U.S. patent application number 17/461425 was filed with the patent office on 2022-02-17 for digital twin for control tower and enterprise management platform managing entity replicas and e-commerce systems.
This patent application is currently assigned to STRONG FORCE VCN PORTFOLIO 2019, LLC. The applicant listed for this patent is STRONG FORCE VCN PORTFOLIO 2019, LLC. Invention is credited to Brent BLIVEN, Andrew CARDNO, Charles Howard CELLA, Joshua DOBROWITSKY, Teymour S. EL-TAHRY, Jenna PARENTI, Richard SPITZ.
Application Number | 20220051171 17/461425 |
Document ID | / |
Family ID | |
Filed Date | 2022-02-17 |
United States Patent
Application |
20220051171 |
Kind Code |
A1 |
CELLA; Charles Howard ; et
al. |
February 17, 2022 |
DIGITAL TWIN FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM
MANAGING ENTITY REPLICAS AND E-COMMERCE SYSTEMS
Abstract
A value chain system that provides recommendations for designing
a logistics system generally includes a machine learning system
that trains machine-learned models that output logistics design
recommendations based on training data sets that each respectively
defines one or more features of a respective logistic system and an
outcome relating to the respective logistics system; an artificial
intelligence system that receives a request for a logistics system
design recommendation and determines the logistics system design
recommendation based on one or more of the machine-learned models
and the request; and a digital twin system that generates an
environment digital twin of a logistics environment that
incorporates the logistics system design recommendation, and one or
more physical asset digital twins of physical assets. The digital
twin system executes a simulation based on the logistics
environment digital twin, the one or more physical asset digital
twins.
Inventors: |
CELLA; Charles Howard;
(Pembroke, MA) ; SPITZ; Richard; (Fort Lauderdale,
FL) ; EL-TAHRY; Teymour S.; (Birmingham, MI) ;
CARDNO; Andrew; (Fort Lauderdale, FL) ; PARENTI;
Jenna; (Fort Lauderdale, FL) ; BLIVEN; Brent;
(Fort Lauderdale, FL) ; DOBROWITSKY; Joshua;
(Birmingham, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STRONG FORCE VCN PORTFOLIO 2019, LLC |
Fort Lauderdale |
FL |
US |
|
|
Assignee: |
STRONG FORCE VCN PORTFOLIO 2019,
LLC
Fort Lauderdale
FL
|
Appl. No.: |
17/461425 |
Filed: |
August 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2020/059227 |
Nov 5, 2020 |
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17461425 |
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63087292 |
Oct 4, 2020 |
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63069533 |
Aug 24, 2020 |
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63054606 |
Jul 21, 2020 |
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63016976 |
Apr 28, 2020 |
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62969153 |
Feb 3, 2020 |
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62931193 |
Nov 5, 2019 |
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International
Class: |
G06Q 10/08 20060101
G06Q010/08; G06Q 10/06 20060101 G06Q010/06; G06Q 30/02 20060101
G06Q030/02; G06F 30/27 20060101 G06F030/27; G06N 3/04 20060101
G06N003/04 |
Claims
1. An information technology system for leveraging digital twins in
a value chain having a plurality of value chain entities, the
information technology system comprising: a plurality of sensors
positioned at least one of in, on, and near a set of value chain
entities of the value chain entities and configured to collect
sensor data related to the set of value chain entities, the sensor
data being substantially real-time sensor data; and an adaptive
intelligence system connected to the plurality of sensors and
configured to receive the sensor data from the plurality of
sensors, the adaptive intelligence system including: an artificial
intelligence system configured to input the sensor data into a
machine learning model such that the sensor data is used as
training data for the machine learning model, and the machine
learning model is configured to transform the sensor data into
simulation data; and a digital twin system configured to create a
digital replica of the set of value chain entities based on the
simulation data, wherein the digital replica of the value chain
entities is configured to be used to provide a substantially
real-time representation of the value chain entities and provide a
simulation of a possible future state of the value chain entities
via the simulation data.
2. The information technology system of claim 1, wherein the
machine learning model is configured to learn which types of sensor
data are relevant to dynamics of each value chain entity of the
value chain entities and simulation thereof.
3. The information technology system of claim 1, wherein the
machine learning model is configured to make suggestions to a user
of the information technology system via an interface regarding
potential changes to the plurality of sensors that would improve
simulation of the value chain entities via the digital twin
system.
4. The information technology system of claim 1, wherein the
machine learning model is configured to prioritize collection and
transmission of sensor data that are relevant to dynamics of the
value chain entities and simulation thereof.
5. A value chain network management platform, comprising: a machine
learning system that trains one or more machine-learned models to
output one or more e-commerce recommendations to a value chain
network customer via an interface using training data that includes
product features and outcomes; and an artificial intelligence
system that receives a request for e-commerce from an e-commerce
system, wherein the artificial intelligence is configured to
determine and generate an e-commerce recommendation based on the
one or more machine-learned models and the request, and the
artificial intelligence is configured to leverage one or more
product digital twins and one or more customer digital twins to
execute a simulation based on the one or more customer digital
twins, the one or more product digital twins, and the e-commerce
recommendation.
6. The value chain network management platform of claim 5, wherein
the machine learning system integrates with a model
interpretability system, and wherein the model interpretability
system is configured to implement Testing with Concept Activation
Vectors (TCAV) functionality, whereby the model interpretability
facilitates learning of human-interpretable concepts by the
machine-learned model.
7. The value chain network management platform of claim 5, wherein
the one or more machine-learned models are at least one of trained
and retrained using simulation data from one or more simulations
involving one or more customer profile digital twins.
8. A value chain network management platform comprising: a machine
learning system that trains one or more machine-learned models to
output one or more risk management decisions using training data
that includes component features and outcomes; and an artificial
intelligence system that receives a request for risk management
from a risk management system, wherein the artificial intelligence
system is configured to determine and generate a risk management
decision based on the one or more machine-learned models and the
request, and the artificial intelligence system is configured to
leverage one or more component digital twins and one or more
environment digital twins to execute a simulation based on the one
or more component digital twins, the one or more environment
digital twins, and the risk management decision.
9. The value chain network management platform of claim 8, wherein
the risk management decision relates to a condition of a
component.
10. The value chain network management platform of claim 8, wherein
the one or more machine-learned models are at least one of trained
and retrained using simulation data from one or more simulations
involving one or more components.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a bypass continuation of International
Application No. PCT/US2020/059227, filed Nov. 5, 2020, which claims
the benefit of priority to the following U.S. Provisional Patent
Applications: Ser. No. 62/931,193, filed Nov. 5, 2019, entitled
"METHODS AND SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;"
Ser. No. 62/969,153 filed Feb. 3, 2020, entitled "METHODS AND
SYSTEMS OF VALUE CHAIN NETWORK MANAGEMENT PLATFORM;" Ser. No.
63/016,976 filed Apr. 28, 2020, entitled "DIGITAL TWIN SYSTEMS AND
METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;" Ser.
No. 63/054,606 filed Jul. 21, 2020, entitled "DIGITAL TWIN SYSTEMS
AND METHODS FOR FACILITATING VALUE CHAIN NETWORKS AND LOGISTICS;"
Ser. No. 63/069,533, filed Aug. 24, 2020, entitled "INFORMATION
TECHNOLOGY SYSTEMS AND METHODS FOR VALUE CHAIN ARTIFICIAL
INTELLIGENCE LEVERAGING DIGITAL TWINS;" and Ser. No. 63/087,292,
filed Oct. 4, 2020, entitled "EXECUTIVE CONTROL TOWER AND
ENTERPRISE MANAGEMENT PLATFORM FOR VALUE CHAIN NETWORK." Each of
the above applications is hereby incorporated by reference in its
entirety as if fully set forth herein.
FIELD
[0002] The present disclosure relates to information technology
methods and systems for management of value chain network entities,
including supply chain and demand management entities. The present
disclosure also relates to the field of enterprise management
platforms, more particularly involving data management, artificial
intelligence, network connectivity and digital twins
BACKGROUND
[0003] Historically, many of the various categories of goods
purchased and used by household consumers, by businesses and by
other customers were been supplied mainly through a relatively
linear fashion, in which manufacturers and other suppliers of
finished goods, components, and other items handed off items to
shipping companies, freight forwarders and the like, who delivered
them to warehouses for temporary storage, to retailers, where
customers purchased them, or directly to customer locations.
Manufacturers and retailers undertook various sales and marketing
activities to encourage and meet demand by customers, including
designing products, positioning them on shelves and in advertising,
setting prices, and the like.
[0004] Orders for products were fulfilled by manufacturers through
a supply chain, such as depicted in FIG. 1, where suppliers 122 in
various supply environments 160, operating production facilities
134 or acting as resellers or distributors for others, made a
product 130 available at a point of origin 102 in response to an
order. The product 130 was passed through the supply chain, being
conveyed and stored via various hauling facilities 138 and
distribution facilities 134, such as warehouses 132, fulfillment
centers 112 and delivery systems 114, such as trucks and other
vehicles, trains, and the like. In many cases, maritime facilities
and infrastructure, such as ships, barges, docks and ports provided
transport over waterways between the points of origin 102 and one
or more destinations 104.
[0005] Organizations have access to an almost unlimited amount of
data. With the advent of smart connected devices, wearable
technologies, the Internet of Things (IoT), and the like, the
amount of data available to an organization that is planning,
overseeing, managing and operating a value chain network has
increased dramatically and will likely to continue to do so. For
example, in a manufacturing facility, warehouse, campus, or other
operating environment, there may be hundreds to thousands of IoT
sensors that provide metrics such as vibration data that measure
the vibration signatures of important machinery, temperatures
throughout the facility, motion sensors that can track throughput,
asset tracking sensors and beacons to locate items, cameras and
optical sensors, chemical and biological sensors, and many others.
Additionally, as wearable technologies become more prevalent,
wearables may provide insight into the movement, health indicators,
physiological states, activity states, movements, and other
characteristics of workers. Furthermore, as organizations implement
CRM systems, ERP systems, operations systems, information
technology systems, advanced analytics and other systems that
leverage information and information technology, organizations have
access to an increasingly wide array of other large data sets, such
as marketing data, sales data, operational data, information
technology data, performance data, customer data, financial data,
market data, pricing data, supply chain data, and the like,
including data sets generated by or for the organization and
third-party data sets.
[0006] The presence of more data and data of new types offers many
opportunities for organizations to achieve competitive advantages;
however, it also presents problems, such as of complexity and
volume, such that users can be overwhelmed, missing opportunities
for insight. A need exists for methods and systems that allow
enterprises not only to obtain data, but to convert the data into
insights and to translate the insights into well-informed decisions
and timely execution of efficient operations.
SUMMARY
[0007] According to some embodiments of the present disclosure,
methods and systems are provided herein for an information
technology system that may include a cloud-based management
platform with a micro-services architecture; a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities; and a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use.
[0008] In embodiments, provided herein are methods, systems,
components and other elements for an information technology system
that may include a cloud-based management platform with a
micro-services architecture, the platform having a set of
interfaces for accessing and configuring features of the platform;
a set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform; a set of
adaptive intelligence facilities for automating a set of
capabilities of the platform; a set of data storage facilities for
storing data collected and handled by the platform; and a set of
monitoring facilities for monitoring the value chain network
entities; wherein the platform hosts a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin of a product of the enterprise to a
point of customer use.
[0009] In embodiments, an information technology system, includes a
cloud-based management platform with a micro-services architecture,
the platform having a set of interfaces that are configured to
access and configure features of the platform; a set of network
connectivity facilities that are configured to direct a set of
value chain network entities to connect to the features of the
platform; a set of adaptive intelligence facilities that are
configured to automate a set of capabilities of the platform
related to at least one of the value chain network entities and the
features of the platform; a set of data storage facilities that are
configured to store data collected and handled by the platform,
wherein the data is related to at least one of the value chain
network entities and the features of the platform; and a set of
monitoring facilities that are configured to monitor the value
chain network entities; wherein the platform is configured to host
a set of applications for directing an enterprise to manage the
value chain network entities from a point of origin of a product of
the enterprise to a point of customer use.
[0010] In embodiments. the set of interfaces includes at least one
of a demand management interface and a supply chain management
interface. In embodiments, the set of network connectivity
facilities includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities includes a cognitive networking system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of network connectivity
facilities includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes
an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence facilities includes a self-configuring data collection
system deployed in a supply chain infrastructure facility operated
by the enterprise. In embodiments, the set of adaptive intelligence
facilities includes a digital twin system representing attributes
of at least one value chain network entity of the value chain
network entities controlled by the enterprise. In embodiments, the
set of adaptive intelligence includes a smart contract system that
is configured to automate a set of interactions among the value
chain network entities.
[0011] In embodiments, the set of data storage facilities uses a
distributed data architecture. In embodiments, the set of data
storage facilities uses a blockchain. In embodiments, the set of
data storage facilities uses a distributed ledger. In embodiments,
the set of data storage facilities uses a graph database
representing a set of hierarchical relationships of the value chain
network entities. In embodiments, the set of monitoring facilities
includes an Internet of Things monitoring system. In embodiments,
the set of monitoring facilities includes a sensor system deployed
in an infrastructure facility operated by the enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications, and enterprise resource management applications. In
embodiments, the set of applications includes an asset management
application.
[0012] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities. In embodiments, the
platform manages a set of demand factors, a set of supply factors,
and a set of supply chain infrastructure facilities.
[0013] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[0014] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[0015] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[0016] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[0017] In embodiments, an information technology system, includes a
cloud-based management platform with a micro-services architecture,
the platform having a set of interfaces that are configured to
access and configure features of the platform, a set of network
connectivity facilities that are configured to direct a set of
value chain network entities to connect to the features of the
platform, a set of adaptive intelligence facilities that are
configured to automate a set of capabilities of the platform
related to at least one of the value chain network entities and the
features of the platform, a set of data storage facilities that are
configured to store data collected and handled by the platform, and
a set of monitoring facilities that are configured to monitor the
value chain network entities, wherein the interfaces, the network
connectivity facilities, the adaptive intelligence facilities, the
data storage facilities, and the monitoring facilities are
coordinated for monitoring and management of the value chain
network entities; a set of applications that are configured to
direct an enterprise to manage the value chain network entities of
the platform from a point of origin to a point of customer use; and
a unified set of robotic process automation systems that provide
coordinated automation among at least two types of applications
from among a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications, and
a set of enterprise resource management applications for a category
of goods with respect to the value chain network entities of the
platform.
[0018] In embodiments, the unified set of robotic process
automation systems automate a process selected from the group
consisting of selection of a quantity of product for an order,
selection of a carrier for a shipment, selection of a vendor for a
component, selection of a vendor for a finished goods order,
selection of a variation of a product for marketing, selection of
an assortment of goods for a shelf, determination of a price for a
finished good, configuration of a service offer related to a
product, configuration of product bundle, configuration of a
product kit, configuration of a product package, configuration of a
product display, configuration of a product image, configuration of
a product description, configuration of a website navigation path
related to a product, determination of an inventory level for a
product, selection of a logistics type, configuration of a schedule
for product delivery, configuration of a logistics schedule,
configuration of a set of inputs for machine learning, preparation
of product documentation, preparation of disclosures about a
product, configuration of a product for a set of local
requirements, configuration of a set of products for compatibility,
configuration of a request for proposals, ordering of equipment for
a warehouse, ordering of equipment for a fulfillment center,
classification of a product defect in an image, inspection of a
product in an image, inspection of product quality data from a set
of sensors, inspection of data from a set of onboard diagnostics on
a. product, inspection of diagnostic data from an Internet of
Things system, review of sensor data from environmental sensors in
a set of supply chain environments, selection of inputs for a
digital twin, selection of outputs from a digital twin, selection
of visual elements for presentation in a digital twin, diagnosis of
sources of delay in a supply chain, diagnosis of sources of
scarcity in a supply chain, diagnosis of sources of congestion in a
supply chain, diagnosis of sources of cost overruns in a supply
chain, diagnosis of sources of product defects in a supply chain,
and prediction of maintenance requirements in supply chain
infrastructure.
[0019] In embodiments, one of the processes automated by the
robotic process automation system involves selection of a quantity
of product for an order. In embodiments, one of the processes
automated by the robotic process automation system involves
selection of a carrier for a shipment. In embodiments, wherein one
of the processes automated by the robotic process automation system
involves selection of a vendor for a component. In embodiments,
wherein one of the processes automated by the robotic process
automation system involves selection of a vendor for a finished
goods order. In embodiments, wherein one of the processes automated
by the robotic process automation system involves selection of a
variation of a product for marketing. In embodiments, wherein one
of the processes automated by the robotic process automation system
involves selection of an assortment of goods for a shelf. In
embodiments, wherein one of the processes automated by the robotic
process automation system involves determination of a price for a
finished good.
[0020] In embodiments, one of the processes automated by the
robotic process automation system involves configuration of a
service offer related to a product. In embodiments, wherein one of
the processes automated by the robotic process automation system
involves configuration of a product bundle. In embodiments, wherein
one of the processes automated by the robotic process automation
system involves configuration of a product kit. In embodiments,
wherein one of the processes automated by the robotic process
automation system involves configuration of a product package. In
embodiments, wherein one of the processes automated by the robotic
process automation system involves configuration of a product
display. In embodiments, wherein one of the processes automated by
the robotic process automation system involves configuration of a
product image. In embodiments, wherein one of the processes
automated by the robotic process automation system involves
configuration of a product description. In embodiments, wherein one
of the processes automated by the robotic process automation system
involves configuration of a website navigation path related to a
product.
[0021] In embodiments, one of the processes automated by the
robotic process automation system involves determination of an
inventory level for a product. In embodiments, wherein one of the
processes automated by the robotic process automation system
involves selection of a logistics type. In embodiments, wherein one
of the processes automated by the robotic process automation system
involves configuration of a schedule for product delivery. In
embodiments, one of the processes automated by the robotic process
automation system involves configuration of a logistics schedule.
In embodiments, one of the processes automated by the robotic
process automation system involves configuration of a set of inputs
for machine learning. In embodiments, one of the processes
automated by the robotic process automation system involves
preparation of product documentation. In embodiments, one of the
processes automated by the robotic process automation system
involves preparation of disclosures about a product. In
embodiments, one of the processes automated by the robotic process
automation system involves configuration of a product for a set of
local requirements. In embodiments, one of the processes automated
by the robotic process automation system involves configuration of
a set of products for compatibility. In embodiments, one of the
processes automated by the robotic process automation system
involves configuration of a request for proposals. In embodiments,
one of the processes automated by the robotic process automation
system involves ordering of equipment for a warehouse. In
embodiments, one of the processes automated by the robotic process
automation system involves ordering of equipment for a fulfillment
center. In embodiments, one of the processes automated by the
robotic process automation system involves classification of a
product defect in an image. In embodiments, one of the processes
automated by the robotic process automation system involves
inspection of a product in an image. In embodiments, one of the
processes automated by the robotic process automation system
involves inspection of product quality data from a set of sensors.
In embodiments, one of the processes automated by the robotic
process automation system involves inspection of data from a set of
onboard diagnostics on a. product.
[0022] In embodiments, one of the processes automated by the
robotic process automation system involves inspection of diagnostic
data from an Internet of Things system. In embodiments, one of the
processes automated by the robotic process automation system
involves review of sensor data from environmental sensors in a set
of supply chain environments. In embodiments, one of the processes
automated by the robotic process automation system involves
selection of inputs for a digital twin. In embodiments, one of the
processes automated by the robotic process automation system
involves selection of outputs from a digital twin. In embodiments,
one of the processes automated by the robotic process automation
system involves selection of visual elements for presentation in a
digital twin. In embodiments, one of the processes automated by the
robotic process automation system involves diagnosis of sources of
delay in a supply chain. In embodiments, one of the processes
automated by the robotic process automation system involves
diagnosis of sources of scarcity in a supply chain. In embodiments,
one of the processes automated by the robotic process automation
system involves diagnosis of sources of congestion in a supply
chain. In embodiments, one of the processes automated by the
robotic process automation system involves diagnosis of sources of
cost overruns in a supply chain. In embodiments, one of the
processes automated by the robotic process automation system
involves diagnosis of sources of product defects in a supply chain.
In embodiments, one of the processes automated by the robotic
process automation system involves prediction of maintenance
requirements in supply chain infrastructure.
[0023] In embodiments, the set of demand management applications,
supply chain applications, intelligent product applications and
enterprise resource management applications are selected from the
group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[0024] In embodiments, the set of interfaces includes at least one
of a demand management interface and a supply chain management
interface. In embodiments, the set of network connectivity
facilities includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities includes a cognitive networking system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of network connectivity
facilities includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes
an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence facilities includes a self-configuring data collection
system deployed in a supply chain infrastructure facility operated
by the enterprise. In embodiments, the set of adaptive intelligence
facilities includes a digital twin system representing attributes
of value chain network entity controlled by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes a
smart contract system that is configured to automate a set of
interactions among a set of value chain network entities.
[0025] In embodiments, the set of data storage facilities uses a
distributed data architecture. In embodiments, the set of data
storage facilities uses a blockchain. In embodiments, the set of
data storage facilities uses a distributed ledger. In embodiments,
the set of data storage facilities uses graph database representing
a set of hierarchical relationships of the value chain network
entities. In embodiments, the set of monitoring facilities includes
an Internet of Things monitoring system. In embodiments, the set of
monitoring facilities includes a sensor system deployed in an
infrastructure facility operated by an enterprise. In embodiments,
the set of applications includes a set of applications of at least
two types from among a set of supply chain management applications,
demand management applications, intelligent product applications
and enterprise resource management applications. In embodiments,
the set of applications includes an asset management
application.
[0026] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[0027] In embodiments, wherein the platform manages a set of demand
factors, a set of supply factors, and a set of supply chain
infrastructure facilities.
[0028] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[0029] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[0030] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[0031] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[0032] In embodiments, an information technology system, includes a
cloud-based management platform with a micro-services architecture,
the platform having a set of interfaces that are configured to
access and configure features of the platform, a set of network
connectivity facilities that are configured to direct a set of
value chain network entities to connect to the features of the
platform, a set of adaptive intelligence facilities that are
configured to automate a set of capabilities of the platform
related to at least one of the value chain network entities and the
features of the platform, a set of data storage facilities that are
configured to store data collected and handled by the platform, and
a set of monitoring facilities that are configured to monitor the
value chain network entities, wherein the interfaces, the network
connectivity facilities, the adaptive intelligence facilities, the
data storage facilities, and the monitoring facilities are
coordinated for monitoring and management of the value chain
network entities; a set of applications that are configured to
direct an enterprise to manage the value chain network entities of
the platform from a point of origin to a point of customer use; and
a set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservices layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management entities
related to the value chain network entities of the platform.
[0033] In embodiments, the set of Internet of Things resources that
collect information with respect to supply chain entities and
demand management entities collects information from entities
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[0034] In embodiments, the set of Internet of Things resources is
selected from the group consisting of camera systems, lighting
systems, motion sensing systems, weighing systems, inspection
systems, machine vision systems, environmental sensor systems,
onboard sensor systems, onboard diagnostic systems, environmental
control systems, sensor-enabled network switching and routing
systems, RF sensing systems, magnetic sensing systems, pressure
monitoring systems, vibration monitoring systems, temperature
monitoring systems, heat flow monitoring systems, biological
measurement systems, chemical measurement systems, ultrasonic
monitoring systems, radiography systems, LIDAR-based monitoring
systems, access control systems, penetrating wave sensing systems,
SONAR-based monitoring systems, radar-based monitoring systems,
computed tomography systems, magnetic resonance imaging systems,
and network monitoring systems.
[0035] In embodiments, the set of Internet of Things resources
includes a set of camera systems. In embodiments, the set of
Internet of Things resources includes a set of lighting systems. In
embodiments, the set of Internet of Things resources includes a set
of machine vision systems. In embodiments, the set of Internet of
Things resources includes a set of motion sensing systems.
[0036] In embodiments, the set of Internet of Things resources
includes a set of weighing systems. In embodiments, the set of
Internet of Things resources includes a set of inspection systems.
In embodiments, the set of Internet of Things resources includes a
set of environmental sensor systems. In embodiments, the set of
Internet of Things resources includes a set of onboard sensor
systems. In embodiments, the set of Internet of Things resources
includes a set of onboard diagnostic systems. In embodiments, the
set of Internet of Things resources includes a set of environmental
control systems. In embodiments, the set of Internet of Things
resources includes a set of sensor-enabled network switching and
routing systems. In embodiments, the set of Internet of Things
resources includes a set of RF sensing systems.
[0037] In embodiments, the set of Internet of Things resources
includes a set of magnetic sensing systems. In embodiments, the set
of Internet of Things resources includes a set of pressure
monitoring systems. In embodiments, the set of Internet of Things
resources includes a set of vibration monitoring systems. In
embodiments, the set of Internet of Things resources includes a set
of temperature monitoring systems. In embodiments, the set of
Internet of Things resources includes a set of heat flow monitoring
systems. In embodiments, the set of Internet of Things resources
includes a set of biological measurement systems. In embodiments,
the set of Internet of Things resources includes a set of chemical
measurement systems. In embodiments, the set of Internet of Things
resources includes a set of ultrasonic monitoring systems. In
embodiments, the set of Internet of Things resources includes a set
of radiography systems. In embodiments, the set of Internet of
Things resources includes a set of LIDAR-based monitoring systems.
In embodiments, the set of Internet of Things resources includes a
set of access control systems. In embodiments, the set of Internet
of Things resources includes a set of penetrating wave sensing
systems. In embodiments, the set of Internet of Things resources
includes a set of SONAR-based monitoring systems. In embodiments,
the set of Internet of Things resources includes a set of
radar-based monitoring systems. In embodiments, the set of Internet
of Things resources includes a set of computed tomography systems.
In embodiments, the set of Internet of Things resources includes a
set of magnetic resonance imaging systems. In embodiments, the set
of Internet of Things resources includes a set of network
monitoring systems. In embodiments, the set of interfaces includes
at least one of a demand management interface and a supply chain
management interface.
[0038] In embodiments, the set of applications is at least one of
demand management applications, supply chain applications,
intelligent product applications, and enterprise resource
management applications that are selected from the group consisting
of supply chain, asset management, risk management, inventory
management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[0039] In embodiments, the set of network connectivity facilities
includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities includes a cognitive networking system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, wherein the set of network connectivity
facilities includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes
an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence includes a self-configuring data collection system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of adaptive intelligence
facilities includes a digital twin system representing attributes
of value chain network entity controlled by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes a
smart contract system that is configured to automate a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities uses a distributed
data architecture. In embodiments, the set of data storage
facilities uses a blockchain. In embodiments, the set of data
storage facilities uses a distributed ledger. In embodiments, the
set of data storage facilities uses a graph database representing a
set of hierarchical relationships of value chain network entities.
In embodiments, the set of monitoring includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities includes a sensor system deployed in an infrastructure
facility operated by an enterprise. In embodiments, the set of
applications includes a set of applications of at least two types
from among a set of supply chain management applications, demand
management applications, intelligent product applications and
enterprise resource management applications. In embodiments, the
set of applications includes an asset management application.
[0040] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[0041] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities.
[0042] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[0043] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[0044] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[0045] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[0046] In embodiments, an information technology system, includes a
cloud-based management platform with a micro-services architecture,
the platform having a set of interfaces that are configured to
access and configure features of the platform, a set of network
connectivity facilities that are configured to direct a set of
value chain network entities to connect to the features of the
platform, a set of adaptive intelligence facilities that are
configured to automate a set of capabilities of the platform
related to at least one of the value chain network entities and the
features of the platform, a set of data storage facilities that are
configured to store data collected and handled by the platform, and
a set of monitoring facilities that are configured to monitor the
value chain network entities, wherein the interfaces, the network
connectivity facilities, the adaptive intelligence facilities, the
data storage facilities, and the monitoring facilities are
coordinated for monitoring and management of the value chain
network entities; a set of applications that are configured to
direct an enterprise to manage the value chain network entities of
the platform from a point of origin to a point of customer use; and
a set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservices layers
include a robotic process automation layer that uses information
collected by a data collection layer and a set of outcomes and
activities involving the applications of the application layer to
automate a set of actions for at least a subset of the applications
with respect to the value chain network entities of the
platform.
[0047] In embodiments, the robotic process automation layer
automates a process selected from the group consisting of selection
of a quantity of product for an order, selection of a carrier for a
shipment, selection of a vendor for a component, selection of a
vendor for a finished goods order, selection of a variation of a
product for marketing, selection of an assortment of goods for a
shelf, determination of a price for a finished good, configuration
of a service offer related to a product, configuration of product
bundle, configuration of a product kit, configuration of a product
package, configuration of a product display, configuration of a
product image, configuration of a product description,
configuration of a website navigation path related to a product,
determination of an inventory level for a product, selection of a
logistics type, configuration of a schedule for product delivery,
configuration of a logistics schedule, configuration of a set of
inputs for machine learning, preparation of product documentation,
preparation of disclosures about a product, configuration of a
product for a set of local requirements, configuration of a set of
products for compatibility, configuration of a request for
proposals, ordering of equipment for a warehouse, ordering of
equipment for a fulfillment center, classification of a product
defect in an image, inspection of a product in an image, inspection
of product quality data from a set of sensors, inspection of data
from a set of onboard diagnostics on a. product, inspection of
diagnostic data from an Internet of Things system, review of sensor
data from environmental sensors in a set of supply chain
environments, selection of inputs for a digital twin, selection of
outputs from a digital twin, selection of visual elements for
presentation in a digital twin, diagnosis of sources of delay in a
supply chain, diagnosis of sources of scarcity in a supply chain,
diagnosis of sources of congestion in a supply chain, diagnosis of
sources of cost overruns in a supply chain, diagnosis of sources of
product defects in a supply chain, and prediction of maintenance
requirements in supply chain infrastructure.
[0048] In embodiments, one of the actions automated by the robotic
process automation layer involves selection of a quantity of
product for an order. In embodiments, one of the actions automated
by the robotic process automation layer involves selection of a
carrier for a shipment. In embodiments, one of the actions
automated by the robotic process automation layer involves
selection of a vendor for a component. In embodiments, one of the
actions automated by the robotic process automation layer involves
selection of a vendor for a finished goods order. In embodiments,
one of the actions automated by the robotic process automation
layer involves selection of a variation of a product for marketing.
In embodiments, one of the actions automated by the robotic process
automation layer involves selection of an assortment of goods for a
shelf. In embodiments, one of the actions automated by the robotic
process automation layer involves determination of a price for a
finished good. In embodiments, one of the actions automated by the
robotic process automation layer involves configuration of a
service offer related to a product. In embodiments, one of the
actions automated by the robotic process automation layer involves
configuration of product bundle. In embodiments, one of the actions
automated by the robotic process automation layer involves
configuration of a product kit. In embodiments, one of the actions
automated by the robotic process automation layer involves
configuration of a product package. In embodiments, one of the
actions automated by the robotic process automation layer involves
configuration of a product display. In embodiments, one of the
actions automated by the robotic process automation layer involves
configuration of a product image. In embodiments, one of the
actions automated by the robotic process automation layer involves
configuration of a product description. In embodiments, one of the
actions automated by the robotic process automation layer involves
configuration of a website navigation path related to a product. In
embodiments, one of the actions automated by the robotic process
automation layer involves determination of an inventory level for a
product. In embodiments, one of the actions automated by the
robotic process automation layer involves selection of a logistics
type. In embodiments, one of the actions automated by the robotic
process automation layer involves configuration of a schedule for
product delivery. In embodiments, one of the actions automated by
the robotic process automation layer involves configuration of a
logistics schedule. In embodiments, one of the actions automated by
the robotic process automation layer involves configuration of a
set of inputs for machine learning. In embodiments, one of the
actions automated by the robotic process automation layer involves
preparation of product documentation. In embodiments, one of the
actions automated by the robotic process automation layer involves
preparation of disclosures about a product. In embodiments, one of
the actions automated by the robotic process automation layer
involves configuration of a product for a set of local
requirements. In embodiments, one of the actions automated by the
robotic process automation layer involves configuration of a set of
products for compatibility. In embodiments, one of the actions
automated by the robotic process automation layer involves
configuration of a request for proposals. In embodiments, one of
the actions automated by the robotic process automation layer
involves ordering of equipment for a warehouse. In embodiments, one
of the actions automated by the robotic process automation layer
involves ordering of equipment for a fulfillment center. In
embodiments, one of the actions automated by the robotic process
automation layer involves classification of a product defect in an
image. In embodiments, one of the actions automated by the robotic
process automation layer involves inspection of a product in an
image. In embodiments, one of the actions automated by the robotic
process automation layer involves inspection of product quality
data from a set of sensors. In embodiments, one of the actions
automated by the robotic process automation layer involves
inspection of data from a set of onboard diagnostics on a. product.
In embodiments, one of the actions automated by the robotic process
automation layer involves inspection of diagnostic data from an
Internet of Things system. In embodiments, one of the actions
automated by the robotic process automation layer involves review
of sensor data from environmental sensors in a set of supply chain
environments. In embodiments, one of the actions automated by the
robotic process automation layer involves selection of inputs for a
digital twin. In embodiments, one of the actions automated by the
robotic process automation layer involves selection of outputs from
a digital twin. In embodiments, one of the actions automated by the
robotic process automation layer involves selection of visual
elements for presentation in a digital twin. In embodiments, one of
the actions automated by the robotic process automation layer
involves diagnosis of sources of delay in a supply chain. In
embodiments, one of the actions automated by the robotic process
automation layer involves diagnosis of sources of scarcity in a
supply chain. In embodiments, one of the actions automated by the
robotic process automation layer involves diagnosis of sources of
congestion in a supply chain. In embodiments, one of the actions
automated by the robotic process automation layer involves
diagnosis of sources of cost overruns in a supply chain. In
embodiments, one of the actions automated by the robotic process
automation layer involves diagnosis of sources of product defects
in a supply chain. In embodiments, one of the actions automated by
the robotic process automation layer involves prediction of
maintenance requirements in supply chain infrastructure.
[0049] In embodiments, the set of interfaces includes at least one
of a demand management interface and a supply chain management
interface.
[0050] In embodiments, the set of applications is at least one of
demand management applications, supply chain applications,
intelligent product applications, and enterprise resource
management applications that are selected from the group consisting
of supply chain, asset management, risk management, inventory
management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[0051] In embodiments, the set of network connectivity facilities
includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities includes a cognitive networking system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of network connectivity
facilities includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes
an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence facilities includes a self-configuring data collection
system deployed in a supply chain infrastructure facility operated
by the enterprise. In embodiments, the set of adaptive intelligence
facilities includes a digital twin system representing attributes
of value chain network entity controlled by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes a
smart contract system for automating a set of interactions among a
set of value chain network entities. In embodiments, the set of
data storage facilities uses a distributed data architecture. In
embodiments, the set of data storage facilities uses a blockchain.
In embodiments, the set of data storage facilities uses a
distributed ledger. In embodiments, the set of data storage
facilities uses a graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities includes an Internet of Things
monitoring system. In embodiments, the set of monitoring facilities
includes a sensor system deployed in an infrastructure facility
operated by an enterprise.
[0052] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application.
[0053] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[0054] In embodiments, the platform manages a set of demand
factors, a set of supply factors, and a set of supply chain
infrastructure facilities.
[0055] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[0056] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[0057] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[0058] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[0059] In embodiments, an information technology system, includes a
cloud-based management platform with a micro-services architecture,
the platform having a set of interfaces that are configured to
access and configure features of the platform, a set of network
connectivity facilities that are configured to direct a set of
value chain network entities to connect to the features of the
platform, a set of adaptive intelligence facilities that are
configured to automate a set of capabilities of the platform
related to at least one of the value chain network entities and the
features of the platform, a set of data storage facilities that are
configured to store data collected and handled by the platform, and
a set of monitoring facilities that are configured to monitor the
value chain network entities, wherein the interfaces, the network
connectivity facilities, the adaptive intelligence facilities, the
data storage facilities, and the monitoring facilities are
coordinated for monitoring and management of the value chain
network entities; a set of applications that are configured to
direct an enterprise to manage the value chain network entities of
the platform from a point of origin to a point of customer use; and
a machine learning/artificial intelligence system configured to
generate recommendations for placing at least one of an additional
sensor and a camera on and/or in proximity to a value chain network
entity of the value chain network entities, and wherein data from
the at least one of the additional sensor and the camera feeds into
a digital twin that represents the value chain network
entities.
[0060] In embodiments, the set of interfaces includes at least one
of a demand management interface and a supply chain management
interface. In embodiments, the set of applications is at least one
of demand management applications, supply chain applications,
intelligent product applications, and enterprise resource
management applications that are selected from the group consisting
of supply chain, asset management, risk management, inventory
management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[0061] In embodiments, the set of network connectivity facilities
includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities includes a cognitive networking system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of network connectivity
facilities includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes
an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence facilities includes a self-configuring data collection
system deployed in a supply chain infrastructure facility operated
by the enterprise. In embodiments, the set of adaptive intelligence
facilities includes a digital twin system representing attributes
of value chain network entity controlled by the enterprise. In
embodiments, the set of adaptive intelligence facilities includes a
smart contract system for automating a set of interactions among a
set of value chain network entities. In embodiments, the set of
data storage facilities uses a distributed data architecture. In
embodiments, the set of data storage facilities uses a blockchain.
In embodiments, the set of data storage facilities uses a
distributed ledger. In embodiments, the set of data storage
facilities uses a graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities includes an Internet of Things
monitoring system. In embodiments, the set of monitoring facilities
includes a sensor system deployed in an infrastructure facility
operated by an enterprise. In embodiments, the set of applications
includes a set of applications of at least two types from among a
set of supply chain management applications, demand management
applications, intelligent product applications and enterprise
resource management applications. In embodiments, the set of
applications includes an asset management application.
[0062] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[0063] In embodiments, the platform manages a set of demand
factors, a set of supply factors, and a set of supply chain
infrastructure facilities.
[0064] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[0065] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[0066] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[0067] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[0068] In embodiments, a value chain system that provides container
fleet management decisions includes a machine learning system that
trains a machine-learned model that outputs a container fleet
management decision given a respective set of input features
relating to a specific shipping event, wherein the machine learning
system trains the machine-learned model based on training data sets
that define features of previous shipping events and outcomes of
the shipping events; an artificial intelligence system that
receives a request for container fleet management and determines a
container fleet management decision based on the machine-learned
model and the request; and a digital twin system that generates an
environment digital twin of an environment of a container fleet and
one or more container digital twins of respective containers in the
container fleet, wherein the digital twin system executes a
container fleet simulation based on the environment digital twin
and the one or more container digital twins, issues a container
fleet management request from the artificial intelligence system
based on a state of the container fleet simulation; and adjusts the
state of the container fleet simulation based on the container
fleet management decision output by the artificial intelligence
system in response to the container fleet management request.
[0069] In embodiments, the digital twin system outputs a simulation
outcome to the machine-learning system, and the machine learning
system reinforces the machine-learned model used to determine the
container fleet management decision based on the simulation
outcome. In embodiments, the artificial intelligence system
receives the container fleet management request from the digital
twin system and determines the container fleet management decision
based on simulation features defined in the container fleet
management request, wherein the simulation features are indicative
of the state of the container fleet simulation. In embodiments, the
request for container fleet management includes one or more
properties of a simulated shipping event. In embodiments, the
artificial intelligence system determines the container fleet
management decision based on the one or more properties of the
simulated shipping event and the machine-learned model. In
embodiments, the one or more properties include a type of good
being shipped. In embodiments, the one or more properties include a
source and a destination of a container. In embodiments, the
digital twin system provides outcome data to the machine-learning
system, wherein the outcome data defines a simulation outcome
resulting from the container fleet management decision.
[0070] In embodiments, a value chain system that provides
recommendations for designing a logistics system includes a machine
learning system that trains a machine-learned model that outputs a
logistics design recommendation given a respective set of input
features relating to a specific respective logistics system,
wherein the machine learning system trains the machine-learned
model based on training data sets that define features of logistics
systems and outcomes of the logistics systems; an artificial
intelligence system that receives a request for logistics system
design and determines a logistics system design recommendation
based on the machine-learned model and the request; and a digital
twin system that generates an environment digital twin of a
logistics environment that incorporates the logistics system design
recommendation and one or more physical asset digital twins of
physical assets, wherein the digital twin system: executes a
logistics simulation based on the logistics environment digital
twin and the one or more physical asset digital twins, issues a
logistics system design request from the artificial intelligence
system based on a state of the logistics simulation; and adjusts
the state of the logistics simulation based on the logistics system
design recommendation output by the artificial intelligence system
in response to the logistics system design request.
[0071] In embodiments, the digital twin system outputs a graphical
representation of the environment digital twin to a display,
whereby a user views the simulation via the display. In
embodiments, the digital twin system outputs a simulation outcome
of the simulation to the machine learning system, and the machine
learning system reinforces the machine-learned model used to
determine the logistics system design recommendation based on the
simulation outcome. In embodiments, the artificial intelligence
system receives the request from a logistics design system that
designs logistics systems, wherein the request includes one or more
logistics factors corresponding to a proposed logistics solution of
an organization. In embodiments, the logistics factors include one
or more of: a type of product corresponding to the proposed
logistics solution, one or more features of the type of product, a
location of a manufacturing site, a location of a distribution
facility, a location of a warehouse, a location of a customer base,
proposed expansion areas of the organization, and supply chain
features. In embodiments, the logistics design system provides
outcome data relating to the logistics system design recommendation
to the machine learning system, and the machine learning system
reinforces the machine-learned model that are used to determine the
logistics system design recommendation based on the outcome data.
In embodiments, the artificial intelligence system determines the
logistics system design recommendation to minimize delay times. In
embodiments, the artificial intelligence system determines the
logistics system design recommendation to comply with regulatory
requirements.
[0072] In embodiments, a value chain system that designs packaging
includes a machine learning system that trains a machine-learned
model that outputs a packaging design recommendation given a
respective set of input features relating to a specific respective
packaging design, wherein the machine learning system trains the
machine-learned model based on training data sets that define
features of packaging designs and outcomes of the packaging
designs; an artificial intelligence system that receives a request
for packaging design and determines a packaging design
recommendation based on the machine-learned model and the request;
and a digital twin system that generates a package digital twin of
a package that incorporates the packaging design recommendation,
wherein the digital twin system: executes a packaging simulation
based on the package digital twin; issues a packaging design
request from the artificial intelligence system based on a state of
the logistics simulation; and adjusts the state of the logistics
simulation based on the packaging design recommendation output by
the artificial intelligence system in response to the packaging
design request.
[0073] In embodiments, the digital twin system outputs a graphical
representation of the package digital twin to a display, whereby a
user views the simulation via the display. In embodiments, the
digital twin system outputs a graphical representation of the
package digital twin in a graphical user interface, whereby a user
edits the packaging design via the graphical user interface. In
embodiments, the digital twin system outputs a simulation outcome
of the simulation to the machine learning system, and the machine
learning system reinforces the machine-learned model used to
determine the packaging design recommendation based on the
simulation outcome. In embodiments, the artificial intelligence
system receives the request from a packaging design system that
designs packaging for physical objects, wherein the request
includes one or more packaging factors corresponding to a proposed
packaging design for the physical objects. In embodiments, the
packaging factors include one or more of: a type of the physical
objects, dimensions of the physical objects, masses of the physical
objects, and shipping methods of the physical objects. In
embodiments, the packaging design system provides outcome data
relating to the packaging design recommendation to the machine
learning system, and the machine learning system reinforces the
machine-learned model that are used to determine the packaging
design recommendation based on the outcome data. In embodiments,
the artificial intelligence system determines the packaging design
recommendation to minimize damage. In embodiments, the artificial
intelligence system determines the packaging design recommendation
to minimize costs. In embodiments, the artificial intelligence
system determines the packaging design recommendation to mitigate
environmental impact.
[0074] In embodiments, an information technology system for
leveraging digital twins in a value chain having a plurality of
value chain entities, the information technology system includes a
plurality of sensors positioned at least one of in, on, and near a
set of value chain entities of the value chain entities and
configured to collect sensor data related to the set of value chain
entities, the sensor data being substantially real-time sensor
data; and an adaptive intelligence system connected to the
plurality of sensors and configured to receive the sensor data from
the plurality of sensors, the adaptive intelligence system
including: an artificial intelligence system configured to input
the sensor data into a machine learning model such that the sensor
data is used as training data for the machine learning model, and
the machine learning model is configured to transform the sensor
data into simulation data; and a digital twin system configured to
create a digital replica of the set of value chain entities based
on the simulation data, wherein the digital replica of the value
chain entities is configured to be used to provide a substantially
real-time representation of the value chain entities and provide a
simulation of a possible future state of the value chain entities
via the simulation data.
[0075] In embodiments, the machine learning model is configured to
learn which types of sensor data are relevant to dynamics of each
value chain entity of the value chain entities and simulation
thereof. In embodiments, the machine learning model is configured
to make suggestions to a user of the information technology system
via an interface regarding potential changes to the plurality of
sensors that would improve simulation of the value chain entities
via the digital twin system. In embodiments, the machine learning
model is configured to prioritize collection and transmission of
sensor data that are relevant to dynamics of the value chain
entities and simulation thereof.
[0076] In embodiments, a value chain network management platform,
includes a machine learning system that trains one or more
machine-learned models to output one or more e-commerce
recommendations to a value chain network customer via an interface
using training data that includes product features and outcomes;
and an artificial intelligence system that receives a request for
e-commerce from an e-commerce system, wherein the artificial
intelligence is configured to determine and generate an e-commerce
recommendation based on the one or more machine-learned models and
the request, and the artificial intelligence is configured to
leverage one or more product digital twins and one or more customer
digital twins to execute a simulation based on the one or more
customer digital twins, the one or more product digital twins, and
the e-commerce recommendation.
[0077] In embodiments, the machine learning system integrates with
a model interpretability system, and wherein the model
interpretability system is configured to implement Testing with
Concept Activation Vectors (TCAV) functionality, whereby the model
interpretability facilitates learning of human-interpretable
concepts by the machine-learned model. In embodiments, the one or
more machine-learned models are at least one of trained and
retrained using simulation data from one or more simulations
involving one or more customer profile digital twins.
[0078] In embodiments, a value chain network management platform
includes a machine learning system that trains one or more
machine-learned models to output one or more risk management
decisions using training data that includes component features and
outcomes; and an artificial intelligence system that receives a
request for risk management from a risk management system, wherein
the artificial intelligence system is configured to determine and
generate a risk management decision based on the one or more
machine-learned models and the request, and the artificial
intelligence system is configured to leverage one or more component
digital twins and one or more environment digital twins to execute
a simulation based on the one or more component digital twins, the
one or more environment digital twins, and the risk management
decision.
[0079] In embodiments, the risk management decision relates to a
condition of a component. In embodiments, the one or more
machine-learned models are at least one of trained and retrained
using simulation data from one or more simulations involving one or
more components.
[0080] In embodiments, an information technology system includes a
value chain network management platform having an asset management
application associated with maritime assets, wherein the platform
comprises a data handling layer including data sources containing
information used to populate a training set based on a set of
maritime activities of one or more of the maritime assets and at
least one of design outcomes, parameters, and data associated with
the one or more of the maritime assets; an artificial intelligence
system that is configured to learn on the training set collected
from the data sources, wherein the artificial intelligence system
is configured to simulate one or more attributes of the one or more
of the maritime assets, and the artificial intelligence system is
configured to generate one or more sets of recommendations for a
change in the one or more attributes based on the training set
collected from the data sources; a digital twin system that is
configured to provide for visualization of a digital twin of the
one or more of the maritime assets including detail generated by
the artificial intelligence system of the one or more attributes in
combination with the one or more generated sets of
recommendations.
[0081] In embodiments, the maritime assets include one or more
container ships, and wherein the digital twin system further
provides for visualization of the digital twin of the one or more
container ships including the one or more attributes in combination
with one or more of the sets of recommendations associated with the
container ships. In embodiments, the maritime assets include one or
more barges, and wherein the digital twin system further provides
for visualization of the digital twin of one or more of the barges
including the one or more attributes in combination with one or
more of the sets of recommendations associated with the barges. In
embodiments, the maritime assets include one or more components of
a port infrastructure installed on or adjacent to land, and wherein
the digital twin system further provides for visualization of the
digital twin of one or more of the components of port
infrastructure including the one or more attributes in combination
with one or more of the sets of recommendations associated with the
components of port infrastructure. In embodiments, the maritime
assets also include a container ship moored to a component of the
port infrastructure. In embodiments, the maritime assets include
one or more moored navigation units deployed on water. In
embodiments, the maritime assets include one or more ships each
connected to a barge. In embodiments, the maritime assets are
associated with a real-world maritime port, and wherein the digital
twin system further provides for visualization of the digital twin
of one or more of the components of the real-world maritime port
including the one or more attributes in combination with one or
more of the sets of recommendations associated with the components
of the real-world maritime port. In embodiments, the maritime
assets are associated with a real-world shipyard, and wherein the
digital twin system further provides for visualization of the
digital twin of one or more of the components of the real-world
shipyard including the one or more attributes in combination with
one or more of the sets of recommendations associated with the
components of the real-world shipyard.
[0082] In embodiments, the digital twin of one or more of the
maritime assets is a floating asset twin associated with a ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a navigation course of the ship relative to a
planned course of the ship and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the navigation course of the ship. In embodiments, the
floating asset twin is configured to provide for visualization of
an engine performance of the ship and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the engine performance of the ship. In embodiments, the
visualization of the engine performance includes an emissions
profile of the ship. In embodiments, the floating asset twin is
configured to provide for visualization of a hull integrity of the
ship and one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of the
hull of the ship. In embodiments, the floating asset twin is
configured to provide for visualization of in-situ hydrodynamic
changes to a portion of a hull disposed below a water line of the
ship and one or more of the sets of recommendations from the
artificial intelligence system for a change in a hydrodynamic
surface to change performance of the ship. In embodiments, the
floating asset twin is configured to determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the
waterline of the ship to improve fuel efficiency based on known
routes of travel and weather patterns.
[0083] In embodiments, the floating asset twin is configured to
provide visualizations of in-situ aerodynamic changes to a portion
of a hull disposed above a water line of the ship and one or more
of the sets of recommendations from the artificial intelligence
system for a change in an aerodynamic surface to change performance
of the ship. In embodiments, the floating asset twin is configured
to determine a schedule for the change to the aerodynamic surface
disposed above the waterline of the ship to improve fuel efficiency
using known routes of travel and historical weather patterns. In
embodiments, the floating asset twin is configured to provide
visualizations of extendable buoyant members from a hull of the
ship to improve stability during certain maneuvers of the ship and
one or more of the sets of recommendations from the artificial
intelligence system for a change in the extendable buoyant members
to change performance of the ship. In embodiments, the floating
asset twin is configured to provide visualizations of a plurality
of inspection points on the ship and maintenance histories
associated with those inspection points. In embodiments, the
floating asset twin is further configured to provide one or more of
the sets of recommendations from the artificial intelligence system
for a change in maintenance of the plurality of inspection points.
In embodiments, the floating asset twin is further configured to
provide for visualizations of the plurality of inspection points on
the ship affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is further configured to provide details of
a ledger of activity associated with the visualization of the
plurality of inspection points on the ship affected by travel
within a geofenced area and maintenance histories associated with
those inspection points. In embodiments, the floating asset twin is
configured to provide for visualization for a first user of one of
a navigation course of the ship and an engine performance of the
ship within a first geofenced area and for visualization for a
second user of one of the navigation course of the ship and the
engine performance of the ship within a second different geofenced
area and where transit between the first and second geofenced areas
motivates a handoff of the floating asset twin of the ship between
the first user and the second user.
[0084] In embodiments, the digital twin is configured to at least
partially represent one or more of the maritime assets associated
with an event investigation and to at least partially detail a
timeline of the event investigation and the associated maritime
assets. In embodiments, the digital twin is further configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the associated maritime assets based on the event
investigation and the timeline. In embodiments, the digital twin is
configured to at least partially represent one or more of the
maritime assets associated with a legal proceeding and to at least
partially detail at least a portion of a timeline pertinent to the
legal proceeding and the associated maritime assets. In
embodiments, the digital twin is further configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated maritime assets based on the legal proceeding and the
timeline. In embodiments, the digital twin is configured to at
least partially represent one or more of the maritime assets
associated with at least one of a casualty forecast and a casualty
report, and to at least partially detail at least a portion of a
timeline pertinent to the at least one of the casualty forecast,
the casualty report, and the associated maritime assets. In
embodiments, the digital twin is further configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated maritime assets to reduce exposure relative to a set of
previous casualty forecasts based on at least one of the casualty
forecast and the casualty report, and the timeline. In embodiments,
the maritime assets include a port infrastructure facility, wherein
the data collected by a value chain network management platform
facilitates identifying theft at or misuse of the port
infrastructure facility by correlating data between a set of data
collectors for one or more physical items in the port
infrastructure facility and the digital twin detailing the one or
more physical items of the port infrastructure facility for the at
least one of the port infrastructure facility and a set of
operators. In embodiments, the digital twin details the one or more
physical items of the port infrastructure facility for at least one
operator that includes a view of expected states of at least a
portion of the one or more physical items. In embodiments, the
maritime assets include a shipyard, wherein the data collected by a
value chain network management platform facilitates identifying
theft at or misuse of one or more physical items in the shipyard by
correlating data between a set of data collectors for the one or
more physical items and the digital twin detailing the one or more
physical items of the shipyard for the at least one of the shipyard
and a set of operators. In embodiments, the digital twin details
the one or more physical items of the shipyard for at least one
operator that includes a view of expected states of at least a
portion of the one or more physical items. In embodiments, the
artificial intelligence system determines a set of geofence
parameters, and wherein the digital twin provides further
visualization of at least one geofence that integrates
representation of a set of the maritime assets with a
representation of a maritime environment adjacent to the geofence.
In embodiments, the digital twin is further configured to provide
one or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
set of maritime assets based on the visualization of the at least
one geofence. In embodiments, the maritime assets are ships capable
of carrying cargo, wherein the artificial intelligence system
determines a set of geofence parameters, and wherein the digital
twin provides further visualization of at least one geofence that
integrates representation of the ships capable of carrying cargo
with a representation of a maritime environment. In embodiments,
the digital twin is further configured to provide one or more of
the sets of recommendations from the artificial intelligence system
for a change of one of the attributes of the ships capable of
carrying cargo based on the visualization of the at least one
geofence.
[0085] In embodiments, an information technology system having a
management platform includes a user interface that provides a set
of adaptive intelligence systems that provide coordinated
artificial intelligence for a set of demand management applications
and a set of supply chain applications for a category of goods by
determining relationships among demand management and supply chain
applications based on inputs used by the applications and results
produced by the applications; and a set of artificial intelligence
systems as part of the set of adaptive intelligence systems that
provide coordinated intelligence for the set of demand management
applications and the set of supply chain applications for the
category of goods by determining a temporal prioritization of
demand management application outputs that impact control of supply
chain applications so as to meet a temporal demand for at least one
of the goods in the category of goods.
[0086] In embodiments, the adaptive intelligence system facilitates
coordinated artificial intelligence for the set of demand
management applications or the set of supply chain applications, or
both for a category of goods by processing data that is available
in any of a plurality of data sources including processes, bill of
materials, weather, traffic, design specification, customer
complaint logs, customer reviews, Enterprise Resource Planning
(ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle
Management (SLM) System, Product Lifecycle Management (PLM) System.
In embodiments, the set of adaptive intelligence systems provide
user access to coordinated artificial intelligence capabilities for
use with the sets of applications. In embodiments, the user
interface presents a set of coordinated artificial intelligence
capabilities responsive to the category of goods. In embodiments,
the user interface facilitates configuring the set of adaptive
intelligence systems with at least one artificial intelligence
system. In embodiments, the at least one artificial intelligence
system is a hybrid artificial intelligence system. In embodiments,
the at least one artificial intelligence system comprises a hybrid
neural network. In embodiments, the set of adaptive intelligence
systems that provide coordinated artificial intelligence operates
on or responsive to data collected by or produced by other systems
of an adaptive intelligence systems layer. In embodiments, the set
of adaptive intelligence systems that provide coordinated
artificial intelligence provides coordinated intelligence for a
specific operator and/or enterprise that participates in the supply
chain for the category of goods. In embodiments, the set of
adaptive intelligence systems that provide coordinated artificial
intelligence employs a neural network that processes at least one
of demand management application outputs and supply chain
application outputs to provide the coordinated intelligence.
[0087] In embodiments, the set of adaptive intelligence systems
that provide coordinated artificial intelligence is configured
through the user interface for at least two demand management
applications selected from the list consisting of a demand planning
application, a demand prediction application, a sales application,
a future demand aggregation application, a marketing application,
an advertising application, an e-commerce application, a marketing
analytics application, a customer relationship management
application, a search engine optimization application, a sales
management application, an advertising network application, a
behavioral tracking application, a marketing analytics application,
a location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service
[0088] In embodiments, the set of adaptive intelligence systems
that provide coordinated artificial intelligence is configured
through the user interface for at least two supply chain
applications selected from the list consisting of a goods timing
management application, a goods quantity management application, a
logistics management application, a shipping application, a
delivery application, an order for goods management application,
and an order for components management application. In embodiments,
the set of adaptive intelligence systems provides a set of
capabilities that facilitate development and deployment of
intelligence for at least one function selected from a list of
functions consisting of supply chain application automation, demand
management application automation, machine learning, artificial
intelligence, intelligent transactions, intelligent operations,
remote control, analytics, monitoring, reporting, state management,
event management, and process management. In embodiments, an
artificial intelligence system of the adaptive intelligence systems
layer operates on or responsive to data collected by or produced by
other systems of the adaptive intelligence systems layer. In
embodiments, a set of artificial intelligence systems may provide
coordinated intelligence for a specific operator and/or enterprise
that participates in the supply chain for the category of goods. In
embodiments, the coordinated intelligence includes a portion of a
set of artificial intelligence systems that employs a neural
network that processes at least one of demand management
application outputs and supply chain application outputs to provide
the coordinated intelligence.
[0089] In embodiments, the demand management applications include
at least two of a demand planning application, a demand prediction
application, a sales application, a future demand aggregation
application, a marketing application, an advertising application,
an e-commerce application, a marketing analytics application, a
customer relationship management application, a search engine
optimization application, a sales management application, an
advertising network application, a behavioral tracking application,
a marketing analytics application, a location-based product or
service-targeting application, a collaborative filtering
application, a recommendation engine for a product or service.
[0090] In embodiments, the supply chain applications include at
least two of a goods timing management application, a goods
quantity management application, a logistics management
application, a shipping application, a delivery application, an
order for goods management application, and an order for components
management application.
[0091] In embodiments, an artificial intelligence system
facilitates coordinated intelligence for the sets of applications
by processing data that is available in any of a plurality of data
sources including processes, bill of materials, weather, traffic,
design specification, customer complaint logs, customer reviews,
Enterprise Resource Planning (ERP) System, Customer Relationship
Management (CRM) System, Customer Experience Management (CEM)
System, Service Lifecycle Management (SLM) System, Product
Lifecycle Management (PLM) System.
[0092] In embodiments, the set of adaptive intelligence systems are
configured in a topology that facilitates shared adaptation
capabilities among at least two adaptive intelligence systems in
the set of adaptive intelligence systems. In embodiments, the set
of adaptive intelligence systems employ artificial intelligence to
provision available network resources for both the set of demand
management applications and for the set of supply chain
applications. In embodiments, the set of demand management
applications comprises a demand planning application. In
embodiments, the set of adaptive intelligence systems employ
artificial intelligence to improve at least one of the list of
outputs consisting of a process output, an application output, a
process outcome and an application outcome.
[0093] One path to distilling information is digital twin
technology, which can present large amounts of data in a digestible
format that represents salient characteristics of an item, often
updated in real time or near real time as the twin is updated to
reflect the current state based on a pipeline of data about a
represented item. While this is helpful, current digital twin
technology has its limitations due to the fact that different roles
within an organization may require different information to draw
their insights. For example, a CEO of an industrial facility makes
decisions based on a "10,000 foot view" of the company. The CEO may
review profit and loss (P&L) data, industry trends, and
employee trends (e.g., employee satisfaction or employee retention
rates) to make overall decisions on behalf of the organization but
does not necessarily need to see the granular data points to make
decisions. In contrast, a different user, such as a CFO, may
require more granular information, such as sales figures by region,
marketing costs, maintenance costs, depreciation information, human
capital costs, and costs of third-party vendors to draw her
conclusions, but may not be as concerned with employee or industry
trends. Similarly, a CTO may have no need for P&L data but may
require an in-depth visualization of the processes within different
manufacturing facilities to gain a better understanding of
opportunities to improve process outcomes or to diagnose issues
within processes, equipment or systems. Thus, a need exists for
digital twins and other interfaces that are configured for
particular roles.
[0094] As a further challenge, a given role may have varying needs
based on context. For example, while the CEO might focus on
higher-level data for many activities, such as strategic decision
making or board communications, the same CEO may find more
granular, micro-scale data useful for other activities, such as
when an issue is escalated from a subdivision of the organization
for input. Thus, a need exists for context-adaptive digital twins
for each role, including ones that provide relevant displays and
information of the right type at the right time for various
situations and activities undertaken by the role.
[0095] More generally, ubiquitous connectivity and the
proliferation of larger and larger data sets offers enterprise
leaders opportunities for an unprecedented degree of awareness and
control over enterprise assets and activities. A need and
opportunity exist for an enterprise control tower by which
executive leaders can, through various interfaces, including
executive digital twins, dashboards, and similar systems, obtain
timely information that is curated to invoke relevant awareness,
support effective decisions and enable operational control.
[0096] According to some embodiments of the present disclosure, an
enterprise management platform is disclosed. In some embodiments,
the enterprise management platform integrates a set of executive
digital twins that take data from an intelligent data and
networking pipeline to provide role-specific features, including
AI-enabled expert agent features and enhanced collaboration
features, and salient views of the entities and workflows of an
enterprise, thereby enabling executives to monitor and control
entities and workflows to an unprecedented degree at appropriate
levels of granularity and using familiar taxonomies and
decision-making frameworks.
[0097] Further provided herein are methods and systems for
enterprise control towers by which executive leaders can, through
various interfaces, including executive digital twins, dashboards,
and similar systems, obtain timely information (often in real-time
or near real-time) that is curated to invoke relevant awareness,
support effective decisions and enable operational control. The
present disclosure further relates to an executive control tower
and enterprise management platform that is configured to provide
and use a converged technology stack that includes intelligent
sensing and data collection, curation and handling of data through
various stages of a distributed storage, networking and
connectivity pipeline (from a set of local operational environments
through information technology networks to various distributed
on-premises and cloud computing environments), and deployment of
various application-specific and general artificial intelligence
capabilities in order to enable executive control towers, including
role-specific executive digital twins, that are used by executives
in management of the value chain network operations of an
enterprise.
[0098] In embodiments of the present disclosure, a method is
provided for configuring role-based digital twins, comprising:
receiving, by a processing system having one or more processors, an
organizational definition of an enterprise, wherein the
organizational definition defines a set of roles within the
enterprise; generating, by the processing system, an organizational
digital twin of the enterprise based on the organizational
definition, wherein the organizational digital twin is a digital
representation of an organizational structure of the enterprise;
determining, by the processing system, a set of relationships
between different roles within the set of roles based on the
organizational definition; determining, by the processing system, a
set of settings for a role from the set of roles based on the
determined set of relationships; linking an identity of a
respective individual to the role; determining, by the processing
system, a configuration of a presentation layer of a role-based
digital twin corresponding to the role based on the settings of the
role that is linked to the identity, wherein the configuration of
the presentation layer defines a set of states that is depicted in
the role-based digital twin associated with the role; determining,
by the processing system, a set of data sources that provide data
corresponding to the set of states, wherein each data source
provides one or more respective types of data; and configuring one
or more data structures that is received from the one or more data
sources, wherein the one or more data structures are configured to
provide data used to populate one or more of the set of states in
the role-based digital twin.
[0099] In embodiments, an organizational definition may further
identify a set of physical assets of the enterprise.
[0100] In embodiments, determining a set of relationships may
include parsing the organizational definition to identify a
reporting structure and one or more business units of the
enterprise.
[0101] In embodiments, a set of relationships may be inferred from
a reporting structure and a business unit.
[0102] In embodiments, a set of identities may be linked to a set
of roles, wherein each identity corresponds to a respective role
from the set of roles.
[0103] In embodiments, a role-based digital twin may integrate with
an enterprise resource planning system that operates on the
organizational digital twin that represents a set of roles in the
enterprise, such that changes in an enterprise resource planning
system are automatically reflected in the organizational digital
twin.
[0104] In embodiments, an organizational structure may include
hierarchical components, which may be embodied in a graph data
structure.
[0105] In embodiments, a set of settings for the set of roles may
include role-based permission settings.
[0106] In embodiments, a role-based permission setting may be based
on hierarchical components defined in the organizational
definition.
[0107] In embodiments, a set of settings for a set of roles may
include role-based preference settings.
[0108] In embodiments, a role-based preference setting may be
configured based on a set of role-specific templates.
[0109] In embodiments, a set of templates may include at least one
of a CEO template, a COO template, a CFO template, a counsel
template, a board member template, a CTO template, a chief
marketing officer template, an information technology manager
template, a chief information officer template, a chief data
officer template, an investor template, a customer template, a
vendor template, a supplier template, an engineering manager
template, a project manager template, an operations manager
template, a sales manager template, a salesperson template, a
service manager template, a maintenance operator template, and a
business development template.
[0110] In embodiments, a set of settings for the set of roles may
include role-based taxonomy settings.
[0111] In embodiments, a taxonomy setting may identify a taxonomy
that is used to characterize data that is presented in a role-based
digital twin, such that the data is presented in a taxonomy that is
linked to the role corresponding to the role-based digital
twin.
[0112] In embodiments, a set of taxonomies includes at least one of
a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy,
a board member taxonomy, a CTO taxonomy, a chief marketing officer
taxonomy, an information technology manager taxonomy, a chief
information officer taxonomy, a chief data officer taxonomy, an
investor taxonomy, a customer taxonomy, a vendor taxonomy, a
supplier taxonomy, an engineering manager taxonomy, a project
manager taxonomy, an operations manager taxonomy, a sales manager
taxonomy, a salesperson taxonomy, a service manager taxonomy, a
maintenance operator taxonomy, and a business development
taxonomy.
[0113] In embodiments, at least one role of the set of roles may be
selected from among a CEO role, a COO role, a CFO role, a counsel
role, a board member role, a CTO role, an information technology
manager role, a chief information officer role, a chief data
officer role, a human resources manager role, an investor role, an
engineering manager role, an accountant role, an auditor role, a
resource planning role, a public relations manager role, a project
manager role, an operations manager role, a research and
development role, an engineer role, including but not limited to
mechanical engineer, electrical engineer, semiconductor engineer,
chemical engineer, computer science engineer, data science
engineer, network engineer, or some other type of engineer, and a
business development role.
[0114] In embodiments, at least one role may be selected from among
a factory manager role, a factory operations role, a factory worker
role, a power plant manager role, a power plant operations role, a
power plant worker role, an equipment service role, and an
equipment maintenance operator role.
[0115] In embodiments, at least one role may be selected from among
a market maker role, a market analyst role, an exchange manager
role, a broker-dealer role, a trading role, a reconciliation role,
a contract counterparty role, an exchange rate setting role, a
market orchestration role, a market configuration role, and a
contract configuration role.
[0116] In embodiments, at least one role may be selected from among
a chief marketing officer role, a product development role, a
supply chain manager role, a product design role, a marketing
analyst role, a product manager role, a competitive analyst role, a
customer service representative role, a procurement operator, an
inbound logistics operator, an outbound logistics operator, a
customer role, a supplier role, a vendor role, a demand management
role, a marketing manager role, a sales manager role, a service
manager role, a demand forecasting role, a retail manager role, a
warehouse manager role, a salesperson role, and a distribution
center manager role.
[0117] In embodiments of the present disclosure, a method is
provided for training an expert agent, comprising; receiving
digital twin data from a set of data sources, the digital twin data
including: sensor data that is received from a set of sensors that
monitor a set of monitored physical entities associated with the
enterprise, the sensor data transported by a set of network
entities; enterprise data streams generated by a set of enterprise
assets, wherein the enterprise assets include at least one of
physical entities associated with the enterprise and digital
entities associated with the enterprise; structuring the digital
twin data into a set of digital twin data structures that are
configured to serve a plurality of different role-based digital
twins; receiving a request for a role-based digital twin from a
client application, wherein the role-based digital twin is
configured with respect to a defined role within the enterprise;
determining a subset of the structured digital twin data to
corresponds to a set of states that are depicted in the role-based
digital twin; providing the subset of the structured digital twin
data to the client application; receiving expert agent training
data sets from the client application, each expert agent training
data set indicating a respective action taken by a user using the
client application and one or more features that correspond to the
respective action; and training an expert agent on behalf of the
user based on the expert agent training data sets, wherein the
expert agent is configured to determine actions to be performed on
behalf of the user, wherein the determined actions are either
recommended to the user or automatically performed on behalf of the
user.
[0118] In embodiments, a defined role may be selected from among a
CEO role, a COO role, a CFO role, a counsel role, a board member
role, a CTO role, an information technology manager role, a chief
information officer role, a chief data officer role, an investor
role, an engineering manager role, a project manager role, an
operations manager role, and a business development role.
[0119] In embodiments, a defined role may be selected from among a
factory manager role, a factory operations role, a factory worker
role, a power plant manager role, a power plant operations role, a
power plant worker role, an equipment service role, and an
equipment maintenance operator role.
[0120] In embodiments, a defined role may be selected from among a
market maker role, an exchange manager role, a broker-dealer role,
a trading role, a reconciliation role, a contract counterparty
role, an exchange rate setting role, a market orchestration role, a
market configuration role, and a contract configuration role.
[0121] In embodiments, a defined role may be selected from among a
chief marketing officer role, a product development role, a supply
chain manager role, a customer role, a supplier role, a vendor
role, a demand management role, a marketing manager role, a sales
manager role, a service manager role, a demand forecasting role, a
retail manager role, a warehouse manager role, a salesperson role,
and a distribution center manager role.
[0122] In embodiments, an expert agent training data may include
interactions training data that indicates a set of interactions
with a set of experts by the user during performance of the
role.
[0123] In embodiments, a set of interactions used to train the
expert agent may include interactions of the user with the physical
entities, interactions of the user with the role-based digital
twin, interactions of the user with the sensor data as depicted in
the role-based digital twin, interactions of the experts with the
data streams generated by the physical entities, interactions of
the experts with one or more computational entities, interactions
of the user with one or more network entities, or some other type
of interaction.
[0124] In embodiments, an expert agent may be trained to determine
an action selected from the group comprising: selection of a tool,
selection of a task, selection of a dimension, setting of a
parameter, selection of an object, selection of a workflow,
triggering of a workflow, ordering of a process, ordering of a
workflow, cessation of a workflow, selection of a data set,
selection of a design choice, creation of a set of design choices,
identification of a failure mode, identification of a fault,
identification of an operating mode, identification of a problem,
selection of a human resource, selection of a workforce resource,
providing an instruction to a human resource, and providing an
instruction to a workforce resource.
[0125] In embodiments, an executive may be trained on a training
set of outcomes resulting from the actions taken by the
executive.
[0126] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0127] In embodiments, an expert agent may be trained to perform an
action selected from among determining an architecture for a
system, reporting on a status, reporting on an event, reporting on
a context, reporting on a condition, determining a model,
configuring a model, populating a model, designing a system,
designing a process, designing an apparatus, engineering a system,
engineering a device, engineering a process, engineering a product,
maintaining a system, maintaining a device, maintaining a process,
maintaining a network, maintaining a computational resource,
maintaining equipment, maintaining hardware, repairing a system,
repairing a device, repairing a process, repairing a network,
repairing a computational resource, repairing equipment, repairing
hardware, assembling a system, assembling a device, assembling a
process, assembling a network, assembling a computational resource,
assembling equipment, assembling hardware, setting a price,
physically securing a system, physically securing a device,
physically securing a process, physically securing a network,
physically securing a computational resource, physically securing
equipment, physically securing hardware, cyber-securing a system,
cyber-securing a device, cyber-securing a process, cyber-securing a
network, cyber-securing a computational resource, cyber-securing
equipment, cyber-securing hardware, detecting a threat, detecting a
fault, tuning a system, tuning a device, tuning a process, tuning a
network, tuning a computational resource, tuning equipment, tuning
hardware, optimizing a system, optimizing a device, optimizing a
process, optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[0128] In embodiments, an expert agent is at least one of trained
and configured via feedback from at least one expert in the defined
role regarding a set of outputs of expert agent.
[0129] In embodiments, a set of outputs of the expert agent upon
which the expert provides feedback may include at least one of a
recommendation, a classification, a prediction, a control
instruction, an input selection, a protocol selection, a
communication, an alert, a target selection for a communication, a
data storage selection, a computational selection, a configuration,
an event detection, and a forecast.
[0130] In embodiments, feedback of the at least one expert may be
solicited to train the expert agent to replicate the expertise of
the expert in the role.
[0131] In embodiments, a feedback of the at least one expert may be
used to modify the set of inputs to the expert agent and/or used to
identify and characterize at least one error by the expert
agent.
[0132] In embodiments, a report on a set of errors may be provided
to a user of the expert agent to enable reconfiguring of the expert
agent based on the feedback from the expert.
[0133] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[0134] In embodiments, an expert agent may be trained learn upon a
training set of outcomes and to provide at least one of training
and guidance to an individual who is responsible for performing the
defined role.
[0135] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0136] In embodiments of the present disclosure, a method is
provided taking an information technology architecture that
supports a digital twin of a set of physical and digital entities,
the architecture including: a set of sensors that provide sensor
data about the set of physical entities; a set of data streams
generated by at least a subset of the set of physical and digital
entities; a set of computational entities for processing data and a
set of network entities for transporting data that is derived from
the set of sensors and the set of data streams; a set of data
processing systems for extracting, transforming and loading the
data that is transported by the network entities into a set of
resources that are sources for the digital twin; and integrating an
artificial intelligence system with the information technology
architecture, wherein the artificial intelligence system is
configured to operate as a double of an expert worker for a defined
role of the enterprise.
[0137] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by a specific expert worker during performance of the
defined role.
[0138] In embodiments, a set of interactions may be used to train
the artificial intelligence system may include interactions of the
expert with the physical entities, wherein the set of interactions
used to train the artificial intelligence system includes
interactions of the expert with the digital twin.
[0139] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the sensor data, wherein the set of interactions used
to train the artificial intelligence system includes interactions
of the expert with the data streams generated by the physical
entities.
[0140] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the computational entities, wherein the set of
interactions used to train the artificial intelligence system may
include interactions of the expert with the network entities.
[0141] In embodiments, a set of interactions may be parsed to
identify a chain of reasoning of the expert worker upon a set of
information and the chain of reasoning is embodied in the
configuration of the artificial intelligence system.
[0142] In embodiments, an artificial intelligence system may be
trained based on the set interactions to determine an action
selected from: selection of a tool, selection of a task, selection
of a dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[0143] In embodiments, a chain of reasoning may be parsed to
identify a type of reasoning of the expert worker and the type of
reasoning is used as a basis for configuration of the artificial
intelligence system.
[0144] In embodiments, a chain of reasoning may be a deductive
chain of reasoning from a set of data.
[0145] In embodiments, a chain of reasoning may be an inductive
chain of reasoning, a classification chain of reasoning, a
predictive chain of reasoning, an iterative chain of reasoning, a
trial-and-error chain of reasoning, a Bayesian chain of reasoning,
a scientific method chain of reasoning, or some other reasoning
method or system.
[0146] In embodiments, an artificial intelligence system may be
trained on a training set to perform an action selected from among
determining an architecture for a system, reporting on a status,
reporting on an event, reporting on a context, reporting on a
condition, determining a model, configuring a model, populating a
model, designing a system, designing a process, designing an
apparatus, engineering a system, engineering a device, engineering
a process, engineering a product, maintaining a system, maintaining
a device, maintaining a process, maintaining a network, maintaining
a computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[0147] In embodiments, a training set of interactions may be parsed
to identify a type of processing of the expert worker upon a set of
information and the type of processing is embodied in the
configuration of the artificial intelligence system.
[0148] In embodiments, a type of processing may use visual
processing of the expert worker and the artificial intelligence
system is configured to operate on image or video information.
[0149] In embodiments, a type of processing may use audio
processing of the expert worker and the artificial intelligence
system may be configured to operate on audio information.
[0150] In embodiments, a type of processing may use touch
processing of the expert worker and the artificial intelligence
system may be configured to operate on physical sensor
information.
[0151] In embodiments, a type of processing may use olfactory
processing of the expert worker and the artificial intelligence
system may be configured to operate on chemical sensing
information.
[0152] In embodiments, a type of processing may use textual
information processing of the expert worker and the artificial
intelligence system may be configured to operate on text
information.
[0153] In embodiments, a type of processing may use motion
processing of the expert worker and the artificial intelligence
system may be configured to operate on motion information.
[0154] In embodiments, a type of processing may use taste
processing of the expert worker and the artificial intelligence
system may be configured to operate on chemical information.
[0155] In embodiments, a type of processing may use mathematical
processing of the expert worker and the artificial intelligence
system may be configured to operate mathematically on available
data.
[0156] In embodiments, a type of processing may use executive
manager processing of the expert worker and the artificial
intelligence system may be configured to provide executive decision
support.
[0157] In embodiments, a type of processing may use creative
processing of the expert worker and the artificial intelligence
system may be configured to provide a set of alternative
options.
[0158] In embodiments, a type of processing may use analytic
processing of the expert worker to select among a set of available
choices and the artificial intelligence system may be configured to
provide a recommendation among a set of choices.
[0159] In embodiments, an artificial intelligence system may be
trained on a training set of outcomes.
[0160] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0161] In embodiments, an artificial intelligence system may be at
least one of trained and configured via feedback from the specific
expert worker regarding a set of outputs of the artificial
intelligence system.
[0162] In embodiments, a set of outputs of the artificial
intelligence system upon which the expert provides feedback may
include at least one of a recommendation, a classification, a
prediction, a control instruction, an input selection, a protocol
selection, a communication, an alert, a target selection for a
communication, a data storage selection, a computational selection,
a configuration, an event detection, and a forecast.
[0163] In embodiments, a feedback of the expert may be solicited to
train the artificial intelligence system to replicate the expertise
of the expert in the role, used to modify the set of inputs to the
artificial intelligence system, and or used to identify and
characterize at least one error by the artificial intelligence
system.
[0164] In embodiments, a report on a set of errors may be provided
to a manager associated with the artificial intelligence system to
enable reconfiguring of the artificial intelligence system based on
the feedback from the expert.
[0165] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[0166] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
another worker to enable the other worker to perform the defined
role.
[0167] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[0168] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0169] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
another worker to enable the other worker to perform the defined
role.
[0170] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[0171] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0172] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to the
expert worker to enable the expert worker to perform the defined
role.
[0173] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[0174] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0175] In embodiments, outcomes may be compared between a set of
actions of the expert worker and a set of outputs of the artificial
intelligence system.
[0176] In embodiments, a comparison may be used to train the expert
worker.
[0177] In embodiments, a comparison may be used to improve the
artificial intelligence system.
[0178] In embodiments, a defined role of the expert worker may be
selected from among a CEO role, a COO role, a CFO role, a counsel
role, a board member role, a CTO role, a chief marketing officer
role, an information technology manager role, a chief information
officer role, a chief data officer role, an investor role, a
customer role, a vendor role, a supplier role, an engineering
manager role, a project manager role, an operations manager role, a
sales manager role, a salesperson role, a service manager role, a
maintenance operator role, and a business development role.
[0179] In embodiments, computational entities and the network
entities may be integrated as a converged computational and network
entity.
[0180] In embodiments of the present disclosure, a method is
provided for maintaining an information technology architecture
that supports a digital twin of a set of physical entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical entities; a set of
computational entities for processing data and a set of network
entities for transporting data that is derived from the set of
sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; and integrating an artificial
intelligence system with the information technology architecture,
wherein the artificial intelligence system is configured to operate
as a double of an expert worker for a defined role of the
enterprise and wherein an electronic account associated with the
expert worker is awarded with a benefit for training the artificial
intelligence system.
[0181] In embodiments, a benefit may be a reward based on the
outcomes of the use of the artificial intelligence system, a reward
based on the productivity of the artificial intelligence system
and/or a reward based on a measure of the expertise of the
artificial intelligence system.
[0182] In embodiments, a benefit may be a share of revenue or
profit generated by the work of the artificial intelligence system
and/or a reward that is tracked via a distributed ledger on a
blockchain that captures information associated with a set of
actions and events involving the artificial intelligence
system.
[0183] In embodiments, a reward may be administered via a smart
contract operating on the blockchain.
[0184] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by a specific expert worker during performance of the
defined role.
[0185] In embodiments, a set of interactions may be used to train
the artificial intelligence system includes interactions of the
expert with the physical entities, used to train the artificial
intelligence system includes interactions of the expert with the
digital twin and/or used to train the artificial intelligence
system includes interactions of the expert with the sensor
data.
[0186] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the data streams generated by the physical entities,
interactions of the expert with the computational entities, and/or
interactions of the expert with the network entities.
[0187] In embodiments, an artificial intelligence system may be
trained based on the interactions to determine an action selected
from: selection of a tool, selection of a task, selection of a
dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[0188] In embodiments, a training set of interactions may be parsed
to identify a chain of reasoning of the expert worker upon a set of
information and the chain of reasoning is embodied in the
configuration of the artificial intelligence system.
[0189] In embodiments, a chain of reasoning may be parsed to
identify a type of reasoning of the expert worker and the type of
reasoning is used as a basis for configuration of the artificial
intelligence system.
[0190] In embodiments, a chain of reasoning may be a deductive
chain of reasoning from a set of data.
[0191] In embodiments, an artificial intelligence system may be
trained to perform an action selected from: determining an
architecture for a system, reporting on a status, reporting on an
event, reporting on a context, reporting on a condition,
determining a model, configuring a model, populating a model,
designing a system, designing a process, designing an apparatus,
engineering a system, engineering a device, engineering a process,
engineering a product, maintaining a system, maintaining a device,
maintaining a process, maintaining a network, maintaining a
computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[0192] In embodiments of the present disclosure, a method is
provided for taking an information technology architecture that
supports a digital twin of a set of physical entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical entities; a set of
computational entities for processing data and a set of network
entities for transporting data that is derived from the set of
sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; and integrating an artificial
intelligence system with the information technology architecture,
wherein the artificial intelligence system is configured to operate
as a double of a defined workforce involving a defined set of roles
of the enterprise.
[0193] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by members of the defined workforce during performance
of the defined set of roles.
[0194] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
workforce with the physical entities, interactions of the workforce
with the digital twin, interactions of the workforce with the
sensor data, interactions of the workforce with the data streams
generated by the physical entities, interactions of the workforce
with the computational entities, and/or interactions of the
workforce with the network entities.
[0195] In embodiments, a training set of interactions may be parsed
to identify a chain of operations of the workforce upon a set of
information and the chain of reasoning may be embodied in the
configuration of the artificial intelligence system.
[0196] In embodiments, a training set of interactions may be parsed
to identify a type of processing of the workforce upon a set of
information and the type of processing may be embodied in the
configuration of the artificial intelligence system.
[0197] In embodiments, an artificial intelligence system may be
trained based on the interactions to determine an action selected
from: selection of a tool, selection of a task, selection of a
dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[0198] In embodiments, an artificial intelligence system may be
trained on a training set of outcomes.
[0199] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0200] In embodiments, an artificial intelligence system may be at
least one of trained and configured via feedback from members of
the workforce regarding a set of outputs of the artificial
intelligence system.
[0201] In embodiments, a set of outputs of the artificial
intelligence system upon which the workforce members provide
feedback may include at least one of a recommendation, a
classification, a prediction, a control instruction, an input
selection, a protocol selection, a communication, an alert, a
target selection for a communication, a data storage selection, a
computational selection, a configuration, an event detection, and a
forecast.
[0202] In embodiments, a feedback of the workforce members may be
solicited to train the artificial intelligence system to replicate
the operation of the workforce in the defined set of roles.
[0203] In embodiments, a feedback of the workforce members may be
used to modify the set of inputs to the artificial intelligence
system.
[0204] In embodiments, a feedback of the workforce members may be
used to identify and characterize at least one error by the
artificial intelligence system.
[0205] In embodiments, a report on a set of errors may be provided
to a manager of the artificial intelligence system to enable
reconfiguring of the artificial intelligence system based on the
feedback.
[0206] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[0207] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
enable the other worker to perform a role within the defined set of
roles of the workforce.
[0208] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[0209] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[0210] In embodiments, an artificial intelligence system may be
trained to perform an action selected from among determining an
architecture for a system, reporting on a status, reporting on an
event, reporting on a context, reporting on a condition,
determining a model, configuring a model, populating a model,
designing a system, designing a process, designing an apparatus,
engineering a system, engineering a device, engineering a process,
engineering a product, maintaining a system, maintaining a device,
maintaining a process, maintaining a network, maintaining a
computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[0211] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to the
workforce to enable the workforce to perform the defined role.
[0212] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[0213] In embodiments, a training set of outcomes may include. data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome
[0214] In embodiments, outcomes may be compared between a set of
actions of the workforce and a set of outputs of the artificial
intelligence system, wherein the comparison is used to train the
workforce and/or is used to improve the artificial intelligence
system.
[0215] In embodiments, at least one role within the set of roles of
the workforce may be selected from among a CEO role, a COO role, a
CFO role, a counsel role, a board member role, a CTO role, an
information technology manager role, a chief information officer
role, a chief data officer role, an investor role, an engineering
manager role, a project manager role, an operations manager role,
and a business development role.
[0216] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, or
some other kind of workforce.
[0217] In embodiments, a workforce may be a brokering workforce for
a marketplace, a trading workforce for a marketplace, a trade
reconciliation workforce for a marketplace, a transactional
execution workforce for a marketplace, or some other kind of
workforce.
[0218] In embodiments, computational entities and the network
entities may be integrated as a converged computational and network
entity.
[0219] In embodiments of the present disclosure, a method is
provided for configuring a digital twin of a workforce, comprising:
representing an enterprise organizational structure in a digital
twin of an enterprise; parsing the structure to infer relationships
among a set of roles within the organizational structure, the
relationships and the roles defining a workforce of the enterprise;
and configuring the presentation layer of a digital twin to
represent the enterprise as a set of workforces having a set of
attributes and relationships.
[0220] In embodiments, a digital twin may integrate with an
enterprise resource planning system that operates on a data
structure representing a set of roles in the enterprise, such that
changes in the enterprise resource planning system are
automatically reflected in the digital twin.
[0221] In embodiments, an organizational structure may include
hierarchical components.
[0222] In embodiments, hierarchical components may be embodied in a
graph data structure.
[0223] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, or some other type of workforce.
[0224] In embodiments, a workforce may be a network operations
workforce responsible for operating a network for an industrial
production environment, wherein the workforce is a supply chain
management workforce, a demand planning workforce, a logistics
planning workforce, a vendor management workforce, a brokering
workforce for a marketplace, a trading workforce for a marketplace,
a trade reconciliation workforce for a marketplace, a transactional
execution workforce for a marketplace, or some other type of
workforce.
[0225] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[0226] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[0227] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[0228] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[0229] In embodiments, a digital twin may represent a
recommendation for training for the workforce, a recommendation for
augmentation of the workforce, a recommendation for configuration
of a set of operations involving the workforce, a recommendation
for configuration of the workforce, or some other kind of
recommendation.
[0230] In embodiments of the present disclosure, a method is
provided for providing a digital twin of a workforce, comprising:
maintaining an information technology architecture that supports a
digital twin of a set of physical and digital entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical and digital entities; a
set of computational entities for processing data and a set of
network entities for transporting data that is derived from the set
of sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; representing an enterprise
organizational structure in a digital twin of an enterprise;
parsing the structure to infer relationships among a set of roles
within the organizational structure, the relationships and the
roles defining a workforce of the enterprise; integrating an
artificial intelligence system with the information technology
architecture, wherein the artificial intelligence system is
configured to operate as a double of a set of workers for a set of
defined roles of the enterprise and configuring the presentation
layer of a digital twin to represent the enterprise as a set of
workforces having a set of attributes and relationships, wherein
the attributes and relationships include human worker attributes
and relationships and artificial intelligence double attributes and
relationships.
[0231] In embodiments, a digital twin may integrate with an
enterprise resource planning system that operates on a data
structure representing a set of roles in the enterprise, such that
changes in the enterprise resource planning system are
automatically reflected in the digital twin.
[0232] In embodiments, an organizational structure may include
hierarchical components.
[0233] In embodiments, hierarchical components may be embodied in a
graph data structure.
[0234] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, a
brokering workforce, a trading workforce, a trade reconciliation
workforce, a transactional execution workforce, or some other type
of workforce.
[0235] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[0236] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[0237] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[0238] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[0239] In embodiments, a digital twin may represent a
recommendation for training for the workforce, a recommendation for
augmentation of the workforce, a recommendation for configuration
of a set of operations involving the workforce, a recommendation
for configuration of the workforce, a set of capacities and
competencies of a set of workers and a set of doubles, and/or a set
of mixed workgroups of human workers and artificial intelligence
doubles.
[0240] In embodiments of the present disclosure, a method is
provided for serving digital twins comprising: receiving, by a
processing system of a digital twin system, a request for a digital
twin from a user device of a user associated with an enterprise,
the enterprise deploying a sensor system to monitor one or more
facilities of the enterprise; determining, by the processing
system, a workforce role of the user with respect to the
enterprise; generating, by the processing system, a role-based
digital twin corresponding to the workforce role of the user based
on a perspective view corresponding to the workforce role of the
user, wherein the role-based digital twin depicts one or more
states and/or entities that are related to the enterprise;
providing, by the processing system, the role-based digital twin to
the user device, wherein providing the role-based digital twin:
identifying, by the processing system, a set of data types that are
used to populate the at least one of the states and/or entities of
the role-based digital twin, wherein the set of data types include
one or more sensor data feeds that are received from the sensor
system deployed by the enterprise; and connecting, by the
processing system, the one or more sensor data streams to the
role-based digital twin.
[0241] In embodiments, generating a role-based digital twin may
include determining the perspective view corresponding to the
workforce role of the user based on the workforce role of the user
and a set of data types that are relevant to the workforce role of
the user.
[0242] In embodiments, determining the perspective view
corresponding to the workforce role of the user may include
determining an appropriate granularity level for each of the data
types.
[0243] In embodiments, an appropriate granularity level for at
least one of the data types may be defined in a default
configuration corresponding to the workforce role.
[0244] In embodiments, an appropriate granularity level for at
least one of the data types may be determined based on previous
interactions of the user with the role-based digital twin.
[0245] In embodiments, a sensor system may include an edge device
that receives sensor data from a set of sensors within the sensor
system and generates the sensor data stream that is provided to the
digital twin system via a network.
[0246] In embodiments, an edge device may receive sensor data from
the set of sensors and selectively compresses the sensor data based
on values indicated in the sensor data to obtain the sensor data
stream.
[0247] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
and routing the sensor data stream to the user device that is
presenting the role-based digital twin to the user.
[0248] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify one or more fault
conditions corresponding to an object being monitored by the sensor
system; and routing an indicator of the fault condition to the user
device that is presenting the role-based digital twin to the
user.
[0249] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify a recommendation
corresponding to the workforce role of the user; and routing an
indicator of the recommendation to the user device that is
presenting the role-based digital twin to the user.
[0250] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify a recommendation
corresponding to the workforce role of the user; and routing an
indicator of the recommendation to the user device that is
presenting the role-based digital twin to the user.
[0251] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, or
some other type of workforce.
[0252] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[0253] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[0254] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[0255] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[0256] In embodiments of the present disclosure, a method is
provided for providing a digital twin of a workforce, comprising:
maintaining an information technology architecture that supports a
digital twin of a set of physical and digital entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical and digital entities; a
set of computational entities for processing data and a set of
network entities for transporting data that is derived from the set
of sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; representing an enterprise
organizational structure in a digital twin of an enterprise;
parsing the structure to infer relationships among a set of roles
within the organizational structure, the relationships and the
roles defining a workforce of the enterprise; determining a set of
parameters with which the digital twin is configured based on the
inferred set of relationships; and configuring the presentation
layer of a digital twin based on the set of parameters.
[0257] A more complete understanding of the disclosure will be
appreciated from the description and accompanying drawings and the
claims, which follow. All documents referenced herein are hereby
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0258] The accompanying drawings, which are included to provide a
better understanding of the disclosure, illustrate embodiments of
the disclosure and together with the description serve to explain
the many aspects of the disclosure. In the drawings:
[0259] FIG. 1 is a block diagram showing prior art relationships of
various entities and facilities in a supply chain.
[0260] FIG. 2 is a block diagram showing components and
interrelationships of systems and processes of a value chain
network in accordance with the present disclosure.
[0261] FIG. 3 is another block diagram showing components and
interrelationships of systems and processes of a value chain
network in accordance with the present disclosure.
[0262] FIG. 4 is a block diagram showing components and
interrelationships of systems and processes of a digital products
network of FIGS. 2 and 3 in accordance with the present
disclosure.
[0263] FIG. 5 is a block diagram showing components and
interrelationships of systems and processes of a value chain
network technology stack in accordance with the present
disclosure.
[0264] FIG. 6 is a block diagram showing a platform and
relationships for orchestrating controls of various entities in a
value chain network in accordance with the present disclosure.
[0265] FIG. 7 is a block diagram showing components and
relationships in embodiments of a value chain network management
platform in accordance with the present disclosure.
[0266] FIG. 8 is a block diagram showing components and
relationships of value chain entities managed by embodiments of a
value chain network management platform in accordance with the
present disclosure.
[0267] FIG. 9 is a block diagram showing network relationships of
entities in a value chain network in accordance with the present
disclosure.
[0268] FIG. 10 is a block diagram showing a set of applications
supported by unified data handling layers in a value chain network
management platform in accordance with the present disclosure.
[0269] FIG. 11 is a block diagram showing components and
relationships in embodiments of a value chain network management
platform in accordance with the present disclosure.
[0270] FIG. 12 is a block diagram showing components and
relationships of a data storage layer in embodiments of a value
chain network management platform in accordance with the present
disclosure.
[0271] FIG. 13 is a block diagram showing components and
relationships of an adaptive intelligent systems layer in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0272] FIG. 14 is a block diagram that depicts providing adaptive
intelligence systems for coordinated intelligence for sets of
demand and supply applications for a category of goods in
accordance with the present disclosure.
[0273] FIG. 15 is a block diagram that depicts providing hybrid
adaptive intelligence systems for coordinated intelligence for sets
of demand and supply applications or a category of goods in
accordance with the present disclosure.
[0274] FIG. 16 is a block diagram that depicts providing adaptive
intelligence systems for predictive intelligence for sets of demand
and supply applications for a category of goods in accordance with
the present disclosure.
[0275] FIG. 17 is a block diagram that depicts providing adaptive
intelligence systems for classification intelligence for sets of
demand and supply applications for a category of goods in
accordance with the present disclosure.
[0276] FIG. 18 is a block diagram that depicts providing adaptive
intelligence systems to produce automated control signals for sets
of demand and supply applications for a category of goods in
accordance with the present disclosure.
[0277] FIG. 19 is a block diagram that depicts training artificial
intelligence/machine learning systems to produce information
routing recommendations for a selected value chain network in
accordance with the present disclosure.
[0278] FIG. 20 is a block diagram that depicts a semi-sentient
problem recognition system for recognition of pain points/problem
states in a value chain network in accordance with the present
disclosure.
[0279] FIG. 21 is a block diagram that depicts a set of artificial
intelligence systems operating on value chain information to enable
automated coordination of value chain activities for an enterprise
in accordance with the present disclosure.
[0280] FIG. 22 is a block diagram showing components and
relationships involved in integrating a set of digital twins in an
embodiment of a value chain network management platform in
accordance with the present disclosure.
[0281] FIG. 23 is a block diagram showing a set of digital twins
involved in embodiments of a value chain network management
platform in accordance with the present disclosure.
[0282] FIG. 24 is a block diagram showing components and
relationships of entity discovery and management systems in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0283] FIG. 25 is a block diagram showing components and
relationships of a robotic process automation system in embodiments
of a value chain network management platform in accordance with the
present disclosure.
[0284] FIG. 26 is a block diagram showing components and
relationships of a set of opportunity miners in an embodiment of a
value chain network management platform in accordance with the
present disclosure.
[0285] FIG. 27 is a block diagram showing components and
relationships of a set of edge intelligence systems in embodiments
of a value chain network management platform in accordance with the
present disclosure.
[0286] FIG. 28 is a block diagram showing components and
relationships in an embodiment of a value chain network management
platform in accordance with the present disclosure.
[0287] FIG. 29 is a block diagram showing additional details of
components and relationships in embodiments of a value chain
network management platform in accordance with the present
disclosure.
[0288] FIG. 30 is a block diagram showing components and
relationships in an embodiment of a value chain network management
platform that enables centralized orchestration of value chain
network entities in accordance with the present disclosure.
[0289] FIG. 31 is a block diagram showing components and
relationships of a unified database in an embodiment of a value
chain network management platform in accordance with the present
disclosure.
[0290] FIG. 32 is a block diagram showing components and
relationships of a set of unified data collection systems in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0291] FIG. 33 is a block diagram showing components and
relationships of a set of Internet of Things monitoring systems in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0292] FIG. 34 is a block diagram showing components and
relationships of a machine vision system and a digital twin in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0293] FIG. 35 is a block diagram showing components and
relationships of a set of adaptive edge intelligence systems in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0294] FIG. 36 is a block diagram showing additional details of
components and relationships of a set of adaptive edge intelligence
systems in embodiments of a value chain network management platform
in accordance with the present disclosure.
[0295] FIG. 37 is a block diagram showing components and
relationships of a set of unified adaptive intelligence systems in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0296] FIG. 38 is a schematic of a system configured to train an
artificial system that is leveraged by a value chain system using
real world outcome data and a digital twin system according to some
embodiments of the present disclosure.
[0297] FIG. 39 is a schematic of a system configured to train an
artificial system that is leveraged by a container fleet management
system using real world outcome data and a digital twin system
according to some embodiments of the present disclosure.
[0298] FIG. 40 is a schematic of a system configured to train an
artificial system that is leveraged by a logistics design system
using real world outcome data and a digital twin system according
to some embodiments of the present disclosure.
[0299] FIG. 41 is a schematic of a system configured to train an
artificial system that is leveraged by a packaging design system
using real world outcome data and a digital twin system according
to some embodiments of the present disclosure.
[0300] FIG. 42 is a schematic of a system configured to train an
artificial system that is leveraged by a waste mitigation system
using real world outcome data and a digital twin system according
to some embodiments of the present disclosure.
[0301] FIG. 43 is a schematic illustrating an example of a portion
of an information technology system for value chain artificial
intelligence leveraging digital twins according to some embodiments
of the present disclosure.
[0302] FIG. 44 is a block diagram showing components and
relationships of a set of intelligent project management facilities
in embodiments of a value chain network management platform in
accordance with the present disclosure.
[0303] FIG. 45 is a block diagram showing components and
relationships of an intelligent task recommendation system in
embodiments of a value chain network management platform in
accordance with the present disclosure.
[0304] FIG. 46 is a block diagram showing components and
relationships of a routing system among nodes of a value chain
network in embodiments of a value chain network management platform
in accordance with the present disclosure.
[0305] FIG. 47 is a block diagram showing components and
relationships of a dashboard for managing a set of digital twins in
embodiments of a value chain network management platform.
[0306] FIG. 48 is a block diagram showing components and
relationships in embodiments of a value chain network management
platform that uses a microservices architecture.
[0307] FIG. 49 is a block diagram showing components and
relationships of an Internet of Things data collection architecture
and sensor recommendation system in embodiments of a value chain
network management platform.
[0308] FIG. 50 is a block diagram showing components and
relationships of a social data collection architecture in
embodiments of a value chain network management platform.
[0309] FIG. 51 is a block diagram showing components and
relationships of a crowdsourcing data collection architecture in
embodiments of a value chain network management platform.
[0310] FIG. 52 is a diagrammatic view that depicts embodiments of a
set of value chain network digital twins representing virtual
models of a set of value chain network entities in accordance with
the present disclosure.
[0311] FIG. 53 is a diagrammatic view that depicts embodiments of a
warehouse digital twin kit system in accordance with the present
disclosure.
[0312] FIG. 54 is a diagrammatic view that depicts embodiments of a
stress test performed on a value chain network in accordance with
the present disclosure.
[0313] FIG. 55 is a diagrammatic view that depicts embodiments of
methods used by a machine for detecting faults and predicting any
future failures of the machine in accordance with the present
disclosure.
[0314] FIG. 56 is a diagrammatic view that depicts embodiments of
deployment of machine twins to perform predictive maintenance on a
set of machines in accordance with the present disclosure.
[0315] FIG. 57 is a schematic illustrating an example of a portion
of a system for value chain customer digital twins and customer
profile digital twins according to some embodiments of the present
disclosure.
[0316] FIG. 58 is a schematic illustrating an example of an
advertising application that interfaces with the adaptive
intelligent systems layer in accordance with the present
disclosure.
[0317] FIG. 59 is a schematic illustrating an example of an
e-commerce application integrated with the adaptive intelligent
systems layer in accordance with the present disclosure.
[0318] FIG. 60 is a schematic illustrating an example of a demand
management application integrated with the adaptive intelligent
systems layer in accordance with the present disclosure.
[0319] FIG. 61 is a schematic illustrating an example of a portion
of a system for value chain smart supply component digital twins
according to some embodiments of the present disclosure.
[0320] FIG. 62 is a schematic illustrating an example of a risk
management application that interfaces with the adaptive
intelligent systems layer in accordance with the present
disclosure.
[0321] FIG. 63 is a diagrammatic view of maritime assets associated
with a value chain network management platform including components
of a port infrastructure in accordance with the present
disclosure.
[0322] FIGS. 64 and 65 are diagrammatic views of maritime assets
associated with a value chain network management platform including
components of a ship in accordance with the present disclosure.
[0323] FIG. 66 is a diagrammatic view of maritime assets associated
with a value chain network management platform including components
of a barge in accordance with the present disclosure.
[0324] FIG. 67 is a diagrammatic view of maritime assets associated
with a value chain network management platform including those
involved in maritime events, legal proceedings and making use of
geofenced parameters in accordance with the present disclosure.
[0325] FIG. 68 is a schematic illustrating an example environment
of the enterprise and executive control tower and management
platform, including data sources in communication therewith,
according to some embodiments of the present disclosure.
[0326] FIG. 69 is a schematic illustrating an example set of
components of the enterprise control tower and management platform
according to some embodiments of the present disclosure.
[0327] FIG. 70 is a schematic illustrating and example of an
enterprise data model according to some embodiments of the
disclosure.
[0328] FIG. 71 is a schematic illustrating examples of different
types of enterprise digital twins, including executive digital
twins, in relation to the data layer, processing layer, and
application layer of the enterprise digital twin framework
according to some embodiments of the present disclosure.
[0329] FIG. 72 is a schematic illustrating an example
implementation of the enterprise and executive control tower and
management platform according to some embodiments of the present
disclosure.
[0330] FIG. 73 is a flow chart illustrating an example set of
operations for configuring and serving an enterprise digital
twin.
[0331] FIG. 74 illustrates an example set of operations of a method
for configuring an organizational digital twin.
[0332] FIG. 75 illustrates an example set of operations of a method
for generating an executive digital twin.
[0333] FIG. 76 through FIG. 103 are schematic diagrams of
embodiments of neural net systems that may connect to, be
integrated in, and be accessible by the platform for enabling
intelligent transactions including ones involving expert systems,
self-organization, machine learning, artificial intelligence and
including neural net systems trained for pattern recognition, for
classification of one or more parameters, characteristics, or
phenomena, for support of autonomous control, and other purposes in
accordance with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0334] Over time, companies have increasingly used technology
solutions to improve outcomes related to a traditional supply chain
like the one depicted in FIG. 1, such as software systems for
predicting and managing customer demand, RFID and asset tracking
systems for tracking goods as they move through the supply chain,
navigation and routing systems to improve the efficiency of route
selection, and the like. However, some large trends have placed
manufacturers, retailers and other businesses under increasing
pressure to improve supply chain performance. First, online and
ecommerce operators, in particular Amazon.TM. have become the
largest retail channels for many categories of goods and have
introduced distribution and fulfillment centers 112 throughout some
geographies like the United States that house hundreds of
thousands, and sometimes more, product categories (SKUs), so that
customers can receive items the day after they are ordered, and in
some cases on the same day (and in some cases delivered to the door
by a drone, robot, and/or autonomous vehicle. For retailers that do
not have extensive geographic distribution of fulfillment centers
or warehouses, customer expectations for speed of delivery place
increased pressure on supply chain efficiency and optimization.
Accordingly, a need still exists for improved supply chain methods
and systems.
[0335] Second, agile manufacturing capabilities (such as using 3D
printing and robotic assembly techniques, among others), customer
profiling technologies, and online ratings and reviews have led to
increased customer expectations for customization and
personalization of products. Accordingly, in order to compete,
manufacturers and retailers need improved methods and systems for
understanding, predicting, and satisfying customer demand.
[0336] Historically, supply chain management and demand planning
and management have been largely separate activities, unified
primarily when demand is converted to an order, which is passed to
the supply side for fulfillment in a supply chain. As expectations
for speed and personalization increase, a need exists for methods
and systems that can provide unified orchestration of supply and
demand.
[0337] In parallel with these other large trends has been the
emergence of the Internet of Things, in which some categories of
products, particularly smart home products like thermostats,
lighting systems, and speakers, are increasingly enabled with
onboard network connectivity and processing capability, often
including a voice controlled intelligent agent like Alexa.TM. or
Siri.TM. that allows device control and triggering of certain
application features, such as playing music, or even ordering a
product. In some cases, smart products 650 even initiate orders,
such as printers that order refill cartridges. Intelligent products
650 are in some cases involved in a coordinated system, such as
where an Amazon.TM. Echo.TM. product controls a television, or
where a sensor-enabled thermostat or security camera connects to a
mobile device, but most intelligent products are still involved in
sets of largely isolated, application-specific interactions. As
artificial intelligence capabilities increase, and as more and more
computing and networking power is moved to network-enabled edge
devices and systems that reside in supply environments 670, in
demand environments 672, and in all of the locations, systems, and
facilities that populate the path of a product 650 from the loading
dock of a manufacturer to the point of destination 612 of a
customer 662 or retailers 664, a need and opportunity exists for
dramatically improved intelligence, control, and automation of all
of the factors involved in demand and supply.
Value Chain Networks
[0338] Referring to FIG. 2, a block diagram is presented at 200
showing components and interrelationships of systems and processes
of a value chain network. In example embodiments, "value chain
network," as used herein, refers to elements and interconnections
of historically segregated demand management systems and processes
and supply chain management systems and processes, enabled by the
development and convergence of numerous diverse technologies. In
example embodiments a value chain control tower 260 (e.g., referred
to herein in some cases as a "value chain network management
platform", a "VCNP", or simply as "the system", or "the platform")
may be connected to, in communication with, or otherwise
operatively coupled with data processing facilities including, but
not limited to, big data centers (e.g., big data processing 230)
and related processing functionalities that receive data flow, data
pools, data streams and/or other data configurations and
transmission modalities received from, for example, digital product
networks 252, directly from customers (e.g., direct connected
customer 250), or some other third party 220. Communications
related to market orchestration activities and communications 210,
analytics 232, or some other type of input may also be utilized by
the value chain control tower for demand enhancement 262,
synchronized planning 234, intelligent procurement 238, dynamic
fulfillment 240 or some other smart operation informed by
coordinated and adaptive intelligence, as described herein.
[0339] Referring to FIG. 3, another block diagram is presented
showing components and interrelationships of systems and processes
of a value chain network and related uses cases, data handling, and
associated entities. In example embodiments, the value chain
control tower 360 may coordinate market orchestration activities
310 including, but not limited to, demand curve management 352,
synchronization of an ecosystem 348, intelligent procurement 344,
dynamic fulfillment 350, value chain analytics 340, and/or smart
supply chain operations 342. In example embodiments, the value
chain control tower 360 may be connected to, in communication with,
or otherwise operatively coupled with adaptive data pipelines 302
and processing facilities that may be further connected to, in
communication with, or otherwise operationally coupled with
external data sources 320 and a data handling stack 330 (e.g.,
value chain network technology) that may include intelligent,
user-adaptive interfaces, adaptive intelligence and control 332,
and/or adaptive data monitoring and storage 334, as described
herein. The value chain control tower 302 may also be further
connected to, in communication with, or otherwise operatively
coupled with additional value chain entities including, but not
limited to, digital product networks 360, customers (e.g., directed
connected customers 362), and/or other connected operations 364 and
entities of a value chain network.
Digital Product Networks ("DPN")
[0340] Referring to FIG. 4, a block diagram is presented showing
components and interrelationships of systems and processes of the
digital products networks at 400. In example embodiments, products
(including goods and services) may create and transmit data, such
as product level data, to a communication layer within the value
chain network technology stack and/or to an edge data processing
facility. This data may produce enhanced product level data and may
be combined with third party data for further processing, modeling
or other adaptive or coordinated intelligence activity, as
described herein. This may include, but is not limited to,
producing and/or simulating product and value chain use cases, the
data for which may be utilized by products, product development
processes, product design, and the like.
Stack View Examples
[0341] Referring to FIG. 5, a block diagram is presented at 500
showing components and interrelationships of systems and processes
of a value chain network technology stack, which may include, but
is not limited to a presentation layer, an intelligence layer, and
serverless functionalities such as platforms (e.g., development and
hosting platforms), data facilities (e.g., relating to data with
IoT and Big Data), and data aggregation facilities. In example
embodiments, the presentation layer may include, but is not limited
to, a user interface, and modules for investigation and discovery
and tracking users' experience and engagements. In example
embodiments, the intelligence layer may include, but is not limited
to, a statistical and computation methods, semantic models, an
analytics library, a development environment for analytics,
algorithms, logic and rules, and machine learning. In example
embodiments, the platforms or the value chain network technology
stack may include a development environment, APIs for connectivity,
cloud and/or hosting applications, and device discovery. In example
embodiments, the data aggregation facilities or layer may include,
but is not limited to, modules for data normalization for common
transmission and heterogeneous data collection from disparate
devices. In example embodiments, the data facilities or layer may
include, but is not limited to, IoT and big data access, control,
and collection and alternatives. In example embodiments, the value
chain network technology stack may be further associated with
additional data sources and/or technology enablers.
Value Chain Orchestration from a Command Platform
[0342] FIG. 6 illustrates a connected value chain network 668 in
which a value chain network management platform 604 (referred to
herein in some cases as a "value chain control tower," the "VCNP,"
or simply as "the system," or "the platform") orchestrates a
variety of factors involved in planning, monitoring, controlling,
and optimizing various entities and activities involved in the
value chain network 668, such as supply and production factors,
demand factors, logistics and distribution factors, and the like.
By virtue of a unified platform 604 for monitoring and managing
supply factors and demand factors as well as status information
(e.g., quality and status, plan, order and confirm, and/or track
and trace) can be shared about and between various entities (e.g.,
including customers/consumers, suppliers, distribution such as
distributors, suppliers, and production such as producers or
production facilities) as demand factors are understood and
accounted for, as orders are generated and fulfilled, and as
products are created and moved through a supply chain. The value
chain network 668 may include not only an intelligent product 650,
but all of the equipment, infrastructure, personnel and other
entities involved in planning and satisfying demand for it.
Value Chain Network and Value Chain Network Management Platform
[0343] Referring to FIG. 7, the value chain network 668 managed by
a value chain management platform 604 may include a set of value
chain network entities 652, such as, without limitation: a product
650, which may be an intelligent product 650; a set of production
facilities 674 involved in producing finished goods, components,
systems, sub-systems, materials used in goods, or the like; various
entities, activities and other supply factors 648 involved in
supply environments 670, such as suppliers 642, points of origin
610, and the like; various entities, activities and other demand
factors 644 involved in demand environments 672, such as customers
662 (including consumers, businesses, and intermediate customers
such as value added resellers and distributors), retailers 664
(including online retailers, mobile retailers, conventional bricks
and mortar retailers, pop-up shops and the like) and the like
located and/or operating at various destinations 612; various
distribution environments 678 and distribution facilities 658, such
as warehousing facilities 654, fulfillment facilities 628, and
delivery systems 632, and the like, as well as maritime facilities
622, such as port infrastructure facilities 660, floating assets
620, and shipyards 638, among others. In embodiments, the value
chain network management platform 604 monitors, controls, and
otherwise enables management (and in some cases autonomous or
semi-autonomous behavior) of a wide range of value chain network
668 processes, workflows, activities, events and applications 630
(collectively referred to in some cases simply as "applications
630").
[0344] Referring still to FIG. 7, a high-level schematic of the
value chain network management platform 604 is illustrated. The
value chain network management platform 604 may include a set of
systems, applications, processes, modules, services, layers,
devices, components, machines, products, sub-systems, interfaces,
connections, and other elements working in coordination to enable
intelligent management of a set of value chain entities 652 that
may occur, operate, transact or the like within, or own, operate,
support or enable, one or more value chain network processes,
workflows, activities, events and/or applications 630 or that may
otherwise be part of, integrated with, linked to, or operated on by
the VCNP 604 in connection with a product 650 (which may be any
category of product, such as a finished good, software product,
hardware product, component product, material, item of equipment,
item of consumer packaged goods, consumer product, food product,
beverage product, home product, business supply product, consumable
product, pharmaceutical product, medical device product, technology
product, entertainment product, or any other type of product and/or
set of related services, and which may, in embodiments, encompass
an intelligent product 650 that is enabled with a set of
capabilities such as, without limitation data processing,
networking, sensing, autonomous operation, intelligent agent,
natural language processing, speech recognition, voice recognition,
touch interfaces, remote control, self-organization, self-healing,
process automation, computation, artificial intelligence, analog or
digital sensors, cameras, sound processing systems, data storage,
data integration, and/or various Internet of Things capabilities,
among others.
[0345] In embodiments, the management platform 604 may include a
set of data handling layers 624 each of which is configured to
provide a set of capabilities that facilitate development and
deployment of intelligence, such as for facilitating automation,
machine learning, applications of artificial intelligence,
intelligent transactions, state management, event management,
process management, and many others, for a wide variety of value
chain network applications and end uses. In embodiments, the data
handling layers 624 are configured in a topology that facilitates
shared data collection and distribution across multiple
applications and uses within the platform 604 by a value chain
monitoring systems layer 614. The value chain monitoring systems
layer 614 may include, integrate with, and/or cooperate with
various data collection and management systems 640, referred to for
convenience in some cases as data collection systems 640, for
collecting and organizing data collected from or about value chain
entities 652, as well as data collected from or about the various
data layers 624 or services or components thereof. In embodiments,
the data handling layers 624 are configured in a topology that
facilitates shared or common data storage across multiple
applications and uses of the platform 604 by a value chain
network-oriented data storage systems layer 624, referred to herein
for convenience in some cases simply as a data storage layer 624 or
storage layer 624. As shown in FIG. 7, the data handling layers 624
may also include an adaptive intelligent systems layer 614. The
adaptive intelligence systems layer 614 may include a set of data
processing, artificial intelligence and computational systems 634
that are described in more detail elsewhere throughout this
disclosure. The data processing, artificial intelligence and
computational systems 634 may relate to artificial intelligence
(e.g., expert systems, artificial intelligence, neural, supervised,
machine learning, deep learning, model-based systems, and the
like). Specifically, the data processing, artificial intelligence
and computational systems 634 may relate to various examples, in
some embodiments, such as use of a recurrent network as adaptive
intelligence system operating on a blockchain of transactions in a
supply chain to determine a pattern, use with biological systems,
opportunity mining (e.g., where artificial intelligence system may
be used to monitor for new data sources as opportunities for
automatically deploying intelligence), robotic process automation
(e.g., automation of intelligent agents for various workflows),
edge and network intelligence (e.g., implicated on monitoring
systems such as adaptively using available RF spectrum, adaptively
using available fixed network spectrum, adaptively storing data
based on available storage conditions, adaptively sensing based on
a kind of contextual sensing), and the like.
[0346] In embodiments, the data handling layers 624 may be depicted
in vertical stacks or ribbons in the figures and may represent many
functionalities available to the platform 604 including storage,
monitoring, and processing applications and resources and
combinations thereof. In embodiments, the set of capabilities of
the data handling layers 624 may include a shared microservices
architecture. By way of these examples, the set of capabilities may
be deployed to provide multiple distinct services or applications,
which can be configured as one or more services, workflows, or
combinations thereof. In some examples, the set of capabilities may
be deployed within or be resident to certain applications or
processes. In some examples, the set of capabilities can include
one or more activities marshaled for the benefit of the platform.
In some examples, the set of capabilities may include one or more
events organized for the benefit of the platform. In embodiments,
one of the sets of capabilities of the platform may be deployed
within at least a portion of a common architecture such as common
architecture that supports a common data schema. In embodiments,
one of the sets of capabilities of the platform may be deployed
within at least a portion of a common architecture that can support
a common storage. In embodiments, one of the sets of capabilities
of the platform may be deployed within at least a portion of a
common architecture that can support common monitoring systems. In
embodiments, one or more sets of capabilities of the platform may
be deployed within at least a portion of a common architecture that
can support one or more common processing frameworks. In
embodiments, the set of capabilities of the data handling layers
624 can include examples where the storage functionality supports
scalable processing capabilities, scalable monitoring systems,
digital twin systems, payments interface systems, and the like. By
way of these examples, one or more software development kits can be
provided by the platform along with deployment interfaces to
facilitate connections and use of the capabilities of the data
handling layers 624. In further examples, adaptive intelligence
systems may analyze, learn, configure, and reconfigure one or more
of the capabilities of the data handling layers 624. In
embodiments, the platform 604 may, for example, include a common
data storage schema serving a shipyard entity related service and a
warehousing entity service. There are many other applicable
examples and combinations applicable to the foregoing example
including the many value chain entities disclosed herein. By way of
these examples, the platform 604 may be shown to create
connectivity (e.g., supply of capabilities and information) across
many value chain entities. In many examples, there are pairings
(doubles, triples, quadruplets, etc.) of similar kinds of value
chain entities using one or more smaller sets of capabilities of
the data handling layers 624 to deploy (interact with, rely on,
etc.) a common data schema, a common architecture, a common
interface, and the like. While services and capabilities can be
provided to single value chain entities, the platform can be shown
to provide myriad benefits to value chains and consumers by
supporting connectivity across value chain entities and
applications used by the entities.
Value Chain Network Entities Managed by the Platform
[0347] Referring to FIG. 8, the value chain network management
platform 604 is illustrated in connection with a set of value chain
entities 652 that may be subject to management by the platform 604,
may integrate with or into the platform 604, and/or may supply
inputs to and/or take outputs from the platform 604, such as ones
involved in or for a wide range of value chain activities (such as
supply chain activities, logistics activities, demand management
and planning activities, delivery activities, shipping activities,
warehousing activities, distribution and fulfillment activities,
inventory aggregation, storage and management activities, marketing
activities, and many others, as involved in various value chain
network processes, workflows, activities, events and applications
630 (collectively "applications 630" or simply "activities")).
Connections with the value chain entities 652 may be facilitated by
a set of connectivity facilities 642 and interfaces 702, including
a wide range of components and systems described throughout this
disclosure and in greater detail below. This may include
connectivity and interface capabilities for individual services of
the platform, for the data handling layers, for the platform as a
whole, and/or among value chain entities 652, among others.
[0348] These value chain entities 652 may include any of the wide
variety of assets, systems, devices, machines, components,
equipment, facilities, individuals or other entities mentioned
throughout this disclosure or in the documents incorporated herein
by reference, such as, without limitation: machines 724 and their
components (e.g., delivery vehicles, forklifts, conveyors, loading
machines, cranes, lifts, haulers, trucks, loading machines,
unloading machines, packing machines, picking machines, and many
others, including robotic systems, e.g., physical robots,
collaborative robots (e.g., "cobots"), drones, autonomous vehicles,
software bots and many others); products 650 (which may be any
category of products, such as a finished goods, software products,
hardware products, component products, material, items of
equipment, items of consumer packaged goods, consumer products,
food products, beverage products, home products, business supply
products, consumable products, pharmaceutical products, medical
device products, technology products, entertainment products, or
any other type of products and/or set of related services); value
chain processes 722 (such as shipping processes, hauling processes,
maritime processes, inspection processes, hauling processes,
loading/unloading processes, packing/unpacking processes,
configuration processes, assembly processes, installation
processes, quality control processes, environmental control
processes (e.g., temperature control, humidity control, pressure
control, vibration control, and others), border control processes,
port-related processes, software processes (including applications,
programs, services, and others), packing and loading processes,
financial processes (e.g., insurance processes, reporting
processes, transactional processes, and many others), testing and
diagnostic processes, security processes, safety processes,
reporting processes, asset tracking processes, and many others);
wearable and portable devices 720 (such as mobile phones, tablets,
dedicated portable devices for value chain applications and
processes, data collectors (including mobile data collectors),
sensor-based devices, watches, glasses, hearables, head-worn
devices, clothing-integrated devices, arm bands, bracelets,
neck-worn devices, AR/VR devices, headphones, and many others);
workers 718 (such as delivery workers, shipping workers, barge
workers, port workers, dock workers, train workers, ship workers,
distribution of fulfillment center workers, warehouse workers,
vehicle drivers, business managers, engineers, floor managers,
demand managers, marketing managers, inventory managers, supply
chain managers, cargo handling workers, inspectors, delivery
personnel, environmental control managers, financial asset
managers, process supervisors and workers (for any of the processes
mentioned herein), security personnel, safety personnel and many
others); suppliers 642 (such as suppliers of goods and related
services of all types, component suppliers, ingredient suppliers,
materials suppliers, manufacturers, and many others); customers 662
(including consumers, licensees, businesses, enterprises, value
added and other resellers, retailers, end users, distributors, and
others who may purchase, license, or otherwise use a category of
goods and/or related services); a wide range of operating
facilities 712 (such as loading and unloading docks, storage and
warehousing facilities 654, vaults, distribution facilities 658 and
fulfillment centers 628, air travel facilities 740 (including
aircraft, airports, hangars, runways, refueling depots, and the
like), maritime facilities 622 (such as port infrastructure
facilities 622 (such as docks, yards, cranes, roll-on/roll-off
facilities, ramps, containers, container handling systems,
waterways 732, locks, and many others), shipyard facilities 638,
floating assets 620 (such as ships, barges, boats and others),
facilities and other items at points of origin 610 and/or points of
destination 628, hauling facilities 710 (such as container ships,
barges, and other floating assets 620, as well as land-based
vehicles and other delivery systems 632 used for conveying goods,
such as trucks, trains, and the like); items or elements factoring
in demand (i.e., demand factors 644) (including market factors,
events, and many others); items or elements factoring in supply
(i.e., supply factors 648)(including market factors, weather,
availability of components and materials, and many others);
logistics factors 750 (such as availability of travel routes,
weather, fuel prices, regulatory factors, availability of space
(such as on a vehicle, in a container, in a package, in a
warehouse, in a fulfillment center, on a shelf, or the like), and
many others); retailers 664 (including online retailers 730 and
others such as in the form of eCommerce sites 730); pathways for
conveyance (such as waterways 732, roadways 734, air travel routes,
railways 738 and the like); robotic systems 744 (including mobile
robots, cobots, robotic systems for assisting human workers,
robotic delivery systems, and others); drones 748 (including for
package delivery, site mapping, monitoring or inspection, and the
like); autonomous vehicles 742 (such as for package delivery);
software platforms 752 (such as enterprise resource planning
platforms, customer relationship management platforms, sales and
marketing platforms, asset management platforms, Internet of Things
platforms, supply chain management platforms, platform as a service
platforms, infrastructure as a service platforms, software-based
data storage platforms, analytic platforms, artificial intelligence
platforms, and others); and many others. In some example
embodiments, the product 650 may be encompassed as an intelligent
product 650 or the VCNP 604 may include the intelligent product
650. The intelligent product 650 may be enabled with a set of
capabilities such as, without limitation data processing,
networking, sensing, autonomous operation, intelligent agent,
natural language processing, speech recognition, voice recognition,
touch interfaces, remote control, self-organization, self-healing,
process automation, computation, artificial intelligence, analog or
digital sensors, cameras, sound processing systems, data storage,
data integration, and/or various Internet of Things capabilities,
among others. The intelligent product 650 may include a form of
information technology. The intelligent product 650 may have a
processor, computer random access memory, and a communication
module. The intelligent product 650 may be a passive intelligent
product that is similar to a RFID type of data structure where the
intelligent product may be pinged or read. The product 650 may be
considered a value chain network entity (e.g., under control of
platform) and may be rendered intelligent by surrounding
infrastructure and adding an RFID such that data may be read from
the intelligent product 650. The intelligent product 650 may fit in
a value chain network in a connected way such that connectivity was
built around the intelligent product 650 through a sensor, an IoT
device, a tag, or another component.
[0349] In embodiments, the monitoring systems layer 614 may monitor
any or all of the value chain entities 652 in a value chain network
668, may exchange data with the value chain entities 652, may
provide control instructions to or take instructions from any of
the value chain entities 652, or the like, such as through the
various capabilities of the data handling layers 624 described
throughout this disclosure.
Network Characteristics of the Value Chain Network Entities
[0350] Referring to FIG. 9, orchestration of a set of deeply
interconnected value chain network entities 652 in a value chain
network 668 by the value chain network management platform 604 is
illustrated. Each of the value chain network entities 652 may have
a connection to the VCNP 604, to a set of other value chain network
entities 652 (which may be a local network connection, a
peer-to-peer connection, a mobile network connection, a connection
via a cloud, or other connection), and/or through the VCNP 604 to
other value chain network entities 652. The value chain network
management platform 604 may manage the connections, configure or
provision resources to enable connectivity, and/or manage
applications 630 that take advantage of the connections, such as by
using information from one set of entities 652 to inform
applications 630 involving another set of entities 652, by
coordinating activities of a set of entities 652, by providing
input to an artificial intelligence system of the VCNP 604 or of or
about a set of entities 652, by interacting with edge computation
systems deployed on or in entities 652 and their environments, and
the like.
[0351] The entities 652 may be external such that the VCNP 604 may
interact with these entities 652. When the VCNP 604 functions as
the control tower to establish monitoring (e.g., establish
monitoring such as common monitoring across several entities 652).
In one unified platform, there may be an interface where a user may
view various items such as user's destinations, ports, air and rail
assets, as well as orders, etc. Then, the next step may be to
establish a common data schema that enables services that work on
or in any one of these applications. This may involve taking any of
the data that is flowing through or about any of these entities 652
and pull the data into a framework where other applications across
supply and demand may interact with the entities 652. This may be a
shared data pipeline coming from an IoT system and other external
data sources, feeding into the monitoring layer, being stored in a
common data schema in the storage layer, and then various
intelligence may be trained to identify implications across these
entities 652. In an example embodiment, a supplier may be bankrupt,
or a determination is made that the supplier is bankrupt, and then
the VCNP 604 may automatically trigger a substitute smart contract
to be sent to a secondary supplier with altered terms. There may be
management of different aspects of the supply chain. For example,
changing pricing instantly and automatically on the demand side in
response to one more supplier's being identified as bankrupt (e.g.,
from bankruptcy announcement). Other similar examples may be used
based on what occurs in that automation layer which may be enabled
by the VCNP 604. Then, at the interface layer of this VCNP 604, a
digital twin may be used by user to view all these entities 652
that are not typically shown together and monitor what is going on
with each of these entities 652 including identification of problem
states. For example, after viewing three quarters of bad financial
reports on a supplier, a report may be flagged to watch it closely
for potential future bankruptcy, etc.
[0352] For example, an IoT system deployed in a fulfillment center
628 may coordinate with an intelligent product 650 that takes
customer feedback about the product 650, and an application 630 for
the fulfillment center 628 may, upon receiving customer feedback
via a connection path to the intelligent product 650 about a
problem with the product 650, initiate a workflow to perform
corrective actions on similar products 650 before the products 650
are sent out from the fulfillment center 628. Similarly, a port
infrastructure facility 660, such as a yard for holding shipping
containers, may inform a fleet of floating assets 620 via
connections to the floating assets 620 (such as ships, barges, or
the like) that the port is near capacity, thereby kicking off a
negotiation process (which may include an automated negotiation
based on a set of rules and governed by a smart contract) for the
remaining capacity and enabling some assets 620 to be redirected to
alternative ports or holding facilities. These and many other
connections among value chain network entities 652, whether
one-to-one connections, one-to-many connections, many-to-many
connections, or connections among defined groups of entities 652
(such as ones controlled by the same owner or operator), are
encompassed herein as applications 630 managed by the VCNP 604.
Value Chain Network Activities and Applications Managed by the
Platform
[0353] Referring to FIG. 10, the set of applications 630 provided
on the VCNP 604, integrated with the VCNP 604 and/or managed by or
for the VCNP 604 and/or involving a set of value chain network
entities 652 may include, without limitation, one or more of any of
a wide range of types of applications, such as: a supply chain
management application 812 (such as, without limitation, for
management of timing, quantities, logistics, shipping, delivery,
and other details of orders for goods, components, and other
items); an asset management application 814 (such as, without
limitation, for managing value chain assets, such as floating
assets (such as ships, boats, barges, and floating platforms), real
property (such as used for location of warehouses, ports,
shipyards, distribution centers and other buildings), equipment,
machines and fixtures (such as used for handling containers, cargo,
packages, goods, and other items), vehicles (such as forklifts,
delivery trucks, autonomous vehicles, and other systems used to
move items), human resources (such as workers), software,
information technology resources, data processing resources, data
storage resources, power generation and/or storage resources,
computational resources and other assets); a finance application
822 (such as, without limitation, for handling finance matters
relating to value chain entities and assets, such as involving
payments, security, collateral, bonds, customs, duties, imposts,
taxes and others); a risk management application 818 (such as,
without limitation, for managing risk or liability with respect to
a shipment, goods, a product, an asset, a person, a floating asset,
a vehicle, an item of equipment, a component, an information
technology system, a security system, a security event, a
cybersecurity system, an item of property, a health condition,
mortality, fire, flood, weather, disability, negligence, business
interruption, injury, damage to property, damage to a business,
breach of a contract, and others); a demand management application
824 (such as, without limitation, an application for analyzing,
planning, or promoting interest by customers of a category of goods
that can be supplied by or with facilities of a value chain product
or service, such as a demand planning application, a demand
prediction application, a sales application, a future demand
aggregation application, a marketing application, an advertising
application, an e-commerce application, a marketing analytics
application, a customer relationship management application, a
search engine optimization application, a sales management
application, an advertising network application, a behavioral
tracking application, a marketing analytics application, a
location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service, and others, including ones that use or are
enabled by one or more features of an intelligent product 650 or
that are executed using intelligence capabilities on an intelligent
product 650); a trading application 858 (such as, without
limitation, a buying application, a selling application, a bidding
application, an auction application, a reverse auction application,
a bid/ask matching application, an analytic application for
analyzing value chain performance, yield, return on investment, or
other metrics, or others); a tax application 850 (such as, without
limitation, for managing, calculating, reporting, optimizing, or
otherwise handling data, events, workflows, or other factors
relating to a tax, a tariff, an impost, a levy, a tariff, a duty, a
credit, a fee or other government-imposed charge, such as, without
limitation, customs duties, value added tax, sales tax, income tax,
property tax, municipal fees, pollution tax, renewal energy credit,
pollution abatement credit, import duties, export duties, and
others); an identity management application 830 (such as for
managing one or more identities of entities 652 involved in a value
chain, such as, without limitation, one or more of an identity
verification application, a biometric identify validation
application, a pattern-based identity verification application, a
location-based identity verification application, a user
behavior-based application, a fraud detection application, a
network address-based fraud detection application, a black list
application, a white list application, a content inspection-based
fraud detection application, or other fraud detection application;
an inventory management application 820 (such as, without
limitation, for managing inventory in a fulfillment center,
distribution center, warehouse, storage facility, store, port, ship
or other floating asset, or other location); a security
application, solution or service 834 (referred to herein as a
security application, such as, without limitation, any of the
identity management applications 830 noted above, as well as a
physical security system (such as for an access control system
(such as using biometric access controls, fingerprinting, retinal
scanning, passwords, and other access controls), a safe, a vault, a
cage, a safe room, a secure storage facility, or the like), a
monitoring system (such as using cameras, motion sensors, infrared
sensors and other sensors), a perimeter security system, a floating
security system for a floating asset, a cyber security system (such
as for virus detection and remediation, intrusion detection and
remediation, spam detection and remediation, phishing detection and
remediation, social engineering detection and remediation,
cyber-attack detection and remediation, packet inspection, traffic
inspection, DNS attack remediation and detection, and others) or
other security application); a safety application 840 (such as,
without limitation, for improving safety of workers, for reducing
the likelihood of damage to property, for reducing accident risk,
for reducing the likelihood of damage to goods (such as cargo), for
risk management with respected to insured items, collateral for
loans, or the like, including any application for detecting,
characterizing or predicting the likelihood and/or scope of an
accident or other damaging event, including safety management based
on any of the data sources, events or entities noted throughout
this disclosure or the documents incorporated herein by reference);
a blockchain application 844 (such as, without limitation, a
distributed ledger capturing a series of transactions, such as
debits or credits, purchases or sales, exchanges of in kind
consideration, smart contract events, or the like, or other
blockchain-based application); a facility management application
850 (such as, without limitation, for managing infrastructure,
buildings, systems, real property, personal property, and other
property involved in supporting a value chain, such as a shipyard,
a port, a distribution center, a warehouse, a dock, a store, a
fulfillment center, a storage facility, or others, as well as for
design, management or control of systems and facilities in or
around a property, such as an information technology system, a
robotic/autonomous vehicle system, a packaging system, a packing
system, a picking system, an inventory tracking system, an
inspection system, a routing system for mobile robots, a workflow
system for human assets, or the like); a regulatory application 852
(such as, without limitation, an application for regulating any of
the applications, services, transactions, activities, workflows,
events, entities, or other items noted herein and in the documents
incorporated by reference herein, such as regulation of permitted
routes, permitted cargo and goods, permitted parties to
transactions, required disclosures, privacy, pricing, marketing,
offering of goods and services, use of data (including data privacy
regulations, regulations relating to storage of data and others),
banking, marketing, sales, financial planning, and many others); a
commerce application, solution or service 854 (such as, without
limitation an e-commerce site marketplace, an online site, an
auction site or marketplace, a physical goods marketplace, an
advertising marketplace, a reverse-auction marketplace, an
advertising network, or other marketplace); a vendor management
application 832 (such as, without limitation, an application for
managing a set of vendors or prospective vendors and/or for
managing procurement of a set of goods, components or materials
that may be supplied in a value chain, such as involving features
such as vendor qualification, vendor rating, requests for proposal,
requests for information, bonds or other assurances of performance,
contract management, and others); an analytics application 838
(such as, without limitation, an analytic application with respect
to any of the data types, applications, events, workflows, or
entities mentioned throughout this disclosure or the documents
incorporated by reference herein, such as a big data application, a
user behavior application, a prediction application, a
classification application, a dashboard, a pattern recognition
application, an econometric application, a financial yield
application, a return on investment application, a scenario
planning application, a decision support application, a demand
prediction application, a demand planning application, a route
planning application, a weather prediction application, and many
others); a pricing application 842 (such as, without limitation,
for pricing of goods, services (including any mentioned throughout
this disclosure and the documents incorporated by reference herein;
and a smart contract application, solution, or service (referred to
collectively herein as a smart contract application 848, such as,
without limitation, any of the smart contract types referred to in
this disclosure or in the documents incorporated herein by
reference, such as a smart contract for sale of goods, a smart
contract for an order for goods, a smart contract for a shipping
resource, a smart contract for a worker, a smart contract for
delivery of goods, a smart contract for installation of goods, a
smart contract using a token or cryptocurrency for consideration, a
smart contract that vests a right, an option, a future, or an
interest based on a future condition, a smart contract for a
security, commodity, future, option, derivative, or the like, a
smart contract for current or future resources, a smart contract
that is configured to account for or accommodate a tax, regulatory
or compliance parameter, a smart contract that is configured to
execute an arbitrage transaction, or many others). Thus, the value
chain management platform 604 may host an enable interaction among
a wide range of disparate applications 630 (such term including the
above-referenced and other value chain applications, services,
solutions, and the like), such that by virtue of shared
microservices, shared data infrastructure, and shared intelligence,
any pair or larger combination or permutation of such services may
be improved relative to an isolated application of the same
type.
[0354] Referring still to FIG. 10, the set of applications 630
provided on the VCNP 604, integrated with the VCNP 604 and/or
managed by or for the VCNP 604 and/or involving a set of value
chain network entities 652 may further include, without limitation:
a payments application 860 (such as for calculating payments
(including based on situational factors such as applicable taxes,
duties and the like for the geography of an entity 652),
transferring funds, resolving payments to parties, and the like,
for any of the applications 630 noted herein); a process management
application 862 (such as for managing any of the processes or
workflows described throughout this disclosure, including supply
processes, demand processes, logistics processes, delivery
processes, fulfillment processes, distribution processes, ordering
processes, navigation processes, and many others); a compatibility
testing application 864, such as for assessing compatibility among
value chain network entities 652 or activities involved in any of
the processes, workflows, activities, or other applications 630
described herein (such as for determining compatibility of a
container or package with a product 650, the compatibility of a
product 650 with a set of customer requirements, the compatibility
of a product 650 with another product 650 (such as where one is a
refill, resupply, replacement part, or the like for the other), the
compatibility of a infrastructure and equipment entities 652 (such
as between a container ship or barge and a port or waterway,
between a container and a storage facility, between a truck and a
roadway, between a drone or robot and a package, between a drone,
AV or robot and a delivery destination, and many others); an
infrastructure testing application 802 (such as for testing the
capabilities of infrastructure elements to support a product 650 or
an application 630 (such as, without limitation, storage
capabilities, lifting capabilities, moving capabilities, storage
capacity, network capabilities, environmental control capabilities,
software capabilities, security capabilities, and many others));
and/or an incident management application 910 (such as for managing
events, accidents, and other incidents that may occur in one or
more environments involving value chain network entities 652, such
as, without limitation, vehicle accidents, worker injuries,
shutdown incidents, property damage incidents, product damage
incidents, product liability incidents, regulatory non-compliance
incidents, health and/or safety incidents, traffic congestion
and/or delay incidents (including network traffic, data traffic,
vehicle traffic, maritime traffic, human worker traffic, and
others, as well as combinations among them), product failure
incidents, system failure incidents, system performance incidents,
fraud incidents, misuse incidents, unauthorized use incidents, and
many others).
[0355] Referring still to FIG. 10, the set of applications 630
provided on the VCNP 604, integrated with the VCNP 604 and/or
managed by or for the VCNP 604 and/or involving a set of value
chain network entities 652 may further include, without limitation:
a predictive maintenance application 910 (such as for anticipating,
predicting, and undertaking actions to manage faults, failures,
shutdowns, damage, required maintenance, required repairs, required
service, required support, or the like for a set of value chain
network entities 652, such as products 650, equipment,
infrastructure, buildings, vehicles, and others); a logistics
application 912 (such as for managing logistics for pickups,
deliveries, transfer of goods onto hauling facilities, loading,
unloading, packing, picking, shipping, driving, and other
activities involving in the scheduling and management of the
movement of products 650 and other items between points of origin
and points of destination through various intermediate locations; a
reverse logistic application 914 (such as for handling logistics
for returned products 650, waste products, damaged goods, or other
items that can be transferred on a return logistics path); a waste
reduction application 920 (such as for reducing packaging waste,
solid waste, waste of energy, liquid waste, pollution,
contaminants, waste of computing resources, waste of human
resources, or other waste involving a value chain network entity
652 or activity); an augmented reality, mixed reality and/or
virtual reality application 930 (such as for visualizing one or
more value chain network entities 652 or activities involved in one
or more of the applications 630, such as, without limitation,
movement of a product 650, the interior of a facility, the status
or condition of an item of goods, one or more environmental
conditions, a weather condition, a packing configuration for a
container or a set of containers, or many others); a demand
prediction application 940 (such as for predicting demand for a
product 650, a category of products, a potential product, and/or a
factor involved in demand, such as a market factor, a wealth
factor, a demographic factor, a weather factor, an economic factor,
or the like); a demand aggregation application 942 (such as for
aggregating information, orders and/or commitments (optionally
embodied in one or more contracts, which may be smart contracts)
for one or more products 650, categories, or the like, including
current demand for existing products and future demand for products
that are not yet available); a customer profiling application 944
(such as for profiling one or more demographic, psychographic,
behavioral, economic, geographic, or other attributes of a set of
customers, including based on historical purchasing data, loyalty
program data, behavioral tracking data (including data captured in
interactions by a customer with a smart product 650), online
clickstream data, interactions with intelligent agents, and other
data sources); and/or a component supply application 948 (such as
for managing a supply chain of components for a set of products
650).
[0356] Referring still to FIG. 10, the set of applications 630
provided on the VCNP 604, integrated with the VCNP 604 and/or
managed by or for the VCNP 604 and/or involving a set of value
chain network entities 652 may further include, without limitation:
a policy management application 868 (such as for deploying one or
more policies, rules, or the like for governance of one or more
value chain network entities 652 or applications 630, such as to
govern execution of one or more workflows (which may involve
configuring polices in the platform 604 on a per-workflow basis),
to govern compliance with regulations (including maritime, food
& drug, medical, environmental, health, safety, tax, financial
reporting, commercial, and other regulations as described
throughout this disclosure or as would be understood in the art),
to govern provisioning of resources (such as connectivity,
computing, human, energy, and other resources), to govern
compliance with corporate policies, to govern compliance with
contracts (including smart contracts, wherein the platform 604 may
automatically deploy governance features to relevant entities 652
and applications 630, such as via connectivity facilities 642), to
govern interactions with other entities (such as involving policies
for sharing of information and access to resources), to govern data
access (including privacy data, operational data, status data, and
many other data types), to govern security access to
infrastructure, products, equipment, locations, or the like, and
many others; a product configuration application 870 (such as for
allowing a product manager and/or automated product configuration
process (optionally using robotic process automation) to determine
a configuration for a product 650, including configuration
on-the-fly, such as during agile manufacturing, or involving
configuration or customization in route (such as by 3D printing one
or more features or elements), or involving configuration or
customization remotely, such as by downloading firmware,
configuring field programmable gate arrays, installing software, or
the like; a warehousing and fulfillment application 872 (such as
for managing a warehouse, distribution center, fulfillment center,
or the like, such as involving selection of products, configuring
storage locations for products, determining routes by which
personnel, mobile robots, and the like move products around a
facility, determining picking and packing schedules, routes and
workflows, managing operations of robots, drones, conveyors, and
other facilities, determining schedules for moving products out to
loading docks or the like, and many other functions); a kit
configuration and deployment application 874 (such as for enabling
a user of the VCNP to configure a kit, box, or otherwise
pre-integrated, pre-provisioned, and/or pre-configured system to
allow a customer or worker to rapidly deploy a subset of
capabilities of the VCNP 604 for a specific value chain network
entity 652 and/or application 630); and/or a product testing
application 878 for testing a product 650 (including testing for
performance, activation of capabilities and features, safety,
compliance with policy or regulations, quality, quality of service,
likelihood of failure, and many other factors).
[0357] Referring still to FIG. 10, the set of applications 630
provided on the VCNP 604, integrated with the VCNP 604 and/or
managed by or for the VCNP 604 and/or involving a set of value
chain network entities 652 may further include, without limitation
a maritime fleet management application 880 (for managing a set of
maritime assets, such as container ships, barges, boats, and the
like, as well as related infrastructure facilities such as docks,
cranes, ports, and others, such as to determine optimal routes for
fleet assets based on weather, market, traffic, and other
conditions, to ensure compliance with policies and regulations, to
ensure safety, to improve environmental factors, to improve
financial metrics, and many others); a shipping management
application 882 (such as for managing a set of shipping assets,
such as trucks, trains, airplanes, and the like, such as to
optimize financial yield, to improve safety, to reduce energy
consumption, to reduce delays, to mitigate environmental impact,
and for many other purposes); an opportunity matching application
884 (such as for matching one or more demand factors with one or
more supply factors, for matching needs and capabilities of value
chain network entities 652, for identifying reverse logistics
opportunities, for identifying opportunities for inputs to enrich
analytics, artificial intelligence and/or automation, for
identifying cost-saving opportunities, for identifying profit
and/or arbitrage opportunities, and many others); a workforce
management application 888 (such as for managing workers in various
work forces, including work forces in, on or for fulfillment
centers, ships, ports, warehouses, distribution centers, enterprise
management locations, retail stores, online/ecommerce site
management facilities, ports, ships, boats, barges, trains, depots,
and other facilities mentioned throughout this disclosure); a
distribution and delivery application 890 (such as for planning,
scheduling, routing, and otherwise managing distribution and
delivery of products 650 and other items); and/or an enterprise
resource planning (ERP) application 892 (such as for planning
utilization of enterprise resources, including workforce resources,
financial resources, energy resources, physical assets, digital
assets, and other resources).
Core Capabilities and Interactions of the Data Handling Layers
(Adaptive Intelligence, Monitoring, Data Storage and
Applications)
[0358] Referring to FIG. 11, a high-level schematic of an
embodiment of the value chain network management platform 604 is
illustrated, including a set of systems, applications, processes,
modules, services, layers, devices, components, machines, products,
sub-systems, interfaces, connections, and other elements working in
coordination to enable intelligent management of sets of the value
chain entities 652 that may occur, operate, transact or the like
within, or own, operate, support or enable, one or more value chain
network processes, workflows, activities, events and/or
applications 630 or that may otherwise be part of, integrated with,
linked to, or operated on by the platform 604 in connection with a
product 650 (which may be a finished good, software product,
hardware product, component product, material, item of equipment,
consumer packaged good, consumer product, food product, beverage
product, home product, business supply product, consumable product,
pharmaceutical product, medical device product, technology product,
entertainment product, or any other type of product or related
service, which may, in embodiments, encompass an intelligent
product that is enabled with processing, networking, sensing,
computation, and/or other Internet of Things capabilities). Value
chain entities 652, such as involved in or for a wide range of
value chain activities (such as supply chain activities, logistics
activities, demand management and planning activities, delivery
activities, shipping activities, warehousing activities,
distribution and fulfillment activities, inventory aggregation,
storage and management activities, marketing activities, and many
others, as involved in various value chain network processes,
workflows, activities, events and applications 630 may include any
of the wide variety of assets, systems, devices, machines,
components, equipment, facilities, individuals or other entities
mentioned throughout this disclosure or in the documents
incorporated herein by reference.
[0359] In embodiments, the value chain network management platform
604 may include the set of data handling layers 624, each of which
is configured to provide a set of capabilities that facilitate
development and deployment of intelligence, such as for
facilitating automation, machine learning, applications of
artificial intelligence, intelligent transactions, intelligent
operations, remote control, analytics, monitoring, reporting, state
management, event management, process management, and many others,
for a wide variety of value chain network applications and end
uses. In embodiments, the data handling layers 624 may include a
value chain network monitoring systems layer 614, a value chain
network entity-oriented data storage systems layer 624 (referred to
in some cases herein for convenience simply as a data storage layer
624), an adaptive intelligent systems layer 614 and a value chain
network management platform layer 604. The value chain network
management platform 604 may include the data handling layers 624
such that the value chain network management platform layer 604 may
provide management of the value chain network management platform
604 and/or management of the other layers such as the value chain
network monitoring systems layer 614, the value chain network
entity-oriented data storage systems layer 624 (e.g., data storage
layer 624), and the adaptive intelligent systems layer 614. Each of
the data handling layers 624 may include a variety of services,
programs, applications, workflows, systems, components and modules,
as further described herein and in the documents incorporated
herein by reference. In embodiments, each of the data handling
layers 624 (and optionally the platform 604 as a whole) is
configured such that one or more of its elements can be accessed as
a service by other layers 624 or by other systems (e.g., being
configured as a platform-as-a-service deployed on a set of cloud
infrastructure components in a microservices architecture). For
example, the platform 604 may have (or may configure and/or
provision), and a data handling layer 608 may use, a set of
connectivity facilities 642, such as network connections (including
various configurations, types and protocols), interfaces, ports,
application programming interfaces (APIs), brokers, services,
connectors, wired or wireless communication links, human-accessible
interfaces, software interfaces, micro-services, SaaS interfaces,
PaaS interfaces, IaaS interfaces, cloud capabilities, or the like
by which data or information may be exchanged between a data
handling layer 608 and other layers, systems or sub-systems of the
platform 604, as well as with other systems, such as value chain
entities 652 or external systems, such as cloud-based or
on-premises enterprise systems (e.g., accounting systems, resource
management systems, CRM systems, supply chain management systems
and many others). Each of the data handling layers 624 may include
a set of services (e.g., microservices), for data handling,
including facilities for data extraction, transformation and
loading; data cleansing and deduplication facilities; data
normalization facilities; data synchronization facilities; data
security facilities; computational facilities (e.g., for performing
pre-defined calculation operations on data streams and providing an
output stream); compression and de-compression facilities; analytic
facilities (such as providing automated production of data
visualizations) and others.
[0360] In embodiments, each data handling layer 608 has a set of
application programming connectivity facilities 642 for automating
data exchange with each of the other data handling layers 624.
These may include data integration capabilities, such as for
extracting, transforming, loading, normalizing, compression,
decompressing, encoding, decoding, and otherwise processing data
packets, signals, and other information as it exchanged among the
layers and/or the applications 630, such as transforming data from
one format or protocol to another as needed in order for one layer
to consume output from another. In embodiments, the data handling
layers 624 are configured in a topology that facilitates shared
data collection and distribution across multiple applications and
uses within the platform 604 by the value chain monitoring systems
layer 614. The value chain monitoring systems layer 614 may
include, integrate with, and/or cooperate with various data
collection and management systems 640, referred to for convenience
in some cases as data collection systems 640, for collecting and
organizing data collected from or about value chain entities 652,
as well as data collected from or about the various data layers 624
or services or components thereof. For example, a stream of
physiological data from a wearable device worn by a worker
undertaking a task or a consumer engaged in an activity can be
distributed via the monitoring systems layer 614 to multiple
distinct applications in the value chain management platform layer
604, such as one that facilitates monitoring the physiological,
psychological, performance level, attention, or other state of a
worker and another that facilitates operational efficiency and/or
effectiveness. In embodiments, the monitoring systems layer 614
facilitates alignment, such as time-synchronization, normalization,
or the like of data that is collected with respect to one or more
value chain network entities 652. For example, one or more video
streams or other sensor data collected of or with respect to a
worker 718 or other entity in a value chain network facility or
environment, such as from a set of camera-enabled IoT devices, may
be aligned with a common clock, so that the relative timing of a
set of videos or other data can be understood by systems that may
process the videos, such as machine learning systems that operate
on images in the videos, on changes between images in different
frames of the video, or the like. In such an example, the
monitoring systems layer 614 may further align a set of videos,
camera images, sensor data, or the like, with other data, such as a
stream of data from wearable devices, a stream of data produced by
value chain network systems (such as ships, lifts, vehicles,
containers, cargo handling systems, packing systems, delivery
systems, drones/robots, and the like), a stream of data collected
by mobile data collectors, and the like. Configuration of the
monitoring systems layer 614 as a common platform, or set of
microservices, that are accessed across many applications, may
dramatically reduce the number of interconnections required by an
owner or other operator within a value chain network in order to
have a growing set of applications monitoring a growing set of IoT
devices and other systems and devices that are under its
control.
[0361] In embodiments, the data handling layers 624 are configured
in a topology that facilitates shared or common data storage across
multiple applications and uses of the platform 604 by the value
chain network-oriented data storage systems layer 624, referred to
herein for convenience in some cases simply as the data storage
layer 624 or storage layer 624. For example, various data collected
about the value chain entities 652, as well as data produced by the
other data handling layers 624, may be stored in the data storage
layer 624, such that any of the services, applications, programs,
or the like of the various data handling layers 624 can access a
common data source (which may comprise a single logical data source
that is distributed across disparate physical and/or virtual
storage locations). This may facilitate a dramatic reduction in the
amount of data storage required to handle the enormous amount of
data produced by or about value chain network entities 652 as
applications 630 and uses of value chain networks grow and
proliferate. For example, a supply chain or inventory management
application in the value chain management platform layer 604, such
as one for ordering replacement parts for a machine or item of
equipment, may access the same data set about what parts have been
replaced for a set of machines as a predictive maintenance
application that is used to predict whether a component of a ship,
or facility of a port is likely to require replacement parts.
Similarly, prediction may be used with respect to the resupply of
items.
[0362] In embodiments, value chain network data objects 1004 may be
provided according to an object-oriented data model that defines
classes, objects, attributes, parameters and other features of the
set of data objects (such as associated with value chain network
entities 652 and applications 630) that are handled by the platform
604.
[0363] In embodiments, the data storage systems layer 624 may
provide an extremely rich environment for collection of data that
can be used for extraction of features or inputs for intelligence
systems, such as expert systems, analytic systems, artificial
intelligence systems, robotic process automation systems, machine
learning systems, deep learning systems, supervised learning
systems, or other intelligent systems as disclosed throughout this
disclosure and the documents incorporated herein by reference. As a
result, each application 630 in the platform 604 and each adaptive
intelligent system in the adaptive intelligent systems layer 614
can benefit from the data collected or produced by or for each of
the others. In embodiments, the data storage systems layer 624 may
facilitate collection of data that can be used for extraction of
features or inputs for intelligence systems such as a development
framework from artificial intelligence. In examples, the
collections of data may pull in and/or house event logs (naturally
stored or ad-hoc, as needed), perform periodic checks on onboard
diagnostic data, or the like. In examples, pre calculation of
features may be deployed using AWS Lambda, for example, or various
other cloud-based on-demand compute capabilities, such as
pre-calculations, multiplexing signals. In many examples, there are
pairings (doubles, triples, quadruplets, etc.) of similar kinds of
value chain entities that may use one or more sets of capabilities
of the data handling layers 624 to deploy connectivity and services
across value chain entities and across applications used by the
entities even when amassing hundreds and hundreds of data types
from relatively disparate entities. In these examples, various
pairings of similar types of value chain entities using, at least
in part, the connectivity and services across value chain entities
and applications, may direct the information from the pairings of
connected data to artificial intelligence services including the
various neural networks disclosed herein and hybrid combinations
thereof. In these examples, genetic programming techniques may be
deployed to prune some of the input features in the information
from the pairings of connected data. In these examples, genetic
programming techniques may also be deployed to add to and augment
the input features in the information from the pairings. These
genetic programming techniques may be shown to increase the
efficacy of the determinations established by the artificial
intelligence services. In these examples, the information from the
pairings of connected data may be migrated to other layers on the
platform including to support or deploy robotic process automation,
prediction, forecasting, and other resources such that the shared
data schema may facilitate as capabilities and resources for the
platform 604.
[0364] A wide range of data types may be stored in the storage
layer 624 using various storage media and data storage types, data
architectures 1002, and formats, including, without limitation:
asset and facility data 1030, state data 1140 (such as indicating a
state, condition status, or other indicator with respect to any of
the value chain network entities 652, any of the applications 630
or components or workflows thereof, or any of the components or
elements of the platform 604, among others), worker data 1032
(including identity data, role data, task data, workflow data,
health data, attention data, mood data, stress data, physiological
data, performance data, quality data and many other types); event
data 1034 ((such as with respect to any of a wide range of events,
including operational data, transactional data, workflow data,
maintenance data, and many other types of data that includes or
relates to events that occur within a value chain network 668 or
with respect to one or more applications 630, including process
events, financial events, transaction events, output events, input
events, state-change events, operating events, workflow events,
repair events, maintenance events, service events, damage events,
injury events, replacement events, refueling events, recharging
events, shipping events, warehousing events, transfers of goods,
crossing of borders, moving of cargo, inspection events, supply
events, and many others); claims data 664 (such as relating to
insurance claims, such as for business interruption insurance,
product liability insurance, insurance on goods, facilities, or
equipment, flood insurance, insurance for contract-related risks,
and many others, as well as claims data relating to product
liability, general liability, workers compensation, injury and
other liability claims and claims data relating to contracts, such
as supply contract performance claims, product delivery
requirements, warranty claims, indemnification claims, delivery
requirements, timing requirements, milestones, key performance
indicators and others); accounting data 730 (such as data relating
to completion of contract requirements, satisfaction of bonds,
payment of duties and tariffs, and others); and risk management
data 732 (such as relating to items supplied, amounts, pricing,
delivery, sources, routes, customs information and many others),
among many other data types associated with value chain network
entities 652 and applications 630.
[0365] In embodiments, the data handling layers 624 are configured
in a topology that facilitates shared adaptation capabilities,
which may be provided, managed, mediated and the like by one or
more of a set of services, components, programs, systems, or
capabilities of the adaptive intelligent systems layer 614,
referred to in some cases herein for convenience as the adaptive
intelligence layer 614. The adaptive intelligence systems layer 614
may include a set of data processing, artificial intelligence and
computational systems 634 that are described in more detail
elsewhere throughout this disclosure. Thus, use of various
resources, such as computing resources (such as available
processing cores, available servers, available edge computing
resources, available on-device resources (for single devices or
peered networks), and available cloud infrastructure, among
others), data storage resources (including local storage on
devices, storage resources in or on value chain entities or
environments (including on-device storage, storage on asset tags,
local area network storage and the like), network storage
resources, cloud-based storage resources, database resources and
others), networking resources (including cellular network spectrum,
wireless network resources, fixed network resources and others),
energy resources (such as available battery power, available
renewable energy, fuel, grid-based power, and many others) and
others may be optimized in a coordinated or shared way on behalf of
an operator, enterprise, or the like, such as for the benefit of
multiple applications, programs, workflows, or the like. For
example, the adaptive intelligence layer 614 may manage and
provision available network resources for both a supply chain
management application and for a demand planning application (among
many other possibilities), such that low latency resources are used
for supply chain management application (where rapid decisions may
be important) and longer latency resources are used for the demand
planning application. As described in more detail throughout this
disclosure and the documents incorporated herein by reference, a
wide variety of adaptations may be provided on behalf of the
various services and capabilities across the various layers 624,
including ones based on application requirements, quality of
service, on-time delivery, service objectives, budgets, costs,
pricing, risk factors, operational objectives, efficiency
objectives, optimization parameters, returns on investment,
profitability, uptime/downtime, worker utilization, and many
others.
[0366] The value chain management platform layer 604, referred to
in some cases herein for convenience as the platform layer 604, may
include, integrate with, and enable the various value chain network
processes, workflows, activities, events and applications 630
described throughout this disclosure that enable an operator to
manage more than one aspect of a value chain network environment or
entity 652 in a common application environment (e.g., shared,
pooled, similarly licenses whether shared data for one person,
multiple people, or anonymized), such as one that takes advantage
of common data storage in the data storage layer 624, common data
collection or monitoring in the monitoring systems layer 614 and/or
common adaptive intelligence of the adaptive intelligence layer
614. Outputs from the applications 630 in the platform layer 604
may be provided to the other data handing layers 624. These may
include, without limitation, state and status information for
various objects, entities, processes, flows and the like; object
information, such as identity, attribute and parameter information
for various classes of objects of various data types; event and
change information, such as for workflows, dynamic systems,
processes, procedures, protocols, algorithms, and other flows,
including timing information; outcome information, such as
indications of success and failure, indications of process or
milestone completion, indications of correct or incorrect
predictions, indications of correct or incorrect labeling or
classification, and success metrics (including relating to yield,
engagement, return on investment, profitability, efficiency,
timeliness, quality of service, quality of product, customer
satisfaction, and others) among others. Outputs from each
application 630 can be stored in the data storage layer 624,
distributed for processing by the data collection layer 614, and
used by the adaptive intelligence layer 614. The cross-application
nature of the platform layer 604 thus facilitates convenient
organization of all of the necessary infrastructure elements for
adding intelligence to any given application, such as by supplying
machine learning on outcomes across applications, providing
enrichment of automation of a given application via machine
learning based on outcomes from other applications or other
elements of the platform 604, and allowing application developers
to focus on application-native processes while benefiting from
other capabilities of the platform 604. In examples, there may be
systems, components, services and other capabilities that optimize
control, automation, or one or more performance characteristics of
one or more value chain network entities 652; or ones that may
generally improve any of process and application outputs and
outcomes 1040 pursued by use of the platform 604. In some examples,
outputs and outcomes 1040 from various applications 630 may be used
to facilitate automated learning and improvement of classification,
prediction, or the like that is involved in a step of a process
that is intended to be automated.
Some Data Storage Layer Details--Alternative Data Architectures
[0367] Referring to FIG. 12, additional details, components,
sub-systems, and other elements of an optional embodiment of the
data storage layer 624 of the platform 604 are illustrated. Various
data architectures may be used, including conventional relational
and object-oriented data architectures, blockchain architectures
1180, asset tag data storage architectures 1178, local storage
architectures 1190, network storage architectures 1174,
multi-tenant architectures 1132, distributed data architectures
1002, value chain network (VCN) data object architectures 1004,
cluster-based architectures 1128, event data-based architectures
1034, state data-based architectures 1140, graph database
architectures 1124, self-organizing architectures 1134, and other
data architectures 1002.
[0368] The adaptive intelligent systems layer 614 of the platform
604 may include one or more protocol adaptors 1110 for facilitating
data storage, retrieval access, query management, loading,
extraction, normalization, and/or transformation to enable use of
the various other data storage architectures 1002, such as allowing
extraction from one form of database and loading to a data system
that uses a different protocol or data structure.
[0369] In embodiments, the value chain network-oriented data
storage systems layer 624 may include, without limitation, physical
storage systems, virtual storage systems, local storage systems
(e.g., part of the local storage architectures 1190), distributed
storage systems, databases, memory, network-based storage,
network-attached storage systems (e.g., part of the network storage
architectures 1174 such as using NVME, storage attached networks,
and other network storage systems), and many others.
[0370] In embodiments, the storage layer 624 may store data in one
or more knowledge graphs (such as a directed acyclic graph, a data
map, a data hierarchy, a data cluster including links and nodes, a
self-organizing map, or the like) in the graph database
architectures 1124. In example embodiments, the knowledge graph may
be a prevalent example of when a graph database and graph database
architecture may be used. In some examples, the knowledge graph may
be used to graph a workflow. For a linear workflow, a directed
acyclic graph may be used. For a contingent workflow, a cyclic
graph may be used. The graph database (e.g., graph database
architectures vpc608) may include the knowledge graph or the
knowledge graph may be an example of the graph database. In example
embodiments, the knowledge graph may include ontology and
connections (e.g., relationships) between the ontology of the
knowledge graph. In an example, the knowledge graph may be used to
capture an articulation of knowledge domains of a human expert such
that there may be an identification of opportunities to design and
build robotic process automation or other intelligence that may
replicate this knowledge set. The platform may be used to recognize
that a type of expert is using this factual knowledge base (from
the knowledge graph) coupled with competencies that may be
replicable by artificial intelligence that may be different
depending on type of expertise involved. For example, artificial
intelligence such as a convolutional neural network may be used
with spatiotemporal aspects that may be used to diagnose issues or
packing up a box in a warehouse. Whereas the platform may use a
different type of knowledge graph for a self-organizing map of an
expert whose main job is to segment customers into customer
segmentation groups. In some examples, the knowledge graph may be
built from various data such as job credentials, job listings,
parsing output deliverables. In embodiments, the data storage layer
624 may store data in a digital thread, ledger, or the like, such
as for maintaining a serial or other records of an entities 652
over time, including any of the entities described herein. In
embodiments, the data storage layer 624 may use and enable an asset
tag 1178, which may include a data structure that is associated
with an asset and accessible and managed, such as by use of access
controls, so that storage and retrieval of data is optionally
linked to local processes, but also optionally open to remote
retrieval and storage options. In embodiments, the storage layer
624 may include one or more blockchains 1180, such as ones that
store identity data, transaction data, historical interaction data,
and the like, such as with access control that may be role-based or
may be based on credentials associated with a value chain entity
652, a service, or one or more applications 630. Data stored by the
data storage systems 624 may include accounting and other financial
data 730, access data 734, asset and facility data 1030 (such as
for any of the value chain assets and facilities described herein),
asset tag data 1178, worker data 1032, event data 1034, risk
management data 732, pricing data 738, safety data 664 and many
other types of data that may be associated with, produced by, or
produced about any of the value chain entities and activities
described herein and in the documents incorporated by
reference.
Adaptive Intelligent Systems and Monitoring Layers
[0371] Referring to FIG. 13, additional details, components,
sub-systems, and other elements of an optional embodiment of the
platform 604 are illustrated. The management platform 604 may, in
various optional embodiments, include the set of applications 630,
by which an operator or owner of a value chain network entity, or
other users, may manage, monitor, control, analyze, or otherwise
interact with one or more elements of a value chain network entity
652, such as any of the elements noted in connection above and
throughout this disclosure.
[0372] In embodiments, the adaptive intelligent systems layer 614
may include a set of systems, components, services and other
capabilities that collectively facilitate the coordinated
development and deployment of intelligent systems, such as ones
that can enhance one or more of the applications 630 at the
application platform layer 604; ones that can improve the
performance of one or more of the components, or the overall
performance (e.g., speed/latency, reliability, quality of service,
cost reduction, or other factors) of the connectivity facilities
642; ones that can improve other capabilities within the adaptive
intelligent systems layer 614; ones that improve the performance
(e.g., speed/latency, energy utilization, storage capacity, storage
efficiency, reliability, security, or the like) of one or more of
the components, or the overall performance, of the value chain
network-oriented data storage systems 624; ones that optimize
control, automation, or one or more performance characteristics of
one or more value chain network entities 652; or ones that
generally improve any of the process and application outputs and
outcomes 1040 pursued by use of the platform 604.
[0373] These adaptive intelligent systems 614 may include a robotic
process automation system 1442, a set of protocol adaptors 1110, a
packet acceleration system 1410, an edge intelligence system 1420
(which may be a self-adaptive system), an adaptive networking
system 1430, a set of state and event managers 1450, a set of
opportunity miners 1460, a set of artificial intelligence systems
1160, a set of digital twin systems 1700, a set of entity
interaction management systems 1900 (such as for setting up,
provisioning, configuring and otherwise managing sets of
interactions between and among sets of value chain network entities
652 in the value chain network 668), and other systems.
[0374] In embodiments, the value chain monitoring systems layer 614
and its data collection systems 640 may include a wide range of
systems for the collection of data. This layer may include, without
limitation, real time monitoring systems 1520 (such as onboard
monitoring systems like event and status reporting systems on ships
and other floating assets, on delivery vehicles, on trucks and
other hauling assets, and in shipyards, ports, warehouses,
distribution centers and other locations; on-board diagnostic (OBD)
and telematics systems on floating assets, vehicles and equipment;
systems providing diagnostic codes and events via an event bus,
communication port, or other communication system; monitoring
infrastructure (such as cameras, motion sensors, beacons, RFID
systems, smart lighting systems, asset tracking systems, person
tracking systems, and ambient sensing systems located in various
environments where value chain activities and other events take
place), as well as removable and replaceable monitoring systems,
such as portable and mobile data collectors, RFID and other tag
readers, smart phones, tablets and other mobile devices that are
capable of data collection and the like); software interaction
observation systems 1500 (such as for logging and tracking events
involved in interactions of users with software user interfaces,
such as mouse movements, touchpad interactions, mouse clicks,
cursor movements, keyboard interactions, navigation actions, eye
movements, finger movements, gestures, menu selections, and many
others, as well as software interactions that occur as a result of
other programs, such as over APIs, among many others); mobile data
collectors 1170 (such as described extensively herein and in
documents incorporated by reference), visual monitoring systems
1930 (such as using video and still imaging systems, LIDAR, IR and
other systems that allow visualization of items, people, materials,
components, machines, equipment, personnel, gestures, expressions,
positions, locations, configurations, and other factors or
parameters of entities 652, as well as inspection systems that
monitor processes, activities of workers and the like); point of
interaction systems 1530 (such as dashboards, user interfaces, and
control systems for value chain entities); physical process
observation systems 1510 (such as for tracking physical activities
of operators, workers, customers, or the like, physical activities
of individuals (such as shippers, delivery workers, packers,
pickers, assembly personnel, customers, merchants, vendors,
distributors and others), physical interactions of workers with
other workers, interactions of workers with physical entities like
machines and equipment, and interactions of physical entities with
other physical entities, including, without limitation, by use of
video and still image cameras, motion sensing systems (such as
including optical sensors, LIDAR, IR and other sensor sets),
robotic motion tracking systems (such as tracking movements of
systems attached to a human or a physical entity) and many others;
machine state monitoring systems 1940 (including onboard monitors
and external monitors of conditions, states, operating parameters,
or other measures of the condition of any value chain entity, such
as a machine or component thereof, such as a machine, such as a
client, a server, a cloud resource, a control system, a display
screen, a sensor, a camera, a vehicle, a robot, or other machine);
sensors and cameras 1950 and other IoT data collection systems 1172
(including onboard sensors, sensors or other data collectors
(including click tracking sensors) in or about a value chain
environment (such as, without limitation, a point of origin, a
loading or unloading dock, a vehicle or floating asset used to
convey goods, a container, a port, a distribution center, a storage
facility, a warehouse, a delivery vehicle, and a point of
destination), cameras for monitoring an entire environment,
dedicated cameras for a particular machine, process, worker, or the
like, wearable cameras, portable cameras, cameras disposed on
mobile robots, cameras of portable devices like smart phones and
tablets, and many others, including any of the many sensor types
disclosed throughout this disclosure or in the documents
incorporated herein by reference); indoor location monitoring
systems 1532 (including cameras, IR systems, motion-detection
systems, beacons, RFID readers, smart lighting systems,
triangulation systems, RF and other spectrum detection systems,
time-of-flight systems, chemical noses and other chemical sensor
sets, as well as other sensors); user feedback systems 1534
(including survey systems, touch pads, voice-based feedback
systems, rating systems, expression monitoring systems, affect
monitoring systems, gesture monitoring systems, and others);
behavioral monitoring systems 1538 (such as for monitoring
movements, shopping behavior, buying behavior, clicking behavior,
behavior indicating fraud or deception, user interface
interactions, product return behavior, behavior indicative of
interest, attention, boredom or the like, mood-indicating behavior
(such as fidgeting, staying still, moving closer, or changing
posture) and many others); and any of a wide variety of Internet of
Things (IoT) data collectors 1172, such as those described
throughout this disclosure and in the documents incorporated by
reference herein.
[0375] In embodiments, the value chain monitoring systems layer 614
and its data collection systems 640 may include an entity discovery
system 1900 for discovering one or more value chain network
entities 652, such as any of the entities described throughout this
disclosure. This may include components or sub-systems for
searching for entities within the value chain network 668, such as
by device identifier, by network location, by geolocation (such as
by geofence), by indoor location (such as by proximity to known
resources, such as IoT-enabled devices and infrastructure, Wifi
routers, switches, or the like), by cellular location (such as by
proximity to cellular towers), by identity management systems (such
as where an entity 652 is associated with another entity 652, such
as an owner, operator, user, or enterprise by an identifier that is
assigned by and/or managed by the platform 604), and the like.
Entity discovery 1900 may initiate a handshake among a set of
devices, such as to initiate interactions that serve various
applications 630 or other capabilities of the platform 604.
[0376] Referring to FIG. 14, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections. The
management platform includes a user interface 3020 that provides,
among other things, a set of adaptive intelligence systems 614. The
adaptive intelligence systems 614 provide coordinated intelligence
(including artificial intelligence 1160, expert systems 3002,
machine learning 3004, and the like) for a set of demand management
applications 824 and for a set of supply chain applications 812 for
a category of goods 3010, which may be produced and sold through
the value chain. The adaptive intelligence systems 614 may deliver
artificial intelligence 1160 through a set of data processing,
artificial intelligence and computational systems 634. In
embodiments, the adaptive intelligence systems 614 are selectable
and/or configurable through the user interface 3020 so that one or
more of the adaptive intelligence systems 614 can operate on or in
cooperation with the sets of value chain applications (e.g., demand
management applications 824 and supply chain applications 812). The
adaptive intelligence systems 614 may include artificial
intelligence, including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference.
[0377] In embodiments, user interface may include interfaces for
configuring an artificial intelligence system 1160 to take inputs
from selected data sources of the value chain (such as data sources
used by the set of demand management applications 824 and/or the
set of supply chain applications 812) and supply them, such as to a
neural network, artificial intelligence system 1160 or any of the
other adaptive intelligence systems 614 described throughout this
disclosure and in the documents incorporated herein by reference to
enhance, control, improve, optimize, configure, adapt or have
another impact on a value chain for the category of goods 3010. In
embodiments, the selected data sources of the value chain may be
applied either as inputs for classification or prediction, or as
outcomes relating to the value chain, the category of goods 3010
and the like.
[0378] In embodiments, providing coordinated intelligence may
include providing artificial intelligence capabilities, such as
artificial intelligence systems 1160 and the like. Artificial
intelligence systems may facilitate coordinated intelligence for
the set of demand management applications 824 or the set of supply
chain applications 812 or both, such as for a category of goods,
such as by processing data that is available in any of the data
sources of the value chain, such as value chain processes, bills of
materials, manifests, delivery schedules, weather data, traffic
data, goods design specifications, customer complaint logs,
customer reviews, Enterprise Resource Planning (ERP) System,
Customer Relationship Management (CRM) System, Customer Experience
Management (CEM) System, Service Lifecycle Management (SLM) System,
Product Lifecycle Management (PLM) System, and the like.
[0379] In embodiments, the user interface 3020 may provide access
to, among other things artificial intelligence capabilities,
applications, systems and the like for coordinating intelligence
for applications of the value chain and particularly for value
chain applications for the category of goods 3010. The user
interface 3020 may be adapted to receive information descriptive of
the category of goods 3010 and configure user access to the
artificial intelligence capabilities responsive thereto, so that
the user, through the user interface is guided to artificial
intelligence capabilities that are suitable for use with value
chain applications (e.g., the set of demand management applications
824 and supply chain applications 812) that contribute to
goods/services in the category of goods 3010. The user interface
3020 may facilitate providing coordinated intelligence that
comprises artificial intelligence capabilities that provide
coordinated intelligence for a specific operator and/or enterprise
that participates in the supply chain for the category of
goods.
[0380] In embodiments, the user interface 3020 may be configured to
facilitate the user selecting and/or configuring multiple
artificial intelligence systems 1160 for use with the value chain.
The user interface may present the set of demand management
applications 824 and supply chain applications 812 as connected
entities that receive, process, and produce outputs each of which
may be shared among the applications. Types of artificial
intelligence systems 1160 may be indicated in the user interface
3020 responsive to sets of connected applications or their data
elements being indicated in the user interface, such as by the user
placing a pointer proximal to a connected set of applications and
the like. In embodiments, the user interface 3020 may facilitate
access to the set of adaptive intelligence systems provides a set
of capabilities that facilitate development and deployment of
intelligence for at least one function selected from a list of
functions consisting of supply chain application automation, demand
management application automation, machine learning, artificial
intelligence, intelligent transactions, intelligent operations,
remote control, analytics, monitoring, reporting, state management,
event management, and process management.
[0381] The adaptive intelligence systems 614 may be configured with
data processing, artificial intelligence and computational systems
634 that may operate cooperatively to provide coordinated
intelligence, such as when an artificial intelligence system 1160
operates on or responds to data collected by or produced by other
systems of the adaptive intelligence systems 614, such as a data
processing system and the like. In embodiments, providing
coordinated intelligence may include operating a portion of a set
of artificial intelligence systems 1160 that employs one or more
types of neural network that is described herein and in the
documents incorporated herein by reference and that processes any
of the demand management application outputs and supply chain
application outputs to provide the coordinated intelligence.
[0382] In embodiments, providing coordinated intelligence for the
set of demand management applications 824 may include configuring
at least one of the adaptive intelligence systems 614 (e.g.,
through the user interface 3020 and the like) for at least one or
more demand management applications selected from a list of demand
management applications including a demand planning application, a
demand prediction application, a sales application, a future demand
aggregation application, a marketing application, an advertising
application, an e-commerce application, a marketing analytics
application, a customer relationship management application, a
search engine optimization application, a sales management
application, an advertising network application, a behavioral
tracking application, a marketing analytics application, a
location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service, and the like.
[0383] Similarly, providing coordinated intelligence for the set of
supply chain applications 812 may include configuring at least one
of the adaptive intelligence systems 614 for at least one or more
supply chain applications selected from a list of supply chain
applications including a goods timing management application, a
goods quantity management application, a logistics management
application, a shipping application, a delivery application, an
order for goods management application, an order for components
management application, and the like.
[0384] In embodiments, the management platform 102 may, such as
through the user interface 3020 facilitate access to the set of
adaptive intelligence systems 614 that provide coordinated
intelligence for a set of demand management applications 824 and
supply chain applications 812 through the application of artificial
intelligence. In such embodiments, the user may seek to align
supply with demand while ensuring profitability and the like of a
value chain for a category of goods 3010. By providing access to
artificial intelligence capabilities 1160, the management platform
allows the user to focus on the applications of demand and supply
while gaining advantages of techniques such as expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and the like.
[0385] In embodiments, the management platform 102 may, through the
user interface 3020 and the like provide a set of adaptive
intelligence systems 614 that provide coordinated artificial
intelligence 1160 for the sets of demand management applications
824 and supply chain applications 812 for the category of goods
3020 by, for example, determining (automatically) relationships
among demand management and supply chain applications based on
inputs used by the applications, results produced by the
applications, and value chain outcomes. The artificial intelligence
1160 may be coordinated by, for example, the set of data
processing, artificial intelligence and computational systems 634
available through the adaptive intelligence systems 614.
[0386] In embodiments, the management platform 102 may be
configured with a set of artificial intelligence systems 1160 as
part of a set of adaptive intelligence systems 614 that provide the
coordinated intelligence for the sets of demand management
applications 824 and supply chain applications 812 for a category
of goods 3010. The set of artificial intelligence systems 1160 may
provide the coordinated intelligence so that at least one supply
chain application of the set of supply chain applications 812
produces results that address at least one aspect of supply for at
least one of the goods in the category of goods as determined by at
least one demand management application of the set of demand
management applications 824. In examples, a behavioral tracking
demand management application may generate results for behavior of
uses of a good in the category of goods 3010. The artificial
intelligence systems 1160 may process the behavior data and
conclude that there is a perceived need for greater consumer access
to a second product in the category of goods 3010. This coordinated
intelligence may be, optionally automatically, applied to the set
of supply chain applications 812 so that, for example, production
resources or other resources in the value chain for the category of
goods are allocated to the second product. In examples, a
distributor who handles stocking retailer shelves may receive a new
stocking plan that allocates more retail shelf space for the second
product, such as by taking away space from a lower margin product
and the like.
[0387] In embodiments, the set of artificial intelligence systems
1160 and the like may provide coordinated intelligence for the sets
of supply chain and demand management applications by, for example,
determining an optionally temporal prioritization of demand
management application outputs that impact control of supply chain
applications so that an optionally temporal demand for at least one
of the goods in the category of goods 3010 can be met. Seasonal
adjustments in prioritization of demand application results are one
example of a temporal change. Adjustments in prioritization may
also be localized, such as when a large college football team is
playing at their home stadium and local supply of tailgating
supplies may temporally be adjusted even though demand management
application results suggest that small propane stoves are not
currently in demand in a wider region.
[0388] A set of adaptive intelligence systems 614 that provide
coordinated intelligence, such as by providing artificial
intelligence capabilities 1160 and the like may also facilitate
development and deployment of intelligence for at least one
function selected from a list of functions consisting of supply
chain application automation, demand management application
automation, machine learning, artificial intelligence, intelligent
transactions, intelligent operations, remote control, analytics,
monitoring, reporting, state management, event management, and
process management. The set of adaptive intelligence systems 614
may be configured as a layer in the platform and an artificial
intelligence system therein may operate on or be responsive to data
collected by and/or produced by other systems (e.g., data
processing systems, expert systems, machine learning systems and
the like) of the adaptive intelligence systems layer.
[0389] In addition to providing coordinated intelligence configured
for specific categories of goods, the coordinated intelligence may
be provided for a specific value chain entity 652, such as a supply
chain operator, business, enterprise, and the like that
participates in the supply chain for the category of goods.
[0390] Providing coordinated intelligence may include employing a
neural network to process at least one of the inputs and outputs of
the sets of demand management and supply chain applications. Neural
networks may be used with demand applications, such as a demand
planning application, a demand prediction application, a sales
application, a future demand aggregation application, a marketing
application, an advertising application, an e-commerce application,
a marketing analytics application, a customer relationship
management application, a search engine optimization application, a
sales management application, an advertising network application, a
behavioral tracking application, a marketing analytics application,
a location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service, and the like. Neural networks may also be used
with supply chain applications such as a goods timing management
application, a goods quantity management application, a logistics
management application, a shipping application, a delivery
application, an order for goods management application, an order
for components management application, and the like. Neural
networks may provide coordinated intelligence by processing data
that is available in any of a plurality of value chain data sources
for the category of goods including without limitation processes,
bill of materials, weather, traffic, design specification, customer
complaint logs, customer reviews, Enterprise Resource Planning
(ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle
Management (SLM) System, Product Lifecycle Management (PLM) System,
and the like. Neural networks configured for providing coordinated
intelligence may share adaptation capabilities with other adaptive
intelligence systems 614, such as when these systems are configured
in a topology that facilitates such shared adaptation. In
embodiments, neural networks may facilitate provisioning available
value chain/supply chain network resources for both the set of
demand management applications and for the set of supply chain
applications. In embodiments, neural networks may provide
coordinated intelligence to improve at least one of the list of
outputs consisting of a process output, an application output, a
process outcome, an application outcome, and the like.
[0391] Referring to FIG. 15, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections. The
management platform includes a user interface 3020 that provides,
among other things, a hybrid set of adaptive intelligence systems
614. The hybrid set of adaptive intelligence systems 614 provide
coordinated intelligence through the application of artificial
intelligence, such as through application of a hybrid artificial
intelligence system 3060, and optionally through one or more expert
systems, machine learning systems, and the like for use with a set
of demand management applications 824 and for a set of supply chain
applications 812 for a category of goods 3010, which may be
produced and sold through the value chain. The hybrid adaptive
intelligence systems 614 may deliver two types of artificial
intelligence systems, type A 3052 and type B 3054 through a set of
data processing, artificial intelligence and computational systems
634. In embodiments, the hybrid adaptive intelligence systems 614
are selectable and/or configurable through the user interface 3020
so that one or more of the hybrid adaptive intelligence systems 614
can operate on or in cooperation with the sets of supply chain
applications (e.g., demand management applications 824 and supply
chain applications 812). The hybrid adaptive intelligence systems
614 may include a hybrid artificial intelligence system 3060 that
may include at least two types of artificial intelligence
capabilities including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference. The hybrid adaptive
intelligence systems 614 may facilitate applying a first type of
artificial intelligence system 1160 to the set of demand management
applications 824 and a second type of artificial intelligence
system 1160 to the set of supply chain applications 812, wherein
each of the first type and second type of artificial intelligence
system 1160 can operate independently, cooperatively, and
optionally coordinate operation to provide coordinated intelligence
for operation of the value chain that produces at least one of the
goods in the category of goods 3010.
[0392] In embodiments, the user interface 3020 may include
interfaces for configuring a hybrid artificial intelligence system
3060 to take inputs from selected data sources of the value chain
(such as data sources used by the set of demand management
applications 824 and/or the set of supply chain applications 812)
and supply them, such as to at least one of the two types of
artificial intelligence systems in the hybrid artificial
intelligence system 3060, types of which are described throughout
this disclosure and in the documents incorporated herein by
reference to enhance, control, improve, optimize, configure, adapt
or have another impact on a value chain for the category of goods
3010. In embodiments, the selected data sources of the value chain
may be applied either as inputs for classification or prediction,
or as outcomes relating to the value chain, the category of goods
3010 and the like.
[0393] In embodiments, the hybrid adaptive intelligence systems 614
provides a plurality of distinct artificial intelligence systems
1160, a hybrid artificial intelligence system 3060, and
combinations thereof. In embodiments, any of the plurality of
distinct artificial intelligence systems 1160 and the hybrid
artificial intelligence system 3060 may be configured as a
plurality of neural network-based systems, such as a
classification-adapted neural network, a prediction-adapted neural
network and the like. As an example of hybrid adaptive intelligence
systems 614, a machine learning-based artificial intelligence
system may be provided for the set of demand management
applications 824 and a neural network-based artificial intelligence
system may be provided for the set of supply chain applications
812. As an example of a hybrid artificial intelligence system 3060,
the hybrid adaptive intelligence systems 614 may provide the hybrid
artificial intelligence system 3060 that may include a first type
of artificial intelligence that is applied to the demand management
applications 824 and which is distinct from a second type of
artificial intelligence that is applied to the supply chain
applications 812. A hybrid artificial intelligence system 3060 may
include any combination of types of artificial intelligence systems
including a plurality of a first type of artificial intelligence
(e.g., neural networks) and at least one second type of artificial
intelligence (e.g., an expert system) and the like. In embodiments,
a hybrid artificial intelligence system may comprise a hybrid
neural network that applies a first type of neural network with
respect to the demand management applications 824 and a second type
of neural network with respect to the supply chain applications
812. Yet further, a hybrid artificial intelligence system 3060 may
provide two types of artificial intelligence to different
applications, such as different demand management applications 824
(e.g., a sales management application and a demand prediction
application) or different supply chain applications 812 (e.g., a
logistics control application and a production quality control
application).
[0394] In embodiments, hybrid adaptive intelligence systems 614 may
be applied as distinct artificial intelligence capabilities to
distinct demand management applications 824. As examples,
coordinated intelligence through a hybrid artificial intelligence
capabilities may be provided to a demand planning application by a
feed-forward neural network, to a demand prediction application by
a machine learning system, to a sales application by a
self-organizing neural network, to a future demand aggregation
application by a radial basis function neural network, to a
marketing application by a convolutional neural network, to an
advertising application by a recurrent neural network, to an
e-commerce application by a hierarchical neural network, to a
marketing analytics application by a stochastic neural network, to
a customer relationship management application by an associative
neural network and the like.
[0395] Referring to FIG. 16, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections for
providing a set of predictions 3070. The management platform
includes a user interface 3020 that provides, among other things, a
set of adaptive intelligence systems 614. The adaptive intelligence
systems 614 provide a set of predictions 3070 through the
application of artificial intelligence, such as through application
of an artificial intelligence system 1160, and optionally through
one or more expert systems, machine learning systems, and the like
for use with a coordinated set of demand management applications
824 and supply chain applications 812 for a category of goods 3010,
which may be produced and sold through the value chain. The
adaptive intelligence systems 614 may deliver the set of prediction
3070 through a set of data processing, artificial intelligence and
computational systems 634. In embodiments, the adaptive
intelligence systems 614 are selectable and/or configurable through
the user interface 3020 so that one or more of the adaptive
intelligence systems 614 can operate on or in cooperation with the
coordinated sets of value chain applications. The adaptive
intelligence systems 614 may include an artificial intelligence
system that provides artificial intelligence capabilities known to
be associated with artificial intelligence including any of the
various expert systems, artificial intelligence systems, neural
networks, supervised learning systems, machine learning systems,
deep learning systems, and other systems described throughout this
disclosure and in the documents incorporated by reference. The
adaptive intelligence systems 614 may facilitate applying adapted
intelligence capabilities to the coordinated set of demand
management applications 824 and supply chain applications 812 such
as by producing a set of predictions 3070 that may facilitate
coordinating the two sets of value chain applications, or at least
facilitate coordinating at least one demand management application
and at least one supply chain application from their respective
sets.
[0396] In embodiments, the set of predictions 3070 includes a least
one prediction of an impact on a supply chain application based on
a current state of a coordinated demand management application,
such as a prediction that a demand for a good will decrease earlier
than previously anticipated. The converse may also be true in that
the set of predictions 3070 includes at least one prediction of an
impact on a demand management application based on a current state
of a coordinated supply chain application, such as a prediction
that a lack of supply of a good will likely impact a measure of
demand of related goods. In embodiments, the set of predictions
3070 is a set of predictions of adjustments in supply required to
meet demand. Other predictions include at least one prediction of
change in demand that impacts supply. Yet other predictions in the
set of predictions predict a change in supply that impacts at least
one of the set of demand management applications, such as a
promotion application for at least one good in the category of
goods. A prediction in the set of predictions may be as simple as
setting a likelihood that a supply of a good in the category of
goods will not meet demand set by a demand setting application.
[0397] In embodiments, the adaptive intelligence systems 614 may
provide a set of artificial intelligence capabilities to facilitate
providing the set of predictions for the coordinated set of demand
management applications and supply chain applications. In one
non-limiting example, the set of artificial intelligence
capabilities may include a probabilistic neural network that may be
used to predict a fault condition or a problem state of a demand
management application such as a lack of sufficient validated
feedback. The probabilistic neural network may be used to predict a
problem state with a machine performing a value chain operation
(e.g., a production machine, an automated handling machine, a
packaging machine, a shipping machine and the like) based on a
collection of machine operating information and preventive
maintenance information for the machine.
[0398] In embodiments, the set of predictions 3070 may be provided
by the management platform 102 directly through a set of adaptive
artificial intelligence systems.
[0399] In embodiments, the set of predictions 3070 may be provided
for the coordinated set of demand management applications and
supply chain applications for a category of goods by applying
artificial intelligence capabilities for coordinating the set of
demand management applications and supply chain applications.
[0400] In embodiments, the set of predictions 3070 may be
predictions of outcomes for operating a value chain with the
coordinated set demand management applications and supply chain
applications for the category of goods, so that a user may conduct
test cases of coordinated sets of demand management applications
and supply chain applications to determine which sets may produce
desirable outcomes (viable candidates for a coordinated set of
applications) and which may produce undesirable outcomes.
[0401] Referring to FIG. 17, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections for
providing a set of classifications 3080. The management platform
includes a user interface 3020 that provides, among other things, a
set of adaptive intelligence systems 614. The adaptive intelligence
systems 614 provide a set of classifications 3080 through, for
example, the application of artificial intelligence, such as
through application of an artificial intelligence system 1160, and
optionally through one or more expert systems, machine learning
systems, and the like for use with a coordinated set of demand
management applications 824 and supply chain applications 812 for a
category of goods 3010, which may be produced, marketed, sold,
resold, rented, leased, given away, serviced, recycled, renewed,
enhanced, and the like through the value chain. The adaptive
intelligence systems 614 may deliver the set of classifications
3080 through a set of data processing, artificial intelligence and
computational systems 634. In embodiments, the adaptive
intelligence systems 614 are selectable and/or configurable through
the user interface 3020 so that one or more of the adaptive
intelligence systems 614 can operate on or in cooperation with the
coordinated sets of value chain applications. The adaptive
intelligence systems 614 may include an artificial intelligence
system that provides, among other things classification
capabilities through any of the various expert systems, artificial
intelligence systems, neural networks, supervised learning systems,
machine learning systems, deep learning systems, and other systems
described throughout this disclosure and in the documents
incorporated by reference. The adaptive intelligence systems 614
may facilitate applying adapted intelligence capabilities to the
coordinated set of demand management applications 824 and supply
chain applications 812 such as by producing a set of
classifications 3080 that may facilitate coordinating the two sets
of value chain applications, or at least facilitate coordinating at
least one demand management application and at least one supply
chain application from their respective sets.
[0402] In embodiments, the set of classifications 3080 includes at
least one classification of a current state of a supply chain
application for use by a coordinated demand management application,
such as a classification of a problem state that may impact
operation of a demand management application, such as a marketing
application and the like. Such a classification may be useful in
determining how to adjust a market expectation for a good that is
going to have a lower yield than previously anticipated. The
converse may also be true in that the set of classifications 3080
includes at least one classification of a current state of a demand
management application and its relationship to a coordinated supply
chain application. In embodiments, the set of classifications 3080
is a set of classifications of adjustments in supply required to
meet demand, such as adjustments to production worker needs would
be classified differently that adjustments in third-party logistics
providers. Other classifications may include at least one
classification of perceived changes in demand and a resulting
potential impact on supply management. Yet other classifications in
the set of classifications may include a supply chain application
impact on at least one of the set of demand management
applications, such as a promotion application for at least one good
in the category of goods. A classification in the set of
classifications may be as simple as classifying a likelihood that a
supply of a good in the category of goods will not meet demand set
by a demand setting application.
[0403] In embodiments, the adaptive intelligence systems 614 may
provide a set of artificial intelligence capabilities to facilitate
providing the set of classifications 3080 for the coordinated set
of demand management applications and supply chain applications. In
one non-limiting example, the set of artificial intelligence
capabilities may include a probabilistic neural network that may be
used to classify fault conditions or problem states of a demand
management application, such as a classification of a lack of
sufficient validated feedback. The probabilistic neural network may
be used to classify a problem state of a machine performing a value
chain operation (e.g., a production machine, an automated handling
machine, a packaging machine, a shipping machine and the like) as
pertaining to at least one of machine operating information and
preventive maintenance information for the machine.
[0404] In embodiments, the set of classifications 3080 may be
provided by the management platform 102 directly through a set of
adaptive artificial intelligence systems. Further, the set of
classifications 3080 may be provided for the coordinated set of
demand management applications and supply chain applications for a
category of goods by applying artificial intelligence capabilities
for coordinating the set of demand management applications and
supply chain applications.
[0405] In embodiments, the set of classifications 3080 may be
classifications of outcomes for operating a value chain with the
coordinated set demand management applications and supply chain
applications for the category of goods, so that a user may conduct
test cases of coordinated sets of demand management applications
and supply chain applications to determine which sets may produce
outcomes that are classified as desirable (e.g., viable candidates
for a coordinated set of applications) and outcomes that are
classified as undesirable.
[0406] In embodiments, the set of classifications may comprise a
set of adaptive intelligence functions, such as a neural network
that may be adapted to classify information associated with the
category of goods. In an example, the neural network may be a
multilayered feed forward neural network.
[0407] In embodiments, performing classifications may include
classifying discovered value chain entities as one of demand
centric and supply centric.
[0408] In embodiments, the set of classifications 3080 may be
achieved through use of artificial intelligence systems 1160 for
coordinating the set of coordinated demand management and supply
chain applications. Artificial intelligence systems may configure
and generate sets of classifications 3080 as a means by which
demand management applications and supply chain applications can be
coordinated. In an example, classification of information flow
throughout a value chain may be classified as being relevant to
both a demand management application and a supply chain
application; this common relevance may be a point of coordination
among the applications. In embodiments, the set of classifications
may be artificial intelligence generated classifications of
outcomes of operating a supply chain that is dependent on the
coordinated demand management applications 824 and supply chain
applications 812.
[0409] Referring to FIG. 18, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections for
achieving automated control intelligence. The management platform
includes a user interface 3020 that provides, among other things, a
set of adaptive intelligence systems 614. The adaptive intelligence
systems 614 provide automated control signaling 3092 for a
coordinated set of demand management applications 824 and supply
chain applications 812 for a category of goods 3010, which may be
produced and sold through the value chain. The adaptive
intelligence systems 614 may deliver the automated control signals
3092 through a set of data processing, artificial intelligence and
computational systems 634. In embodiments, the adaptive
intelligence systems 614 are selectable and/or configurable through
the user interface 3020 so that one or more of the adaptive
intelligence systems 614 can automatically control the sets of
supply chain applications (e.g., demand management applications 824
and supply chain applications 812). The adaptive intelligence
systems 614 may include artificial intelligence including any of
the various expert systems, artificial intelligence systems, neural
networks, supervised learning systems, machine learning systems,
deep learning systems, and other systems described throughout this
disclosure and in the documents incorporated by reference.
[0410] In embodiments, the user interface 3020 may include
interfaces for configuring an adaptive intelligence systems 614 to
take inputs from selected data sources of the value chain 3094
(such as data sources used by the coordinated set of demand
management applications 824 and/or the set of supply chain
applications 812) and supply them, such as to a neural network,
artificial intelligence system 1160 or any of the other adaptive
intelligence systems 614 described throughout this disclosure and
in the documents incorporated herein by reference for producing
automated control signals 3092, such as to enhance, control,
improve, optimize, configure, adapt or have another impact on a
value chain for the category of goods 3010. In embodiments, the
selected data sources of the value chain may be used for
determining aspects of the automated control signals, such as for
temporal adjustments to control outcomes relating to the value
chain at least for the category of goods 3010 and the like.
[0411] In an example, the set of automated control signals may
include at least one control signal for automating execution of a
supply chain application, such as a production start, an automated
material order, an inventory check, a billing application and the
like in the coordinated set of demand management applications and
supply chain applications. In yet another example of automated
control signal generation, the set of automated control signals may
include at least one control signal for automating execution of a
demand management application, such as a product recall
application, an email distribution application and the like in the
coordinated set of demand management applications and supply chain
applications. In yet other examples, the automate control signals
may control timing of demand management applications based on goods
supply status.
[0412] In embodiments, the adaptive intelligence systems 614 may
apply machine learning to outcomes of supply to automatically adapt
a set of demand management application control signals. Similarly,
the adaptive intelligence systems 614 may apply machine learning to
outcomes of demand management to automatically adapt a set of
supply chain application control signals. The adaptive intelligence
systems 614 may provide further processing for automated control
signal generation, such as by applying artificial intelligence to
determine aspects of a value chain that impact automated control of
the coordinated set of demand management applications and supply
chain applications for a category of goods. The determined aspects
could be used in the generation and operation of automated control
intelligence/signals, such as by filtering out value chain
information for aspects that do not impact the targeted demand
management and supply chain applications.
[0413] Automated control of, for example, supply chain applications
may be restricted, such as by policy, operational limits, safety
constraints and the like. The set of adaptive intelligence systems
may determine a range of supply chain application control values
within which control can be automated. In embodiments, the range
may be associated with a supply rate, a supply timing rate, a mix
of goods in a category of goods, and the like.
[0414] Embodiments are described herein for using artificial
intelligence systems or capabilities to identify, configure and
regulate automated control signals. Such embodiments may further
include a closed loop of feedback from the coordinated set of
demand management and supply chain applications (e.g., state
information, output information, outcomes and the like) that is
optionally processed with machine learning and used to adapt the
automated control signals for at least one of the goods in the
category of goods. An automated control signal may be adapted based
on, for example, an indication of feedback from a supply chain
application that yield of a good suggests a production problem. In
this example, the automated control signal may impact production
rate and the feedback may cause the signal to automatically
self-adjust to a slower production rate until the production
problem is resolved.
[0415] Referring to FIG. 19, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections for
providing information routing recommendations. The management
platform includes a set of value chain networks 3102 from which
network data 3110 is collected from a set of information routing
activities, the information including outcomes, parameters, routing
activity information and the like. Within the set of value chain
networks 3102 is selected a select value chain network 3104 for
which at least one information routing recommendation 3130 is
provided. An artificial intelligence system 1160 may include a
machine learning system and may be trained using a training set
derived from the network data 3110 outcomes, parameters and routing
activity information for the set of value chain networks 3102. The
artificial intelligence system 1160 may further provide an
information routing recommendation 3130 based on a current status
3120 of the select value chain network 3104. The artificial
intelligence system may use machine learning to train on
information transaction types within the set of value chain
networks 3102, thereby learning pertinent factors regarding
different transaction types (e.g., real-time inventory updates,
buyer credit checks, engineering signoff, and the like) and
contributing to the information routing recommendation accordingly.
The artificial intelligence system may also use machine learning to
train on information value for different types and/or classes of
information routed in and throughout the set of value chain
networks 3102. Information may be valued on a wide range of
factors, including timing of information availability and timing of
information consumption as well as information content-based value,
such as information without which a value chain network element
(e.g., a production provider) cannot perform a desired action
(e.g., starting volume production without a work order). Therefore
information routing recommendations may be based on training on
transaction type, information value, and a combination thereof.
These are merely exemplary information routing recommendation
training and recommendation basis factors and are presented here
without limitation on other elements for training and
recommendation basis.
[0416] In embodiments, the artificial intelligence system 1160 may
provide an information routing recommendation 3130 based on
transaction type, transaction type and information type, network
type and the like. An information routing recommendation may be
based on combinations of factors, such as information type and
network type, such as when an information type (streaming) is not
compatible with a network type (small transactions).
[0417] In embodiments, the artificial intelligence system 1160 may
use machine learning to develop an understanding of networks within
the selected value chain network 3104, such as network topology,
network loading, network reliability, network latency and the like.
This understanding may be combined with, for example, detected or
anticipated network conditions to form an information routing
recommendation. Aspects such as existence of edge intelligence in a
value chain network 3104 can influence one or more information
routing recommendations. In an example, a type of information may
be incompatible with a network type; however the network may be
configured with edge intelligence that can be leveraged by the
artificial intelligence system 1160 to adapt the form of the
information being routed so that it is compatible with a targeted
network type. This is also an example of more general consideration
for information routing recommendation--network resources (e.g.,
presence, availability, and capability), such as edge computing,
server access, network-based storage resources and the like.
Likewise, value chain network entities may impact information
routing recommendations. In embodiments, an information routing
recommendation may avoid routing information that is confidential
to a first supplier in the value chain through network nodes
controlled by competitors of the supplier. In embodiments, an
information routing recommendation may include routing information
to a first node where it is partially consumed and partially
processed for further routing, such as by splitting up the portion
partially processed for further routing into destination-specific
information sets.
[0418] In embodiments, an artificial intelligence system 1160 may
provide an information routing recommendation based on goals, such
as goals of a value chain network, goals of information routing,
and the like. Goal-based information routing recommendations may
include routing goals, such as Quality of Service routing goals,
routing reliability goals (which may be measured based on a
transmission failure rate and the like). Other goals may include a
measure of latency associated with one or more candidate routes. An
information routing recommendation may be based on the availability
of information in a selected value chain network, such as when
information is available and when it needs to be delivered. For
information that is available well ahead of when it is needed
(e.g., a nightly production report that is available for routing at
2 AM is first needed by 7 AM), routing recommendations may include
using resources that are lower cost, may involve short delays in
routing and the like. For information that is available just before
it is needed (e.g., a result of product testing is needed within a
few hundred milliseconds of when the test is finished to maintain a
production operation rate, and the like).
[0419] An information routing recommendation may be formed by the
artificial intelligence system 1160 based on information
persistence factors, such as how long information is available for
immediate routing within the value chain network. An information
routing recommendation that factors information persistence may
select network resources based on availability, cost and the like
during a time of information persistence.
[0420] Information value and an impact on information value may
factor into an information routing recommendation. As an example,
information that is valid for a single shipment (e.g., a production
run of a good) may substantively lose value once the shipment has
been satisfactorily received. In such an example, an information
routing recommendation may indicate routing the relevant
information to all of the highest priority consumers of the
information while it is still valid. Likewise, routing of
information that is consumed by more than one value chain entity
may need to be coordinated so that each value chain entity receives
the information at a desired time/moment, such as during the same
production shift, at their start of day, which may be different if
the entities are in different time zones, and the like.
[0421] In embodiments, information routing recommendations may be
based on a topology of a value chain, based on location and
availability of network storage resources, and the like.
[0422] In embodiments, one or more information routing
recommendations may be adapted while the information is routed
based on, for example, changes in network resource availability,
network resource discovery, network dynamic loading, priority of
recommendations that are generated after information for a first
recommendation is in-route, and the like.
[0423] Referring to FIG. 20, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections for
semi-sentient problem recognitions of pain points in a value chain
network. The management platform includes a set of value chain
network entities 3152 from which entity-related data 3160 is
collected and includes outcomes, parameters, activity information
and the like associated with the entities. Within the set of value
chain network entities 3152 is selected a set of select value chain
network entities 3154 for which at least one pain point problem
state 3172 is detected. An artificial intelligence system 1160 may
be training on a training set derived from the entity-related data
3160 including training on outcomes associated with value chain
entities, parameters associated with, for example, operation of the
value chain, value chain activity information and the like. The
artificial intelligence system may further employ machine learning
to facilitate learning problem state factors 3180 that may
characterize problem states input as training data. These factors
3180 may further be used by an instance of artificial intelligence
1160' that operates on computing resources 3170 that are local to
value chain network entities that are experiencing the
problem/result of a pain point. A goal of such a configuration of
artificial intelligence systems, data sets, and value chain
networks is to recognize a problem state in a portion of the
selected value chain.
[0424] In embodiments, recognizing problem states may be based on
variance analysis, such as variances that occur in value chain
measures (e.g., loading, latency, delivery time, cost, and the
like), particularly in a specific measure over time. Variances that
exceed a variance threshold (e.g., an optionally dynamic range of
results of a value chain operation, such as production, shipping,
clearing customs, and the like) may be indicative of a pain
point.
[0425] In addition to detecting problem states, the platform 102,
such as through the methods of semi-sentient problem recognition,
predict a pain point based at least in part on a correlation with a
detected problem state. The correlation may be derived from the
value chain, such as a shipper cannot deliver international goods
until they are processed through customs, or a sales forecast
cannot be provided with a high degree of confidence without high
quality field data and the like. In embodiments, a predicted pain
point may be a point of value chain activity further along a supply
chain, an activity that occurs in a related activity (e.g., tax
planning is related to tax laws), and the like. A predicted pain
point may be assigned a risk value based on aspects of the detected
problem state and correlations between the predicted pain point
activity and the problem state activity. If a production operation
can receive materials from two suppliers, a problem state with one
of the suppliers may indicate a low risk of a pain point of use of
the material. Likewise, if a demand management application
indicates high demand for a good and a problem is detected with
information on which the demand is based, a risk of excess
inventory (pain point) may be high depending on, for example how
far along in the value chain the good has progressed.
[0426] In embodiments, semi-sentient problem recognition may
involve more than mere linkages of data and operational states of
entities engaged in a value chain. Problem recognition may also be
based on human factors, such as perceived stress of production
supervisors, shippers, and the like. Human factors for use in
semi-sentient problem recognition may be collected from sensors
that facilitate detection of human stress level and the like (e.g.,
wearable physiological sensors, and the like).
[0427] In embodiments, semi-sentient problem recognition may also
be based on unstructured information, such as digital
communication, voice messaging, and the like that may be shared
among, originate with, or be received by humans involved in the
value chain operations. As an example, natural language processing
of email communications among workers in an enterprise may indicate
a degree of discomfort with, for example, a supplier to a value
chain. While data associated with the supplier (e.g., on-time
production, quality, and the like) may be within a variance range
deemed acceptable, information within this unstructured content may
indicate a potential pain point, such as a personal issue with a
key participant at the supplier and the like. By employing natural
language processing, artificial intelligence, and optionally
machine learning, problem state recognition may be enhanced.
[0428] In embodiments, semi-sentient problem recognition may be
based on analysis of variances of measures of a value chain
operation/entity/application including variance of a given measure
over time, variance of two related measures, and the like. In
embodiments, variance in outcomes over time may indicate a problem
state and/or suggest a pain point. In embodiments, an artificial
intelligence-based system may determine an acceptable range of
outcome variance and apply that range to measures of a select set
of value chain network entities, such as entities that share one or
more similarities, to facilitate detection of a problem state. In
embodiments, an acceptable range of outcome variance may indicate a
problem state trigger threshold that may be used by a local
instance of artificial intelligence to signal a problem state. In
such a scenario, a problem state may be detected when at least one
measure of the value chain activity/entity and the like is greater
than the artificial intelligence-determined problem state
threshold. Variance analysis for problem state detection may
include detecting variances in start/end times of scheduled value
chain network entity activities, variances in at least one of
production time, production quality, production rate, production
start time, production resource availability or trends thereof,
variances in a measure of shipping supply chain entity, variances
in a duration of time for transfer from one mode of transport to
another (e.g., when the variance is greater than a transport mode
problem state threshold), variances in quality testing, and the
like.
[0429] In embodiments, a semi-sentient problem recognition system
may include a machine learning/artificial intelligence prediction
of a correlated pain point further along a supply chain due to a
detected pain point, such as a risk and/or need for overtime,
expedited shipping, discounting goods prices, and the like.
[0430] In embodiments, a machine learning/artificial intelligence
system may process outcomes, parameters, and data collected from a
set of data sources relating to a set of value chain entities and
activities to detect at least one pain point selected from the list
of pain points consisting of late shipment, damaged container,
damaged goods, wrong goods, customs delay, unpaid duties, weather
event, damaged infrastructure, blocked waterway, incompatible
infrastructure, congested port, congested handling infrastructure,
congested roadway, congested distribution center, rejected goods,
returned goods, waste material, wasted energy, wasted labor force,
untrained workforce, poor customer service, empty transport vehicle
on return route, excessive fuel prices, excessive tariffs, and the
like.
[0431] Referring to FIG. 21, a management platform of an
information technology system, such as a management platform for a
value chain of goods and/or services is depicted as a block diagram
of functional elements and representative interconnections
automated coordination of a set of value chain network activities
for a set of products of an enterprise. The management platform
includes a set of network-connected value chain network entities
3202 that produce activity information 3208 that is used by an
artificial intelligence system 1160 to provide automate
coordination 3220 of value chain network activities 3212 for a set
of products 3210 for an enterprise 3204. In embodiments, value
chain monitoring systems 614 may monitor activities of the set of
network-connected value chain entities 3202 and work cooperatively
with data collection and management systems 640 to gather and store
value chain entity monitored information, such as activity
information, configuration information, and the like. This gathered
information may be configured as activity information 3208 for a
set of activities associated with a set of products 3210 of an
enterprise 3204. In embodiments, the artificial intelligence
systems 1160 may use application programming connectivity
facilities 642 for automating access to the monitored activity
information 3208.
[0432] A value chain may include a plurality of interconnected
entities that each perform several activities for completing the
value chain. While humans play a critical role in some activities
within a value chain network, greater automated coordination and
unified orchestration of supply and demand may be achieved using
artificial intelligence-type systems (e.g., machine learning,
expert systems, self-organizing systems, and the like including
such systems describe herein and in the documents incorporated
herein by reference) for coordinating supply chain activities. Use
of artificial intelligence may further enrich the emerging nature
of self-adapting systems, including Internet of Things (IoT)
devices and intelligent products and the like that not only provide
greater capabilities to end users, but can play a critical role in
automated coordination of supply chain activities.
[0433] For example, an IoT system deployed in a fulfillment center
628 may coordinate with an intelligent product 650 that takes
customer feedback about the product 650, and an application 630 for
the fulfillment center 628 may, upon receiving customer feedback
via a connection path to the intelligent product 650 about a
problem with the product 650, initiate a workflow to perform
corrective actions on similar products 650 before the products 650
are sent out from the fulfillment center 628. The workflow may be
configured by an artificial intelligence system 1160 that analyzes
the problem with the product 650, develops an understanding of
value chain network activities that produce the product, determines
resources required for the workflow, coordinates with inventory and
production systems to adapt any existing workflows and the like.
Artificial intelligence systems 1160 may further coordinate with
demand management applications to address any temporary impact on
product availability and the like.
[0434] In embodiments, automated coordination of a set of value
chain network activities for a set of products for an enterprise
may rely on the methods and systems of coordinated intelligence
described herein, such as to facilitate coordinating demand
management activities, supply chain activities and the like,
optionally using artificial intelligence for providing the
coordinated intelligence, coordinating the activities and the like.
As an example, artificial intelligence may facilitate determining
relationships among value change network activities based on inputs
used by the activities and results produced by the activities.
Artificial intelligence may be integrated with and/or work
cooperatively with activities of the platform, such as value chain
network entity activities to continuously monitor activities,
identify temporal aspects needing coordination (e.g., when changes
in supply temporally impact demand activities), and automate such
coordination. Automated coordination of value chain network
activities within and across value chain network entity activities
may benefit from advanced artificial intelligence systems that may
enable use of differing artificial intelligence capabilities for
any given value chain set of entities, applications, or conditions.
Use of hybrid artificial intelligence systems may provide benefits
by applying more than one type of intelligence to a set of
conditions to facilitate human and/or computer automated selection
thereof. Artificial intelligence can further enhance automated
coordination of value chain network entity activities through
intelligent operations such as generating sets of predictions, sets
of classifications, generation of automate control signals (that
may be communicated across value chain network entities and the
like). Other exemplary artificial intelligence-based influences on
automated coordination of value chain network entity activities
include machine learning-based information routing and
recommendations thereto, semi-sentient problem recognition based on
both structured (e.g., production data) and unstructured (e.g.,
human emotions) sources, and the like. Artificial intelligence
systems may facilitate automated coordination of value chain
network entity activities for a set of products or an enterprise
based on adaptive intelligence provided by the platform for a
category of goods under which the set of products of an enterprise
may be grouped. In an example, adaptive intelligence may be
provided by the platform for a drapery hanging category of goods
and a set of products for an enterprise may include a line of
adaptable drapery hangers. Through understanding developed for the
overall drapery hanging category, artificial intelligence
capabilities may be applied to value chain network activities of
the enterprise for automating aspects of the value chain, such as
information exchange among activities and the like.
Digital Twin System in Value Chain Entity Management Platform
[0435] Referring to FIG. 22, the adaptive intelligence layer 614
may include a value chain network digital twin system 1700, which
may include a set of components, processes, services, interfaces
and other elements for development and deployment of digital twin
capabilities for visualization of various value chain entities 652,
environments, and applications 630, as well as for coordinated
intelligence (including artificial intelligence 1160, edge
intelligence 1400, analytics and other capabilities) and other
value-added services and capabilities that are enabled or
facilitated with a digital twin 1700. Without limitation, a digital
twin 1700 may be used for and/or applied to each of the processes
that are managed, controlled, or mediated by each of the set of
applications 630 of the platform application layer.
[0436] In embodiments, the digital twin 1700 may take advantage of
the presence of multiple applications 630 within the value chain
management platform layer 604, such that a pair of applications may
share data sources (such as in the data storage layer 624) and
other inputs (such as from the monitoring layer 614) that are
collected with respect to value chain entities 652, as well as
sharing outputs, events, state information and outputs, which
collectively may provide a much richer environment for enriching
content in a digital twin 1700, including through use of artificial
intelligence 1160 (including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference) and through use of content
collected by the monitoring layer 614 and data collection systems
640.
[0437] In embodiments, a digital twin 1700 may be used in
connection with shared or converged processes among the various
pairs of the applications 630 of the application layer 604, such
as, without limitation, of a converged process involving a security
application 834 and an inventory management application 820,
integrated automation of blockchain-based applications 844 with
facility management applications 850, and many others. In
embodiments, converged processes may include shared data structures
for multiple applications 630 (including ones that track the same
transactions on a blockchain but may consume different subsets of
available attributes of the data objects maintained in the
blockchain or ones that use a set of nodes and links in a common
knowledge graph) that may be connected to with the digital twin
1700 such that the digital twin 1700 is updated accordingly. For
example, a transaction indicating a change of ownership of an
entity 652 may be stored in a blockchain and used by multiple
applications 630, such as to enable role-based access control,
role-based permissions for remote control, identity-based event
reporting, and the like that may be connected to and shared with
the digital twin 1700 such that the digital twin 1700 may be
updated accordingly. In embodiments, converged processes may
include shared process flows across applications 630, including
subsets of larger flows that are involved in one or more of a set
of applications 630 that may be connected to and shared with the
digital twin 1700 such that the digital twin 1700 may be updated
accordingly. For example, an inspection flow about a value chain
network entity 652 may serve an analytics solution 838, an asset
management solution 814, and others.
[0438] In embodiments, a digital twin 1700 may be provided for the
wide range of value chain network applications 630 mentioned
throughout this disclosure and the documents incorporated herein by
reference. An environment for development of a digital twin 1700
may include a set of interfaces for developers in which a developer
may configure an artificial intelligence system 1160 to take inputs
from selected data sources of the data storage layer 624 and events
or other data from the monitoring systems layer 614 and supply them
for inclusion in a digital twin 1700. A digital twin 1700
development environment may be configured to take outputs and
outcomes from various applications 630.
Value Chain Network Digital Twins
[0439] Referring to FIG. 23, any of the value chain network
entities 652 can be depicted in a set of one or more digital twins
1700, such as by populating the digital twin 1700 with value chain
network data object 1004, such as event data 1034, state data 1140,
or other data with respect to value chain network entities 652,
applications 630, or components or elements of the platform 604 as
described throughout this disclosure.
[0440] Thus, the platform 604 may include, integrate, integrate
with, manage, control, coordinate with, or otherwise handle any of
a wide variety of digital twins 1700, such as distribution twins
1714 (such as representing distribution facilities, assets,
objects, workers, or the like); warehousing twins 1712 (such as
representing warehouse facilities, assets, objects, workers and the
like); port infrastructure twins 1714 (such as representing a
seaport, an airport, or other facility, as well as assets, objects,
workers and the like); shipping facility twins 1720; operating
facility twins 1722; customer twins 1730 (such as representing
physical, behavioral, demographic, psychographic, financial,
historical, affinity, interest, and other characteristics of groups
of customers or individual customers); worker twins 1740 (such as
representing physical attributes, physiologic data, status data,
psychographic information, emotional states, states of
fatigue/energy, states of attention, skills, training,
competencies, roles, authority, responsibilities, work status,
activities, and other attributes of or involving workers);
wearable/portable device twins 1750; process twins 1760; machine
twins 1770 (such as for various machines used to support a value
chain network 668); product twins 1780; point of origin twins 1560;
supplier twins 1630; supply factor twins 1650; maritime facility
twins 1572; floating asset twins 1570; shipyard twins 1620;
destination twins 1562; fulfillment twins 1600; delivery system
twins 1610; demand factor twins 1640; retailer twins 1790;
ecommerce and online site and operator twins 1800; waterway twins
1810; roadway twins 1820; railway twins 1830; air facility twins
1840 (such as twins of aircraft, runways, airports, hangars,
warehouses, air travel routes, refueling facilities and other
assets, objects, workers and the like used in connection with air
transport of products 650); autonomous vehicle twins 1850; robotics
twins 1860; drone twins 1870; and logistics factor twins 1880;
among others. Each of these may have characteristics of digital
twins described throughout this disclosure and the documents
incorporated by reference herein, such as mirroring or reflecting
changes in states of associated physical objects or other entities,
providing capabilities for modeling behavior or interactions of
associated physical objects or other entities, enabling
simulations, providing indications of status, and many others.
[0441] In example embodiments, a digital twin system may be
configured to generate a variety of enterprise digital twins 1700
in connection with a value chain (e.g., specifically value chain
network entities 652). For example, an enterprise that produces
goods internationally (or at multiple facilities) may configure a
set of digital twins 1700, such as supplier twins that depict the
enterprise's supply chain, factory twins of the various production
facilities, product twins that represent the products made by the
enterprise, distribution twins that represent the enterprise's
distribution chains, and other suitable twins. In doing so, the
enterprise may define the structural elements of each respective
digital twin as well as any system data that corresponds to the
structural elements of the digital twin. For instance, in
generating a production facility twin, the enterprise may the
layout and spatial definitions of the facility and any processes
that are performed in the facility. The enterprise may also define
data sources corresponding to the value chain network entities 652,
such as sensor systems, smart manufacturing equipment, inventory
systems, logistics systems, and the like that provide data relevant
to the facility. The enterprise may associate the data sources with
elements of the production facility and/or the processes occurring
the facility. Similarly, the enterprise may define the structural,
process, and layout definitions of its supply chain and its
distribution chain and may connect relevant data sources, such as
supplier databases, logistics platforms, to generate respective
distribution chain and supply chain twins. The enterprise may
further associate these digital twins to have a view of its value
chain. In embodiments, the digital twin system may perform
simulations of the enterprise's value chain that incorporate
real-time data obtained from the various value chain network
entities 652 of the enterprise. In some of these embodiments, the
digital twin system may recommend decisions to a user interacting
with the enterprise digital twins 1700, such as when to order
certain parts for manufacturing a certain product given a predicted
demand for the manufactured product, when to schedule maintenance
on machinery and/or replace machinery (e.g., when digital
simulations on the digital twin indicates the demand for certain
products may be the lowest or when it would have the least effect
on the enterprise's profits and losses statement), what time of day
to ship items, or the like. The foregoing example is a non-limiting
example of the manner by which a digital twin may ingest system
data and perform simulations in order to further one or more
goals.
Entity Discovery and Interaction Management
[0442] Referring to FIG. 24, the monitoring systems layer 614,
including various data collection systems 640 (such as IoT data
collection systems, data collection systems that search social
networks, websites, and other online resources, crowdsourcing
systems, and others) may include a set of entity discovery systems
1900, such as for identifying sets of value chain network entities
652, identifying types of value chain network entities 652,
identifying specific value chain network entities 652 and the like,
as well as for managing identities of the value chain network
entities 652, including for resolving ambiguities (such as where a
single entity is identified differently in different systems, where
different entities are identified similarly, and the like), for
entity identity deduplication, for entity identity resolution, for
entity identity enhancement (such as by enriching data objects with
additional data that is collected about an entity within the
platform), and the like. Entity discovery 1900 may also include
discovery of interactions among entities, such as how entities are
connected (e.g., by what network connections, data integration
systems, and/or interfaces), what data is exchanged among entities
(including what types of data objects are exchanged, what common
workflows involve entities, what inputs and outputs are exchanged
between entities, and the like), what rules or policies govern the
entities, and the like. The platform 604 may include a set of
entity interaction management systems 1902, which may comprise one
or more artificial intelligence systems (including any of the types
described throughout this disclosure) for managing a set of
interactions among entities that are discovered through entity
discovery 1900, including ones that learn on a training set of data
to manage interactions among entities based on how entities have
been managed by human supervisors or by other systems.
[0443] As an illustrative example among many possible ones, the
entity discovery system 1900 may be used to discover a
network-connected camera that shows the loading dock of facility
that produces a product for an enterprise, as well as to identify
what interfaces or protocols are needed to access a feed of video
content from the camera. The entity interaction management system
1902 may then be used to interact with the interfaces or protocols
to set up access to the feed and to provide the feed to another
system for further processing, such as to have an artificial
intelligence system 1160 process the feed to discovery content that
is relevant to an activity of the enterprise. For example, the
artificial intelligence system 1160 may process image frames of the
video feed to find markings (such as produce labels, SKUs, images,
logos, or the like), shapes (such as packages of a particular size
or shape), activities (such as loading or unloading activities) or
the like that may indicate that a product has moved through the
loading dock. This information may substitute for, augment, or be
used to validate other information, such as RFID tracking
information or the like. Similar discovery and interaction
management activities may be undertaken with any of the types of
value chain network entities 652 described throughout this
disclosure.
Robotic Process Automation in Value Chain Network
[0444] Referring to FIG. 25, the adaptive intelligence layer 614
may include a robotic process automation (RPA) system 1442, which
may include a set of components, processes, services, interfaces
and other elements for development and deployment of automation
capabilities for various value chain entities 652, environments,
and applications 630. Without limitation, robotic process
automation 1442 may be applied to each of the processes that are
managed, controlled, or mediated by each of the set of applications
630 of the platform application layer, to functions, components,
workflows, processes of the VCNP 604 itself, to processes involving
value chain network entities 652 and other processes.
[0445] In embodiments, robotic process automation 1442 may take
advantage of the presence of multiple applications 630 within the
value chain management platform layer 604, such that a pair of
applications may share data sources (such as in the data storage
layer 624) and other inputs (such as from the monitoring layer 614)
that are collected with respect to value chain entities 652, as
well as sharing outputs, events, state information and outputs,
which collectively may provide a much richer environment for
process automation, including through use of artificial
intelligence 1160 (including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference). For example, an asset
management application 814 may use robotic process automation 1442
for automation of an asset inspection process that is normally
performed or supervised by a human (such as by automating a process
involving visual inspection using video or still images from a
camera or other that displays images of an entity 652, such as
where the robotic process automation 1442 system is trained to
automate the inspection by observing interactions of a set of human
inspectors or supervisors with an interface that is used to
identify, diagnose, measure, parameterize, or otherwise
characterize possible defects or favorable characteristics of a
facility or other asset. In embodiments, interactions of the human
inspectors or supervisors may include a labeled data set where
labels or tags indicate types of defects, favorable properties, or
other characteristics, such that a machine learning system can
learn, using the training data set, to identify the same
characteristics, which in turn can be used to automate the
inspection process such that defects or favorable properties are
automatically classified and detected in a set of video or still
images, which in turn can be used within the value chain network
asset management application 814 to flag items that require further
inspection, that should be rejected, that should be disclosed to a
prospective buyer, that should be remediated, or the like. In
embodiments, robotic process automation 1442 may involve
multi-application or cross-application sharing of inputs, data
structures, data sources, events, states, outputs or outcomes. For
example, the asset management application 814 may receive
information from a marketplace application 854 that may enrich the
robotic process automation 1442 of the asset management application
814, such as information about the current characteristics of an
item from a particular vendor in the supply chain for an asset,
which may assist in populating the characteristics about the asset
for purposes of facilitating an inspection process, a negotiation
process, a delivery process, or the like. These and many other
examples of multi-application or cross-application sharing for
robotic process automation 1442 across the applications 630 are
encompassed by the present disclosure. Robotic process automation
1442 may be used with various functionality of the VCNP 604. For
example, in some embodiments, robotic process automation 1442 may
be described as training a robot to operate and automate a task
that was, to at least a large extent, governed by a human. One of
these tasks may be used to train a robot that may train other
robots. The robotic process automation 1442 may be trained (e.g.,
through machine learning) to mimic interactions on a training set,
and then have this trained robotic process automation 1442 (e.g.,
trained agent or trained robotic process automation system) execute
these tasks that were previously performed by people. For example,
the robotic process automation 1442 may utilize software that may
provide software interaction observations (such as mouse movements,
mouse clicks, cursor movements, navigation actions, menu
selections, keyboard typing, and many others), such as logged
and/or tracked by software interaction observation system 1500,
purchase of the product by a customer 714, and the like. This may
include monitoring of a user's mouse clicks, mouse movements,
and/or keyboard typing to learn to do the same clicks and/or
typing. In another example, the robotic process automation 1442 may
utilize software to learn physical interactions with robots and
other systems to train a robotic system to sequence or undertake
the same physical interactions. For example, the robot may be
trained to rebuild a set of bearings by having the robot watch a
video of someone doing this task. This may include tracking
physical interactions and tracking interactions at a software
level. The robotic process automation 1442 may understand what the
underlying competencies are that are being deployed such that the
VCNP 604 preconfigure combinations of neural networks that may be
used to replicate performance of human capabilities.
[0446] In embodiments, robotic process automation may be applied to
shared or converged processes among the various pairs of the
applications 630 of the application layer 604, such as, without
limitation, of a converged process involving a security application
834 and an inventory application 820, integrated automation of
blockchain-based applications 844 with vendor management
applications 832, and many others. In embodiments, converged
processes may include shared data structures for multiple
applications 630 (including ones that track the same transactions
on a blockchain but may consume different subsets of available
attributes of the data objects maintained in the blockchain or ones
that use a set of nodes and links in a common knowledge graph). For
example, a transaction indicating a change of ownership of an
entity 652 may be stored in a blockchain and used by multiple
applications 630, such as to enable role-based access control,
role-based permissions for remote control, identity-based event
reporting, and the like. In embodiments, converged processes may
include shared process flows across applications 630, including
subsets of larger flows that are involved in one or more of a set
of applications 630. For example, a risk management or inspection
flow about an entity 652 may serve an inventory management
application 832, an asset management application 814, a demand
management application 824, and a supply chain application 812,
among others.
[0447] In embodiments, robotic process automation 1442 may be
provided for the wide range of value chain network processes
mentioned throughout this disclosure and the documents incorporated
herein by reference, including without limitation all of the
applications 630. An environment for development of robotic process
automation for value chain networks may include a set of interfaces
for developers in which a developer may configure an artificial
intelligence system 1160 to take inputs from selected data sources
of the VCN data storage layer 624 and event data 1034, state data
1140 or other value chain network data objects 1004 from the
monitoring systems layer 614 and supply them, such as to a neural
network, either as inputs for classification or prediction, or as
outcomes relating to the platform 102, value chain network entities
652, applications 630, or the like. The RPA development environment
1442 may be configured to take outputs and outcomes 1040 from
various applications 630, again to facilitate automated learning
and improvement of classification, prediction, or the like that is
involved in a step of a process that is intended to be automated.
In embodiments, the development environment, and the resulting
robotic process automation 1442 may involve monitoring a
combination of both software program interaction observations 1500
(e.g., by workers interacting with various software interfaces of
applications 630 involving value chain network entities 652) and
physical process interaction observations 1510 (e.g., by watching
workers interacting with or using machines, equipment, tools or the
like in a value chain network 668). In embodiments, observation of
software interactions 1500 may include interactions among software
components with other software components, such as how one
application 630 interacts via APIs with another application 630. In
embodiments, observation of physical process interactions 1510 may
include observation (such as by video cameras, motion detectors, or
other sensors, as well as detection of positions, movements, or the
like of hardware, such as robotic hardware) of how human workers
interact with value chain entities 652 (such as locations of
workers (including routes taken through a location, where workers
of a given type are located during a given set of events, processes
or the like, how workers manipulate pieces of equipment, cargo,
containers, packages, products 650 or other items using various
tools, equipment, and physical interfaces, the timing of worker
responses with respect to various events (such as responses to
alerts and warnings), procedures by which workers undertake
scheduled deliveries, movements, maintenance, updates, repairs and
service processes; procedures by which workers tune or adjust items
involved in workflows, and many others). Physical process
observation 1510 may include tracking positions, angles, forces,
velocities, acceleration, pressures, torque, and the like of a
worker as the worker operates on hardware, such as on a container
or package, or on a piece of equipment involved in handling
products, with a tool. Such observations may be obtained by any
combination of video data, data detected within a machine (such as
of positions of elements of the machine detected and reported by
position detectors), data collected by a wearable device (such as
an exoskeleton that contains position detectors, force detectors,
torque detectors and the like that is configured to detect the
physical characteristics of interactions of a human worker with a
hardware item for purposes of developing a training data set). By
collecting both software interaction observations 1500 and physical
process interaction observations 1510 the RPA system 1442 can more
comprehensively automate processes involving value chain entities
652, such as by using software automation in combination with
physical robots.
[0448] In embodiments, robotic process automation 1442 is
configured to train a set of physical robots that have hardware
elements that facilitate undertaking tasks that are conventionally
performed by humans. These may include robots that walk (including
walking up and down stairs to deliver a package), climb (such as
climbing ladders in a warehouse to reach shelves where products 650
are stored), move about a facility, attach to items, grip items
(such as using robotic arms, hands, pincers, or the like), lift
items, carry items, remove and replace items, use tools and many
others.
Value Chain Management Platform--Unified Robotic Process Automation
for Demand Management and Supply Chain
[0449] In embodiments, provided herein are methods, systems,
components and other elements for an information technology system
that may include a cloud-based management VCNP 604 with a
micro-services architecture, a set of interfaces 702, a set of
network connectivity facilities 642, adaptive intelligence
facilities 614, data storage facilities 624, data collection
systems 640, and monitoring facilities 614 that are coordinated for
monitoring and management of a set of value chain network entities
652; a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a unified set of robotic process
automation systems 1442 that provide coordinated automation among
various applications 630, including demand management applications,
supply chain applications, intelligent product applications and
enterprise resource management applications for a category of
goods.
[0450] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
Value Chain Management Platform--Robotic Process Automation
Services in Microservices Architecture for Value Chain Network
[0451] In embodiments, provided herein are methods, systems,
components and other elements for an information technology system
that may include a cloud-based management VCNP 102 with a
micro-services architecture, a set of interfaces 702, a set of
network connectivity facilities 642, adaptive intelligence
facilities 614, data storage facilities 624, data collection
systems 640, and monitoring facilities 614 that are coordinated for
monitoring and management of a set of value chain network entities
652; a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a set of microservices layers including
an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the microservice layers include a robotic process automation layer
1442 that uses information collected by a data collection layer 640
and a set of outcomes and activities 1040 involving the
applications of the application layer 630 to automate a set of
actions for at least a subset of the applications 630.
[0452] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a robotic process automation layer that
uses information collected by a data collection layer and a set of
outcomes and activities involving the applications of the
application layer to automate a set of actions for at least a
subset of the applications.
Value Chain Management Platform--Robotic Process Automation for
Value Chain Network Processes
[0453] In embodiments, provided herein are methods, systems,
components and other elements for an information technology system
that may include a cloud-based management VCNP 102 with a
micro-services architecture, a set of interfaces 702, a set of
network connectivity facilities 642, adaptive intelligence
facilities 614, data storage facilities 624, data collection
systems 640, and monitoring facilities 614 that are coordinated for
monitoring and management of a set of value chain network entities
652; a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a set of robotic process automation
systems 1442 for automating a set of processes in a value chain
network, wherein the robotic process automation systems 1442 learn
on a training set of data involving a set of user interactions with
a set of interfaces 702 of a set of software systems that are used
to monitor and manage the value chain network entities 652, as well
as from various process and application outputs and outcomes 1040
that may occur with or within the VCNP 102.
[0454] In embodiments, the value chain network entities 652 may
include, for example, products, suppliers, producers,
manufacturers, retailers, businesses, owners, operators, operating
facilities, customers, consumers, workers, mobile devices, wearable
devices, distributors, resellers, supply chain infrastructure
facilities, supply chain processes, logistics processes, reverse
logistics processes, demand prediction processes, demand management
processes, demand aggregation processes, machines, ships, barges,
warehouses, maritime ports, airports, airways, waterways, roadways,
railways, bridges, tunnels, online retailers, ecommerce sites,
demand factors, supply factors, delivery systems, floating assets,
points of origin, points of destination, points of storage, points
of use, networks, information technology systems, software
platforms, distribution centers, fulfillment centers, containers,
container handling facilities, customs, export control, border
control, drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, port infrastructure facilities, or
many others.
[0455] In embodiments, the robotic process automation layer
automates a process that may include, for example, without
limitation, selection of a quantity of product for an order,
selection of a carrier for a shipment, selection of a vendor for a
component, selection of a vendor for a finished goods order,
selection of a variation of a product for marketing, selection of
an assortment of goods for a shelf, determination of a price for a
finished good, configuration of a service offer related to a
product, configuration of product bundle, configuration of a
product kit, configuration of a product package, configuration of a
product display, configuration of a product image, configuration of
a product description, configuration of a website navigation path
related to a product, determination of an inventory level for a
product, selection of a logistics type, configuration of a schedule
for product delivery, configuration of a logistics schedule,
configuration of a set of inputs for machine learning, preparation
of product documentation, preparation of required disclosures about
a product, configuration of a product for a set of local
requirements, configuration of a set of products for compatibility,
configuration of a request for proposals, ordering of equipment for
a warehouse, ordering of equipment for a fulfillment center,
classification of a product defect in an image, inspection of a
product in an image, inspection of product quality data from a set
of sensors, inspection of data from a set of onboard diagnostics on
a product, inspection of diagnostic data from an Internet of Things
system, review of sensor data from environmental sensors in a set
of supply chain environments, selection of inputs for a digital
twin, selection of outputs from a digital twin, selection of visual
elements for presentation in a digital twin, diagnosis of sources
of delay in a supply chain, diagnosis of sources of scarcity in a
supply chain, diagnosis of sources of congestion in a supply chain,
diagnosis of sources of cost overruns in a supply chain, diagnosis
of sources of product defects in a supply chain, prediction of
maintenance requirements in supply chain infrastructure, or
others.
[0456] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; and a set of
robotic process automation systems for automating a set of
processes in a value chain network, wherein the robotic process
automation systems learn on a training set of data involving a set
of user interactions with a set of interfaces of a set of software
systems that are used to monitor and manage the value chain network
entities.
[0457] In embodiments, one of the processes automated by robotic
process automation as described in any of the embodiments disclosed
herein may involve the following. In embodiments, RPA involves
selection of a quantity of product for an order. In embodiments,
one of the processes automated by robotic process automation
involves selection of a carrier for a shipment. In embodiments, one
of the processes automated by robotic process automation involves
selection of a vendor for a component. In embodiments, one of the
processes automated by robotic process automation involves
selection of a vendor for a finished goods order. In embodiments,
one of the processes automated by robotic process automation
involves selection of a variation of a product for marketing. In
embodiments, one of the processes automated by robotic process
automation involves selection of an assortment of goods for a
shelf. In embodiments, one of the processes automated by robotic
process automation involves determination of a price for a finished
good. In embodiments, one of the processes automated by robotic
process automation involves configuration of a service offer
related to a product. In embodiments, one of the processes
automated by robotic process automation involves configuration of
product bundle. In embodiments, one of the processes automated by
robotic process automation involves configuration of a product kit.
In embodiments, one of the processes automated by robotic process
automation involves configuration of a product package. In
embodiments, one of the processes automated by robotic process
automation involves configuration of a product display. In
embodiments, one of the processes automated by robotic process
automation involves configuration of a product image. In
embodiments, one of the processes automated by robotic process
automation involves configuration of a product description. In
embodiments, one of the processes automated by robotic process
automation involves configuration of a website navigation path
related to a product. In embodiments, one of the processes
automated by robotic process automation involves determination of
an inventory level for a product. In embodiments, one of the
processes automated by robotic process automation involves
selection of a logistics type. In embodiments, one of the processes
automated by robotic process automation involves configuration of a
schedule for product delivery. In embodiments, one of the processes
automated by robotic process automation involves configuration of a
logistics schedule. In embodiments, one of the processes automated
by robotic process automation involves configuration of a set of
inputs for machine learning. In embodiments, one of the processes
automated by robotic process automation involves preparation of
product documentation. In embodiments, one of the processes
automated by robotic process automation involves preparation of
required disclosures about a product. In embodiments, one of the
processes automated by robotic process automation involves
configuration of a product for a set of local requirements. In
embodiments, one of the processes automated by robotic process
automation involves configuration of a set of products for
compatibility. In embodiments, one of the processes automated by
robotic process automation involves configuration of a request for
proposals.
[0458] In embodiments, one of the processes automated by robotic
process automation involves ordering of equipment for a warehouse.
In embodiments, one of the processes automated by robotic process
automation involves ordering of equipment for a fulfillment center.
In embodiments, one of the processes automated by robotic process
automation involves classification of a product defect in an image.
In embodiments, one of the processes automated by robotic process
automation involves inspection of a product in an image.
[0459] In embodiments, one of the processes automated by robotic
process automation involves inspection of product quality data from
a set of sensors. In embodiments, one of the processes automated by
robotic process automation involves inspection of data from a set
of onboard diagnostics on a product. In embodiments, one of the
processes automated by robotic process automation involves
inspection of diagnostic data from an Internet of Things system. In
embodiments, one of the processes automated by robotic process
automation involves review of sensor data from environmental
sensors in a set of supply chain environments.
[0460] In embodiments, one of the processes automated by robotic
process automation involves selection of inputs for a digital twin.
In embodiments, one of the processes automated by robotic process
automation involves selection of outputs from a digital twin. In
embodiments, one of the processes automated by robotic process
automation involves selection of visual elements for presentation
in a digital twin. In embodiments, one of the processes automated
by robotic process automation involves diagnosis of sources of
delay in a supply chain. In embodiments, one of the processes
automated by robotic process automation involves diagnosis of
sources of scarcity in a supply chain. In embodiments, one of the
processes automated by robotic process automation involves
diagnosis of sources of congestion in a supply chain.
[0461] In embodiments, one of the processes automated by robotic
process automation involves diagnosis of sources of cost overruns
in a supply chain. In embodiments, one of the processes automated
by robotic process automation involves diagnosis of sources of
product defects in a supply chain. In embodiments, one of the
processes automated by robotic process automation involves
prediction of maintenance requirements in supply chain
infrastructure.
[0462] In embodiments, the set of demand management applications,
supply chain applications, intelligent product applications and
enterprise resource management applications may include, for
example, ones involving supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, enterprise resource planning, and many others.
Introduction of Opportunity Miners for Automated Improvement of
Adaptive Intelligence
[0463] Referring to FIG. 26, a set of opportunity miners 1460 may
be provided as part of the adaptive intelligence layer 614, which
may be configured to seek and recommend opportunities to improve
one or more of the elements of the platform 604, such as via
addition of artificial intelligence 1160, automation (including
robotic process automation 1442), or the like to one or more of the
systems, sub-systems, components, applications or the like of the
VCNP 102 or with which the VCNP 102 interacts. In embodiments, the
opportunity miners 1460 may be configured or used by developers of
AI or RPA solutions to find opportunities for better solutions and
to optimize existing solutions in a value chain network 668. In
embodiments, the opportunity miners 1460 may include a set of
systems that collect information within the VCNP 102 and collect
information within, about and for a set of value chain network
entities 652 and environments, where the collected information has
the potential to help identify and prioritize opportunities for
increased automation and/or intelligence about the value chain
network 668, about applications 630, about value chain network
entities 652, or about the VCNP 102 itself. For example, the
opportunity miners 1460 may include systems that observe clusters
of value chain network workers by time, by type, and by location,
such as using cameras, wearables, or other sensors, such as to
identify labor-intensive areas and processes in a set of value
chain network 668 environments. These may be presented, such as in
a ranked or prioritized list, or in a visualization (such as a heat
map showing dwell times of customers, workers or other individuals
on a map of an environment or a heat map showing routes traveled by
customers or workers within an environment) to show places with
high labor activity. In embodiments, analytics 838 may be used to
identify which environments or activities would most benefit from
automation for purposes of improved delivery times, mitigation of
congestion, and other performance improvements.
[0464] In embodiments, opportunity mining may include facilities
for solicitation of appropriate training data sets that may be used
to facilitate process automation. For example, certain kinds of
inputs, if available, would provide very high value for automation,
such as video data sets that capture very experienced and/or highly
expert workers performing complex tasks. Opportunity miners 1460
may search for such video data sets as described herein; however,
in the absence of success (or to supplement available data), the
platform may include systems by which a user, such as a developer,
may specify a desired type of data, such as software interaction
data (such as of an expert working with a program to perform a
particular task), video data (such as video showing a set of
experts performing a certain kind of delivery process, packing
process, picking process, a container movement process, or the
like), and/or physical process observation data (such as video,
sensor data, or the like). The resulting library of interactions
captured in response to specification may be captured as a data set
in the data storage layer 624, such as for consumption by various
applications 630, adaptive intelligence systems 614, and other
processes and systems. In embodiments, the library may include
videos that are specifically developed as instructional videos,
such as to facilitate developing an automation map that can follow
instructions in the video, such as providing a sequence of steps
according to a procedure or protocol, breaking down the procedure
or protocol into sub-steps that are candidates for automation, and
the like. In embodiments, such videos may be processed by natural
language processing, such as to automatically develop a sequence of
labeled instructions that can be used by a developer to facilitate
a map, a graph, or other models of a process that assists with
development of automation for the process. In embodiments, a
specified set of training data sets may be configured to operate as
inputs to learning. In such cases the training data may be
time-synchronized with other data within the platform 604, such as
outputs and outcomes from applications 630, outputs and outcomes of
value chain entities 652, or the like, so that a given video of a
process can be associated with those outputs and outcomes, thereby
enabling feedback on learning that is sensitive to the outcomes
that occurred when a given process that was captured (such as on
video, or through observation of software interactions or physical
process interactions). For example, this may relate to an
instruction video such as a video of a person who may be building
or rebuilding (e.g., rebuilding a bearing set). This instruction
video may include individual steps for rebuild that may allow a
staging of the training to provide instructions such as parsing the
video into stages that mimic the experts staging in the video. For
example, this may include tagging of the video to include
references to each stage and status (e.g., stage one complete,
stage two, etc.) This type of example may utilize artificial
intelligence that may understand that there may be a series of
sub-functions that add up to a final function.
[0465] In embodiments, opportunity miners 1460 may include methods,
systems, processes, components, services and other elements for
mining for opportunities for smart contract definition, formation,
configuration and execution. Data collected within the platform
604, such as any data handled by the data handling layers 624,
stored by the data storage layer 624, collected by the monitoring
layer 614 and collection systems 640, collected about or from
entities 652 or obtained from external sources may be used to
recognize beneficial opportunities for application or configuration
of smart contracts. For example, pricing information about an
entity 652, handled by a pricing application 842, or otherwise
collected, may be used to recognize situations in which the same
item or items is disparately priced (in a spot market, futures
market, or the like), and the opportunity miner 1460 may provide an
alert indicating an opportunity for smart contract formation, such
as a contract to buy in one environment at a price below a given
threshold and sell in another environment at a price above a given
threshold, or vice versa.
[0466] In some examples, as shown in FIG. 26, the adaptive
intelligent systems 614 may include value translators 1470. The
value translators 1470 may relate to demand side of transactions.
Specifically, for example, the value translators 1470 may
understand negative currencies of two marketplaces and may be able
to translate value currencies into other currencies (e.g., not only
fiat currencies that already have clear translation functions). In
some examples, value translators 1470 may be associated with points
of a point-based system (e.g., in a cost-based routing system). In
an example embodiment, value translators 1470 may be loyalty points
offered that may be convertible into airline seats and/or may
translate to refund policies for staying in a hotel room. In some
examples, different types of entities may be connected as having
native pricing or cost functions that do not always use the same
currency or any currency. In another example, value translators
1470 may be used with network prioritization or cost-based routing
that happens in networks off of priorities where the point system
in these cost-based routing systems is not monetary-based.
Broad Management Platform
[0467] Referring to FIG. 28, additional details of an embodiment of
the platform 604 are provided, in particular relating to an overall
architecture for the platform 604. These may include, for the
cloud-based management platform 604, employing a micro-services
architecture, a set of network connectivity facilities 642 (which
may include or connect to a set of interfaces 702 of various layers
of the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0468] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture; a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities; and a set of applications for enabling an
enterprise to manage a set of value chain network entities from a
point of origin to a point of customer use.
[0469] Also provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, the platform having: a set of interfaces for
accessing and configuring features of the platform; a set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform; a set of adaptive
intelligence facilities for automating a set of capabilities of the
platform; a set of data storage facilities for storing data
collected and handled by the platform; and a set of monitoring
facilities for monitoring the value chain network entities; wherein
the platform hosts a set of applications for enabling an enterprise
to manage a set of value chain network entities from a point of
origin of a product of the enterprise to a point of customer
use.
Broad Management Platform--Details
[0470] Referring to FIG. 29, additional details of an embodiment of
the platform 604 are provided, in particular relating to an overall
architecture for the platform 604. These may include, for the
cloud-based management platform 604, employing a micro-services
architecture, a set of network connectivity facilities 642 (which
may include or connect to a set of interfaces 702 of various layers
of the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0471] In embodiments, the set of interfaces 702 may include a
demand management interface MPVC104 and a supply chain management
interface MPVC108.
[0472] In embodiments, the set of network connectivity facilities
642 for enabling a set of value chain network entities 652 to
connect to the platform 604 may include a 5G network system
MPVC110, such as one that is deployed in a supply chain
infrastructure facility operated by the enterprise.
[0473] In embodiments, the set of network connectivity facilities
642 for enabling a set of value chain network entities 652 to
connect to the platform 604 may include an Internet of Things
system 1172, such as one that is deployed in a supply chain
infrastructure facility operated by the enterprise, in, on or near
a value chain network entity 652, in a network system, and/or in a
cloud computing environment (such as where data collection systems
640 are configured to collect and organize IoT data).
[0474] In embodiments, the set of network connectivity facilities
642 for enabling a set of value chain network entities 652 to
connect to the VCNP 102 may include a cognitive networking system
MPVC114 deployed in a supply chain infrastructure facility operated
by the enterprise.
[0475] In embodiments, the set of network connectivity facilities
642 for enabling a set of value chain network entities 652 to
connect to the VCNP 102 may include a peer-to-peer network system
MPVC118, such as one that is deployed in a supply chain
infrastructure facility operated by the enterprise.
[0476] In embodiments, the set of adaptive intelligence facilities
or adaptive intelligent systems 614 for automating a set of
capabilities of the platform 604 may include an edge intelligence
system 1420, such as one that is deployed in a supply chain
infrastructure facility operated by the enterprise.
[0477] In embodiments, the set of adaptive intelligence facilities
or adaptive intelligent systems 614 for automating a set of
capabilities of the platform 604 may include a robotic process
automation system 1442.
[0478] In embodiments, the set of adaptive intelligence facilities
or adaptive intelligent systems 614 for automating a set of
capabilities of the platform 604 may include or may integrate with
a self-configuring data collection system 1440, such as one that
deployed in a supply chain infrastructure facility operated by the
enterprise, one that is deployed in a network, and/or one that is
deployed in a cloud computing environment. This may include
elements of the data collection systems 640 of the data handling
layers 624 that interact with or integrate with elements of the
adaptive intelligent systems 614.
[0479] In embodiments, the set of adaptive intelligence facilities
or adaptive intelligent systems 614 for automating a set of
capabilities of the platform 604 may include a digital twin system
1700, such as one representing attributes of a set of value chain
network entities, such as the ones controlled by an enterprise.
[0480] In embodiments, the set of adaptive intelligence facilities
or adaptive intelligent systems 614 for automating a set of
capabilities of the platform 604 may include a smart contract
system 848, such as one for automating a set of interactions or
transactions among a set of value chain network entities 652 based
on status data, event data, or other data handled by the data
handling layers 624.
[0481] In embodiments, the set of data storage facilities or data
storage systems 624 for storing data collected and handled by the
platform 604 uses a distributed data architecture 1122.
[0482] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
blockchain 844.
[0483] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger 1452.
[0484] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses graph
database 1124 representing a set of hierarchical relationships of
value chain network entities.
[0485] In embodiments, the set of monitoring facilities 614 for
monitoring the value chain network entities 652 includes an
Internet of Things monitoring system 1172, such as for collecting
data from IoT systems and devices deployed throughout a value chain
network.
[0486] In embodiments, the set of monitoring facilities 614 for
monitoring the value chain network entities 652 includes a set of
sensor systems 1462, such as ones deployed in a value chain
environment or in, one or near a value chain network entity 652,
such as in or on a product 650.
[0487] In embodiments, the set of applications 630 includes a set
of applications, which may include a variety of types from among,
for example, a set of supply chain management applications 1500,
demand management applications 1502, intelligent product
applications 1510 and enterprise resource management applications
1520.
[0488] In embodiments, the set of applications includes an asset
management application 1530.
[0489] In embodiments, the value chain network entities 652 as
mentioned throughout this disclosure may include, for example,
without limitation, products, suppliers, producers, manufacturers,
retailers, businesses, owners, operators, operating facilities,
customers, consumers, workers, mobile devices, wearable devices,
distributors, resellers, supply chain infrastructure facilities,
supply chain processes, logistics processes, reverse logistics
processes, demand prediction processes, demand management
processes, demand aggregation processes, machines, ships, barges,
warehouses, maritime ports, airports, airways, waterways, roadways,
railways, bridges, tunnels, online retailers, ecommerce sites,
demand factors, supply factors, delivery systems, floating assets,
points of origin, points of destination, points of storage, points
of use, networks, information technology systems, software
platforms, distribution centers, fulfillment centers, containers,
container handling facilities, customs, export control, border
control, drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, port infrastructure facilities, or
others.
[0490] In embodiments, the platform 604 manages a set of demand
factors 1540, a set of supply factors 1550 and a set of value chain
infrastructure facilities 1560.
[0491] In embodiments, the supply factors 1550 as mentioned
throughout this disclosure may include, for example and without
limitation, ones involving component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, route safety, and many
others.
[0492] In embodiments, the demand factors 1540 as mentioned
throughout this disclosure may include, for example and without
limitation, ones involving product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, inferred interest, and many others.
[0493] In embodiments, the supply chain infrastructure facilities
1560 as mentioned throughout this disclosure may include, for
example and without limitation, ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, border control, and other facilities.
[0494] In embodiments, the set of applications 630 as mentioned
throughout this disclosure may include, for example and without
limitation, supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, enterprise resource planning and other applications.
Control Tower
[0495] Referring to FIG. 30, an embodiment of the platform 604 is
provided. The platform 604 may employ a micro-services architecture
with the various data handling layers 614, a set of network
connectivity facilities 642 (which may include or connect to a set
of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence facilities or adaptive intelligent systems
1160, a set of data storage facilities or systems 624, and a set of
monitoring facilities or systems 614. The platform 604 may support
a set of applications 630 (including processes, workflows,
activities, events, use cases and applications) for enabling an
enterprise to manage a set of value chain network entities 652,
such as from a point of origin to a point of customer use of a
product 650, which may be an intelligent product.
[0496] In embodiments, the platform 604 may include a user
interface 1570 that provides a set of unified views for a set of
demand management information and supply chain information for a
category of goods, such as one that displays status information,
event information, activity information, analytics, reporting, or
other elements of, relating to, or produced by a set of supply
chain management applications 1500, demand management applications
1502, intelligent product applications 1510 and enterprise resource
management applications 1520 that monitor and/or manage a value
chain network and a set of value chain network entities 652. The
unified view interface 1570 may thus provide, in embodiments, a
control tower for an enterprise over a range of assets, such as
supply chain infrastructure facilities 1560 and other value chain
network entities 652 that are involved as a product 650 travels
from a point of origin through distribution and retail channels to
an environment where it is used by a customer. These may include
views of demand factors 1540 and supply factors 1550, so that a
user may develop insights about connections among the factors and
control one or both of them with coordinated intelligence.
Population of a set of unified views may be adapted over time, such
as by learning on outcomes 1040 or other operations of the adaptive
intelligent systems 614, such as to determine which views of the
interface 1570 provide the most impactful insights, control
features, or the like.
[0497] In embodiments, the user interface includes a voice operated
assistant 1580.
[0498] In embodiments, the user interface includes a set of digital
twins 1700 for presenting a visual representation of a set of
attributes of a set of value chain network entities 652.
[0499] In embodiments, the user interface 1570 may include
capabilities for configuring the adaptive intelligent systems 614
or adaptive intelligence facilities, such as to allow user
selection of attributes, parameters, data sources, inputs to
learning, feedback to learning, views, formats, arrangements, or
other elements.
Value Chain Management Platform--Control Tower UI for Demand
Management and Supply Chain
[0500] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods.
Unified Database
[0501] Referring to FIG. 31, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0502] In embodiments, the platform 604 may include a unified
database 1590 that supports a set of applications of multiple
types, such as ones among a set of supply chain management
applications 1500, demand management applications 1502, intelligent
product applications 1510 and enterprise resource management
applications 1520 that monitor and/or manage a value chain network
and a set of value chain network entities 652. The unified database
1590 may thus provide, in embodiments, unification of data storage,
access and handling for an enterprise over a range of assets, such
as supply chain infrastructure facilities 1560 and other value
chain network entities 652 that are involved as a product 650
travels from a point of origin through distribution and retail
channels to an environment where it is used by a customer. This
unification may provide a number of advantages, including reduced
need for data entry, consistency across applications 630, reduced
latency (and better real-time reporting), reduced need for data
transformation and integration, and others. These may include data
relating to demand factors 1540 and supply factors 1550, so that an
application 630 may benefit from information collected by,
processed, or produced by other applications 630 of the platform
604 and a user can develop insights about connections among the
factors and control one or both of them with coordinated
intelligence. Population of the unified database 1590 may be
adapted over time, such as by learning on outcomes 1040 or other
operations of the adaptive intelligent systems 614, such as to
determine which elements of the database 1590 should be made
available to which applications, what data structures provide the
most benefit, what data should be stored or cached for immediate
retrieval, what data can be discarded versus saved, what data is
most beneficial to support adaptive intelligent systems 614, and
for other uses.
[0503] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified database that supports a set of
applications of at least two types from among a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[0504] In embodiments, the unified database that supports a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods is a
distributed database.
[0505] In embodiments, the unified database that supports a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods uses a
graph database architecture. In embodiments, the set of demand
management applications includes a demand prediction application.
In embodiments, the set of demand management applications includes
a demand aggregation application. In embodiments, the set of demand
management applications includes a demand activation
application.
[0506] In embodiments, the set of supply chain management
applications includes a vendor search application. In embodiments,
the set of supply chain management applications includes a route
configuration application. In embodiments, the set of supply chain
management applications includes a logistics scheduling
application.
Unified Data Collection Systems
[0507] Referring to FIG. 32, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0508] In embodiments, the platform 604 may include a set of
unified set of data collection and management systems 640 of the
set of monitoring facilities or systems 614 that support a set of
applications 630 of various types, including a set of supply chain
management applications 1500, demand management applications 1502,
intelligent product applications 1510 and enterprise resource
management applications 1520 that monitor and/or manage a value
chain network and a set of value chain network entities 652. The
unified data collection and management systems 640 may thus
provide, in embodiments, unification of data monitoring, search,
discovery, collection, access and handling for an enterprise or
other user over a range of assets, such as supply chain
infrastructure facilities 1560 and other value chain network
entities 652 that are involved as a product 650 travels from a
point of origin through distribution and retail channels to an
environment where it is used by a customer. This unification may
provide a number of advantages, including reduced need for data
entry, consistency across applications 630, reduced latency (and
better real-time reporting), reduced need for data transformation
and integration, and others. These may include collection of data
relating to demand factors 1540 and supply factors 1550, so that an
application 630 may benefit from information collected by,
processed, or produced by other applications 630 of the platform
604 and a user can develop insights about connections among the
factors and control one or both of them with coordinated
intelligence. The unified data collection and management systems
640 may be adapted over time, such as by learning on outcomes 1040
or other operations of the adaptive intelligent systems 614, such
as to determine which elements of the data collection and
management systems 640 should be made available to which
applications 630, what data types or sources provide the most
benefit, what data should be stored or cached for immediate
retrieval, what data can be discarded versus saved, what data is
most beneficial to support adaptive intelligent systems 614, and
for other uses. In example embodiments, the unified data collection
and management systems 640 may use a unified data schema which
relates data collection and management for various applications.
This may be a single point of truth database at the most tightly
bound or a set of distributed data systems that may follow a schema
that may be sufficiently common enough that a wide variety of
applications may consume the same data as received. For example,
sensor data may be pulled from a smart product that may be consumed
by a logistics application, a financial application, a demand
prediction application, or a genetic programming artificial
intelligence (AI) application to change the product, and the like.
All of these applications may consume data from a data framework.
In an example, this may occur from blockchains that may contain a
distributed ledger or transactional data for purchase and sales or
blockchains where there may be an indication of whether or not
events had occurred. In some example embodiments, as data moves
through a supply chain, this data flow may occur through
distributed databases, relational databases, graph databases of all
types, and the like that may be part of the unified data collection
and management systems 640. In other examples, the unified data
collection and management systems 640 may utilize memory that may
be dedicated memory on an asset, in a tag or part of a memory
structure of the device itself that may come from a robust pipeline
tied to the value chain network entities. In other examples, the
unified data collection and management systems 640 may use classic
data integration capabilities that may include adapting protocols
such that they can ultimately get to the unified system or
schema.
[0509] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of data collection systems that
support a set of applications of at least two types from among a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[0510] In embodiments, the unified set of data collection systems
includes a set of crowdsourcing data collection systems. In
embodiments, the unified set of data collection systems includes a
set of Internet of Things data collection systems. In embodiments,
the unified set of data collection systems includes a set of
self-configuring sensor systems. In embodiments, the unified set of
data collection systems includes a set of data collection systems
that interact with a network-connected product.
[0511] In embodiments, the unified set of data collection systems
includes a set of mobile data collectors deployed in a set of value
chain network environments operated by an enterprise. In
embodiments, the unified set of data collection systems includes a
set of edge intelligence systems deployed in set of value chain
network environments operated by an enterprise. In embodiments, the
unified set of data collection systems includes a set of
crowdsourcing data collection systems. In embodiments, the unified
set of data collection systems includes a set of Internet of Things
data collection systems. In embodiments, the unified set of data
collection systems includes a set of self-configuring sensor
systems. In embodiments, the unified set of data collection systems
includes a set of data collection systems that interact with a
network-connected product. In embodiments, the unified set of data
collection systems includes a set of mobile data collectors
deployed in a set of value chain network environments operated by
an enterprise. In embodiments, the unified set of data collection
systems includes a set of edge intelligence systems deployed in a
set of value chain network environments operated by an
enterprise.
Unified IoT Monitoring Systems
[0512] Referring to FIG. 33, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0513] In embodiments, the platform 604 may include a unified set
of Internet of Things systems 1172 that provide coordinated
monitoring of various value chain entities 652 in service of a set
of multiple applications 630 of various types, such as a set of
supply chain management applications 1500, demand management
applications 1502, intelligent product applications 1510 and
enterprise resource management applications 1520 that monitor
and/or manage a value chain network and a set of value chain
network entities 652.
[0514] The unified set of Internet of Things systems 1172 may thus
provide, in embodiments, unification of monitoring of, and
communication with, a wide range of facilities, devices, systems,
environments, and assets, such as supply chain infrastructure
facilities 1560 and other value chain network entities 652 that are
involved as a product 650 travels from a point of origin through
distribution and retail channels to an environment where it is used
by a customer. This unification may provide a number of advantages,
including reduced need for data entry, consistency across
applications 630, reduced latency, real-time reporting and
awareness, reduced need for data transformation and integration,
and others. These may include Internet of Things systems 1172 that
are used in connection with demand factors 1540 and supply factors
1550, so that an application 630 may benefit from information
collected by, processed, or produced by the unified set of Internet
of Things systems 1172 for other applications 630 of the platform
604, and a user can develop insights about connections among the
factors and control one or both of them with coordinated
intelligence. The unified set of Internet of Things systems 1172
may be adapted over time, such as by learning on outcomes 1040 or
other operations of the adaptive intelligent systems 614, such as
to determine which elements of the unified set of Internet of
Things systems 1172 should be made available to which applications
630, what IoT systems 1172 provide the most benefit, what data
should be stored or cached for immediate retrieval, what data can
be discarded versus saved, what data is most beneficial to support
adaptive intelligent systems 614, and for other uses. In some
examples, the unified set of Internet of Things (IoT) systems 1172
may be IoT devices that may be installed in various environments.
One goal of the unified set of Internet of Things systems 1172 may
be coordination across a city or town involving citywide
deployments where collectively a set of IOT devices may be
connected by wide area network protocols (e.g., longer range
protocols). In another example, the unified set of Internet of
Things systems 1172 may involve connecting a mesh of devices across
several different distribution facilities. The IoT devices may
identify collection for each warehouse and the warehouses may use
the IoT devices to communicate with each other. The IoT devices may
be configured to process data without using the cloud.
[0515] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications integrated with the platform for enabling an
enterprise user of the platform to manage a set of value chain
network entities from a point of origin to a point of customer use;
and a unified set of Internet of Things systems that provide
coordinated monitoring of a set of applications of at least two
types from among a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[0516] In embodiments, the unified set of Internet of Things
systems includes a set of smart home Internet of Things devices to
enable monitoring of a set of demand factors and a set of Internet
of Things devices deployed in proximity to a set of supply chain
infrastructure facilities to enable monitoring of a set of supply
factors.
[0517] In embodiments, the unified set of Internet of Things
systems includes a set of workplace Internet of Things devices to
enable monitoring of a set of demand factors for a set of business
customers and a set of Internet of Things devices deployed in
proximity to a set of supply chain infrastructure facilities to
enable monitoring of a set of supply factors.
[0518] In embodiments, the unified set of Internet of Things
systems includes a set of Internet of Things devices to monitor a
set of consumer goods stores to enable monitoring of a set of
demand factors for a set of consumers and a set of Internet of
Things devices deployed in proximity to a set of supply chain
infrastructure facilities to enable monitoring of a set of supply
factors.
[0519] In embodiments, the Internet of Things systems as mentioned
throughout this disclosure may include, for example and without
limitations, camera systems, lighting systems, motion sensing
systems, weighing systems, inspection systems, machine vision
systems, environmental sensor systems, onboard sensor systems,
onboard diagnostic systems, environmental control systems,
sensor-enabled network switching and routing systems, RF sensing
systems, magnetic sensing systems, pressure monitoring systems,
vibration monitoring systems, temperature monitoring systems, heat
flow monitoring systems, biological measurement systems, chemical
measurement systems, ultrasonic monitoring systems, radiography
systems, LIDAR-based monitoring systems, access control systems,
penetrating wave sensing systems, SONAR-based monitoring systems,
radar-based monitoring systems, computed tomography systems,
magnetic resonance imaging systems, network monitoring systems, and
many others.
Machine Vision Feeding Digital Twin
[0520] Referring to FIG. 34, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0521] In embodiments, the platform 604 may include a machine
vision system 1600 and a digital twin system 1700, wherein the
machine vision system 1600 feeds data to the digital twin system
1700 (which may be enabled by a set of adaptive intelligent systems
614, including artificial intelligence 1160, and may be used as
interfaces or components of interfaces 702, such as ones by which
an operator may monitor twins 1700 of various value chain network
entities 652). The machine vision system 1600 and digital twin
system 1700 may operate in coordination for a set of multiple
applications 630 of various types, such as a set of supply chain
management applications 1500, demand management applications 1502,
intelligent product applications 1510 and enterprise resource
management applications 1520 that monitor and/or manage a value
chain network and a set of value chain network entities 652.
[0522] The machine vision system 1600 and digital twin system 1700
may thus provide, in embodiments, image-based monitoring (with
automated processing of image data) a wide range of facilities,
devices, systems, environments, and assets, such as supply chain
infrastructure facilities 1560 and other value chain network
entities 652 that are involved as a product 650 travels from a
point of origin through distribution and retail channels to an
environment where it is used by a customer, as well as
representation of images, as well as extracted data from images, in
a digital twin 1700. This unification may provide a number of
advantages, including improved monitoring, improved visualization
and insight, improved visibility, and others. These may include
machine vision systems 1600 and digital twin systems 1700 that are
used in connection with demand factors 1540 and supply factors
1550, so that an application 630 may benefit from information
collected by, processed, or produced by the machine vision system
1600 and digital twin system 1700 for other applications 630 of the
platform 604, and a user can develop insights about connections
among the factors and control one or both of them with coordinated
intelligence. The machine vision system 1600 and/or digital twin
system 1700 may be adapted over time, such as by learning on
outcomes 1040 or other operations of the adaptive intelligent
systems 614, such as to determine which elements collected and/or
processed by the machine vision system 1600 and/or digital twin
system 1700 should be made available to which applications 630,
what elements and/or content provide the most benefit, what data
should be stored or cached for immediate retrieval, what data can
be discarded versus saved, what data is most beneficial to support
adaptive intelligent systems 614, and for other uses.
[0523] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and for a set of applications of at least two types
from among a set of supply chain applications, a set of demand
management applications, a set of intelligent product applications
and a set of enterprise resource management applications and having
a machine vision system and a digital twin system, wherein the
machine vision system feeds data to the digital twin system.
[0524] In embodiments, the set of supply chain applications and
demand management applications is among any described throughout
this disclosure or in the documents incorporated by reference
herein.
[0525] In embodiments, the set of supply chain applications and
demand management applications includes, for example and without
limitation one or more involving inventory management, demand
prediction, demand aggregation, pricing, blockchain, smart
contract, positioning, placement, promotion, analytics, finance,
trading, arbitrage, customer identity management, store planning,
shelf-planning, customer route planning, customer route analytics,
commerce, ecommerce, payments, customer relationship management,
sales, marketing, advertising, bidding, customer monitoring,
customer process monitoring, customer relationship monitoring,
collaborative filtering, customer profiling, customer feedback,
similarity analytics, customer clustering, product clustering,
seasonality factor analytics, customer behavior tracking, customer
behavior analytics, product design, product configuration, A/B
testing, product variation analytics, augmented reality, virtual
reality, mixed reality, customer demand profiling, customer mood,
emotion or affect detection, customer mood, emotion of affect
analytics, business entity profiling, customer enterprise
profiling, demand matching, location-based targeting,
location-based offering, point of sale interface, point of use
interface, search, advertisement, entity discovery, entity search,
enterprise resource planning, workforce management, customer
digital twin, product pricing, product bundling, product and
service bundling, product assortment, upsell offer configuration,
customer feedback engagement, customer survey, or others.
[0526] In embodiments, the set of supply chain applications and
demand management applications may include, without limitation, one
or more of supply chain, asset management, risk management,
inventory management, blockchain, smart contract, infrastructure
management, facility management, analytics, finance, trading, tax,
regulatory, identity management, commerce, ecommerce, payments,
security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
supply chain digital twin, vendor profiling, supplier profiling,
manufacturer profiling, logistics entity profiling, enterprise
profiling, worker profiling, workforce profiling, component supply
policy management, warehousing, distribution, fulfillment, shipping
fleet management, vehicle fleet management, workforce management,
maritime fleet management, navigation, routing, shipping
management, opportunity matching, search, entity discovery, entity
search, distribution, delivery, enterprise resource planning or
other applications.
[0527] In embodiments, the set of supply chain applications and
demand management applications may include, without limitation, one
or more of asset management, risk management, inventory management,
blockchain, smart contract, analytics, finance, trading, tax,
regulatory, identity management, commerce, ecommerce, payments,
security, safety, compatibility testing, compatibility management,
incident management, predictive maintenance, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, waste reduction, augmented reality, virtual
reality, mixed reality, product design, product configuration,
product updating, product maintenance, product support, product
testing, kit configuration, kit deployment, kit support, kit
updating, kit maintenance, kit modification, kit management,
product digital twin, opportunity matching, search, advertisement,
entity discovery, entity search, variation, simulation, user
interface, application programming interface, connectivity
management, natural language interface, voice/speech interface,
robotic interface, touch interface, haptic interface, vision system
interface, enterprise resource planning, or other applications.
[0528] In embodiments, the set of supply chain applications and
demand management applications may include, without limitation, one
or more of operations, finance, asset management, supply chain
management, demand management, human resource management, product
management, risk management, regulatory and compliance management,
inventory management, infrastructure management, facilities
management, analytics, trading, tax, identity management, vendor
management, process management, project management, operations
management, customer relationship management, workforce management,
incident management, research and development, sales management,
marketing management, fleet management, opportunity analytics,
decision support, strategic planning, forecasting, resource
management, property management, or other applications.
[0529] In embodiments, the machine vision system includes an
artificial intelligence system that is trained to recognize a type
of value chain asset based on a labeled data set of images of such
type of value chain assets.
[0530] In embodiments, the digital twin presents an indicator of
the type of asset based on the output of the artificial
intelligence system.
[0531] In embodiments, the machine vision system includes an
artificial intelligence system that is trained to recognize a type
of activity involving a set of value chain entities based on a
labeled data set of images of such type of activity.
[0532] In embodiments, the digital twin presents an indicator of
the type of activity based on the output of the artificial
intelligence system.
[0533] In embodiments, the machine vision system includes an
artificial intelligence system that is trained to recognize a
safety hazard involving a value chain entity based on a training
data set that includes a set of images of value chain network
activities and a set of value chain network safety outcomes.
[0534] In embodiments, the digital twin presents an indicator of
the hazard based on the output of the artificial intelligence
system.
[0535] In embodiments, the machine vision system includes an
artificial intelligence system that is trained to predict a delay
based on a training data set that includes a set of images of value
chain network activities and a set of value chain network timing
outcomes.
[0536] In embodiments, the digital twin presents an indicator of a
likelihood of delay based on the output of the artificial
intelligence system.
[0537] As noted elsewhere herein and in documents incorporated by
reference, artificial intelligence (such as any of the techniques
or systems described throughout this disclosure) in connection with
value chain network entities 652 and related processes and
applications may be used to facilitate, among other things: (a) the
optimization, automation and/or control of various functions,
workflows, applications, features, resource utilization and other
factors, (b) recognition or diagnosis of various states, entities,
patterns, events, contexts, behaviors, or other elements; and/or
(c) the forecasting of various states, events, contexts or other
factors. As artificial intelligence improves, a large array of
domain-specific and/or general artificial intelligence systems have
become available and are likely to continue to proliferate. As
developers seek solutions to domain-specific problems, such as ones
relevant to value chain entities 652 and applications 630 described
throughout this disclosure they face challenges in selecting
artificial intelligence models (such as what set of neural
networks, machine learning systems, expert systems, or the like to
select) and in discovering and selecting what inputs may enable
effective and efficient use of artificial intelligence for a given
problem. As noted above, opportunity miners 1460 may assist with
the discovery of opportunities for increased automation and
intelligence; however, once opportunities are discovered, selection
and configuration of an artificial intelligence solution still
presents a significant challenge, one that is likely to continue to
grow as artificial intelligence solutions proliferate.
[0538] One set of solutions to these challenges is an artificial
intelligence store 3504 that is configured to enable collection,
organization, recommendation and presentation of relevant sets of
artificial intelligence systems based on one or more attributes of
a domain and/or a domain-related problem. In embodiments, an
artificial intelligence store 3504 may include a set of interfaces
to artificial intelligence systems, such as enabling the download
of relevant artificial intelligence applications, establishment of
links or other connections to artificial intelligence systems (such
as links to cloud-deployed artificial intelligence systems via
APIs, ports, connectors, or other interfaces) and the like. The
artificial intelligence store 3504 may include descriptive content
with respect to each of a variety of artificial intelligence
systems, such as metadata or other descriptive material indicating
suitability of a system for solving particular types of problems
(e.g., forecasting, NLP, image recognition, pattern recognition,
motion detection, route optimization, or many others) and/or for
operating on domain-specific inputs, data or other entities. In
embodiments, the artificial intelligence store 3504 may be
organized by category, such as domain, input types, processing
types, output types, computational requirements and capabilities,
cost, energy usage, and other factors. In embodiments, an interface
to the application store 3504 may take input from a developer
and/or from the platform (such as from an opportunity miner 1460)
that indicates one or more attributes of a problem that may be
addressed through artificial intelligence and may provide a set of
recommendations, such as via an artificial intelligence attribute
search engine, for a subset of artificial intelligence solutions
that may represent favorable candidates based on the developer's
domain-specific problem. Search results or recommendations may, in
embodiments, be based at least in part on collaborative filtering,
such as by asking developers to indicate or select elements of
favorable models, as well as by clustering, such as by using
similarity matrices, k-means clustering, or other clustering
techniques that associate similar developers, similar
domain-specific problems, and/or similar artificial intelligence
solutions. The artificial intelligence store 3504 may include
e-commerce features, such as ratings, reviews, links to relevant
content, and mechanisms for provisioning, licensing, delivery and
payment (including allocation of payments to affiliates and or
contributors), including ones that operate using smart contract
and/or blockchain features to automate purchasing, licensing,
payment tracking, settlement of transactions, or other
features.
[0539] Referring to FIG. 43, the artificial intelligence system
1160 may define a machine learning model 3000 for performing
analytics, simulation, decision making, and prediction making
related to data processing, data analysis, simulation creation, and
simulation analysis of one or more of the value chain entities 652.
The machine learning model 3000 is an algorithm and/or statistical
model that performs specific tasks without using explicit
instructions, relying instead on patterns and inference. The
machine learning model 3000 builds one or more mathematical models
based on training data to make predictions and/or decisions without
being explicitly programmed to perform the specific tasks. The
machine learning model 3000 may receive inputs of sensor data as
training data, including event data 1034 and state data 1140
related to one or more of the value chain entities 652. The sensor
data input to the machine learning model 3000 may be used to train
the machine learning model 3000 to perform the analytics,
simulation, decision making, and prediction making relating to the
data processing, data analysis, simulation creation, and simulation
analysis of the one or more of the value chain entities 652. The
machine learning model 3000 may also use input data from a user or
users of the information technology system. The machine learning
model 3000 may include an artificial neural network, a decision
tree, a support vector machine, a Bayesian network, a genetic
algorithm, any other suitable form of machine learning model, or a
combination thereof. The machine learning model 3000 may be
configured to learn through supervised learning, unsupervised
learning, reinforcement learning, self learning, feature learning,
sparse dictionary learning, anomaly detection, association rules, a
combination thereof, or any other suitable algorithm for
learning.
[0540] The artificial intelligence system 1160 may also define the
digital twin system 1700 to create a digital replica of one or more
of the value chain entities 652. The digital replica of the one or
more of the value chain entities 652 may use substantially
real-time sensor data to provide for substantially real-time
virtual representation of the value chain entity 652 and provides
for simulation of one or more possible future states of the one or
more value chain entities 652. The digital replica exists
simultaneously with the one or more value chain entities 652 being
replicated. The digital replica provides one or more simulations of
both physical elements and properties of the one or more value
chain entities 652 being replicated and the dynamics thereof, in
embodiments, throughout the lifestyle of the one or more value
chain entities 652 being replicated. The digital replica may
provide a hypothetical simulation of the one or more value chain
entities 652, for example during a design phase before the one or
more value chain entities are constructed or fabricated, or during
or after construction or fabrication of the one or more value chain
entities by allowing for hypothetical extrapolation of sensor data
to simulate a state of the one or more value chain entities 652,
such as during high stress, after a period of time has passed
during which component wear may be an issue, during maximum
throughput operation, after one or more hypothetical or planned
improvements have been made to the one or more value chain entities
652, or any other suitable hypothetical situation. In some
embodiments, the machine learning model 3000 may automatically
predict hypothetical situations for simulation with the digital
replica, such as by predicting possible improvements to the one or
more value chain entities 652, predicting when one or more
components of the one or more value chain entities 652 may fail,
and/or suggesting possible improvements to the one or more value
chain entities 652, such as changes to timing settings,
arrangement, components, or any other suitable change to the value
chain entities 652. The digital replica allows for simulation of
the one or more value chain entities 652 during both design and
operation phases of the one or more value chain entities 652, as
well as simulation of hypothetical operation conditions and
configurations of the one or more value chain entities 652. The
digital replica allows for invaluable analysis and simulation of
the one or more value chain entities, by facilitating observation
and measurement of nearly any type of metric, including
temperature, wear, light, vibration, etc. not only in, on, and
around each component of the one or more value chain entities 652,
but in some embodiments within the one or more value chain entities
652. In some embodiments, the machine learning model 3000 may
process the sensor data including the event data 1034 and the state
data 1140 to define simulation data for use by the digital twin
system 1700. The machine learning model 3000 may, for example,
receive state data 1140 and event data 1034 related to a particular
value chain entity 652 of the plurality of value chain entities 652
and perform a series of operations on the state data 1140 and the
event data 1034 to format the state data 1140 and the event data
1034 into a format suitable for use by the digital twin system 1700
in creation of a digital replica of the value chain entity 652. For
example, one or more value chain entities 652 may include a robot
configured to augment products on an adjacent assembly line. The
machine learning model 3000 may collect data from one or more
sensors positioned on, near, in, and/or around the robot. The
machine learning model 3000 may perform operations on the sensor
data to process the sensor data into simulation data and output the
simulation data to the digital twin system 1700. The digital twin
simulation 1700 may use the simulation data to create one or more
digital replicas of the robot, the simulation including for example
metrics including temperature, wear, speed, rotation, and vibration
of the robot and components thereof. The simulation may be a
substantially real-time simulation, allowing for a human user of
the information technology to view the simulation of the robot,
metrics related thereto, and metrics related to components thereof,
in substantially real time. The simulation may be a predictive or
hypothetical situation, allowing for a human user of the
information technology to view a predictive or hypothetical
simulation of the robot, metrics related thereto, and metrics
related to components thereof.
[0541] In some embodiments, the machine learning model 3000 and the
digital twin system 1700 may process sensor data and create a
digital replica of a set of value chain entities of the plurality
of value chain entities 652 to facilitate design, real-time
simulation, predictive simulation, and/or hypothetical simulation
of a related group of value chain entities. The digital replica of
the set of value chain entities may use substantially real-time
sensor data to provide for substantially real-time virtual
representation of the set of value chain entities and provide for
simulation of one or more possible future states of the set of
value chain entities. The digital replica exists simultaneously
with the set of value chain entities being replicated. The digital
replica provides one or more simulations of both physical elements
and properties of the set of value chain entities being replicated
and the dynamics thereof, in embodiments throughout the lifestyle
of the set of value chain entities being replicated. The one or
more simulations may include a visual simulation, such as a
wire-frame virtual representation of the one or more value chain
entities 652 that may be viewable on a monitor, using an augmented
reality (AR) apparatus, or using a virtual reality (VR) apparatus.
The visual simulation may be able to be manipulated by a human user
of the information technology system, such as zooming or
highlighting components of the simulation and/or providing an
exploded view of the one or more value chain entities 652. The
digital replica may provide a hypothetical simulation of the set of
value chain entities, for example during a design phase before the
one or more value chain entities are constructed or fabricated, or
during or after construction or fabrication of the one or more
value chain entities by allowing for hypothetical extrapolation of
sensor data to simulate a state of the set of value chain entities,
such as during high stress, after a period of time has passed
during which component wear may be an issue, during maximum
throughput operation, after one or more hypothetical or planned
improvements have been made to the set of value chain entities, or
any other suitable hypothetical situation. In some embodiments, the
machine learning model 3000 may automatically predict hypothetical
situations for simulation with the digital replica, such as by
predicting possible improvements to the set of value chain
entities, predicting when one or more components of the set of
value chain entities may fail, and/or suggesting possible
improvements to the set of value chain entities, such as changes to
timing settings, arrangement, components, or any other suitable
change to the value chain entities 652. The digital replica allows
for simulation of the set of value chain entities during both
design and operation phases of the set of value chain entities, as
well as simulation of hypothetical operation conditions and
configurations of the set of value chain entities. The digital
replica allows for invaluable analysis and simulation of the one or
more value chain entities, by facilitating observation and
measurement of nearly any type of metric, including temperature,
wear, light, vibration, etc. not only in, on, and around each
component of the set of value chain entities, but in some
embodiments within the set of value chain entities. In some
embodiments, the machine learning model 3000 may process the sensor
data including the event data 1034 and the state data 1140 to
define simulation data for use by the digital twin system 1700. The
machine learning model 3000 may, for example, receive state data
1140 and event data 1034 related to a particular value chain entity
652 of the plurality of value chain entities 652 and perform a
series of operations on the state data 1140 and the event data 1034
to format the state data 1140 and the event data 1034 into a format
suitable for use by the digital twin system 1700 in the creation of
a digital replica of the set of value chain entities. For example,
a set of value chain entities may include a die machine configured
to place products on a conveyor belt, the conveyor belt on which
the die machine is configured to place the products, and a
plurality of robots configured to add parts to the products as they
move along the assembly line. The machine learning model 3000 may
collect data from one or more sensors positioned on, near, in,
and/or around each of the die machines, the conveyor belt, and the
plurality of robots. The machine learning model 3000 may perform
operations on the sensor data to process the sensor data into
simulation data and output the simulation data to the digital twin
system 1700. The digital twin simulation 1700 may use the
simulation data to create one or more digital replicas of the die
machine, the conveyor belt, and the plurality of robots, the
simulation including for example metrics including temperature,
wear, speed, rotation, and vibration of the die machine, the
conveyor belt, and the plurality of robots and components thereof.
The simulation may be a substantially real-time simulation,
allowing for a human user of the information technology to view the
simulation of the die machine, the conveyor belt, and the plurality
of robots, metrics related thereto, and metrics related to
components thereof, in substantially real time. The simulation may
be a predictive or hypothetical situation, allowing for a human
user of the information technology to view a predictive or
hypothetical simulation of the die machine, the conveyor belt, and
the plurality of robots, metrics related thereto, and metrics
related to components thereof.
[0542] In some embodiments, the machine learning model 3000 may
prioritize collection of sensor data for use in digital replica
simulations of one or more of the value chain entities 652. The
machine learning model 3000 may use sensor data and user inputs to
train, thereby learning which types of sensor data are most
effective for creation of digital replicate simulations of one or
more of the value chain entities 652. For example, the machine
learning model 3000 may find that a particular value chain entity
652 has dynamic properties such as component wear and throughput
affected by temperature, humidity, and load. The machine learning
model 3000 may, through machine learning, prioritize collection of
sensor data related to temperature, humidity, and load, and may
prioritize processing sensor data of the prioritized type into
simulation data for output to the digital twin system 1700. In some
embodiments, the machine learning model 3000 may suggest to a user
of the information technology system that more and/or different
sensors of the prioritized type be implemented in the information
technology and value chain system near and around the value chain
entity 652 being simulation such that more and/or better data of
the prioritized type may be used in simulation of the value chain
entity 652 via the digital replica thereof.
[0543] In some embodiments, the machine learning model 3000 may be
configured to learn to determine which types of sensor data are to
be processed into simulation data for transmission to the digital
twin system 1700 based on one or both of a modeling goal and a
quality or type of sensor data. A modeling goal may be an objective
set by a user of the information technology system or may be
predicted or learned by the machine learning model 3000. Examples
of modeling goals include creating a digital replica capable of
showing dynamics of throughput on an assembly line, which may
include collection, simulation, and modeling of, e.g., thermal,
electrical power, component wear, and other metrics of a conveyor
belt, an assembly machine, one or more products, and other
components of the value chain. The machine learning model 3000 may
be configured to learn to determine which types of sensor data are
necessary to be processed into simulation data for transmission to
the digital twin system 1700 to achieve such a model. In some
embodiments, the machine learning model 3000 may analyze which
types of sensor data are being collected, the quality and quantity
of the sensor data being collected, and what the sensor data being
collected represents, and may make decisions, predictions,
analyses, and/or determinations related to which types of sensor
data are and/or are not relevant to achieving the modeling goal and
may make decisions, predictions, analyses, and/or determinations to
prioritize, improve, and/or achieve the quality and quantity of
sensor data being processed into simulation data for use by the
digital twin system 1700 in achieving the modeling goal.
[0544] In some embodiments, a user of the information technology
system may input a modeling goal into the machine learning model
3000. The machine learning model 3000 may learn to analyze training
data to output suggestions to the user of the information
technology system regarding which types of sensor data are most
relevant to achieving the modeling goal, such as one or more types
of sensors positioned in, on, or near a value chain entity or a
plurality of value chain entities that is relevant to the
achievement of the modeling goal is and/or are not sufficient for
achieving the modeling goal, and how a different configuration of
the types of sensors, such as by adding, removing, or repositioning
sensors, may better facilitate achievement of the modeling goal by
the machine learning model 3000 and the digital twin system 1700.
In some embodiments, the machine learning model 3000 may
automatically increase or decrease collection rates, processing,
storage, sampling rates, bandwidth allocation, bitrates, and other
attributes of sensor data collection to achieve or better achieve
the modeling goal. In some embodiments, the machine learning model
3000 may make suggestions or predictions to a user of the
information technology system related to increasing or decreasing
collection rates, processing, storage, sampling rates, bandwidth
allocation, bitrates, and other attributes of sensor data
collection to achieve or better achieve the modeling goal. In some
embodiments, the machine learning model 3000 may use sensor data,
simulation data, previous, current, and/or future digital replica
simulations of one or more value chain entities 652 of the
plurality of value chain entities 652 to automatically create
and/or propose modeling goals. In some embodiments, modeling goals
automatically created by the machine learning model 3000 may be
automatically implemented by the machine learning model 3000. In
some embodiments, modeling goals automatically created by the
machine learning model 3000 may be proposed to a user of the
information technology system, and implemented only after
acceptance and/or partial acceptance by the user, such as after
modifications are made to the proposed modeling goal by the
user.
[0545] In some embodiments, the user may input the one or more
modeling goals, for example, by inputting one or more modeling
commands to the information technology system. The one or more
modeling commands may include, for example, a command for the
machine learning model 3000 and the digital twin system 1700 to
create a digital replica simulation of one value chain entity 652
or a set of value chain entities of the plurality of 652, may
include a command for the digital replica simulation to be one or
more of a real-time simulation, and a hypothetical simulation. The
modeling command may also include, for example, parameters for what
types of sensor data should be used, sampling rates for the sensor
data, and other parameters for the sensor data used in the one or
more digital replica simulations. In some embodiments, the machine
learning model 3000 may be configured to predict modeling commands,
such as by using previous modeling commands as training data. The
machine learning model 3000 may propose predicted modeling commands
to a user of the information technology system, for example, to
facilitate simulation of one or more of the value chain entities
652 that may be useful for the management of the value chain
entities 652 and/or to allow the user to easily identify potential
issues with or possible improvements to the value chain entities
652.
[0546] In some embodiments, the machine learning model 3000 may be
configured to evaluate a set of hypothetical simulations of one or
more of the value chain entities 652. The set of hypothetical
simulations may be created by the machine learning model 3000 and
the digital twin system 1700 as a result of one or more modeling
commands, as a result of one or more modeling goals, one or more
modeling commands, by prediction by the machine learning model
3000, or a combination thereof. The machine learning model 3000 may
evaluate the set of hypothetical simulations based on one or more
metrics defined by the user, one or more metrics defined by the
machine learning model 3000, or a combination thereof. In some
embodiments, the machine learning model 3000 may evaluate each of
the hypothetical simulations of the set of hypothetical simulations
independently of one another. In some embodiments, the machine
learning model 3000 may evaluate one or more of the hypothetical
simulations of the set of hypothetical simulations in relation to
one another, for example by ranking the hypothetical simulations or
creating tiers of the hypothetical simulations based on one or more
metrics.
[0547] In some embodiments, the machine learning model 3000 may
include one or more model interpretability systems to facilitate
human understanding of outputs of the machine learning model 3000,
as well as information and insight related to cognition and
processes of the machine learning model 3000, i.e., the one or more
model interpretability systems allow for human understanding of not
only "what" the machine learning model 3000 is outputting, but also
"why" the machine learning model 3000 is outputting the outputs
thereof, and what process led to the 3000 formulating the outputs.
The one or more model interpretability systems may also be used by
a human user to improve and guide training of the machine learning
model 3000, to help debug the machine learning model 3000, to help
recognize bias in the machine learning model 3000. The one or more
model interpretability systems may include one or more of linear
regression, logistic regression, a generalized linear model (GLM),
a generalized additive model (GAM), a decision tree, a decision
rule, RuleFit, Naive Bayes Classifier, a K-nearest neighbors
algorithm, a partial dependence plot, individual conditional
expectation (ICE), an accumulated local effects (ALE) plot, feature
interaction, permutation feature importance, a global surrogate
model, a local surrogate (LIME) model, scoped rules, i.e. anchors,
Shapley values, Shapley additive explanations (SHAP), feature
visualization, network dissection, or any other suitable machine
learning interpretability implementation. In some embodiments, the
one or more model interpretability systems may include a model
dataset visualization system. The model dataset visualization
system is configured to automatically provide to a human user of
the information technology system visual analysis related to
distribution of values of the sensor data, the simulation data, and
data nodes of the machine learning model 3000.
[0548] In some embodiments, the machine learning model 3000 may
include and/or implement an embedded model interpretability system,
such as a Bayesian case model (BCM) or glass box. The Bayesian case
model uses Bayesian case-based reasoning, prototype classification,
and clustering to facilitate human understanding of data such as
the sensor data, the simulation data, and data nodes of the machine
learning model 3000. In some embodiments, the model
interpretability system may include and/or implement a glass box
interpretability method, such as a Gaussian process, to facilitate
human understanding of data such as the sensor data, the simulation
data, and data nodes of the machine learning model 3000.
[0549] In some embodiments, the machine learning model 3000 may
include and/or implement testing with concept activation vectors
(TCAV). The TCAV allows the machine learning model 3000 to learn
human-interpretable concepts, such as "running," "not running,"
"powered," "not powered," "robot," "human," "truck," or "ship" from
examples by a process including defining the concept, determining
concept activation vectors, and calculating directional
derivatives. By learning human-interpretable concepts, objects,
states, etc., TCAV may allow the machine learning model 3000 to
output useful information related to the value chain entities 652
and data collected therefrom in a format that is readily understood
by a human user of the information technology system.
[0550] In some embodiments, the machine learning model 3000 may be
and/or include an artificial neural network, e.g. a connectionist
system configured to "learn" to perform tasks by considering
examples and without being explicitly programmed with task-specific
rules. The machine learning model 3000 may be based on a collection
of connected units and/or nodes that may act like artificial
neurons that may in some ways emulate neurons in a biological
brain. The units and/or nodes may each have one or more connections
to other units and/or nodes. The units and/or nodes may be
configured to transmit information, e.g. one or more signals, to
other units and/or nodes, process signals received from other units
and/or nodes, and forward processed signals to other units and/or
nodes. One or more of the units and/or nodes and connections
therebetween may have one or more numerical "weights" assigned. The
assigned weights may be configured to facilitate learning, i.e.
training, of the machine learning model 3000. The weights assigned
weights may increase and/or decrease one or more signals between
one or more units and/or nodes, and in some embodiments may have
one or more thresholds associated with one or more of the weights.
The one or more thresholds may be configured such that a signal is
only sent between one or more units and/or nodes, if a signal
and/or aggregate signal crosses the threshold. In some embodiments,
the units and/or nodes may be assigned to a plurality of layers,
each of the layers having one or both of inputs and outputs. A
first layer may be configured to receive training data, transform
at least a portion of the training data, and transmit signals
related to the training data and transformation thereof to a second
layer. A final layer may be configured to output an estimate,
conclusion, product, or other consequence of processing of one or
more inputs by the machine learning model 3000. Each of the layers
may perform one or more types of transformations, and one or more
signals may pass through one or more of the layers one or more
times. In some embodiments, the machine learning model 3000 may
employ deep learning and being at least partially modeled and/or
configured as a deep neural network, a deep belief network, a
recurrent neural network, and/or a convolutional neural network,
such as by being configured to include one or more hidden
layers.
[0551] In some embodiments, the machine learning model 3000 may be
and/or include a decision tree, e.g. a tree-based predictive model
configured to identify one or more observations and determine one
or more conclusions based on an input. The observations may be
modeled as one or more "branches" of the decision tree, and the
conclusions may be modeled as one or more "leaves" of the decision
tree. In some embodiments, the decision tree may be a
classification tree. the classification tree may include one or
more leaves representing one or more class labels, and one or more
branches representing one or more conjunctions of features
configured to lead to the class labels. In some embodiments, the
decision tree may be a regression tree. The regression tree may be
configured such that one or more target variables may take
continuous values.
[0552] In some embodiments, the machine learning model 3000 may be
and/or include a support vector machine, e.g. a set of related
supervised learning methods configured for use in one or both of
classification and regression-based modeling of data. The support
vector machine may be configured to predict whether a new example
falls into one or more categories, the one or more categories being
configured during training of the support vector machine.
[0553] In some embodiments, the machine learning model 3000 may be
configured to perform regression analysis to determine and/or
estimate a relationship between one or more inputs and one or more
features of the one or more inputs. Regression analysis may include
linear regression, wherein the machine learning model 3000 may
calculate a single line to best fit input data according to one or
more mathematical criteria.
[0554] In embodiments, inputs to the machine learning model 3000
(such as a regression model, Bayesian network, supervised model, or
other type of model) may be tested, such as by using a set of
testing data that is independent from the data set used for the
creation and/or training of the machine learning model, such as to
test the impact of various inputs to the accuracy of the model
3000. For example, inputs to the regression model may be removed,
including single inputs, pairs of inputs, triplets, and the like,
to determine whether the absence of inputs creates a material
degradation of the success of the model 3000. This may assist with
recognition of inputs that are in fact correlated (e.g., are linear
combinations of the same underlying data), that are overlapping, or
the like. Comparison of model success may help select among
alternative input data sets that provide similar information, such
as to identify the inputs (among several similar ones) that
generate the least "noise" in the model, that provide the most
impact on model effectiveness for the lowest cost, or the like.
Thus, input variation and testing of the impact of input variation
on model effectiveness may be used to prune or enhance model
performance for any of the machine learning systems described
throughout this disclosure.
[0555] In some embodiments, the machine learning model 3000 may be
and/or include a Bayesian network. The Bayesian network may be a
probabilistic graphical model configured to represent a set of
random variables and conditional independence of the set of random
variables. The Bayesian network may be configured to represent the
random variables and conditional independence via a directed
acyclic graph. The Bayesian network may include one or both of a
dynamic Bayesian network and an influence diagram.
[0556] In some embodiments, the machine learning model 3000 may be
defined via supervised learning, i.e. one or more algorithms
configured to build a mathematical model of a set of training data
containing one or more inputs and desired outputs. The training
data may consist of a set of training examples, each of the
training examples having one or more inputs and desired outputs,
i.e. a supervisory signal. Each of the training examples may be
represented in the machine learning model 3000 by an array and/or a
vector, i.e. a feature vector. The training data may be represented
in the machine learning model 3000 by a matrix. The machine
learning model 3000 may learn one or more functions via iterative
optimization of an objective function, thereby learning to predict
an output associated with new inputs. Once optimized, the objective
function may provide the machine learning model 3000 with the
ability to accurately determine an output for inputs other than
inputs included in the training data. In some embodiments, the
machine learning model 3000 may be defined via one or more
supervised learning algorithms such as active learning, statistical
classification, regression analysis, and similarity learning.
Active learning may include interactively querying, by the machine
learning model AILD102T, a user and/or an information source to
label new data points with desired outputs. Statistical
classification may include identifying, by the machine learning
model 3000, to which a set of subcategories, i.e. subpopulations, a
new observation belongs based on a training set of data containing
observations having known categories. Regression analysis may
include estimating, by the machine learning model 3000
relationships between a dependent variable, i.e. an outcome
variable, and one or more independent variables, i.e. predictors,
covariates, and/or features. Similarity learning may include
learning, by the machine learning model 3000, from examples using a
similarity function, the similarity function being designed to
measure how similar or related two objects are.
[0557] In some embodiments, the machine learning model 3000 may be
defined via unsupervised learning, i.e. one or more algorithms
configured to build a mathematical model of a set of data
containing only inputs by finding structure in the data such as
grouping or clustering of data points. In some embodiments, the
machine learning model 3000 may learn from test data, i.e. training
data, that has not been labeled, classified, or categorized. The
unsupervised learning algorithm may include identifying, by the
machine learning model 3000, commonalities in the training data and
learning by reacting based on the presence or absence of the
identified commonalities in new pieces of data. In some
embodiments, the machine learning model 3000 may generate one or
more probability density functions. In some embodiments, the
machine learning model 3000 may learn by performing cluster
analysis, such as by assigning a set of observations into subsets,
i.e. clusters, according to one or more predesignated criteria,
such as according to a similarity metric of which internal
compactness, separation, estimated density, and/or graph
connectivity are factors.
[0558] In some embodiments, the machine learning model 3000 may be
defined via semi-supervised learning, i.e. one or more algorithms
using training data wherein some training examples may be missing
training labels. The semi-supervised learning may be weakly
supervised learning, wherein the training labels may be noisy,
limited, and/or imprecise. The noisy, limited, and/or imprecise
training labels may be cheaper and/or less labor intensive to
produce, thus allowing the machine learning model 3000 to train on
a larger set of training data for less cost and/or labor.
[0559] In some embodiments, the machine learning model 3000 may be
defined via reinforcement learning, such as one or more algorithms
using dynamic programming techniques such that the machine learning
model 3000 may train by taking actions in an environment in order
to maximize a cumulative reward. In some embodiments, the training
data is represented as a Markov Decision Process.
[0560] In some embodiments, the machine learning model 3000 may be
defined via self-learning, wherein the machine learning model 3000
is configured to train using training data with no external rewards
and no external teaching, such as by employing a Crossbar Adaptive
Array (CAA). The CAA may compute decisions about actions and/or
emotions about consequence situations in a crossbar fashion,
thereby driving teaching of the machine learning model 3000 by
interactions between cognition and emotion.
[0561] In some embodiments, the machine learning model 3000 may be
defined via feature learning, i.e. one or more algorithms designed
to discover increasingly accurate and/or apt representations of one
or more inputs provided during training, e.g. training data.
Feature learning may include training via principal component
analysis and/or cluster analysis. Feature learning algorithms may
include attempting, by the machine learning model 3000, to preserve
input training data while also transforming the input training data
such that the transformed input training data is useful. In some
embodiments, the machine learning model 3000 may be configured to
transform the input training data prior to performing one or more
classifications and/or predictions of the input training data.
Thus, the machine learning model 3000 may be configured to
reconstruct input training data from one or more unknown
data-generating distributions without necessarily conforming to
implausible configurations of the input training data according to
the distributions. In some embodiments, the feature learning
algorithm may be performed by the machine learning model 3000 in a
supervised, unsupervised, or semi-supervised manner.
[0562] In some embodiments, the machine learning model 3000 may be
defined via anomaly detection, i.e. by identifying rare and/or
outlier instances of one or more items, events and/or observations.
The rare and/or outlier instances may be identified by the
instances differing significantly from patterns and/or properties
of a majority of the training data. Unsupervised anomaly detection
may include detecting of anomalies, by the machine learning model
3000, in an unlabeled training data set under an assumption that a
majority of the training data is "normal." Supervised anomaly
detection may include training on a data set wherein at least a
portion of the training data has been labeled as "normal" and/or
"abnormal."
[0563] In some embodiments, the machine learning model 3000 may be
defined via robot learning. Robot learning may include generation,
by the machine learning model 3000, of one or more curricula, the
curricula being sequences of learning experiences, and cumulatively
acquiring new skills via exploration guided by the machine learning
model 3000 and social interaction with humans by the machine
learning model 3000. Acquisition of new skills may be facilitated
by one or more guidance mechanisms such as active learning,
maturation, motor synergies, and/or imitation.
[0564] In some embodiments, the machine learning model 3000 can be
defined via association rule learning. Association rule learning
may include discovering relationships, by the machine learning
model 3000, between variables in databases, in order to identify
strong rules using some measure of "interestingness." Association
rule learning may include identifying, learning, and/or evolving
rules to store, manipulate and/or apply knowledge. The machine
learning model 3000 may be configured to learn by identifying
and/or utilizing a set of relational rules, the relational rules
collectively representing knowledge captured by the machine
learning model 3000. Association rule learning may include one or
more of learning classifier systems, inductive logic programming,
and artificial immune systems. Learning classifier systems are
algorithms that may combine a discovery component, such as one or
more genetic algorithms, with a learning component, such as one or
more algorithms for supervised learning, reinforcement learning, or
unsupervised learning. Inductive logic programming may include
rule-learning, by the machine learning model 3000, using logic
programming to represent one or more of input examples, background
knowledge, and hypothesis determined by the machine learning model
3000 during training. The machine learning model 3000 may be
configured to derive a hypothesized logic program entailing all
positive examples given an encoding of known background knowledge
and a set of examples represented as a logical database of
facts.
[0565] In embodiments, another set of solutions, which may be
deployed alone or in connection with other elements of the
platform, including the artificial intelligence store 3504, may
include a set of functional imaging capabilities FMRP102, which may
comprise monitoring systems 640 and in some cases physical process
observation systems 1510 and/or software interaction observation
systems 1500, such as for monitoring various value chain entities
652. Functional imaging systems FMRP102 may, in embodiments,
provide considerable insight into the types of artificial
intelligence that are likely to be most effective in solving
particular types of problems most effectively. As noted elsewhere
in this disclosure and in the documents incorporated by reference
herein, computational and networking systems, as they grow in
scale, complexity and interconnections, manifest problems of
information overload, noise, network congestion, energy waste, and
many others. As the Internet of Things grows to hundreds of
billions of devices, and virtually countless potential
interconnections, optimization becomes exceedingly difficult. One
source for insight is the human brain, which faces similar
challenges and has evolved, over millennia, reasonable solutions to
a wide range of very difficult optimization problems. The human
brain operates with a massive neural network organized into
interconnected modular systems, each of which has a degree of
adaptation to solve particular problems, from regulation of
biological systems and maintenance of homeostasis, to detection of
a wide range of static and dynamic patterns, to recognition of
threats and opportunities, among many others. Functional imaging
FMRP102, such as functional magnetic resonance imaging (fMRI),
electroencephalogram (EEG), computed tomography (CT) and other
brain imaging systems have improved to the point that patterns of
brain activity can be recognized in real time and temporally
associated with other information, such behaviors, stimulus
information, environmental condition data, gestures, eye movements,
and other information, such that via functional imaging FMRP102,
either alone or in combination with other information collected by
monitoring systems IPX106, the platform may determine and classify
what brain modules, operations, systems, and/or functions are
employed during the undertaking of a set of tasks or activities,
such as ones involving software interaction 1500, physical process
observations 1510, or a combination thereof. This classification
may assist in selection and/or configuration of a set of artificial
intelligence solutions, such as from an artificial intelligence
store 3504, that includes a similar set of capabilities and/or
functions to the set of modules and functions of the human brain
when undertaking an activity, such as for the initial configuration
of a robotic process automation (RPA) system 1442 that automates a
task performed by an expert human. Thus, the platform may include a
system that takes input from a functional imaging system FRMP102 to
configure, optionally automatically based on matching of attributes
between one or more biological systems, such as brain systems, and
one or more artificial intelligence systems, a set of artificial
intelligence capabilities for a robotic process automation system.
Selection and configuration may further comprise selection of
inputs to robotic process automation and/or artificial intelligence
that are configured at least in part based on functional imaging of
the brain while workers undertake tasks, such as selection of
visual inputs (such as images from cameras) where vision systems of
the brain are highly activated, selection of acoustic inputs where
auditory systems of the brain are highly activated, selection of
chemical inputs (such as chemical sensors) where olfactory systems
of the brain are highly activated, or the like. Thus, a
biologically aware robotic process automation system may be
improved by having initial configuration, or iterative improvement,
be guided, either automatically or under developer control, by
imaging-derived information collected as workers perform expert
tasks that may benefit from automation.
[0566] Referring to FIG. 27, additional details of an embodiment of
the platform 604 are provided, in particular relating to elements
of the adaptive intelligence layer 614 that facilitate improved
edge intelligence, including the adaptive edge compute management
system 1400 and the edge intelligence system 1420. These elements
provide a set of systems that adaptively manage "edge" computation,
storage and processing, such as by varying storage locations for
data and processing locations (e.g., optimized by AI) between
on-device storage, local systems, in the network and in the cloud.
These elements enable facilitation of a dynamic definition by a
user, such as a developer, operator, or host of the platform 102,
of what constitutes the "edge" for purposes of a given application.
For example, for environments where data connections are slow or
unreliable (such as where a facility does not have good access to
cellular networks (such as due to remoteness of some environments
(such as in geographies with poor cellular network infrastructure),
shielding or interference (such as where density of network-using
systems, thick metals hulls of container ships, thick metal
container walls, underwater or underground location, or presence of
large metal objects (such as vaults, hulls, containers and the
like) interferes with networking performance), and/or congestion
(such as where there are many devices seeking access to limited
networking facilities), edge computing capabilities can be defined
and deployed to operate on the local area network of an
environment, in peer-to-peer networks of devices, or on computing
capabilities of local value chain entities 652. For example, in an
environment with a limited set of computational and/or networking
resources, tasks may be intelligently load balanced based on a
current context (e.g., network availability, latency, congestion,
and the like) and, in an example, one type of data may be
prioritized for processing, or one workflow prioritized over
another workflow, and the like. Where strong data connections are
available (such as where good backhaul facilities exist), edge
computing capabilities can be disposed in the network, such as for
caching frequently used data at locations that improve input/output
performance, reduce latency, or the like. Thus, adaptive definition
and specification of where edge computing operations are enabled,
under control of a developer or operator, or optionally determined
automatically, such as by an expert system or automation system,
such as based on detected network conditions for an environment,
for a financial entity 652, or for a network as a whole.
[0567] In embodiments, edge intelligence 1420 enables adaptation of
edge computation (including where computation occurs within various
available networking resources, how networking occurs (such as by
protocol selection), where data storage occurs, and the like) that
is multi-application aware, such as accounting for QoS, latency
requirements, congestion, and cost as understood and prioritized
based on awareness of the requirements, the prioritization, and the
value (including ROI, yield, and cost information, such as costs of
failure) of edge computation capabilities across more than one
application, including any combinations and subsets of the
applications 630 described herein or in the documents incorporated
herein by reference.
[0568] Referring to FIG. 35, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0569] In embodiments, the platform 604 may include a unified set
of adaptive edge computing and other edge intelligence systems 1420
that provide coordinated edge computation and other edge
intelligence 1420 capabilities for a set of multiple applications
630 of various types, such as a set of supply chain management
applications 1500, demand management applications 1502, intelligent
product applications 1510 and enterprise resource management
applications 1520 that monitor and/or manage a value chain network
and a set of value chain network entities 652. In embodiments, edge
intelligence capabilities of the systems and methods described
herein may include, but are not limited to, on-premise edge devices
and resources, such as local area network resources, and network
edge devices, such as those deployed at the edge of a cellular
network or within a peripheral data center, both of which may
deploy edge intelligence, as described herein, to, for example,
carry out intelligent processing tasks at these edge locations
before transferring data or other matter, to the primary or core
cellular network command or central data center.
[0570] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of applications
of at least two types from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[0571] The adaptive edge computing and other edge intelligence
systems 1420 may thus provide, in embodiments, intelligence for
monitoring, managing, controlling, or otherwise handling a wide
range of facilities, devices, systems, environments, and assets,
such as supply chain infrastructure facilities 1560 and other value
chain network entities 652 that are involved as a product 650
travels from a point of origin through distribution and retail
channels to an environment where it is used by a customer. This
unification may provide a number of advantages, including improved
monitoring, improved remote control, improved autonomy, improved
prediction, improved classification, improved visualization and
insight, improved visibility, and others. These may include
adaptive edge computing and other edge intelligence systems 1420
that are used in connection with demand factors 1540 and supply
factors 1550, so that an application 630 may benefit from
information collected by, processed by, or produced by the adaptive
edge computing and other edge intelligence systems 1420 for other
applications 630 of the platform 604, and a user can develop
insights about connections among the factors and control one or
both of them with coordinated intelligence. For example,
coordinated intelligence may include, but is not limited to,
analytics and processing for monitoring data streams, as described
herein, for the purposes of classification, prediction or some
other type of analytic modeling. Such coordinated intelligence
methods and systems may be applied in an automated manner in which
differing combinations of intelligence assets are applied. As an
example, within an industrial environment the coordinated
intelligence system may monitor signals coming from machinery
deployed in the environment. The coordinated intelligence system
may classify, predict or perform some other intelligent analytics,
in combination, for the purpose of, for example, determining a
state of a machine, such as a machine in a deteriorated state, in
an at-risk state, or some other state. The determination of a state
may cause a control system to alter a control regime, for example,
slowing or shutting down a machine that is in a deteriorating
state. In embodiments, the coordinated intelligence system may
coordinate across multiple entities of a value chain, supply chain
and the like. For example, the monitoring of the deteriorating
machine in the industrial environment may simultaneously occur with
analytics related to parts suppliers and availability, product
supply and inventory predictions, or some other coordinated
intelligence operation. The adaptive edge computing and other edge
intelligence systems 1420 may be adapted over time, such as by
learning on outcomes 1040 or other operations of the other adaptive
intelligent systems 614, such as to determine which elements
collected and/or processed by the adaptive edge computing and other
edge intelligence systems 1420 should be made available to which
applications 630, what elements and/or content provide the most
benefit, what data should be stored or cached for immediate
retrieval, what data can be discarded versus saved, what data is
most beneficial to support adaptive intelligent systems 614, and
for other uses.
[0572] Referring to FIG. 36, in embodiments, the unified set of
adaptive edge computing systems that provide coordinated edge
computation include a wide range of systems, such as classification
systems 1610 (such as image classification systems, object type
recognition systems, and others), video processing systems 1612
(such as video compression systems), signal processing systems 1614
(such as analog-to-digital transformation systems,
digital-to-analog transformation systems, RF filtering systems,
analog signal processing systems, multiplexing systems, statistical
signal processing systems, signal filtering systems, natural
language processing systems, sound processing systems, ultrasound
processing systems, and many others), data processing systems 1630
(such as data filtering systems, data integration systems, data
extraction systems, data loading systems, data transformation
systems, point cloud processing systems, data normalization
systems, data cleansing system, data deduplication systems,
graph-based data storage systems, object-oriented data storage
systems, and others), predictive systems 1620 (such as motion
prediction systems, output prediction systems, activity prediction
systems, fault prediction systems, failure prediction systems,
accident prediction systems, event predictions systems, event
prediction systems, and many others), configuration systems 1630
(such as protocol selection systems, storage configuration systems,
peer-to-peer network configuration systems, power management
systems, self-configuration systems, self-healing systems,
handshake negotiation systems, and others), artificial intelligence
systems 1160 (such as clustering systems, variation systems,
machine learning systems, expert systems, rule-based systems, deep
learning systems, and many others), system management and control
systems 1640 (such as autonomous control systems, robotic control
systems, RF spectrum management systems, network resource
management systems, storage management systems, data management
systems, and others), robotic process automation systems, analytic
and modeling systems 1650 (such as data visualization systems,
clustering systems, similarity analysis systems, random forest
systems, physical modeling systems, interaction modeling systems,
simulation systems, and many others), entity discovery systems,
security systems 1670 (such as cybersecurity systems, biometric
systems, intrusion detection systems, firewall systems, and
others), rules engine systems, workflow automation systems,
opportunity discovery systems, testing and diagnostic systems 1660,
software image propagation systems, virtualization systems, digital
twin systems, Internet of Things monitoring systems, routing
systems, switching systems, indoor location systems, geolocation
systems, and others.
[0573] In embodiments, the interface is a user interface for a
command center dashboard by which an enterprise orchestrates a set
of value chain entities related to a type of product.
[0574] In embodiments, the interface is a user interface of a local
management system located in an environment that hosts a set of
value chain entities.
[0575] In embodiments, the local management system user interface
facilitates configuration of a set of network connections for the
adaptive edge computing systems.
[0576] In embodiments, the local management system user interface
facilitates configuration of a set of data storage resources for
the adaptive edge computing systems.
[0577] In embodiments, the local management system user interface
facilitates configuration of a set of data integration capabilities
for the adaptive edge computing systems.
[0578] In embodiments, the local management system user interface
facilitates configuration of a set of machine learning input
resources for the adaptive edge computing systems.
[0579] In embodiments, the local management system user interface
facilitates configuration of a set of power resources that support
the adaptive edge computing systems.
[0580] In embodiments, the local management system user interface
facilitates configuration of a set of workflows that are managed by
the adaptive edge computing systems.
[0581] In embodiments, the interface is a user interface of a
mobile computing device that has a network connection to the
adaptive edge computing systems.
[0582] In embodiments, the interface is an application programming
interface.
[0583] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a cloud-based artificial intelligence system.
[0584] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a real-time operating system of a cloud data management
platform.
[0585] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a computational facility of a cloud data management
platform.
[0586] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of environmental sensors that collect data about
an environment that hosts a set of value chain network
entities.
[0587] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of sensors that collect data about a product.
[0588] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of sensors that collect data published by an
intelligent product.
[0589] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of sensors that collect data published by a set
of Internet of Things systems that are disposed in an environment
that hosts a set of value chain network entities.
[0590] In embodiments, the set of demand management applications,
supply chain applications, intelligent product applications and
enterprise resource management applications may include, for
example, any of the applications mentioned throughout this
disclosure or in the documents incorporated by reference
herein.
Unified Adaptive Intelligence
[0591] Referring to FIG. 37, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0592] In embodiments, the VCNP 102 may include a unified set of
adaptive intelligent systems 614 that provide coordinated
intelligence for a set of various applications, such as demand
management applications 1502, a set of supply chain applications
1500, a set of intelligent product applications 1510, a set of
enterprise resource management applications 1520 and a set of asset
management applications 1530 for a category of goods.
[0593] In embodiments, the unified set of adaptive intelligence
systems include a wide variety of systems described throughout this
disclosure and in the documents incorporated herein by reference,
such as, without limitation, the edge intelligence systems 1420,
classification systems 1610, data processing systems 1612, signal
processing systems 1614, artificial intelligence systems 1160,
prediction systems 1620, configuration systems 1630, control
systems 1640, analytic systems 1650, testing/diagnostic systems
1660, security systems 1670 and other systems, whether used for
edge intelligence or for intelligence within a network, within an
application, or in the cloud, as well as to serve various layers of
the platform 604. These include neural networks, deep learning
systems, model-based systems, expert systems, machine learning
systems, rule-based systems, opportunity miners, robotic process
automation systems, data transformation systems, data extraction
systems, data loading systems, genetic programming systems, image
classification systems, video compression systems,
analog-to-digital transformation systems, digital-to-analog
transformation systems, signal analysis systems, RF filtering
systems, motion prediction systems, object type recognition
systems, point cloud processing systems, analog signal processing
systems, signal multiplexing systems, data fusion systems, sensor
fusion systems, data filtering systems, statistical signal
processing systems, signal filtering systems, signal processing
systems, protocol selection systems, storage configuration systems,
power management systems, clustering systems, variation systems,
machine learning systems, event prediction systems, autonomous
control systems, robotic control systems, robotic process
automation systems, data visualization systems, data normalization
systems, data cleansing systems, data deduplication systems,
graph-based data storage systems, intelligent agent systems,
object-oriented data storage systems, self-configuration systems,
self-healing systems, self-organizing systems, self-organizing map
systems, cost-based routing systems, handshake negotiation systems,
entity discovery systems, cybersecurity systems, biometric systems,
natural language processing systems, speech processing systems,
voice recognition systems, sound processing systems, ultrasound
processing systems, artificial intelligence systems, rules engine
systems, workflow automation systems, opportunity discovery
systems, physical modeling systems, testing systems, diagnostic
systems, software image propagation systems, peer-to-peer network
configuration systems, RF spectrum management systems, network
resource management systems, storage management systems, data
management systems, intrusion detection systems, firewall systems,
virtualization systems, digital twin systems, Internet of Things
monitoring systems, routing systems, switching systems, indoor
location systems, geolocation systems, parsing systems, semantic
filtering systems, machine vision systems, fuzzy logic systems,
recommendation systems, dialog management systems, and others.
[0594] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[0595] In embodiments, the unified set of adaptive intelligent
systems includes a set of artificial intelligence systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of neural networks. In embodiments, the unified set
of adaptive intelligent systems includes a set of deep learning
systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of model-based systems.
[0596] In embodiments, the unified set of adaptive intelligent
systems includes a set of expert systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
machine learning systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of rule-based systems.
In embodiments, the unified set of adaptive intelligent systems
includes a set of opportunity miners.
[0597] In embodiments, the unified set of adaptive intelligent
systems includes a set of robotic process automation systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of data transformation systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of data
extraction systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of data loading systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of genetic programming systems.
[0598] In embodiments, the unified set of adaptive intelligent
systems includes a set of image classification systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of video compression systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
analog-to-digital transformation systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
digital-to-analog transformation systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
signal analysis systems.
[0599] In embodiments, the unified set of adaptive intelligent
systems includes a set of RF filtering systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
motion prediction systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of object type
recognition systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of point cloud processing
systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of analog signal processing systems.
[0600] In embodiments, the unified set of adaptive intelligent
systems includes a set of signal multiplexing systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of data fusion systems. In embodiments, the unified
set of adaptive intelligent systems includes a set of sensor fusion
systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of data filtering systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
statistical signal processing systems.
[0601] In embodiments, the unified set of adaptive intelligent
systems includes a set of signal filtering systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
signal processing systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of protocol selection
systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of storage configuration systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of power management systems.
[0602] In embodiments, the unified set of adaptive intelligent
systems includes a set of clustering systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
variation systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of machine learning systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of event prediction systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
autonomous control systems.
[0603] In embodiments, the unified set of adaptive intelligent
systems includes a set of robotic control systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
robotic process automation systems. In embodiments, the unified set
of adaptive intelligent systems includes a set of data
visualization systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of data normalization systems.
In embodiments, the unified set of adaptive intelligent systems
includes a set of data cleansing systems.
[0604] In embodiments, the unified set of adaptive intelligent
systems includes a set of data deduplication systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of graph-based data storage systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
intelligent agent systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of object-oriented data
storage systems.
[0605] In embodiments, the unified set of adaptive intelligent
systems includes a set of self-configuration systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of self-healing systems. In embodiments, the unified
set of adaptive intelligent systems includes a set of
self-organizing systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of self-organizing map
systems.
[0606] In embodiments, the unified set of adaptive intelligent
systems includes a set of cost-based routing systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of handshake negotiation systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
entity discovery systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of cybersecurity
systems.
[0607] In embodiments, the unified set of adaptive intelligent
systems includes a set of biometric systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
natural language processing systems. In embodiments, the unified
set of adaptive intelligent systems includes a set of speech
processing systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of voice recognition
systems.
[0608] In embodiments, the unified set of adaptive intelligent
systems includes a set of sound processing systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
ultrasound processing systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of artificial
intelligence systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of rules engine systems.
[0609] In embodiments, the unified set of adaptive intelligent
systems includes a set of workflow automation systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of opportunity discovery systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
physical modeling systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of testing systems.
[0610] In embodiments, the unified set of adaptive intelligent
systems includes a set of diagnostic systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
software image propagation systems. In embodiments, the unified set
of adaptive intelligent systems includes a set of peer-to-peer
network configuration systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of RF spectrum
management systems.
[0611] In embodiments, the unified set of adaptive intelligent
systems includes a set of network resource management systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of storage management systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of data
management systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of intrusion detection
systems.
[0612] In embodiments, the unified set of adaptive intelligent
systems includes a set of firewall systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
virtualization systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of digital twin systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of Internet of Things monitoring systems.
[0613] In embodiments, the unified set of adaptive intelligent
systems includes a set of routing systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
switching systems. In embodiments, the unified set of adaptive
intelligent systems includes a set of indoor location systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of geolocation systems.
[0614] In embodiments, the unified set of adaptive intelligent
systems includes a set of parsing systems. In embodiments, the
unified set of adaptive intelligent systems includes a set of
semantic filtering systems. In embodiments, the unified set of
adaptive intelligent systems includes a set of machine vision
systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of fuzzy logic systems.
[0615] In embodiments, the unified set of adaptive intelligent
systems includes a set of recommendation systems. In embodiments,
the unified set of adaptive intelligent systems includes a set of
dialog management systems. In embodiments, the set of interfaces
includes a demand management interface and a supply chain
management interface. In embodiments, the interface is a user
interface for a command center dashboard by which an enterprise
orchestrates a set of value chain entities related to a type of
product.
[0616] In embodiments, the interface is a user interface of a local
management system located in an environment that hosts a set of
value chain entities. In embodiments, the local management system
user interface facilitates configuration of a set of network
connections for the adaptive intelligence systems. In embodiments,
the local management system user interface facilitates
configuration of a set of data storage resources for the adaptive
intelligence systems. In embodiments, the local management system
user interface facilitates configuration of a set of data
integration capabilities for the adaptive intelligence systems.
[0617] In embodiments, the local management system user interface
facilitates configuration of a set of machine learning input
resources for the adaptive intelligence systems. In embodiments,
the local management system user interface facilitates
configuration of a set of power resources that support the adaptive
intelligence systems. In embodiments, the local management system
user interface facilitates configuration of a set of workflows that
are managed by the adaptive intelligence systems.
[0618] In embodiments, the interface is a user interface of a
mobile computing device that has a network connection to the
adaptive intelligence systems.
[0619] In embodiments, the interface is an application programming
interface. In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a cloud-based artificial intelligence system. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive intelligence systems and a
real-time operating system of a cloud data management platform.
[0620] In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a computational facility of a cloud data management
platform.
[0621] In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a set of environmental sensors that collect data about
an environment that hosts a set of value chain network entities. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive intelligence systems and a
set of sensors that collect data about a product.
[0622] In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a set of sensors that collect data published by an
intelligent product.
[0623] In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a set of sensors that collect data published by a set
of Internet of Things systems that are disposed in an environment
that hosts a set of value chain network entities.
[0624] In embodiments, the set of demand management applications,
supply chain applications, intelligent product applications and
enterprise resource management applications may include, any of the
applications mentioned throughout this disclosure or the documents
incorporated herein by reference.
[0625] In embodiments, the adaptive intelligent systems layer 614
is configured to train and deploy artificial intelligence systems
to perform value-chain related tasks. For example, the adaptive
intelligent systems layer 614 may be leveraged to manage a
container fleet, design a logistics system, control one or more
aspects of a logistics system, select packaging attributes of
packages in the value chain, design a process to meet regulatory
requirements, automate processes to mitigate waste production
(e.g., solid waste or waste water), and/or other suitable tasks
related to the value-chain.
[0626] In some of these embodiments, one or more digital twins may
be leveraged by the adaptive intelligent systems layer 614. A
digital twin may refer to a digital representation of a physical
object (e.g., an asset, a device, a product, a package, a
container, a vehicle, a ship, or the like), an environment (e.g., a
facility), an individual (e.g., a customer or worker), or other
entity (including any of the value chain network entities 652
described herein), or combination thereof. Further examples of
physical assets include containers (e.g., boxes, shipping
containers, boxes, palates, barrels, and the like), goods/products
(e.g., widgets, food, household products, toys, clothing, water,
gas, oil, equipment, and the like), components (e.g., chips,
boards, screens, chipsets, wires, cables, cards, memory, software
components, firmware, parts, connectors, housings, and the like),
furniture (e.g., tables, counters, workstations, shelving, etc.),
and the like. Examples of devices include computers, sensors,
vehicles (e.g., cars, trucks, tankers, trains, forklifts, cranes,
and the like), equipment, conveyer belts, and the like. Examples of
environments may include facilities (e.g., factories, refineries,
warehouses, retail locations, storage buildings, parking lots,
airports, commercial buildings, residential buildings, and the
like), roads, water ways, cities, countries, land masses, and the
like. Examples of different types of physical assets, devices, and
environments are referenced throughout the disclosure.
[0627] In embodiments, a digital twin may be comprised of (e.g.,
via reference, or by partial or complete integration) other digital
twins. For example, a digital twin of a package may include a
digital twin of a container and one or more digital twins of one or
more respective goods enclosed within the container. Taking this
example one step further, one or more digital twins of the packages
may be contained in a digital twin of a vehicle traversing a
digital twin of a road or may be positioned on a digital twin of a
shelf within a digital twin of a warehouse, which would include
digital twins of other physical assets and devices.
[0628] In embodiments, the digital representation for a digital
twin may include a set of data structures (e.g., classes of
objects) that collectively define a set of properties, attributes,
and/or parameters of a represented physical asset, device, or
environment, possible behaviors or activities thereof and/or
possible states or conditions thereof, among other things. For
example, a set of properties of a physical asset may include a type
of the physical asset, the shape and/or dimensions of the asset,
the mass of the asset, the density of the asset, the material(s) of
the asset, the physical properties of the material(s), the chemical
properties of the asset, the expected lifetime of the asset, the
surface of the physical asset, a price of the physical asset, the
status of the physical asset, a location of the physical asset,
and/or other properties, as well as identifiers of other digital
twins contained within or linked to the digital twin and/or other
relevant data sources that may be used to populate the digital twin
(such as data sources within the management platform described
herein or external data sources, such as environmental data sources
that may impact properties represented in the digital twin (e.g.,
where ambient air pressure or temperature affects the physical
dimensions of an asset that inflates or deflates). Examples of a
behavior of a physical asset may include a state of matter of the
physical asset (e.g., a solid, liquid, plasma or gas), a melting
point of the physical asset, a density of the physical asset when
in a liquid state, a viscosity of the physical asset when in a
liquid state, a freezing point of the physical asset, a density of
the physical asset when in a solid state, a hardness of the
physical asset when in a solid state, the malleability of the
physical asset, the buoyancy of the physical asset, the
conductivity of the physical asset, electromagnetic properties of
the physical asset, radiation properties, optical properties (e.g.,
reflectivity, transparency, opacity, albedo, and the like), wave
interaction properties (e.g., transparency or opacity to radio
waves, reflection properties, shielding properties, or the like), a
burning point of the physical asset, the manner by which humidity
affects the physical asset, the manner by which water or other
liquids affect the physical asset, and the like. In another
example, the set of properties of a device may include a type of
the device, the dimensions of the device, the mass of the device,
the density of the density of the device, the material(s) of the
device, the physical properties of the material(s), the surface of
the device, the output of the device, the status of the device, a
location of the device, a trajectory of the device, identifiers of
other digital twins that the device is connected to and/or
contains, and the like. Examples of the behaviors of a device may
include a maximum acceleration of a device, a maximum speed of a
device, possible motions of a device, possible configurations of
the device, operating modes of the device, a heating profile of a
device, a cooling profile of a device, processes that are performed
by the device, operations that are performed by the device, and the
like. Example properties of an environment may include the
dimensions of the environment, environmental air pressure, the
temperature of the environment, the humidity of the environment,
the airflow of the environment, the physical objects in the
environment, currents of the environment (if a body of water), and
the like. Examples of behaviors of an environment may include
scientific laws that govern the environment, processes that are
performed in the environment, rules or regulations that must be
adhered to in the environment, and the like.
[0629] In embodiments, the properties of a digital twin may be
adjusted. For example, the temperature of a digital twin, a
humidity of a digital twin, the shape of a digital twin, the
material of a digital twin, the dimensions of a digital twin, or
any other suitable parameters may be adjusted to conform to current
status data and/or to a predicted status of a corresponding
entity.
[0630] In embodiments, a digital twin may be rendered by a
computing device, such that a human user can view a digital
representation of a set of physical assets, devices, or other
entities, and/or an environment thereof. For example, the digital
twin may be rendered and provided as an output, or may provide an
output, to a display device. In some embodiments, the digital twin
may be rendered and output in an augmented reality and/or virtual
reality display. For example, a user may view a 3D rendering of an
environment (e.g., using monitor or a virtual reality headset).
While doing so, the user may inspect digital twins of physical
assets or devices in the environment. In embodiments, a user may
view processes being performed with respect to one or more digital
twins (e.g., inventorying, loading, packing, shipping, and the
like). In embodiments, a user may provide input that controls one
or more properties of a digital twin via a graphical user
interface.
[0631] In some embodiments, the adaptive intelligent systems layer
614 is configured to execute simulations using the digital twin.
For example, the adaptive intelligent systems layer 614 may
iteratively adjust one or more parameters of a digital twin and/or
one or more embedded digital twins. In embodiments, the adaptive
intelligent systems layer 614 may, for each set of parameters,
execute a simulation based on the set of parameters and may collect
the simulation outcome data resulting from the simulation. Put
another way, the adaptive intelligent systems layer 614 may collect
the properties of the digital twin and the digital twins within or
containing the digital twin used during the simulation as well as
any outcomes stemming from the simulation. For example, in running
a simulation on a digital twin of a shipping container, the
adaptive intelligent systems layer 614 can vary the materials of
the shipping container and can execute simulations that outcomes
resulting from different combinations. In this example, an outcome
can be whether the goods contained in the shipping container arrive
to a destination undamaged. During the simulation, the adaptive
intelligent systems layer 614 may vary the external temperatures of
the container (e.g., a temperature property of the digital twin of
an environment of the container may be adjusted between simulations
or during a simulation), the dimensions of the container, the
products inside (represented by digital twins of the products) the
container, the motion of the container, the humidity inside the
container, and/or any other properties of the container, the
environment, and/or the contents in the container. For each
simulation instance, the adaptive intelligent systems layer 614 may
record the parameters used to perform the simulation instance and
the outcome of the simulation instance. In embodiments, each
digital twin may include, reference, or be linked to a set of
physical limitations that define the boundary conditions for a
simulation. For example, the physical limitations of a digital twin
of an outdoor environment may include a gravity constant (e.g., 9.8
m/s2), a maximum temperature (e.g., 60 degrees Celsius), a minimum
temperature (e.g., -80 degrees Celsius), a maximum humidity (e.g.,
110% humidity), friction coefficients of surfaces, maximum
velocities of objects, maximum salinity of water, maximum acidity
of water, minimum acidity of water. Additionally or alternatively,
the simulations may adhere to scientific formulas, such as ones
reflecting principles or laws of physics, chemistry, materials
science, biology, geometry, or the like. For example, a simulation
of the physical behavior of an object may adhere to the laws of
thermodynamics, laws of motion, laws of fluid dynamics, laws of
buoyancy, laws of heat transfer, laws of cooling, and the like.
Thus, when the adaptive intelligent systems layer 614 performs a
simulation, the simulation may conform to the physical limitations
and scientific laws, such that the outcomes of the simulations
mimic real world outcomes. The outcome from a simulation can be
presented to a human user, compared against real world data (e.g.,
measured properties of a container, the environment of the
container, the contents of the container, and resultant outcomes)
to ensure convergence of the digital twin with the real world,
and/or used to train machine learning models.
[0632] FIG. 38 illustrates example embodiments of a system for
controlling and/or making decisions, predictions, and/or
classification on behalf of a value chain system 2030. In
embodiments, an artificial intelligence system 2010 leverages one
or more machine-learned models 2004 to perform value chain-related
tasks on behalf of the value chain system 2030 and/or to make
decisions, classifications, and/or predictions on behalf of the
value chain system 2030. In some embodiments, a machine learning
system 2002 trains the machine learned models 2004 based on
training data 2062, outcome data 2060, and/or simulation data 2022.
As used herein, the term machine-learned model may refer to any
suitable type of model that is learned in a supervised,
unsupervised, or hybrid manner. Examples of machine-learned models
include neural networks (e.g., deep neural networks, convolution
neural networks, and many others), regression based models,
decision trees, hidden forests, Hidden Markov models, Bayesian
models, and the like. In embodiments, the artificial intelligence
system 2010 and/or the value chain system 2030 may provide outcome
data 2060 to the machine-learning system 2002 that relates to a
determination (e.g., decision, classification, prediction) made by
the artificial intelligence system 2010 based in part on the one or
more machine-learned models and the input to those models. The
machine learning system may in-turn reinforce/retrain the
machine-learned models 2004 based on the feedback. Furthermore, in
embodiments, the machine-learning system 2002 may train the
machine-learning models based on simulation data 2022 generated by
the digital twin simulation system 2020. In these embodiments, the
digital twin simulation system 2020 may be instructed to run
specific simulations using one or more digital twins that represent
objects and/or environments that are managed, maintained, and/or
monitored by the value chain system. In this way, the digital twin
simulation system 2020 may provide richer data sets that the
machine-learning system 2002 may use to train/reinforce the
machine-learned models. Additionally or alternatively, the digital
twin simulation system 2020 may be leveraged by the artificial
intelligence system 2010 to test a decision made by the artificial
intelligence system 2010 before providing the decision to the value
chain entity.
[0633] In the illustrated example, a machine learning system 2002
may receive training data 2062, outcome data 2060, and/or
simulation data 2022. In embodiments, the training data may be data
that is used to initially train a model. The training data may be
provided by a domain expert, collected from various data sources,
and/or obtained from historical records and/or scientific
experimentation. The training data 2062 may include quantified
properties of an item or environment and outcomes relating from the
quantified properties. In some embodiments, the training data may
be structured in n-tuples, whereby each tuple includes an outcome
and a respective set of properties relating to the outcome. In
embodiments, the outcome data 2060 includes real world data (e.g.,
data measured or captured from one or more of IoT sensors, value
chain entities, and/or other sources). The outcome data may include
an outcome and properties relating to the outcome. Outcome data may
be provided by the value chain system 2030 leveraging the
artificial intelligence system 2010 and/or other data sources
during operation of the value chain entity system 2010. Each time
an outcome is realized (whether negative or positive), the value
chain entity system 2010, the artificial intelligence system 2010,
as well as any other data source 2050, may output data relating to
the outcome to the machine learning system 2002. In embodiments,
this data may be provided to the machine-learning system via an API
of the adaptive intelligent systems layer 614. Furthermore, in
embodiments, the adaptive intelligent systems layer 614 may obtain
data from other types of external data sources that are not
necessarily a value chain entity but may provide insightful data.
For example, weather data, stock market data, news events, and the
like may be collected, crawled, subscribed to, or the like to
supplement the outcome data (and/or training data and/or simulation
data).
[0634] In some embodiments, the machine learning system 2002 may
receive simulation data 2022 from the digital twin simulation
system 2020. Simulation data 2022 may be any data relating to a
simulation using a digital twin. Simulation data 2022 may be
similar to outcome data 2060, but the results are simulated results
from an executed simulation rather than real-world data. In
embodiments, simulation data 2022 may include the properties of the
digital twin and any other digital twins that were used to perform
the simulation and the outcomes stemming therefrom. In embodiments,
the digital twin simulation system 2020 may iteratively adjust the
properties of a digital twin, as well as other digital twins that
are contained or contain the digital twin. During each iteration,
the digital twin simulation system 2020 may provide the properties
of the simulation (e.g., the properties of all the digital twins
involved in the simulation) to the artificial intelligence system
2010, which then outputs predictions, classifications, or any other
decisions to the digital twin simulation system 2020. The digital
twin simulation system 2020 may use the decisions from the
artificial intelligence system 2010 to execute the simulation
(which may result in a series of decisions stemming from a state
change in the simulation). At each iteration, the digital twin
simulation system 2020 may output the properties used to run the
simulation to the machine learning system 2002, any decisions from
the artificial intelligence system 2010 used by the digital twin
simulation system 2020, and outcomes from the simulation to the
machine learning system 2002, such that the properties, decisions,
and outcomes of the simulation are used to further train the
model(s) used by the artificial intelligence system during the
simulation.
[0635] In some embodiments, training data, outcome data 2060,
and/or simulation data 2022 may be fed into a data lake (e.g., a
Hadoop data lake). The machine learning system 2002 may structure
the data from the data lake. In embodiments, the machine learning
system 2002 may train/reinforce the models using the collected data
to improve the accuracy of the models (e.g., minimize the error
value of the model). The machine learning system may execute
machine-learning algorithms on the collected data (e.g., training
data, outcome data, and/or simulation data) to obtain the model.
Depending on the type of model, the machine-learning algorithm will
vary. Examples of learning algorithms/models include (e.g., deep
neural networks, convolution neural networks, and many others as
described throughout this disclosure), statistical models (e.g.,
regression-based models and many others), decision trees and other
decision models, random/hidden forests, Hidden Markov models,
Bayesian models, and the like. In collecting data from the digital
twin simulation system 2020, the machine-learning system 2002 may
train the model on scenarios not yet encountered by the value chain
system 2030. In this way, the resultant models will have less
"unexplored" feature spaces, which may lead to improved decisions
by the artificial intelligence system 2010. Furthermore, as digital
twins are based partly on assumptions, the properties of a digital
twin may be updated/corrected when a real-world behavior differs
from that of the digital twin. Examples are provided below.
[0636] FIG. 39 illustrates an example of a container fleet
management system 2070 that interfaces with the adaptive
intelligent systems layer 614. In example embodiments, a container
fleet management system 2070 may be configured to automate one or
more aspects of the value chain as it applies to containers and
shipping. In embodiments, the container fleet management system
2070 may be include one or more software modules that are executed
by one or more server devices. These software modules may be
configured to select containers to use (e.g., a size of container,
the type of the container, the provider of the container, etc.) for
a set of one or more shipments, schedule delivery/pickup of
container, selection of shipping routes, determining the type of
storage for a container (e.g., outdoor or indoor), select a
location of each container while awaiting shipping, manage bills of
lading and/or other suitable container fleet management tasks. In
embodiments, the machine-learning system 2002 trains one or more
models that are leveraged by the artificial intelligence system
2010 to make classifications, predictions, and/or other decisions
relating to container fleet management. In example embodiments, a
model 2004 is trained to select types of containers given one or
more task-related features to maximize the likelihood of a desired
outcome (e.g., that the contents of the container arrive in a
timely manner with minimal loss at the lowest possible cost). As
such, the machine-learning system 2002 may train the models using
n-tuples that include the task-related features pertaining to a
particular event and one or more outcomes associated with the
particular event. In this example, task-related features for a
particular event (e.g., a shipment) may include, but are not
limited to, the type of container used, the contents of the
container, properties of the container contents (e.g., cost,
perishability, temperature restrictions, and the like), the source
and destination of the container, whether the container is being
shipped via truck, rail, or ship, the time of year, the cost of
each container, and/or other relevant features. In this example,
outcomes relating to the particular event may include whether the
contents arrived safely, replacement costs (if any) associated with
any damage or loss, total shipping time, and/or total cost of
shipment (e.g., how much it cost to ship container). Furthermore,
as international and/or interstate logistics may include many
different sources, destinations, contents, weather conditions, and
the like, simulations that simulate different shipping events may
be run to richen the data used to train the model. For instance,
simulations may be run for different combinations of ports and/or
train depots for different combinations of sources, destinations,
products, and times of year. In this example, different digital
twins may be generated to represent the different combinations
(e.g., digital twins of products, containers, and shipping-related
environments), whereby one or more properties of the digital twins
are varied for different simulations and the outcomes of each
simulation may be recorded in a tuple with the proprieties. In this
way, the model may be trained on certain combinations of routes,
contents, time of year, container type, and/or cost that may not
have been previously encountered in the real-world outcome data.
Other examples of training a container fleet management model may
include a model that is trained to determine where a container
should be stored in a storage facility (e.g., where in a stack,
indoors or outdoors, and/or the like) given the contents of the
container, when the container needs to be moved, the type of
container, the location, the time of year, and the like.
[0637] In operation, the artificial intelligence system 2010 may
use the above-discussed models 2004 to make container fleet
management decisions on behalf of a container fleet management
system 2070 given one or more features relating to a task or event.
For example, the artificial intelligence system 2010 may select a
type of container (e.g., materials of the container, the dimensions
of the container, the brand of the container, and the like) to use
for a particular shipment. In this example, the container fleet
management system 2070 may provide the features of an upcoming
shipment to the artificial intelligence system 2010. These features
may include what is being shipped (e.g., the type(s) of goods in
the shipment), the size of the shipment, the source and
destination, the date when the shipment is to be sent off, and/or
the desired date or range of dates for delivery. In embodiments,
the artificial intelligence system 2010 may feed these features
into one or more of the models discussed above to obtain one or
more decisions. These decisions may include which type of container
to use and/or which shipping routes to use, whereby the decisions
may be selected to minimize overall shipping costs (e.g., costs for
container and transit+any replacement costs). The container fleet
management system 2070 may then initiate the shipping event using
the decision(s) made by the artificial intelligence system 2010.
Furthermore, after the shipping event, the outcomes of the event
(e.g., total shipping time, any reported damages or loss,
replacement costs, total costs) may be reported to the
machine-learning system 2002 to reinforce the models used to make
the decisions. Furthermore, in some embodiments, the output of the
container fleet management system 2070 and/or the other value chain
entity data sources 2050 may be used to update one or more
properties of one or more digital twins via the digital twin system
2020.
[0638] FIG. 40 illustrates an example of a logistics design system
that interfaces with the adaptive intelligent systems layer 614. In
embodiments, a logistics design system may be configured to design
one or more aspects of a logistics solution. For example, the
logistics design system may be configured to receive one or more
logistics factors (e.g., from a user via a GUI). In embodiments,
logistics factors may include one or more present conditions,
historical conditions, or future conditions of an organization (or
potential organization) that are relevant to forming a logistics
solution. Examples of logistics factors may include, but are not
limited to the type(s) of products being produced/farmed/shipped,
features of those products (e.g., dimensions, weights, shipping
requirements, shelf life, etc.), locations of manufacturing sites,
locations of distribution facilities, locations of warehouses,
locations of customer bases, market penetration in certain areas,
expansion locations, supply chain features (e.g., required
parts/supplies/resources, suppliers, supplier locations, buyers,
buyer locations), and/or the like) and may determine one or more
design recommendations based on the factors. Examples of design
recommendations may include supply chain recommendations (e.g.,
proposed suppliers (e.g., resource or parts suppliers),
implementations of a smart inventory systems that order on-demand
parts from available suppliers, and the like), storage and
transport recommendations (e.g., proposed shipping routes, proposed
shipping types (e.g., air, freight, truck, ship), proposed storage
development (e.g., locations and/or dimensions of new warehouses),
infrastructure recommendations (e.g., updates to machinery, adding
cooled storage, adding heated storage, or the like), and
combinations thereof. In embodiments, the logistics design system
determines the recommendations to optimize an outcome. Examples of
outcomes can include manufacturing times, manufacturing costs,
shipping times, shipping costs, loss rate, environmental impact,
compliance to a set of rules/regulations, and the like. Examples of
optimizations include increased production throughput, reduced
production costs, reduced shipping costs, decreased shipping times,
reduced carbon footprint, and combinations thereof.
[0639] In embodiments, the logistics design system may interface
with the artificial intelligence system 2010 to provide the
logistics factors and to receive design recommendations that are
based thereon. In embodiments, the artificial intelligence system
2010 may leverage one or more machine-learned models 2004 (e.g.,
logistics design recommendations models) to determine a
recommendation. As will be discussed, a logistics design
recommendation model may be trained to optimize one or more
outcomes given a set of logistics factors. For example, a logistics
design recommendation model trained to design supply chains may
identify a set of suppliers that can supply a given manufacturer,
the location of the manufacturer, the supplies needed, and/or other
factors. The set of suppliers may then be used to implement an
on-demand supply side inventory. In another example, the logistics
design recommendation may take the same features of another
manufacturer and recommend the purchase and use of one or more 3D
printers.
[0640] In embodiments, the artificial intelligence system 2010 may
leverage the digital twin system 2020 to generate a digital twin of
a logistics system that implements the logistics design
recommendation (and, in some embodiments, alternative systems that
implement other design recommendations). In these embodiments, the
digital twin system 2010 may receive the design recommendations and
may generate a digital twin of a logistics environment that mirrors
the recommendations. In embodiments, the artificial intelligence
system 2010 may leverage the digital twin of the logistics
environment to run simulations on the proposed solution. In
embodiments, the digital twin system 2010 may display the digital
twin of the logistics environment to a user via a display device
(e.g., a monitor or a VR headset). In embodiments, the user may
view the simulations in the digital twin. Furthermore, in
embodiments, the digital twin system 2010 may provide a graphical
user interface that the user may interact with to adjust the design
of the logistics environment to adjust the design. The design
provided (at least in part) by a user may also be represented in a
digital twin of a logistics environment, whereby the digital twin
system 2020 may perform simulations using the digital twin.
[0641] In some embodiments, the simulations run by the digital twin
system 2010 may be used to train the recommendation models.
Furthermore, when the design recommendations are implemented by an
organization, the logistics system of the organization may be
configured to report (e.g., via sensors, computing devices, manual
human input) outcome data corresponding to the design
recommendations to the machine learning system 2002, which may use
the outcome data to reinforce the logistics design recommendation
models.
[0642] FIG. 41 illustrates an example of a packaging design system
that interfaces with the adaptive intelligent systems layer 614. In
embodiments, the packaging design system may be configured to
design one or more aspects of packaging for a physical object being
conveyed in the value chain network. In some embodiments, the
packaging design system may select one or more packaging attributes
(e.g., size, material, padding, etc.) of the packaging to optimize
one or more outcomes associated with the transport of the physical
object. For example, the packaging attributes may be selected to
reduce costs, decrease loss/damage, decrease weight, decrease
plastic or other non-biodegradable waste, or the like. In
embodiments, the packaging design system leverages the artificial
intelligence system 2010 to obtain packaging attribute
recommendations. In embodiments, the packaging design system may
provide one or more features of the physical object. In
embodiments, the features of the physical object may include the
dimensions of the physical object, the mass of the physical object,
the source of the physical object, one or more potential
destinations of the physical object, the manner by which the
physical object is shipped, and the like. In embodiments, the
packaging design system may further provide one or more
optimization goals for the package design (e.g., reduce cost,
reduce damage, reduce environmental impact). In response, the
artificial intelligence system 2010 may determine one or more
recommended packaging attributes based on the physical asset
features and the given objective. In embodiments, the packaging
design system receives the packaging attributes and generates a
package design based thereon. The package design may include a
material to be used, the external dimensions of the packaging, the
internal dimensions of the packaging, the shape of the packaging,
the padding/stuffing for the packaging, and the like.
[0643] In some embodiments, the packaging design system may provide
a packaging design to the digital twin system 2020, which generates
a digital twin of the packaging and physical asset based on the
packaging design. The digital twin of the packaging and physical
asset may be used to run simulations that test the packaging (e.g.,
whether the packaging holds up in shipping, whether the packaging
provides adequate insulation/padding, and the like). In
embodiments, the results of the simulation may be returned to the
packaging design system, which may output the results to a user. In
embodiments, the user may accept the packaging design, may adjust
the packaging design, or may reject the design. In some
embodiments, the digital twin system may run simulations on one or
more digital twins to test different conditions that the package
may be subjected to (e.g., outside in the snow, rocking in a boat,
being moved by a forklift, or the like). In some embodiments, the
digital twin system may output the results of a simulation to the
machine-learning system 2002, which can train/reinforce the
packaging design models based on the properties used to run the
simulation and the outcomes stemming therefrom.
[0644] In embodiments, the machine-learning system 2002 may receive
outcome data from the packaging design system and/or other value
chain entity data sources (e.g., smart warehouses, user feedback,
and the like). The machine-learning system 2002 may use this
outcome data to train/reinforce the packaging design models.
Furthermore, in some embodiments, the outcome data may be used by
the digital twin system 2020 to update/correct any incorrect
assumptions used by the digital twin system (e.g., the flexibility
of a packaging material, the water resistance of a packaging
material, and the like).
[0645] FIG. 42 illustrates examples of a waste mitigation system
that interfaces with the adaptive intelligent systems layer 614. In
embodiments, the waste mitigation system is configured to analyze a
process within the value chain (e.g., manufacturing of a product,
oil refining, fertilization, water treatment, or the like) to
mitigate waste (e.g., solid waste, wastewater, discarded packaging,
wasted energy, wasted time, wasted resources, or other waste). In
embodiments, the waste mitigation system may interface with the
artificial intelligence system 2010 to automate one or more
processes to mitigate waste.
[0646] In embodiments, the artificial intelligence system 2010 may
provide control decisions to the waste mitigation system to
mitigate solid waste production. Examples of waste production may
include excess plastic or other non-biodegradable waste, hazardous
or toxic waste (e.g., nuclear waste, petroleum coke, or the like),
and the like. In some of these embodiments, the artificial
intelligence system 2010 may receive one or more features of the
process (or "process features"). Examples of process features may
include, but are not limited to, the steps in the process, the
materials being used, the properties of the materials being used,
and the like. The artificial intelligence system 2010 may leverage
one or more machine-learned models to control the process.
[0647] In embodiments, the machine-learned models may be trained to
classify a waste condition and/or the cause of the waste condition.
In some of these embodiments, the artificial intelligence system
2010 may determine or select a waste mitigation solution based on
the classified waste condition. For example, in some embodiments,
the artificial intelligence system 2010 may apply rules-based logic
to determine an adjustment to make to the process to reduce or
resolve the waste condition. Additionally, or alternatively, the
artificial intelligence may leverage a model that recommends an
adjustment to make to the process to reduce or resolve the waste
condition.
[0648] In embodiments, the artificial intelligence system 2010 may
leverage the digital twin system 2020 to mitigate the waste
produced by a process. In embodiments, the digital twin system 2020
may execute iterative simulations of the process in a digital twin
of the environment in which the process is performed. When the
simulation is executed, the artificial intelligence system 2010 may
monitor the results of the simulation to determine a waste
condition and/or the cause of the waste condition. During the
simulations, the artificial intelligence system 2010 may adjust one
or more aspects of the process to determine whether the adjustments
mitigated the waste condition, worsened the waste condition, or had
no effect. When an adjustment is found to mitigate the waste
condition, the artificial intelligence system 2010 may adjust other
aspects of the process to determine if an improvement can be
realized. In embodiments, the artificial intelligence system 2010
may perform a genetic algorithm when iteratively adjusting the
aspects of the process in the digital twin simulations. In these
embodiments, the artificial intelligence system 2010 may identify
aspects of the process that can be adjusted to mitigate the waste
production.
Smart Project Management Facilities
[0649] Referring to FIG. 43, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
624, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 614 (including artificial intelligence
1160), a set of data storage facilities or systems 624, and a set
of monitoring facilities or systems 614. The platform 604 may
support a set of applications 630 (including processes, workflows,
activities, events, use cases and applications) for enabling an
enterprise to manage a set of value chain network entities 652,
such as from a point of origin to a point of customer use of a
product 650, which may be an intelligent product.
[0650] In embodiments, the adaptive intelligence systems layer 614
may further include a set of automated project management
facilities MPVC1102 that provide automated recommendations for a
set of value chain project management tasks based on processing
current status information, a set of application outputs and/or a
set of outcomes 1040 for a set of demand management applications
1502, a set of supply chain applications 1500, a set of intelligent
product applications 1510, a set of asset management applications
1530 and a set of enterprise resource management applications 1520
for a category of goods.
[0651] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of project management facilities that
provide automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[0652] In embodiments, the set of project management facilities are
configured to manage a wide variety of types of projects, such as
procurement projects, logistics projects, reverse logistics
projects, fulfillment projects, distribution projects, warehousing
projects, inventory management projects, product design projects,
product management projects, shipping projects, maritime projects,
loading or unloading projects, packing projects, purchasing
projects, marketing projects, sales projects, analytics projects,
demand management projects, demand planning projects, resource
planning projects and many others.
[0653] In embodiments, the project management facilities are
configured to manage a set of procurement projects. In embodiments,
the project management facilities are configured to manage a set of
logistics projects. In embodiments, the project management
facilities are configured to manage a set of reverse logistics
projects. In embodiments, the project management facilities are
configured to manage a set of fulfillment projects.
[0654] In embodiments, the project management facilities are
configured to manage a set of distribution projects. In
embodiments, the project management facilities are configured to
manage a set of warehousing projects. In embodiments, the project
management facilities are configured to manage a set of inventory
management projects. In embodiments, the project management
facilities are configured to manage a set of product design
projects.
[0655] In embodiments, the project management facilities are
configured to manage a set of product management projects. In
embodiments, the project management facilities are configured to
manage a set of shipping projects. In embodiments, the project
management facilities are configured to manage a set of maritime
projects. In embodiments, the project management facilities are
configured to manage a set of loading or unloading projects.
[0656] In embodiments, the project management facilities are
configured to manage a set of packing projects. In embodiments, the
project management facilities are configured to manage a set of
purchasing projects. In embodiments, the project management
facilities are configured to manage a set of marketing projects. In
embodiments, the project management facilities are configured to
manage a set of sales projects.
[0657] In embodiments, the project management facilities are
configured to manage a set of analytics projects. In embodiments,
the project management facilities are configured to manage a set of
demand management projects. In embodiments, the project management
facilities are configured to manage a set of demand planning
projects. In embodiments, the project management facilities are
configured to manage a set of resource planning projects.
[0658] Smart Task Recommendations
[0659] Referring to FIG. 282, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
624, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 614 (including artificial intelligence
1160), a set of data storage facilities or systems 624, and a set
of monitoring facilities or systems 614.
[0660] The platform 604 may support a set of applications 630
(including processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0661] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of project management facilities that
provide automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[0662] In embodiments, the adaptive intelligent systems layer 614
may further include a set of process automation facilities 14402
that provide automated recommendations for a set of value chain
process tasks MPVC1102 that provide automated recommendations for a
set of value chain processes based on processing current status
information, a set of application outputs and/or a set of outcomes
1040 for a set of demand management applications 1502, a set of
supply chain applications 1500, a set of intelligent product
applications 1510, a set of asset management applications 1530 and
a set of enterprise resource management applications 1520 for a
category of goods. In some examples, the process automation
facilities 14402 may be used with basic rule-based training and
recommendations. This may relate to following a set of rules that
an expert has articulated such as when a trigger occurs, undertake
a task. In another example, the process automation facilities 14402
may utilize deep learning to observe interactions such as deep
learning on outcomes to learn to recommend decisions or tasks that
produce a highest return on investment (ROI) or other outcome-based
yield. The process automation facilities 14402 may be used to
provide collaborative filtering such as look at a set of experts
that are most similar in terms of work done and tasks completed
being most similar. For example, the underlying software may be
used to find customers similar to another set of customers to sell
to, make a different offering to, or change price accordingly. In
general, given a set of underlying pattern data, contextually,
about a customer segment, purchasing patterns may be determined for
that customer segment such as knowledge of cost and pricing
patterns for that customer. This information may be used to learn
to focus a next set of activities around pricing, promotion, demand
management towards an ideal that may be based on deep learning or
rules or collaborative filtering type work trying to leverage off
of similar decisions made by similarly situated people (e.g.,
recommending movies to a similar cohort of people).
[0663] In embodiments, the set of facilities that provide automated
recommendations for a set of value chain process tasks provide
recommendations involving a wide range of types of activities, such
as, without limitation, product configuration activities, product
selection activities for a customer, supplier selection activities,
shipper selection activities, route selection activities, factory
selection activities, product assortment activities, product
management activities, logistics activities, reverse logistics
activities, artificial intelligence configuration activities,
maintenance activities, product support activities, product
recommendation activities and many others.
[0664] In embodiments, the automated recommendations relate to a
set of product configuration activities. In embodiments, the
automated recommendations relate to a set of product selection
activities for a customer. In embodiments, the automated
recommendations relate to a set of supplier selection activities.
In embodiments, the automated recommendations relate to a set of
shipper selection activities.
[0665] In embodiments, the automated recommendations relate to a
set of route selection activities. In embodiments, the automated
recommendations relate to a set of factory selection activities. In
embodiments, the automated recommendations relate to a set of
product assortment activities. In embodiments, the automated
recommendations relate to a set of product management activities.
In embodiments, the automated recommendations relate to a set of
logistics activities.
[0666] In embodiments, the automated recommendations relate to a
set of reverse logistics activities. In embodiments, the automated
recommendations relate to a set of artificial intelligence
configuration activities. In embodiments, the automated
recommendations relate to a set of maintenance activities. In
embodiments, the automated recommendations relate to a set of
product support activities. In embodiments, the automated
recommendations relate to a set of product recommendation
activities.
[0667] Optimized Routing Among Nodes
[0668] Referring to FIG. 44, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
624, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 614 (including artificial intelligence
1160), a set of data storage facilities or systems 624, and a set
of monitoring facilities or systems 614. The platform 604 may
support a set of applications 630 (including processes, workflows,
activities, events, use cases and applications) for enabling an
enterprise to manage a set of value chain network entities 652,
such as from a point of origin to a point of customer use of a
product 650, which may be an intelligent product.
[0669] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform for a value chain
network with a micro-services architecture, a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities that are
coordinated for monitoring and management of a set of value chain
network entities; and a set of applications for enabling an
enterprise to manage a set of value chain network entities from a
point of origin to a point of customer use; wherein a set of
routing facilities generate a set of routing instructions for
routing information among a set of nodes in the value chain network
based on current status information for the value chain
network.
[0670] In embodiments, the adaptive intelligent systems layer 614
may further include a set of routing facilities 1720 that generate
a set of routing instructions for routing information among a set
of nodes in the value chain network, such as based on processing
current status information 1730, a set of application outputs
and/or a set of outcomes 1040, or other information collected by or
used in the VCNP 102. Routing may include routing for the benefit
of a set of demand management applications 1502, a set of supply
chain applications 1500, a set of intelligent product applications
1510, a set of asset management applications 1530 and a set of
enterprise resource management applications 1520 for a category of
goods.
[0671] In embodiments, the set of routing facilities that generate
a set of routing instructions for routing information among a set
of nodes in the value chain network use a wide variety of routing
systems or configurations, such as involving, without limitation,
priority-based routing, master controller routing, least cost
routing, rule-based routing, genetically programmed routing, random
linear network coding routing, traffic-based routing,
spectrum-based routing, RF condition-based routing, energy-based
routing, latency-sensitive routing, protocol compatibility based
routing, dynamic spectrum access routing, peer-to-peer negotiated
routing, queue-based routing, and others.
[0672] In embodiments, the routing includes priority-based routing.
In embodiments, the routing includes master controller routing. In
embodiments, the routing includes least cost routing. In
embodiments, the routing includes rule-based routing. In
embodiments, the routing includes genetically programmed
routing.
[0673] In embodiments, the routing includes random linear network
coding routing. In embodiments, the routing includes traffic-based
routing. In embodiments, the routing includes spectrum-based
routing.
[0674] In embodiments, the routing includes RF condition-based
routing. In embodiments, the routing includes energy-based routing.
In embodiments, the routing includes latency-sensitive routing.
[0675] In embodiments, the routing includes protocol
compatibility-based routing.
[0676] In embodiments, the routing includes dynamic spectrum access
routing. In embodiments, the routing includes peer-to-peer
negotiated routing. In embodiments, the routing includes
queue-based routing.
[0677] In embodiments, the status information for the value chain
network involves a wide range of states, events, workflows,
activities, occurrences, or the like, such as, without limitation,
traffic status, congestion status, bandwidth status, operating
status, workflow progress status, incident status, damage status,
safety status, power availability status, worker status, data
availability status, predicted system status, shipment location
status, shipment timing status, delivery status, anticipated
delivery status, environmental condition status, system diagnostic
status, system fault status, cybersecurity status, compliance
status, demand status, supply status, price status, volatility
status, need status, interest status, aggregate status for a group
or population, individual status, and many others.
[0678] In embodiments, the status information involves traffic
status. In embodiments, the status information involves congestion
status. In embodiments, the status information involves bandwidth
status. In embodiments, the status information involves operating
status. In embodiments, the status information involves workflow
progress status.
[0679] In embodiments, the status information involves incident
status. In embodiments, the status information involves damage
status. In embodiments, the status information involves safety
status.
[0680] In embodiments, the status information involves power
availability status. In embodiments, the status information
involves worker status. In embodiments, the status information
involves data availability status.
[0681] In embodiments, the status information involves predicted
system status. In embodiments, the status information involves
shipment location status. In embodiments, the status information
involves shipment timing status. In embodiments, the status
information involves delivery status.
[0682] In embodiments, the status information involves anticipated
delivery status. In embodiments, the status information involves
environmental condition status.
[0683] In embodiments, the status information involves system
diagnostic status. In embodiments, the status information involves
system fault status. In embodiments, the status information
involves cybersecurity status. In embodiments, the status
information involves compliance status.
Dashboard for Managing Digital Twins
[0684] Referring to FIG. 14, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
624, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 614 (including artificial intelligence
1160), a set of data storage facilities or systems 624, and a set
of monitoring facilities or systems 614. The platform 604 may
support a set of applications 630 (including processes, workflows,
activities, events, use cases and applications) for enabling an
enterprise to manage a set of value chain network entities 652,
such as from a point of origin to a point of customer use of a
product 650, which may be an intelligent product.
[0685] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows.
[0686] In embodiments, the VCNP 604 may further include a dashboard
1740 for managing a set of digital twins 1700. In embodiments, this
may include different twins, such as where one digital twin 1700
represents a set of supply chain entities, workflows and assets and
another digital twin 1700 represents a set of demand management
entities and workflows. In some example embodiments, managing a set
of digital twins 1700 may refer to configuration (e.g., via the
dashboard 1740) as described in the disclosure. For example, the
digital twin 1700 may be configured through use of a digital twin
configuration system to set up and manage the enterprise digital
twins and associated metadata of an enterprise, to configure the
data structures and data listening threads that power the
enterprise digital twins, and to configure features of the
enterprise digital twins, including access features, processing
features, automation features, reporting features, and the like,
each of which may be affected by the type of enterprise digital
twin (e.g., based on the role(s) that it serves, the entities it
depicts, the workflows that it supports or enables and the like).
In example embodiments, the digital twin configuration system may
receive the types of digital twins that may be supported for the
enterprise, as well as the different objects, entities, and/or
states that are to be depicted in each type of digital twin. For
each type of digital twin, the digital twin configuration system
may determine one or more data sources and types of data that feed
or otherwise support each object, entity, or state that is depicted
in the respective type of digital twin and may determine any
internal or external software requests (e.g., API calls) that
obtain the identified data types or other suitable data
acquisitions mechanisms, such as webhooks, that may configured to
automatically receive data from an internal or external data source
In some embodiments, the digital twin configuration system may
determine internal and/or external software requests that support
the identified data types by analyzing the relationships between
the different types of data that correspond to a particular
state/entity/object and the granularity thereof. Additionally or
alternatively, a user may define (e.g., via a GUI) the data sources
and/or software requests and/or other data acquisition mechanisms
that support the respective data types that are depicted in a
respective digital twin. In these example embodiments, the user may
indicate the data source that may be accessed and the types of data
to be obtained from the respective data source.
[0687] The dashboard may be used to configure the digital twins
1700 for use in collection, processing, and/or representation of
information collected in the platform 604, such as status
information 1730, such as for the benefit of a set of demand
management applications 1502, a set of supply chain applications
1500, a set of intelligent product applications 1510, a set of
asset management applications 1530 and a set of enterprise resource
management applications 1520 for a category of goods.
[0688] In embodiments, the dashboard for managing a set of digital
twins, wherein at least one digital twin represents a set of supply
chain entities and workflows and at least one other digital twin
represents a set of demand management entities and workflows.
[0689] In embodiments, the entities and workflows relate to a set
of products of an enterprise. In embodiments, the entities and
workflows relate to a set of suppliers of an enterprise. In
embodiments, the entities and workflows relate to a set of
producers of a set of products. In embodiments, the entities and
workflows relate to a set of manufacturers of a set of
products.
[0690] In embodiments, the entities and workflows relate to a set
of retailers of a line of products. In embodiments, the entities
and workflows relate to a set of businesses involved in an
ecosystem for a category of products. In embodiments, the entities
and workflows relate to a set of owners of a set of assets involved
in a value chain for a set of products. In embodiments, the
entities and workflows relate to a set of operators of a set of
assets involved in a value chain for a set of products.
[0691] In embodiments, the entities and workflows relate to a set
of operating facilities. In embodiments, the entities and workflows
relate to a set of customers. In embodiments, the entities and
workflows relate to a set of consumers. In embodiments, the
entities and workflows relate to a set of workers.
[0692] In embodiments, the entities and workflows relate to a set
of mobile devices. In embodiments, the entities and workflows
relate to a set of wearable devices. In embodiments, the entities
and workflows relate to a set of distributors. In embodiments, the
entities and workflows relate to a set of resellers.
[0693] In embodiments, the entities and workflows relate to a set
of supply chain infrastructure facilities. In embodiments, the
entities and workflows relate to a set of supply chain processes.
In embodiments, the entities and workflows relate to a set of
logistics processes. In embodiments, the entities and workflows
relate to a set of reverse logistics processes.
[0694] In embodiments, the entities and workflows relate to a set
of demand prediction processes. In embodiments, the entities and
workflows relate to a set of demand management processes. In
embodiments, the entities and workflows relate to a set of demand
aggregation processes. In embodiments, the entities and workflows
relate to a set of machines.
[0695] In embodiments, the entities and workflows relate to a set
of ships. In embodiments, the entities and workflows relate to a
set of barges. In embodiments, the entities and workflows relate to
a set of warehouses. In embodiments, the entities and workflows
relate to a set of maritime ports.
[0696] In embodiments, the entities and workflows relate to a set
of airports. In embodiments, the entities and workflows relate to a
set of airways. In embodiments, the entities and workflows relate
to a set of waterways. In embodiments, the entities and workflows
relate to a set of roadways.
[0697] In embodiments, the entities and workflows relate to a set
of railways. In embodiments, the entities and workflows relate to a
set of bridges. In embodiments, the entities and workflows relate
to a set of tunnels. In embodiments, the entities and workflows
relate to a set of online retailers.
[0698] In embodiments, the entities and workflows relate to a set
of ecommerce sites. In embodiments, the entities and workflows
relate to a set of demand factors. In embodiments, the entities and
workflows relate to a set of supply factors. In embodiments, the
entities and workflows relate to a set of delivery systems.
[0699] In embodiments, the entities and workflows relate to a set
of floating assets. In embodiments, the entities and workflows
relate to a set of points of origin. In embodiments, the entities
and workflows relate to a set of points of destination. In
embodiments, the entities and workflows relate to a set of points
of storage.
[0700] In embodiments, the entities and workflows relate to a set
of points of product usage. In embodiments, the entities and
workflows relate to a set of networks. In embodiments, the entities
and workflows relate to a set of information technology systems. In
embodiments, the entities and workflows relate to a set of software
platforms.
[0701] In embodiments, the entities and workflows relate to a set
of distribution centers. In embodiments, the entities and workflows
relate to a set of fulfillment centers. In embodiments, the
entities and workflows relate to a set of containers. In
embodiments, the entities and workflows relate to a set of
container handling facilities.
[0702] In embodiments, the entities and workflows relate to a set
of customs. In embodiments, the entities and workflows relate to a
set of export control. In embodiments, the entities and workflows
relate to a set of border control. In embodiments, the entities and
workflows relate to a set of drones.
[0703] In embodiments, the entities and workflows relate to a set
of robots. In embodiments, the entities and workflows relate to a
set of autonomous vehicles. In embodiments, the entities and
workflows relate to a set of hauling facilities. In embodiments,
the entities and workflows relate to a set of drones, robots and
autonomous vehicles. In embodiments, the entities and workflows
relate to a set of waterways. In embodiments, the entities and
workflows relate to a set of port infrastructure facilities.
[0704] In embodiments, the set of digital twins may include, for
example and without limitation, distribution twins, warehousing
twins, port infrastructure twins, shipping facility twins,
operating facility twins, customer twins, worker twins, wearable
device twins, portable device twins, mobile device twins, process
twins, machine twins, asset twins, product twins, point of origin
twins, point of destination twins, supply factor twins, maritime
facility twins, floating asset twins, shipyard twins, fulfillment
twins, delivery system twins, demand factors twins, retailer twins,
ecommerce twins, online twins, waterway twins, roadway twins,
roadway twins, railway twins, air facility twins, aircraft twins,
ship twins, vehicle twins, train twins, autonomous vehicle twins,
robotic system twins, drone twins, logistics factor twins and many
others.
Microservices Architecture
[0705] Referring to FIG. 15, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
624, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 614, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0706] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
applications of the application layer use a common set of services
among a set of data processing services, data collection services,
and data storage services.
[0707] In embodiments, the VCNP 604 may further include a set of
microservices layers including an application layer supporting at
least two applications among a set of demand management
applications 1502, a set of supply chain applications 1500, a set
of intelligent product applications 1510, a set of asset management
applications 1530 and a set of enterprise resource management
applications 1520 for a category of goods.
[0708] A microservices architecture provides several advantages to
the platform 604. For example, one advantage may be the ability to
leverage creation of improved microservices created by others such
that developer may only need to define inputs and outputs such that
the platform may use readily adapted services created by others.
Also, use of the microservices architecture may provide ability to
modularize microservices into collections that may be used to
achieve tasks. For example, a goal to determine what is happening
in a warehouse may be achieved with a variety of microservices with
minimal cost such as vision-based service, series of regular
prompts that may ask and receive, reading off of event logs or
feeds, and the like. Each one of these microservices may be a
distinct microservice that may be easily plugged in and used. If a
particular microservice does not work effectively, the microservice
may be replaced easily with another service with minimal impact to
other components in the platform. Other microservices that may be
used include recommendation service, collaborative filtering
service, deep learning with semi-supervised learning service, etc.
The microservice architecture may provide modularity at each stage
in building a full workflow. In an example embodiment, a
microservice may be built for multiple applications that may be
consumed including shared data steam and anything else enabled by
the microservices architecture.
IoT Data Collection Architecture Recommendation of Other Sensors
and Cameras
[0709] Referring to FIG. 16, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0710] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of Internet of Things resources that collect
information with respect to supply chain entities and demand
management entities.
[0711] Also provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a machine learning/artificial intelligence system
configured to generate recommendations for placing an additional
sensor/and or camera on and/or in proximity to a value chain entity
and wherein data from the additional sensor and/or camera feeds
into a digital twin that represents a set of value chain
entities.
[0712] In embodiments, the VCNP 604 may further include a set of
microservices, wherein the microservice layers include a monitoring
systems and data collections systems layer 614 having data
collection and management systems 640 that collect information from
a set of Internet of Things resources 1172 that collect information
with respect to supply chain entities and demand management
entities 652. The microservices may support various applications
among a set of demand management applications 1502, a set of supply
chain applications 1500, a set of intelligent product applications
1510, a set of asset management applications 1530 and a set of
enterprise resource management applications 1520 for a category of
goods.
[0713] In embodiments, the platform 604 may further include a
machine learning/artificial intelligence system 1160 that includes
a sensor recommendation system 1750 that is configured to generate
recommendations for placing an additional sensor 1462 and/or camera
on and/or in proximity to a value chain network entity 652. For
example, in some embodiments, the sensor recommendation system 1750
may generate recommendations by using load, array of signals,
emergent situations, frequency response, maintenance, diagnosis,
etc. Data from the additional sensor 1462 and/or camera may feed
into a digital twin 1700 that represents a set of value chain
entities 652. In embodiments, the set of Internet of Things
resources that collect information with respect to supply chain
entities and demand management entities collects information from
entities of any of the types described throughout this disclosure
and in the documents incorporated by reference herein.
[0714] In embodiments, the set of Internet of Things resources may
be of a wide variety of types such as, without limitation, camera
systems, lighting systems, motion sensing systems, weighing
systems, inspection systems, machine vision systems, environmental
sensor systems, onboard sensor systems, onboard diagnostic systems,
environmental control systems, sensor-enabled network switching and
routing systems, RF sensing systems, magnetic sensing systems,
pressure monitoring systems, vibration monitoring systems,
temperature monitoring systems, heat flow monitoring systems,
biological measurement systems, chemical measurement systems,
ultrasonic monitoring systems, radiography systems, LIDAR-based
monitoring systems, access control systems, penetrating wave
sensing systems, SONAR-based monitoring systems, radar-based
monitoring systems, computed tomography systems, magnetic resonance
imaging systems, network monitoring systems, or others.
[0715] In embodiments, the set of Internet of Things resources
includes a set of camera systems. In embodiments, the set of
Internet of Things resources includes a set of lighting systems. In
embodiments, the set of Internet of Things resources includes a set
of machine vision systems. In embodiments, the set of Internet of
Things resources includes a set of motion sensing systems.
[0716] In embodiments, the set of Internet of Things resources
includes a set of weighing systems. In embodiments, the set of
Internet of Things resources includes a set of inspection systems.
In embodiments, the set of Internet of Things resources includes a
set of environmental sensor systems. In embodiments, the set of
Internet of Things resources includes a set of onboard sensor
systems.
[0717] In embodiments, the set of Internet of Things resources
includes a set of onboard diagnostic systems. In embodiments, the
set of Internet of Things resources includes a set of environmental
control systems. In embodiments, the set of Internet of Things
resources includes a set of sensor-enabled network switching and
routing systems. In embodiments, the set of Internet of Things
resources includes a set of RF sensing systems. In embodiments, the
set of Internet of Things resources includes a set of magnetic
sensing systems.
[0718] In embodiments, the set of Internet of Things resources
includes a set of pressure monitoring systems. In embodiments, the
set of Internet of Things resources includes a set of vibration
monitoring systems. In embodiments, the set of Internet of Things
resources includes a set of temperature monitoring systems. In
embodiments, the set of Internet of Things resources includes a set
of heat flow monitoring systems. In embodiments, the set of
Internet of Things resources includes a set of biological
measurement systems.
[0719] In embodiments, the set of Internet of Things resources
includes a set of chemical measurement systems. In embodiments, the
set of Internet of Things resources includes a set of ultrasonic
monitoring systems. In embodiments, the set of Internet of Things
resources includes a set of radiography systems. In embodiments,
the set of Internet of Things resources includes a set of
LIDAR-based monitoring systems. In embodiments, the set of Internet
of Things resources includes a set of access control systems.
[0720] In embodiments, the set of Internet of Things resources
includes a set of penetrating wave sensing systems. In embodiments,
the set of Internet of Things resources includes a set of
SONAR-based monitoring systems. In embodiments, the set of Internet
of Things resources includes a set of radar-based monitoring
systems. In embodiments, the set of Internet of Things resources
includes a set of computed tomography systems. In embodiments, the
set of Internet of Things resources includes a set of magnetic
resonance imaging systems. In embodiments, the set of Internet of
Things resources includes a set of network monitoring systems.
Social Data Collection Architecture
[0721] Referring to FIG. 17, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0722] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities.
[0723] In embodiments, the VCNP 604 may further include a set of
microservices layers that include a data collection layer (e.g.,
monitoring systems and data collection systems layer 614) with a
social data collection facility 1760 that collects information from
a set of social network resources MPVC1708 that provide information
with respect to supply chain entities and demand management
entities. The social network data collection facilities 1760 may
support various applications among a set of demand management
applications 1502, a set of supply chain applications 1500, a set
of intelligent product applications 1510, a set of asset management
applications 1530 and a set of enterprise resource management
applications 1520 for a category of goods. Social network data
collection (using social network data collection facilities 1760)
may be facilitated by a social data collection configuration
interface, such as for configuring queries, identifying social data
sources of relevance, configuring APIs for data collection, routing
data to appropriate applications 630, and the like.
Crowdsourcing Data Collection Architecture
[0724] Referring to FIG. 18, an embodiment of the platform 604 is
provided. As with other embodiments, the platform 604 may employ a
micro-services architecture with the various data handling layers
614, a set of network connectivity facilities 642 (which may
include or connect to a set of interfaces 702 of various layers of
the platform 604), a set of adaptive intelligence facilities or
adaptive intelligent systems 1160, a set of data storage facilities
or systems 624, and a set of monitoring facilities or systems 614.
The platform 604 may support a set of applications 630 (including
processes, workflows, activities, events, use cases and
applications) for enabling an enterprise to manage a set of value
chain network entities 652, such as from a point of origin to a
point of customer use of a product 650, which may be an intelligent
product.
[0725] Thus, provided herein are methods, systems, components and
other elements for an information technology system that may
include: a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[0726] In embodiments, the VCNP 604 may further include a set of
microservices layers that include a monitoring systems and data
collection systems layer 614 with a crowdsourcing facility 1770
that collects information from a set of crowdsourcing resources
that provide information with respect to supply chain entities and
demand management entities. The crowdsourcing services 1770 may
support various applications among a set of demand management
applications 1502, a set of supply chain applications 1500, a set
of intelligent product applications 1510, a set of asset management
applications 1530 and a set of enterprise resource management
applications 1520 for a category of goods. Crowdsourcing may be
facilitated by a crowdsourcing interface 1770, such as for
configuring queries, setting rewards for information, configuring
workflows, determining eligibility for participation, and other
elements of crowdsourcing.
Value Chain Digital Twin Processing (DTPT)
[0727] Referring now to FIG. 52 a set of value chain network
digital twins 1700 representing a set of value chain network
entities 652 is depicted. The digital twins 1700 are configured to
simulate properties, states, operations, behaviors and other
aspects of the value chain network entities 652. The digital twins
1700 may have a visual user interface, e.g., in the form of 3D
models, or may consist of system specifications or ontologies
describing the architecture, including components and their
interfaces, of the value chain network entities 652. The digital
twins 1700 may include configuration or condition of the value
chain network entities 652, including data records of the past and
current state of the value chain network entities 652, such as
captured through sensors, through user input, and/or determined by
outputs of behavioral models that describe the behavior of the
value chain network entities 652. The digital twins 1700 may be
updated continuously to reflect the current condition of the value
chain network entities 652, based on sensor data, test and
inspection results, conducted maintenance, modifications, etc. The
digital twins 1700 may also be configured to communicate with a
user via multiple communication channels, such as speech, text,
gestures, and the like. For example, a digital twin 1700 may
receive queries from a user about the value chain network entities
652, generate responses for the queries, and communicate such
responses to the user. Additionally or alternatively, digital twins
1700 may communicate with one another to learn from and identify
similar operating patterns and issues in other value chain network
entities 652, as well as steps taken to resolve those issues. The
digital twins 1700 may be used for monitoring, diagnostics,
simulation, management, remote control, and prognostics, such as to
optimize the individual and collective performance and utilization
of value chain network entities 652.
[0728] For example, machine twins 1770 may continuously capture the
key operational metrics of the machines 724 and may be used to
monitor and optimize machine performance in real time. Machine
twins 1770 may combine sensor, performance, and environmental data,
including insights from similar machines 724, enabling prediction
of life span of various machine components and informed maintenance
decisions. In embodiments, machine twins 1770 may generate an alert
or other warning based on a change in operating characteristics of
the machine 724. The alert may be due to an issue with a component
of the machine 724. Additionally, machine twins 1770 may determine
similar issues that have previously occurred with the machine or
similar machines, provide a description of what caused the issues,
what was done to address the issues, and explain differences
between the present issue and the previous issues and what actions
to take to resolve the issue, etc.
[0729] Similarly, warehousing twins 1712 may combine a 3D model of
the warehouse with inventory and operational data including the
size, quantity, location, and demand characteristics of different
products. The warehousing twins 1712 may also collect sensor data
in a connected warehouse, as well as data on the movement of
inventory and personnel within the warehouse. Warehousing twins
1712 may help in optimizing space utilization and aid in
identification and elimination of waste in warehouse operations.
The simulation using warehousing twins 1712 of the movement of
products, personnel, and material handling equipment may enable
warehouse managers to test and evaluate the potential impact of
layout changes or the introduction of new equipment and new
processes.
[0730] In embodiments, multiple digital twins of the value chain
network entities 652 may be integrated, thereby aggregating data
across the value chain network to drive not only entity-level
insights but also system-level insights. For example, consider a
simple value chain network with an operating facility 712
comprising different machines 724 including conveyors, robots, and
inspection devices. The operating facility digital twin 1172 may
need to integrate the data from digital twins 1770 of different
machines to get a holistic picture of the complete conveyor line in
the operating facility 712 (e.g., a warehouse, distribution center,
or fulfillment center where packages are moved along a conveyor and
inspected before being sent out for delivery. While the digital
twin of conveyor line may provide insights about only its
performance, the composite digital twin may aggregate data across
the different machines in the operating facility 712. Thus, it may
provide an integrated view of individual machines and their
interactions with environmental factors in the operating facility
leading to insights about the overall health of the conveyor line
within the operating facility 712. As another example, the supply
factor twins 1650 and demand factor twins 1640 may be integrated to
create a holistic picture of demand-supply equilibrium for a
product 650. The integration of digital twins also enables the
querying of multiple value chain network entities 652 and create a
360-degree view of the value chain network 668 and its various
systems and subsystems.
[0731] It will be apparent that the ability to integrate digital
twins of the value chain network entities 652 may be used to
generate a value chain network digital twin system from a plurality
of digital twin subsystems representing entities selected from
among supply chain entities, demand management entities and value
chain network entities. For example, a machine digital twin 1770 is
comprised of multiple digital twins of sub-systems and individual
components constituting the machine 724. The machine's digital twin
may integrate all such component twins and their inputs and outputs
to build the model of the machine. Also, for example, a
distribution facility twins system 1714 may be comprised of
subsystems, such as warehousing twins 1712, fulfilment twins 1600
and delivery system twins 1610.
[0732] Similarly, the process digital twin may be seen as comprised
of digital twins of multiple sub-processes representing entities
selected from among supply chain entities, demand management
entities and value chain network entities. For example, the digital
twin of a packaging process is comprised of digital twins of
sub-processes for picking, moving, inspecting and packing the
product. As another example, the digital twin of warehousing
process may be seen as comprised of digital twins of multiple
sub-processes including receiving, storing, picking and shipping of
stored inventories.
[0733] It will be apparent that a value chain network digital twin
system may be generated from a plurality of digital twin subsystems
or conversely a digital twin subsystem may be generated from a
digital twin system, wherein at least one of the digital twin
subsystem and the digital twin system represents entities selected
from among supply chain entities, demand management entities and
value chain network entities.
[0734] Similarly, a value chain network digital twin process may be
generated from a plurality of digital twin sub-processes or
conversely digital twin sub-process generated from a digital twin
process wherein at least one of the digital twin sub-process and
the digital twin process represents entities selected from among
supply chain entities, demand management entities and value chain
network entities.
[0735] The analytics obtained from digital twins 1700 of the value
chain network entities 652 and their interactions with one another
provide a systemic view of the value chain network as well as its
systems, sub-systems, processes and sub-processes. This may help in
generating new insights into ways the various systems and processes
may be evolved to improve their performance and efficiency.
[0736] In embodiments, the platform 604 and applications 630 may
have a system for generating and updating a self-expanding digital
twin that represents a set of value chain entities. The
self-expanding digital twin continuously keeps learning and
expanding in scope, with more and more data it collects and
scenarios it encounters. As a result, the self-expanding twin can
evolve with time and take on more complex tasks and answer more
complex questions posed by a user of the self-expanding digital
twin.
[0737] In embodiments, the platform 604 and applications 630 may
have a system for scheduling the synchronization of a physical
value chain entity's changing condition to a digital twin that
represents a set of value chain entities. In embodiments, the
synchronization between the physical value chain entity and its
digital twin is on a near real-time basis.
[0738] In embodiments, the platform 604 and applications 630 may
have an application programming interface for extracting, sharing,
and/or harmonizing data from information technology systems
associated with multiple value chain network entities that
contribute to a single digital twin representing a set of value
chain entities.
[0739] In embodiments, value chain network management platform 604
may include various subsystems that may be implemented as micro
services, such that other subsystems of the system access the
functionality of a subsystem providing a micro service via
application programming interface API. In some embodiments, the
various services that are provided by the subsystems may be
deployed in bundles that are integrated, such as by a set of
APIs.
[0740] In embodiments, value chain network management platform 604
may include a set of microservices for managing a set of value
chain network entities for an enterprise and having a set of
processing capabilities for at least one of creating, modifying,
and managing the parameters of a digital twin that is used in the
platform to represent a set of value chain network entities.
Value Chain Digital Twin Kit (DTIB)
[0741] The value chain network management platform may provide a
digital twin sub-system in the form of an out-of-the-box kit system
with self-configuring capabilities. The kit may provide a data-rich
and interactive overview of a set of value chain network entities
constituting the sub-system. For example, a supply chain
out-of-the-box digital twin kit system may represent a set of
supply chain entities that are linked to the identity of an owner
or operator of the supply chain entities. The owner or operator of
the supply chain entity may then use the kit to get a holistic
picture of its complete portfolio. The owner may investigate for
information related to various supply chain entities and ask
interactive questions from the digital twin kit system.
[0742] In embodiments, a demand management out-of-the-box digital
twin kit system may represent a set of demand management entities
that are linked to the identity of an owner or operator of the
demand management entities.
[0743] In embodiments, a value chain network digital twin kit
system for providing out-of-the-box, self-configuring capabilities
may represent a set of demand management entities and a set of
supply chain entities that are linked to the identity of an owner
or operator of the demand management entities and the supply chain
entities.
[0744] In embodiments, a warehouse digital twin kit system for
providing out-of-the-box, self-configuring capabilities may
represent a set of warehouse entities that are linked to the
identity of an owner or operator of the warehouse.
[0745] Referring now to FIG. 53, an example warehouse digital twin
kit system 5000 is depicted. The warehouse digital twin kit system
5000 includes warehousing twins in the virtual space 5002
representing models of warehouses 654 in the real space 5004.
[0746] The warehouse digital twin kit system 5000 allows an owner
or operator 5008 of the one or more warehouse entities 654 to get
complete portfolio overview of all these entities--existing or in
design or construction. The owner 5008 may navigate a wealth of
information including warehouse photographs 5010, 3D images 5012,
live video feeds 5014 of real-time construction progress and AR or
VR renderings 5018 of the warehousing entities 654. The owner 5008
may investigate about the health of one or more entities 654 and
ask interactive questions and search for detailed information about
one or more warehouse entities 654. The warehouse digital twin kit
system 5000 has access to real time dynamic data captured by IoT
devices and sensors at warehouse entities 654 and may be supported
with natural language capabilities enabling it to interact with the
owner 5008 and answer any questions about the condition of the
warehouse entities 654.
[0747] In embodiments, warehouse digital twin kit system 5000 may
provide the portfolio overview of warehouse entities 654 to owner
5008 in the form of a 3D information map containing all the
warehouse entities 654. Owner 5008 may select a specific entity on
the map and get information about inventory, operational and health
data from the warehousing twin 1710. Alternatively, the owner 5008
may ask for information about the overall portfolio of warehouse
entities 654 owned. The warehouse digital twin kit system 5000
consolidates information from the multiple warehousing twins 1710
and provides a holistic view. The consolidated view may help owner
5008 to optimize operations across warehouse entities 654 by
adjusting stock locations and staffing levels to match current or
forecasted demand. The owner 5008 may also display the information
from warehouse digital twin kit system 5000 on a website or
marketing material to be accessed by any customers, suppliers,
vendors and other partners.
[0748] In embodiments, a container ship digital twin kit system for
providing out-of-the-box, self-configuring capabilities may
represent a set of container ship entities that are linked to the
identity of an owner or operator of the container ship.
[0749] In embodiments, a port infrastructure digital twin kit
system for providing out-of-the-box, self-configuring capabilities
may represent a set of port infrastructure entities that are linked
to the identity of an owner or operator of the port
infrastructure.
Value Chain Compatibility Testing (VCCT)
[0750] The platform 604 may deploy digital twins 1700 of value
chain network entities 652 for testing the compatibility between
different value chain network entities 652 interacting with one
another and forming various systems and subsystems of the value
chain network.
[0751] This brings visibility to the compatibility and performance
of various systems and subsystems within the value chain network
before there are any physical impacts. Any incompatibilities or
performance deficiencies of different value chain network entities
652 may be highlighted through digital models and simulations
rather than having to rely on physical systems to perform such
tests which is both expensive and impractical.
[0752] The digital twin 1700 may make use of artificial
intelligence 1160 (including any of the various expert systems,
artificial intelligence systems, neural networks, supervised
learning systems, machine learning systems, deep learning systems,
and other systems described throughout this disclosure and in the
documents incorporated by reference) for carrying out the
compatibility testing in the value chain network.
[0753] In embodiments, the platform may provide a system for
testing compatibility or configuration of a set of vendor
components for a container ship using a set of digital twins
representing the container ship and the vendor components.
[0754] In embodiments, the platform may provide a system for
testing compatibility or configuration of a set of vendor
components for a warehouse using a set of digital twins
representing the warehouse and the vendor components.
[0755] In embodiments, the platform may provide a system for
testing compatibility or configuration of a set of vendor
components for a port infrastructure facility using a set of
digital twins representing the port infrastructure facility and the
vendor components.
[0756] In embodiments, the platform may provide a system for
testing compatibility or configuration of a set of vendor
components for a shipyard facility using a set of digital twins
representing the shipyard facility and the vendor components.
[0757] In embodiments, the platform may provide a system for
testing compatibility or configuration of a container ship and a
set of port infrastructure facilities using a set of digital twins
representing the container ship and the port infrastructure
facility.
[0758] In embodiments, the platform may provide a system for
testing compatibility or configuration of a barge and a set of
waterways for a navigation route using a set of digital twins
representing the barge and the set of waterways.
[0759] In embodiments, the platform may provide a system for
testing compatibility or configuration of a container ship and a
set of cargo for an identified shipment using a set of digital
twins representing the container ship and the cargo.
[0760] In embodiments, the platform may provide a system for
testing compatibility or configuration of a barge and a set of
cargo for an identified shipment using a set of digital twins
representing the barge and the cargo.
[0761] In embodiments, the platform may provide a system for
testing compatibility or configuration of a set of cargo handling
infrastructure facilities and a set of cargo for an identified
shipment using a set of digital twins representing the cargo
handling infrastructure facilities and the cargo.
Value Chain Infrastructure Testing (VCIT)
[0762] The platform 604 may deploy digital twins 1700 of value
chain network entities 652 to perform stress tests on a set of
value chain network entities. The digital twins may help simulate
behavior of value chain network systems and sub-systems in a wide
variety of environments. The stress tests may help run any
"what-if" scenarios to understand the impact of change in relevant
parameters beyond normal operating values and evaluate the
resilience of the infrastructure of value chain network.
[0763] The platform 604 may include a system for learning on a
training set of outcomes, parameters, and data collected from data
sources relating to a set of value chain network activities to
train artificial intelligence system 1160 (including any of the
various expert systems, artificial intelligence systems, neural
networks, supervised learning systems, machine learning systems,
deep learning systems, and other systems described throughout this
disclosure and in the documents incorporated) for performing such
stress tests on the value chain network.
[0764] In embodiments, the platform may include a system for
learning on a training set of machine outcomes, parameters, and
data collected from data sources relating to a set of value chain
network activities to train an artificial intelligence/machine
learning system to perform stress tests on the machine using a
digital twin that represents a set of value chain entities.
[0765] As described, the value chain network comprises a plurality
of interrelated sub-systems and sub-processes that manage and
control all aspects associated with the production and delivery of
a finished product to an end-user-from the acquisition and
distribution of raw materials between a supplier and a
manufacturer, through the delivery, distribution, and storage of
materials for a retailer or wholesaler, and, finally, to the sale
of the product to an end-user. The complex interconnected nature of
the value chain network means that an adverse event within one
subsystem or one or more value chain entities reflect through the
entire value chain network.
[0766] FIG. 54 is an example method for performing a stress test on
the value chain network. The stress test may comprise a simulation
exercise to test the resilience of the value chain network
(including its subsystems) and determine its ability to deal with
an adverse scenario, say a natural calamity, a congested route, a
change in law, or a deep economic recession. Such adverse or stress
scenarios may affect one or more entities or subsystems within the
value chain network depending on the nature of the scenario. Hence,
any stress tests would require simulating scenarios and analyzing
the impact of different scenarios across different subsystems and
on the overall value chain network.
[0767] At 5102, all historical and current data related to the
value chain network are received. The data may include information
related to various operating parameters of the value chain network
over a particular historical time period, say last 12 months. The
data may also provide information on the typical values of various
operating parameters under normal conditions. Some examples of
operating parameters include: product demand, procurement lead
time, productivity, inventory level at one or more warehouses,
inventory turnover rates, warehousing costs, average time to
transport product from warehouse to shipping terminals, overall
cost of product delivery, service levels, etc. At 5104, one or more
simulation models of value chain network are created based on the
data. The simulation models help in visualizing the value chain
network as a whole and in predicting how changes in operating
parameters affect the operation and performance of the value chain
network. In embodiments, the simulation model may be a sum of
multiple models of different subsystems of the value chain
network.
[0768] At 5106, one or more stress scenarios may be simulated by
changing one or more parameters beyond the normal operating values.
The simulating of stress scenarios overcome the limitation of any
analysis based only on historical data and helps analyze the
network performance across a range of hypothetical yet plausible
stress conditions. The simulation involves varying (shocking) one
or more parameters while keeping the other parameters as fixed to
analyze the impact of such variations on value chain network. In
embodiments, a single parameter may be varied while keeping
remaining parameters as fixed. In other embodiments, multiple
parameters may be varied simultaneously. At 5108, the outcomes of
stress scenario simulations are determined, and the performance of
value chain network and its different subsystems is estimated
across various scenarios. At 5110, the data, parameters and
outcomes are fed into a machine learning process in the artificial
intelligence system 1160 for further analysis.
[0769] An advantage of generating data through simulations and then
training machine learning algorithms on this data is the control
this approach provides on the features in the data as well as
volume and frequency of data.
[0770] In embodiments, the platform may include a system for
learning on a training set of outcomes, parameters, and data
collected from data sources relating to a set of value chain
network activities to train an artificial intelligence/machine
learning system to perform stress tests on a physical object using
a digital twin that represents a set of value chain entities.
[0771] In embodiments, the platform may include a system for
learning on a training set of outcomes, parameters, and data
collected from data sources relating to a set of value chain
network activities to train an artificial intelligence/machine
learning system to perform stress tests on a telecommunications
network using a digital twin that represents a set of value chain
entities in a connected network of entities and the
telecommunications network.
[0772] For example, the telecommunications network may be stress
tested for resiliency by deliberately increasing network traffic by
generating and sending data packets to a specific target node
within the telecommunications network. Further, the amount of
traffic may be varied to create varying load conditions on the
target node by manipulating the number, rate or amount of data in
the data packets. The response from the target node may be
determined to evaluate how the node performed in the stress test.
The target node may be selected at different parts of the
telecommunications network for stress testing so as to test
robustness of any portion of the network in any topology. The
simulated stress tests on the telecommunications network may be
utilized to identify vulnerabilities in any portion of a network so
that the vulnerability can be rectified before users experience
network outages in a deployed network.
[0773] In embodiments, the platform may include a system for using
a digital twin that represents a set of value chain entities in a
demand management environment to perform a set of stress tests on a
set of workflows in the demand management environment using the
digital twin, wherein the stress tests represent impacts in the
digital twin of varying a set of demand-relevant parameters to
levels that exceed normal operating levels. For example, the demand
of a product in the value chain network may be affected by factors
like changes in consumer confidence, recessions, excessive
inventory levels, substitute product pricing, overall market
indices, currency exchange changes, etc. The demand factors twin
1640 may simulate such scenarios by varying supply parameters and
evaluate the impact of such stresses on the demand environments
672. The stress tests performed using the digital twins may help in
testing and evaluating the resiliency of the value chain network
both in cases of over-demand and under-demand.
[0774] In embodiments, the platform may include a system for using
a digital twin that represents a set of value chain entities in the
supply chain to perform a set of stress tests on a set of workflows
in the supply chain using the digital twin, wherein the stress
tests represent impacts in the digital twin of varying a set of
supply chain-relevant parameters to levels that exceed normal
operating levels. For example, the supply of a product in the value
chain network may be affected by factors like weather, natural
calamities, traffic congestion, regulatory changes including taxes
and subsidies and border restrictions, etc. The supply factors twin
1650 may simulate such scenarios by varying supply parameters and
evaluate the impact of such stresses on the supply environments
670. The stress tests performed using the digital twins may help in
testing and evaluating the resiliency of the value chain network
both in cases of over-supply and under-supply.
[0775] Value Chain Incident Management (VCIM)
[0776] The platform 604 may deploy digital twins 1700 of value
chain network entities 652 for automatically managing a set of
incidents relating to a set of value chain network entities and
activities. The incidents may include any events causing disruption
to the value chain network like accidents, fires, explosions, labor
strikes, increases in tariffs, changes in law, changes in market
prices (e.g., of fuel, components, materials, or end products),
changes in demand, activities of cartels, closures of borders or
routes, and/or natural events and/or disasters (including storms,
heat waves, winds, earthquakes, floods, hurricanes, tsunamis,
etc.), among many others.
[0777] Also, the platform 604 may provide real-time visualization
and analysis of mobility flows in the value chain network. This may
help in quantifying risks, improving visibility and reacting to the
disruptions in the value chain network. For example, real-time
visualization of a utility flow for shipping activities using a
digital twin may help in detecting the occurrence and location of
an emergency involving a shipping system and deploying emergency
services to the detected location.
[0778] In embodiments, the platform may deploy digital twins 1700
of value chain network entities 652 for more accurate determination
of accident fault. The platform may learn on a training set of
accident outcomes, parameters, and data collected from the
monitoring layer 614 and data sources of the data storage layer 624
to train artificial intelligence system 1160 using a set of digital
twins 1700 of involved value chain network entities 652 to
determine accident fault. For example, data from digital twins of
two colliding vehicles may be compared with each other in addition
to data from the drivers, witnesses and police reports to determine
accident fault.
[0779] In embodiments, the platform may include a system for
learning on a training set of vehicular event outcomes, parameters,
and data collected from data sources related to a set of value
chain network entities 652 to train artificial intelligence system
1160 to use a digital twins 1700 of a selected set of value chain
network entities 652 to detect an incidence of fraud. For example,
comparing vehicular event data from digital twins of vehicles to
any insurance claims, contract claims, maritime claims on such
vehicles may help in detecting any mismatch in the two.
[0780] In embodiments, the platform may include a system for
learning on a training set of vehicle outcomes, parameters, and
data collected from data sources related to a set of value chain
network entities 652 to train artificial intelligence system 1160
to use a digital twin 1700 of a selected set of value chain network
entities 652 to detect unreported abnormal events with respect to
selected set of value chain network entities 652. Consider an
example where the digital twin of a vehicle shows an abnormal event
like an accident but this event has not been reported by the driver
of the vehicle. The unreported event may be added to the record of
the vehicle and the driver by a lessor of the vehicle. Also, the
lessor of the vehicle may charge the lessee for repairs or
diminished value of the vehicle at lease-end and adjust residual
value forecast for the same. Similarly, an insurer may add the
unreported event to the record of the vehicle and the driver. The
reporting may be as detailed as the exact nature, timing, location,
fault, etc. of the accident or just the fact there was unreported
accident. This information may then be used for calculating the
insurance premium.
[0781] Finally, in case there are multiple entities involved in the
accident, the data may be triangulated with the digital twin of
another entity for validation.
Value Chain Predictive Maintenance (PMVC)
[0782] The platform 604 may deploy digital twins 1700 of value
chain network entities 652 to predict when a set of value chain
network entities should receive maintenance.
[0783] The digital twin may predict the anticipated wear and
failure of components of a system by reviewing historical and
current operational data thereby reducing the risk of unplanned
downtime and the need for scheduled maintenance. Instead of
over-servicing or over-maintaining products to avoid costly
downtime, repairs or replacement, any product performance issues
predicted by the digital twin may be addressed in a proactive or
just-in-time manner.
[0784] The digital twins 1700 may collect events or state data
about value chain entities 652 from the monitoring layer 614 and
historical or other data from selected data sources of the data
storage layer 624. Predictive analytics powered by artificial
intelligence system 1160 dissect the data, search for correlations,
and formulate predictions about maintenance need and remaining
useful life of a set of value chain entities 652.
[0785] The platform 604 may include a system for learning on a
training set of outcomes, parameters, and data collected from data
sources relating to a set of value chain network activities to
train artificial intelligence 1160 (including any of the various
expert systems, artificial intelligence systems, neural networks,
supervised learning systems, machine learning systems, deep
learning systems, and other systems described throughout this
disclosure and in the documents incorporated) for performing
condition monitoring, anomaly detection, failure forecasting and
predictive maintenance of a set of value chain entities 652.
[0786] In embodiments, the platform may include a system for
learning on a training set of machine maintenance outcomes,
parameters, and data collected from data sources relating to a set
of machine activities to train an artificial intelligence/machine
learning system to perform predictive maintenance on a machine
using a digital twin of the machine.
[0787] In embodiments, artificial intelligence system 1160 may
train models, such as predictive models (e.g., various types of
neural networks, classification-based models, regression based
models, and other machine-learned models). In embodiments, training
can be supervised, semi-supervised, or unsupervised. In
embodiments, training can be done using training data, which may be
collected or generated for training purposes.
[0788] An example artificial intelligence system 1160 trains a
machine predictive maintenance model. A predictive maintenance
model may be a model that receives machine related data and outputs
one or more predictions or answers regarding the remaining life of
the machine. The training data can be gathered from multiple
sources including machine specifications, environmental data,
sensor data, run information, outcome data and notes maintained by
machine operators. The artificial intelligence system 1160 takes in
the raw data, pre-processes it and applies machine learning
algorithms to generate the predictive maintenance model. In
embodiments, the artificial intelligence system 1160 may store the
predictive model in a model datastore within data storage layer
624.
[0789] Some examples of questions that the predictive model may
answer are: when will the machine fail, what type of failure it
will be, what is the probability that a failure will occur within
the next X hours, what is the remaining useful life of the machine,
is the machine behaving in an uncharacteristic manner, which
machine requires maintenance most urgently and the like.
[0790] The artificial intelligence system 1160 may train multiple
predictive models to answer different questions. For example, a
classification model may be trained to predict failure within a
given time window, while a regression model may be trained to
predict the remaining useful life of the machine.
[0791] In embodiments, training may be done based on feedback
received by the system, which is also referred to as "reinforcement
learning." In embodiments, the artificial intelligence system 1160
may receive a set of circumstances that led to a prediction (e.g.,
attributes of a machine, attributes of a model, and the like) and
an outcome related to the machine and may update the model
according to the feedback.
[0792] In embodiments, artificial intelligence system 1160 may use
a clustering algorithm to identify the failure pattern hidden in
the failure data to train a model for detecting uncharacteristic or
anomalous behavior. The failure data across multiple machines and
their historical records may be clustered to understand how
different patterns correlate to certain wear-down behavior and
develop a maintenance plan resonant with the failure.
[0793] In embodiments, artificial intelligence system 1160 may
output scores for each possible prediction, where each prediction
corresponds to a possible outcome. For example, in using a
predictive model used to determine a likelihood that a machine will
fail in the next one week, the predictive model may output a score
for a "will fail" outcome and a score for a "will not fail"
outcome. The artificial intelligence system 1160 may then select
the outcome with the greater score as the prediction.
Alternatively, the system 1160 may output the respective scores to
a requesting system. In embodiments, the output from system 1160
includes a probability of the prediction's accuracy.
[0794] FIG. 55 is an example method used by machine twin 1770 for
detecting faults and predicting any future failures of machine
724.
[0795] At 5202, a plurality of streams of machine related data from
multiple data sources are received at the machine twin 1770. This
includes machine specifications like mechanical properties, data
from maintenance records, operating data collected from the
sensors, historical data including failure data from multiple
machines running at different times and under different operating
conditions and so on. At 5205, the raw data is cleaned by removing
any missing or noisy data, which may occur due to any technical
problems in the machine at the time of collection of data. At 5208,
one or more models are selected for training by machine twin 1770.
The selection of model is based on the kind of data available at
the machine twin 1770 and the desired outcome of the model. For
example, there may be cases where failure data from machines is not
available, or only a limited number of failure datasets exist
because of regular maintenance being performed. Classification or
regression models may not work well for such cases and clustering
models may be most suitable. As another example, if the desired
outcome of the model is determining current condition of the
machine and detecting any faults, then fault detection models may
be selected, whereas if the desired outcome is predicting future
failures then remaining useful life prediction model may be
selected. At 5210, the one or more models are trained using
training dataset and tested for performance using testing dataset.
At 5212, the trained model is used for detecting faults and
predicting future failure of the machine on production data.
[0796] FIG. 56 is an example embodiment depicting the deployment of
machine twins 1770 perform predictive maintenance on machines 724.
Machine twin 1770 receives data from data storage systems 624 on a
real-time or near real-time basis. The data storage systems 624 may
store different types of data in different datastores. For example,
machine datastore 5202 may store data related to machine
identification and attributes, machine state and event data, data
from maintenance records, historical operating data, notes from
machine operator, etc. Sensor datastore 5204 may store sensor data
from operation such as temperature, pressure, and vibration that
may be stored as signal or time series data. Failure datastore 5310
may store failure data from machine 724 or similar machines running
at different times and under different operating conditions. Model
datastore 5312 may store data related to different predictive
models including fault detection and remaining life prediction
models.
[0797] Machine twin 1770 then coordinates with artificial
intelligence system to select one or more of models based on the
kind and quality of available data and the desired answers or
outcomes. For example, physical models 5320 may be selected if the
intended use of machine twin 1770 is to simulate what-if scenarios
and predict how the machine will behave under such scenarios. Fault
Detection and Diagnostics Models 5322 may be selected to determine
the current health of the machine and any fault conditions. A
simple fault detection model may use one or more condition
indicators to distinguish between regular and faulty behaviors and
may have a threshold value for the condition indicator that is
indicative of a fault condition when exceeded. A more complex model
may train a classifier to compare the value of one or more
condition indicators to values associated with fault states and
returns the probability of presence of one or more fault
states.
[0798] Remaining Useful Life (RUL) Prediction models 5324 are used
for predicting future failures and may include degradation models
5326, survival models 5328 and similarity models 5330. An example
RUL prediction model may fit the time evolution of a condition
indicator and predicts how long it will be before the condition
indicator crosses some threshold value indicative of a failure.
Another model may compare the time evolution of the condition
indicator to measured or simulated time series from similar systems
that ran to failure.
[0799] In embodiments, a combination of one or more of these models
may be selected by the machine twin 1770.
[0800] Artificial Intelligence system 1160 may include machine
learning processes 5340, clustering processes 5342, analytics
processes 5344 and natural language processes 5348. Machine
learning processes 5340 work with machine twin 1770 to train one or
more models as identified above. An example of such machine learned
model is the RUL prediction model 5324. The model 5324 may be
trained using training dataset pmvc 230 from the Data Storage
Systems 624. The performance of the model 5324 and classifier may
then be tested using testing dataset 5350.
[0801] Clustering processes 5342 may be implemented to identify the
failure pattern hidden in the failure data to train a model for
detecting uncharacteristic or anomalous behavior. The failure data
across multiple machines and their historical records may be
clustered to understand how different patterns correlate to certain
wear-down behavior. Analytics processes 5344 perform data analytics
on various data to identify insights and predict outcomes. Natural
language processes 4348 coordinate with machine twin 1770 to
communicate the outcomes and results to the user of machine twin
1770.
[0802] The outcomes 5360 may be in the form of modeling results
5362, alerts and warnings 5364 or remaining useful life (RUL)
predictions 5368. Machine twin 1770 may communicate with a user via
multiple communication channels such as speech, text, gestures to
convey outcomes 5360.
[0803] In embodiments, models may then be updated or reinforced
based on the model outcomes 5360. For example, the artificial
intelligence system may receive a set of circumstances that led to
a prediction of failure and the outcome and may update the model
based on the feedback.
[0804] In embodiments, the platform may include a system for
learning on a training set of ship maintenance outcomes,
parameters, and data collected from data sources relating to a set
of ship activities to train an artificial intelligence/machine
learning system to perform predictive maintenance on a ship using a
digital twin of the ship.
[0805] In embodiments, the platform may include a system for
learning on a training set of barge maintenance outcomes,
parameters, and data collected from data sources relating to a set
of barge activities to train an artificial intelligence/machine
learning system to perform predictive maintenance on a barge using
a digital twin of the barge.
[0806] In embodiments, the platform may include a system for
learning on a training set of port maintenance outcomes,
parameters, and data collected from data sources relating to a set
of port activities to train an artificial intelligence/machine
learning system to perform predictive maintenance on a port
infrastructure facility using a digital twin of the port
infrastructure facility.
[0807] In embodiments, the platform may include a system for
learning on a training set of repair outcomes, parameters, and data
collected from data sources related to a set of value chain
entities to train an artificial intelligence/machine learning
system to use a digital twin of a selected set of value chain
entities to estimate the cost of repair of a damaged object.
[0808] In embodiments, the platform may include a system for
learning on a training set of infrastructure outcomes, parameters,
and data collected from data sources to train an artificial
intelligence/machine learning system to predict deterioration of
infrastructure using a digital twin of the infrastructure.
[0809] In embodiments, the platform may include a system for
learning on a training set of natural hazard outcomes, parameters,
and data collected from data sources relating to a set of shipping
activities to train an artificial intelligence/machine learning
system to model natural hazard risks for a set of shipping
infrastructure facilities using a digital twin of a city.
[0810] In embodiments, the platform may include a system for
learning on a training set of maintenance outcomes, parameters, and
data collected from data sources relating to a set of shipping
activities to train an artificial intelligence/machine learning
system to monitor shipping infrastructure maintenance activities
for a set of shipping infrastructure facilities using a digital
twin of the set of facilities
[0811] In embodiments, the platform may include a system for
learning on a training set of maintenance outcomes, parameters, and
data collected from data sources relating to a set of shipping
activities to train an artificial intelligence/machine learning
system to detect the occurrence and location of a maintenance issue
using a digital twin of a set of shipping infrastructure facilities
and having a system for automatically deploying maintenance
services to the detected location.
[0812] Referring to FIG. 57, the platform 604 may include,
integrate, integrate with, manage, control, coordinate with, or
otherwise handle customer digital twins 5502 and/or customer
profile digital twins 1730.
[0813] Customer digital twins 5502 may represent evolving,
continuously updated digital representations of value chain network
customers 662. In embodiments, value chain network customers 662
include consumers, licensees, businesses, enterprises, value-added
resellers and other resellers, distributors, retailers (including
online retailers, mobile retailers, conventional brick and mortar
retailers, pop-up shops and the like), end users, and others who
may purchase, license, or otherwise use a category of goods and/or
related services.
[0814] Customer profile digital twins 1730, on the other hand, may
represent one or more demographic (age, gender, race, marital
status, number of children, occupation, annual income, education
level, living status (homeowner, renter, and the like)
psychographic, behavioral, economic, geographic, physical (e.g.,
size, weight, health status, physiological state or condition, or
the like) or other attributes of a set of customers. In
embodiments, customer profile digital twins 1730 may be enterprise
customer profile digital twins that represent attributes of a set
of enterprise customers. In embodiments, a customer profiling
application may be used to manage customer profiles 5504 based on
historical purchasing data, loyalty program data, behavioral
tracking data (including data captured in interactions by a
customer with an intelligent product 650), online clickstream data,
interactions with intelligent agents, and other data sources.
[0815] Customers 662 can be depicted in a set of one or more
customer digital twins 5502, such as by populating the customer
digital twin 1730 with value chain network data objects 1004, such
as event data 1034, state data 1140, or other data with respect to
value chain network customers 662. Likewise, customer profiles 5504
can be depicted in a set of one or more customer profile digital
twins 1730, such as by populating the customer profile digital
twins 1730 with value chain network data objects 1004, such as
described throughout this disclosure.
[0816] Customer digital twins 5502 and customer profile digital
twins 1730 may allow for modeling, simulation, prediction,
decision-making, classification, and the like.
[0817] Where customers 662 are consumers, for example, the
respective customer digital twins 1730 may be populated with
identity data, account data, payment data, contact data, age data,
gender data, race data, location data, demographic data, living
status data, mood data, stress data, behavior data, personality
data, interest data, preference data, style data, medical data,
physiological data, psychological data, physical attribute data,
education data, employment data, salary data, net worth data,
family data, household data, relationship data, pet data,
contact/connection data (such as mobile phone contacts, social
media connections, and the like), transaction history data,
political data, travel data, product interaction data, product
feedback data, customer service interaction data (such as a
communication with a chatbot, or a telephone communication with a
customer service agent at a call center), fitness data, sleep data,
nutrition data, software program interaction observation data 1500
(e.g., by customers interacting with various software interfaces of
applications 630 involving value chain entities 652) and physical
process interaction observation data 1510 (e.g., by watching
customers interacting with products or other value chain entities
652), and the like.
[0818] In another example, where customers 662 are enterprises or
businesses, the customer digital twin 1730 may be populated with
identity data, account data, payment data, transaction data,
product feedback data, location data, revenue data, enterprise type
data, product and/or service offering data, worker data (such as
identity data, role data, and the like), and other
enterprise-related attributes.
[0819] Customer digital twins and customer profile digital twins
1730 may include a set of components, processes, services,
interfaces, and other elements for development and deployment of
digital twin capabilities for visualization of value chain network
customers 662 and customer profiles 5504 as well as for coordinated
intelligence (including artificial intelligence 1160, edge
intelligence, analytics and other capabilities) and other
value-added services and capabilities that are enabled or
facilitated with digital twins.
[0820] In embodiments, the customer digital twins 5502 and customer
profile digital twins 1730 may take advantage of the presence of
multiple applications 630 within the value chain management
platform layer 604, such that a pair of applications may share data
sources (such as in the data storage layer 624) and other inputs
(such as from the monitoring layer 614) that are collected with
respect to value chain entities 652, as well as sharing events,
state information and outputs, which collectively may provide a
much richer environment for enriching content in the digital twins,
including through use of artificial intelligence 1160 (including
any of the various expert systems, artificial intelligence systems,
neural networks, supervised learning systems, machine learning
systems, deep learning systems, and other systems described
throughout this disclosure and in the documents incorporated by
reference) and through use of content collected by the monitoring
layer 614 and data collection systems 640.
[0821] An environment for development of a customer digital twin
5502 may include a set of interfaces for developers in which a
developer may configure an artificial intelligence system 1160 to
take inputs from selected data sources of the data storage layer
624 and events or other data from the monitoring systems layer 614
and supply them for inclusion in a customer digital twin 5502. A
customer digital twin development environment may be configured to
take outputs and outcomes from various applications 630. In
embodiments, a customer digital twin 1730 may be provided for the
wide range of value chain network applications 630 mentioned
throughout this disclosure and the documents incorporated herein by
reference.
[0822] In embodiments, the customer digital twin 5502 may be
rendered by a computing device, such that a user can view a digital
representation of the customer 714. For example, a customer digital
twin 5502 may be rendered and output to a display device. In
another example, a 5502 may be rendered in a three-dimensional
environment and viewed using a virtual reality headset.
[0823] An environment for development of a customer profile digital
twin 1730 may include a set of interfaces for developers in which a
developer may configure an artificial intelligence system 1160 to
take inputs from selected data sources of the data storage layer
624 and events or other data from the monitoring systems layer 614
and supply them for inclusion in a customer profile digital twin
1730. A customer profile digital twin development environment may
be configured to take outputs and outcomes from various
applications 630. In embodiments, a customer profile digital twin
1730 may be provided for the wide range of value chain network
applications 630 mentioned throughout this disclosure and the
documents incorporated herein by reference.
[0824] In embodiments, the adaptive intelligent systems layer 614
is configured to train and implement artificial intelligence
systems to perform tasks related to the value chain network 668
and/or value chain network entities 652. For example, the adaptive
intelligent systems layer 614 may be leveraged to recommend
products, enhance customer experience, select advertising
attributes for advertisements relating to value chain products
and/or services, and/or other appropriate value-chain tasks.
[0825] In embodiments, a customer profile digital twin 1730 or
other customer digital twin may be created interactively and
cooperatively with a customer, such as by allowing a customer to
request, select, modify, delete, or otherwise influence a set of
properties, states, behaviors, or other aspects represented in the
digital twin 1730. For example, a customer could refine sizes
(e.g., shoe size, dress size, shirt size, pant size, and the like),
indicate interests and needs (e.g., what the customer is interested
in buying), indicate behaviors (e.g., projects planned by an
enterprise), update current states (e.g., to reflect changes), and
the like. A version of the digital twin 1730 may thus be made
available to a customer, such as in a graphical user interface,
where the customer may manipulate one or more aspects of the
digital twin 1730, request changes, and the like. In embodiments,
multiple versions of a digital twin 1730 may be maintained for a
given customer, such as a version for customer review, an internal
version for an enterprise or host, a version for each of a specific
set of brands (e.g., where a customer's appropriate clothing sizes
vary by brand), a public version (such as one shared with a
customer's social network for feedback, such as from friends), a
private version (such as one where a customer is provided complete
control over features and properties), a simulation version, a
real-time version, and the like. In embodiments, the adaptive
intelligent systems layer 614 is configured to leverage the
customer digital twins 5502, customer profile digital twins 1730,
and/or other digital twins 1700 of other value chain network
entities 652. In embodiments, the adaptive intelligent systems
layer 614 is configured to perform simulations using the customer
digital twins 5502, customer profile digital twins 1730, and/or
digital twins of other value chain network entities 652. For
example, the adaptive intelligent systems layer 614 may vary one or
more features of a product digital twin 1780 as its use is
simulated by a customer digital twin 1730.
[0826] In embodiments, a simulation management system 5704 may set
up, provision, configure, and otherwise manage interactions and
simulations between and among digital twins 1700 representing value
chain entities 652.
[0827] In embodiments, the adaptive intelligent systems layer 614
may, for each set of features, execute a simulation based on the
set of features and may collect the simulation outcome data
resulting from the simulation. For example, in executing a
simulation involving the interactions of an intelligent product
digital twin 1780 representing an intelligent product 650 and a
customer digital twin 1730, the adaptive intelligent systems layer
614 can vary the dimensions of the intelligent product digital twin
1780 and can execute simulations that generate outcomes in a
simulation management system 5704. In this example, an outcome can
be an amount of time taken by a customer digital twin 5502 to
complete a task using the intelligent product digital twin 1780.
During the simulations, the adaptive intelligent systems layer 614
may vary the intelligent product digital twin 1780 display screen
size, available capabilities (processing, speech recognition, voice
recognition, touch interfaces, remote control, self-organization,
self-healing, process automation, computation, artificial
intelligence, data storage, and the like), materials, and/or any
other properties of the intelligent product digital twin 1780.
Simulation data 5710 may be created for each simulation and may
include feature data used to perform the simulations, as well as
outcome data. In the example described above, the simulation data
5710 may be the properties of the customer digital twin 5502 and
the intelligent product digital twin 1780 that were used to perform
the simulation and the outcomes resulting therefrom. In
embodiments, a machine learning system 5720 may receive training
data 5730, outcome data 5740, simulation data 5710, and/or data
from other types of external data sources 5702 (weather data, stock
market data, sports event data, news event data, and the like). In
embodiments, this data may be provided to the machine-learning
system 5720 via an API of the adaptive intelligent systems layer
614. The machine learning system 5720 may train, retrain, or
reinforce machine leaning models 5750 using the received data
(training data, outcome data, simulation data, and the like).
[0828] FIG. 58 illustrates an example of an advertising application
that interfaces with the adaptive intelligent systems layer 614. In
example embodiments, the advertising application may be configured
automate advertising-related tasks for a value chain product or
service.
[0829] In embodiments, the machine-learning system 5720 trains one
or more models 5750 that are leveraged by the artificial
intelligence system 1160 to make classifications, predictions,
and/or other decisions relating to advertisements for a set of
value chain products and/or services.
[0830] In example embodiments, a model 5750 is trained to select
advertisement features to optimize one or more outcomes (e.g.,
maximize product sales for a product 650 in the value chain network
668). The machine-learning system 5720 may train the models 5750
using n-tuples that include the features pertaining to
advertisements and one or more outcomes associated with the
advertisements. In this example, features for an advertisement may
include, but are not limited to, product and/or service category
advertised, advertised product features (price, product vendor, and
the like), advertised service features, advertisement type
(television, radio, podcast, social media, e-mail or the like),
advertisement length (10 seconds, 30 seconds, or the like),
advertisement timing (in the morning, before a holiday, and the
like), advertisement tone (comedic, informational, emotional, or
the like), and/or other relevant advertisement features. In this
example, outcomes relating to the advertisement may include product
sales, total cost of the advertisement, advertisement interaction
measures, and the like. In this example, one or more digital twins
1700 may be used to simulate the different arrangements (e.g.,
digital twins of advertisements, customers, customer profiles, and
environments), whereby one or more properties of the digital twins
are varied for different simulations and the outcomes of each
simulation may be recorded in a tuple with the proprieties. Other
examples of training advertising models may include a model that is
trained to generate advertisements for value chain products 650, a
model that is trained to manage an advertising campaign for value
chain products 650, and the like. In operation, the artificial
intelligence system 1160 may use such models 5750 to make
advertisement decisions on behalf of an advertising application
5602 given one or more features relating to an advertising-related
task or event. For example, the artificial intelligence system 1160
may select a type of advertisement (e.g., social media, podcast,
and the like) to use for a value chain product 650. In this
example, the advertising application 5602 may provide the features
of the product to artificial intelligence system 1160. These
features may include product vendor, the price of the product, and
the like. In embodiments, the artificial intelligence system 1160
may insert these features into one or more of the models 5750 to
obtain one or more decisions, which may include which type of
advertisement to use. In embodiments, the artificial intelligence
system 1160 may leverage the customer digital twins 5502 and/or
customer profile digital twins 1730 to run simulations on the one
or more decisions and generate simulation data 5710. The machine
learning system 5720 may receive the simulation data 5710 and other
data as described throughout this disclosure to retrain or
reinforce machine leaning models. In embodiments, the customer
digital twins 5502, customer profile digital twins 1730, and other
digital twins 1700 may be leveraged by the artificial intelligence
system 1160 to simulate a decision made by the artificial
intelligence system 1160 before providing the decision to the value
chain entity 652. In the present example, the customer profile
digital twins 1730 may be leveraged by the artificial intelligence
system 1160 to simulate decisions made by the artificial
intelligence system 1160 before providing the decision to the
advertising application 5602. In embodiments, where simulation
outcomes are unacceptable, simulation data 5710 may be reported to
the machine learning system 5720, which may use the received data
to re-train machine learning models 5750, which may then be
leveraged by the artificial intelligence system 1160 to make a new
decision. The advertising application 824 may initiate an
advertising event using the decision(s) made by the artificial
intelligence system 1160. In embodiments, after the advertising
event, the outcomes of the event (e.g., product sales) may be
reported to the machine-learning system 5720 to reinforce the
models 5750 used to make the decisions. Furthermore, in some
embodiments, the output of the advertising application and/or the
other value chain entity data sources may be used to update one or
more properties of customer digital twins 5502, customer profile
digital twins 1730 and/or other digital twins 1700.
[0831] FIG. 59 illustrates an example of an e-commerce application
5604 integrated with the adaptive intelligent systems layer 614. In
embodiments, an e-commerce application 5604 may be configured to
generate product recommendations for value chain customers 662. For
example, the ecommerce application 5604 may be configured to
receive one or more product features for a value chain network
product 650. Examples of product features may include, but are not
limited to product types, product capabilities, product price,
product materials, product vendor, and the like. In embodiments,
the e-commerce application 5604 determines recommendations to
optimize an outcome. Examples of outcomes can include software
interaction observations (such as mouse movements, mouse clicks,
cursor movements, navigation actions, menu selections, and many
others), such as logged and/or tracked by software interaction
observation system 1500, purchase of the product by a customer 714,
and the like. In embodiments, the e-commerce application 5604 may
interface with the artificial intelligence system 1160 to provide
product features and to receive product recommendations that are
based thereon. In embodiments, the artificial intelligence system
1160 may utilize one or more machine-learned models 5750 to
determine a recommendation. In some embodiments, the simulations
run by the customer digital twin 1730 may be used to train the
product recommendation machine-learning models.
[0832] FIG. 60 is a schematic illustrating an example of demand
management application 824 integrated with the adaptive intelligent
systems layer 614. In embodiments, the artificial intelligence
system 1160 may use machine-learning models 5750 trained to make
demand management decisions for a demand environment 672 on behalf
of a demand management application 824 given one or more demand
factors 644. Demand factors 644 may include product type, product
capabilities, product price, product materials, time of year,
location, and the like. In embodiments, the artificial intelligence
system 1160 may determine a demand management decision for a value
chain product 650. For example, the artificial intelligence system
1160 may generate a demand management decision relating to how many
printer ink cartridges should be supplied to a particular region
for an upcoming month. In this example, the demand management
system 824 may provide the demand factors 644 to artificial
intelligence system 1160. In embodiments, the artificial
intelligence system 1160 may insert these factors 644 into one or
more machine-learning models 5750 to obtain one or more demand
management decisions. These decisions may include the volume of ink
cartridges should be sent to the select region during the select
month.
[0833] In embodiments, the artificial intelligence system 1160 may
leverage the customer profile digital twins 1730 to run simulations
on the proposed decisions related to the demand management. The
demand management application 824 may then initiate an ink resupply
event using the decision(s) made by the artificial intelligence
system 1160. Furthermore, after the ink resupply event, the
outcomes of the event (e.g., ink cartridge sales) may be reported
to the machine-learning system 5720 to reinforce the models used to
make the decisions. Furthermore, in some embodiments, the output of
the demand management system 824 and/or the other value chain
entity data sources may be used to update one or more properties of
customer profile digital twins 1730 and/or other digital twins
1700.
[0834] In embodiments, an API enables users to access the customer
digital twins 5502 and/or customer profile digital twins 1730. In
embodiments, an API enables users to receive one or more reports
related to the digital twins.
[0835] The platform 604 may include, integrate, integrate with,
manage, control, coordinate with, or otherwise handle household
demand digital twins 5902. Household demand digital twins 5902 may
be a digital representation of a household demand for a product
category or for a set of product categories.
[0836] An environment for development of a household demand digital
twin 5902 may include a set of interfaces for developers in which a
developer may configure an artificial intelligence system 1160 to
take inputs from selected data sources of the data storage layer
624 and events or other data from the monitoring systems layer 614
and supply them for inclusion in a household demand digital twin
5902. A household demand digital twin development environment may
be configured to take outputs and outcomes from various
applications 630. In embodiments, a household demand digital twin
5902 may be provided for the wide range of value chain network
applications 630 mentioned throughout this disclosure and the
documents incorporated herein by reference.
[0837] In embodiments, a digital twin 1700 may be generated from
other digital twins. For example, a customer digital twin 5502 may
be used to generate an anonymized customer digital twin 5902. The
platform may include, integrate, integrate with, manage, control,
coordinate with, or otherwise handle anonymized customer digital
twins 5902. Anonymized customer digital twins 5902 may be an
anonymized digital representation of a customer 714. In
embodiments, anonymized customer digital twins 5902 are not
populated with personally identifiable information but may
otherwise be populated using the same data sources as its
corresponding customer digital twin 5502.
[0838] In embodiments, an environment for development of an
anonymized customer digital twin 1730 may include a set of
interfaces for developers in which a developer may configure an
artificial intelligence system 1160 to take inputs from selected
data sources of the data storage layer 624 and events or other data
from the monitoring systems layer 614 and supply them for inclusion
in an anonymized customer digital twin 5902. An anonymized digital
twin development environment may be configured to take outputs and
outcomes from various applications 630. In embodiments, an
anonymized customer digital twin 5902 may be provided for the wide
range of value chain network applications 630 mentioned throughout
this disclosure and the documents incorporated herein by
reference.
[0839] In embodiments, the anonymized customer digital twin 5902
comprises an API that can receive an access request to the
anonymized customer digital twin 5902. A requesting entity can use
the API of the anonymized customer digital twin 5902 to issue an
access request. The access request may be routed from the API to an
access logic of the anonymized customer twin 5902, which can
determine if the requesting entity is entitled to access. In
embodiments, users may monetize access to anonymized customer
digital twins 5902, such as by subscription or any other suitable
monetization method.
[0840] The platform 604 may include, integrate, integrate with,
manage, control, coordinate with, or otherwise handle enterprise
customer engagement digital twins. Enterprise customer engagement
digital twins may be a digital representation of a set of
attributes of the enterprise customer that are relevant to
engagement by the customer with a set of offerings of an
enterprise.
[0841] An environment for development of an enterprise customer
engagement digital twin may include a set of interfaces for
developers in which a developer may configure an artificial
intelligence system 1160 to take inputs from selected data sources
of the data storage layer 624 and events or other data from the
monitoring systems layer 614 and supply them for inclusion in an
enterprise customer engagement digital twin. An enterprise customer
engagement digital twin development environment may be configured
to take outputs and outcomes from various applications 630. In
embodiments, an enterprise customer engagement digital twin may be
provided for the wide range of value chain network applications 630
mentioned throughout this disclosure and the documents incorporated
herein by reference.
[0842] Referring to FIG. 61, the platform 604 may include,
integrate, integrate with, manage, control, coordinate with, or
otherwise handle component digital twins 6002. Component digital
twins 6002 may represent evolving, continuously updated digital
profiles of components 6002 of value chain products 650. Component
digital twins 6002 may allow for modeling, simulation, prediction,
decision-making, classification, and the like.
[0843] Product components 6002 can be depicted in a set of one or
component digital twins 6002, such as by populating the component
digital twins 6002 with value chain network data objects 1004, such
as event data 1034, state data 1140, or other data with respect to
value chain network product components 6002.
[0844] A product 650 may be any category of product, such as a
finished good, software product, hardware product, component
product, material, item of equipment, consumer packaged good,
consumer product, food product, beverage product, home product,
business supply product, consumable product, pharmaceutical
product, medical device product, technology product, entertainment
product, or any other type of product and/or set of related
services, and which may, in embodiments, encompass an intelligent
product 650 that is enabled with a set of capabilities such as,
without limitation data processing, networking, sensing, autonomous
operation, intelligent agent, natural language processing, speech
recognition, voice recognition, touch interfaces, remote control,
self-organization, self-healing, process automation, computation,
artificial intelligence, analog or digital sensors, cameras, sound
processing systems, data storage, data integration, and/or various
Internet of Things capabilities, among others. A component 6002 may
be any category of product component.
[0845] As an example, a component digital twin 6002 may be
populated with supplier data, dimension data, material data,
thermal data, price data, and the like.
[0846] A component digital twin 6002 may include a set of
components, processes, services, interfaces, and other elements for
development and deployment of digital twin capabilities for
visualization of value chain network components 714 as well as for
coordinated intelligence (including artificial intelligence 1160,
edge intelligence, analytics and other capabilities) and other
value-added services and capabilities that are enabled or
facilitated with a component digital twin 1730.
[0847] In embodiments, the component digital twin 6002 may take
advantage of the presence of multiple applications 630 within the
value chain management platform layer 604, such that a pair of
applications may share data sources (such as in the data storage
layer 624) and other inputs (such as from the monitoring layer 614)
that are collected with respect to value chain entities 652, as
well sharing outputs, events, state information and outputs, which
collectively may provide a much richer environment for enriching
content in a component digital twin 6002, including through use of
artificial intelligence 1160 (including any of the various expert
systems, artificial intelligence systems, neural networks,
supervised learning systems, machine learning systems, deep
learning systems, and other systems described throughout this
disclosure and in the documents incorporated by reference) and
through use of content collected by the monitoring layer 614 and
data collection systems 640.
[0848] An environment for development of a component digital twin
6002 may include a set of interfaces for developers in which a
developer may configure an artificial intelligence system 1160 to
take inputs from selected data sources of the data storage layer
624 and events or other data from the monitoring systems layer 614
and supply them for inclusion in a component digital twin 6002. A
component digital twin development environment may be configured to
take outputs and outcomes from various applications 630. In
embodiments, a component digital twin 6002 may be provided for the
wide range of value chain network applications 630 mentioned
throughout this disclosure and the documents incorporated herein by
reference. In embodiments, a digital twin 650 may be generated from
other digital twins 1700. For example, a product digital twin 1780
may be used to generate component digital twins 6002. In another
example, component digital twins 6002 may be used to generate
product digital twins 1780. In embodiments, a digital twin 1700 may
be embedded in another digital twin 1700. For example, a component
digital twin 6002 may be embedded in a product digital twin 1780
which may be embedded in an environment digital twin 6004.
[0849] In embodiments, a simulation management system 6110 may set
up, provision, configure, and otherwise manage interactions and
simulations between and among digital twins 1700 representing value
chain entities 652.
[0850] In embodiments, the adaptive intelligent systems layer 614
is configured to execute simulations in a simulation management
system 6110 using the component digital twins 6002 and/or digital
twins 1700 of other value chain network entities 652. For example,
the adaptive intelligent systems layer 614 may adjust one or more
features of an environment digital twin 6004 as a set of component
digital twins 6002 are subjected to an environment. In embodiments,
the adaptive intelligent systems layer 614 may, for each set of
features, execute a simulation based on the set of features and may
collect the simulation outcome data resulting from the
simulation.
[0851] For example, in executing a simulation on a set of component
digital twins 6002 representing components of value chain product
650 in an environment digital twin 6004, the adaptive intelligent
systems layer 614 can vary the properties of the environment
digital twin 6110 and can execute simulations that generate
outcomes. During the simulation, the adaptive intelligent systems
layer 614 may vary the environment digital twin temperature,
pressure, lighting, and/or any other properties of the environment
digital twin 6004. In this example, an outcome can be a condition
of the component digital twin 6002 after being subjected to a high
temperature. The outcomes from simulations can be used to train
machine learning models 6120.
[0852] In embodiments, a machine learning system 6150 may receive
training data 6170, outcome data 6160, simulation data 6140, and/or
data from other types of external data sources 6150 (weather data,
stock market data, sports event data, news event data, and the
like). In embodiments, this data may be provided to the
machine-learning system 6150 via an API of the adaptive intelligent
systems layer 614. In embodiments, the machine learning system 6150
may receive simulation data 6140 relating to a component digital
twin 6002 simulation. In this example, the simulation data 6140 may
be the properties of the component digital twins 6002 that were
used to perform the simulation and the outcomes resulting
therefrom.
[0853] In embodiments, the machine learning system 6150 may
train/reinforce machine leaning models 6120 using the received data
to improve the models.
[0854] Fig. SCDT-2 illustrates an example of a risk management
application 818 that interfaces with the adaptive intelligent
systems layer 614. In example embodiments, the risk management
application 818 may be configured to manage risk or liability with
respect to a good or good component.
[0855] In embodiments, the machine-learning system 6150 trains one
or more models 6120 that are utilized by the artificial
intelligence system 1160 to make classifications, predictions,
and/or other decisions relating to risk management, including for
products 650 and product components 6002. In embodiments, may be
equipment components. In example embodiments, a model 6120 is
trained to mitigate risk and liability by detecting the condition
of a set of components. The machine-learning system 6150 may train
the models using n-tuples that include the features pertaining to
components and one or more outcomes associated with the component
condition. In this example, features for a component 6002 may
include, but are not limited to, component material (plastic,
glass, metal, or the like), component history (manufacturing dates,
usage history, repair history), component properties, component
dimensions, component thermal properties, component price,
component supplier, and/or other relevant features. In this
example, outcomes may include whether the digital twin of the
component 6002 is in operating condition. In this example, one or
more properties of the digital twins are varied for different
simulations and the outcomes of each simulation may be recorded in
a tuple with the proprieties. Other examples of training risk
management models may include a model 6120 that is trained to
optimize product safety, a model that is trained to identify
components with a high likelihood of causing an undesired event,
and the like.
[0856] In operation, the artificial intelligence system 1160 may
use the above-discussed models 6120 to make risk management
decisions on behalf of a risk management application 818 given one
or more features relating to a task or event. For example, the
artificial intelligence system 1160 may determine the condition of
a component. In this example, the risk management application 818
may provide the features of the component to the artificial
intelligence system 1160. These features may include component
material, component history, component dimensions, component cost,
component thermal properties, component supplier, and the like. In
embodiments, the artificial intelligence system 1160 may feed these
features into one or more of the models discussed above to obtain
one or more decisions. These decisions may include whether the
component is in operating condition.
[0857] In embodiments, the artificial intelligence system 1160 may
leverage the component digital twins 6002 to run simulations on the
proposed decisions.
[0858] The risk management application 818 may then initiate a
component resupply event using the decision(s) made by the
artificial intelligence system 1160. Furthermore, after the
component resupply event, the outcomes of the event (e.g., improved
product performance) may be reported to the machine-learning system
6150 to reinforce the models used to make the decisions.
[0859] The platform 604 may include, integrate, integrate with,
manage, control, coordinate with, or otherwise handle component
attribute digital twins 6140. Component attribute digital twins
6140 may be a digital representation of a set of attributes of a
set of supply chain components in a supply for a set of products of
an enterprise.
[0860] An environment for development of a component attribute
digital twin 6140 may include a set of interfaces for developers in
which a developer may configure an artificial intelligence system
1160 to take inputs from selected data sources of the data storage
layer 624 and events or other data from the monitoring systems
layer 614 and supply them for inclusion in a component attribute
digital twin 6140. A component attribute digital twin development
environment may be configured to take outputs and outcomes from
various applications 630. In embodiments, a component attribute
digital twin 6140 may be provided for the wide range of value chain
network applications 630 mentioned throughout this disclosure and
the documents incorporated herein by reference.
[0861] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform with an asset management application associated
with maritime assets and a data handling layer of the management
platform including data sources containing information used to
populate a training set based on a set of maritime activities of
one or more of the maritime assets and one of design outcomes,
parameters, and data associated with the one or more maritime
assets. The information technology system also has an artificial
intelligence system that is configured to learn on the training set
collected from the data sources, that simulates one or more
attributes of one or more of the maritime assets, and that
generates one or more sets of recommendations for a change in the
one or more attributes based on the training set collected from the
data sources. The information technology system also has a digital
twin system included in the value chain network management platform
that provides for visualization of a digital twin of one or more of
the maritime assets including detail generated by the artificial
intelligence system of one or more of the attributes in combination
with the one or more sets of recommendations.
[0862] In embodiments, the maritime assets include one or more
container ships. In embodiments, the digital twin system further
provides for visualization of the digital twin of one or more of
the container ships including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the container ships.
[0863] In embodiments, the maritime assets include one or more
barges. In embodiments, the digital twin system further provides
for visualization of the digital twin of one or more of the barges
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with the barges.
[0864] In embodiments, the maritime assets include one or more
components of the port infrastructure installed on or adjacent to
land. In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the components
of port infrastructure including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of port infrastructure.
[0865] In embodiments, the maritime assets also include a container
ship moored to a component of the port infrastructure. In
embodiments, the maritime assets include one or more moored
navigation units deployed on water. In embodiments, the maritime
assets include one or more ships each connected to a barge.
[0866] In embodiments, the maritime assets are associated with a
real-world maritime port. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world maritime port including
one or more of the attributes in combination with one or more of
the sets of recommendations associated with the components of the
real-world maritime port.
[0867] In embodiments, the maritime assets are associated with a
real-world shipyard In embodiments, the digital twin system further
provides for visualization of the digital twin of one or more of
the components of the real-world shipyard including one or more of
the attributes in combination with one or more of the sets of
recommendations associated with the components of the real-world
shipyard.
[0868] In embodiments, the digital twin of one or more of the
maritime assets is a floating asset twin associated with a ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a navigation course of the ship relative to a
planned course of the ship and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the navigation course of the ship. In embodiments, the
floating asset twin is configured to provide for visualization of
an engine performance of the ship and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the engine performance of the ship. In embodiments, the
visualization of an engine performance includes an emissions
profile of the ship.
[0869] In embodiments, the floating asset twin is configured to
provide for visualization of a hull integrity of the ship and one
or more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the hull of the
ship. In embodiments, the floating asset twin is configured to
provide for visualization of in-situ hydrodynamic changes to a
portion of a hull disposed below a water line of the ship and one
or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of the ship. In embodiments, the floating asset
twin is configured to determine a schedule for the change to the
hydrodynamic surface of the hull disposed below the waterline of
the ship to improve fuel efficiency based on known routes of travel
and weather patterns. In embodiments, the floating asset twin is
configured to provide visualizations of in-situ aerodynamic changes
to a portion of a hull disposed above a water line of the ship and
one or more of the sets of recommendations from the artificial
intelligence system for a change in an aerodynamic surface to
change performance of the ship. In embodiments, the floating asset
twin is configured to determine a schedule for the change to the
aerodynamic surface disposed above the waterline of the ship to
improve fuel efficiency using known routes of travel and historical
weather patterns.
[0870] In embodiments, the floating asset twin is configured to
provide visualizations of extendable buoyant members from a hull of
the ship to improve stability during certain maneuvers of the ship
and one or more of the sets of recommendations from the artificial
intelligence system for a change in the extendable buoyant members
to change performance of the ship. In embodiments, the floating
asset twin is configured to provide visualizations of a plurality
of inspection points on the ship and maintenance histories
associated with those inspection points. In embodiments, the
floating asset twin is also configured to provide one or more of
the sets of recommendations from the artificial intelligence system
for a change in maintenance of the plurality of inspection points.
In embodiments, the floating asset twin is configured to provide
for visualizations of the plurality of inspection points on the
ship affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is also configured to provide one or more
of the sets of recommendations from the artificial intelligence
system for a change in maintenance of the plurality of inspection
points. In embodiments, the floating asset twin is configured to
provide details of a ledger of activity associated with the
visualization of the plurality of inspection points on the ship
affected by travel within a geofenced area and maintenance
histories associated with those inspection points.
[0871] In embodiments, the floating asset twin is configured to
provide for visualization for a first user of one of a navigation
course of the ship and an engine performance of the ship within a
first geofenced area and for visualization for a second user of one
of the navigation course of the ship and the engine performance of
the ship within a second different geofenced area and where transit
between the first and second geofenced areas motivates a handoff of
the floating asset twin of the ship between the first user and the
second user.
[0872] In embodiments, the digital twin is configured to at least
partially represent one or more of the maritime assets associated
with an event investigation and to at least partially detail a
timeline of the event investigation and associated maritime assets.
In embodiments, the digital twin is also configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated maritime assets.
[0873] In embodiments, the digital twin is configured to at least
partially represent one or more of the maritime assets associated
with a legal proceeding and to at least partially detail at least a
portion of a timeline pertinent to the legal proceeding and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated maritime assets.
In embodiments, the digital twin is configured to at least
partially represent one or more of the maritime assets associated
with a casualty forecast and to at least partially detail at least
a portion of a timeline pertinent to the casualty report and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated maritime assets
to reduce exposure relative to a set of previous casualty
forecasts.
[0874] In embodiments, the maritime assets include a port
infrastructure facility. In embodiments, the data collected by a
value chain network management platform facilitates identifying
theft at or misuse of the port infrastructure facility by
correlating data between a set of data collectors for one or more
physical items in the port infrastructure facility and the digital
twin detailing the one or more physical items of the port
infrastructure facility for the at least one of the port
infrastructure facility and the set of operators.
[0875] In embodiments, the digital twin details the one or more
physical items of the port infrastructure facility for at least one
operator that includes a view of expected states of at least a
portion of the one or more physical items.
[0876] In embodiments, the maritime assets include a shipyard. In
embodiments, the data collected by a value chain network management
platform facilitates identifying theft at or misuse of one or more
physical items in the shipyard by correlating data between a set of
data collectors for the one or more physical items and the digital
twin detailing the one or more physical items of the shipyard for
the at least one of the shipyard and the set of operators. In
embodiments, the digital twin details the one or more physical
items of the shipyard for at least one operator that includes a
view of expected states of at least a portion of the one or more
physical items.
[0877] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets with a representation of a maritime environment adjacent to
the geofence. In embodiments, the digital twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the set of maritime assets.
[0878] In embodiments, the maritime assets are ships capable of
carrying cargo. In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of the ships capable of
carrying cargo with a representation of a maritime environment. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
ships capable of carrying cargo.
[0879] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform including an asset management application
associated with one or more ships and a data handling layer of the
management platform including data sources containing information
used to populate a training set based on a set of maritime
activities of one or more of the ships and one of design outcomes,
parameters, and data associated with the one or more of the ships.
The information technology system also has an artificial
intelligence system that is configured to learn on the training set
collected from the data sources, that simulates one or more design
attributes of one or more of the ships, and that generates one or
more sets of design recommendations based on the training set
collected from the data sources. The information technology system
also has a digital twin system included in the value chain network
management platform that provides for visualization of a digital
twin of one or more of the ships including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations.
[0880] In embodiments, one or more of the ships include one or more
container ships. In embodiments, the digital twin system further
provides for visualization of the digital twin of one or more of
the container ships including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the container ships. In embodiments, one or more of
the container ships are moored to a component of port
infrastructure. In embodiments, one or more of the ships are
connected to a barge. In embodiments, the digital twin is
configured to provide further visualization of a navigation course
relative to a planned course and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the navigation course associated with one or more of the
ships. In embodiments, the digital twin is configured to provide
further visualization of an engine performance of one or more of
the ships and one or more of the sets of recommendations from the
artificial intelligence system for a change in the engine
performance. In embodiments, the visualization of the engine
performance includes an emissions profile of one or more of the
ships.
[0881] In embodiments, the digital twin is configured to provide
further visualization of a hull integrity of one or more of the
ships and one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of a
hull of one or more of the ships. In embodiments, the digital twin
is configured to provide further visualization of in-situ
hydrodynamic changes to a portion of a hull disposed below a water
line of one or more of the ships and one or more of the sets of
recommendations from the artificial intelligence system for a
change in a hydrodynamic surface to change performance of one or
more of the ships. In embodiments, the digital twin is configured
to determine a schedule for the change to the hydrodynamic surface
of the hull disposed below the waterline of one or more of the
ships to improve fuel efficiency based on known routes of travel
and weather patterns. In embodiments, the digital twin is
configured to provide further visualization of in-situ aerodynamic
changes to a portion of a hull disposed above a water line of one
or more of the ships and one or more of the sets of recommendations
from the artificial intelligence system for a change in an
aerodynamic surface to change performance of one or more of the
ships. In embodiments, the digital twin is configured to determine
a schedule for the change to the aerodynamic surface disposed above
the waterline of one or more of the ships to improve fuel
efficiency using known routes of travel and historical weather
patterns.
[0882] In embodiments, digital twin is configured to provide
further visualization of extendable buoyant members from a hull of
one or more of the ships to improve stability during certain
maneuvers and one or more of the sets of recommendations from the
artificial intelligence system for a change in the extendable
buoyant members to change performance of one or more of the
ships.
[0883] In embodiments, the digital twin is configured to provide
further visualization of a plurality of inspection points on one or
more of the ships and maintenance histories associated with those
inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide further visualization of the
plurality of inspection points on the ship affected by travel
within a geofenced area and maintenance histories associated with
those inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on one or more of the ships affected by travel within a
geofenced area and maintenance histories associated with those
inspection points.
[0884] In embodiments, the digital twin is configured to provide
for visualization for a first user of one of a navigation course
and an engine performance of one more of the ships within a first
geofenced area and for visualization for a second user of one of
the navigation course and the engine performance of one or more the
ships within a second different geofenced area and where transit
between the first and second geofenced areas motivates a handoff of
one or more of the ships visualized by the digital twin of one or
more of the ships between the first user and the second user.
[0885] In embodiments, the digital twin is configured to at least
partially represent one or more of the ships associated with an
event investigation and to at least partially detail a timeline of
the event investigation and associated ships. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the associated ships. In
embodiments, the digital twin is configured to at least partially
represent one or more of the ships associated with a legal
proceeding and to at least partially detail at least a portion of a
timeline pertinent to the legal proceeding and associated ships. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated ships.
[0886] In embodiments, the digital twin is configured to at least
partially represent one or more of the ships associated with a
casualty forecast and to at least partially detail at least a
portion of a timeline pertinent to the casualty report and
associated ships. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated ships to reduce exposure relative to a
set of previous casualty forecasts.
[0887] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items at one of the ships by correlating data between a
set of data collectors for one or more physical items in one of the
ships and the digital twin detailing one or more of the physical
items associated with one of the ships for the at least one of the
port infrastructure facility and the set of operators. In
embodiments, the digital twin details the one or more physical
items associated with one of the ships for at least one operator
that includes a view of expected states of at least a portion of
the one or more physical items.
[0888] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of one or more of the ships
with a representation of a maritime environment adjacent to the
geofence. In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of one or more of the ships.
[0889] In embodiments, one or more of the ships are capable of
carrying cargo. In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of one or more of the ships
capable of carrying cargo with a representation of a maritime
environment. In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of one or more of the ships capable of carrying
cargo.
[0890] In embodiments, the maritime activities include the forward
speed of one or more of the ships relative to water and weather
conditions based on the parameters associated with energy
consumption of the propulsion units on one or more of the
ships.
[0891] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of design
outcomes, parameters, and data collected from data sources relating
to a set of shipping activities to train an artificial intelligence
system to simulate attributes of a container ship and generate a
set of recommendations of changes to the attributes using a digital
twin of the container ship.
[0892] In embodiments, the container ship is moored to port
infrastructure installed on or adjacent to land. In embodiments,
the shipping activities include the forward speed of the container
ship relative to water and weather conditions based on the
parameters associated with energy consumption of propulsion units
on the container ship. In embodiments, the information technology
system further includes an asset management application associated
with one or more maritime facilities connected to the container
ship. In embodiments, the asset management application is
associated with one or more ships connected to barges.
[0893] In embodiments, the digital twin of the container ship
provides for visualization of a navigation course of the container
ship. In embodiments, the digital twin of the container ship
provides for visualization of an engine performance of the
container ship. In embodiments, the digital twin of the container
ship provides for visualization of a hull integrity of the
container ship. In embodiments, the digital twin of the container
ship provides for visualization of in-situ hydrodynamic changes to
a portion of a hull disposed below a water line of the container
ship. In embodiments, the digital twin of the container ship
determines a schedule of the in-situ hydrodynamic changes to the
portion of the hull disposed below the waterline of the container
ship to improve fuel efficiency using known routes of travel and
historical weather patterns. In embodiments, the digital twin of
the container ship provides for visualization of in-situ
aerodynamic changes to a portion of a hull disposed above a water
line of the container ship. In embodiments, the digital twin of the
container ship determines a schedule of in-situ aerodynamic changes
to the portion of the hull disposed above the waterline of the
container ship to improve fuel efficiency using known routes of
travel and historical weather patterns.
[0894] In embodiments, the digital twin of the container ship
provides for visualization of extendable buoyant members from a
hull of the container ship to improve stability during certain
maneuvers of the container ship. In embodiments, the digital twin
of the container ship provides for visualization of extendable
buoyant members from a hull of the container ship to improve
stability during certain maneuvers of the container ship.
[0895] In embodiments, the digital twin of the container ship
provides for visualization of a plurality of inspection points on
the container ship and maintenance histories associated with those
inspection points. In embodiments, the digital twin of the
container ship provides for the visualization of the plurality of
inspection points on the container ship affected by travel within a
geofenced area and maintenance histories associated with those
inspection points when maintenance follows travel through the
geofenced area. In embodiments, the digital twin of the container
ship provides for details of a ledger of activity associated with
the visualization of the plurality of inspection points on the
container ship affected by travel within a geofenced area and
maintenance histories associated with those inspection points when
maintenance follows travel through the geofenced area.
[0896] In embodiments, the digital twin of the container ship
provides for visualization for a first user of one of a navigation
course of the container ship and an engine performance of the
container ship within a first geofenced area and for visualization
for a second user of one of the navigation course of the container
ship and the engine performance of the container ship within a
second geofenced area and where transit between the first and
second geofenced areas motivates a handoff of the digital twin of
the container ship between the first user and the second user.
[0897] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform including an asset management application
associated with one or more barges and a data handling layer of the
management platform including data sources containing information
used to populate a training set based on a set of maritime
activities of one or more of the barges and one of design outcomes,
parameters, and data associated with the one or more of the barges.
The information technology system also has an artificial
intelligence system that is configured to learn on the training set
collected from the data sources, that simulates one or more design
attributes of one or more of the barges, and that generates one or
more sets of design recommendations based on the training set
collected from the data sources. The information technology system
also has a digital twin system included in the value chain network
management platform that provides for visualization of a digital
twin of one or more of the barges including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations.
[0898] In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the barges
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with the barges. In
embodiments, one of the barges is connected to a ship. In
embodiments, the digital twin is configured to provide for
visualization of a navigation course of one of the barges relative
to a planned course of one of the barges and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the navigation course of one of the barges.
[0899] In embodiments, the digital twin is configured to provide
for visualization of a hull integrity of one of the barges relative
to a planned course of one of the barges and one or more of the
sets of recommendations from the artificial intelligence system for
a change in maintenance of the hull of one of the barges.
[0900] In embodiments, the digital twin is configured to provide
for visualization of in-situ hydrodynamic changes to a portion of a
hull disposed below a water line of one or more of the barges and
one or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of one or more of the barges. In embodiments,
the digital twin is configured to determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the
waterline of one or more of the barges to improve fuel efficiency
based on known routes of travel and weather patterns.
[0901] In embodiments, the digital twin is configured to provide
visualizations of extendable buoyant members from a hull of one or
more of the barges to improve stability during certain maneuvers of
one or more of the barges and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the extendable buoyant members to change performance of
one or more of the barges. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points on one or more of the barges and maintenance histories
associated with those inspection points. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide for
visualizations of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the plurality of
inspection points. In embodiments, the digital twin is configured
to provide details of a ledger of activity associated with the
visualization of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points.
[0902] In embodiments, the digital twin is configured to provide
for visualization for a first user of one of a navigation course of
one or more of the barges within a first geofenced area and for
visualization for a second user of one of the navigation course of
one or more of the barges within a second different geofenced area
and where transit between the first and second geofenced areas
motivates a handoff of the digital twin of one or more of the
barges between the first user and the second user. In embodiments,
the digital twin is configured to at least partially represent one
or more of the barges associated with an event investigation and to
at least partially detail a timeline of the event investigation and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges.
[0903] In embodiments, the digital twin is configured to at least
partially represent one or more of the barges associated with a
legal proceeding and to at least partially detail at least a
portion of a timeline pertinent to the legal proceeding and
associated barges. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated barges. In embodiments, the digital
twin is configured to at least partially represent one or more of
the barges associated with a casualty forecast and to at least
partially detail at least a portion of a timeline pertinent to the
casualty report and associated barges. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges to reduce
exposure relative to a set of previous casualty forecasts.
[0904] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items at on one of the barges by correlating data between
a set of data collectors for one or more physical items on one of
the barges and the digital twin detailing the one or more physical
items on one of the barges for at least one of a port
infrastructure facility and a set of operators. In embodiments, the
digital twin details the one or more physical items on of the
barges for at least one operator that includes a view of expected
states of at least a portion of the one or more physical items. In
embodiments, the artificial intelligence system determines a set of
geofence parameters. In embodiments, the digital twin provides
further visualization of at least one geofence that integrates
representation of one or more of the barges with a representation
of a maritime environment adjacent to the geofence. In embodiments,
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the set of one or more of the
barges.
[0905] In embodiments, the asset management application is
associated with one or more ships connected to one of the barges.
In embodiments, the data handling layer of the management platform
includes data sources containing information used to populate the
training set based on a set of maritime activities of one or more
of the barges underway and each connected to a ship and one of
design outcomes, parameters, and data associated with the one or
more of the barges and its associated ship.
[0906] In embodiments, the artificial intelligence system is
configured to learn on the training set collected from the data
sources and to simulate one or more design attributes of one or
more of the barges each connected to a ship. In embodiments, the
digital twin system provides for visualization of a digital twin of
one or more of the barges and each of the ships to which they are
connected.
[0907] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of design
outcomes, parameters, and data collected from data sources relating
to a set of shipping activities to train an artificial intelligence
system to simulate attributes of a barge and generate a set of
recommendations of changes to the attributes using a digital twin
of the barge.
[0908] In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the barges
including one or more of the attributes in combination with one or
more of the sets of recommendations of changes to the attributes
associated with the barges. In embodiments, one of the barges is
connected to a ship. In embodiments, the digital twin is configured
to provide for visualization of a navigation course of one of the
barges relative to a planned course of one of the barges and one or
more of the sets of recommendations from the artificial
intelligence system for a change in the navigation course of one of
the barges.
[0909] In embodiments, the digital twin is configured to provide
for visualization of a hull integrity of one of the barges relative
to a planned course of one of the barges and one or more of the
sets of recommendations from the artificial intelligence system for
a change in maintenance of the hull of one of the barges. In
embodiments, digital twin is configured to provide for
visualization of in-situ hydrodynamic changes to a portion of a
hull disposed below a water line of one or more of the barges and
one or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of one or more of the barges. In embodiments,
the digital twin is configured to determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the
waterline of one or more of the barges to improve fuel efficiency
based on known routes of travel and weather patterns.
[0910] In embodiments, the digital twin is configured to provide
visualizations of extendable buoyant members from a hull of one or
more of the barges to improve stability during certain maneuvers of
one or more of the barges and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the extendable buoyant members to change performance of
one or more of the barges. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points on one or more of the barges and maintenance histories
associated with those inspection points. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide for
visualizations of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the plurality of
inspection points. In embodiments, the digital twin is configured
to provide details of a ledger of activity associated with the
visualization of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points.
[0911] In embodiments, the digital twin is configured to provide
for visualization for a first user of one of a navigation course of
one or more of the barges within a first geofenced area and for
visualization for a second user of one of the navigation course of
one or more of the barges within a second different geofenced area
and where transit between the first and second geofenced areas
motivates a handoff of the digital twin of one or more of the
barges between the first user and the second user. In embodiments,
the digital twin is configured to at least partially represent one
or more of the barges associated with an event investigation and to
at least partially detail a timeline of the event investigation and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges.
[0912] In embodiments, the digital twin is configured to at least
partially represent one or more of the barges associated with a
legal proceeding and to at least partially detail at least a
portion of a timeline pertinent to the legal proceeding and
associated barges. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated barges. In embodiments, the digital
twin is configured to at least partially represent one or more of
the barges associated with a casualty forecast and to at least
partially detail at least a portion of a timeline pertinent to the
casualty report and associated barges. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges to reduce
exposure relative to a set of previous casualty forecasts.
[0913] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items on one of the barges by correlating data between a
set of data collectors for one or more physical items on one of the
barges and the digital twin detailing the one or more physical
items on one of the barges for at least one of a port
infrastructure facility and a set of operators. In embodiments, the
digital twin details the one or more physical items on of the
barges for at least one operator that includes a view of expected
states of at least a portion of the one or more physical items.
[0914] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of one or more of the
barges with a representation of a maritime environment adjacent to
the geofence. In embodiments, the digital twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the set of one or more of the barges.
[0915] In embodiments, the asset management application is
associated with one or more ships connected to one of the barges.
In embodiments, the data handling layer of the management platform
includes data sources containing information used to populate the
training set based on a set of maritime activities of one or more
of the barges underway and each connected to a ship and one of
design outcomes, parameters, and data associated with the one or
more of the barges and its associated ship. In embodiments, the
artificial intelligence system is configured to learn on the
training set collected from the data sources and to simulate one or
more design attributes of one or more of the barges each connected
to a ship. In embodiments, the digital twin system provides for
visualization of a digital twin of one or more of the barges and
each of the ships to which they are connected.
[0916] In embodiments, the methods, systems and apparatuses
includes an information technology system having a value chain
network management platform including an asset management
application associated with port infrastructure and a data handling
layer of the management platform including data sources containing
information used to populate a training set based on a set of
maritime activities around the port infrastructure and one of
design outcomes, parameters, and data associated with the port
infrastructure. The information technology system also has an
artificial intelligence system that is configured to learn on the
training set collected from the data sources, that simulates one or
more attributes of the port infrastructure, and that generates one
or more sets of recommendations for a change in the one or more
attributes based on the training set collected from the data
sources. The information technology system also has a digital twin
system included in the value chain network management platform that
provides for visualization of a digital twin of the port
infrastructure including detail generated by the artificial
intelligence system of one or more of the attributes in combination
with the one or more sets of recommendations.
[0917] In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of container ships
in the port infrastructure including one or more of the attributes
in combination with one or more of the sets of recommendations
associated with one or more of the container ships.
[0918] In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of barges in the
port infrastructure including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with one or more of the barges. In embodiments, the port
infrastructure includes one or more moored navigation units
deployed on water. In embodiments, the port infrastructure includes
one or more ships each connected to a barge. In embodiments, the
port infrastructure is associated with a real-world maritime port.
In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the components
of the real-world maritime port including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of the real-world
maritime port.
[0919] In embodiments, the port infrastructure is associated with a
real-world shipyard. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world shipyard including one or
more of the attributes in combination with one or more of the sets
of recommendations associated with the components of the real-world
shipyard.
[0920] In embodiments, the digital twin is configured to provide
for visualization of an engine performance of the port
infrastructure and one or more of the sets of recommendations from
the artificial intelligence system for a change in the engine
performance installed in the port infrastructure. In embodiments,
the visualization of an engine performance includes an emissions
profile. In embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points on the port
infrastructure and maintenance histories associated with those
inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide for visualizations of the plurality
of inspection points on the port infrastructure includes within a
geofenced area and maintenance histories associated with those
inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on the port infrastructure includes within a geofenced area
and maintenance histories associated with those inspection
points.
[0921] In embodiments, the digital twin is configured to at least
partially represent the port infrastructure associated with an
event investigation and to at least partially detail a timeline of
the event investigation. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated port infrastructure.
[0922] In embodiments, the digital twin is configured to at least
partially represent the port infrastructure associated with a legal
proceeding and to at least partially detail at least a portion of a
timeline pertinent to the legal proceeding. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the associated port
infrastructure.
[0923] In embodiments, the digital twin is configured to at least
partially represent the port infrastructure associated with a
casualty forecast and to at least partially detail at least a
portion of a timeline pertinent to the casualty report. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated port infrastructure to reduce exposure relative to a set
of previous casualty forecasts.
[0924] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft at or misuse at
the port infrastructure by correlating data between a set of data
collectors for one or more physical items at the port
infrastructure and the digital twin detailing the one or more
physical items of the port infrastructure for the at least one of a
facility at the port infrastructure and the set of operators. In
embodiments, the digital twin details the one or more physical
items at the port infrastructure for at least one operator that
includes a view of expected states of at least a portion of the one
or more physical items.
[0925] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft at or misuse of
one or more physical items at the port infrastructure by
correlating data between a set of data collectors for the one or
more physical items and the digital twin detailing the one or more
physical items at the port infrastructure includes for the at least
one of a facility at the port infrastructure and the set of
operators. In embodiments, the digital twin details the one or more
physical items at the port infrastructure for at least one operator
that includes a view of expected states of at least a portion of
the one or more physical items.
[0926] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of at least a portion of
the port infrastructure with a representation of a maritime
environment adjacent to the geofence. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the port infrastructure.
[0927] In embodiments, one or more components of the port
infrastructure are installed on land. In embodiments, the one or
more components of the port infrastructure include one or more
moored navigation units deployed on water. In embodiments, the
methods, systems and apparatuses include an information technology
system having a value chain network management platform for
learning on a training set of design outcomes, parameters, and data
collected from data sources relating to a set of shipping
activities to train an artificial intelligence system to simulate
design attributes of a port infrastructure facility and generate a
set of design recommendations using a digital twin of the port
infrastructure facility. In embodiments, the digital twin system
further provides for visualization of the digital twin of the port
infrastructure facility including one or more of the attributes in
combination with one or more of the sets of recommendations of
changes to the attributes associated with the port infrastructure
facility.
[0928] In embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points on the port
infrastructure facility and maintenance histories associated with
those inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide details of a
ledger of activity associated with the visualization of the
plurality of inspection points on the port infrastructure facility
within a geofenced area and maintenance histories associated with
those inspection points.
[0929] In embodiments, the digital twin is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with an event investigation and to at least
partially detail a timeline of the event investigation and
associated with the port infrastructure facility. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change of one of the attributes of the port infrastructure
facility.
[0930] In embodiments, the digital twin is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with a legal proceeding and to at least
partially detail at least a portion of a timeline pertinent to the
legal proceeding and associated with the port infrastructure
facility. In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the associated port infrastructure facility
[0931] In embodiments, the digital twin is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with a casualty forecast and to at least
partially detail at least a portion of a timeline pertinent to the
casualty report and associated port infrastructure facility. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of at
least a portion of the port infrastructure facility to reduce
exposure relative to a set of previous casualty forecasts.
[0932] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items in at least a portion of the port infrastructure
facility by correlating data between a set of data collectors for
one or more physical items in at least a portion of the port
infrastructure facility and the digital twin detailing the one or
more physical items in at least a portion of the port
infrastructure facility for at least one of the port infrastructure
facility and a set of operators. In embodiments, the digital twin
details the one or more physical items in the port infrastructure
facility for at least one operator that includes a view of expected
states of at least a portion of the one or more physical items.
[0933] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of at least a portion of
the port infrastructure facility with a representation of a
maritime environment adjacent to the geofence. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of at least a portion of the port
infrastructure facility.
[0934] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform including an asset management application
associated with maritime assets involved in a maritime event and a
data handling layer of the management platform including data
sources containing information used to populate a training set
based on a set of maritime activities of the maritime assets
involved in the maritime event and one of design outcomes,
parameters, and data associated with the maritime assets involved
in the maritime event. The information technology system also has
an artificial intelligence system that is configured to learn on
the training set collected from the data sources, that simulates
one or more design attributes of the maritime assets involved in a
maritime event, and that generates one or more sets of design
recommendations based on the training set collected from the data
sources. The information technology system also has a digital twin
system included in the value chain network management platform that
provides for visualization of a digital twin of the maritime assets
involved in a maritime event including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations applicable to at least one of the maritime assets
involved in the maritime event.
[0935] In embodiments, the maritime assets include one or more
container ships involved in the maritime event. In embodiments, the
digital twin system further provides for visualization of the
digital twin of one or more of the container ships including one or
more of the attributes in combination with one or more of the sets
of recommendations associated with the container ships.
[0936] In embodiments, the maritime assets include one or more
barges involved in the maritime event. In embodiments, the digital
twin system further provides for visualization of the digital twin
of one or more of the barges including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the barges.
[0937] In embodiments, the maritime assets include one or more
components of port infrastructure involved in the maritime event.
In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the components
of port infrastructure including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of port infrastructure.
[0938] In embodiments, the maritime assets are associated with a
real-world maritime port. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world maritime port involved in
the maritime event including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of the real-world maritime port.
[0939] In embodiments, the maritime assets are associated with a
real-world shipyard In embodiments, the digital twin system further
provides for visualization of the digital twin of one or more of
the components of the real-world shipyard involved in the maritime
event including one or more of the attributes in combination with
one or more of the sets of recommendations associated with the
components of the real-world shipyard.
[0940] In embodiments, the digital twin of one or more of the
maritime assets is a floating asset twin associated with a ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a navigation course of the ship involved in the
maritime event relative to a planned course of the ship and one or
more of the sets of recommendations from the artificial
intelligence system for a change in the navigation course of the
ship. In embodiments, the floating asset twin is configured to
provide for visualization of an engine performance of the ship
involved in the maritime event and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the engine performance of the ship. In embodiments, the
visualization of an engine performance includes an emissions
profile of the ship. In embodiments, the floating asset twin is
configured to provide for visualization of a hull integrity of the
ship involved in the maritime event and one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the hull of the ship. In embodiments, the
floating asset twin is configured to provide visualizations of a
plurality of inspection points on the ship involved in the maritime
event and maintenance histories associated with those inspection
points. In embodiments, the floating asset twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of the
plurality of inspection points associated with the maritime event.
In embodiments, the floating asset twin is configured to provide
for visualizations of the plurality of inspection points on the
ship affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is also configured to provide one or more
of the sets of recommendations from the artificial intelligence
system for a change in maintenance of the plurality of inspection
points associated with the maritime event. In embodiments, the
floating asset twin is configured to provide details of a ledger of
activity associated with the visualization of the plurality of
inspection points on the ship involved in the maritime event within
a geofenced area and maintenance histories associated with those
inspection points.
[0941] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets involved in the maritime event with a representation of a
maritime environment adjacent to the geofence. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the set of maritime assets
involved in the maritime event. In embodiments, the methods,
systems and apparatuses include an information technology system
having a value chain network management platform for learning on a
training set of maritime event outcomes, parameters, and data
collected from data sources to train an artificial intelligence
system to use a digital twin to facilitate investigation of a
maritime event.
[0942] In embodiments, the maritime event outcomes are associated
with a real-world shipyard. In embodiments, the digital twin is
configured to detail at least a portion of the real-world shipyard
to facilitate investigation of the maritime event. In embodiments,
the maritime event outcomes are associated with a real-world
maritime port. In embodiments, the digital twin is configured to
detail at least a portion of the real-world maritime port to
facilitate investigation of the maritime event.
[0943] In embodiments, the maritime event outcomes are associated
with one or more container ships. In embodiments, the digital twin
is configured to detail one or more of the container ships to
facilitate investigation of the maritime event. In embodiments, the
maritime event outcomes are associated with one or more barges. In
embodiments, the digital twin is configured to detail one or more
of the barges to facilitate investigation of the maritime
event.
[0944] In embodiments, the maritime event outcomes are associated
with at least a portion of port infrastructure. In embodiments, the
digital twin is configured to detail at least a portion of the port
infrastructure to facilitate investigation of the maritime event.
In embodiments, the digital twin is configured to at least
partially represent activity of one or more maritime value chain
network entities during a timeline associated with the maritime
event. In embodiments, the one or more maritime value chain network
entities are associated with a legal proceeding. In embodiments,
the digital twin is further configured to at least partially
represent activity of one or more maritime value chain network
entities during a timeline associated with the legal proceeding. In
embodiments, the one or more maritime value chain network entities
are associated with a legal proceeding. In embodiments, the digital
twin is further configured to at least partially represent activity
of one or more maritime value chain network entities during a
timeline associated with the legal proceeding.
[0945] In embodiments, the one or more maritime value chain network
entities are associated with a casualty forecast. In embodiments,
the digital twin is further configured to at least partially
represent activity of one or more maritime value chain network
entities during a timeline associated with the casualty forecast.
In embodiments, one or more of the maritime value chain network
entities is a port infrastructure facility. In embodiments, the
data collected by the value chain network management platform
facilitates identifying theft or misuse of one or more physical
items of the port infrastructure facility by correlating data
between a set of data collectors for one or more of the physical
items in the port infrastructure facility and the digital twin
detailing one or more of the physical items of the port
infrastructure facility for the at least one of the port
infrastructure facility and the set of operators to further
facilitate investigation of the maritime event.
[0946] In embodiments, the maritime event includes a container ship
that is moored to port infrastructure installed on or adjacent to
land. In embodiments, the maritime event includes at least a
container ship having a forward speed relative to water and weather
conditions and parameters associated with energy consumption of
propulsion units on the container ship.
[0947] In embodiments, the maritime event includes one or more
ships connected to barges. In embodiments, the maritime event
includes one or more ships. In embodiments, the digital twin
provides for visualization of a navigation course of one or more of
the ships during the maritime event. In embodiments, the maritime
event includes one or more ships. In embodiments, the digital twin
provides for visualization of an engine performance of one or more
of the ships during the maritime event. In embodiments, the
maritime event includes one or more ships. In embodiments, the
digital twin provides for visualization of a hull integrity of one
or more of the ships involved in the maritime event.
[0948] In embodiments, the maritime event includes one or more
ships. In embodiments, the digital twin provides for visualization
of a plurality of inspection points associated with one or more of
the ships and maintenance histories associated with those
inspection points.
[0949] In embodiments, the digital twin further provides for the
visualization of the plurality of inspection points associated with
one or more of the ships within a geofenced area related to the
maritime event and maintenance histories associated with those
inspection points. In embodiments, the digital twin further
provides for details of a ledger of activity associated with the
visualization of the plurality of inspection points associated with
one or more of the ships within a geofenced area related to the
maritime event and maintenance histories associated with those
inspection points.
[0950] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform including an asset management application
associated with maritime assets involved in a maritime legal
proceeding and a data handling layer of the management platform
including data sources containing information used to populate a
training set based on a set of maritime activities of the maritime
assets involved in the maritime legal proceeding and one of
parameters and data associated with the maritime assets involved in
the maritime legal proceeding. The information technology system
also has an artificial intelligence system that is configured to
learn on the training set collected from the data sources, that
simulates one or more attributes of one or more of the maritime
assets involved in the maritime legal proceeding, and that
generates one or more sets of recommendations for a change in the
one or more attributes based on the training set collected from the
data sources. The information technology system also has a digital
twin system included in the value chain network management platform
that provides for visualization of a digital twin of one or more of
the maritime assets involved in the maritime legal proceeding
including detail generated by the artificial intelligence system of
one or more of the attributes in combination with the one or more
sets of recommendations.
[0951] In embodiments, the maritime assets include one or more
container ships involved in the maritime legal proceeding. In
embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the container
ships including one or more of the attributes in combination with
one or more of the sets of recommendations associated with the
container ships.
[0952] In embodiments, the maritime assets include one or more
barges involved in the maritime legal proceeding. In embodiments,
the digital twin system further provides for visualization of the
digital twin of one or more of the barges including one or more of
the attributes in combination with one or more of the sets of
recommendations associated with the barges.
[0953] In embodiments, the maritime assets include one or more
components of port infrastructure involved in the maritime legal
proceeding. In embodiments, the digital twin system further
provides for visualization of the digital twin of one or more of
the components of port infrastructure including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of port
infrastructure.
[0954] In embodiments, the maritime assets are associated with a
real-world maritime port. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world maritime port involved in
the maritime legal proceeding including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of the real-world
maritime port.
[0955] In embodiments, the maritime assets are associated with a
real-world shipyard. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world shipyard involved in the
maritime legal proceeding including one or more of the attributes
in combination with one or more of the sets of recommendations
associated with the components of the real-world shipyard.
[0956] In embodiments, the digital twin of one or more of the
maritime assets is a floating asset twin associated with a ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a navigation course of the ship involved in the
maritime legal proceeding relative to a planned course of the ship
and one or more of the sets of recommendations from the artificial
intelligence system for a change in the navigation course of the
ship. In embodiments, the floating asset twin is configured to
provide for visualization of an engine performance of the ship
involved in the maritime legal proceeding and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the engine performance of the ship.
[0957] In embodiments, the visualization of an engine performance
includes an emissions profile of the ship. In embodiments, the
floating asset twin is configured to provide for visualization of a
hull integrity of the ship involved in the maritime legal
proceeding and one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of the
hull of the ship. In embodiments, the floating asset twin is
configured to provide visualizations of a plurality of inspection
points on the ship involved in the maritime legal proceeding and
maintenance histories associated with those inspection points. In
embodiments, the floating asset twin is also configured to provide
one or more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the plurality of
inspection points associated with the maritime event. In
embodiments, the floating asset twin is configured to provide for
visualizations of the plurality of inspection points on the ship
affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is also configured to provide one or more
of the sets of recommendations from the artificial intelligence
system for a change in maintenance of the plurality of inspection
points associated with the maritime event. In embodiments, the
floating asset twin is configured to provide details of a ledger of
activity associated with the visualization of the plurality of
inspection points on the ship involved in the maritime legal
proceeding within a geofenced area and maintenance histories
associated with those inspection points.
[0958] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets involved in the maritime legal proceeding with a
representation of a maritime environment adjacent to the geofence.
In embodiments, the digital twin is also configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
set of maritime assets involved in the maritime legal
proceeding.
[0959] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of maritime
legal outcomes, parameters, and data collected from data sources to
train an artificial intelligence system to use a digital twin to
generate a recommendation relating to a maritime legal
proceeding.
[0960] In embodiments, the maritime legal outcomes are associated
with a real-world shipyard. In embodiments, the digital twin is
configured to detail at least a portion of the real-world shipyard
associated with the maritime legal proceeding. In embodiments, the
maritime legal outcomes are associated with a real-world maritime
port. In embodiments, the digital twin is configured to detail at
least a portion of the real-world maritime port associated with the
maritime legal proceeding.
[0961] In embodiments, the maritime legal outcomes are associated
with one or more container ships. In embodiments, the digital twin
is configured to detail at least a portion of the one or more
container ships associated with the maritime legal proceeding. In
embodiments, the maritime legal outcomes are associated with one or
more barges. In embodiments, the digital twin is configured to
detail at least a portion of the one or more barges associated with
the maritime legal proceeding.
[0962] In embodiments, the maritime legal outcomes are associated
with at least a portion of port infrastructure. In embodiments, the
digital twin is configured to detail at least a portion of the port
infrastructure associated with the maritime legal proceeding.
[0963] In embodiments, the digital twin is configured to at least
partially represent activity of one or more maritime value chain
network entities during a timeline associated with the maritime
legal proceeding. In embodiments, one or more of the maritime value
chain network entities is a port infrastructure facility. In
embodiments, the data collected by the value chain network
management platform facilitates identifying theft or misuse of one
or more physical items of the port infrastructure facility relating
to the maritime legal proceeding by correlating data between a set
of data collectors for one or more of the physical items in the
port infrastructure facility. In embodiments, the digital twin is
configured to further detail one or more of the physical items of
the port infrastructure facility for the at least one of the port
infrastructure facility and the set of operators.
[0964] In embodiments, the maritime legal proceeding includes a
situation involving a container ship that is moored to port
infrastructure installed on or adjacent to land. In embodiments,
the maritime legal proceeding includes a situation involving a
container ship having a forward speed relative to water and weather
conditions and parameters associated with energy consumption of
propulsion units on the container ship. In embodiments, the
maritime legal proceeding includes a situation involving one or
more ships connected to barges. In embodiments, the maritime legal
proceeding includes a situation involving one or more ships. In
embodiments, the digital twin provides for visualization of a
navigation course of one or more of the ships relevant to the
maritime legal proceeding. In embodiments, the maritime legal
proceeding includes a situation involving one or more ships. In
embodiments, the digital twin provides for visualization of an
engine performance of one or more of the ships relevant to the
maritime legal proceeding. In embodiments, the maritime legal
proceeding includes a situation involving one or more ships. In
embodiments, the digital twin provides for visualization of a hull
integrity of one or more of the ships relevant to the maritime
legal proceeding.
[0965] In embodiments, the maritime legal proceeding includes a
situation involving one or more ships. In embodiments, the digital
twin provides for visualization of a plurality of inspection points
associated with one or more of the ships and maintenance histories
associated with those inspection points. In embodiments, the
digital twin further provides for the visualization of the
plurality of inspection points associated with one or more of the
ships within a geofenced area relevant to the maritime legal
proceeding and maintenance histories associated with those
inspection points. In embodiments, the digital twin further
provides for details of a ledger of activity associated with the
visualization of the plurality of inspection points associated with
one or more of the ships within a geofenced area relevant to the
maritime legal proceeding and maintenance histories associated with
those inspection points.
[0966] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform including an asset management application
associated with maritime assets and a data handling layer of the
management platform including data sources containing information
used to populate a training set based on a set of maritime
activities of one or more of the maritime assets involved in a loss
event and one of outcomes, parameters, and data associated with the
one or more maritime assets experiencing the loss event. The
information technology system also has an artificial intelligence
system that is configured to learn on the training set collected
from the data sources, that simulates one or more attributes of one
or more of the maritime assets, and that generates one or more sets
of casualty forecasts based on the training set collected from the
data sources. The information technology system also has a digital
twin system included in the value chain network management platform
that provides for visualization of one or more digital twins
associated with one or more of the maritime assets involved in the
loss event including detail generated by the artificial
intelligence system of at least a portion of one of the sets of
casualty forecasts.
[0967] In embodiments, the maritime assets include one or more
container ships associated with at least a portion of one of the
sets of casualty forecasts. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the container ships including one or more of the attributes
in combination with one or more of the sets of recommendations
associated with the container ships.
[0968] In embodiments, the maritime assets include one or more
barges with at least a portion of one of the sets of casualty
forecasts. In embodiments, the digital twin system further provides
for visualization of the digital twin of one or more of the barges
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with the barges.
[0969] In embodiments, the maritime assets include one or more
components of port infrastructure with at least a portion of one of
the sets of casualty forecasts. In embodiments, the digital twin
system further provides for visualization of the digital twin of
one or more of the components of port infrastructure including one
or more of the attributes in combination with one or more of the
sets of recommendations associated with the components of port
infrastructure associated with the sets of casualty forecasts.
[0970] In embodiments, the maritime assets are associated with a
real-world maritime port. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world maritime port associated
at least a portion of one of the sets of casualty forecasts
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with the components
of the real-world maritime port.
[0971] In embodiments, the maritime assets are associated with a
real-world shipyard. In embodiments, the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world shipyard associated at
least a portion of one of the sets of casualty forecasts including
one or more of the attributes in combination with one or more of
the sets of recommendations associated with the components of the
real-world shipyard.
[0972] In embodiments, the digital twin of one or more of the
maritime assets is a floating asset twin associated with a ship
associated with at least a portion of one of the sets of casualty
forecasts. In embodiments, the floating asset twin is configured to
provide for visualization of a navigation course of the ship
associated at least a portion of one of the sets of casualty
forecasts relative to a planned course of the ship and one or more
of the sets of recommendations from the artificial intelligence
system for a change in the navigation course of the ship. In
embodiments, the floating asset twin is configured to provide for
visualization of an engine performance of the ship associated at
least a portion of one of the sets of casualty forecasts and one or
more of the sets of recommendations from the artificial
intelligence system for a change in the engine performance of the
ship. In embodiments, the visualization of an engine performance
includes an emissions profile of the ship. In embodiments, the
floating asset twin is configured to provide for visualization of a
hull integrity of the ship associated at least a portion of one of
the sets of casualty forecasts and one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the hull of the ship. In embodiments, the
floating asset twin is configured to provide visualizations of a
plurality of inspection points on the ship associated with at least
a portion of one of the sets of casualty forecasts and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is also configured to provide one or more
of the sets of recommendations from the artificial intelligence
system for a change in maintenance of the plurality of inspection
points associated with the maritime event. In embodiments, the
floating asset twin is configured to provide for visualizations of
the plurality of inspection points on the ship affected by travel
within a geofenced area and maintenance histories associated with
those inspection points. In embodiments, the floating asset twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points
associated with the maritime event. In embodiments, the floating
asset twin is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on the ship associated at least a portion of one of the sets
of casualty forecasts within a geofenced area and maintenance
histories associated with those inspection points.
[0973] In embodiments, the artificial intelligence system
determines a set of geofence parameters. In embodiments, the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets associated at least a portion of one of the sets of casualty
forecasts with a representation of a maritime environment adjacent
to the geofence. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the set of maritime assets associated with at least a
portion of one of the sets of casualty forecasts.
[0974] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of maritime
outcomes, parameters, and data collected from data sources to train
an artificial intelligence system to use a digital twin to predict
and display a casualty forecast for a set of maritime assets.
[0975] In embodiments, the set of maritime assets includes a
real-world shipyard. In embodiments, the digital twin is configured
to detail at least a portion of the real-world shipyard associated
with the casualty forecast.
[0976] In embodiments, the set of maritime assets includes a
real-world maritime port. In embodiments, the digital twin is
configured to detail at least a portion of the real-world maritime
port associated with the casualty forecast.
[0977] In embodiments, the set of maritime assets includes one or
more container ships. In embodiments, the digital twin is
configured to detail at least a portion of the one or more
container ships associated with the casualty forecast.
[0978] In embodiments, the set of maritime assets includes one or
more barges. In embodiments, the digital twin is configured to
detail at least a portion of the one or more barges associated with
the casualty forecast. In embodiments, the set of maritime assets
includes at least a portion of port infrastructure. In embodiments,
the digital twin is configured to detail at least a portion of the
port infrastructure associated with the casualty forecast. In
embodiments, the digital twin is configured to at least partially
represent activity of the set of maritime assets during a timeline
associated with the casualty forecast.
[0979] In embodiments, the set of maritime assets includes a port
infrastructure facility. In embodiments, data collected by the
value chain network management platform facilitates identifying
theft or misuse of one or more physical items of the port
infrastructure facility relating to the casualty forecast by
correlating data between a set of data collectors for one or more
of the physical items in the port infrastructure facility. In
embodiments, the digital twin is configured to further detail one
or more of the physical items of the port infrastructure facility
for the at least one of the port infrastructure facility and the
set of operators.
[0980] In embodiments, the set of maritime assets includes a
container ship that is moored to port infrastructure installed on
or adjacent to land. In embodiments, the set of maritime assets
includes one or more ships connected to barges. In embodiments, the
set of maritime assets includes one or more ships. In embodiments,
the digital twin provides for visualization of a navigation course
of one or more of the ships relevant to the casualty forecast. In
embodiments, the set of maritime assets includes one or more ships.
In embodiments, the digital twin provides for visualization of an
engine performance of one or more of the ships relevant to the
casualty forecast. In embodiments, the set of maritime assets
includes one or more ships. In embodiments, the digital twin
provides for visualization of a hull integrity of one or more the
ships relevant to the casualty forecast.
[0981] In embodiments, the set of maritime assets includes one or
more ships. In embodiments, the digital twin provides for
visualization of a plurality of inspection points associated with
one or more of the ships and maintenance histories associated with
those inspection points relevant to the casualty forecast. In
embodiments, the digital twin further provides for the
visualization of the plurality of inspection points associated with
one or more of the ships within a geofenced area relevant to the
casualty forecast and maintenance histories associated with those
inspection points. In embodiments, the digital twin further
provides for details of a ledger of activity associated with the
visualization of the plurality of inspection points associated with
one or more of the ships within a geofenced area relevant to the
casualty forecast and maintenance histories associated with those
inspection points.
[0982] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for identifying theft or misuse of a port
infrastructure facility by correlating data between a set of data
collectors for the physical item and a set of digital twins for at
least one of the port infrastructure facility and a set of
operators.
[0983] In embodiments, the set of digital twins of the port
infrastructure facility includes one or more of the attributes in
combination with one or more of the sets of recommendations of
changes to attributes associated with the port infrastructure
facility. In embodiments, the set of digital twins is configured to
provide visualizations of a plurality of inspection points on the
port infrastructure facility and maintenance histories associated
with those inspection points. In embodiments, the set of digital
twins is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on the port infrastructure facility within a geofenced area
and maintenance histories associated with those inspection
points.
[0984] In embodiments, the set of digital twins is configured to at
least partially represent at least a portion of the port
infrastructure facility associated with an event investigation and
to at least partially detail a timeline of the event investigation
and associated with the port infrastructure facility. In
embodiments, the set of digital twins is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with a legal proceeding and to at least
partially detail at least a portion of a timeline pertinent to the
legal proceeding and associated with the port infrastructure
facility. In embodiments, the set of digital twins is configured to
at least partially represent at least a portion of the port
infrastructure facility associated with a casualty forecast and to
at least partially detail at least a portion of a timeline
pertinent to the casualty report and associated port infrastructure
facility.
[0985] In embodiments, the digital twin details the one or more
physical items in the port infrastructure facility for at least one
operator that includes a view of expected states of at least a
portion of the one or more physical items. In embodiments, the set
of digital twins provides further visualization of at least one
geofence that integrates representation of at least a portion of
the port infrastructure facility with a representation of a
maritime environment adjacent to the geofence.
[0986] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform identifying theft or misuse of a shipyard
facility by correlating data between a set of data collectors for
the physical item and a set of digital twins for at least one of
the shipyard facility and a set of operators.
[0987] In embodiments, the set of digital twins for at least one of
the shipyard facility and a set of operators includes one or more
of the attributes in combination with one or more of the sets of
recommendations of changes to attributes associated with the
shipyard facility.
[0988] In embodiments, the set of digital twins is configured to
provide visualizations of a plurality of inspection points on in
the shipyard facility and maintenance histories associated with
those inspection points. In embodiments, the set of digital twins
is configured to provide details of a ledger of activity associated
with the visualization of the plurality of inspection points on the
shipyard facility within a geofenced area and maintenance histories
associated with those inspection points.
[0989] In embodiments, the set of digital twins is configured to at
least partially represent at least a portion of the shipyard
facility associated with an event investigation and to at least
partially detail a timeline of the event investigation and
associated with the port infrastructure facility. In embodiments,
the set of digital twins is configured to at least partially
represent at least a portion of the shipyard facility associated
with a legal proceeding and to at least partially detail at least a
portion of a timeline pertinent to the legal proceeding and
associated with the shipyard facility. In embodiments, the set of
digital twins is configured to at least partially represent at
least a portion of the shipyard facility associated with a casualty
forecast and to at least partially detail at least a portion of a
timeline pertinent to the casualty report and associated shipyard
facility.
[0990] In embodiments, the digital twin details the one or more
physical items in the shipyard facility for at least one operator
that includes a view of expected states of at least a portion of
the one or more physical items. In embodiments, the set of digital
twins provides further visualization of at least one geofence that
integrates representation of at least a portion of the shipyard
facility with a representation of a maritime environment adjacent
to the geofence.
[0991] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of maritime
outcomes, parameters, and data collected from data sources to train
an artificial intelligence system to determine a set of geofence
parameters and represent at least one geofence in a digital twin
that integrates representation of a set of maritime entities with a
representation of a maritime environment.
[0992] In embodiments, the set of maritime entities is associated
with a real-world shipyard. In embodiments, the digital twin is
configured to represent the real-world shipyard and geofence
parameters include a location within the real-world shipyard.
[0993] In embodiments, the set of maritime entities is associated
with a real-world maritime port. In embodiments, the digital twin
is configured to represent the real-world maritime port and
geofence parameters include a location within the real-world
maritime port.
[0994] In embodiments, the set of maritime entities is associated
with one or more container ships. In embodiments, the digital twin
is configured to represent the one or more container ships relative
to the geofence parameters. In embodiments, the set of maritime
entities is associated with one or more container barges. In
embodiments, the digital twin is configured to represent the one or
more barges relative to the geofence parameters. In embodiments,
the set of maritime entities is associated with an event
investigation. In embodiments, the digital twin is configured to at
least partially represent the set of maritime entities as it at
least one of interacted during a timeline associated with the event
investigation or is predicted to act based on a suggestion
associated with the event investigation.
[0995] In embodiments, the set of maritime entities is associated
with a legal proceeding. In embodiments, the digital twin is
configured to at least partially represent the set of maritime
entities as it at least one of interacted during a timeline
associated with the legal proceeding or is predicted to act based
on a suggestion associated with the legal proceeding.
[0996] In embodiments, the data collected by the value chain
network management platform relates to a casualty report. In
embodiments, the digital twin of the set of maritime entities is
configured to simulate possibilities of a loss relevant to the
casualty report based on the data collected by the value chain
network management platform.
[0997] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items contained on the set of maritime entities by
correlating data between a set of data collectors for one or more
physical items on the set of maritime entities and the digital twin
detailing the one or more physical items associated with the set of
maritime entities for the at least one of a port infrastructure
facility and a set of operators.
[0998] In embodiments, the set of maritime entities is a container
ship that is moored to port infrastructure installed on or adjacent
to land. In embodiments, data collected by a value chain network
management platform is based on at least a ship having a forward
speed relative to water and weather conditions and parameters
associated with energy consumption of propulsion units on the
ship.
[0999] The information technology system also includes an asset
management application associated with the value chain network
management platform and one or more maritime entities connected to
a ship. In embodiments, the asset management application is
associated with one or more ships connected to barges.
[1000] In embodiments, the set of maritime entities includes one or
more ships. In embodiments, the digital twin provides for
visualization of a navigation course of one or more of the ships.
In embodiments, the set of maritime entities includes one or more
ships. In embodiments, the digital twin provides for visualization
of an engine performance of one or more of the ships. In
embodiments, the set of maritime entities includes one or more
ships. In embodiments, the digital twin provides for visualization
of a hull integrity of one or more of the ships.
[1001] In embodiments, the digital twin provides for visualization
of a plurality of inspection points on the set of the maritime
entities and maintenance histories associated with those inspection
points.
[1002] In embodiments, the digital twin further provides for the
visualization of the plurality of inspection points on the set of
the maritime entities within the geofenced parameters and
maintenance histories associated with those inspection points. In
embodiments, the digital twin further provides for details of a
ledger of activity associated with the visualization of the
plurality of inspection points on the maritime entities within the
geofenced parameters and maintenance histories associated with
those inspection points. In embodiments, the training set of
maritime outcomes, parameters, and data collected from the data
sources is related to a set of shipping activities.
[1003] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for learning on a training set of maritime
outcomes, parameters, and data collected from data sources relating
to a set of shipping activities to train an artificial intelligence
system to determine a set of geofence parameters and represent at
least one geofence in a digital twin that integrates representation
of a set of maritime entities with a representation of a maritime
environment.
[1004] In embodiments, the set of maritime entities is associated
with a real-world shipyard. In embodiments, the digital twin is
configured to represent the real-world shipyard, its associated set
of the shipping activities and geofence parameters include a
location within the real-world shipyard. In embodiments, the set of
maritime entities is associated with a real-world maritime port. In
embodiments, the digital twin is configured to represent the
real-world maritime port, its associated set of the shipping
activities and geofence parameters include a location within the
real-world maritime port. In embodiments, the set of maritime
entities is associated with one or more container ships. In
embodiments, the digital twin is configured to represent the one or
more container ships and its associated set of the shipping
activities relative to the geofence parameters.
[1005] In embodiments, the set of maritime entities is associated
with one or more container barges. In embodiments, the digital twin
is configured to represent the one or more barges and its
associated set of the shipping activities relative to the geofence
parameters. In embodiments, the set of maritime entities is
associated with an event investigation. In embodiments, the digital
twin is configured to at least partially represent the set of
maritime entities and its associated set of the shipping activities
at least partially detailed on a timeline associated with the event
investigation.
[1006] In embodiments, the set of maritime entities is associated
with a legal proceeding. In embodiments, the digital twin is
configured to at least partially represent the set of maritime
entities as it at least one of interacted during a timeline
associated with the legal proceeding or is predicted to act based
on a suggestion associated with the legal proceeding.
[1007] In embodiments, the data collected by the value chain
network management platform relates to a casualty report. In
embodiments, the digital twin of the set of maritime entities is
configured to simulate possibilities of a loss relevant to the
casualty report based on the data collected by the value chain
network management platform.
[1008] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items contained on the set of maritime entities by
correlating data between a set of data collectors for one or more
physical items on the set of maritime entities and the digital twin
detailing the one or more physical items associated with the set of
maritime entities for the at least one of a port infrastructure
facility and a set of operators.
[1009] In embodiments, the set of maritime entities is a container
ship that is moored to port infrastructure installed on or adjacent
to land. In embodiments, data collected by a value chain network
management platform is based on at least a ship having a forward
speed relative to water and weather conditions and parameters
associated with energy consumption of propulsion units on the
ship.
[1010] In embodiments, the information technology system also has
an asset management application associated with the value chain
network management platform and one or more maritime entities
connected to a ship. In embodiments, the asset management
application is associated with one or more ships connected to
barges. In embodiments, the set of maritime entities includes one
or more ships. In embodiments, the digital twin provides for
visualization of a navigation course of one or more of the ships.
In embodiments, the set of maritime entities includes one or more
ships. In embodiments, the digital twin provides for visualization
of an engine performance of one or more of the ships. In
embodiments, the set of maritime entities includes one or more
ships. In embodiments, the digital twin provides for visualization
of a hull integrity of one or more of the ships.
[1011] In embodiments, the digital twin provides for visualization
of a plurality of inspection points on the set of the maritime
entities and one of maintenance histories and the set of shipping
activities associated with those inspection points. In embodiments,
the digital twin further provides for the visualization of the
plurality of inspection points on the set of the maritime entities
within the geofenced parameters and one of maintenance histories
and the set of shipping activities associated with those inspection
points. In embodiments, the digital twin further provides for
details of a ledger of activity associated with the visualization
of the plurality of inspection points on the maritime entities
within the geofenced parameters and one of maintenance histories
and the set of shipping activities associated with those inspection
points.
[1012] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform generating a digital twin representing a
real-world maritime port.
[1013] In embodiments, the digital twin representing the real-world
maritime port includes one or more container ships. In embodiments,
the digital twin representing the real-world maritime port includes
one or more barges. In embodiments, the digital twin representing
the real-world maritime port includes one or more components of the
port infrastructure installed on or adjacent to land.
[1014] In embodiments, the digital twin representing the real-world
maritime port also includes a container ship moored to a component
of the port infrastructure. In embodiments, the digital twin
representing the real-world maritime port includes include one or
more moored navigation units deployed on water. In embodiments, the
digital twin representing the real-world maritime port includes
include one or more ships connected to barges.
[1015] In embodiments, the digital twin representing the real-world
maritime port includes a ship. In embodiments, the digital twin is
configured to provide for visualization of a navigation course of
the ship in a simulated maritime port based on the real-world
maritime port. In embodiments, the digital twin is configured to
provide for visualization of an engine performance of the ship
including an emissions profile as the ship moves around the
real-world maritime port. In embodiments, the digital twin is
configured to provide for visualization of a hull of the ship as it
moves through the real-world maritime port on a path having a water
depth. In embodiments, the digital twin is configured to further
provide for visualization of a proximity of a portion of the hull
to a portion of a seafloor in the real-word shipyard. In
embodiments, the digital twin displays suggestions from an
artificial intelligence system that generates a portion of a
maintenance schedule to maintain the water depth through the
real-world maritime port based on at least a combination of a
portion of actual activity in the real-world maritime port and
simulations provided by the digital twin of the real-world maritime
port. In embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points in the
real-world maritime port and maintenance histories associated with
those inspection points. In embodiments, the digital twin is
configured to provide for visualizations of the plurality of
inspection points in the real-world maritime port and maintenance
histories associated with those inspection points when within a
geofenced area. In embodiments, the digital twin is configured to
provide details of a ledger of activity associated with the
visualization of the plurality of inspection points and maintenance
histories associated with those inspection points within a
geofenced of the real-world maritime port. In embodiments, the
digital twin is configured to provide for further visualization for
a first user of one of a navigation course of a ship and an engine
performance of the ship within a first geofenced area of the
real-world maritime port and for further visualization for a second
user of one of the navigation course of the ship and the engine
performance of the ship within a second different geofenced area in
the real-world maritime port and where transit between the first
and second geofenced areas motivates a handoff of the ship between
the first user and the second user as depicted by the digital twin
representing the real-world maritime port including the ship.
[1016] In embodiments, the methods, systems and apparatuses include
an information technology system having a value chain network
management platform for generating a digital twin representing a
real-world shipyard. In embodiments, the digital twin representing
the real-world shipyard includes one or more container ships. In
embodiments, the digital twin representing the real-world shipyard
includes one or more barges. In embodiments, the digital twin
representing the real-world shipyard includes one or more
components of the port infrastructure installed on or adjacent to
land. In embodiments, the digital twin representing the real-world
shipyard also includes a container ship moored to a component of
the port infrastructure.
[1017] In embodiments, the digital twin representing the real-world
shipyard includes include one or more moored navigation units
deployed on water. In embodiments, the digital twin representing
the real-world shipyard includes include one or more ships
connected to barges. In embodiments, the digital twin representing
the real-world shipyard includes a ship. In embodiments, the
digital twin is configured to provide for visualization of a
navigation course of the ship in a simulated shipyard based on the
real-world shipyard. In embodiments, the digital twin is configured
to provide for visualization of an engine performance of the ship
including an emissions profile as the ship moves around the
real-world shipyard. In embodiments, the digital twin is configured
to provide for visualization of a hull of the ship as it moves
through the real-world shipyard on a path having a water depth. In
embodiments, the digital twin is configured to further provide for
visualization of a proximity of a portion of the hull to a portion
of a seafloor in the real-word shipyard. In embodiments, the
digital twin displays suggestions from an artificial intelligence
system that generates a portion of a maintenance schedule to
maintain the water depth through the real-world shipyard based on
at least a combination of a portion of actual activity in the
real-world shipyard and simulations provided by the digital twin of
the real-world shipyard. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points in the real-world shipyard and maintenance histories
associated with those inspection points. In embodiments, the
digital twin is configured to provide for visualizations of the
plurality of inspection points in the real-world shipyard and
maintenance histories associated with those inspection points when
within a geofenced area. In embodiments, the digital twin is
configured to provide details of a ledger of activity associated
with the visualization of the plurality of inspection points and
maintenance histories associated with those inspection points
within a geofenced of the real-world shipyard.
[1018] In embodiments, the digital twin is configured to provide
for further visualization for a first user of one of a navigation
course of a ship and an engine performance of the ship within a
first geofenced area of the real-world shipyard and for further
visualization for a second user of one of the navigation course of
the ship and the engine performance of the ship within a second
different geofenced area in the real-world shipyard and where
transit between the first and second geofenced areas motivates a
handoff of the ship between the first user and the second user as
depicted by the digital twin representing the real-world shipyard
including the ship.
[1019] In embodiments, the methods, systems and apparatuses include
an information technology system having a set of intelligent
systems for automatically populating a digital twin of a maritime
value chain network entity based on data collected by a value chain
network management platform.
[1020] In embodiments, the maritime value chain network entity is
associated with a real-world shipyard. In embodiments, the digital
twin is configured to represent the real-world shipyard. In
embodiments, the maritime value chain network entity is associated
with a real-world maritime port. In embodiments, the digital twin
is configured to represent the real-world maritime port. In
embodiments, the maritime value chain network entity is associated
with a container ship. In embodiments, the digital twin is
configured to represent the container ship.
[1021] In embodiments, the maritime value chain network entity is
associated with a barge. In embodiments, the digital twin is
configured to represent the barge. In embodiments, the maritime
value chain network entity is associated with port infrastructure.
In embodiments, the digital twin is configured to represent one or
more components of the port infrastructure. In embodiments, the
maritime value chain network entity is associated with an event
investigation. In embodiments, the digital twin is configured to at
least partially represent the maritime value chain network entity
as it interacted during a timeline associated with the event
investigation.
[1022] In embodiments, the maritime value chain network entity is
associated with a legal proceeding. In embodiments, the digital
twin is configured to at least partially represent the maritime
value chain network entity as it interacted during a timeline
associated with the legal proceeding.
[1023] In embodiments, the data collected by a value chain network
management platform relates to a casualty report. In embodiments,
the digital twin of the maritime value chain network entity is
configured to simulate possibilities of a loss relevant to the
casualty report based on the data collected by a value chain
network management platform.
[1024] In embodiments, the maritime value chain network entity is a
port infrastructure facility. In embodiments, the data collected by
a value chain network management platform facilitates identifying
theft or misuse of the port infrastructure facility by correlating
data between a set of data collectors for one or more physical
items in the port infrastructure facility and the digital twin
detailing the one or more physical items of the port infrastructure
facility for the at least one of the port infrastructure facility
and the set of operators.
[1025] In embodiments, the maritime value chain network entity is a
container ship that is moored to port infrastructure installed on
or adjacent to land. In embodiments, data collected by a value
chain network management platform is based on at least a container
ship having a forward speed relative to water and weather
conditions and parameters associated with energy consumption of
propulsion units on the container ship. The information technology
system also has an asset management application associated with the
value chain network management platform and one or more maritime
facilities connected to a container ship. In embodiments, the asset
management application is associated with one or more ships
connected to barges. In embodiments, the maritime value chain
network entity is one or more ships. In embodiments, the digital
twin provides for visualization of a navigation course of one or
more of the ships.
[1026] In embodiments, the maritime value chain network entity is
one or more ships. In embodiments, the digital twin provides for
visualization of an engine performance of one or more of the ships.
In embodiments, the maritime value chain network entity is one or
more ships. In embodiments, the digital twin provides for
visualization of a hull integrity of one or more of the ships. In
embodiments, the digital twin provides for visualization of a
plurality of inspection points on the maritime value chain network
entity and maintenance histories associated with those inspection
points. In embodiments, the digital twin further provides for the
visualization of the plurality of inspection points on the maritime
value chain network entity within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the digital twin further provides for details of a ledger of
activity associated with the visualization of the plurality of
inspection points on the maritime value chain network entity within
a geofenced area and maintenance histories associated with those
inspection points.
[1027] A more complete understanding of the disclosure will be
appreciated from the description and accompanying drawings and the
claims, which follow.
[1028] Referring to FIG. 6, the value chain network management
platform 604 orchestrates a variety of factors involved in
planning, monitoring, controlling, and optimizing various entities
and activities involved in the value chain network 668 as it is
applied to maritime assets, activities, logistics, and planning
including supply and production factors, demand factors, logistics
and distribution factors, and the like. The management platform 604
can facilitate the monitoring and managing of supply factors and
demand factors, the sharing of status information about and between
various entities as demand factors are understood and accounted
for, as orders are generated and fulfilled, and as products are
created and moved through a supply chain. Referring to FIG. 7, the
management platform 604 may include a set of value chain network
entities 652 including various delivery systems 632 that can
include and connect to maritime facilities 622. The maritime
facilities 622 can include port infrastructure facilities 660,
floating assets 620, and shipyards 638, and the like. In
embodiments, the value chain network management platform 604
monitors, controls, and otherwise enables management (and in some
cases autonomous or semi-autonomous behavior) of a wide range of
value chain network 668 processes, workflows, activities, events
and applications 630 applicable in the maritime environment.
[1029] Referring to FIGS. 6 and 11, the management platform 604
deployed in the maritime environment may include a set of data
handling layers 624 each of which is configured to provide a set of
capabilities that facilitate development and deployment of
intelligence, such as for facilitating automation, machine
learning, applications of artificial intelligence, intelligent
transactions, state management, event management, process
management, and many others, for a wide variety of value chain
network applications and end uses in the maritime environment. In
embodiments, the data handling layers 624 are configured in a
topology that facilitates shared data collection and distribution
across multiple applications and uses within the management
platform 604 by the value chain monitoring systems layer 614. The
value chain monitoring systems layer 614 may include, integrate
with, and/or cooperate with various data collection and management
systems 640, referred to for convenience in some cases as data
collection systems 640, for collecting and organizing data
collected from or about value chain entities 652, as well as data
collected from or about the various data layers 624 or services or
components thereof.
[1030] In embodiments, the data handling layers 624 are configured
in a topology that facilitates shared or common data storage across
multiple applications and uses of the platform 604 by the value
chain network-oriented data storage systems layer 624, referred to
herein for convenience in some cases simply as the data storage
layer 624 or storage layer 624. For example, various data collected
about the value chain entities 652, as well as data produced by the
other data handling layers 624, may be stored in the data storage
layer 624, such that any of the services, applications, programs,
or the like of the various data handling layers 624 can access a
common data source, which may comprise a single logical data source
that is distributed across disparate physical and/or virtual
storage locations. This may facilitate a dramatic reduction in the
amount of data storage required to handle the enormous amount of
data produced by or about value chain network entities 652 as
applications 630 and uses of value chain networks grow and
proliferate. For example, a supply chain or inventory management
application in the value chain management platform layer 604, such
as one for ordering replacement parts for a machine or item of
equipment, may access the same data set about what parts have been
replaced for a set of machines as a predictive maintenance
application that is used to predict whether a component of a ship,
or facility of a port is likely to require replacement parts.
Similarly, prediction may be used with respect to resupply of
items.
[1031] Referring to FIGS. 6 and 12, the value chain
network-oriented data storage systems layer 624 may include,
without limitation, physical storage systems, virtual storage
systems, local storage systems 1190, distributed storage systems,
databases, memory, network-based storage, network-attached storage
systems. In embodiments, the storage layer 624 may store data in
one or more knowledge graphs in the graph database architectures
1124, such as a directed acyclic graph, a data map, a data
hierarchy, a data cluster including links and nodes, a
self-organizing map, or the like. In embodiments, the data storage
layer 624 may store data in a digital thread, ledger, distributed
ledger or the like, such as for maintaining a serial or other
records of an entity 652 over time, including any of the entities
described herein. In embodiments, the storage layer 624 may include
one or more blockchains 1180, such as ones that store identity
data, transaction data, historical interaction data, and the like,
such as with access control that may be role-based or may be based
on credentials associated with a value chain entity 652, a service,
or one or more applications 630. Data stored by the data storage
systems 624 may include accounting and other financial data 730,
access data 734, asset and facility data 1032, asset tag data 1178,
worker data 1032, event data 1034, risk management data 732,
pricing data 738, safety data 664 and the like.
[1032] Referring to FIG. 8, the value chain network management
platform 604 includes one or more sets of value chain entities 652
that may be subject to management by the management platform 604,
may integrate with or into the management platform 604, and/or may
supply inputs to and/or take outputs from the management platform
604, such as ones involved in or for a wide range of value chain
activities. These value chain entities 652 may include any of the
wide variety of assets, systems, devices, machines, components,
equipment, facilities, and individuals that can support a wide
range of operating facilities 712 including maritime facilities
622. Referring to FIG. 63, the maritime facilities can include port
infrastructure facilities 1034. In embodiments, the port
infrastructure facilities 1034 can include docks 7002, yards 7004,
cranes 7008, roll-on/roll-off facilities 7010, ramps 7012,
containers 7014, container handling systems 7018, waterways 732,
and locks 7020, as applicable. In embodiments, the docks 7002 and
their adjacent areas may include piers 7022, basins 7024, stacking
areas 7028, storage areas 7030, and warehouses 7032. In
embodiments, the container handling systems 7018 can include
portainer tracking system and sensors 7040, such as for monitoring,
reporting on, or managing one or more portainers or other systems
for moving shipping containers, such as cranes (e.g., Gottwald
cranes, gantry cranes, and others), straddle carriers,
multitrailers, reach stackers, and the like. In embodiments, the
port infrastructure facilities 1034 can further include gantry
cranes 7042 and the port vehicles 7044 that can be used to move
containers 7014, such as straddle carriers. In embodiments, the
port infrastructure facilities 1034 also include refrigerated
containers 7050 with dedicated stacking areas 7052 and cooling
infrastructure to maintain the controlled environments in the
refrigerated containers 7050.
[1033] The port infrastructure facilities 1034 further include
shipyard facilities 638 and floating assets 620. The floating
assets 620 can include ships 7060 and boats, container ships 7062,
barges 7064, tugboats 7068, 7070, and dinghies 7072, as well as
partially floating assets, such as submarines, underwater drones,
and the like. By way of these examples, the floating assets 620 can
operate among facilities and other items at points of origin 610
and/or points of destination 628. The shipyard facilities 638 can
include the hauling facilities 710 such as many of the floating
assets 620 as well as land-based vehicles and other delivery
systems 632 used for conveying goods, such as trucks, trains, and
the like
[1034] Referring to FIGS. 63, orchestration of a set of deeply
interconnected value chain network entities 652 by the management
platform 604 can include providing interconnectivity for the value
chain network entities 652 using local network connections, a
peer-to-peer connections, connections through one or more mobile
networks, and connections via a cloud network facility, satellite
uplinks, microwave communications or other connections. The
management platform 604 may manage the connections, configure or
provision resources to enable connectivity, and/or manage
applications 630 that take advantage of the connections knowing
that are many maritime environments where connectivity may be poor
or non-existent relative to when the floating assets 620 are closer
to port or other land-based communication systems. In many
examples, a port infrastructure facility 660, such as a yard for
holding shipping containers 7080, may inform a fleet of floating
assets 620 via connections to the floating assets 620 that the port
is near capacity. With this knowledge, the floating assets 620
movement can be varied to extend times including reducing approach
speeds to delay arrival, direction to other ports, and the like. In
further examples, the news of the port reaching capacity can result
in starting a negotiation process with the floating assets 620
looking to arrive at port. In embodiments, the negotiation process
with the floating assets 620 can include an automated negotiation
based on a set of rules and governed by a smart contract for the
remaining capacity and enabling some floating assets 620 to be
redirected to alternative ports or holding facilities.
[1035] In embodiments, the maritime facilities 622 can include
floating assets 620 including many different ships 7060. Referring
to FIGS. 64 and 65, the ship 7060 can be one or more container
ships 7062 that can haul many shipping containers 7080. In other
examples, the ship 7060 can be one or more container ships 7062
that can haul raw materials, processed goods in bulk, gaseous cargo
and many other forms of cargo not otherwise transported in shipping
containers 7080. In many examples, the ship 7060 can include a bow
area 7100. The bow area 7100 can include a bulbous bow 7102. In
some examples, the bulbous bow 7102 can be configured in-situ in
response to control from the management platform 604. Inboard from
the bow area 7100 and traveling toward the stern area 7104 of the
ship 7060, the ship 7060 can include a forepeak tank 7110. In this
same area, the ship 7060 can include one or more bow anchors 7112
and bow thrusters 7114. Various passageways 7118 connect these
areas in the bow area 7100. Depending on the configuration of the
ship 7060, the hold 7120 can be configured and re-configured to
accommodate various products such as product 650, raw materials,
material in process, and combinations thereof. In some examples,
the ship 7060 can include multiple holds 7120. In examples, the
container ship 7062 can be configured with eight holds: container
hold 7130, 7132, 7134, 7138, 7140, 7142, 7144, and 7148. Toward the
stern area 7104, the ship 7060 includes an engine room 7150
including one or more propulsion units 7152. Each of the one or
more propulsion units 7152 is fed by a fuel system 7154 and its
emissions are controlled by an exhaust system 7158. In various
locations on the ship 7060, one or more fin stabilizers 7160 may be
deployed. In the stern area 7104, the ship 7060 includes a steering
gear area 7160 below a rear deck area 7162. One or more rudders
7164 can extend from the steering gear area 7160.
[1036] One or more propellers 7170 can extend from the stern area
7104 with a rotating power connection to the propulsion units. In
embodiments, one or more propellers 7170 can extend from the ship
7060 with an electrical connection to the propulsion units but no
physical rotating power connection. In embodiments, one or more
propellers 7170 can extend from the ship 7060 with a hydraulic
connection to the propulsion units but no physical rotating power
connection. In further examples, steam or other working fluids may
be employed to drive the propulsion of the ship 7060. In further
examples, mechanical rotating power, electrical drive, hydraulic
drive, steam and various combinations thereof can be used for
propulsion. In various examples, the one or more propellers 7170
can include side propellers 7172 and a central propeller 7174. In
other examples, two propellers 7170 can be deployed. In
embodiments, the propellers 7170 can be fixed such that the plane
in which the propeller rotates is fixed relative to the ship 7060.
By way of these examples, the propellers 7170 can be fixed and can
be driven by mechanical linkage to propulsion units of the ship
7060. In other examples, the propellers 7170 can be fixed and can
be driven by electrical motors adjacent each of the propellers
7170. In embodiments, the position of the propellers 7170 can be
variable such that the plane in which the propeller rotates is
movable relative to the ship 7060. By way of these examples, the
propellers 7170 can be driven by electrical motors adjacent to each
of the propellers 7170. In one or more locations on the ship 7060,
the propellers 7170 can be deployed in pods that can include an
independently controlled and movable electrical drivetrain and
propeller so that the entire pod can be moved into various
positions to facilitate forward propulsion, steering, maneuvering,
docking, evasive maneuvers, and the like.
[1037] In further examples, the ship 7060 is configured with one or
more ballast tanks 7180. In various examples, the ship 7060 can
include side ballast tanks 7182 and deep ballast tanks 7184. The
ballast tanks 7180 can each include pumping and draining systems
7190, cleaning systems 7192, sensors 7194 to determine
characteristics of the ballast water such as salinity, foreign
particles, organic material, garbage, restricted content relative
to geofenced areas, regulated zones, ad-hoc demarcated areas, and
the like. The sensors 7194 can also determine tank characteristics
including wear from fatigue, corrosion, physical damage, or the
like. In the bow area 7100, the ship 7060 can include a windlass
7200, a foremast 7202, and a crow's-nest 7204 on which various
sensors 7208 can be located to observe characteristics of the ship
7060, the weather and ambient conditions 7210, and navigational
inputs 7212. In various locations on the ship 7060, one of more
mooring winches 7220 can be deployed to assist in docking, in
connection to suitable mooring connections points, connection other
vessels in transit such as tenders, and the like. In various
locations on the ship 7060, one or more hatch covers 7222 can be
deployed to permit access to various areas and passageways on the
ship 7060.
[1038] In further examples, the ship 7060 is configured as a
container ship 7062 that can be configured with eight holds:
container hold 7130, 7132, 7134, 7138, 7140, 7142, 7144, and 7148.
In further examples, the ship 7060 is configured as a container
ship 7062 with various numbers of holds 7120. In further examples,
the ship 7060 is configured as a container ship 7062 with in-situ
configurable holds. In further examples, the ship 7060 is
configured as a container ship 7062 with various numbers of holds
some of which are in-situ configurable. In embodiments, the holds
7120 can include one or more vents 7240 deployed to facilitate an
atmosphere in the hold suitable for transit and for the care of the
cargo. In embodiments, the holds 7120 can include one or more
rigging and anchoring systems 7242 to secure one or more loads
within holds 7120 configured or reconfigured for such cargo. In
embodiments, the holds 7120 can include one or more movable baffle
and dunnage 7244 to secure one or more loads within holds 7120
configured or reconfigured for such cargo.
[1039] In further examples, the ship 7060 includes a wheelhouse
7250 and one or more life rafts 7252 and lifeboats 7254. In further
examples, the ship 7060 includes nautical and satellite
navigational equipment 7260. By way of these examples, the ship can
include direction finder antennae 7262, radar scanner 7264, a
signal yard 7268. In these examples, the ship 7060 includes a radar
mast 7270 and a Suez signal light 7272, a funnel 7274 and an
antenna pole 7278.
[1040] In further examples, the ship 7060 includes one or more
cranes 7280 that can be used to move things in and about the decks
7282 and in and out of the holds 7120 of the ship 7060. In these
examples, the ship 7060 can contain or carry on top many containers
of various sizes including twenty-foot and forty-foot containers.
In these examples, the ship 7060 can contain or carry on top many
containers of various sizes including twenty-foot dry freight
containers, twenty-foot open-top containers, twenty-foot
collapsible flat rack containers, twenty-foot refrigerated
containers, and the like. In these examples, the ship 7060 can
contain or carry on top many containers of various sizes including
forty-foot high cube containers, forty-foot open-top containers,
forty-foot collapsible flat rack containers, forty-foot high cube
refrigerated containers, and the like. In these examples, the ship
7060 can contain or carry on top many containers of various sizes
including forty-five-foot high cube dry containers, and the
like.
[1041] In embodiments, the ship 7060 can contain engine units that
include a diesel generator 7280 that can supply electrical power
throughout the ship 7060. The ship 7060 can also contain engine
units that include a center main diesel engine 7282 and one or more
side main diesel engines 7284. In embodiments, the ship 7060 can
contain engine units that are configured to combust natural gas,
propane, gasoline, methanol, and the like. In embodiments, the ship
7060 can contain engine units that are configured to be powered by
nuclear units that can be used to heat water to steam-driven
electrical systems. In embodiments, the ship 7060 can contain
engine units that are configured to be powered by nuclear units and
internal combustion engines in a hybrid arrangement. In
embodiments, the ship 7060 can contain engine units that are
configured to be powered by nuclear units and internal combustion
engines, and other renewables in a hybrid arrangement such as solar
and wind where each of these can feed an electrical and battery
system to power propulsion and ship operations.
[1042] In embodiments, the ship 7060 can contain multiple bulkheads
7290. By way of these examples, the engine room can be framed in
engine room bulkheads 7292 to contain the various powerplant units.
In embodiments, the cargo and hold region of the ship 7060 can
contain hold bulkheads 7294 to contain the various powerplant
units. In embodiments, the ship 7060 can contain structural
transverse bulkheads 7300 and axial bulkheads 7302.
[1043] In embodiments, the maritime facilities 622 can include
floating assets 620 including many different barges 7500. Referring
to FIG. 66, one or more of the barges 7500 can be transport barges,
cargo barges, submersible barges, and the like that can in size and
capacity. In many examples, barges are available in many varieties
of towed barges and self-propelled ships including submersible
heavy lift vessels. In many examples, the barges 7500 can be towed
or pushed by tug boats 7510 to transport from one location to
another. In many examples, the barges 7500 can be flat top and
bottom and can be equipped with navigational lights 7520, fairleads
7522 and towing points 7524.
[1044] In some examples, the barges 7500 can be designed to be
submerged so as to pick up cargoes 7530 such as floating cargoes.
By way of these examples, the barges 7500 can be equipped with a
forecastle 7540 and a deck structure 7542 at a bow area 7550
opposite a deck structure 7544 at a stern area 7552. There can be
additional deck structure 7548 between the bow area 7550 and the
stern area 7552 that can be configured and re-configured to hold
the cargoes 7530. In these examples, the barges 7500 can be
equipped with their own ballast system 7560. In embodiments, the
barges 7500 can include a modular steel box 7570 and stability
casings 7572 that may be added at the stern area 7552 to some
predetermined degree to effectively provide additional portions of
a hull 7580 in the water 7582 that can be shown to enhance the
stability of the barge 7500 and its cargoes 7530 as the deck
structures 7542, 7544, 7548 go through a waterline 7584. In these
examples, the modular steel box 7570 and stability casings 7572 can
be removable and can be stowed away on one of the deck structures
7542, 7544, 7548 of the barge 7500 or stored onshore when not
required. In doing so, the barge 7500 can be relatively more
efficient when lighter loads warrant the relatively smaller hull
structure.
[1045] In many examples, barges 7500 can be classified not only by
their length and width but also how they are used, launched and the
like. In some examples, one or more of the barges 7500 can be less
than 200 feet in length and 50 feet wide. By way of these examples,
the barge 7500 can include small pontoons can be used for carrying
small structures in sheltered inshore waters. In some examples, one
or more of the barges 7500 can be about 250 feet by 70 feet and can
include small pontoons to support the barge 7500 that is otherwise
configured without an onboard ballast system. By way of these
examples, barges in these configurations can be used to transport
small offshore loads, do work in and near port infrastructures,
perform maintenance in a shipyard, etc. In some examples, one or
more of the barges 7500 can be about 300 feet and can be 90 or 100
feet wide. By way of these examples, one or more barges in these
configurations can be used as standard cargo barges but may not be
equipped with an onboard ballast system. In some examples, one or
more barges 7500 can be about 400 feet by 100 feet and these barges
can be equipped with an onboard ballast system.
[1046] In some examples, one or more of the barges 7500 can be
about 450 feet and longer and can be deployed with an onboard
ballasting systems 7590. By way of these examples, one or more of
the barges 7500 can also be deployed with skid beams 7592. One or
more of the barges 7500 can also be deployed with rocker arms 7594
at the stern area 7552 to enable, for example, the launching of
jackets or other loads that may be too heavy to lift. In examples,
the Heerema H851 brand barge is nominally 850 feet long by 200 feet
wide and can be a suitable example of one of the largest
commercially available barges.
[1047] In embodiments, one or more of the barges 7500 can also be
configured as a submersible barge 7600, which can be a towed barge
that can be equipped with stability casings 7602 in the stern area
7552. In examples, the submersible barge 7600 can be configured
with a ship-like bow structure 7604. In these examples, the ship
like bow structure 7604 can be configured with a bridge 7608
sufficiently tall to enable the submerging of the barge above at
least a portion of its deck structures. In examples, the Boa brand
barges have nominal dimensions of 400 feet by 100 feet, the AMT
brand barges have nominal dimensions 470 feet by 120 feet and
Hyundai brand barges having nominal dimensions 460 feet by 120 feet
can be suitable examples of commercially available submersible
barges. By way of these examples, these barges can submerge up to
18 to 24 feet above their decks.
[1048] It will be appreciated in light of the disclosure that
barges are rated and paired with jobs in terms of deadweight which
provides a broad indication of the barges' carrying capacity. The
barges, however, have additional requirements such as their global
strength, local deck and frame strengths and height of the cargo's
center of gravity. With regard to center of gravity, one exemplary
barge may be able to transport a 20,000-ton structure with its
center of gravity very close to the deck sufficiently tied and
supported on the deck. The same exemplary barge may only be able to
transport a half of the weight if the cargo has a relatively high
center of gravity. With that in mind, many attributes of one or
more of the barges are the placement, orientation, center of
gravity and weight of the cargoes on their decks.
[1049] In embodiments, one of the barges can be towed by one of the
ships, tugboats 7510, or the like with a towing bridle 7610. In
many examples, two lines 7612 can run from tow brackets 7614
through fairleads 7618 on one of the barges and connect to a
triplate 7620 on the barge through towing shackles 7622. By way of
this example, a third line 7630 can connect the triplate 7620 to a
winch 7640 on one of the tugboats 7510. In further examples, an
emergency wire 7642 can be installed along the length of the barge.
The emergency wire 7642 can be attached to a connector 7644 that
can terminate with a buoy 7650. The buoy 7650 can trail behind the
barge 7650 during tow and can form part of the towing
arrangement.
[1050] In some examples, roll accelerations of the barge can be
directly proportional to the transverse stiffness of the barge,
which can be measured by its metacentric height. In some
arrangements, a barge can have a large metacentric height and as a
result, roll accelerations can be severe. In further examples with
relatively tall cargo, the metacentric height can be low resulting
in the period and amplitude of roll and the static force resulting
from the load being greater but the dynamic component may be less.
In many examples, attributes of the barge 7500 include positioning
of cargoes 7530 on its deck structures and its effective
metacentric height. In further examples, counter-roll mechanisms
7660 can be installed on the barge 7500. By way of these examples,
the adaptive intelligence layer 614 can update the program of the
counter-roll mechanisms 7660 and can be shown to increase its
efficacy to changing cargo load and water and weather conditions.
In embodiments, the adaptive intelligence layer 614 can update the
speed and angles of the of the counter-roll mechanisms 7660 and can
be shown to increase its efficacy to changing cargo load and water
and weather conditions.
[1051] In embodiments, the management platform 604 may include a
set of value chain network entities 652 including various delivery
systems 632 that can include and connect to the maritime facilities
622. The maritime facilities 622 can include port infrastructure
facilities 660, floating assets 620, and shipyards 638, and the
like. In embodiments, the value chain network management platform
604 monitors, controls, and otherwise enables management (and in
some cases autonomous or semi-autonomous behavior) of a wide range
of value chain network 668 processes, workflows, activities, events
and applications 630 applicable in the maritime environment.
[1052] The maritime facilities 622 can include one or more ships
7060 of various sizes to service the facilities. The maritime
facilities 622 can include one or more fixed or moored navigation
aids within the water or on land to facilitate the movement ships
of various sizes and vehicles on land. In embodiments, the maritime
facilities 622 can be configured as a seaport in that it can be
configured to accept deep-draft ships with a draft of 20 feet or
more. In embodiments, some of the larger maritime facilities 622
can include areas outside the boundaries of the seaports, shipyard,
maritime ports, and the like that are related to port operations or
to an intermodal connection to the seaports, shipyard, maritime
ports, and the like.
[1053] In embodiments, the management platform 604 can manage port
gate-in and gate-out improvements to the logistics of the flow of
assets and cargoes around the maritime facilities 622. In
embodiments, the management platform 604 can manage road
improvements both within and connecting to the maritime facilities
622. In embodiments, the management platform 604 can manage rail
improvements both within and connecting to the maritime facilities
622. In embodiments, the management platform 604 can manage berth
improvements in the maritime facilities 622 including to docks,
wharves, piers and the like. In embodiments, the management
platform 604 can manage berth improvements including dredging at
the berths, approach and departure areas adjacent to the berth, and
in areas around maritime facilities. In embodiments, the management
platform 604 can manage cargo moving equipment used on land. In
embodiments, the management platform 604 can manage facilities
necessary to improve cargo transport including silos, elevators,
conveyors, container terminals, roll-on/roll-off facilities
including parking garages necessary for intermodal freight
transfer, warehouses including refrigerated facilities, bunkering
facilities for oil or gas products, lay-down areas, transit sheds,
and the like. In embodiments, the management platform 604 can
manage utilities necessary for standard operations including
lighting, stormwater, and the like that can be incidental to a
larger set of maritime facilities. In embodiments, the management
platform 604 can manage port-related intelligent transportation
system hardware and software including all technologies used to
promote efficient port movements including routing and
communications for vessels, trucks, and rail cargo movements as
well as flow-through processing for import/export requirements,
storage and tracking, and asset/equipment management. In
embodiments, the management platform 604 can manage phytosanitary
treatment facilities to support phytosanitary treatment
requirements. In embodiments, the management platform 604 can
manage, configure and re-configure fully automated cargo-handling
equipment.
[1054] In embodiments, the adaptive intelligent systems layer 614
may include a set of systems, components, services and other
capabilities that collectively facilitate the coordinated
development and deployment of intelligent systems, such as ones
that can enhance one or more of the applications 630 at the
application platform layer 604; ones that can improve the
performance of one or more of the components, or the overall
performance (e.g., speed/latency, reliability, quality of service,
cost reduction, or other factors) of the connectivity facilities
642; ones that can improve other capabilities within the adaptive
intelligent systems layer 614; ones that improve the performance
(e.g., speed/latency, energy utilization, storage capacity, storage
efficiency, reliability, security, or the like) of one or more of
the components, or the overall performance, of the value chain
network-oriented data storage systems 624; ones that optimize
control, automation, or one or more performance characteristics of
one or more value chain network entities 652; or ones that
generally improve any of the process and application outputs and
outcomes 1040 pursued by use of the platform 604.
[1055] These adaptive intelligent systems 614 may be deployed in
and among the maritime facilities 622 and floating assets 620.
These adaptive intelligent systems 614 may include a robotic
process automation system 1442, a set of protocol adaptors 1110, a
packet acceleration system 1410, an edge intelligence system 1430
(which may be a self-adaptive system), an adaptive networking
system 1430, a set of state and event managers 1450, a set of
opportunity miners 1460, a set of artificial intelligence systems
1160, a set of digital twin systems 1700, a set of entity
interaction management systems 1902 (such as for setting up,
provisioning, configuring and otherwise managing sets of
interactions between and among sets of value chain network entities
652 in the value chain network 668), and other systems.
[1056] In embodiments, a set of digital twin systems 1700 may be
deployed for each of the maritime facilities 622 and each of the
floating assets 620. Referring to FIG. 6, the connected value chain
network 668 benefits from digital twin systems deployed throughout
the value chain network management platform 604 to facilitate the
management, visualization, and modeling of the orchestration of a
variety of factors involved in planning, monitoring, controlling,
and optimizing various entities and activities involved in the
value chain network 668, such as supply and production factors,
demand factors, logistics and distribution factors, and the like.
By virtue of the unified platform 604 for monitoring and managing
supply factors and demand factors, digital twins for status
information can be shared about and between various entities to
facilitate modeling and analytics and to provide for visualization
of changing demand factors becomes operational realities, as orders
are generated and fulfilled, and as products are created and moved
through a supply chain.
[1057] In embodiments, the value chain monitoring systems layer 614
and its data collection systems 640 may include a wide range of
systems for the collection of data from the maritime facilities 622
and the floating assets 620. This layer may include, without
limitation, real time monitoring systems 1520 (such as onboard
monitoring systems like event and status reporting systems on ships
and other floating assets, on delivery vehicles, on trucks and
other hauling assets, and in shipyards, ports, warehouses,
distribution centers and other locations; on-board diagnostic (OBD)
and telematics systems on floating assets, vehicles and equipment;
systems providing diagnostic codes and events via an event bus,
communication port, or other communication system; monitoring
infrastructure (such as cameras, motion sensors, beacons, RFID
systems, smart lighting systems, satellite connections, asset
tracking systems, person tracking systems, and ambient sensing
systems located in various environments where value chain
activities and other events take place), as well as removable and
replaceable monitoring systems on maritime assets and cargo or
other assets contained therein or in transit thereon, such as
portable and mobile data collectors, RFID and other tag readers,
smart phones, tablets and other mobile devices that are capable of
data collection and the like); software interaction observation
systems 1500 that can be deployed into portable and onboard systems
of the maritime facilities 622 and floating assets 620; visual
monitoring systems 1930 such as using video and still imaging
systems, LIDAR, IR and other systems that allow visualization of
items, people, materials, components, machines, equipment,
personnel, and the like to detail cargo in the hold of floating
assets 620, to detail activity of personal and gear deployed at the
maritime facilities 622 and on the floating assets 620; point of
interaction systems 1530 (such as dashboards, user interfaces, and
control systems for value chain entities); physical process
observation systems 1510 (such as for tracking physical activities
of operators, workers, customers, or the like, physical activities
of individuals (such as shippers, delivery workers, packers,
pickers, assembly personnel, customers, merchants, vendors,
distributors and others), physical interactions of workers with
other workers, interactions of workers with physical entities like
machines and equipment, and interactions of physical entities with
other physical entities, including, without limitation, by use of
video and still image cameras, motion sensing systems (such as
including optical sensors, LIDAR, IR and other sensor sets),
robotic motion tracking systems (such as tracking movements of
systems attached to a human or a physical entity) and many others;
machine state monitoring systems 1940 (including onboard monitors
and external monitors of conditions, states, operating parameters,
or other measures of the condition of any value chain entity, such
as a machine or component thereof, such as a machine, such as a
client, a server, a cloud resource, a control system, a display
screen, a sensor, a camera, a vehicle, a robot, or other machine);
sensors and cameras 1950 and other IoT data collection systems 1172
(including onboard sensors, sensors or other data collectors
(including click tracking sensors) in or about a value chain
environment (such as, without limitation, a point of origin, a
loading or unloading dock, a vehicle or floating asset used to
convey goods, a container, a port, a distribution center, a storage
facility, a warehouse, a delivery vehicle, and a point of
destination), cameras for monitoring an entire environment,
dedicated cameras for a particular machine, process, worker, or the
like, wearable cameras, portable cameras, cameras disposed on
mobile robots, cameras of portable devices like smart phones and
tablets, and many others, including any of the many sensor types
disclosed throughout this disclosure or in the documents
incorporated herein by reference); indoor location monitoring
systems 1532 (including cameras, IR systems, motion-detection
systems, beacons, RFID readers, smart lighting systems,
triangulation systems, RF and other spectrum detection systems,
time-of-flight systems, chemical noses and other chemical sensor
sets, as well as other sensors); user feedback systems 1534
(including survey systems, touch pads, voice-based feedback
systems, rating systems, expression monitoring systems, affect
monitoring systems, gesture monitoring systems, and others);
behavioral monitoring systems 1538 (such as for monitoring
movements, shopping behavior, buying behavior, clicking behavior,
behavior indicating fraud or deception, user interface
interactions, product return behavior, behavior indicative of
interest, attention, boredom or the like, mood-indicating behavior
(such as fidgeting, staying still, moving closer, or changing
posture) and many others); and any of a wide variety of Internet of
Things (IoT) data collectors 1172, such as those described
throughout this disclosure and in the documents incorporated by
reference herein.
[1058] Referring to FIG. 26, a set of opportunity miners 1460 may
be provided as part of the adaptive intelligence layer 614, which
may be configured to seek and recommend opportunities to improve
one or more of the elements of the platform 604, such as via
addition of artificial intelligence 1160, automation (including
robotic process automation 1402), or the like to one or more of the
maritime facilities 622 and for each of floating assets 620
including their systems, sub-systems, components, applications with
which the platform 100 interacts. In embodiments, the opportunity
miners 1460 may be configured or used by developers of AI or RPA
solutions to find opportunities for better solutions and to
optimize existing solutions in a value chain network 668. In
embodiments, the opportunity miners 1460 may include a set of
systems that collect information within the management platform 604
and collect information within, about and for a set of maritime
facilities 622 and for each of floating assets 620, where the
collected information has the potential to help identify and
prioritize opportunities for increased automation and/or
intelligence about the value chain network 668, about applications
630, one or more of the maritime facilities 622 and the floating
assets 620. For example, the opportunity miners 1460 may include
systems that observe clusters of value chain network workers by
time, by type, and by location (whether on the water or land), such
as using cameras, wearables, or other sensors, such as to identify
labor-intensive areas and processes in set of value chain network
668 environments. These may be presented, such as in a ranked or
prioritized list, or in a visualization (such as a heat map showing
dwell times of customers, workers or other individuals on a map of
an environment or a heat map showing routes traveled by customers
or workers within an environment) to show places with high labor
activity. In embodiments, analytics 838 may be used to identify
which environments or activities would most benefit from automation
for purposes of improved delivery times, mitigation of congestion,
and other performance improvements.
[1059] In embodiments, opportunity mining may include facilities
for solicitation of appropriate training data sets that may be used
to facilitate process automation. For example, certain kinds of
inputs, if available, would provide very high value for automation,
such as video data sets that capture very experienced and/or highly
expert workers performing complex tasks. This information becomes
even more valuable when collected in close proximity to other
maritime facilities 622 and with deployed floating assets 620.
Opportunity miners 1460 may search for such video data sets as
described herein; however, in the absence of success (or to
supplement available data), the management platform 604 may include
systems by which a user at a maritime facility or deployed on a
maritime asset may specify a desired type of data, such as software
interaction data (such as of an expert working with a program to
perform a particular task), video data (such as video showing a set
of experts performing a certain kind of delivery process, unloading
process, securing and logistics process, cleaning and maintenance
process, a container movement process, or the like), and/or
physical process observation data (such as video, sensor data, or
the like). The resulting library of interactions captured in
response to the specification may be captured as a data set in the
data storage layer 624, such as for consumption by various
applications 630, adaptive intelligence systems 614, and other
processes and systems. In embodiments, the library may include
videos that are specifically developed as instructional videos,
such as to facilitate developing an automation map that can follow
instructions in the video, such as providing a sequence of steps
according to a procedure or protocol, breaking down the procedure
or protocol into sub-steps that are candidates for automation, and
the like. In embodiments, such videos may be processed by natural
language processing, such as to automatically develop a sequence of
labeled instructions that can be used by a developer to facilitate
a map, a graph, or other models of a process that assists with
development of automation for the process.
[1060] In embodiments, the value chain monitoring systems layer 614
and its data collection systems 640 may include an entity discovery
system 1900 for discovering one or more value chain network
entities 652, such as any of the entities described throughout this
disclosure and especially those that can be loaded and offloaded as
control passes from various maritime facilities 622 and floating
assets 620. This may include components or sub-systems for
searching for entities at maritime facilities 622 and floating
assets 620 within the value chain network 668, such as by device
identifier, by network location, by geolocation (such as by
geofence), by indoor location (such as by proximity to known
resources, such as IoT-enabled devices and infrastructure, Wifi
routers, switches, or the like), by cellular location (such as by
proximity to cellular towers), by maritime navigation aids and
vessel identity beacons, by identity management systems (such as
where an entity 652 is associated with another entity 652, such as
an owner, operator, user, or enterprise by an identifier that is
assigned by and/or managed by the platform 604), and the like. In
these examples, an entity discovery system 1900 may interact with
established maritime asset logistic systems used to track traffic
and location. In these examples, an entity discovery system 1900
may interact with established maritime asset autopilot and
auto-navigation systems obtaining information relevant to intended
navigation destinations and from there, the error and magnitude of
corrective action need to arrive at the navigation destination.
[1061] Referring to FIG. 22, the adaptive intelligence layer 614
may include a value chain network digital twin system 1700, which
may include a set of components, processes, services, interfaces
and other elements for development and deployment of digital twin
capabilities for visualization of various value chain entities 652
in environments, and applications 630, as well as for coordinated
intelligence (including artificial intelligence 1160, edge
intelligence 1420, analytics and other capabilities) and other
value-added services and capabilities that are enabled or
facilitated with a digital twin 1700. In embodiments, a digital
twin system 1700 may be deployed with each facility (or groups
thereof) among the maritime facilities 622 and may be deployed for
each of floating assets 620. In many instances, each floating asset
620 and physical assets in the maritime facilities 622 can be
coordinated and managed with its digital twin supported by the
digital twin system 1700. Without limitation, a digital twin system
1700 may be used for and/or applied to each of the processes that
is managed, controlled, or mediated by each of the set of
applications 630 of the platform application layer that may be
deployed in various systems, networks, and infrastructures (or
across groups thereof) of the floating assets 620 and in and among
the maritime facilities 622.
[1062] In embodiments, the digital twin 1700 may take advantage of
the presence of multiple applications 630 within the value chain
management platform layer 604, such that a pair of applications may
share data sources (such as in the data storage layer 624) and
other inputs (such as from the monitoring layer 614) that are
collected (to support fusion of collected signals and the like)
with respect to value chain entities 652, as well sharing outputs,
events, state information and outputs, which collectively may
provide a much richer environment for enriching content in a
digital twin 1700, including through use of artificial intelligence
1160 including any of the various expert systems, artificial
intelligence systems, neural networks, supervised learning systems,
machine learning systems, deep learning systems, and other systems
described throughout this disclosure and in the documents
incorporated by reference and through use of content collected by
the monitoring layer 614 and data collection systems 640.
[1063] Referring to FIG. 23, any of the value chain network
entities 652 can be depicted in a set of one or more digital twins
1700, such as by populating the digital twin 1700 with value chain
network data object 1004, such as event data 1034, state data 1140,
or other data with respect to value chain network entities 652,
applications 630, or components or elements of the platform 604 as
described throughout this disclosure.
[1064] Thus, the platform 604 may include, integrate, integrate
with, manage, control, coordinate with, or otherwise handle any of
a wide variety of digital twins 1700, such as distribution twins
1714 (such as representing distribution facilities, assets,
objects, workers, or the like); warehousing twins 1712 (such as
representing warehouse facilities, assets, objects, workers and the
like); port infrastructure twins 1714 (such as representing a
seaport, an airport, or other facility, as well as assets, objects,
workers and the like); shipping facility twins 1720; operating
facility twins 1172; customer twins 1730; worker twins 1740;
wearable/portable device twins 1750; process twins 1760; machine
twins 1770 (such as for various machines used to support a value
chain network 668); product twins 1780; point of origin twins 1502;
supplier twins 1630; supply factor twins 1650; maritime facility
twins 1572; floating asset twins 1570; shipyard twins 1620;
destination twins 1562; fulfillment twins 1600; delivery system
twins 1610; demand factor twins 1640; retailer twins 1790;
ecommerce and online site and operator twins 1800; waterway twins
1810; roadway twins 1820; railway twins 1830; air facility twins
1840 (such as twins of aircraft, runways, airports, hangars,
warehouses, air travel routes, refueling facilities and other
assets, objects, workers and the like used in connection with air
transport of products 650); autonomous vehicle twins 1850; robotics
twins 1860; drone twins 1870; and logistics factor twins 1880;
among others.
[1065] Referring to FIG. 27, additional details of an embodiment of
the platform 604 are provided, in particular relating to elements
of the adaptive intelligence layer 614 that facilitate improved
edge intelligence, including the adaptive edge compute management
system 1400 and the edge intelligence system 1420. These elements
provide a set of systems that adaptively manage "edge" computation,
storage and processing, such as by varying storage locations for
data and processing locations (e.g., optimized by AI) between
on-device storage, local systems, peer-to-peer, in the network and
in the cloud. These elements can enable facilitation of a dynamic
definition by a user, such as a developer, operator, or host of the
platform 102, of what constitutes the "edge" for purposes of a
given application anywhere in the world and especially in regions
of the oceans where connectivity can be constrained. For example,
for environments where data connections are slow or unreliable
(such as where a facility does not have good access to cellular
networks (such as due to remoteness on the globe), shielding or
interference (such as where density of network-using systems, thick
metals hulls of container ships, thick metal container walls,
underwater or underground location, or presence of large metal
objects (such as vaults, hulls, containers, cranes, stacked raw
materials, and the like,) interferes with networking performance),
and/or congestion (such as where there are many devices seeking
access to limited networking facilities), edge computing
capabilities can be defined and deployed to operate on the local
area network of an environment, in peer-to-peer networks of
devices, or on computing capabilities of local value chain entities
652. Where strong data connections are available (such as where
good backhaul facilities exist), edge computing capabilities can be
disposed in the network, such as for caching frequently used data
at locations that improve input/output performance, reduce latency,
or the like. Thus, adaptive definition and specification of where
edge computing operations are enabled, under control of a developer
or operator, or optionally determined automatically among a fleet
or deployed in a geographic region, such as by an expert system or
automation system that may be based on detected network conditions
for an environment. In embodiments, edge intelligence 1420 enables
adaptation of edge computation (including where computation occurs
within various available networking resources, how networking
occurs (such as by protocol selection), where data storage occurs,
and the like) that is multi-application aware, such as accounting
for QoS, latency requirements, congestion, and cost as understood
and prioritized based on awareness of the requirements, the
prioritization, and the value of edge computation capabilities
across more than one application.
[1066] In embodiments, the digital twin system 1700 may host
floating asset twins 1570 that can be associated with one or more
of the floating assets 620. By way of these examples, one or more
of the floating asset twins 1570 can simulate how one or more of
the floating assets 620 will perform without needing to test the
one or more of the floating assets 620 in the real world. Further
examples include visualization of all systems of the ship, its
navigation course, and functional needs including various details
all forms of information on a ship, from engine performance to hull
integrity, available at a glance throughout the full lifetime of
the vessel through its floating asset twins 1570.
[1067] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide beneficial visualization of any
and all important components of the one or more the floating assets
620. The use of the floating asset twins 1570 during operation can
be shown to be beneficial to carry out analyses and improve the
operation on the structural and functional components of the
floating assets 620. In further examples, use of the floating asset
twins 1570 during operation of the one or more of the floating
assets 620 can be used to model in-situ hydrodynamic and
aerodynamic changes to the structures and hull surfaces of the
floating assets 620. In embodiments, the floating assets 620 can
deploy systems to alter the configuration of the cross-sections of
certain portions of the hull, alter the configuration of
hydrodynamic control surfaces below the water line, alter the
configuration of aerodynamic control surfaces above the waterline,
extended additional buoyant members from the hull to improve hull
stability during certain maneuvers, and the like. In these
examples, artificial intelligence systems 1160 can study simulated
hull configurations deployed on the floating asset twins 1570 to
determine a schedule of hull configuration changes to improve fuel
efficiency using known routes of travel and historical weather
patterns.
[1068] In embodiments, use of the floating asset twins 1570 during
operation can be shown to benefit operators as they can plan for
more efficient inspections and maintenance of one or more floating
assets 620. In embodiments, use of the port infrastructure twins
1714 during operation can be shown to benefit operators that can
plan for more efficient inspections and maintenance of one or more
physical assets in the maritime facilities 622. This can also lead
to an extension of the physical assets' lifetimes, as preventive
measures will be taken to avoid damages.
[1069] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide operators with an ability to
create visual models of the ship and its underlying systems, such
as engine spaces and pumps, and continuously record its fuel
consumption, distributed on sources of energy, such as engines,
boilers and batteries. By way of these examples, operators can plan
for more efficient operations, inspections and maintenance of one
or more floating assets 620. In embodiments, use of the port
infrastructure twins 1714 during operation can be shown to provide
operators with ability to create visual models of the maritime
assets at a port, on land, moored in location and placed as
navigation aids including their underlying systems, such as systems
powerplants, and continuously record their energy consumption,
distributed on sources of energy, such as engines, boilers and
batteries. By way of these examples, operators can plan for more
efficient operations, inspections and maintenance of one or more
physical assets in the maritime facilities 622. In embodiments, the
digital twin systems can include simulation and analytical models
that can be developed to acquire the optimum fuel consumption for a
particular voyage with a specific cargo, by including external
factors such as wind, current and weather conditions. In
embodiments, the digital twin systems can include simulation and
analytical models that can be developed to acquire the optimum
energy consumption for a particular port activity such as unloading
with a specific cargo, by including external factors such as
weather conditions and other assets monitored by the adaptive
intelligence layer 614.
[1070] In embodiments, use of the floating asset twins 1570 and the
port infrastructure twins 1714 during operation can be shown to
provide operators with ability to visualize control and adapt the
operation of machinery systems in one or more floating assets 620
or deployed in the physical assets in the maritime facilities 622,
especially when the supply chain is across the one or more floating
assets 620 and the physical assets in the maritime facilities 622
and processes can be held, increased, decreased based on the
progress of other processed on land or on the water.
[1071] In embodiments, use of the floating asset twins 1570 and the
port infrastructure twins 1714 during operation can be shown to
provide optimal points during the voyage or during service life on
land to retrofit batteries and replace other switchgear. In
embodiments, use of the floating asset twins 1570 during operation
can be shown to provide a basis for changing to more powerful, more
efficient, or more versatile engines, thrusters or other propulsion
systems upon the usual maintenance cycles or at opportune times for
retrofit of components.
[1072] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide a basis for tuning a schedule to
adjust the front bulbous bow of the floating assets 620 to improve
efficient flow around the bow of the vessel in various combinations
of vessel speed, water activity and weather. In these examples, the
front bulbous bow can adjust its shape based on the predetermined
schedule or the revised schedule adjust by the adaptive
intelligence layer 614 for a shape of the bow for most efficient
running.
[1073] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide optimal points during the voyage
to perform hull cleaning, maintenance or painting or perform
propeller cleaning, maintenance or replacement. In embodiments, use
of the floating asset twins 1570 during operation can be shown to
provide basis for scheduling when hull or propeller cleaning is
needed, where in the journey contributes to greatest need to clean
systems and determining with simulation using the floating asset
twins 1570 whether such maintenance justified or routing of the
floating assets 620 to different passages may inflict less of a
maintenance burden.
[1074] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide detailed simulation and
visualization of optimal points during the voyage to perform hull
cleaning, maintenance or painting or perform propeller cleaning,
maintenance or replacement. In embodiments, use of the floating
asset twins 1570 during operation can be shown to provide basis for
scheduling when hull or propeller cleaning is needed, where in the
journey contributes to greatest need to clean systems and
determining with simulation using the floating asset twins 1570
whether such maintenance justified or routing of the floating
assets 620 to different passages may inflict less of a maintenance
burden.
[1075] In embodiments, use of the floating asset twins 1570 during
operation can be shown to provide detailed simulation and
visualization the performance of one or more ships or floating
assets 620 on a detailed level so users can see the effects of
design choices and changes on the one or more ships or floating
assets 620 as they simulate historical voyages, predicted voyages,
and previous voyages modified to further simulate activity
encountered to enhance training and safety. In embodiments, use of
the floating asset twins 1570 during operation can be shown to
provide detailed simulation and visualization the performance of
multiple ships or floating assets 620 on a detailed level so users
can make use of the digital twins for benchmarking performance
towards the other ships or maritime assets and these comparisons
can be used to simulate historical voyages, predicted voyages, and
previous voyages modified to further simulate activity encountered
to enhance training and safety.
[1076] In embodiments, use of the floating asset twins 1570 can be
shown to provide ship owners a tool for visualization of ships and
their subsystems (and various other maritime assets), qualification
and analytics of operational data, optimization of ship
performance, improved internal and external communication, safe
handling of increased levels of autonomy and safe
decommissioning.
[1077] In embodiments, use of the floating asset twins 1570 can be
shown to provide equipment manufacturers a tool to facilitate
system integration, demonstrate technology performance, perform
system quality assurance and promote additional services for
monitoring and maintenance.
[1078] In embodiments, use of the floating asset twins 1570 and the
port infrastructure twins 1714 can be shown to provide authorities
a systematic framework that can be set up with applications to feed
live information and generate required reports from each maritime
asset whether ships, barges, other floating assets, and port
infrastructure including moored navigation aids, cargo in unloaded
and loaded conditions and even personnel that move throughout the
port infrastructure to ensure its operation. In many examples, use
of the floating asset twins 1570 and the port infrastructure twins
1714 can be shown to ensure higher quality reporting on critical
issues without putting additional burdens or cognitive load on crew
already ensuring operations of the various maritime assets. In many
examples, use of the floating asset twins 1570 and the port
infrastructure twins 1714 can be shown to ensure higher quality
reporting on legal and regulatory issues by providing time-stamped
ledgers of activity paired with agreements and contracts underlying
the commerce supporting the maritime activity without putting
additional burdens or cognitive load on crew already ensuring
operations of the various maritime assets.
[1079] In embodiments, use of the floating asset twins 1570 and the
port infrastructure twins 1714 can be shown to provide
universities, colleges, and municipalities with platforms on which
to increase system understanding and facilitate knowledge exchange
enhancing research and development and education in a range of
technological disciplines. By way of these examples, use of the
floating asset twins 1570 and the port infrastructure twins 1714
can be shown to provide maritime academies platforms for training
that can increase the candidates' understanding of the whole ship
or specific maritime asset and train them in systems understanding
to see the integrated consequences of actions taken as it affects
that asset, all (or some) of the assets including floating and
infrastructure assets. In these examples, systems understanding can
be shown to be improved because the integrated consequences of
actions taken can be seen at the asset level, the fleet of asset
level, the infrastructure level, and the business level showing how
activity in fleet can affect the profitability of the fleet with
combinations of improving revenues and reducing expense where it
makes sense all of which can be visualized and interpreted from the
floating asset twins 1570 and the port infrastructure twins 1714
including suggestions from the adaptive intelligence layer 614.
[1080] In embodiments, an information technology system including a
value chain network management platform 604 can have an asset
management application 814 such as a maritime fleet management
application 880 associated with one or more maritime assets such as
one or more floating assets 620 or assets in the maritime
facilities 622. In embodiments, a data handling layer 608 of the
management platform 604 including data sources such as in the data
storage layer 624 and from other inputs such as from the monitoring
layer 614 that are collected with respect to any of the value chain
entities 652 including one more maritime assets. In embodiments,
the data sources contain information used to populate a training
set based on a set of maritime activities of one or more of the
maritime assets and one of design outcomes, parameters, and data
from one or more of the data handling layers 624 is associated with
the one or more maritime assets. In embodiments, an artificial
intelligence system such as the adaptive intelligence layer 614 can
be configured to learn on one or more of the training sets obtained
from the data sources from the one or more data handling layers
624. In doing so, the artificial intelligence system can simulate
one or more design attributes of one or more of the maritime
assets. The artificial intelligence system can also generate one or
more sets of design recommendations based on the training sets
collected from the data sources. In embodiments, a digital twin
system 1700 in the value chain network management platform 604 can
provides for visualization of one or more digital twins of one or
more of the maritime assets including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations.
[1081] In embodiments, the maritime assets can include one or more
container ships. In embodiments, the maritime assets include one or
more barges. In embodiments, the maritime assets include one or
more components of the port infrastructure installed on or adjacent
to land. In embodiments, the maritime assets include one or more
moored navigation units deployed on water. In embodiments, the
maritime assets include a ship and the maritime activities include
the forward speed of the ship relative to water and weather
conditions based on the parameters associated with energy
consumption of the propulsion units on the ship.
[1082] In embodiments, an information technology system includes a
set of intelligent systems for automatically populating a digital
twin of a maritime value chain network entity based on data
collected by the value chain network management platform 604. In
embodiments, the maritime value chain network entity is associated
with one or more of the real-world shipyards and the digital twin
can be configured to represent one or more of the real-world
shipyards. In embodiments, the maritime value chain network entity
is associated with a real-world maritime port and the digital twin
can be configured to represent one or more of the real-world
maritime ports. In embodiments, the maritime value chain network
entity is associated with one or more of the container ships and
the digital twin can be configured to represent one or more of the
container ships. In embodiments, the maritime value chain network
entity is associated with one or more of the barges and the digital
twin can be configured to represent one or more of the barges.
[1083] In embodiments, the maritime value chain network entity is
associated with one or more event investigations 7700 and the
digital twin can be configured to at least partially represent the
maritime value chain network entity as it can act and interact with
other assets during a timeline associated with one or more of the
event investigations 7700. In embodiments, the maritime value chain
network entity is associated with one or more legal proceedings
7702 and the digital twin can be configured to at least partially
represent the maritime value chain network entity as it can act and
interact with other assets during a timeline associated with the
one or more of the legal proceedings 7702. In embodiments, the data
collected by a value chain network management platform relates to a
casualty report 7704 and the digital twin of the maritime value
chain network entity is configured to simulate possibilities of a
loss 7708 relevant to the casualty report 7704 based on the data
collected by a value chain network management platform.
[1084] In embodiments, the maritime value chain network entity is a
port infrastructure facility, wherein the data collected by a value
chain network management platform facilitates identifying theft or
misuse of the port infrastructure facility by correlating data
between a set of data collectors for one or more physical items
7710 in the port infrastructure facility and the digital twin can
be configured to detail the one or more physical items 7710 of the
port infrastructure facility for the at least one of the port
infrastructure facility and the set of operators 7720.
[1085] In embodiments, the maritime value chain network entity is a
container ship that is moored to port infrastructure installed on
or adjacent to land.
[1086] In embodiments, data collected by a value chain network
management platform is based on at least a container ship having a
forward speed relative to water and weather conditions and
parameters associated with energy consumption of propulsion units
on the container ship.
[1087] In embodiments, the value chain network management platform
604 includes an asset management application 814 associated with
the value chain network management platform and one or more
maritime facilities connected to a container ship.
[1088] In embodiments, the asset management application is
associated with one or more ships connected to barges.
[1089] In embodiments, the maritime value chain network entity is
one or more ships and the digital twin can provide for
visualization of a navigation course of one or more of the ships.
In embodiments, the maritime value chain network entity is one or
more ships and the digital twin can provide for visualization of an
engine performance of one or more of the ships. In embodiments, the
maritime value chain network entity is one or more ships and the
digital twin can provide for visualization of a hull integrity of
one or more of the ships.
[1090] In embodiments, the digital twin can provide for
visualization of a plurality of inspection points 7730 on the
maritime value chain network entity and maintenance histories 7732
associated with those inspection points. In embodiments, the
digital twin can further provide for the visualization of the
plurality of the inspection points 7730 on the maritime value chain
network entity within geofenced parameters 7740 and maintenance
histories 7732 associated with those inspection points 7730.
[1091] In embodiments, the digital twin can further provide for
details of a ledger 7750 of activity associated with the
visualization of the plurality of inspection points 7730 on the
maritime value chain network entity within geofenced parameters
7740 and maintenance histories mardst 832 associated with those
inspection points 7730.
Control Tower and Enterprise Management Platform for Value Chain
Network
[1092] In embodiments, the control tower may include or interface
with an enterprise management platform (or "EMP"). In embodiments,
an EMP may be configured to generate, integrate with, support,
and/or or operate on one or more digital twins. In general, digital
twins merge data from multiple data sources into a model and
representation of the salient characteristics of things, assets,
systems, devices, machines, components, equipment, facilities,
individuals or other entities mentioned throughout this disclosure
or in the documents incorporated herein by reference, such as,
without limitation: machines and their components (e.g., delivery
vehicles, forklifts, conveyors, loading machines, cranes, lifts,
haulers, trucks, loading machines, unloading machines, packing
machines, picking machines, and many others, including robotic
systems (e.g., physical robots, collaborative robots, "cobots"),
drones, autonomous vehicles, software bots and many others); value
chain processes, such as shipping processes, hauling processes,
maritime processes, inspection processes, hauling processes,
loading/unloading processes, packing/unpacking processes,
configuration processes, assembly processes, installation
processes, quality control processes, environmental control
processes (e.g., temperature control, humidity control, pressure
control, vibration control, and others), border control processes,
port-related processes, software processes (including applications,
programs, services, and others), packing and loading processes,
financial processes (e.g., insurance processes, reporting
processes, transactional processes, and many others), testing and
diagnostic processes, security processes, safety processes,
reporting processes, asset tracking processes, and many others;
wearable and portable devices, such as mobile phones, tablets,
dedicated portable devices for value chain applications and
processes, data collectors (including mobile data collectors),
sensor-based devices, watches, glasses, wearables, head-worn
devices, clothing-integrated devices, bands, bracelets, neck-worn
devices, AR/VR devices, headphones, and many others; workers, such
as delivery workers, shipping workers, barge workers, port workers,
dock workers, train workers, ship workers, distribution of
fulfillment center workers, warehouse workers, vehicle drivers,
business managers, engineers, floor managers, demand managers,
marketing managers, inventory managers, supply chain managers,
cargo handling workers, inspectors, delivery personnel,
environmental control managers, financial asset managers, process
supervisors and workers (for any of the processes mentioned
herein), security personnel, safety personnel and many others);
suppliers, such as suppliers of goods and related services of all
types, component suppliers, ingredient suppliers, materials
suppliers, manufacturers, and many others; customers, including
consumers, licensees, businesses, enterprises, value added and
other resellers, retailers, end users, distributors, and others who
may purchase, license, or otherwise use a category of goods and/or
related services; a wide range of operating facilities, such as
loading and unloading docks, storage and warehousing facilities,
vaults, distribution facilities and fulfillment centers, air travel
facilities, including aircraft, airports, hangars, runways,
refueling depots, and the like, maritime facilities, such as port
infrastructure facilities, such as docks, yards, cranes,
roll-on/roll-off facilities, ramps, containers, container handling
systems, waterways, locks, and many others), shipyard facilities,
floating assets, such as ships, barges, boats and others),
facilities and other items at points of origin and/or points of
destination, hauling facilities, such as container ships, barges,
and other floating assets, as well as land-based vehicles and other
delivery systems used for conveying goods, such as trucks, trains,
and the like; items or elements factoring in demand (i.e., demand
factors), including market factors, events, and many others; items
or elements factoring in supply (i.e., supply factors), including
market factors, weather, availability of components and materials,
and many others; logistics factors, such as availability of travel
routes, weather, fuel prices, regulatory factors, availability of
space, such as on a vehicle, in a container, in a package, in a
warehouse, in a fulfillment center, on a shelf, or the like, and
many others; retailers, including online retailers and others;
pathways for conveyance, such as waterways, roadways, air travel
routes, railways and the like; robotic systems, including mobile
robots, cobots, robotic systems for assisting human workers,
robotic delivery systems, and others; drones, including for package
delivery, site mapping, monitoring or inspection, and the like;
autonomous vehicles, such as for package delivery; software
platforms, such as enterprise resource planning platforms, customer
relationship management platforms, sales and marketing platforms,
asset management platforms, Internet of Things platforms, supply
chain management platforms, platform-as-a-service platforms,
infrastructure-as-a-service platforms, software-based data storage
platforms, analytic platforms, artificial intelligence platforms,
and others; and many others.
[1093] In embodiments, a digital twin can represent a process, such
as a workflow, such as with moving elements that represent steps of
the process, such as the flow of items through a plant or
warehouse. A digital twin can also provide a logical
representation, such as various topologies, clusters, networks,
hierarchies or the like of logically related elements, such as an
organizational chart of roles and/or personnel, the logical steps
of a process, or the like. Thus, the term digital twin may refer to
a digital representation of a thing or set of things. An enterprise
digital twin may refer to any digital twin related to an enterprise
and the wide array of things that relate to the enterprise and its
operations. This may include digital twins of other enterprises and
cohorts related to the enterprise, such as competitors, vendors,
suppliers, distributors, customers, and the like. An enterprise may
refer to a company, organization, corporation, LLC, non-profit
organization, or the like. Enterprise digital twins may be used for
a wide variety of user-facing applications that benefit from
digital representation of salient features of elements of the
enterprise, including monitoring of assets and operations,
convenient generation and representation of a wide variety of
analytic results, generation and display of simulations, such as
for scenario planning, generation and display of recommendations
and other decision support, collaborative decision support, and
control of assets and operations, among many others. Enterprise
digital twins may include organizational digital twins, executive
digital twins, cohort digital twins, process digital twins, logical
digital twins, real-time digital twins, AI-driven digital twins,
environment digital twins, infrastructure and equipment digital
twins, workforce digital twins, asset digital twins, product
digital twins, system digital twins, and/or the like, which are
discussed in greater detail throughout the disclosure.
[1094] In embodiments, digital twins may be visual digital twins
and/or data-based digital twins or combinations of visual and
data-based digital twins. A visual digital twin may refer to a
digital twin that is capable of being depicted in a display such as
a traditional 2D display (optionally with touch, voice, optical,
auditory, or other control features), a 3D display, an augmented
reality display, a virtual-reality display, and/or a mixed-reality
display, any of which may include various combinations of
computer-generated display elements (such as animations and other
computer-generated graphics, including ones generated or derived
from CAD and/or 3D models), elements captured by cameras (such as
video and still images), visual elements captured or derived from
various sensor systems, such as LIDAR and other point cloud
systems, structured light systems, waveforms or other
representations of information from acoustic sensor systems,
vibration sensing systems, electromagnetic sensing systems, and
many others, and/or elements captured, received, or derived from
data collection and generation systems of enterprise assets, such
as onboard diagnostic and reporting systems, IT systems (e.g.,
logs), information from wearable devices, and many others. A
data-based digital twin may refer to a data structure that contains
a set of parameters that are parameterized to represent a state of
a thing or group of things, such that a data-based digital twin may
be leveraged by a computing application, such as for simulation,
modeling, predictions, classifications, and the like. As used
herein, the term "depict" may refer to the visual display of a
thing and/or a digital representation of a thing in a data
structure (e.g., in a data-based digital twin). It is noted that
visual digital twins may also be data-based digital twins, or
combinations of visual and data-based digital twins.
[1095] In some embodiments, a digital twin may be updated with
real-time data, such that the digital twin reflects the state of a
thing or set of things in real-time. For example, a digital twin of
an operating environment or facility (e.g., a factory, warehouse,
campus, or the like) may depict the physical structure of the
environment (e.g., walls, floors, ceilings, rooms and the like), as
well as objects appearing in the environment (e.g., machines,
products, employees, robots, and the like). Furthermore, depending
on the manner in which this digital twin is configured, the digital
twin of the operating facility may include things such as piping,
conduits, wiring, foundations, and the like. In embodiments the
digital twin may represent the information technology
infrastructure of the facility, including wireless and fixed
networking devices and systems and their operating capabilities and
characteristics. In some implementations, the digital twin of the
manufacturing environment may be updated with data received from
sensors (e.g., IoT sensors deployed in or around a facility or
equipment or machinery within the facility, wearable devices worn
by workers within the facility, and other suitable data sources).
For example, as a worker wearing a wearable device moves through
the facility, the wearable device may communicate the relative
location of the worker within the environment to the EMP, which in
turn may update the digital twin to reflect the location of a
representation of the worker in the digital twin of the facility.
In scenarios where the digital twin is of a process, the digital
twin may depict the process. For example, in the context of a
manufacturing process, a digital twin of the process may depict the
status and/or outcomes of different stages in the manufacturing
pipeline. In some implementations, the EMP 80 may receive data from
various sources (e.g., IoT sensors, data from smart equipment,
computing devices, smart products, smart infrastructure, or the
like) and may update the digital twin of the process to reflect the
received data. The EMP may be configured to generate, update,
and/or provide enterprise digital twins for different types of
enterprises, including manufacturing enterprises, retail and
marketing enterprises (merchants, advertisers, retail chains,
restaurant chains, malls, and the like), technology enterprises
(e.g., software, database and information technology companies),
logistics enterprises (e.g., shipping and delivery entities),
service-based enterprises (e.g., airlines, law firms, hospitals,
accounting firms, and the like) and many others. For example,
enterprise digital twins of a fast food enterprise may include
digital twins of food production facilities, food production
processes, food shipping facilities (e.g., warehouses and/or
trucks), retail locations (e.g., individual restaurant locations),
and/or retail processes (e.g., food preparation processes and/or
customer workflows). In this example, these digital twins may
identify the sources of contaminations (e.g., based on abnormal
temperature readings in a food production facility), delays (e.g.,
based on outcomes of the production and/or shipping processes),
customer satisfaction (e.g., based on data related to food
preparation and/or customer workflows), and the like.
[1096] In embodiments, the EMP may be configured to perform
simulations using and/or with respect to one or more enterprise
digital twins. In embodiments, digital twins (including enterprise
digital twins) may be configured to behave in accordance with a set
of constraints, such as laws of nature, laws of physics, mechanical
properties, material properties, economic principals, chemical
properties, and the like. In this way, the EMP may vary one or more
parameters of an enterprise digital twin and may execute a
simulation within the digital twin that conforms with real-word
conditions and behaviors. For example, in executing a simulation of
a logistics process that simulates outcomes associated with
different packaging materials, the EMP may simulate variation of
the packaging materials of one or more products. During the
simulation, the products may be "exposed" to different conditions
(e.g., different temperatures, humidity, motions, and the like) by
varying one or more parameters of an environment digital twin of an
environment of the products, a product digital twin of the product,
and/or the logistics digital twin. The simulation may be executed
to determine the fraction of products that are likely to be damaged
using the different packaging materials, which may affect the
profitability of shipments vis-a-vis the cost of the different
packaging materials and cost of replacing damaged products. In this
way, the simulation may be run to help select the most
cost-effective packaging material, such that estimated product loss
is taken into account. Furthermore, in some embodiments, digital
twins may be leveraged to perform simulations to predict future
states of the thing or group of things and/or modeling behaviors in
order to extrapolate states of the thing or group of things; to
represent results of such simulations (including states, event and
flows); and to offer opportunities to control things that are
represented in the digital twins based on the simulations. For
example, the EMP may receive sensor readings from temperature
sensors, humidity sensors, and fan speed sensors deployed
throughout an environment. The EMP may apply one or more
thermodynamics equations to the received sensor readings and the
dimensions of the environment to model the thermodynamic behavior
of the environment to determine, to represent in the digital twin
the temperatures in areas that do not have temperature sensors and
to offer opportunities to adjust one or more systems, such as HVAC
systems, or components thereof, to induce a change in the
environment.
[1097] In some embodiments, the EMP is configured to generate
organizational digital twins. In some embodiments, an
organizational digital twin incorporates the organization chart
("org chart") of an enterprise. In embodiments, an org chart may
define the different divisions (also referred to as business units)
within an enterprise, the roles within each division, the reporting
structure of the enterprise, and the individuals filling these
roles. In embodiments, the organizational digital twin may further
include additional data for the business units, roles, and/or
individuals filling the roles. For example, the organizational
digital twin may include budgets for each business unit, salary
ranges for roles, titles for roles, salaries for individuals, open
roles, start dates for individuals, and the like. In some
embodiments, an organizational digital twin may further incorporate
data access rules for different divisions and/or roles within the
organization, including permissions, access rights, and
restrictions.
[1098] In some embodiments an organizational digital twin may
represent the organization as a hierarchy or other topology, where
entities and relationships are represented, such as reporting
relationships, relationships of authority or decision-making, or
the like. In embodiments the organizational structure may be
represented and maintained in a graph structure, such as a directed
acyclic graph, a tree, or the like. In embodiments, an
organizational structure, such as an organizational chart or graph,
may be parsed by an artificial intelligence system to automatically
infer a set of entities, relationships, and roles, which in turn
may be used to determine, or recommend, a set of default parameters
for configuration of a digital twin. In embodiments, the default
parameters may be automatically configured for each user based on a
role of the user within the organization, as inferred by the
artificial intelligence system. In embodiments parameters may be
adjusted by one or more authorized users, such as to adjust or
correct the roles, using a digital twin configuration interface of
the organizational digital twin. The parameters for configuration
of a role-specific digital twin may include permissions (such as
for data access), communication settings, availability of features
(such as role-specific views of data and analytics, simulation
features, control features, and many other features described
throughout this disclosure), and the like. In embodiments the
artificial intelligence services system may incorporate any of the
techniques described throughout this disclosure or the documents
incorporated by reference, such as a machine learning, deep
learning, convolutional neural networks, robotic process
automation, or the like. In embodiments, the artificial
intelligence system may include a machine learning system that is
trained to infer roles within an organizational chart or structure
based on a training set of data, such as one where roles and
relationships within an organizational chart are provided by a set
of human experts and/or where roles and relationships are
explicitly stated within the organizational chart. For example, the
artificial intelligence system may learn that the top of the
organizational chart is likely to comprise the role of CEO and/or
President of an organization, and that other roles, such as the CFO
or COO, are likely to be represented in nodes that link directly to
the CEO role. In embodiments, the artificial intelligence system
may be trained to operate on various data sources to determine
and/or augment understanding of an organizational structure, such
as public data sets, such as securities filings, social media
information, web sites (such as securities information sites),
public relations and other news about the organization, or the
like. In embodiments, the machine learning system may parse social
media sites, such as LinkedIn.TM., to determine roles of
individuals and/or to help infer roles. In embodiments, data
sources such as social data, web data, new articles, or the like
may be used to determine competencies of individuals, which may be
associated with roles (e.g., the AI system may infer that a person
with a finance degree is likely to be in a financial role within
the organization). In embodiments, settings for a user may be
automatically configured to provide features that are appropriate
for the training, education, experience and/or competencies of the
user, as explicitly entered into the system or as inferred from
information associated with the identity of the individual. For
example, an individual who has a degree in physics and an MBA may
be provided default access to physical model simulations and to
financial simulations, while an individual who did not have those
educational credentials might be required to obtain authorization
and/or training before those features are made available in the
digital twin. Thus, the EMP may include artificial intelligence
systems that have been trained and/or configured to provide
automated understanding of organizational structures and
relationships, automated configuration of digital twins for roles
within an organization based on the understanding of structures and
relationships, and automated configuration of digital twin
parameters, settings, and features based on the role and/or the
identity of the user filling the role (including the competencies,
education, experience, training, or the like of the user).
[1099] In embodiments, a digital twin may be provided to represent
the organizational structure of a third-party organization in the
cohort of the organization of the user of the EMP, such as a
supplier, vendor, distributor, logistics partner, value added
reseller, representative, agent, venture partner, competitor,
advertiser, marketplace or the like. An organizational digital twin
of a cohort organization may represent structure, relationships,
roles, identities, and competencies of individuals within roles or
the organization, such that a user of the EMP may quickly and
readily view salient information about the relevant parts of the
organization. The organizational digital twin of the cohort
organization may be automatically maintained by an artificial
intelligence system of the EMP, such as by spidering, webscraping,
and parsing websites, news feeds, press releases, social media
data, and other available data sources, in order to maintain an
accurate representation of the organization. The artificial
intelligence system may be trained on a training set of data
labelled by human users and/or automatically labelled to maintain
an updated organizational structure. The resulting cohort digital
twin may be configured to provide various role-specific views
within the EMP. For example, a salesperson may be presented a
digital twin view of the part of the cohort organization that is
most likely to include individuals who are likely to be involved in
a decision to purchase the user's offerings, while an HR person's
view may be configured to present a digital twin view of the part
of the cohort organization that provides the most comparable
benchmark information for human resources. Digital twin views of
cohort organizations may be automatically populated and/or
configured, by training artificial intelligence systems on a
process-specific or role-specific basis, to support a wide range of
processes and features within the EMP, such as identification of
recruiting candidates, benchmarking as to organizational
structures, benchmarking as to competencies and talent,
identification and/or configuration of sales and business
development targets, identification of competitive offerings and/or
projects, identification of targets for mergers and acquisitions,
identification of targets for competitive research, and many
others.
[1100] Digital twins can be helpful for visualizing the current
state of a system, running simulations on those systems, and
modeling behaviors, amongst many other uses. Depending on the
configuration of the digital twin, however, a particular view or
feature may not be useful for some members of an organization, as
the configuration of the digital twin dictates the data that is
depicted/visualized by the digital twin. Thus, as noted above, in
some embodiments, the EMP is configured to generate role-based
digital twins. Role-based digital twins may refer to digital twins
of one or more segments/aspects of an enterprise, where the one or
more segments/aspects and/or the granularity of the data
represented by the role-based digital twin are tailored to a
particular role within the entity and/or to the identity of a user
that is associated with the role (optionally accounting for the
competencies, training, education, experience, authority and/or
permissions of the user, or other characteristics). In embodiments,
the role-based digital twins include executive digital twins.
Executive digital twins may refer to digital twins that are
configured for a respective executive within an enterprise.
Examples of executive digital twins may include CEO digital twins,
CFO (Financial) digital twins, COO (Operations) digital twins, HR
digital twins, CTO (Technology) digital twins, CMO (Marketing)
digital twins, General Counsel (Legal) digital twins, CIO
(Information) digital twins, and the like. In some of these
embodiments, the EMP generates different types of executive digital
twins for users having different roles within the organization. In
some of these embodiments, the respective configuration of each
type of executive digital twin may be predefined with default
digital twin data types, default relationships among entities,
default features, and default granularities, among other elements.
The default data types, entities, features and granularities may be
determined based on a model of an organization, which may in turn
be based on an industry-specific or domain-specific model or
template, such as one that is based on a typical organizational
structure for an industry (e.g., an automotive manufacturer, a
consumer packaged goods manufacturer, a nationwide retailer, a
regional grocery chain, or many others). In embodiments, an
artificial intelligence system may be trained, such as on a labeled
industry-specific or domain-specific data set, to automatically
generate an industry-specific or domain-specific digital twin for
an instance of an EMP for an organization, with default
configuration of data types, entities, features and granularities
for various roles within an organization of that industry or
domain. The defaults can then be reconfigured in a user interface
of an authorized user to reflect company-specific variations from
the industry-specific or domain-specific defaults. In some
embodiments, a user (e.g., during an on-boarding process) may
define the types of data depicted in the different types of
executive digital twins, the entities to be represented, the
features to be provided and/or the granularities of the different
types of executive digital twins. Features may include what data is
permitted to be accessed, what views are represented, levels of
granularity of views, what analytic models and results can be
accessed, what simulations can be undertaken, what changes can be
made (including changes relevant to permissions of other users),
communication and collaboration features (including receipt of
alerts and the capacity to communicate directly to digital twins of
other roles and users), control features, and many others. For
convenience of reference, references to views, data, features,
control or granularity throughout this disclosure should be
understood to encompass any and all of the above, except where
context specifically indicates otherwise. Granularity may refer to
the level of detail at which a particular type of data or types of
data is/are represented in a digital twin. For example, a CEO
digital twin may include P&L data for a particular time period
but may not depict the various revenue streams and costs that
contribute to the P&L data during the time period. Continuing
this example, the CFO digital twin may depict the various revenue
streams and costs during the time period in addition to the
high-level P&L data. The foregoing examples are not intended to
limit the scope of the disclosure. Additional examples and
configurations of different executive digital twins are described
throughout the disclosure.
[1101] In some embodiments, executive digital twins may allow a
user (e.g., a CEO, CFO, COO, VP, Board member, GC, or the like) to
increase the granularity of a particular state depicted in the
digital twin (also referred to "drilling down into" a state of the
digital twin). For example, a CEO digital twin may depict low
granularity snapshots or summaries of P&L data, sales figures,
customer satisfaction, employee satisfaction, and the like. A user
(e.g., the CEO of an enterprise) may opt to drill down into the
P&L data via a client application depicting the CEO digital
twin. In response, the EMP may provide higher resolution P&L
data, such as real-time revenue streams, real-time cost streams,
and the like. In another example, the CEO digital twin may include
visual indicators of different states of the enterprise. For
example, the CEO digital twin may depict different colored icons to
differentiate a condition (e.g., current and/or forecasted
condition) of a respective data item. For example, a red icon may
indicate a warning state, a yellow icon may indicate a neutral
state, and a green icon may indicate a satisfactory state. In this
example, the user (e.g., a CEO) may drill down into a particular
data item (e.g., may select a red sales icon to drill down into the
sales data, to see more specific and/or additional data, in order
to determine why there is the warning state). In response, the CEO
digital twin may depict one or more different data streams relating
to the selected data item.
[1102] In embodiments, a user interacting with a digital twin may
escalate or deescalate a state to another user associated within an
enterprise. For example, a COO or other operations executive may
view a COO digital twin that depicts various operations related
data. In this example, the COO may escalate a particular data set
depicted in the COO digital twin to the CEO. Once escalated, the
particular data set may appear in the CEO digital twin (e.g., with
a message from the escalating executive).
[1103] In some embodiments, the EMP supports rolled-up real-time
reporting. In some of these embodiments, data from IoT systems,
sensors, onboard diagnostic systems, wearable devices, enterprise
software systems, and/or other data sources (such as data feeds,
news feeds, social media data sources, crowdsourced data, data
obtained by spidering websites, sales data, marketing data,
advertising data, market data, weather data, pricing data, and many
others) may undergo one or more data fusion operations and an
AI-based agent may determine which individuals within the
enterprise to report results of analytics performed on the unfused
or fused data. In embodiments, the EMP may access data of or about
an organization (and third-party or external data) that is
available from a range of connected information technology and
connectivity systems of the organization, including data
collection, monitoring and storage systems as described elsewhere
in this disclosure and in the documents incorporated herein by
reference. In embodiments, the data collection, monitoring, and
storage systems may include a "data pipeline" of such connected
information technology and connectivity systems that may include
one or more of individual sensors that are disposed on or around
and/or are integrated into items (such as enterprise assets),
packages of such sensors, data collection, detection and reading
systems (such as asset tag readers, sensor readers, and many
others); onboard diagnostic systems, log systems, and other onboard
reporting systems producing feeds of data from machines, components
or systems; networking devices, including switches, access points,
routers, repeaters, mesh networking nodes, gateways, and the like,
as well as a host of different types of smart or network-connected
edge and IoT devices, and including Bluetooth, BLE, WIFI, NFC, IR
and other wireless devices, as well as 5G, 4G, 3G, LTE and other
cellular infrastructure systems, including cellular chips and
boards, gateways, towers and backhaul systems; data storage and
processing systems, including local storage, distributed storage,
database systems, caching systems, local memory systems, and many
others; computational systems, including edge computational
systems, serverless computational systems; and clients, servers,
on-premises IT systems, cloud-based systems, and many others. Data
may be transmitted and/or stored at points along this pipeline in
raw form (such as in packets of raw data, with metadata, in
streams, as events or transactions, as syndicated data, and in
other forms) and/or in various processed forms, such as compressed
data (including where compression is undertaken by trained
artificial intelligence systems), summarized data (including where
summarization is undertaking by trained artificial intelligence
system), augmented data (such as by metadata and/or one or more
analytic results), fused (e.g., multiplexed with one or more other
sources), or the like. Collection, processing, storage and or
transmission may be automated by one or more intelligence services
systems as disclosed elsewhere in this document and the documents
incorporated by reference herein, such as to provide for improved
reliability, quality-of-service, efficiency, or the like, such as
by intelligent protocol selection for data paths among nodes,
intelligent filtering of RF-domain wireless transmission, and the
like. As an example, a set of vibration sensors deployed on
industrial machines/equipment in a factory may report vibration
signatures of various components of the industrial
machines/equipment. Edge devices may be configured to fuse the
sensor data from an environment (e.g., a factory, warehouse,
distribution center, office building, or many others) with other
data collected with respect to the environment, whereby the fused
data is fed to the digital twin. The EMP may then update the
digital twin with the fused data and an AI system may analyze the
digital twin and/or the fused data to identify data items to
report, the proper role(s) to report to (e.g., CEO, COO, CFO, or
the like), and then may provide the report to the appropriate
individual(s). Enterprise digital twins, including executive
digital twins, are discussed in greater detail throughout the
application.
[1104] In embodiments, the EMP may be configured to provide a set
of collaboration tools that allow for collaboration between users
(e.g., members of an organization and/or with third parties). In
some embodiments, the collaboration tools allow users to
collaborate with respect to and/or within one or more enterprise
digital twins. In some embodiments, users can interact while
viewing the same digital twin or multiple digital twins showing
different aspects of the enterprise, showing different views or
features of the digital twin(s) and/or displaying information at
different granularities.
[1105] In embodiments, the collaboration tools include a video
conferencing service. In some of these embodiments, the video
conferencing service includes a graphical user interface that
allows a user to create subchats during a video conference. A
subchat may refer to an embedded video conferencing session where
the members of the subchat are selected from an ongoing video chat.
In some embodiments, the video conferencing service allows users to
participate in video conferences within a digital twin. For
example, users may access an environment digital twin via a VR-head
set, whereby the participants may view the environment digital twin
and see avatars of other participants within the "in-twin" video
conference. In embodiments, configuration of subchats may be
created based on roles within an enterprise, such as where a role
has authority to pull other roles into a subchat, such as roles
that report to the authority role.
[1106] In embodiments, the collaboration tools include interactive
white boards, productivity tools (e.g., word processors,
spreadsheets applications, slide decks/presentation applications,
and the like), or some other type of collaboration tool. In these
embodiments, users may import data from a digital twin (e.g., an
executive twin) into a medium, such as into a word processor
document or a spreadsheet. For example, when preparing a quarterly
report, a CFO may import data from a CFO digital twin directly into
the document containing the quarterly report. Collaboration tools
are described in greater detail throughout the disclosure. In
embodiments, a digital twin may import data from one or more other
collaboration environments into the digital twin, such that
collaboration entities can be viewed alongside other entities
represented in the digital twin. For example, a Google.TM. document
containing an analytic report on the performance of a logistics
system may be presented in a view of the elements of the logistics
system in a digital twin.
[1107] In embodiments, the EMP trains and deploys expert agents on
behalf of enterprise users. In embodiments, an expert agent is an
AI-based software agent, using, for example, robotic process
automation, that performs tasks on behalf of and/or suggests
actions to a respective user having a defined role that requires
some expertise in a particular field. In embodiments, the expert
agent may be trained within the EMP or otherwise, such as based on
interactions of the user with a client application, such as actions
taken by a user with respect to an executive digital twin,
interactions with sensor data or other data collected by the EMP,
interactions with systems or components of a workflow, and the
like. In embodiments, an expert agent may be an executive agent
trained for executive roles. For example, an executive agent may be
trained for performing or recommending actions to a user in an
executive role, such as CEO role, a COO role, a CFO role, a CTO
role, a CIO role, a CTO role, a CMO (chief marketing officer) role,
a GC (general counsel) role, an HR (human resources) executive
role, a board member role, a CDO (chief data officer) role, a CPO
(chief product officer) role, and the like. In embodiments, the EMP
includes capabilities to train expert agents for other roles within
an enterprise, such as an investor role, an engineering manager
role, a project manager role, an operations manager role, and a
business development role, a factory manager role, a factory
operations role, a factory worker role, a power plant manager role,
a power plant operations role, a power plant worker role, an
equipment service role, an equipment maintenance operator role, a
logistics manager role, a supply chain manager, and the like.
[1108] In embodiments, the expert agents are trained based on
training data that includes actions taken by users and features
relating to the circumstances surrounding the action (e.g., the
type of action taken, the scenario that prompted the action, and
the like). In embodiments, the EMP receives telemetry data from a
client application associated with a particular user and learns the
workflows performed by the particular user based on the telemetry
data and the surrounding circumstances. For example, the user may
be a COO that is presented a COO digital twin. Among the
responsibilities of the COO may be scheduling maintenance and
replacement of equipment or other infrastructure in a facility. The
states depicted in the COO digital twin may include depictions of
the condition of different pieces of equipment or infrastructure
within the facility. In this example, the COO may schedule
maintenance via the digital twin when a piece of equipment is
determined to be in a first condition (e.g., a deteriorating
condition) and may issue a request to the CFO via the COO digital
twin for authorization of finances to replace the piece of
equipment when the equipment is determined to be in a second
condition (e.g., a critical condition). The executive agent may be
trained to identify the COO's tendencies based on the COO's
previous interaction with the COO digital twin. Once trained, the
executive agent may automatically request replacements from the CEO
when a particular piece of equipment is determined to be in the
second condition and may automatically schedule maintenance if the
piece of equipment is in the first condition. Further discussion of
executive agents is provided throughout the disclosure. While
reference is made to an expert agent being trained for a particular
user, it is understood that an expert agent may be trained using
the actions of one or more different users and may be used in
connection with users that were not involved in training the expert
agent. Expert agents, including executive agents, are discussed in
greater detail throughout the disclosure.
[1109] FIG. 68 is a schematic of an example environment of an
enterprise management platform 8000. In embodiments, the EMP 8000
may be integrated with or accessible to a control tower via an
application programming interface (API). In some of these
embodiments, the EMP 8000 may be a series of microservices that are
accessible to the control tower.
[1110] In embodiments, the EMP 8000 includes an enterprise
configuration system 8002, a digital twin system 8004, a
collaboration suite 8006, an expert agent system 8008, and an
intelligence service system 8010. In embodiments, the EMP 8000
includes an API system 8014 that facilitates the transfer of data
between one or more external systems and the EMP 8000. In some
embodiments, the EMP8010 includes an enterprise data store 8012
that stores data relating to enterprises, whereby the enterprise
data is used by the digital twin system 8004, the collaboration
suite 8006, and/or the expert agent system 8008. The enterprise
data store 8012 may store any of a wide variety of data, such as
any data involved in the data pipeline described above and
throughout this disclosure and the documents incorporated herein by
reference. In embodiments, the enterprise data store 8012 may store
data that is being used to update digital twins in real-time or
substantially real time. In embodiments, the enterprise data store
8012 may store databases, file systems, folders, files, documents,
transient data (e.g., real-time data or substantially real-time
data), sensor data, and the like.
[1111] In embodiments, the enterprise configuration system 8002
provides an interface (e.g., a graphical user interface (GUI)) by
which a user (e.g., an "on-boarding" user) may upload or otherwise
provide data relating to an enterprise. As used herein, an
enterprise may refer to a for-profit or non-profit organization,
company, governmental agency, non-governing organization, or the
like. While described as an on-boarding user, the configuration of
the enterprise management platform 8000 for a particular enterprise
may be performed by any number of users, including individuals
associated with the enterprise, individuals associated with the
EMP, and/or individuals associated with a third-party, such as a
third host of a hosted EMP for an enterprise (which may be deployed
on cloud resources, platform-as-a-service, software-as-a-service,
multi-tenant data resources and/or similar resources) and/or a
service provider.
[1112] In embodiments, the on-boarding user may define the types of
enterprise digital twins that may be generated by the digital twin
system 8004 on behalf of the enterprise being on-boarded. In
embodiments, the on-boarding user may select different types of
digital twins that will be supported for the enterprise by the EMP
8000 via a GUI presented by the enterprise configuration system
8002. For example, the user may select different types role-based
digital twins from a menu of digital twin types, where the
different types of role-based digital twins include executive
digital twins. As another example, the user may select a type of
organizational digital twin that is suitable for the user's
organization, such as from a library of industry-specific or
domain-specific organizational templates. In some embodiments, each
type of executive digital twin has a predefined set of states (such
term as referenced herein encompassing states, entities,
relationships, parameters, and other characteristics) that are
depicted in the respective executive digital twin and predefined
granularity levels and/or other features for each state of the set.
In some embodiments, the set of states that are depicted in the
executive digital twin, the granularity of each, and/or other
features may be customized (e.g., by the on-boarding user). In
these embodiments, a user may define the different states that are
represented in each type of executive digital twin and/or the
granularity for each of the states depicted in the digital twin.
For example, if the CEO of an enterprise has a financial
background, the CEO may wish to have more financial data depicted
in the CEO digital twin, such that the financial data is displayed
at a higher granularity, or the CEO may wish to have access to
underlying information on financial models that are available to
the digital twin, such as models used for determination of state
information (e.g., financial predictions or forecasts) or models
used for augmentation of states (such as highlighting important
deviations from expectations). By contrast, if the CEO has less
financial experience or training, the CEO digital twin may be
configured with summary financial data and may include prompts
(which may be generated by an intelligent agent trained on a set of
enterprise and/or industry outcomes) to obtain CFO input when
states deviate from normal operating conditions. In this example,
the CEO digital twin may be configured to depict the desired
financial data fields at a granularity level set defined by a user
(e.g., the financial data may include various revenue streams, cost
streams, and the like). In another example, the CEO may have a
technical background. In this example, the CEO digital twin may be
configured to depict one or more states related to the enterprise's
product and R&D efforts, patent development, and product
roadmaps at higher granularity levels. In yet another example, a
COO may be tasked with overseeing a product team, a marketing team,
and an HR department of the enterprise. In this example, the COO
may wish to view marketing-related states, product
development-related states, and HR-related states at a lower
granularity level. In this example, the COO digital twin may be
configured to show visual indicators that indicate whether any of
the states are at a critical condition, an exceptional condition,
or a satisfactory condition. For instance, if employee turnover is
very high and employee satisfaction is low, the COO digital twin
may depict that the HR-state is at a critical level. In this
configuration, the COO may select to drill down into the HR-state,
where she may view the employee turnover rate, hiring rate, and
employee satisfaction survey results.
[1113] In another example, a COO or CTO digital twin may be
configured to represent and assist with discovery and management of
interconnections, relationships and dependencies between enterprise
operations and information technology. For example, a COO digital
twin or a CFO digital twin may be configured to depict a set of
operations entities and workflows (e.g., flow diagrams that
represent a production process, an assembly process, a logistics
process, or the like), where entities (including human workers,
robots, processing equipment, and other assets) are depicted to
operate on a set of inputs such as materials, components, products,
containers and information) in order produce and hand off a set of
outputs (of similar varied types) to the next set of entities in
the workflow for further processing. These may be represented, for
example, in a flow diagram that depicts each entity and its
relationship in the flow to other entity. In embodiments, a
role-based digital twin (such as a CIO digital twin) may also
represent an information technology system, such as representing
sensors, IoT devices, data collection and monitoring systems, data
storage systems, edge and other computational systems, wired and
wireless networking systems, and the like, including any of the
types described throughout this disclosure. Each information
technology component or system may be depicted in the role-based
digital twin, along with related data, such as specifications,
configuration parameters and settings, processing capabilities,
along with its relationship to other components, such as
representing data and networking connectivity to other components
or systems. In embodiments, a role-based digital twin may provide a
converged view that depicts operations technology entities and
information technology entities in relation to each other, such as
indicating which information technology entities are located with
wired or proximal wireless connectivity to which operational
entities, indicating which informational technology entities are
logically associated to which operational entities (such as where
cloud resources, computational resources, artificial intelligence
resources, database resources, application resources, or other
resources are provisioned to support or interact with operational
entities, such as in virtual machine, container or other logical
relationships). In embodiments, the converged view presented in the
role-based digital twin may thus depict location-based and/or
logical interconnections between operations and information
technologies. In embodiments, alerts, such as indicating failure
modes, congestion, delays, interruptions in service, poor latency,
diminished quality of service, bandwidth constraints, poor
performance on key performance indicators, downtime, or other
issues may be provided as augmentations or overlays of the
converged information technology and operations digital twin, so
that the COO, CTO, CIO or other user may see interconnections
between information technology entities and operational entities
that may be contributing to problems. Other types of issues that
may be provided as augmentations or overlays may include alerts as
to existing conditions and/or forecasts or predictions of such
conditions, such as by analytic systems or forecasting artificial
intelligence systems, such as expert agents trained to make such
forecasts. In an example, if high latency in a control system for a
warehouse is slowing down the process of picking and packing goods
due to a related edge computational node experiencing congestion on
an input data path, the user of the role-based digital twin may be
alerted to the fact that operations are being adversely impacted by
the congestion, and a recommendation may be presented to augment,
update, upgrade, or replace either the system providing
connectivity to the edge node or the edge node itself. Thus, a
converged digital twin of operations technology entities and
information technology entities may provide for insight into how an
executive may adjust operations and/or information technology to
improve results and/or avoid anticipated problems before they
become catastrophic failures.
[1114] In embodiments, a user (e.g., an on-boarding user) may
connect one or more data sources 8020 to the EMP 8000. Examples of
data sources 8020 that may be connected to the EMP may include, but
are not limited to, a sensor system 8022 (e.g., a set of IoT
sensors), a sales database 8024 that is updated with sales figures
in real time, a customer relationship management (CRM) system 8026,
a marketing campaign platform 8028, news websites 8048, a financial
database 8030 that tracks costs of the business, surveys 8032
(e.g., customer satisfaction and/or employee satisfaction surveys),
an org chart 8034, a workflow management system 8036, customer
databases 1S40 that store customer data, external data feeds (such
as news feeds, public relations feeds, weather feeds, trade data,
pricing data, market data, and the like), data obtained by
spidering, webscraping, or otherwise parsing website and social
media sites, data obtained by crowdsourcing, and/or data from many
and various third-party data sources 8038 that store third-party
data. The data sources 8020 may include additional or alternative
data sources without departing from the scope of the disclosure.
Once the user has defined the configuration of each respective
executive digital twin, where the configuration includes the
selected states to be depicted (which may include entities,
relationships, and characteristics), the features that are to be
enabled, and/or the desired granularity of each state, the user may
then define the data sources 8020 that are fed into the respective
executive digital twin, including any of the data sources in the
data pipeline described above. In some embodiments, data from one
or more of the data sources may be fused and/or analyzed before
being fed into a respective digital twin.
[1115] In some embodiments, the on-boarding user may select among
various types of enterprise digital twins that are supported for
the enterprise, including environment digital twins, information
technology digital twins, operations digital twins, organizational
digital twins, supply chain digital twins, product digital twins,
facility digital twins, customer digital twins, cohort digital
twins and/or process digital twins, among others. In some of these
embodiments, the user may define the data sources used to generate
these digital twins and to update the enterprise digital twins. In
embodiments, the user may define any physical locations that will
be represented as an environment digital twin (which may be a
digital twin of a facility or other suitable environments). For
example, the user may define manufacturing facilities (e.g.,
factories), shipping facilities, warehouses, office buildings, and
the like. Each facility may be given a location (which may include
a logical and/or virtual location and/or a geo-location) and an
identifier, such as a name and type description. In embodiments,
the enterprise configuration system 8002 may assign an identifier
to each facility and may associate the location of the facility
with the identifier. In embodiments, the user may define the types
of objects that are included in the environment and/or may be found
within an environment. For example, the user may define the types
of enterprise resources (e.g., factory, warehouse, or distribution
center equipment and machines, assembly lines, conveyors, vehicles,
robots, high-lows, and the like, IT systems, workers, and many
others) that are in the environment, the types of products,
materials and components that are made in, stored in, moved around,
assembled, used as inputs within, produced in, sold from, and/or
received in the environment, the types of sensors/sensor kits
and/or data collection, storage and/or processing devices that are
used in the environment, the workers and workflows involved, and
the like. Examples of how environment and process digital twins are
generated and updated may be found in the U.S. Provisional
Application No. 62/931,193, filed Nov. 5, 2019, entitled Methods
and Systems of Value Chain Network Management Platform and U.S.
Provisional Application No. 62/969,153, filed Feb. 3, 2020,
entitled Methods and Systems of Value Chain Network Management
Platform, the contents of which are herein incorporated by
reference.
[1116] In embodiments, the enterprise configuration system 8002 (in
combination with the digital twin system 8004) is configured to
generate organizational digital twins that represent an
organizational structure of an enterprise. In some embodiments, the
organizational digital twin may depict individuals/roles occupying
the management and expert levels of an enterprise. Alternatively,
the organizational digital twin may include a workforce digital
twin that represents the entire workforce of an enterprise,
including all the employees and/or contractors of the enterprise,
or a defined part thereof. For example, in an enterprise setting,
workforces may include a logistics workforce, a warehouse
workforce, a distribution workforce, a reverse logistics workforce,
a delivery workforce, a factory operations workforce, a plant
operations workforce, a resource extraction operations workforce, a
network operations workforce (e.g., for operating internal networks
of an industrial enterprise), a sales workforce, a marketing
workforce, an advertising workforce, a retail workforce, an R&D
workforce, a technology workforce, an engineering workforce, and/or
the like. In another example, with respect to a value chain
network, workforces may include a supply chain management
workforce, a logistics planning workforce, a vendor management
workforce, and the like. In another example, in the context of a
marketplace setting, workforces may include a brokering workforce
for a marketplace, a trading workforce for a marketplace, a trade
reconciliation workforce for a marketplace, a transactional
execution workforce for a marketplace, and/or the like. Enterprises
may include additional or alternative workforces. In some
embodiments, an organizational digital twin may include
management-level roles within a workforce. Examples of
management-level roles of an enterprise include a CEO role, a COO
role, a CFO role, a counsel role, a board member role, a CTO role,
an information technology manager role, a chief information officer
role, a chief data officer role, an investor role, an engineering
manager role, a project manager role, an operations manager role, a
business development role. Furthermore, the management-level roles
of a workforce may include a factory manager role, a factory
operations role, a factory worker role, a power plant manager role,
a power plant operations role, a power plant worker role, an
equipment service role, and an equipment maintenance operator role.
In a value chain context, the management-level roles of a workforce
may include a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[1117] In the context of marketplaces, the management-level roles
of a workforce may include a market maker role, an exchange manager
role, a broker-dealer role, a trading role, a reconciliation role,
a contract counterparty role, an exchange rate setting role, a
market orchestration role, a market configuration role, and a
contract configuration role. It is appreciated that not all of the
roles defined above apply to a particular workforce type.
Furthermore, some roles may be associated with different types of
workforces.
[1118] In some embodiments, an organizational digital twin may
further incorporate data access rules for different divisions
and/or roles within the organization. For example, the CEO may be
granted access to most or all of the organization's data, the CFO
may be granted access to financial-related data and restricted from
viewing R&D data, the CTO may be granted access to
R&D-related data and restricted from viewing financial data,
members of the engineering team may be restricted in accessing
financial related data, or the like. Similar rules may be applied
to access to features, such as analytic models, artificial
intelligence systems, intelligent agents, and the like, including
role-based or identity-based control of the ability to view
results, to configure inputs, to configure or adjust models (e.g.,
weights, inputs, or processing functions), to undertake control
actions, or the like. In some embodiments, the EMP may utilize the
organizational digital twin when determining the level of access a
particular individual may be granted and/or whether to deny certain
types of access to the individual. In some embodiments, the access
rights may limit the types of data that particular users can
access, such as information about each individual listed in the
organizational digital twin (e.g., salary, start date,
availability, work status, and the like). For example, lower level
employees may not be granted access to sensitive information, such
as financial data, product strategies, marketing strategies, trade
secrets, or the like. In some embodiments, certain users may be
granted permission to change the access rights of other employees,
which may be reflected in the organizational digital twin. For
example, certain executives and managers may be granted permission
to grant access rights to members of their respective teams when
working on certain projects.
[1119] In embodiments, the enterprise configuration system 8002
receives an organization chart ("org chart") definition of an
enterprise and generates an organizational digital twin based on
the org chart definition. In embodiments, the org chart definition
may define the business units/departments of the enterprise, the
reporting structure of the enterprise, various roles of the
enterprise/within each business unit, and the individuals in the
respective roles. In some embodiments, the user can upload the
enterprise's org chart to the EMP 8000 via the enterprise
configuration system 8002. Additionally or alternatively, the user
can define the structure of the org chart (e.g., roles, business
units, reporting structure) and may populate the various roles with
names and/or other identifiers of the individuals filling the
respective roles defined in the org chart. In some embodiments, the
enterprise configuration system 8002 may access an enterprise
resource planning system 8044 and/or an HR system 8046 of the
enterprise to obtain organizational data of the enterprise, such as
the roles of the enterprise, the individuals that fill the roles,
the salaries of the individuals that fill the roles, the reporting
structure of the enterprise, and the like. In these embodiments,
the digital twin system 8004 (discussed below) may continue to
communicate with the ERP system 8044 and/or HR system 8046 to
receive the data needed to maintain the organizational digital twin
in a real-time or near-real-time manner.
[1120] In embodiments, the enterprise configuration system 8002 (in
cooperation with the digital twin system 8004, discussed below) may
generate an organizational digital twin of the enterprise based on
the org chart definition and the individuals that populate the
roles within the org chart definition. In embodiments, a user may
define one or more restrictions, permissions, and/or access rights
of the individuals indicated in the organizational digital twin via
the enterprise configuration system 8002. In embodiments, a
restriction may define one or more types of data or features that a
particular user or group of users is not allowed to access (either
directly or in a digital twin). In embodiments, an access right may
define one or more types of data or features that a particular user
or group of users may access and the type of access that a user or
group of users can access. In embodiments, a permission may define
operations that a user or a group of users may perform with respect
to the EMP 8000. In embodiments, one or more of the access rights,
permissions, and restrictions may be defined geographically and/or
temporally limited. For example, some types of data or features may
only be viewed or otherwise accessed in certain areas (e.g.,
sensitive data may only be viewed in the corporate offices) or at
certain times (e.g., during Board meetings). In embodiments, the
restrictions, permissions, and/or access rights may be set with
respect to roles or the users themselves. As such, defining access
rights, permissions, and/or restrictions for a user or a group of
users may also include defining access rights, permissions, and/or
restrictions to a role and/or business unit within the enterprise.
In embodiments, the organizational digital twin may be deployed to
manage the rights, permissions, and/or restrictions for the users
of an enterprise. Furthermore, in embodiments, the organizational
digital twin may define the types of role-based digital twins (and
other enterprise digital twins) that various users may have access
to. In some embodiments, the organizational digital twin may depict
additional or alternative information.
[1121] In embodiments, the digital twin system 8004 is configured
to generate, update, and serve enterprise digital twins of an
enterprise. In some embodiments, the digital twin system 8004 is
configured to generate and serve role-based digital twins on behalf
of an enterprise and may serve the role-based digital twins to a
client device 8050 (e.g., a mobile device, a tablet, a personal
computer, a laptop, AR/VR-enabled device, workflow-specific device
or equipment, or the like). As discussed, during the configuration
phase, a user may define the different types of data and the
corresponding data sources, data sets, and features that are used
to generate and maintain each respective type of the different
types of enterprise digital twins. Initially, the digital twin
system 8004 configures the data structures that support each type
of enterprise digital twin, including any underlying data
sources/databases (e.g., SQL databases, graph databases, relational
databases, distributed databases, blockchains, distributed ledgers,
data feeds, data streams, and the like) that store or produce data
that is ingested by the respective enterprise digital twins. Once
the data structures that support a digital twin are configured, the
digital twin system 8004 receives data from one or more data
sources 8020. In embodiments, the digital twin system 8004 may
structure and/or store the received data in one or more databases.
When a specific digital twin is requested (e.g., by a user via a
client application 8052 or by a software component of the EMP
8000), the digital twin system may determine the views that are
represented in the requested digital twin and may generate the
requested digital twin based on data from the configured databases
and/or real-time data received via an API. The digital twin system
8004 may serve the requested digital twin to the requestor (e.g.,
the client application or a backend software component of the EMP
8000). After an enterprise digital twin is served, some enterprise
digital twins may be subsequently updated with real-time data
received via the API system 8014. In embodiments an API may provide
information to the data pipeline as to the type of data required
for the digital twin, such that the data pipeline may be configured
(by a user, or by an automated/intelligence systems) to handle the
data effectively. For example, the data pipeline may be configured
to deliver data over a data path that uses an appropriate protocol
for efficient delivery, delivering the data over a cost-appropriate
path (e.g., an inexpensive path for data that does not require low
latency or real-time updating), or the like. Thus, in some
embodiments, configuration of a digital twin may include providing
inputs as to the requirements of the digital twin for low-latency,
high quality-of-service, high accuracy, high granularity, high
reliability, or the like, based on, for example, the priority of
the mission served by the data type. In embodiments, an intelligent
expert agent (or "intelligent agent" or "expert agent") may be
trained on a training set of configurations of inputs to one or
more data pipelines that were previously configured by experts,
such that the intelligent agent may learn to automatically
configure APIs for digital twins to provide appropriate inputs to
data pipelines for subsequent digital twins involving similar or
analogous workflows for similar or analogous roles, identities,
industries and/or domains. In embodiments, such training of an
intelligent agent may include learning as to specific user
interactions, such as learning which users within a role use which
types of data at what times and for what purposes, such that data
resources are appropriately allocated to support actual user
requirements. For example, an automated intelligent agent managing
the configuration of a data pipeline for a COO digital twin may
learn that an operations executive (e.g., a COO user) checks
production data for each facility at the end of each eight-hour
shift (e.g., after 5:00 pm), such that mid-shift data updates are
delivered over lower-cost data resources, but end-of-shift data is
delivered over low-latency data paths that have high reliability
and quality-of-service. Continuing this example, the intelligent
agent may determine the frequency at which the production data is
updated with respect to the COO digital twin, such that the COO
digital twin is updated less frequently in the mornings and
mid-afternoons, but is updated more frequently at the end of
business hours. In embodiments, the intelligent agent may be
configured with business logic that defines overall strategies
(e.g., when to use low-latency networks v. higher-latency networks
and/or how often to update a certain type of data within a
particular digital twin) and customized based on the preferences
and use by the end user of the digital twin, whereby the overall
strategies may be learned from training data sets obtained from
experts and/or may be hard-coded by a developer, and the
customization piece may be learned from monitoring the use of the
digital twin by the end intended user (e.g., when she typically
checks the production data of each facility). Additional or
alternative examples of such data prioritization strategies and/or
other configuration strategies should be understood to be
encompassed herein. For example, upon receipt of inputs as to
performance requirements, artificial intelligence capabilities of
the data pipeline that is integrated with, linked to, or supporting
of the EMP 100 may automatically or under user control employ
techniques to provide appropriate resources at the right time and
place, including, but not limited to: adaptive coding of data path
transmissions between networked data communication nodes; adaptive
filtering, repeating and amplification of RF/wireless signals
(including software-implemented bandpass filtering); dynamic
allocation of use of cellular and other wireless spectrum,
adaptive, ad-hoc, cognitive management of wireless mesh network
nodes; adaptive data storage; cost-based routing of wireless and
wired signals; priority-based routing; channel- and
performance-aware protocol selection for communications;
context-aware allocation of computational resources, serverless
computational systems, adaptive edge computational systems,
channel-aware error correction, smart-contract-implemented network
resource allocation; and/or other suitable techniques.
[1122] In embodiments, the digital twin system 8004 may be further
configured to perform simulations and modeling with respect to the
enterprise digital twins. In embodiments, the digital twin system
8004 is configured to run data simulations and/or environment
simulations using a digital twin. For example, a user may, via a
client device, instruct the digital twin system 8004 to perform a
simulation with respect to one or more states and/or workflows
depicted in a digital twin. The digital twin system 8004 may run
the simulation on the digital twin and may depict the results of
the simulation in the digital twin. In this example, the digital
twin may need to simulate at least some of the data used to run the
simulation of the environment, so that there is reliable data when
performing the requested environment simulation. The digital twin
system 8004 is discussed in greater detail throughout the
disclosure.
[1123] In embodiments, the collaboration suite 8006 provides a set
of various collaboration tools that may be leveraged by various
users of an enterprise. The collaboration tools may include video
conferencing tools, "in-twin" collaboration tools, whiteboard
tools, presentation tools, word processing tools, spreadsheet
tools, and the like. In embodiments, an "in-twin" collaboration
tool allows multiple users to view and collaborate within a digital
twin. For example, in embodiments, the collaboration tools may
include an in-twin collaboration tool that that enables a digital
twin experience and a collaboration experience within the same
interface (e.g., within a AR/VR-enabled user interface, a standard
GUI, or the like), such as where collaboration entities and events
(such as version-controlled objects, comment streams, editing
events and other changes) are represented within the digital twin
interface and linked to digital twin entities. For example,
multiple users may be granted access to view an environment digital
twin of a facility, such as a warehouse or factory, via an in-twin
collaboration tool. Once viewing the environment digital twin, the
users may then change one or more features of the environment
depicted in the environment digital twin and may instruct the
digital twin system to perform a simulation. In this example, the
results of the simulation may be presented to the users in the
digital twin and may be automatically populated into a shared
document (e.g., a spreadsheet or presentation document). Users may
collaborate in additional manners with respect to a digital twin,
as will be discussed throughout the disclosure. For example, in
some embodiments, the collaboration suite 8006 may allow a user to
call a video conference with another user, where the users see each
other and see aspects of a specific digital twin that relates to
the topics of discussion for the conference. In this example, users
may, for example, see a representation of workpiece under
discussion and see each other, so that a user can see gestures or
indications from another user about how the workpiece should be
acted upon. In another example, a conferencing feature of the twin
may show participants in a view of a set of environments of
facilities by their locations, so that users can recognize which
participants may have closest proximity to relevant assets that are
the subject of collaboration. In some embodiments, the
collaboration suite 8006 interfaces with third-party applications,
whereby data may be imported to and/or from the third-party
application. For example, in collaborating on a Board presentation,
different executives may export data from their respective
executive digital twin into a shared presentation file (e.g.,
PowerPoint.TM. file or Google.TM. slide presentation). In another
example, a first user (e.g., the CEO of an enterprise) may request
certain information (e.g., financial projections for the
enterprise) from a second user (e.g., the CTO of the enterprise)
via a first executive digital twin configured for the first user
(e.g., a CEO digital twin of the enterprise). In response, the
second user may upload/export the requested data from a second
executive digital twin that was configured for the second user
(e.g., the CTO) to the EMP100 (e.g., to the collaboration suite
8006 and/or the digital twin system 8004, which may then update the
executive digital twin configured for the first user. Additional
examples and descriptions of the collaboration suite 8006 and
underlying collaboration tools are discussed throughout the
disclosure.
[1124] In embodiments, the collaboration suite 8006 may be
configured to interface with the digital twin system 8004 (e.g.,
independent of or under control of the digital twin system 8004) to
provide role-specific views and other features within a
collaboration environment and/or workflow of a collaboration tool,
such that different participants in the same collaboration
environment and/or workflow experience different views or features
of the same digital twin entities and/or workflows. For example, a
CFO may collaborate with a COO and a CTO about the possible
replacement of an internal system or a piece of machinery or
equipment, where the current system, machinery or equipment and/or
the potential replacement system, machinery, or equipment is/are
represented in the digital twin by visual and other elements.
During collaboration, the collaboration suite 8006 may recognize
the identities/roles of the CFO, COO and CTO and may automatically
configure their respective collaboration views into the example
digital twin based on those roles. For example, the CFO may be
presented with a view that is augmented with financial data, such
as the cost of the item and various possible replacements, terms
and conditions of leasing agreements, depreciation information,
information on the financial impacts on productivity, or the like.
Meanwhile, the collaboration suite 8006 may present the COO with
information depicting the relationship of the item to operational
processes, such as linkages to other systems involved in a
production line, timing information (such as scheduled downtimes
for a facility) and the like. In this example, the CTO may be
presented with performance specifications and capability
information for an item and various possible replacements,
including, for example, compatibility information that indicates
the extent to which various possible replacements are compatible
with other items represented in the digital twin (including
physical/mechanical compatibility, data compatibility, software
compatibility, and many other forms of technology compatibility),
reviews and ratings, and other technical information. Each
executive user may be presented with respective information that is
in the respective user's "native language" (e.g., information that
is tailored to each executive's respective expertise and needs) and
with respective views and/or features that are comfortable for that
user, while the group can collaborate (in live or asynchronous
modes) to raise issues, engage in commentary and dialog, perform
analysis (including simulations as described herein) to arrive at a
decision (e.g., about selection and timing of a replacement, or an
alternative like a repair) that is financially prudent,
operationally effective, and technologically sound. Thus, a
role-sensitive collaboration environment integrated with respect to
a shared enterprise digital twin enables collaboration around
digital twin entities and workflows while allowing users to engage
with role-sensitive views and features. In embodiments the
collaboration suite 8006 and or other systems of the EMP100 (e.g.,
the digital twin system 8004) may access a semantic model of an
enterprise taxonomy to automatically generate and/or provide
information that is presented in a shared digital twin (such as
role-specific augmentation of entities with text or symbols that is
derived from data or metadata based on state information or other
data). In embodiments, the enterprise taxonomy may be learned by
the EMP100 via an analysis of data provided by the enterprise or
may be manually uploaded by a user (e.g., a configurating user
associated with the enterprise). The information in the digital
twin may be presented with a role-specific understanding of the
taxonomy, such as where the same entity (e.g., a piece of
equipment) is given a different name by different groups in the
enterprise (e.g., referred to as an "asset" by the finance
department and a "machine" by the operations team) and/or where
attributes of the entity or related workflows use different
terminology, codes, symbols, or the like that are role-specific or
group-specific. In embodiments the collaboration suite 8006 may
automatically enable translation of terminology between roles, such
as translating commentary that uses the name of an entity or that
describes attributes of the entity from one role-specific form to
another role-specific form. Automatic translation may present
alternative terms together (e.g., as the "asset/machine" or "code
red/urgent"). In embodiments, automated translation may be
performed by translation models (e.g., enterprise-specific
translation models) that are trained by machine learning or similar
techniques, whereby the translation models may be leveraged to
provide automated translation for role-sensitive entity, workflow
and attribute presentation. In embodiments, the translation models
may be trained using a training data set of translations generated
by human experts and/or by unsupervised learning techniques that
operate on the data of the enterprise to identify associations
between different terms used by different roles and/or groups to
describe the same thing. In embodiments, translation models may be
seeded by an explicit translation model or may be accomplished by
deep learning or similar techniques known to those of skill in the
art.
[1125] In embodiments, the expert agent system 8008 trains expert
agents that perform/recommend actions on behalf of an expert. An
expert agent may be a software module that implements and/or
leverages artificial intelligence services to perform/recommend
actions on behalf of or in lieu of an expert. In embodiments, an
expert agent may include one or more machine-learned models (e.g.,
neural networks, prediction models, classification models, Bayesian
models, Gaussian models, decision trees, random forests, and the
like, including any of the artificial intelligence systems, expert
systems, or the like described throughout this disclosure and/or
the documents incorporated herein by reference) that perform
machine-learning tasks, including robotic process automation, in
connection with a defined role. Additionally or alternatively, an
expert agent may be configured with artificial intelligence rules
that determine actions in connection with a defined role. The
artificial intelligence rules may be programmed by a user or may be
generated by the expert agent system 8008. An expert agent may be
executed at a client device 8050 and/or may be executed by or by a
system that is linked to or integrated with the EMP 8000. In
embodiments, the expert agent may be accessed as a service (e.g.,
via an API), such as in a service-oriented architecture, which in
embodiments may be integrated with the EMP as service that is part
of a microservices architecture. In embodiments where an expert
agent is at least partially executed at a client device, the EMP
8000 may train an executive agent and may serve the trained
executive agent to a client application 8052. In embodiments, an
expert agent may be implemented as a container (e.g., a Docker
container), virtual machine, virtualized application, or the like
that may execute at the client device 8050 or at the EMP 8000. In
embodiments the expert agent is further configured to collect and
report data to the expert agent system 8008, which the expert agent
system 8008 uses to train/reinforce/reconfigure the expert agent.
Many examples of such training are described throughout this
disclosure and many others are intended to be encompassed by the
disclosure.
[1126] In some embodiments, the expert agent system 8008 (working
in connection with the artificial intelligence services system
8010) may train expert agents (e.g., executive agents and other
expert agents), such as using robotic process automation
techniques, machine learning techniques, or other artificial
intelligence or expert systems as described throughout this
disclosure and/or the documents incorporated by reference herein to
perform one or more executive actions on behalf of respective
users, such as executives or other users who are responsible for
undertaking activities that are automated by the robotic process
automation or other techniques. In some of these embodiments, a
client application 8052 may execute on a client device 8050 (e.g.,
a user device, such as a tablet, an AR and/or VR headset, a mobile
device, or a laptop, an embedded device, an enterprise server, or
the like) associated with a user (e.g., an executive, an
administrative assistant of the executive, a board member, a
role-based expert, a manager, a worker, or any other suitable
employee or affiliate). In embodiments, the client application 8052
may record the interactions of a user with the client application
8052 and may report the interactions to the expert agent system
8008. In these embodiments, the client application 8052 may further
record and report features relating to the interaction, such as any
stimuli or inputs that were presented to the user, what the user
was viewing at the time of the interaction, the type of
interaction, the role of the user, whether the interaction was
requested by someone else, the role of the individual that
requested the interaction, contextual information, state
information, workflow information, event information, and the like.
The expert agent system 8008 may receive the interaction data and
related features and may generate, train, configure, and/or update
an executive agent based thereon. In embodiments, the interactions
may be interactions by the user with an enterprise digital twin
(e.g., an environment digital twin, a role-based digital twin, a
process digital twin, and the like). In embodiments, the
interactions may be interactions by the user with data, such as
sensor data (e.g., vibration data, temperature data, pressure data,
humidity data, radiation data, electromagnetic radiation data,
motion data, and/or the like) and/or data streams collected form
physical entities of the enterprise (e.g., machinery, a building, a
shipping container, or the like), data from various enterprise
and/or third-party data sources (as described throughout this
disclosure and incorporated documents), entity data (such as
characteristics, features, parameters, settings, configurations,
attributes and the like), workflow data (such as timing, decision
steps, events, tasks activities, dependencies, resources, or the
like), and many other types of data. For example, a user may be
presented with sensor data from a particular piece of machinery or
equipment and, in response, may determine that a corrective action
to be taken with respect to the piece of machinery or equipment. In
this example, the expert agent may be trained on the conditions
that cause the user to take a corrective action as well as
instances where the user did not take corrective actions. In this
example, the expert agent may learn the circumstances in which
corrective action is taken.
[1127] In embodiments, the expert agent system 8008 may train
expert agents based on user interactions with network entities
and/or computation entities. For example, the expert agent system
8008 may train an expert agent to learn the manner by which an IT
expert diagnoses and handles a security breach. In this example,
the expert agent may be trained to learn the steps undertaken by
the expert to diagnose a security breach, the individuals within
the enterprise that the security breach is reported to, and any
actions undertaken by the expert to resolve the security
breach.
[1128] In embodiments, the types of actions that an expert agent
may be trained to perform/recommend include: selection of a tool,
selection of a task, selection of a dimension, setting of a
parameter, configuration of settings, flagging an item for review,
providing an alert, providing a summary report of data, selection
of an object, selection of a workflow, triggering of a workflow,
ordering of a process, ordering of a workflow, cessation of a
workflow, selection of a data set, selection of a design choice,
creation of a set of design choices, identification of a failure
mode, identification of a fault, identification of an operating
mode, identification of a problem, selection of a human resource,
selection of a workforce resource, providing an instruction to a
human resource, and providing an instruction to a workforce
resource, amongst other possible types of actions. In embodiments,
an expert agent may be trained to perform other types of tasks,
such as: determining an architecture for a system, reporting on a
status, reporting on an event, reporting on a context, reporting on
a condition, determining a model, configuring a model, populating a
model, designing a system, designing a process, designing an
apparatus, engineering a system, engineering a device, engineering
a process, engineering a product, maintaining a system, maintaining
a device, maintaining a process, maintaining a network, maintaining
a computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware. As discussed, an expert agent is configured to determine
an action and may output the action to a client application 8052.
Examples of an output of an expert agent may include a
recommendation, a classification, a prediction, a control
instruction, an input selection, a protocol selection, a
communication, an alert, a target selection for a communication, a
data storage selection, a computational selection, a configuration,
an event detection, a forecast, and the like. Furthermore, in some
embodiments, the expert agent system 8008 may train expert agents
to provide training and/or guidance rather in addition to or in
lieu of outputting an action. In these embodiments, the training
and/or guidance may be specific for a particular individual or role
or may be used for other individuals.
[1129] In embodiments, the expert agent system 8008 is configured
to provide benefits to experts that participate in the training of
expert agents. In some embodiments, the benefit is a reward that is
provided based on the outcomes stemming from the user of an expert
agent that is trained at least in part based on actions by the
expert user. In some embodiments, the benefit is a reward that is
provided based on the productivity of the expert agent. For
example, if an expert agent trained by an individual is leveraged
in connection with a set of users in the enterprise (or outside the
enterprise), an account with the individual may be credited with a
benefit such as a cash rewards, stock rewards, gift card rewards,
or the like. As the expert agent is used more, the benefit to the
individual may be increased. In some embodiments, the benefit is a
reward that is provided based on a measure of expertise of the
expert agent. For example, individuals having a more sought
after/valuable skill may be awarded greater benefits than
individuals having a less sought after/valuable skill. In some
embodiments, the benefit is a share of the revenue or profit
generated by, or cost savings resulting from, the work produced by
the expert agent. In some embodiments, the benefit is tracked using
a distributed ledger (e.g., a blockchain) that captures information
associated with a set of actions and events involving the expert
agent. In some of these embodiments, a smart contract may govern
the administration of the reward to the expert user.
[1130] In some embodiments, a set of expert agents trained by the
expert agent system 8008 may be deployed as a double of at least a
portion of a workforce of an enterprise, where the expert agents
perform tasks of different roles within the enterprise. In some of
these embodiments, the expert agents may be trained upon a training
set of data that includes a set of interactions by members of a
defined workforce of the enterprise during performance of the
defined set of roles of the defined workforce (e.g., interactions
with physical entities, digital twins, sensor data, data streams,
computational entities, and/or network entities, among many
others). In some embodiments, the interactions may be parsed to
identify a chain of operations performed by the workforce and/or a
chain of reasoning, whereby the chain of operations and/or chain of
reasoning are used to train the expert agents. In some embodiments,
the interactions may be parsed to identify types of processing
performed by the workforce upon a set of information, whereby the
type of processing is embodied in the configuration of the
respective expert agents. Examples of workforces may include,
factory operations, plant operations, resource extraction
operations, network operations (e.g., responsible for operating a
network for an industrial enterprise), a supply chain workforce, a
logistics planning workforce, a vendor management workforce, a
brokering workforce for a marketplace, a trading workforce for a
marketplace, a trade reconciliation workforce for a marketplace, a
transactional execution workforce for a marketplace, and the
like.
[1131] In some embodiments, the expert agent system 8008 and/or a
client application 8052 can monitor outcomes related to the user's
interactions and may reinforce the training of the expert agent
based on the outcomes. For example, each time the user takes a
corrective action, the expert agent system 8008 may determine the
outcome (e.g., whether a particular condition or issue was
resolved) and whether the outcome is a positive outcome or a
negative outcome. The expert agent system 8008 may then retrain the
expert agent based on the outcome. Examples of outcomes may include
data relating to at least one of a financial outcome, an
operational outcome, a fault outcome, a success outcome, a
performance indicator outcome, an output outcome, a consumption
outcome, an energy utilization outcome, a resource utilization
outcome, a cost outcome, a profit outcome, a revenue outcome, a
sales outcome, and a production outcome. In these embodiments, the
expert agent system 8008 may monitor data obtained from the various
data sources after an action is taken to determine an outcome
(e.g., sales increased/decreased and by how much, energy
utilization decreased/increased and by how much, costs
decreased/increased and by how much, revenue increased/decreased
and by how much, whether consumption decreased/increased and by how
much, whether a fault condition was resolved, and the like). The
expert agent system 8008 may include the outcome in the training
data set associated with the action undertaken by the expert that
resulted in the outcome.
[1132] In some embodiments, the expert agent system 8008 receives
feedback from users regarding respective executive agents. For
example, in some embodiments, a client application 8052 that
leverages an expert agent may provide an interface by which a user
can provide feedback regarding an action output by an expert agent.
In embodiments, the user provides the feedback that identifies and
characterizes any errors by the expert agent. In some of these
embodiments, a report may be generated (e.g., by the client
application or the EMP 8000) that indicates the set of errors
encountered by the expert. The report may be used to
reconfigure/retrain the executive agent. In embodiments, the
reconfiguring/retraining an executive agent may include removing an
input that is the source of the error, reconfiguring a set of nodes
of the artificial intelligence system, reconfiguring a set of
weights of the artificial intelligence system, reconfiguring a set
of outputs of the artificial intelligence system, reconfiguring a
processing flow within the artificial intelligence system, and/or
augmenting the set of inputs to the artificial intelligence
system.
[1133] In embodiments, the expert agent may be configured to, at
least partially, operate as a double of the expert for a defined
role within an enterprise. In these embodiments, the expert agent
system 8008 trains an expert agent based on a training data set
that includes a set of interactions by a specific expert worker
during the performance of their respective role. For example, the
set of interactions that may be used to train the executive agent
may include interactions of the expert with the physical entities
of an enterprise, interactions of the expert with an enterprise
digital twin, interactions of the expert with sensor data obtained
from a sensor system of the enterprise, interactions of the expert
with data streams generated by the physical entities of the
enterprise, interactions of the expert with the computational
entities of the enterprise, interactions of the expert with the
network entities, and the like. In some embodiments, the expert
agent system 8008 parses the training data set of interactions to
identify a chain of reasoning of the expert upon a set of
interactions. In some of these embodiments, the chain of reasoning
may be parsed to identify a type of reasoning of the worker, which
may be used as a basis for configuring/training the expert agent.
For example, the chain of reasoning may be a deductive chain of
reasoning, an inductive chain of reasoning, a predictive chain of
reasoning, a classification chain of reasoning, an iterative chain
of reasoning, a trial-and-error chain of reasoning, a Bayesian
chain of reasoning, a scientific method chain of reasoning, and the
like. In some embodiments, the expert agent system parses the
training data set of interactions to identify a type of processing
undertaking by the expert in analyzing the set of interactions. For
example, types of processing may include audio processing in
analyzing audible information, tactile or "touch" processing in
analyzing physical sensor information, olfactory processing in
analyzing chemical sensing information, textual information
processing in analyzing text, motion processing in analyzing motion
information, taste processing in analyzing chemical information,
mathematical processing in mathematically operating on numerical
data, executive manager processing in making executive decisions,
creative processing when deriving alternative options, analytic
processing when selecting from a set of options, and the like.
[1134] In embodiments the expert agents include executive agents
that are trained to output actions on behalf of executive and/or an
administrator of an executive. In these embodiments, an expert
agent may be trained for executive roles, such that a user in an
executive role can train the executive agent by performing their
respective role. For example, an executive agent may be trained for
performing actions on behalf of or recommending actions to a user
in an executive role. In some of these embodiments, the client
application 8052 may provide the functionality of the enterprise
management platform 8000. For example, in some embodiments, users
may view executive digital twins and/or may use the collaboration
tools via the client application 8052. During the use of the client
application 8052, an executive may either escalate issues
identified in the respective executive digital twin to another
member of the enterprise. Each time the user interacts with the
client application 8052, the client application 8052 may monitor
the user's actions and may report the actions back to the expert
agent system 8008. Over time, the expert agent system 8008 may
learn how the particular user responds to certain situations. For
instance, if the user is the CFO and each time a critical state
with revenue or costs is identified in the CFO digital, the CFO
escalates the critical state to the CEO, the expert agent system
8008 may learn to automatically escalate critical revenue states
and critical cost states to the CEO. Further implementations of the
expert agent system 8008 are discussed further in the
disclosure.
[1135] In embodiments, the artificial intelligence services system
8010 performs machine learning, artificial intelligence, and
analytics tasks on behalf of the EMP 8000. In embodiments, the
artificial intelligence services system 8010 includes a machine
learning system that trains machine learned models that are used by
the various systems of the EMP 8000 to perform some intelligence
tasks, including robotic process automation, predictions,
classifications, natural language processing, and the like. In
embodiments, the EMP 8000 includes an artificial intelligence
system that performs various AI tasks, such as automated decision
making, robotic process automation, and the like. In embodiments,
the EMP 8000 includes an analytics system that performs different
analytics across enterprise data to identify insights to various
states of an enterprise. For example, in embodiments, the analytics
system may analyze the financial data of an enterprise to determine
whether the enterprise is financially stable, in a critical
condition, or a desirable condition. In embodiments, the analytics
system may perform the analytics in real-time as data is ingested
from the various data sources to update one or more states of an
enterprise digital twin. In embodiments, the intelligence system
includes a robotic process automation system that learns behaviors
of respective users and automates one or more tasks on behalf of
the users based on the learned behaviors. In some of these
embodiments, the robotic process automation system may configure
expert agents on behalf of an enterprise. The robotic process
automation system may configure machine-learned models and/or AI
logic that operate to output actions given stimulus. In
embodiments, the robotic process automation system receives
training data sets of interactions by experts and configures the
machine-learned models and/or AI logic based on the training data
sets. In embodiments, the artificial intelligence services system
8010 includes a natural language processing system that receives
text/speech and determines a context of the text and/or generates
text in response to a request to generate text. The intelligence
services are discussed in greater detail throughout the
disclosure.
[1136] In embodiments, the EMP 8000 includes an enterprise data
store 8012 that stores data on behalf of customer enterprises. In
embodiments, each customer enterprise may have an associated data
lake that receives data from various data sources 8020. In some
embodiments, the EMP 8000 receives the data via one or more APIs
8014. For example, in embodiments, the API may be configured to
obtain real-time sensor data from one or more sensor systems 8022
of an enterprise. The sensor data may be collected in a data lake
associated with the enterprise. The digital twin system 8004 and
the artificial intelligence services system 8010 may structure the
data in the data lake and may populate one or more respective
enterprise digital twins based on the collected data. In some
embodiments, the data sources 8020 may include a set of edge
devices 8042 that collect, receive and process data from a sensor
system 8022, from suitable IoT devices, from local networking
devices (e.g., wireless and fixed network resources, including
repeaters, switches, mesh network nodes, routers, access points,
gateways, and others), from general purpose networking devices
(e.g., computers, laptops, tablets, smartphones and the like), from
smart products, from telemetry systems of machinery, equipment,
systems and components (e.g., onboard diagnostic systems, reporting
systems, streaming systems, syndication systems, event logs and the
like), data collected by data collectors (including drones, mobile
robots, RFID and other readers, and human-portable collectors)
and/or other suitable data sources. In some of these embodiments,
the edge devices 8042 may be configured to process sensor data (or
other suitable data) collected at a "network edge" of the
enterprise. Edge processing of enterprise data may include sensor
fusion, data compression, computation, filtering, aggregation,
multiplexing, selective switching, batching, packetization,
streaming, summarization, fusion, fragmentation, encoding,
decoding, transcoding, copying, storage, decompression,
syndication, augmentation (e.g., by metadata), content inspection,
classification, extraction, transformation, normalization, loading,
formatting, error correction, data structuring, and/or many other
processing actions. In some embodiments, the edge device 8042 may
be configured to operate on the collected data and to adjust an
output data stream or feed based on the contents of the collected
data and/or based on contextual information, such as network
conditions, operational conditions, environmental conditions,
workflow conditions, entity state information, data
characteristics, or many others. For example, an edge device 8042
may stream granular sensor data that is identified to be anomalous
without compression, while the edge device 8042 may compress,
summarize, or otherwise pass on a less granular data that is
considered to be within a tolerance range of normal conditions or
that reflects characteristics (e.g., statistical or signal
characteristics) that suggest a lower likelihood that the data is
likely to be of high interest. In this way, the edge device 8042
may provide semi-sentient data streams. Semi-sentience at the edge
device 8042 may be improved by machine learning and training on a
set of outcomes or feedback from users using process automation,
machine learning, deep learning, or other artificial intelligence
techniques as described herein. In embodiments, the EMP 8000 may
store the data streams in the data lake and/or may update one or
more enterprise digital twins with some or all of the received
data.
[1137] In embodiments, the client devices 8050 may execute one or
more client applications 8052 that interface with the EMP 8000. In
embodiments, a client application 8052 may request and display one
or more enterprise digital twins. In some of these embodiments, a
client application 8052 may depict an executive digital twin
corresponding to the role of the user. For example, if the user is
designated as the Chief Marketing Officer, the EMP 8000 may provide
a CMO digital twin of the enterprise of the user. In some of these
embodiments, the user data stored at the EMP 8000 and/or the client
device 8050 may indicate the role of the user and/or the types of
enterprise digital twins (and features thereof) to which the user
has access.
[1138] In embodiments, the client application 8052 may display the
requested executive digital twin and may provide one or more
options to perform one or more respective actions/operations
corresponding to the executive digital twin and the states depicted
therein. In embodiments, the actions/operations may include one or
more of "drilling down" into a particular state, escalating or
otherwise notifying another user of a state or set of states,
exporting a state or set of states into a collaborative environment
(e.g., into a word processor document, a spreadsheet, a
presentation document, a slide show, a model (e.g., a CAD model, a
3D model, or the like), a report (e.g., an annual report, a
quarterly report, or the like), a website, a Wiki, a dashboard, a
collaboration environment location (e.g. a Slack.TM. location), a
workflow application, or the like), sending a request for action
with respect to one or more states from another user, performing a
simulation, adjusting interface elements (such as changing sizes,
colors, locations, brightness, presence/absence of display, etc.),
or the like. For example, a COO or other operations executive may
view an operations or COO digital twin. The states that may be
depicted in the COO digital twin may include notifications of
potential issues with one or more pieces of machinery or equipment
(e.g., among many others, as observed from analyzing a stream of
data from one or more sensors on a piece of robotic equipment). In
viewing the COO digital twin, the user may wish to escalate the
issue, such as to the CEO, request input from another executive
and/or to instruct an operations manager, such as a warehouse or
plant manager, to handle the issue. In this example, the client
application depicting the COO digital twin may allow the user to
select an option to escalate the issue. In response to the user
selecting the "escalate" option, the client application 8052
transmits the escalate request to the EMP 8000. The EMP 8000 may
then determine the appropriate user or users to which the issue is
escalated. In some embodiments, the EMP 8000 may determine the
reporting structure of the enterprise from an organizational
digital twin of the enterprise to which the users belong. In this
example, if the operations executive elects to have the operations
manager handle the issue, the user may select an option to share
the state with another user. The user may then enter an identifier
of the intended recipient (e.g., an email address, phone number,
text address, user name, role description, or other identifier of
the recipient (such as identifiers for the recipient in various
workflow environments, collaboration environments and the like
(including other digital twins), and the like) and may input a
message indicating instructions to the intended recipient. In
response, the EMP 8000 may communicate the identified state to the
intended recipient.
[1139] In another example, the client application 8052 may depict a
CFO digital twin to a user (e.g., the CFO of an enterprise). In
this example, the CFO may be tasked with preparing a quarterly
report at the request of the CEO. In this example, the CFO may view
a set of different financial states, including a P&L data,
historical sales data (e.g., quarterly sales data and/or annual
sales data), real-times sales data, projected sales data,
historical cost data (e.g., quarterly costs and/or annual costs),
projected costs, and the like. In this example, the CFO may select
the states to include in the annual report, including the P&L
data, quarterly sales data, and quarterly cost data. In response to
the user selection, the client application 8052 may transmit a
request to export the selected states into the annual report. In
this example, the EMP 8000 may receive the request, identify the
document (e.g., the annual report), and may include the selected
states into the identified document.
[1140] In embodiments, the client application 8052 may include a
monitoring agent that monitors the manner by which a user responds
to specific requests (e.g., a request from the CEO to populate a
report) or notifications (e.g., a notification that a piece of
machinery requires maintenance). The monitoring agent may report
the user's response to such prompts to the EMP 8000. In response,
the EMP 8000 may train an executive agent (which may include one or
more machine-learned models) to handle such notifications when they
next arrive. In some embodiments, the monitoring agent may be
incorporated in an executive agent that is incorporated in the
client application 8052.
[1141] FIG. 69 illustrates an example set of components of a
digital twin system 8004. As discussed, a digital twin system 8004
is configured to generate visual and/or data-based digital twins,
including enterprise digital twins, and to serve the digital twins
to a client (e.g., a user device, a server, and/or internal and/or
external applications that leverage digital twins). In embodiments,
the digital twin system 8004 is an infrastructure component of the
EMP 8000. In embodiments, the digital twin system 8004 is a
microservice that is accessible by the EMP 8000 and/or other
components of a value chain control tower.
[1142] In embodiments, the digital twin system 8004 is executed by
a computing system (e.g., one or more servers) that may include a
processing system 8100 that includes one or more processors, a
storage system 8120 that includes one or more computer-readable
mediums, and a network interface 8130 that includes one or more
communication units that communicate with a network (e.g., the
Internet, a private network, and the like). In the illustrated
example embodiments, the processing system 8100 may execute one or
more of a digital twin configuration system 8102, digital twin I/O
system 8104, a data structuring system 8106, a digital twin
generation system 8108, a digital twin perspective builder 8110, a
digital twin access controller 8112, a digital twin interaction
manager 8114, an digital twin simulation system 8116, and a digital
twin notification system 8118. The processing system 8100 may
execute additional or alternative components without departing from
the scope of the disclosure. In embodiments, the storage system
8120 may store enterprise data, such as an enterprise data lake
8122, a digital twin data store 8124, a behavior datastore 8126
and/or other datastore, such as a distributed datastore, such as a
set of blockchains or distributed data storage resources. The
storage system 8120 may store additional or alternative data stores
without departing from the scope of the disclosure. In embodiments,
the digital twin system 8004 may interface with the other
components of the EMP 8000, such as the enterprise configuration
system 8002, the collaboration suite 8006, the expert agent system
8008, and/or the artificial intelligence services system 8010.
[1143] In embodiments, the digital twin configuration system 8102
is configured to set up and manage the enterprise digital twins and
associated metadata of an enterprise, to configure the data
structures and data listening threads that power the enterprise
digital twins, and to configure features of the enterprise digital
twins, including access features, processing features, automation
features, reporting features, and the like, each of which may be
affected by the type of enterprise digital twin (e.g., based on the
role(s) that it serves, the entities it depicts, the workflows that
it supports or enables and the like). In embodiments, the digital
twin configuration system 8102 receives the types of digital twins
that will be supported for the enterprise, as well as the different
objects, entities, and/or states that are to be depicted in each
type of digital twin. For each type of digital twin, the digital
twin configuration system 8102 determines one or more data sources
and types of data that feed or otherwise support each object,
entity, or state that is depicted in the respective type of digital
twin and may determine any internal or external software requests
(e.g., API calls) that obtain the identified data types or other
suitable data acquisitions mechanisms, such as webhooks, that are
configured to automatically receive data from an internal or
external data source In some embodiments, the digital twin
configuration system 8102 determines internal and/or external
software requests that support the identified data types by
analyzing the relationships between the different types of data
that correspond to a particular state/entity/object and the
granularity thereof. Additionally or alternatively, a user may
define (e.g., via a GUI) the data sources and/or software requests
and/or other data acquisition mechanisms that support the
respective data types that are depicted in a respective digital
twin. In these embodiments, the user may indicate the data source
that are to be accessed and the types of data to be obtained from
the respective data source. For example, if a user is configuring
an enterprise digital twin of a supply chain process, the user may
identify an inventory management system to obtain inventory levels,
various supplier systems to obtain pricing data of particular
items, sensor systems to obtain sensor data from various points
within the enterprise's supply chain (e.g., manufacturing
facilities, warehouse facilities, and the like), and other suitable
systems for other suitable data types. In this data definition
process a user may associate specific data types and/or data
sources to corresponding structural elements of a digital twin
(e.g., layouts, spatial elements, processes, or components
thereof). For example, the user can match a specific cost of a good
(e.g., the cost of a bearing on a compressor, a headlight that goes
into an automobile, an automobile, or any other suitable good) that
is obtained via an API request to a seller of the good with a
digital twin element representing the good (e.g., a 3D model of the
good). In this example, the digital twin of the good may depict the
cost of the good, and as the price of the good changes, so too may
the depiction of the good.
[1144] In embodiments, the configuration system 8102 generates one
or more foreign keys for each digital twin that collectively
associate different data types with the structural elements of the
digital twin. Thus, when a digital twin is generated, the foreign
key may be leveraged to connect data obtained from the data sources
to the structural elements of the digital twin. In some
embodiments, a configuring user may define the associations that
are used to generate the set of foreign keys.
[1145] In embodiments, the digital twin configuration system 8102
determines, defines, and manages the data structures needed to
support each type of digital twin, such as data lakes, relational
databases, SQL databases, NOSQL databases, graph databases, and the
like. For example, for an environment digital twin, the digital
twin configuration system 8102 may instantiate a database (e.g., a
graph database that defines the ontology of the environment and the
objects existing (or potentially existing) within the environment
and the relationships therebetween), whereby the instantiated
database contains and/or references the underlying data that powers
the environmental digital twin (e.g., sensor data and analytics
relating thereto, 3D maps, physical asset twins within the
environment, and the like). In some embodiments, a user may define
an ontology of a respective digital twin, such that the ontology
defines the types of data depicted in the digital twin and the
relationships between those data types. Additionally or
alternatively, the digital twin configuration system 8102 may
derive the ontology based on the types of digital twins that are to
be configured.
[1146] In some embodiments, the different types of enterprise
digital twins may be configured in accordance with a set of
preference settings, granularity settings, alert settings, taxonomy
settings, topology settings, and the like. In some embodiments, the
configuration system 8102 may utilize pre-defined preferences
(e.g., default preference templates for different types of
enterprise digital twins, including ones that are domain-specific,
role-specific, industry-specific, workflow-specific and the like),
taxonomies (e.g., default taxonomies for different types of
enterprise digital twins), and/or topologies (e.g., default
topologies for different types of twins, such as graph-based
topologies, tree-based topologies, serial topologies, flow-based
topologies, loop-based topologies, network-based topologies, mesh
topologies, and others)). Additionally or alternatively, the
configuration system 8102 may receive custom preference settings
and taxonomies from a configuring user. Non-limiting examples of
role-specific templates that are used to configure a role-based
digital twin may include may include CEO template, a COO template,
a CFO template, a counsel template, a board member template, a CTO
template, a chief marketing officer template, an information
technology manager template, a chief information officer template,
a chief data officer template, an investor template, a customer
template, a vendor template, a supplier template, an engineering
manager template, a project manager template, an operations manager
template, a sales manager template, a salesperson template, a
service manager template, a maintenance operator template, and/or a
business development template. Similarly, examples of taxonomies
that are used to configure different types of role-based digital
twins may include CEO taxonomy, a COO taxonomy, a CFO taxonomy, a
counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief
marketing officer taxonomy, an information technology manager
taxonomy, a chief information officer taxonomy, a chief data
officer taxonomy, an investor taxonomy, a customer taxonomy, a
vendor taxonomy, a supplier taxonomy, an engineering manager
taxonomy, a project manager taxonomy, an operations manager
taxonomy, a sales manager taxonomy, a salesperson taxonomy, a
service manager taxonomy, a maintenance operator taxonomy, and/or a
business development taxonomy. Each of the role-specific templates
may include data types that are specific to the kinds of
interactions the role might have and the specific responses to
interactions, which may be role-based. For example, a CEO template
may include data type definitions for supplier information and
labor cost information across the entire organization, and may
include responses to interactions with a CEO digital twin, such as
drilling down to specific suppliers and/or labor groups within the
enterprise.
[1147] In embodiments, the digital twin configuration system 8102
may be configured to configure and instantiate the databases that
support each respective enterprise digital twin of an enterprise
(e.g., role-based digital twins, environment digital twins,
organizational digital twins, process digital twins, and the like),
which may be stored on the digital twin data store 8124. In
embodiments, for each database configuration, the digital twin
configuration system 8102 may identify and connect any external
resources needed to collect data for each respective data type. For
each identified external resource, the digital twin configuration
system 8102 may configure one or more data collection threads to
access an API, SDK, port, webhook, search facility, database access
facility, and/or other connection facility For example, certain
executive digital twins (e.g., CEO digital twin, CFO digital twin,
COO digital twin, and CMO digital twin) may each require data
derived and/or obtained from a CRM 8026 of the enterprise. In this
example, the digital twin configuration system 8102 may configure
one or more data collection threads to access an API, SDK, port,
webhook, search facility, database access facility, and/or other
connection facility of the CRM 8026 of the enterprise on behalf of
the enterprise and may obtain any necessary security credentials to
access the API. In another example, in order to collect data from
one or more edge devices 8042 of the enterprise, the configuration
system 8102 may initiate a process of granting access to the edge
devices 8042 of the enterprise to the APIs of the EMP 8000, such
that the edge devices 8042 may provide digital twin data to the EMP
8000.
[1148] In embodiments, the digital twin I/O system 8104 is
configured to obtain data from a set of data sources (e.g., users,
sensor systems, internal and/or external databases, software
platforms (e.g., CRMs, ERPs, CRMs, workflow management system),
surveys, customers, and the like). In some embodiments, the digital
twin I/O system 8104 (or other suitable component) may provide a
graphical user interface that allows a user affiliated with an
enterprise to upload various types of data that may be leveraged to
generate the enterprise digital twins of the enterprise. For
example, in providing data to support an environment digital twin,
a user may upload 3D scans, still and video images, LIDAR scans,
structured light scans, blueprints, 3D floor plans, object types
(e.g., products, sensors, machinery, furniture, and the like),
object properties (e.g., materials, physical properties,
descriptions, price, and the like), output type (e.g., sensor
units), architectural drawings, CAD documents, equipment
specifications, and many others via the digital twin I/O system
8104. In embodiments, the digital twin I/O system 8104 may
subscribe to or otherwise automatically receive data streams (e.g.,
publicly available data streams, such as RSS feeds, news streams,
event streams, log streams, sensor system streams, and the like) on
behalf of an enterprise. Additionally or alternatively, the digital
twin system I/O system 8104 may periodically query and/or receive
data from a connected data source 8020, such as a sensor system
8022 having sensors that sensor data from facilities (e.g.,
manufacturing facilities, shipping facilities, warehouse
facilities, logistics facilities, retail facilities, distribution
facilities, agricultural facilities, resource extraction
facilities, computing facilities, transportation facilities,
infrastructure facilities, networking facilities, data center
facilities, and many others) and/or other physical entities of the
enterprise, a sales database 8024 that is updated with sales
figures in real time, a CRM system 8026, a content marketing
platform 8028, financial databases 8030, surveys 8032, org charts
8034, workflow management systems 8036, third-party data sources
8038, customer databases 8040 that store customer data, and/or
third-party data sources 8038 that store third-party data, edge
devices 8042 that report data relating to physical assets (e.g.,
smart machinery/manufacturing equipment, sensor kits, autonomous
vehicles, of the enterprise, wearable devices, and the like),
enterprise resource management systems 8044, HR systems 8046,
content management systems 8026, and the like). In embodiments, the
digital twin I/O system 8104 may employ a set of web crawlers to
obtain data. In embodiments, the digital twin I/O system 8104 may
include listening threads that listen for new data from a
respective data source. In embodiments, the digital twin I/O system
8104 may be configured with a set of webhooks that receive data
from a respective set of data sources. In these embodiments, the
digital twin I/O system 8104 may receive data that is pushed from
an external data source, such as real-time data.
[1149] In some embodiments, the digital twin I/O system 8104 is
configured to serve the obtained data to instances of enterprise
digital twins (which is used to populate digital twins) that are
executed by a client device 8050 or the EMP 8000. In embodiments,
the digital twin I/O system 8104 receives data stream feeds
received data streams received and/or collected on behalf on an
enterprise and stores at least a portion of the streams into a data
lake 8122 associated with the enterprise. In embodiments, the data
that is streamed into the data lake 8122 may be structured and
stored in one or more databases stored in the digital twin data
stores 8124.
[1150] In embodiments, the data structuring system 8106 is
configured to process and structure data into a format that can be
consumed by an enterprise digital twin. In embodiments, processing
by the data structuring system 8106 may include compression,
computation, filtering, aggregation, multiplexing, selective
switching, batching, packetization, streaming, summarization,
fusion, fragmentation, encoding, decoding, transcoding, encryption,
decryption, duplication, deduplication, normalization, cleansing,
identification, copying, storage, decompression, syndication,
augmentation (e.g., by metadata), content inspection,
classification, extraction, transformation, loading, formatting,
error correction, data structuring, and/or many other processing
actions. In embodiments, the data structuring system 8106 may
leverage ETL (extract, transform, load) tools, data streaming, and
other data integration tooling to structure the various types of
digital twin data. In embodiments, the data structuring system 8106
structures the data according to a digital twin data model that may
be defined by the digital twin configuration system 8102 and/or a
user. In embodiments, a digital twin data model may refer to an
abstract model that organizes elements of enterprise-related data
and standardizes the manner by which those elements relate to one
another and to the properties of digital twin entities. For
instance, a digital twin data model of an environment that includes
vehicles (e.g., a vehicle assembly facility or an environment where
vehicles operate) may specify that the data element representing a
vehicle be composed of a number of other elements which represent
sub-elements or attributes of the vehicle (the color of the
vehicle, the dimensions of the vehicle, the engine of the vehicle,
the engine parts of the vehicle, the owner of the vehicle, the
performance specifications of the vehicle, and the like). In this
example, the digital twin model components may define how the
physical attributes are tied to respective physical locations on
the vehicle. In embodiments, digital twin data models may define a
formalization of the objects and relationships found in a
particular application domain. For example, a digital twin data
model may represent the customers, products, and orders found in a
manufacturing enterprise and how they relate to each other within
the various digital twins. In another example, a digital twin data
model may define a set of concepts (e.g., entities, attributes,
relations, tables, and/or the like) used in defining such
formalizations of data or metadata within the environment. For
example, a digital twin data model used in connection with a
banking application may be defined using the entity-relationship
data model and how the entity-relationship data model is then
related to the various executive digital twin views.
[1151] In embodiments, the digital twin generation system 8108
serves enterprise digital twins on behalf of an enterprise. In some
instances, the digital twin generation system 8108 receives a
request for a specific type of digital twin from a client
application 8052 being executed by a client device 8050 (e.g., via
an API). Additionally or alternatively, the digital twin generation
system 8108 receives a request for a specific type of digital twin
from a component of EMP 8000 (e.g., the digital twin simulation
system 8116). The request may indicate the enterprise, the type of
digital twin, the user (whose access rights may be verified or
determined by the digital access controller 8112), and/or a role of
the user. In some embodiments, the digital twin generation system
8108 may determine and provide the client device 8050 (or
requesting component) with the data structures, definition of grain
of data the, response patterns to specific inputs, animation
sequences for illustrating behaviors, display aggregation methods
for smaller displays (such as mobile phone), immersive data
interaction systems, security constraints on the data viewing,
viewing interaction speed (frame rate), nature of light sources
(simulate actual or continuous), multiple user engagement
protocols, network bandwidth constraints, metadata, ontology and
information on hooks to data feeds as well as the digital twin
constructs. This information may be used by the client to generate
the digital twin in the end user device (e.g., an immersive device,
such as AR devices or VR devices, tablet, personal computer,
mobile, or the like). In embodiments, the digital twin generation
system 8108 may determine the appropriate perspective for the
requested digital twin (e.g., via the digital twin perspective
builder 8110, which may include device-sensitive perspectives, such
as delivering in appropriate formats based on the type of end user
device) and any data restrictions, interaction restrictions, depth
of data restrictions, usage restrictions, length of visibility
restrictions, that the user may have (e.g., via the access
controller 8112). In response to determining the perspective and
data restrictions, the digital twin generation system 8108 may
generate the requested digital twin. In some embodiments,
generating the requested digital twin may include identifying the
appropriate data structure given the perspective and obtaining the
data that parameterizes the digital twin, as well as any additional
metadata that is served with the enterprise digital twin.
[1152] In embodiments, the digital twin generation system 8108 may
deliver the enterprise digital twin to the requesting client
application 8052 (or requesting component). In embodiments, the
digital twin generation system 8108 (or another suitable component)
may continue to update a served digital twin with real-time data
(or data that is derived from real-time data) as the real-time data
is received and potentially analyzed, extrapolated, derived,
predicted, and/or simulated by the EMP 8000.
[1153] In some embodiments, the digital twin generation system 8108
(in combination with the digital twin I/O system 8104) may obtain
data streams from traditional data sources, such as relational
databases, API interfaces, direct sensor input, human generated
input, Hadoop file stores, graph databases that underlie
operational and reporting tooling in the environment, telemetry
data sources, onboard diagnostic systems, blockchains, distributed
ledgers, distributed data sources, feed, streams, and many other
sources. In embodiments, the digital twin generation system 8108
may obtain data streams that are associated with the structural
aspects of the data, such as the layout and 3D object properties of
entities within facilities, geospatial information systems, the
hierarchical design of a system of accounts, and/or the logical
relationships of entities and actions in a workflow. In
embodiments, the data streams may include metadata streams that are
associated with the nature of the data and data streams containing
primary data (e.g., sensor data, sales data, survey data, and the
like). For example, the metadata associated with a physical
facility or other entity may include the types and layers of data
that are being managed, while the primary data may include the
instances of objects that fall within each layer. Layers for which
metadata may be tracked and/or created may include, for example,
metadata with respect to attributes, parameters or representations
of a whole facility, component systems and assets within the
facility (equipment, network entities, workforce entities, assets,
and the like), sub-components and sub-systems, and further
sub-components and sub-systems down to arbitrarily lower levels of
granularity (e.g., a ball bearing of a rotating axle assembly of a
fan that is part of a motor assembly driving an assembly line in a
location of a warehouse). In embodiments, layers may include, in
another example, logical or operational layers, such as a reporting
structure, such as from a COO to a VP of operations to a
distribution manager to a warehouse manager to a shift manager to a
warehouse worker. In embodiments, layers may include workflow or
process flow layers, such as from an overall process to its
sub-components and decision points, such as an overall assembly
process having sub-layers of gathering of input materials and
components, positioning of workers, a series of assembly steps,
inspection of outputs, and delivery to a post-assembly
location.
[1154] In embodiments, the digital twin perspective builder 8110
leverages metadata, artificial intelligence, heuristic methods, 3D
rendering algorithms and/or other data processing techniques to
produce a definition of information required for generation of the
digital twin in the digital twin generation system 8108. In some
embodiments, different relevant datasets are hooked to a digital
twin (e.g., an executive digital twin, an environment digital twin,
or the like) at the appropriate level of granularity, thereby
allowing for the structural aspects of the data (e.g., system of
accounts, sensor readings, sales data, or the like) to be a part of
the data analytics process. One aspect of making a perspective
function is that the user can change the structural view or the
granularity of data while potentially forecasting future events or
changes to the structure to guide control of the area of the
business at question. In embodiments, the term "grain of data" may
refer to the base unit of a type of data, such as a single line of
data, a single aggregated line of data, a single byte of data, a
single file, a single instance, or the like. Examples of "grains of
data" may include a detailed record on a single sale, a single
block in a blockchain in a distributed ledger, a single event in an
event log, a single vibration reading from a vibration sensor, or
similar singular or atomic data units, and the like. Grain or
atomicity may impose a constraint in how the data can be combined
or processed to form different outputs. For example if some element
of data is captured only at the level of once-per-day, then it can
only be broken down to single days (or aggregation of days) and
cannot be broken down to hours or minutes, unless derived from the
day representation (e.g., using inference techniques and/or
statistical models). Similarly, if data is provided only at the
aggregate business unit level, it can be broken down to the level
of an individual employee only by, for example, averaging,
modeling, or inductive functions. Generally, role-based and other
enterprise digital twins may often benefit from finer levels of
data, as aggregations and other processing steps may produce
outputs that are dynamic in nature and/or that relate to dynamic
processes and/or real-time decision-making. It is noted that
different types of digital twins may have different "sized" grains
of data. For example, the grains of data that feed a CEO digital
twin may be at a higher granularity level than the grains of data
that feed a COO digital twin. In some embodiments, however, a CEO
may drill down into a state of the CEO digital twin and the
granularity for the selected state may be increased.
[1155] In embodiments, the perspective builder 8110 adds relevant
perspective to the data underlying the digital twin, which is
provided to the digital twin generation system 8108. In
embodiments, "perspective" may refer to the adjustments to,
aggregations of, simplifications of, and/or detail additions to the
ontology of a particular digital twin (e.g., a role-based digital
twin) that provide the appropriate ontological view of the
underlying data with the correct types at the appropriate
granularity level. For example, a CEO digital twin may link in
fuzzy data with markets data and depict the potential impacts of
market forces on a simulated digital twin environment for different
scenarios. In another example, in a CFO level digital twin, the
internal financial system of accounts may be allocated across the
physical structure of the digital twin providing an ability to
understand the relationship between revenue generation, cost
allocation, and the structural aspects of the business (e.g. the
layout of a factory floor, a warehouse, a distribution center, a
logistics facility, an office building, a retail location, a
container ship, or the like). Continuing this example, the CTO
digital twin may include data overlays with current market
information on new technologies and linkages therebetween. In this
example, the CTO digital twin builds in linkages between an impact
of changing technology platforms and outside information that may
be used for enhancement of the facility. These different
perspectives generated by the perspective builder 8110 combine with
the digital twin simulation system 8116 to provide relevant
simulations of how scenario-based future states might be handled by
the facility, the digital twin simulation system 8116 provides for,
recommendations on how to enhance the digitally twin represented
facility structurally to meet the needs of the future states,
responses to specific changes in the digital twin environment or
alterations in the information relating to digital twin simulate
elements. In embodiments, the perspective builder 8110 may build
perspectives that depict intersections or overlays of operational
states and entities with information technology states and
entities, which may facilitate recognition of opportunities and/or
problems involving the interplay and convergence of information
technology and operations technology within the operations of a
wide range of industries and domains. In further embodiments, the
perspective builder 8110 may build perspectives that allow for
different roles to interact with the same digital twin while
maintaining different perspectives on the operational states and
entities, which allows for these different roles to have a
meaningful interaction while maintaining their role-specific
perspective. In embodiments, the perspective builder 8110 builds a
perspective for a digital twin by providing each different
user/role with a respective diagrammatic view expressed as in the
digital twin where that diagram includes information and structure
at a level relevant to the specific user's role. This user-specific
diagram is then connected to the underlying data to provide for the
role-based digital twin experience.
[1156] In embodiments, the digital twin access controller 8112
informs the generation system 8108 of specific constraints around
the roles of users able to view the digital twin as well as
providing for dynamically adjustable digital twins that can adapt
to constrain or release views of the data or other features
specific to each user role. For example sensitive salary data might
be obfuscated from most administrative employees when viewing an
organizational digital twin, but the CEO may be granted access to
view the salary information directly. In embodiments, the digital
twin access controller 8112 may receive a user identifier and one
or more data types. In response, the digital twin access controller
8112 may determine whether the user indicated by the user
identifier has access to the one more data types or other features.
In some of these embodiments, the digital twin access controller
may look up the user in the organizational digital twin of the
enterprise of the user and may determine the user's permissions and
restrictions based thereon. Alternatively, the user's permissions
and restrictions may be indicated in a user database. In
embodiments the organizational digital twin may, as noted above, be
generated automatically, such as by parsing available data sources
to automatically construct a representation of the organization,
such as a hierarchical organizational chart, a graph of the
organization with nodes representing organizational entities (e.g.,
workgroups, roles, assets and personnel), links or connections
indicating relationships (e.g., reporting relationships, lines of
authority, group affiliations, and the like), and data or metadata
indicating other attributes of the entities and relationship, and
the like.
[1157] In embodiments, the digital twin interaction manager 8114
manages the relationship between the structural view of the data in
an enterprise digital twin (e.g., as depicted/represented by the
client application 8052) and the underlying data streams and data
sources. In embodiments, this interaction layer makes the digital
twin into a window into the underlying data streams through the
lens of the structure of the data. In embodiments, the digital twin
interaction manager 8114 determines the types of data, or the
nature of the human interface for building these interactions, that
are being fed to an instance of an enterprise digital twin (e.g.,
an environment digital twin or an executive digital twin) while the
instance is being executed by a client application 8052. Put
another way, the digital twin interaction manager 8114 determines
and serves data for an in-use digital twin. In embodiments, the
digital twin interaction manager 8114 has specific user
interactions and controls that govern the relationship between a
user interface and the role based digital twin. Furthermore, in
embodiments, these role-based digital twin interactions can be with
a shared digital twin with different roles interacting seamlessly.
In embodiments, the digital twin interaction manager 8114 feeds raw
data received from a data source to the digital twin or from the
digital twin I/O system 8104, or a combination of the digital twin
I/O system 8104 and role-based human interactions For example,
sensor readings of temperatures throughout an environment may be
fed directly to the executing environment digital twin of the
environment through the digital twin I/O system 8104 and in
response to a human interaction with the environment digital twin
to adjust a temperature setting of the environment, the digital
twin interaction manager 8114 may issue a control signal to a
temperature controller within the environment to increase or
decrease the temperature.
[1158] In embodiments, the digital twin interaction manager 8114
obtains data and/or instructions that are derived by another
component of the EMP 8000. For example, a CEO digital twin may
depict analytical data obtained from the artificial intelligence
services system 8010 that is derived from incoming financial data,
marketing data, operational data, and sensor data. In this example,
the digital twin interaction manager 8114 may receive a request to
drill down into the analytical data from the user and in response,
the digital twin interaction manager 8114 may obtain the financial
data, marketing data, and/or the sensor data from which the
analytical data was derived. In another example, the digital twin
interaction manager 8114 may receive simulated cost data from the
digital twin simulation system 8116 to convey revenue/costs with
respect to different asset maintenance schedules, whereby the
simulated data is derived using historical maintenance data of the
enterprise, historical sensor data collected by sensors in a
facility of the enterprise. In this example, the digital twin
interaction manager 8114 may receive requests for different
maintenance schedules from a client device depicting an executive
digital twin (e.g., a CFO digital twin, a CTO digital twin, or a
CEO digital twin) and may initiate the simulations for each of the
different maintenance schedules. The digital twin interaction
manager 8114 may then serve the results of the simulation to the
requesting client application.
[1159] In embodiments, the digital twin interaction manager 8114
may manage one or more workflows that are performed via an
executive digital twin. For example, the EMP 8000 may store a set
of executive workflows, where each executive workflow corresponds
to a role within an enterprise and includes one or more stages. In
embodiments, the digital twin interaction manager 8114 may receive
a request to execute a workflow. The request may indicate the
workflow and a user identifier. In response, the digital twin
interaction manager 8114 may retrieve the requested workflow and
may provide specific instructions, including role-based
interactions, and/or data to the client device 8052
[1160] In embodiments, the digital twin simulation system 8116
receives requests to run simulations using one or more digital
twins. In embodiments, the request may indicate a set of parameters
that are to be varied and/or one or more simulation outcomes to
output. In embodiments, the digital twin simulation system 8116 may
request one or more digital twins from the digital twin generation
system 8108 and may varying a set of different parameters for the
simulation. In embodiments, the digital twin simulation system 8116
may construct new digital twins and new data streams within
existing digital twins. In embodiments, the digital twin simulation
system 8116 may perform environment simulations and/or data
simulations. The environment simulation is focused on simulation of
the digital twin ontology rather than the underlying data streams.
In embodiments, the digital twin simulation system 8116 generates
simulated data streams appropriate for respective digital twin
environments. This simulation allows for real world simulations of
how a digital twin will respond to specific events such as changes
in the cost of good supplied, or changes in the demand on the
output of the facility.
[1161] In embodiments, the digital twin simulation system 8116
implements a set of models, in some instances including
role-specific response patterns, (e.g., physical mathematical
forecasts, logical representations, or process diagrams) that
develop the framework where data and the response of the digital
twin can be simulated in response to different situational or
contextual inputs/stimuli. In embodiments, the digital twin
simulation system 8116 may include or leverage a computerized model
builder that constructs a predicted future state of either the data
and/or the response of the digital twin to the input data. In some
embodiments, the computerized model library may be obtained from a
behavior model data store 8126 that stores one or more models that
defines one or more behaviors of entities, such as based on
scientific, economic, statistical, psychological, sociological,
econometric, engineering, mathematical, physical, chemical,
biological, architectural, computational, or other models,
formulas, functions, processes, algorithms, or the like of the
various types described herein or in the documents incorporated by
reference herein (collectively referred to herein as "behavior
models" or "models" except where context indicates otherwise). In
embodiments, value chain network data objects may be provided
according to an object-oriented data model that defines classes,
objects, attributes, parameters and other features of the set of
data objects (such as associated with value chain network entities
and applications) that are handled by the platform. The
computerized digital twin model calculates the results of the model
based on available inputs to build an interactive environment where
users can watch and manipulate salient features of the simulated
environment seeing how the entire system responds to specific
changes in the environment. For example, the digital twin
simulation may display how a set of objects that are stacked in a
container will respond to tilting the container, where the behavior
of the objects is based on a mechanical engineering model and/or an
architectural model of the stacked objects, including structural
features, weight distributions, and the like. This may assist in
assessing the probability and/or impact of various fault modes,
such as breaking, spilling, or the like, in response to seismic
events, road conditions, weather conditions, wave action, or the
like, as well as in simulating the response of other objects in the
simulated environment, including in a chain of events. This may,
for example, allow a user to identify events and consequences that
occur as a result of multiple simultaneous or related faults or
other events.
[1162] In embodiments, digital twin behavior models may be updated
and improved using results of actual experiments and real-world
events. The use of such digital twin mathematical models and their
simulations avoids actual experimentation, which can be costly and
time-consuming. Instead, acquired knowledge about behavior of
entities and computational power are used to diagnose and solve
real-world problems cheaply and/or in a time-efficient manner. As
such, the digital twin simulation system 8116 can facilitate
understanding a system's behavior without actually testing the
system in the real world. For example, to determine which type of
wheel configuration would improve traction the most while designing
a tractor, a digital twin model simulation of the tractor could be
used to estimate the effect of different wheel configurations on
towing capacity. Useful insights about different decisions in the
design may be gleaned without actually building the tractor. In
addition, the digital twin simulation can support experimentation
that occurs totally in software, or in human-in-the-loop
environments where the digital twin represents systems or generates
data needed to meet experiment objectives. Furthermore, digital
twin simulations can be used to train persons using a
perspective-appropriate virtual environment that would otherwise be
difficult or expensive to produce.
[1163] In embodiments, simulation environments may be constructed
using models configured to predict a set of future states. These
models may include deep learning, regression models, quantum
prediction engines, inference engines, pattern recognition engines,
and many other forms of modelling engines that use historical
outcomes, current state information, and other inputs to build a
future state prediction. In some embodiments, a consideration in
making the digital twin models' function is the ability to also
show the response of the perspective-based digital twin structural
elements (e.g., defining the deformation of the axle of a vehicle
in response to different size loads). For example, the resultant
digital twin representation can then be presented to the user in a
virtual reality or augmented reality environment where specific
perspectives are shown in their digital twin form.
[1164] In embodiments, digital twins, as described herein, may
operate in coordination with an adaptive edge computing system
and/or a set of adaptive edge computing systems that provide
coordinated edge computation include a wide range of systems, such
as classification systems (such as image classification systems,
object type recognition systems, and others), video processing
systems (such as video compression systems), signal processing
systems (such as analog-to-digital transformation systems,
digital-to-analog transformation systems, RF filtering systems,
analog signal processing systems, multiplexing systems, statistical
signal processing systems, signal filtering systems, natural
language processing systems, sound processing systems, ultrasound
processing systems, and many others), data processing systems (such
as data filtering systems, data integration systems, data
extraction systems, data loading systems, data transformation
systems, point cloud processing systems, data normalization
systems, data cleansing system, data deduplication systems,
graph-based data storage systems, object-oriented data storage
systems, and others), predictive systems (such as motion prediction
systems, output prediction systems, activity prediction systems,
fault prediction systems, failure prediction systems, accident
prediction systems, event predictions systems, event prediction
systems, and many others), configuration systems (such as protocol
selection systems, storage configuration systems, peer-to-peer
network configuration systems, power management systems,
self-configuration systems, self-healing systems, handshake
negotiation systems, and others), artificial intelligence systems
(such as clustering systems, variation systems, machine learning
systems, expert systems, rule-based systems, deep learning systems,
and many others), system management and control systems (such as
autonomous control systems, robotic control systems, RF spectrum
management systems, network resource management systems, storage
management systems, data management systems, and others), robotic
process automation systems, analytic and modeling systems (such as
data visualization systems, clustering systems, similarity analysis
systems, random forest systems, physical modeling systems,
interaction modeling systems, simulation systems, and many others),
entity discovery systems, security systems (such as cybersecurity
systems, biometric systems, intrusion detection systems, firewall
systems, and others), rules engine systems, workflow automation
systems, opportunity discovery systems, testing and diagnostic
systems, software image propagation systems, virtualization
systems, digital twin systems, IoT monitoring systems, routing
systems, switching systems, indoor location systems, geolocation
systems, and others.
[1165] In embodiments, the digital twin notification system 8118
provides notifications to users via enterprise digital twins
associated with the respective users. In some embodiments, digital
twin notifications are an important part of the overall
interaction. Digital twin notification system 8118 may provide the
digital twin notifications within the context of the digital twin
setting so that the perspective view of the notification is set up
specifically to enable enlightenment of how the notification fits
into the general digital twin represented ontology, taxonomy,
topology or the like.
[1166] As discussed, a digital twin model is based on a combination
of data and the data's relationship to the digital twin
environments and/or processes. As such, different digital twins may
share the same data and different digital twin perspectives can be
the results of a set of metadata built on top of a digital twin
data model or data environment. In embodiments, the digital twin
data model provides the details of the information to be stored and
it is used to build a layered system where the final computer
software code is able to represent the information in the lower
levels in a form that is appropriate for the digital twin
perspective being used. One aspect of the digital twin model is
that one digital can be shared across multiple perspectives, each
perspective viewer can then interact with the same underlying
digital twin model. In this way the multiple perspectives are like
translations allowing each type of user to interact in an
appropriate way for their skill sets or their level of
knowledge.
[1167] FIG. 70 illustrates an example of a digital twin data model
and the manner by which a digital twin is generated, executed, and
served to a requesting digital twin application, wherein the
digital twin data model defines the physical implementation of the
underlying data streams from existing systems and digital twin
structures to achieve a digital twin representation. In
embodiments, the digital twin data model 81B00 defines the manner
by which traditional data streams are tied together with the
digital twin structures to achieve the digital twin representation.
In embodiments, digital twins are a combination of
processes/structures and system data streams. Put another way,
process and structure definitions define the real-world "things"
(for example a factory, a robot, a cargo container, a ship, a road,
or the like) or logical "things" (for example an organizational
chart, a hiring process, a marketing campaign, a tax reporting
workflow, or the like) that are representable by a digital twin,
while the system data stream definitions define the manner by which
real-world data may be ingested into digital twin representations
of the real-world and/or logical "things". Thus, configuring a
digital twin includes structural configuration and ingestion and
data configuration and ingestion.
[1168] During structural configuration and ingestion, the digital
twin system 8004 receives the structural aspects of a digital twin.
In embodiments, the structural aspects may include process
definitions, layout definitions, and/or spatial definitions. In
embodiments, a process definition defines a logical process that
can be mapped to a diagrammatic format that forms the basis of what
a digital twin viewer can interact with. Examples of processes may
include workflows, hiring processes, manufacturing processes,
logistics processes, inventory processes, product management
processes, software processes, and the like. In embodiments, the
spatial definition defines the geospatial configuration of an
object or an environment. In embodiments, the spatial definition
may be a 2D or 3D representation of an object or an environment.
The spatial definition of an object or an environment may be
provided as a CAD file, a LIDAR scan, a 2D or 3D image, or the
like, including logical relationships, organizational hierarchy,
physical relationships, schematic relationships, and/or
interconnectivity between objects and/or environments. In
embodiments, a layout definition defines the relationship between
objects with other objects and/or an environment. In embodiments,
the layout definition may further define the manner by which
objects move with respect to other objects and/or an environment.
Examples of layouts may include electrical wiring diagrams, piping
schematics, assembly line diagrams, circuit diagrams, hierarchical
relationships, network layouts, network schematics, organizational
charts, and the like. In embodiments, a layout definition may
include a set of properties of an object or environment. Examples
of properties of an object may include physical properties, such as
a material of an object, a weight of an object, a density of an
object, a conductivity of an object, a resistance of an object, a
maximum speed of an object, a maximum acceleration of an object,
possible movements of an object, a reactivity of an object, and/or
the like. Examples of properties of an environment may include
materials of the floors, walls, the roof, and the like, coefficient
of friction of the floor, restricted areas within the environment,
paths within the environment, and/or other suitable properties. In
some embodiments, users may upload layout definitions, process
definitions, and/or spatial definitions to the digital twin system
8004. Additionally or alternatively, the digital twin system 8004
may provide a graphical user interface that allows users to define
the layout definitions, process definitions, and/or spatial
definitions. In some embodiments, users may import digital twins
from 3rd party sources. For example, a producer of a particular
object may also provide a digital twin of the object, which may
then be imported to the digital twin system 8004.
[1169] During system data configuration and ingestion, a user
defines the data sources that provide data that hydrates or
populates a digital twin and configures a data bus to receive data
from the various data sources. As discussed, the data sources may
be received from various systems, including sensor systems, ERPs,
CRMs, financial systems, inventory management systems, invoicing
systems, 3rd party systems (e.g., weather services, news services,
government databases, and the like), and other suitable systems. In
embodiments, the user may identify the data sources and may provide
any information required to enable a data bus to receive data from
the data sources and may further define the associations between
the data derived from the data sources and the digital twin
elements. A data bus may refer to a middleware layer that provides
the data wiring and data infrastructure for moving data from one
system to another. The data bus may be configured to handle
real-time data, near real-time data, aggregated data, and/or stored
data, or any combination thereof. The data bus may provide data
directly to a digital twin and/or may store the data in the data
warehouse that hydrates the digital twins. In embodiments, the user
may provide API interface or keys and/or webhook URLs to the
digital twin system 8004 (e.g., via a GUI) thereby enabling data
acquisition from the data sources. In embodiments, the digital twin
system 8004 may configure the data bus to access the data sources
and/or to receive data from the data sources. In some of these
embodiments, the digital twin system 8004 may generate a webhook
URL for a particular digital twin or set of digital twins and may
provide the webhook URL to the data source, such that the data
source can push real-time or near real-time data to the data bus.
Additionally or alternatively, the digital twin system 8004 may
obtain an API interface or key from the data source, such that the
data bus can request data from the data source using the API
interface or key.
[1170] In embodiments, the digital twin system 8004 may generate a
foreign key that associates different types of data with the
structural elements of the digital twin. In this way, the foreign
key ties particular data types to various structural or logical or
schematic elements, such that when the digital twin is depicted,
the real-world data collected from the various data sources is
connected to the corresponding states of the digital twin. For
example, sensor data received from a subset of sensors of a sensor
system that monitor a particular machine component in a real world
environment may be associated with a digital twin of a machine
component, such that the sensor data may be depicted in the digital
twin of the machine component. In embodiments, the user may provide
input to the digital twin system 8004 during the configuration
phase to tie particular data types to various elements of a digital
twin. The data types that are associated with the digital twin may
include raw data, processed data, analytical data, derived data,
and the like. To the extent a particular data stream is processed
before being served into a digital twin (e.g., sensor data that is
averaged over a period of time or a warning condition that is
depicted when sales data dips below a threshold), the user may
define the operations or the associated display highlight that are
performed on the data before it is served into a digital twin. In
these scenarios, the processed data may be associated with a
respective digital twin component in the foreign key.
[1171] Once the data bus is configured for a particular digital
twin and the structural, logical, or schematic elements (e.g.,
layout definitions, process definitions, and spatial definitions)
of the digital twin are defined, the digital twin system 8004 may
perform digital simulations on the digital twin and/or may serve
the digital twin to a digital twin-enabled application based on the
structural elements of the digital twin, the connected systems data
sources, and the foreign key of the digital twin. In embodiments,
the digital twins may be role-based digital twin, whereby the views
into the digital twin that are served to a user occupying a
particular role within an organization. In this way, each user can
interact with a respective role-based digital twin and may gain
appropriate perspectives based on their respective needs with
respect to an organization. In another embodiment, a plurality of
users can interact with a shared role-enabled digital twin and may
gain appropriate perspectives based on their respective needs with
respect to an organization to that single digital twin. In
embodiments, a role-based digital twin may allow the user to
provide feedback to the source systems to allow for controls of the
source system environments, such as corrective actions taken with
respect to a source system. In some embodiments, a plurality of
users can make operational changes with a shared role-based digital
twin and each user sees these changes in an appropriate way for
their role. Furthermore if the operational change involves multiple
users, the digital twin can enable a role-based workflow management
of the depicted environment (e.g., the CEO may approve an
expenditure to change machinery as requested by the CTO).
[1172] In embodiments, the digital twin system 8004 may receive
requests to execute digital twin simulations with respect to a
digital twin. Requests to perform digital twin simulations may be
received from digital twin applications and/or from internal
processes. In embodiments, a digital twin simulation allows for the
building of interactive models based on the processes, layouts,
and/or spatial representations of a digital twin. The digital twin
simulations may provide the degrees of freedom to allow for the
different processes to be altered in response to dynamic data
inputs. For example, a digital twin simulation may be executed to
depict how a bearing can move on a compressor when the compressor
is operated at different operating conditions or how water flows
through a systems of pipes model at different temperatures or with
different amounts of buildup in the piping. In embodiments, the
digital twin system 8004 may output the results of the simulation,
which may, for example, depict the impact of the simulation
parameters on a particular aspect of the digital twin.
[1173] In embodiments, a digital twin application may request and
depict a digital twin to a user, this digital twin can be a new
twin for that user or role specific access with role specific views
to an existing or shared digital twin. A digital twin application
may be provided on mobile applications, virtual reality
applications, PCs, and the like. In embodiments, a digital twin
application provides a request to the digital twin system 8004 for
a particular digital twin, where the request may include a user
identifier of the user and/or a role of the user. In embodiments,
the digital twin system 8004 may include or interface with digital
twin application coordinators that receive requests from digital
twin applications for a digital twin. In embodiments, a digital
twin application controller maintains and leverages a set of
business rules for a particular digital twin that are required by a
digital twin application. In some of these embodiments, the set of
role-based rules are a set of role-based rules that control the
states that a user can access given their role within an
organization and a clearance of the user. In these embodiments, the
digital twin application controller may determine whether to grant
an instance of a digital twin application access to a particular
user based on the business rules and the role of the user. In
embodiments, the digital twin system 8004 may include an
application services layer that allows multiple users to connect to
the back end of the digital twin application coordinator, either
directly or through a shared digital twin.
[1174] In embodiments, these connections may include web services,
publish and subscribe information buses, simple object access
protocols, and/or other suitable application interfaces. The
application services layer may return a requested digital twin to a
requesting instance of a digital twin application, which in turn
depicts the digital twin to the user. The user may then interact
with the digital twin via the application to view different states
of the digital twin, to request simulations, or to interact with
other users of the same role or different roles in the digital twin
environment, and the like.
[1175] In an example implementation of the framework discussed in
FIG. 70, the digital twin system 8004 may be configured to generate
enterprise digital twins in connection with a value chain. For
example, an enterprise that produces goods internationally (or at
multiple facilities) may configure a set of digital twins, such as
supplier twins that depict the enterprise's supply chain, factory
twins of the various production facilities, product twins that
represent the products made by the enterprise, distribution twins
that represent the enterprise's distribution chains, and other
suitable twins. In doing so, the enterprise may define the
structural elements of each respective digital twin as well as any
system data that corresponds to the structural elements of the
digital twin. For instance, in generating a production facility
twin, the enterprise may the layout and spatial definitions of the
facility and any processes that are performed in the facility. The
enterprise may also define data sources corresponding to value
chain entities, such as sensor systems, smart manufacturing
equipment, inventory systems, logistics systems, and the like that
provide data relevant to the facility. The enterprise may associate
the data sources with elements of the production facility and/or
the processes occurring the facility. Similarly, the enterprise may
define the structural, process, and layout definitions of its
supply chain and its distribution chain and may connect relevant
data sources, such as supplier databases, logistics platforms, to
generate respective distribution chain and supply chain twins. The
enterprise may further associate these digital twins to have a view
of its value chain. In embodiments, the digital twin system 8004
may perform simulations of the enterprise's value chain that
incorporate real-time data obtained from the various value chain
entities of the enterprise. In some of these embodiments, the
digital twin system 8004 may recommend decisions to a user
interacting with the enterprise digital twins, such as when to
order certain parts for manufacturing a certain product given a
predicted demand for the manufactured product, when to schedule
maintenance on machinery and/or replace machinery (e.g., when
digital simulations on the digital twin indicates the demand for
certain products may be the lowest or when it would have the least
effect on the enterprise's profits and losses statement), what time
of day to ship items, or the like. The foregoing example is a
non-limiting example of the manner by which a digital twin may
ingest system data and perform simulations in order to further one
or more goals.
[1176] FIG. 71 illustrates examples of different types of
enterprise digital twins, including executive digital twins, in
relation to the data layer, processing layer, and application layer
of the enterprise digital twin framework. In embodiments, executive
digital twins may include, but are not limited to, CEO digital
twins 8302, CFO digital twins 8304, COO digital twins 8306, CMO
digital twins 8308, CTO digital twins 8310, CIO digital twins 8312,
GC digital twins 8314, HR digital twins 8316, and the like.
Additionally, the enterprise digital twins that may be relevant to
the executive suite may include cohort digital twins 8320, agility
digital twins 8322, CRM digital twins 8324, and the like. The
discussion of the different types of digital twins is provided for
example and not intended to limit the scope of the disclosure. It
is understood that in some embodiments, users may alter the
configuration of the various executive digital twins based on the
business needs of the enterprise, the reporting structure of the
enterprise, and the roles and responsibilities of the various
executives within the enterprise.
[1177] In embodiments, executive digital twins and the additional
enterprise digital twins are generated using various types of data
collected from different data sources. As discussed, the data may
include real-time data 8330, historical data 8332, analytics data
8334, simulation/modeled data 8336, CRM data 8338, organizational
data, such as org charts and/or an organizational digital twin
8340, an enterprise data lake 8342, and market data 8344. In
embodiments, the real-time data 8330 may include sensor data
collected from one or more IoT sensor systems, which may be
collected directly from each sensor and/or by various data
collection devices associated with the enterprise, including
readers (e.g., RFID, NFC, and Bluetooth readers), beacons,
gateways, repeaters, mesh network nodes, WIFI systems, access
points, routers, switches, gateways, local area network nodes, edge
devices, and the like. Real-time data 8330 may include additional
or alternative types of data that are collected in real-time, such
as real-time sales data, real-time cost data, project management
data that indicates the status of current projects, and the like.
Historical data may be any data collected by the enterprise and/or
on behalf of the enterprise in the past. This may include sensor
data collected from the sensor systems of the enterprise, sales
data, cost data, maintenance data, purchase data, employee hiring
data, employee on-boarding data, employee retention data,
legal-related data indicating legal proceedings, patent filing data
indicating patent filings and issued patents, project management
data indicating historical progress of past and current projects,
product data indicating products that are on the market, and the
like. Analytics data 8334 may be data derived by performing one or
more analytics processes on data collected by and/or on behalf of
the enterprise. Simulation/modeled data 8336 may be any data
derived from simulation and/or behavior modeling processes that are
performed with respect to one or more digital twins. CRM data 8336
may include data obtained from a CRM of the enterprise. An
organizational digital twin 8340 may be a digital twin of the
enterprise. The enterprise data lake 8342 may be a data lake that
includes data collected from any number of sources. In embodiments,
the market data 8342 may include data that is collected from
disparate data sources concerning or related to competitors and
other cohorts in the marketplace and supply chain. Market data 8342
may be collected from many different sources and may be structured
or unstructured. In embodiments, market data 8342 may contain an
element of uncertainty that may be depicted in a digital twin that
relies on such market data 8342, such as by showing error bars,
probability cones, random walk paths, or the like. It is
appreciated that the different types of data highlighted above may
overlap. For example: historical data may be obtained from the CRM
data; the enterprise data lake 8342 may include real-time data
8330, historical data 8332, analytics data 8332, simulated/modeled
data 8336, and/or CRM data 8336; and analytics data 8334 may be
based on historical data 8332, real-time data 8332, CRM data 8336,
and/or market data 8342. Additional or alternative types of data
may be used to populate an enterprise digital twin.
[1178] In embodiments, the data structuring system 8106 may
structure the various data collected by and/or on behalf of the
enterprise. In embodiments, the digital twin generation system 8108
generates the enterprise digital twins. As discussed, the digital
twin generation system 8108 may receive a request for a particular
type of digital twin (e.g., a CEO digital twin 8302 or a CTO
digital twin 8310) and may determine the types of data needed to
populate the digital twin based on the configuration of the
requested type of digital twin. In embodiments, the digital twin
generation system 8108 may then generate the requested digital twin
based on the various types of data (which may include structured
data structured by the data structuring system 8106). In some
embodiments, the digital twin generation system 8108 may output the
generated digital twin to a client application 8052, which may then
display the requested digital twins.
[1179] In embodiments, a CEO digital twin 8302 is a digital twin
configured for the CEO or analogous top-level decision maker of an
enterprise. The CEO digital twin 8302 may include high-level views
of different states and/or operations data of the enterprise,
including real-time and historical representations of major assets,
processes, divisions, performance metrics, the condition of
different business units of the enterprise, and any other
mission-critical information type. In embodiments, the CEO digital
twin 8302 may work in connection with the EMP 8000 to provide
simulations, predictions, statistical summaries, decision-support
based on analytics, machine learning, and/or other AI and
learning-type processing of inputs (e.g., fiscal data, competitor
data, product data, and the like). In embodiments, a CEO digital
twin 8302 may provide functionality including, but not limited to,
management of personnel, delegation of tasks, decisions, or tasks,
coordination with the Board of Directors and/or strategic partners,
risk management, policy management, oversight of budgets, resource
allocation, investments, and other executive-related resources.
[1180] In embodiments, the types of data that may be populate a CEO
digital twin 8302 may include, but are not limited to:
macroeconomic data, microeconomic analytic data, forecast data,
demand planning data, employment and salary data, analytic results
of AI and/or machine learning modeling (e.g., financial
forecasting), prediction data, recommendation data,
securities-relevant financial data (e.g., earnings, profitability),
industry analyst data (e.g., Gartner quadrant), strategic
competitive data (e.g., news and events regarding industry trends
and competitors), business performance metrics by business unit
that may be relevant to evaluating performance of the business
units (e.g., P&L, head count, factory health, supply chain
metrics, sales metrics, R&D metrics, marketing metrics, and
many others), Board package data, or some other type of data
relevant to the operations of the CEO and/or executive department.
In embodiments, the digital twin system 8004 may obtain
securities-relevant financial data from, for example, the
enterprise's accounting software (e.g., via an API), publicly
disclosed financial statements, third-party reports, tax filings,
and the like. In embodiments, the digital twin system 8004 may
obtain strategic competitive data from public news sources, from
publicly disclosed financial reports, and the like. In embodiments,
macroeconomic data may be derived analytically from various
financial and operational data collected by the EMP 8000. In
embodiments, the business performance metrics may be derived
analytically, based at least in part on real time operations data,
by the artificial intelligence services system 8010 and/or provided
from other users and/or their respective executive digital twins.
The CEO digital twin 8302 may be used to define real time
operations data parameters of interest and to monitor, collect,
analyze, and interpret real time operations data for conformance to
and alignment with an organization's stated business objects, Board
requirements, industry best practice, regulation, or some other
criterion.
[1181] In embodiments, a CEO digital twin 8302 may include
high-level views of different states of the enterprise, including
real-time and historical representations of major assets, the
condition of different business units of the enterprise, and any
mission-critical information. The CEO digital twin 8302 may
initially depict the various states at a lower granularity level.
In embodiments, a user that is viewing the CEO digital twin 8302
may select a state to drill down into the selected state and view
the selected state at a higher level of granularity. For example,
the CEO digital twin 8302 may initially depict a subset of the
various states of the enterprise at a lower granularity level,
including a financial-department state (e.g., a visual indicator
indicating an overall financial health score of the enterprise). In
response to selection, the CEO digital twin 8302 may provide data,
analytics, summary, and/or reporting including, but not limited to,
real-time, historical, aggregated, comparison, and/or forecasted
financial information (e.g., real-time, historical, simulated,
and/or forecasted revenues, liabilities, and the like). In this
way, the CEO digital twin 8302 may initially present the user
(e.g., the CEO) with a view of various different aspects of the
enterprise (e.g., different indicators to indicate different
"health" levels of a respective business unit or part of the
enterprise) but may allow the user to select which aspects require
more of her attention. In response to such a selection, the CEO
digital twin 8302 may request a more granular view of the selected
state(s) from the EMP 8000, which may return the requested states
at the more granular level.
[1182] In embodiments, a CEO digital twin 8302 may include an
executive-level digital twin of the executive department (e.g.,
C-suite, directors, Board members, and the like), which the user
may use to identify, assign, instruct, oversee and review executive
department personnel and third-party personnel, departments,
organizations and the like that are associated with the activities
of the executive of an organization, including the Board of
Directors and the like that are involved in the oversight of the
organization's management. In embodiments, the executive-level
digital twin may include a definition of the various roles,
employees, and departments working under the CEO, the reporting
structure for each individual in the business unit and may be
populated with the various names and/or other identifiers of the
individuals filling the respective roles. In embodiments, the CEO
digital twin 8302 may include a graphical user interface that
provides the user the ability to define/redefine personnel
groupings, assign performance criteria and metrics to business
units, roles, and/or individuals, and/or assign/delegate tasks to
business units, roles, and/or individuals, and the like via the
executive-level digital twin. In embodiments, the executive-level
digital twin may provide real-time operations data of the
organization to continuously evaluate the personnel groupings'
performance against the stored performance criteria.
[1183] In embodiments, a CEO digital twin 8302 may be configured to
interface with the collaboration suite 8006 to specify and provide
a set of collaboration tools that may be leveraged by the executive
department and associated parties. The collaboration tools may
include video conferencing tools, "in-twin" collaboration tools
(e.g., where the collaboration occurs to some extent within a
common interface by which the digital twin entities are viewed and
collaboration activities take place and/or where the components of
the EMP that used to configure, operate or support the digital twin
also govern collaboration around digital twin entities and
workflows), whiteboard tools, agile development environment tools
(such as features in Slack.TM. environments), presentation tools,
word processing tools, spreadsheet tools, and the like, as
described herein. Collaboration and communication rules may be
configured based at least in part on using the AI reporting tool,
as described herein. The collaboration tools may include
collaborative communication (e.g., facilitating live conferencing
where participants are simultaneously presented with
conference-related views of digital twin entities or workflows),
asynchronous collaboration (such as where actions on digital twin
entities, comments, or the like are represented to different users
who interact with the entities), version control features, and many
others.
[1184] In embodiments, a CEO digital twin 8302 may be configured to
provide research, track, and report on an executive department
initiative including, but not limited to, an overall strategic
goal, policy implementation, product roll-out, Board interaction,
investment or acquisition, investor relations, public relations and
press handling, budgeting, or some other type of executive
initiative. The CEO digital twin 8302 may interact with and share
such data and reporting with other executive digital twins,
including, but not limited to, a CFO digital twin, a COO digital
twin, and the like. In embodiments, the CEO digital twin 8302 or an
executive agent integrated with or within it (such as one trained
to undertake expert executive actions as described elsewhere
herein) may leverage intelligence services (e.g., data analytics,
machine learning and A.I. processes) to analyze financial reports,
projections, simulations, budgets, and related summaries to
identify key departments, personnel, third-party or others that
are, for example, listed in, or subject to, a project, initiative,
budget line item and the like, and who therefore may have an
interest in such material. Such material pertaining to a given
party may be abstracted and summarized for presentation, and
formatted and presented automatically, or at the direction of the
CEO or other user, to the party that is the origin of the expense
and/or subject of the material. For example, the CEO digital twin
8302 may assemble materials for the purposes of developing
presentations, speaking points, press releases, or some other
material for the CEO or other executive personnel to use for public
presentation. In an example, a CEO in anticipation of giving a
conference presentation on the introduction of anew company product
may use the CEO digital twin 8302 to specify and configure the
identification, collection and assembly of operations data that is
relevant to the upcoming presentation, such as product data (e.g.,
units produced, units shipped), financial data (e.g., products
sold, products reserved), graphic presentation information (e.g.,
product photos, maps of product distribution, graphs of anticipated
sales), forecasting data (e.g., market growth expected), or some
other type of data and assemble such information in a presentation
format, such as presentation slides, white paper template, speech
talking points, press release, or some other summary format that
may form the basis of the presentation or be distributed in
conjunction with the presentation and/or its marketing.
[1185] In embodiments, a CEO digital twin 8302 may be configured to
track and report on stakeholder communications (e.g., reports,
Board requests, investor requests) related to the executive
department. The CEO digital twin 8302 may present, store, analyze,
reconcile and/or report on executive activities related to parties
with whom the executive department is contracting, cooperating
with, reporting to and so forth, such as key personnel, outside
contractors, the press, the Board of Directors, or others.
[1186] In embodiments, the CEO digital twin 8302 may be configured
to simulate one or more aspects of the enterprise. Such simulations
may assist the user (e.g., the CEO) in making executive level
decisions. For example, simulations of a proposed executive
initiative may be tested, for example using the modeling, machine
learning, and/or AI techniques, as described herein, by simulating
temporal effects on initiatives (e.g., introduction of a new
product), varying financial parameters (e.g., potential investment
levels), targeting parameters (e.g., geographic, demographic, or
the like), and/or other suitable executive parameters. In
embodiments, the digital twin simulation system 8116 may receive a
request to perform an executive simulation requested by the CEO
digital twin 8302, where the request indicates one or more
parameters that are to be varied in one or more enterprise digital
twins. In response, the digital twin simulation system 8116 may
return the simulation results to the CEO digital twin 8302, which
in turn outputs the results to the user via the client device
display. In this way, the user may be provided with various
outcomes corresponding to different parameter configurations. For
example, a user may request a set of simulations to be run to test
different supply chain strategies to see how the different
strategies affect the throughput of a manufacturing facility and
the overall impact on the profits and losses of the enterprise. The
digital twin simulation system 8116 may perform the simulations by
varying the different supply chain strategies and may output the
throughputs and P&L forecasts for each respective supply chain
strategy. In some embodiments, the user may select a parameter set
based on the various outcomes, and iterate simulations based at
least on the varied prior outcomes. Drawing from the previous
example, the user may decide to select the supply chain strategy
that maximizes P&L forecasts but does not adversely affect
throughput of the manufacturing facility. In some embodiments, an
executive agent may be trained to recommend and/or select a
parameter set based on the respective outcomes associated with each
respective parameter set.
[1187] In embodiments, a CEO digital twin 8302 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to an executive strategy, executive planning,
executive activities, and/or executive initiatives. For example,
the CEO digital twin 8302 may be associated with a plurality of
databases or other repositories of financial materials, summaries
and reports and analytics, including such materials, summaries and
reports and analytics related to prior executive activity (e.g.,
prior quarterly financial performance, prior investments, prior
strategic partners, co-developments, and the like), each of which
may be further associated with financial and performance metrics
pertaining to the campaign and which are also accessible to the CEO
digital twin 8302.
[1188] In embodiments, a CEO digital twin 8302 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to financial reporting, ratings, rankings,
financial trend data, income data, or other data related to an
executive's responsibilities. A CEO digital twin 8302 may link to,
interact with, and be associated with external data sources, and
able to upload, download, aggregate external data sources,
including with the EMP's internal data, and analyze such data, as
described herein. Data analysis, machine learning, AI processing,
and other analysis may be coordinated between the CEO digital twin
8302 and an analytics team based at least in part on using the
artificial intelligence services system 8010. This cooperation and
interaction may include assisting with seeding executive-related
data elements and domains in the enterprise data store 8012 for use
in modeling, machine learning, and AI processing to identify an
optimal business strategy, or some other executive-relating metric
or aspect, as well as identification of the optimal data
measurement parameters on which to base judgement of an executive
initiative's success. Examples of data sources 8020 that may be
connected to, associated with, and/or accessed from the CEO digital
twin 8302 may include, but are not limited to, a sensor system 8022
having sensors that sensor data from facilities (e.g.,
manufacturing facilities, shipping and logistics facilities,
transportation facilities, agricultural facilities, resource
extraction facilities, computing facilities, and many others)
and/or other physical entities of the enterprise, a sales database
8024 that is updated with sales figures in real time, a CRM system
8026, a content marketing platform 8028, financial databases 8030,
surveys 8032, org charts 8034, workflow management systems 8036,
third-party data sources 8038, customer databases 8040 that store
customer data, and/or third-party data sources 8038 that store
third-party data, edge devices 8042 that report data relating to
physical assets (e.g., smart machinery/manufacturing equipment,
sensor kits, autonomous vehicles of the enterprise, wearable
devices, and the like), enterprise resource management systems
8044, HR systems 8046, content management systems 8016, and the
like). In embodiments, the digital twin system 8004 abstracts the
different views (or states) within the digital twin to the
appropriate granularity. For instance, the digital twin system 8004
may have access to all the sensor data collected on behalf of the
enterprise as well as access to real-time sensor data streams.
Typically, such data is far too granular for an executive such as a
CEO, and sensor data readings are often of little importance to the
CEO unless associated with a mission critical state or operation.
In this example, however, if the sensor readings from a particular
physical asset (e.g., a critical piece of manufacturing equipment)
are indicative of a potentially critical situation (e.g., failure
state, dangerous condition, or the like), then the analytics that
indicate the potentially critical situation may become very
important to the CEO. Thus, the digital twin system 8004 may, when
building the appropriate perspective for the CEO, include a state
indicator of the physical asset in the CEO digital twin. In this
way, the CEO can drill down into the state indicator of the
physical asset to view the potentially critical situation at a
greater granularity (e.g., the machinery and an analysis of the
sensor data used to identify the situation).
[1189] In embodiments, a CEO digital twin 8302 may be configured to
monitor an organization's performance based at least in part on
real time operations data and the use of the monitoring agent of
the client application 8052, as described herein, that is
associated with the CEO digital twin 8302. The monitoring agent may
report on such activities to the EMP 8000 for presentation in a
user interface that is associated with the CEO digital twin 8302.
In response, the EMP 8000 may train an executive agent (which may
include one or more machine-learned models) to handle and process
such notifications when they next arrive, and escalate and/or alert
the CEO when such notifications are of an urgent nature, such as an
announcement of an acquisition by a competitor, a report indicating
an under-performing business unit, a high-profile press article, a
radical change in the stock market (for the CEO's company, a cohort
member, or the market as a whole), a downgrade in rating by an
industry analyst, an external event likely to disrupt operations
(such as a natural disaster or epidemic) or some other important
event. In embodiments, the CEO digital twin 8302 may generate
performance alerts based on real time operations data, performance
trends, and the like. This may allow a CEO to optimize initiatives
in real-time without having to manually request such real-time
data; the CEO digital twin 8302 may automatically present such
information and related/necessary alerts as configured by the
organization, CEO, or some other interested party.
[1190] In embodiments, a CEO digital twin 8302 may be configured to
report on the performance of the executive department, personnel of
the executive department, executive activities, executive content,
executive platforms, executive partners, or some other aspect of
management within a CEO's responsibilities. Reporting may be to the
CEO, the executive department, to other executives of an
organization (e.g., the COO), or to outside third parties (e.g.,
partners, press releases, and the like). As described herein,
reporting may include stakeholder summaries, minutes of meetings,
presentations, sales data, customer data, financial performance
metrics, personnel metrics, data regarding resource usage, industry
summaries (e.g., summaries of merger and acquisition activity in an
industry segment), or some other type of reporting data. Reporting
and the content of reporting may be shared by the CEO digital twin
8302 with other executive digital twins. The reporting
functionality of the CEO digital twin 8302 may also be used for
populating new or preset reporting formats, and the like. Templets
of common reporting formats may be stored and associated with the
CEO digital twin 8302 to automate the presentation of data and
analytics according to pre-defined formats, styles and system
requirements. In embodiments, an executive agent trained by the
user may be trained to surface the most important reports to the
user. For example, if the user (e.g., the CEO) consistently views
and follows up on sales data reports but routinely skips over
reports relating to the manufacturing KPIs, the executive agent may
automatically surface sales data reports to the user and may
automatically delegate manufacturing KPIs to another executive
digital twin (e.g., the COO digital twin).
[1191] In embodiments, a CEO digital twin 8302 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to competitors of a CEO's
organization, or named entities of interest. In embodiments, such
data may be collected by the EMP 8000 via data aggregation,
spidering, web-scraping, or other techniques to search and collect
competitor information from sources including, but not limited to,
information on investment and/or acquisitions, press releases, SEC
or other financial reports, or some other publicly available data.
For example, a user wishing to monitor a certain competitor may
request that the CEO digital twin 8302 provide materials relating
to the certain competitor. In response, the EMP 8000 may identify a
set of data sources that are either publicly available or to which
the enterprise of the CEO has access (e.g., internal data sources,
licensed third-party data, or the like). The EMP 8000 may configure
a cohort digital twin 8320 based on the types of
data/analysis/services the user requests and the identified set of
data sources. The EMP 8000 may then serve the cohort digital twin
8320 associated with the requested party (e.g., competitor) to the
CEO digital twin 8302.
[1192] In embodiments, a CEO digital twin 8302 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to regulatory activity, such as
government regulations, industry best practices or some other
requirement or standard. For example, the CEO digital twin 8302 may
be in communication with another enterprise digital twin, such as a
General Counsel digital twin 8314, through which the legal team can
keep the CEO apprised of new regulation or regulation changes as
they occur.
[1193] In embodiments, the client application 8052 that executes
the CEO digital twin 8302 may be configured with an executive agent
8364 that is trained on the CEO's actions (which may be indicative
of behaviors, and/or preferences). In embodiments, the executive
agent 8364 may record the features relating to the actions (e.g.,
the circumstances relating to the user's action) to the expert
agent system 8008. For example, the executive agent 8364 may record
each time the user delegates a task to a subordinate (which is the
action) as well as the features surrounding the delegation of the
task (e.g., an event that caused the user to delegate the task, the
type of task that was delegated, the role to which the task was
delegated, instructions provided by the user with the delegation,
and the like). The executive agent 8364 may report the actions and
features to the expert agent system 8008 and the expert agent
system 8008 may train the executive agent 8364 on the manner by
which the executive agent 8364 can delegate or recommend delegation
of tasks in the future. Once trained, the executive agent 8364 may
automatically perform actions and/or recommend actions to the user.
Furthermore, in embodiments, the executive agent 8364 may record
outcomes related to the performed/recommended actions, thereby
creating a feedback loop with the expert agent system 8008.
[1194] References to features and functions of the EMP and digital
twins in this example of a CEO digital twin 8302 should be
understood to apply to other digital twins, and their respective
projects and workflows, except where context indicates
otherwise.
[1195] In embodiments, a Chief Financial officer (CFO) digital twin
8304 may be a digital twin configured for a CFO of an enterprise,
or an analogous executive tasked with overseeing the
finance-related tasks of the enterprise. A CFO digital twin 8304
may provide data, analytics, summary, and/or reporting including,
but not limited to, real-time, historical, aggregated, comparison,
and/or forecasted financial information (e.g., real-time,
historical, simulated, and/or forecasted sales figures,
expenditures, revenues, liabilities, and the like). In embodiments,
the CFO digital twin may work in connection with the EMP 8000 to
provide simulations, predictions, statistical summaries, decision
support based on analytics, machine learning, and/or other AI and
learning-type processing of inputs (e.g., accounting data, sales
data, sensor data and the like).
[1196] In embodiments, a CFO digital twin 8304 may provide features
and functionality including, but not limited to, management of
financial personnel, partners and outside consultants and
contractors (e.g., accounting firms, auditors and the like),
oversight of budgets, procurement, expenditures, receivables, and
other finance-related resources, compliance, oversight of sales and
sales staff and departments' financial performance, management of
contracting, management of internal policies (e.g., policies
related to expenditures and reporting), tax law, finance-related
privacy law (e.g., pertaining to credit agency data), reporting,
compliance, and regulatory analysis.
[1197] In embodiments, the types of data that may populate a CFO
digital twin may include, but are not limited to, financial
performance metrics by business unit, by product, by geography, by
factory, by store location(s), by asset class, earnings, cash,
balance sheet data, cash flow, profitability, resource utilization,
audit data, general ledger data, asset performance data, securities
and commodities data, insurance and risk management data, asset
aging and depreciation data, asset allocation data, macroeconomic
data, microeconomic analytic data, tax data, pricing data,
competitive product and pricing data, forecast data, demand
planning data, employment and salary data, analytic results of AI
and/or machine learning modeling (e.g., financial forecasting),
prediction data, recommendation data, or some other type of data
relevant to the operations of the CFO and/or finance department. In
embodiments, "datum," "data," "dataset," "datastore," "data
warehouse," and/or "database," as used herein, may refer to
information that is stored in a numeric or statistical format,
including summaries, inputs or outputs in statistical or scientific
notation, and also includes information that is stored in natural
language format (e.g., text excerpts from reports, press releases,
statutes and the like), information in a graphic format (e.g.,
financial performance graphs), information in audio and/or
audio-visual format (e.g., recorded audio from conference calls or
video from presentations, including natural language transcript
summaries of audio and/or audio-visual formatted information), or
some other type of information.
[1198] In embodiments, a CFO digital twin 8304 may depict a finance
department twin of the finance department, which the user may use
to identify, assign, instruct, oversee and review finance
department personnel and third-party personnel that are associated
with the finance activities of an organization, including
third-party partners and other outside contractors, such as
accounting firms, tax lawyers and the like that are involved in the
organization's finance endeavors. Examples of such organization
personnel include, but are not limited to, finance department
staff, sales analysts, statisticians, data scientists, executive
personnel, human resources staff, Board Members, advisors, or some
other type of organization personnel relevant to the functioning of
a finance department. Examples of a finance department's
third-party personnel include, but are not limited to, lawyers,
accountants, management consultants, social media platform
personnel, finance partners, consultants, contractors, financial
firm staff, auditors, or some other type of third-party
personnel.
[1199] In embodiments, the CFO digital twin 8304 may include a
definition of the various roles/employees working under the CFO,
the reporting structure, and associated permissions, for each
individual in the business unit, and may be populated with the
various names and/or other identifiers of the individuals filling
the respective roles. In embodiments, a user (e.g., the CFO of an
enterprise) may use the CFO digital twin 8304 to adjust the
reporting structure within the finance department and/or to grant
permissions to one or more individuals within the department.
[1200] In embodiments, a CFO digital twin 8304 may be configured to
interface with the collaboration suite 8006 to specify and provide
a set of collaboration tools that may be leveraged by the finance
department and associated parties. The collaboration tools may
include video conferencing tools, "in-twin" collaboration tools,
whiteboard tools, presentation tools, word processing tools,
spreadsheet tools, and the like, as described herein. Collaboration
and communication rules may be configured based at least in part on
using the AI reporting tool, as described herein.
[1201] In embodiments, a CFO digital twin 8304 may be configured to
research, create, track and report on a finance department
initiative including, but not limited to, an overall department
budget, a budget for a single or group of finance initiatives, an
audit, a third-party vendor activity, or some other type of expense
or budget. In embodiments, the CFO digital twin 8304 may interact
with and share such expense or budget data and reporting with other
enterprise twins, as described herein, including, but not limited
to, a digital twin related to accounts payable, executive staff
such as the CEO (e.g., CEO digital twin) or COO (e.g., the COO
digital twin), or other suitable enterprise digital twins. In
embodiments, the CFO digital twin 8304 may leverage one or more
intelligence services of the EMP 8000 based at least in part on the
data analytics, machine learning and A.I. processes, as described
herein, to provide financial reports, projections, simulations,
budgets and related summaries. In some of these embodiments, the
CFO digital twin 8304 may use the intelligence services to identify
key departments, personnel, third-party or others that are, for
example, listed in, or subject to, the budget line item and who
therefore may have an interest in such material. Budget material
pertaining to a given party may be abstracted and summarized for
presentation independent from the entirety of the budget, and
formatted and presented automatically, or at the direction of the
CFO or other user, to the party that is the origin of the expense
and/or subject of the budget item.
[1202] In some embodiments, a CFO digital twin 8304 may be
configured to track and report on inbound and outbound billing
(i.e., accounts receivable and payable) related to the finance
department and/or organization. In embodiments, the CFO digital
twin 8304 may include a billing digital twin that identifies the
billing department, personnel, processes and systems associated
with the billing workflows of the enterprise. In these embodiments,
the billing digital twin may interact present, store, analyze,
reconcile and/or report on billing activities related to parties
with whom the finance department is interacting. In some
embodiments, the user of the CFO digital twin 8304 may approve
bills, issue bills, drill down into a set of bills, initiate
investigations of bills or the like via the GUI if the CFO digital
twin 8304.
[1203] In embodiments, a CFO digital twin 8304 may be configured to
provide a user (e.g., a CFO or other finance department executive)
with information that is unique to the CFO digital twin 8304 and
thus can provide insights and perspectives on financial performance
that are unique to the CFO digital twin 8304. For example, in
supply chain planning, demand forecasting, operational planning and
other of the CFO's activities, traditional data sources, models and
projections may be "siloed" in ways, meaning they may be
quantitatively robust within a particular domain, but that domain
may be constrained by factors including, but not limited to, the
origins of the data, the format within which the data is recorded,
the statistical weights used in creating or transforming the data
that is available, or some other constraint. In embodiments, the
EMP 8000 in connection with the CFO digital twin 8304 may create
and derive new financial metrics and analytics including, but not
limited to, functionalities such as native data and model creation,
and data and model combinations and aggregations based at least
in-part on the real time operations of an organization. Native data
and model creation, such as specifying the data to be collected,
the format within which to collect and store the data, the data
transformations to model, and so forth gives one the ability to
craft, combine, aggregate, modify, transform, and/or weight the
native data (including in combination with other third-party data)
in manners that are appropriately mathematically tuned to the
modeling, analysis, machine learning, and/or AI techniques that are
performed by the EMP 8000 and CFO digital twin 8304, rather than
being reliant on data and/or model presets. Similarly, in the
analytic context of the CFO's operations and the function of the
EMP and CFO digital twin 8304, native data and model creation and
structuring by the EMP and CFO digital twin 8304 enables analytics,
machine learning, AI operations and the like, yielding new analytic
results and insights, based at least in part on the real time
operations of an organization, because the EMP and CFO digital twin
8304 has enabled the CFO to move further up in financial data
creation and modeling operations to assert greater creative control
over the types of data and other input material to be used in
developing analytic insights that may be created and reported for
the purpose of improving performance including, but not limited to,
product margins (e.g., gross, contribution, net and the like),
product features, upsell opportunities or some other performance
metric.
[1204] In embodiments, the CFO digital twin 8304 may be configured
to simulate finance-related activities on behalf of a user. In
these embodiments, the user may identify one or more parameters
that can be varied during for a simulation including, but not
limited to, financial and/or budget parameters, pricing and sales
goal settings, process designs, and maintenance/infrastructure
upgrades, internal controls design, product testing
frequencies/types, manufacturing down-times, flexible workforce
planning, and the like. In these embodiments, the digital twin
simulation system 8116 may receive a request to perform the
simulation requested by the CFO digital twin 8304, where the
request indicates features and the parameters, including financial
parameters, that are to be varied. In response, the digital twin
simulation system 8116 may return the simulation results to the CFO
digital twin 8304, which in turn outputs the results to the user
via the client device display. In this way, the user is provided
with various outcomes corresponding to different parameter
configurations. In some embodiments, the user may select a
parameter set based on the various outcomes. In some embodiments,
an executive agent trained by the user may select the parameter
sets based on the various outcomes. The simulations, analytics
and/or modeling performed by the CFO digital twin 8304 may be used
to mitigate risk for IPO, M&A, equity and debt offerings, or
some other type of transaction. The simulations, analytics and/or
modeling performed by the CFO digital twin 8304 may be used to
create and structure sales incentives, including commissions and
other performance-based compensation. The simulations, analytics
and/or modeling performed by the CFO digital twin 8304 may be used
to evaluate insurance offerings and other information related to
business interruption preparedness. The simulations, analytics
and/or modeling performed by the CFO digital twin 8304 may be used
to analyze loan covenant monitoring and projections. The CFO
equipped with digital twin 8304 will be better able to adapt
quickly to change by predicting headwinds, forecasting operational
performance, and making informed decisions across departments while
mitigating risk.
[1205] In embodiments, a CFO digital twin 8304 may be configured to
manage operational planning, based at least in part by leveraging
predictive analytics for sales planning, and supply chain
management in order to increase company efficacy while optimizing
operating expenses.
[1206] In embodiments, a CFO digital twin 8304 may be configured to
access insights across environmental resource management (ERM)
solutions for risk oversight that includes, but is not limited to,
internal controls design, testing, certification, and reporting
while directing listed actions into a repository. In embodiments, a
CFO digital twin 8304 may be configured to streamline governance,
risk management, and compliance processes in order to connect risk
and compliance across the organization and manage complex audit
fieldwork and work papers.
[1207] In embodiments, a CFO digital twin 8304 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to a financial strategy, plan, activity or
initiative. For example, the CFO digital twin 8304 may be
associated with a plurality of databases or other repositories of
financial materials, summaries and reports and analytics, including
such materials, summaries and reports and analytics related to
prior financial activity (e.g., prior quarterly financial
performance), each of which may be further associated with
third-party financial or economic data.
[1208] In embodiments, a CFO digital twin 8304 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to financial reporting, ratings, rankings,
financial trend data, income data, or other finance
department-related data. A CFO digital twin 8304 may link to,
interact with, and be associated with external data sources, and
able to upload, download, aggregate external data sources,
including with the EMP's internal data, and analyze such data. Data
analytics, machine learning, AI processing, and other data-driven
processes may be coordinated between the CFO digital twin 8304 and
an analytics team based at least in-part on insights derived by the
artificial intelligence services system 8010. This cooperation and
interaction may include assisting with seeding finance-related data
elements and domains in the enterprise data store 8012 for use in
modeling, machine learning, and AI processing to identify the
optimal financial strategy, or some other finance-related metric or
aspect, as well as identification of the optimal data measurement
parameters on which to base judgement of a finance endeavor's
success. Examples of data sources 8020 that may be connected to,
associated with, and/or accessed from the CFO digital twin 8304 may
include, but are not limited to, a sensor system 8022, a sales
database 8024 that is updated with sales figures in real time, a
CRM system 8026, news websites 8048, a financial database 8030 that
tracks costs of the business, an org chart 8034, a workflow
management system 8036, customer databases 1S40 that store customer
data, and/or third-party data sources 8038 that store third-party
data.
[1209] In embodiments, a CFO digital twin 8304 may aggregate data
sources and types, creating new data types, summaries and reports
that are not available elsewhere. This may reduce reliance upon the
need of multiple third-party providers and current solutions. This
may, among other benefits and improvements, reduce expenses
associated with acquiring data needed for sound financial decision
making.
[1210] In embodiments, a CFO digital twin 8304 may be configured to
monitor a user's performance of finance-related tasks via a
monitoring function of an agent of the client application 8052
executing the CFO digital twin 8304. In embodiments, the monitoring
function of the executive agent may report on certain activities to
the EMP 8000 that are undertaken by the user when interfacing with
the CFO digital twin 8304. In response, the EMP 8000 may train the
executive agent (which may include one or more machine-learned
models) to handle and process such finance-related tasks when they
next arrive. For example, the monitoring function may monitor when
the user (e.g., the CFO) escalates a state of the CFO digital twin
8304 to the CEO and/or when the user delegates a task to a
subordinate via the CFO digital twin 8304. Each time such
escalations and/or delegation events occur and/or when the user
(e.g., the CFO or other finance executive) responds to an alert or
other notifications of an urgent nature and may report and may
report the actions taken by the user in response to each respective
account to the EMP 8000. In response, the expert agent system 8008
may train an executive agent 8364 based on the reported actions,
which in turn may be leveraged by the CFO digital twin to respond
to certain later occurring events on which the executive agent 8364
was trained on (e.g., analytics showing poor financial performance
or finance activity (e.g., a new investment). For example, an
executive agent 8364 trained with respect to a CFO digital twin
8304 may automatically issue financial performance alerts to
certain employees based on performance trends of one or more
business units. In another example, the executive agent 8304 may
automatically escalate a notification to the CEO (which may be
depicted in the CEO digital twin 8302) when certain metrics
indicate a poor financial forecast. In embodiments, the executive
agent 8364 in connection with the CFO digital twin 8304 may allow a
CFO to optimize initiatives in real-time without having to manually
request such real-time financial performance data. In some
embodiments, the CFO digital twin 8304 may automatically present
such information and related/necessary alerts as configured by the
configuring user, the CFO, or some other user having such
permissions.
[1211] In embodiments, an executive agent 8364 trained in
connection with a CFO digital twin 8304 may be configured to report
on the performance of the finance department, personnel of the
finance department, finance activities, finance content, finance
platforms, finance partners, or some other aspect of management
within a CFO's responsibilities. Reporting may be to the CEO, the
Board of Directors, other executives of an organization (e.g., the
COO), or to outside third parties (e.g., partners, press releases,
and the like). The reporting functionality of the CFO digital twin
8304 may also be used for populating required data for formal
reporting requirements such as shareholder statements, annual
reports, SEC filings, and the like. Templets of common reporting
formats may be stored and associated with the CFO digital twin 8304
to automate the presentation of data and analytics according to
pre-defined formats, styles and system requirements.
[1212] In embodiments, a CFO digital twin 8304 in combination with
the EMP 8000 may be configured to monitor, store, aggregate, merge,
analyze, prepare, report and distribute material relating to
competitors of a CFO's organization, or named entities of interest.
In embodiments, such data may be collected by the EMP 8000 via data
aggregation, spidering, web-scraping, or other techniques to search
and collect competitor information from sources including, but not
limited to, press releases, SEC or other financial reports, mergers
and acquisitions activity, or some other publicly available
data.
[1213] In embodiments, a CFO digital twin 8304 in combination with
the EMP 8000 may be configured to monitor, store, aggregate, merge,
analyze, prepare, report and distribute material relating to
regulatory activity, such as government regulations, industry best
practices or some other requirement or standard. For example, the
CFO digital twin 8304 may be in communication with another
enterprise digital twin, such as a General Counsel digital twin
8314, through which the legal team can keep the CFO apprised of new
regulations or regulation changes as they occur.
[1214] In embodiments, the client application 8052 that executes
the CFO digital twin 8304 may be configured with an executive agent
that reports a CFO's behaviors and preferences (or other finance
personnel's behaviors and preferences) to the expert agent system
8008, as described herein, and the expert agent system 8008 may
train the executive agent on how the CFO or other finance personnel
respond to certain situations and adjust its operation based at
least in part on the data collection, analysis, machine learning
and A.I. techniques, as described herein. The foregoing examples
are optional examples and are not intended to limit the scope of
the disclosure.
[1215] References to features and functions of the EMP and digital
twins in this example of a finance department and a CFO digital
twin 8304 should be understood to apply to other departments and
digital twins, and their respective projects and workflows, except
where context indicates otherwise.
[1216] In embodiments, a Chief Operating officer (COO) digital twin
8306 may be a digital twin configured for a COO of an enterprise,
or an analogous executive tasked with overseeing the operations
tasks of the enterprise. A COO digital twin 8306 may provide
functionality including, but not limited to, management of
personnel and partners, oversight of various departments (e.g.,
oversight over marketing department, HR department, sales
department, and the like), project management, implementation
and/or rollouts of business processes and workflows, budgeting,
reporting, and many other operations-related tasks.
[1217] In embodiments, a COO digital twin 8306 may provide data,
analytics, summary, and/or reporting including, but not limited to,
real-time, historical, aggregated, comparison, and/or forecasted
financial information (e.g., sales, expenditures, revenues,
liabilities, profitability, cash flow and the like), mergers and
acquisitions information, systems data, reporting and controls
data, or some other operations related information. In embodiments,
the COO digital twin 8306 may work in connection with the EMP 8000
to provide simulations, predictions, statistical summaries,
decision support based on analytics, machine learning, and/or other
AI and learning-type processing of inputs (e.g., equipment data,
sensor data and the like), for example those related to the
development, communication and implementation of effective growth
strategies and processes for an organization.
[1218] In embodiments, the types of data that may populate a COO
digital twin may include, but are not limited to, operations data,
key performance indicators (KPIs) for factories/plants, business
units, assets/equipment; uptime/downtime, safety data, risk
management data, supply chain/component availability data, demand
plan data, logistics data, workflow data, financial performance
metrics by business unit, by product, by geography, by factory, by
store location(s), by asset class, earnings, resource utilization;
audit data, asset performance data, asset aging and depreciation
data, asset allocation data, or some other type of
operations-relevant data or information.
[1219] In embodiments, a COO digital twin 8306 may depict a twin of
the operations department, which the user may use to identify,
assign, instruct, oversee and review operations department
personnel and third-party personnel that are associated with the
design, implementation and evaluation of operational processes,
internal infrastructures, reporting systems, company policies, and
the like.
[1220] In embodiments, the COO digital twin 8306 may include a
definition of the various roles/employees working under the COO,
the reporting structure, and associated permissions, for each
individual in the business unit, and may be populated with the
various names and/or other identifiers of the individuals filling
the respective roles.
[1221] In embodiments, a COO digital twin 8306 may be configured to
interface with the collaboration suite 8006 to specify and provide
a set of collaboration tools that may be leveraged by the
operations department and associated parties. The collaboration
tools may include video conferencing tools, "in-twin" collaboration
tools, whiteboard tools, presentation tools, word processing tools,
spreadsheet tools, and the like, as described herein. Collaboration
and communication rules may be configured based at least in part on
using the AI reporting tool, as described herein.
[1222] In some of these embodiments, the COO digital twin 8306 may
be configured to simulate operations activities, such as a proposed
new operational plan, process or program. In these embodiments, the
digital twin simulation system 8116 may receive a request to
perform the simulation requested by the COO digital twin 8306,
where the request indicates features and the parameters of the
operational plan or other activity that is proposed for
implementation, the associated variables for which may be altered
or varied to produce differing simulation environments. In
response, the digital twin simulation system 8116 may return the
simulation results to the COO digital twin 8306, which in turn
outputs the results to the user via the client device display. In
this way, the user is provided with various outcomes corresponding
to different operational parameter configurations. In embodiments,
an executive agent trained by the user may select the parameter
sets based on the various outcomes.
[1223] In embodiments, a COO digital twin 8306 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to an operations strategy, plan, activity or
initiative. For example, the COO digital twin 8306 may be
associated with a plurality of databases or other repositories of
operational data, summaries and reports and analytics, including
such materials, summaries and reports and analytics related to
prior operations activity, each of which may be further associated
with financial and performance metrics pertaining to the activity
and which are also accessible to the COO digital twin 8306.
[1224] In embodiments, a COO digital twin 8306 may be configured to
monitor operational performance, including in real time, based at
least in part on use of the monitoring agent of the client
application 8052, as described herein, that is associated with the
COO digital twin 8306. The monitoring agent may report on such
activities to the EMP 8000 for presentation in a user interface
that is associated with the COO digital twin 8306. In response, the
EMP 8000 may train an executive agent (which may include one or
more machine-learned models) to handle and process such
notifications when they next arrive and escalate and/or alert the
COO when such notifications are of an urgent nature.
[1225] In embodiments, a COO digital twin 8306 may be configured to
report on the performance of the operations department, personnel
of the operations department, operations activities, operations
content, operations platforms, operations partners, or some other
aspect of management within a COO's responsibilities.
[1226] In embodiments, the EMP 100 trains and deploys executive
agents on behalf of enterprise users. In embodiments, an executive
agent is an AI-based software system that performs tasks on behalf
of and/or suggests actions to a respective executive user. In
embodiments, the EMP 100 receives data from various data sources
associated with a particular entity or workflow and learns the
workflows performed by the particular user based on the data and
the surrounding circumstances or context. For example, the user may
be a COO that is presented a COO digital twin 8306. Among the
responsibilities of the COO may be scheduling maintenance and
replacement of equipment in a manufacturing, warehouse, or other
operational facility. The states depicted in the COO digital twin
8306 may include depictions of the condition of different pieces of
equipment within the operational facility. In this example, the COO
may schedule maintenance via the digital twin when a piece of
equipment is determined to be in a first condition (e.g., a
deteriorating condition) and may issue a request to the COO via the
COO digital twin 8306 to replace the piece of equipment when the
equipment is determined to be in a second condition (e.g., a
critical condition). The executive agent may learn the COO's
tendencies based on the COO's previous interaction with the COO
digital twin 8306. Once trained, the executive agent may
automatically request replacements from the COO when a particular
piece of equipment is determined to be in the second condition and
may automatically schedule maintenance if the piece of equipment is
in the first condition.
[1227] In embodiments, the client application 8052 that executes
the COO digital twin 8306 may be configured with an executive agent
that reports a COO's behaviors and preferences (or other operations
personnel's behaviors and preferences) to the executive agent
system 8008, as described herein, and the executive agent system
8008 may train the executive agent on how the COO or other
executive personnel respond to certain situations and adjust its
operation based at least in part on the data collection, analysis,
machine learning and A.I. techniques, as described herein. The
foregoing examples are optional examples and are not intended to
limit the scope of the disclosure.
[1228] References to features and functions of the EMP and digital
twins in this example of an operations department and a COO digital
twin 8306 should be understood to apply to other departments and
digital twins, and their respective projects and workflows, except
where context indicates otherwise.
[1229] In embodiments, a Chief Marketing officer (CMO) digital twin
8308 may be a digital twin configured for a CMO of an enterprise,
or an analogous executive tasked with overseeing the marketing
tasks of the enterprise. A CMO digital twin 8308 may provide
functionality including, but not limited to, management of
personnel and partners, development and oversight of marketing
budgets and resources, management of marketing and advertising
platforms, development and management of marketing content,
strategies and campaigns, reporting, competitor analysis,
regulatory analysis, and management of data privacy and
security.
[1230] In embodiments, the types of data that may populate and/or
be utilized by a CMO digital twin 8308 may include, but are not
limited to, macroeconomic data; market pricing data; competitive
product and pricing data; microeconomic analytic data; forecast
data; demand planning data; competitive matrix data; product
roadmap; product capability data; consumer behavior data; consumer
profile data; collaborative filtering data; analytic results of AI
and/or machine learning modeling; channel data; demographic data;
geographic data; prediction data; recommendation data, or some
other type of data relevant to the operations of the CMO and/or
marketing department.
[1231] In embodiments, an executive digital twin, such as a CMO
digital twin 8308 or other executive digital twin may depict a twin
of a department, such as the marketing department or other
department, which the user may use to identify, assign, instruct,
oversee and review department personnel and third-party personnel
that are associated with the activities of a particular department
of an organization, including third-party partners and other
outside associates involved in the organization's related
endeavors. Examples of such organization personnel include, but are
not limited to, an organization's marketing staff, sales staff,
finance staff, product design personnel, engineers, analysts,
statisticians, data scientists, advertising staff, executive
personnel, human resources staff, Board Members, advisors, or some
other type of organization personnel. Examples of an organization's
third-party personnel include, but are not limited to, advertising
firm staff, ad exchange staff, outside creative or content
developers, social media platform personnel, co-marketing partners,
consultants, contractors, financial firm staff, auditors, or some
other type of third-party personnel. In embodiments, the
departmental twin (in this example a marketing department twin) may
include a definition of the various roles/employees working under
the executive (e.g., CMO), the reporting structure, and associated
permissions, for each individual in the business unit, and may be
populated with the various the names and/or other identifiers of
the individuals filling the respective roles. In embodiments, the
department twin (e.g., marketing department twin) may include
subsections that are specific to an activity or initiative, such as
a marketing or advertising campaign. In this way, the executive
(e.g., a CMO) may easily identify the personnel and third-party
providers that are involved in the initiative and/or assign
individuals and/or third parties to the initiative. A user may
define one or more restrictions, permissions, and/or access rights
of the individuals indicated in the business unit (e.g., using the
enterprise configuration system 8002), as described herein, such
that the restrictions, permissions, and/or access rights can be
controlled by the CMO (or analogous user). In embodiments, the
permissions to define such restrictions and/or rights may be, for
example, defined in the organizational digital twin that lists the
user as having a role that permits implementing permissions,
restrictions, and/or access rights to roles/individuals In
embodiments, a personnel restriction or right associated with a
role/individual may be specific to a project, such as a marketing
or advertising campaign, and may define one or more types of data
that a particular user or group of users is allowed, or not
allowed, to access (either directly or in a digital twin). For
example, a first marketing campaign twin may allow a marketing
department employee to review the first marketing budget for a
first marketing campaign and approve marketing expenditures for the
first marketing campaign up to $10,000, but a second marketing
campaign twin may disallow the same employee from any budgetary
review or expenditures. Similar approaches can be used by projects
of various types across an organization and its departments, such
as product development projects, logistics projects, corporate
development projects, service projects, and many others. In
embodiments, a breach, or attempted breach, of a restriction,
permission or access right may invoke a notice, alert, warning or
some other action to an individual notifying them of the breach or
attempted breach. In an example such a notice, alert, or warning
may be sent to an individual that is identified based at least in
part on the individual's position in the org chart relative to the
person breaching or attempting to breach a restriction, permission
or access right. In another example, such a notice, alert, or
warning may be sent to an individual that is not identified in a
departmental org chart and/or specific project or campaign, but
rather may be sent to an individual that is identified based at
least in part on a rule that is defined in the organizational twin
of the entire enterprise. For example, a rule stored within an
organizational digital twin of the entity may specify that an alert
must be sent to an Information Security Department staff member, or
some other staff member, upon an attempted login to a forbidden
file, or other, system. Other rules may be related to geographic,
temporal, or other types of restrictions, as described herein. In
embodiments, an alert may be an email, phone call, text, or some
other communication type.
[1232] In embodiments, a CMO digital twin 8308 may be configured to
oversee and manage personnel and human resources issues and
activities related to the marketing department. For example, a
marketing department twin may map each individual within the
marketing department to her respective marketing department. Using
the CMO digital twin 8308, the user may be able to select a
department to see greater detail on the functioning of the
department. Alternatively, this step may be automatically performed
by the CMO digital twin 8308, requiring no action from the user
(e.g., the CMO) (e.g., via an executive agent trained by the user).
For example, the greater detail might include the number of
vacancies currently associated with the department and the duration
that each of the open positions has remained unfilled, estimated
salary data associated with the open positions, and the like. The
user may be able to also select to see more information on the
budget associated with a given department, such as a department
with a personnel vacancy, in order to see if there is currently
available budget to cover a new hire for the department.
Alternatively, this step may be automatically performed by the CMO
digital twin 8308, requiring no action from the user. Continuing
the example, if there is budget to cover a new hire, the CMO
digital twin 8308 may provide a link or other opportunity for the
user to initiate a communication with human resources or some other
department personnel to begin the process of posting a job listing.
Alternatively, this step may be automatically performed by the CMO
digital twin 8308 (e.g., via an executive agent executing on behalf
of the user), requiring no action from the user. This communication
may be drawn from a repository of form emails, letters or other
communications so that the user need not compose the communication,
but rather only signal within the CMO digital twin 8308 that such
communication should be sent. Similarly, based on the communication
type (e.g., "initiate a new marketing job posting") the user may
not need to select the receiving party, whom may be stored in the
EMP as the appropriate recipient based at least in part on a rule
associated with the communication type. Continuing the example
further, alternatively, if there is not budget available to cover a
new hire, a second type of communication may be invoked by the CMO
digital twin 8308, for example, an email, calendar invitation to
reserve a meeting, or some other type of communication may be
selected to be sent to the CFO, or other financial personnel, to
request a meeting to discuss the marketing department's budget or
initiate some other activity. Following this example, if and when
the new hires are approved, the CMO digital twin may allow the user
to delegate the hiring task to a subordinate or herself. In the
event the user is assigned the hire the new employee, the CMO
digital twin 8308 may provide materials regarding candidates (e.g.,
resume, referrals, interview notes from interviewers, or the like)
and the user may select one or more candidates to further consider,
interview, or hire.
[1233] In an example, a user may be able to select a sub-department
within the marketing department to view the performance of the
sub-department in greater detail. For example, the greater detail
might include the number of types of training sessions, tutorials,
events, conferences, and the like that personnel in the selected
marketing department have received. The user may be able to compare
such training and event attendance levels with a specified target
criterion that is stored in EMP, or that is associated with the
EMP. This may result in the CMO digital twin 8308 reporting to the
CMO a listing of personnel in her department whose training and/or
event attendance fails to meet the target criterion. This listing
may be prioritized by the CMO digital twin 8308 to highlight those
staff members most in need of further training. The user may be
able to also select to see more information on the budget
associated with a given department, such as a department with staff
who do not have adequate training according to the target
criterion, in order to see if there is currently available budget
to cover additional training for the department. If there is budget
to cover additional training, the CMO digital twin 8308 may
provide, for example, a link or other opportunity for the user to
initiate a communication to a staff member in need of training to
alert them that they must schedule training and/or attendance at an
event within a timeframe. This communication may be drawn from a
repository of form emails, letters or other communications so that
the user need not compose the communication, but rather only signal
within the CMO digital twin 8308 that such communication should be
sent. Continuing the example further, a second type of
communication may be invoked by the CMO digital twin 8308, for
example, a request for information, training registration, or some
other type of communication may be selected to be sent to a
third-party training vendor that is used by the marketing
department, a conference event registration, or other training or
event entity, to request scheduling training and/or event
registration, or some other activity. Alternatively, the steps,
discussed above, for tracking and reporting on marketing personnel
training and attendance may be automatically performed by the CMO
digital twin 8308, requiring no action from the user. References to
features and functions of the EMP and digital twins in this example
of a marketing department and a CMO digital twin 8308 should be
understood to apply to other departments and digital twins, and
their respective projects and workflows, except where context
indicates otherwise.
[1234] In embodiments, a CMO digital twin 8308 may be configured to
interface with the collaboration suite 8006 to specify and provide
a set of collaboration tools that may be leveraged by the marketing
department and associated parties. The collaboration tools may
include video conferencing tools, "in-twin" collaboration tools,
whiteboard tools, presentation tools, word processing tools,
spreadsheet tools, and the like, as described herein. Collaboration
and communication rules may be configured based at least in part on
using the AI reporting tool, as described herein.
[1235] In embodiments, a CMO digital twin 8308 may be configured to
research, create, track and report on a marketing department budget
including, but not limited to, an overall department budget, a
budget for a single or group of marketing or advertising campaigns,
a budget for a third-party vendor, or some other type of budget.
The CMO digital twin 8308 may interact with and share such budget
data and reporting with other executive twins, as described herein,
including, but not limited to, a digital twin related to the
finance department, accounts payable, executive staff such as the
CEO and CFO, or others. The CMO digital twin 8308 may include
intelligence, based at least in part on the data analytics, machine
learning and A.I. processes, as described herein, to read marketing
budgets and related summaries and data in order to identify key
departments, personnel, third-party or others that are, for
example, listed in, or subject to, the budget line item and who
therefore may have an interest in such material. Budget material
pertaining to a given party may be abstracted and summarized for
presentation independent from the entirety of the budget, and
formatted and presented automatically, or at the direction of a
user, to the party that is the subject of the budget item. In a
simplified example, a CMO may create a new marketing campaign,
"Airline--Airfare coupon texting campaign--January," which includes
the following line items: Third-party advertising firm content
creation $15,000; Social media platform placement $50,000;
analytics department $25,000, and so forth. The entirety of the
budget may be shared (at the election of the user or automatically)
with parties that must approve the full budget, such as a CFO. As
described herein this sharing may be accomplished by the CMO
digital twin 8308 communicating directly with a CFO digital twin,
so that the information is presented to the CFO without requiring
the CFO to have knowledge of the budget or requesting the budget.
Subparts of the budget, for example, the analytics department line
item, may be automatically sent to the head of the analytics
department by the CMO digital twin 8308 to inform that department
of the total amount of authorized spending that is approved for
that department for the specific marketing campaign.
[1236] In embodiments, a CMO digital twin 8308 may be configured to
track and report on inbound and outbound billing (i.e., accounts
receivable and payable) related to the marketing department. The
billing department, personnel, processes and systems, including a
Billing digital twin may interact with the CMO digital twin 8308 to
present, store, analyze, reconcile and/or report on billing
activities related to parties with whom the marketing department is
contracting, such as ad agencies, ad networks, ad exchanges,
content creators, advertisers, social media platforms, television,
radio, online entities, or others.
[1237] In embodiments, a CMO digital twin 8308 may be configured to
depict marketing campaign twins. In these embodiments, the CMO
digital twin 8308 may depict various states and/or items relating
to a marking campaign such as marketing content associated with a
marketing campaign, market research performed with respect to a
marketing campaign, tracking data of marketing content associated
with marketing campaigns (e.g., geographic reach of marketing
campaigns, demographic data associated with campaigns, etc.),
analyses of marketing campaigns (e.g., outcomes related to
marketing campaigns on various platforms), and the like. In some
embodiments, a CMO digital twin may be configured to automatically
report on marketing campaign-related activity via a user interface
associated with the CMO digital twin 8308. Such activities may be
determined using marketing department metadata that indicates state
changes, such as an alteration to a website content, a change to a
product photograph in an advertisement, a change in wording of a
mailing, and the like. The CMO digital twin 8308 may also depict
activity among a class of entities that are monitored or that are
specified for monitoring in the CMO digital twin 8308, such as a
new press release regarding a discounted advertising opportunity
available from an ad exchange. In embodiments, a CMO digital twin
8308 may be configured to provide research, tracking, monitoring,
and analyses of media content performance across various marketing
related platforms, and automatically report on such activity to a
user interface associated with the CMO digital twin 8308. Such
platforms may include, but are not limited to, customer
relationship platforms (CRMs), organization website(s), social
media, blogs, press releases, mailings, in-store or other
promotions, or some other type of marketing platform-related
material or activity.
[1238] In some of these embodiments, the CMO digital twin 8308 may
be configured to simulate marketing campaigns, such that the
simulations of the marketing campaign may vary parameters such as
vehicles (e.g., social media, television, billboards, print, etc.),
budget, targeting parameters (e.g., geographic, demographic, or the
like), and/or other suitable marketing campaign parameters. In
these embodiments, the digital twin simulation system 8116 may
receive a request to perform the simulation CMO digital twin, where
the request indicates campaign features and the parameters that are
to be varied. In response, the digital twin simulation 8116 may
return the simulation results to the CMO digital twin 8308, which
in turn outputs the results to the user via the client device
display. In this way, the user is provided with various outcomes
corresponding to different parameter configurations. In some
embodiments, the user may select a parameter set based on the
various outcomes. In some embodiments, an executive agent trained
by the user may select the parameter sets based on the various
outcomes.
[1239] In embodiments, a CMO digital twin 8308 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to a marketing strategy, plan, campaign or
initiative. For example, the CMO digital twin 8308 may be
associated with a plurality of databases or other repositories of
marketing presentation materials, summaries and reports and
analytics, including such presentation materials, summaries and
reports and analytics related to prior marketing campaigns, each of
which may be further associated with financial and performance
metrics pertaining to the campaign and which are also accessible to
the CMO digital twin 8308. Such historical marketing campaign
material may consist of advertising, marketing or other content
that may be categorized based in part on the financial and
performance metrics with which it is associated. For example, there
may be a first category called "Market Tested Content," which
consists of content that has been field deployed in a marketing
campaign within a customer population, the actual performance of
which is therefore fully known based on actual market testing.
Because the marketing content from this category has been field
tested, the content may be scored based at least in part on the
financial, performance or other data with which it is associated. A
second category may be "New Content--Simulation Tested," which
consists of content that has not been deployed in the field, but
which has been subject to analytic testing such as simulated
customer segmentation analysis, simulated A/B testing, simulated
attribution modeling, simulated market mix modeling, machine
learning, A.I. techniques including, but not limited to,
classification, probabilistic modeling, learning techniques, and
the like. Because the marketing content from this category has been
simulation tested, the content may be scored based at least in part
on the simulated performance data or other data with which it is
associated. Continuing the example, a third category of content may
be "New Content--Panel Tested," which consists of content that has
not been deployed in the field, nor simulation tested, but which
has been subject to testing among a human panel for their views,
opinions and impressions. Because the marketing content from this
category has been human panel tested, the content may be scored
based at least in part on the performance data, as reported by the
human panel, or other data with which it is associated. A final,
fourth category of content may be "New--Untested," which is newly
developed or other content that has not been tested in the field,
in simulation, or by a human panel. The CMO digital twin 8308 may
utilize the machine learning, A.I. and other analytic capabilities,
as described herein, to analyze the content of the four categories
of content and classify and score the content characteristics that
are probabilistically associated with improved financial or other
performance for stated types of marketing campaigns or marketing
subject matter. Statistical weights may be applied to such
characteristics, where the weight is indicative of a greater degree
of financial or some performance metric of interest. Similarly, the
characteristics of the market may be analyzed vis-a-vis the
marketing content to determine the consumer characteristics that
are probabilistically associated with improved financial or other
performance for given marketing content. The CMO digital twin 8308
may provide a user interface within which access to this repository
of stored data on content category, consumer and performance is
available. When planning a marketing campaign, the CMO, or other
marketing personnel, may use the CMO digital twin 8308 to select
from this repository of content, that content which
probabilistically will perform better with the intended consumer
targets of the new campaign. For example, from historical marketing
field tests from actual prior marketing campaigns, the data may
show that marketing content having images of large dogs
outperformed (based on, for example, ad conversion rates) content
picturing small dogs, and this effect was positively correlated
with age (i.e., older persons have an even greater preference for
larger dogs). The performance data from the simulation-tested
content may show a similar, but smaller effect based on the size of
the dog images in the content, and the panel-tested data may show a
similar effect for large dog imagery in content, but also have
performance data indicating that the effect appears, based on the
panel data, to be muted for persons 15 years or younger (i.e.,
young persons are more attracted to smaller dog breeds than older
persons). For the CMO using the CMO digital twin 8308 this data,
and the characteristics of the more successful content, may be used
to select from the fourth category of content ("New--Untested")
that content that is most appropriate for a new marketing campaign
intended to sell a soft drink. In embodiments, the artificial
intelligence services system 8010 of the EMP 8000 may select the
content and segment its presentation based at least in part on the
prior performance data, so that the ads that are presented on
platforms that tend to have persons over 15 will use content having
a predominance of large breed dogs, and those platforms with
younger audiences will offer a greater mix of dog breeds and
possibly a preference for small breed dogs in marketing images. As
the marketing campaign deployed to the field, the CMO digital twin
8308 may monitor, track and report on the marketing campaign's
performance so that the CMO can review and intervene as necessary.
Once the new content has been field tested it may be stored and
classified in the first category of content, "Market Tested
Content," along with the related financial and performance metrics.
In another example, similar stored content, content categories,
characteristics and financial and performance metrics may be used
by the CMO digital twin 8308 to recommend, for example, search
engine optimization (SEO), or other marketing strategies and
techniques.
[1240] In embodiments, a CMO digital twin 8308 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to market surveys, online surveys, customer
panels, ratings, rankings, marketing trend data or other data
related to marketing. A CMO digital twin 8308 may link to, interact
with, and be associated with external data sources, and able to
upload, download, aggregate external data sources, including with
the EMP's internal data, and analyze such data, as described
herein. Data analysis, machine learning, AI processing, and other
analysis may be coordinated between the CMO digital twin 8308 and
an analytics team based at least in part on using the artificial
intelligence services system 8010. This cooperation and interaction
may include assisting with seeding data elements and domains in the
enterprise data store 8012 for use in modeling, machine learning,
and AI processing to identify the optimal marketing content, sales
channels, target consumers, price points, timing, or some other
marketing-relating metric or aspect, as well as identification of
the optimal data measurement parameters on which to base judgement
of a marketing endeavor's success. Examples of data sources 8020
that may be connected to, associated with, and/or accessed from the
CMO digital twin 8308 may include, but are not limited to, a sensor
system 8022, a sales database 8024 that is updated with sales
figures in real time, a CRM system 8026, a content marketing
platform 8028, news websites, a financial database 8030 that tracks
costs of the business, surveys 8032 (e.g., customer satisfaction
surveys), an org chart 8034, a workflow management system 8036,
customer databases 8040 that store customer data, and/or
third-party data sources 8038 that store third-party data.
[1241] In embodiments, a CMO digital twin 8308 may be configured to
assist in the development of a new marketing campaign. For example,
the CMO digital twin 8308 may identify an internal and external
partner team for a marketing campaign. For example, individuals who
are ideal candidates to assist with a marketing campaign may be
identified based at least in part on experience and expertise data
that is stored within or in association with the CMO digital twin
8308. In another example, the CMO digital twin 8308 may identify
marketing campaign goals and record, monitor and track the
campaign's performance relative to those goals and present, in
real-time, the tracking of the campaign to the CMO within a user
interface that is associated with the CMO digital twin 8308.
Examples of marketing targets include, but are not limited to, unit
distribution, customer acquisition customer retention, customer
churn, customer loyalty (e.g., repeat purchases), customer
acquisition costs, duration of average sales cycle, ad conversion
rate, sales growth, geographic expansion of sales, demographic
expansion of sales, market penetration, percentage of market
control, marketing campaign ROI, regional comparison of
performance, channel analysis, sales partner analysis, marketing
partner analysis, or some other marketing target.
[1242] In embodiments, a CMO digital twin 8308 may be configured to
monitor customer feedback loops, customer opinions, customer
satisfaction, complaints, product returns and the like based at
least in part on use of the monitoring agent of the client
application 8052, as described herein, that is associated with the
CMO digital twin 8308. Such feedback data may include, but is not
limited to, data that derives from call center activity, chatbot
activity, email (e.g., complaints), product returns, Better
Business Bureau submissions, or some other type of customer
feedback or manifestation of customer opinion. The client
application 8052 may include a monitoring agent that monitors the
manner by which customers or others respond to a marketing
campaign. The monitoring agent may report the customer's response
to such campaigns to the EMP 8000 for presentation in a user
interface that is associated with the CMO digital twin 8308. In
response, the EMP 8000 may train an executive agent (which may
include one or more machine-learned models) to handle and process
such notifications when they next arrive, and escalate and/or alert
the CMO when such notifications are of an urgent nature, for
example, an announcement of a class action lawsuit related to a
product that is the subject of a marketing campaign. In
embodiments, the CMO digital twin 8308 may generate performance
alerts based on performance trends. This may allow a CMO to
optimize marketing campaigns in real-time without having to
manually request such real-time performance data; the CMO digital
twin 8308 may automatically present such information and
related/necessary alerts as configured by the organization, CMO, or
some other interested party.
[1243] In embodiments, a CMO digital twin 8308 may be configured to
report on the performance of the marketing department, personnel of
the marketing department, marketing campaigns, marketing content,
marketing platforms, marketing partners, or some other aspect of
management within a CMO's purview. Reporting may be to the CMO, the
marketing department, to other executives of an organization (e.g.,
the CEO), or to outside third parties (e.g., marketing partners,
press releases, and the like). As described herein, reporting may
include sales summaries, customer data, marketing campaign
performance metrics, cost-per-sale data, cost-per-conversion data,
customer analysis, such as predicted customer lifetime value for
newly acquired customers, or some other type of reporting data.
Reporting and the content of reporting may be shared by the CMO
digital twin 8308 with other executive digital twins, for example,
data related to new customers having a particularly high predicted
customer lifetime value may be shared with a sales staff for the
purpose of exploring cross-selling opportunities. The reporting
functionality of the CMO digital twin 8308 may also be used for
populating required data for formal reporting requirements such as
shareholder statements, annual reports, SEC filings, and the like.
Templets of common reporting formats may be stored and associated
with the CMO digital twin 8308 to automate the presentation of data
and analytics according to pre-defined formats, styles and system
requirements
[1244] In embodiments, a CMO digital twin 8308 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to competitors of a CMO's
organization, or named entities of interest. In embodiments, such
data may be collected by the EMP 8000 via data aggregation,
spidering, web-scraping, or other techniques to search and collect
competitor information from sources including, but not limited to,
press releases, SEC or other financial reports, mergers and
acquisitions activity, or some other publicly available data.
[1245] In embodiments, a CMO digital twin 8308 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to regulatory activity, such as
government regulations, industry best practices or some other
requirement or standard. For example, the marketing industry is
subject to data privacy and security laws in many jurisdictions,
and it is an area of law and regulation that is experiencing rapid
change. In embodiments, the CMO digital twin 8308 may be in
communication with another enterprise digital twin, such as a
General Counsel digital twin 8314, through which the legal team can
keep the CMO apprised of new regulation or regulation changes as
they occur. Similarly, as a CMO develops new market campaigns and
selects the jurisdictions (e.g., United States vs Europe) and
populations that will be a part of the campaigns (e.g., minors vs.
adults), the CMO digital twin 8308 may automatically send a
synopsis of the aspects of the campaigns that are relevant for
privacy law review so that the campaign may be vetted for legal and
regulatory compliance prior to launch. In an example, such a
marketing campaign synopsis might include a summary of the
jurisdictions of the campaign, intended audience, means of
obtaining consent, the type of consent to be obtained (e.g.,
opt-in, opt-out, passive), and so forth. Once approved and
launched, as customer consents and other data privacy-related
information is received by an organization, the CMO digital twin
8308 may facilitate the CMO tracking metrics, for example the
percentage of customers choosing to opt-in to receive future
marketing material (e.g., email solicitations). As the organization
receives privacy related material it may store such information for
future retrieval, summary, deletion or other activity, for example,
in response to a data subject request from an EU citizen who has
requested their data be deleted (i.e., exercising their "right to
be forgotten"). In embodiments, the CMO digital twin 8308 may
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to what customer data is collected,
the party responsible for its collection and storage, the location
and duration of storage, and so forth. This data may be called
forth by the CMO digital twin 8308, for example, in the event of a
data breach. The CMO digital twin 8308 may be able to summarize,
for example, a list of persons affected by the breach and the type
of data that was breached and share this information with a Chief
Privacy Officer (CPO), including sharing with the CPO digital
twin.
[1246] In embodiments, the client application 8052 that executes
the CMO digital twin 8308 may be configured with an executive agent
that reports a CMO's behaviors and preferences (or other marketing
personnel's behaviors and preferences) to the expert agent system
8008, as described herein, and the expert agent system 8008 may
train the executive agent on how the CMO or other marketing
personnel respond to certain situations and adjust its operation
based at least in part on the data collection, analysis, machine
learning and A.I. techniques, as described herein.
[1247] In embodiments, a Chief Technical officer (CTO) digital twin
8310 may be a digital twin configured for a CTO or other technology
executive of an enterprise tasked with overseeing and managing the
R&D, technology development, technical implementations of the
enterprise, and/or engineering activities of the enterprise. In
embodiments, a CTO digital twin 8310 provides real-time views of
enterprise technology assets, including technology capabilities and
versions. For example, in a manufacturing enterprise, a CTO digital
twin 8310 may depict where environment-compatible updates,
upgrades, or substitutions may be available. A CTO digital twin
8310 may provide data, analytics, summary, and/or technical
reporting including, but not limited to, real-time, historical,
aggregated, comparison, and/or forecasted technical information
(e.g., real-time, historical, simulated, and/or forecasted
technical performance data related to company products,
benchmarking results, and the like). A CTO using by a CTO digital
twin 8310 may be better able to stay abreast of technical
developments and software engineering impacts by engaging in
continuous virtualized learning using the CTO digital twin 8310. In
embodiments, a CTO digital twin 8310 may assist in virtual
collaboration (a CTO-essential skill), as a CTO will need to
partner with in-house engineers and external vendors in a virtual
environment to imagine and ideate to achieve something, often
something that hasn't been done before. In embodiments, the CTO
digital twin may work in connection with the EMP 8000 to provide
simulations, predictions, statistical summaries, decision support
based on analytics, machine learning, and/or other AI and
learning-type processing of inputs (e.g., technical performance
data, sensor data and the like).
[1248] In embodiments, a CTO digital twin 8310 may provide features
and functionality including, but not limited to, management of
technical personnel, partners and outside consultants and
contractors (e.g., developers, beta testers, and the like),
oversight of budgets, procurement, expenditures, policy compliance
(e.g., policies related to code usage, storage, documentation, and
the like), and other technology, development, and/or
engineering-related resources, and/or reporting.
[1249] In embodiments, the types of data that may populate a CTO
digital twin may include, but are not limited to, technology
performance and specification data, interoperability and
compatibility data, cybersecurity data, competitor data, failure
mode effects analysis (FMEA) data, technology/engineering roadmap
data, information technology systems data (including with respect
to any of the hardware, software, networking, and other types
mentioned or described herein), operations technology and systems
data, uptime/downtime/operational performance data, asset
aging/vintage/timing data, technical performance metrics by
business unit, by product, by geography, by factory, by store
location(s), resource utilization, competitive product and pricing
data, forecast data, demand planning data, analytic results of AI
and/or machine learning modeling (e.g., technical forecasting),
prediction data, metrics relating to patent disclosures, patent
filings, and/or patent grants, recommendation data, and/or other
types of data relevant to the operations of the CTO and/or
technology, development, and/or engineering department.
[1250] In embodiments, a CTO digital twin 8310 may depict a twin of
a set of technology, development, and/or engineering departments,
which the user may use to identify, assign, instruct, oversee and
review technology, development, and/or engineering department
personnel and third-party personnel that are associated with the
technology, development, and/or engineering activities of an
organization, including third-party partners and other outside
contractors, such as third-party developers and/or testers that are
involved in the organization's technology, development, and/or
engineering activities. Examples of such organization personnel
include, but are not limited to, technology, development, and/or
engineering department staff, sales staff and analysts,
statisticians, data scientists, or some other type of organization
personnel relevant to the functioning of a technology, development,
and/or engineering department. Examples of a technology,
development, and/or engineering department's third-party personnel
include, but are not limited to, management consultants,
developers, software engineers, testers, and/or engineering
partners, consultants, contractors, technical firm staff, auditors,
or some other type of third-party personnel.
[1251] In embodiments, the CTO digital twin 8310 may include a
definition of the various roles/employees working under the CTO,
the reporting structure, and associated permissions, for each
individual in the business unit, and may be populated with the
various names and/or other identifiers of the individuals filling
the respective roles.
[1252] In embodiments, a client application 8052 executing a CTO
digital twin 8310 may interface with the collaboration suite 8006
to specify and provide a set of collaboration tools that may be
leveraged by the technology, development, and/or engineering
department and associated parties. The collaboration tools may
include video conferencing tools, "in-twin" collaboration tools,
whiteboard tools, presentation tools, word processing tools,
spreadsheet tools, and the like, as described herein. Collaboration
and communication rules may be configured based at least in part on
using the AI reporting tool, as described herein. Collaboration and
communication tools and associated rules may be configured to use
company-, industry- and domain-specific taxonomies and lexicons
when representing entities, states and flows within the CTO digital
twin 8310.
[1253] In embodiments, a CTO digital twin 8310 may be configured to
allow a user to research, create, track and report on a technology,
development, and/or technology or engineering department initiative
including, but not limited to, a new product development, update,
enhancement, replacement, upgrade, or the like. In embodiments, the
CTO digital twin 8310 may be associated and/or in communication
with databases, including databases storing analytic and/or product
data and product performance data, and present information to an
interface associated with the CTO digital twin 8310, as described
herein. As product development advances, real time operations and
other technical information may be used to continuously update the
product development summary that is available for the CTO or other
technical personnel to review. The CTO digital twin 8310 may be
also be associated and/or in communication with databases,
including databases storing analytic and/or competitive product
data and product performance data, and present this information to
an interface associated with the CTO digital twin 8310, as
described herein. As the CTO's company's products change, and
competitor products change, their current state and specifications
may be presented by the CTO digital twin 8310 for the CTO or other
technical personnel to review direct product comparisons. Such
comparisons may be used, in part, to produce analytics, scores,
reports and the like indicating the relative advantages and/or
disadvantages that a company's product(s) has relative to
competitor product(s). In an example, a report may be automatically
provided to the marketing department to emphasize the relative
advantages that a company product has over a competitor product
(e.g., speed of processing) that should be used in a new marketing
campaign. Sharing with the marketing department may be
accomplished, in part, by the CTO digital twin 8310 communicating
with the CMO digital twin 8308 to present reports or other
information to the CMO or marketing staff.
[1254] In embodiments, the CTO digital twin 8310 may be configured
to present simulations of technology development and/or engineering
activities. For example, in some embodiments, the digital twin
system 8004 may simulate product usage under a plurality of
constraints that might impact product performance, such as an
operating environment, processing speed, storage or other platform
characteristics. In embodiments, real time operations data, such as
operations data available through the EMP 100, may be incorporated
into simulated data for the purposes of running operational
simulations. This may allow a CTO to a gain a deeper understanding
of the operation of the company's products in the real world and
within an altered, simulated real world environment. It may also
allow operational digital twin-based product architectures to be
built that link actual product production with business priorities
to enable simulated decision making in a virtual environment and
assist in the evaluation of vendor supplied solutions by enabling
the review of such digital twins in the context of their supplied
solutions and the relationship to the business. In embodiments,
simulations may also include simulations related to varying
technical and/or product specification parameters, product design
and monitoring, internal controls design, testing, certification,
and deliver technical and non-technical data in reports,
presentations, and dashboards for technical decision making. In
these embodiments, the digital twin simulation system 8116 may
receive a request to perform the simulation requested by the CTO
digital twin 8310, where the request indicates features and the
parameters, including technical parameters, that are to be varied.
In response, the digital twin simulation system 81D16 may return
the simulation results to the CTO digital twin 8310, which in turn
outputs the results to the user via the client device display. In
this way, the user is provided with various outcomes corresponding
to different technical and/or product parameter configurations. In
some embodiments, the user may select a parameter set based on the
various outcomes. In some embodiments, an executive agent trained
by the user may select a technical parameter set based on the
various outcomes. The simulations, analytics and/or modeling
performed by the CTO digital twin 8310 may be used to reduce
testing time, design time, or some other type of technical cost.
The simulations, analytics and/or modeling performed by the CTO
digital twin 8310 may be used to create and structure product
development and testing plans. The simulations, analytics and/or
modeling performed by the CTO digital twin 8310 may be used to
evaluate product go-to-market timing and preparedness. The CTO
equipped with a CTO digital twin 8310 will be better able to adapt
quickly to identify product and/or technical parameters in need of
further development and predict products' operational performance.
This may reduce errors, speed testing and reduce the need for
patches, bug fixes, updates and the like and flatten agile process
management.
[1255] In embodiments, a CTO digital twin 8310 may provide an
interface that allows a user to research, create, track and report
on a technology, development, and/or engineering department
initiative including, but not limited to, an overall department
budget, a budget for a single or group of technology, development,
and/or engineering initiatives, a third-party vendor activity, or
some other type of expense or budget. The CTO digital twin 8310 may
interact with and share such expense or budget data and reporting
with other executive twins, including, but not limited to, a
digital twin related to accounts payable, executive staff such as
the CEO, and/or others.
[1256] In embodiments, the CTO digital twin 8310 may leverage the
artificial intelligence services system 8010 (e.g., data analytics,
machine learning and A.I. processes) to read technical reports,
projections, simulations, and related summaries and data in order
to identify key departments, personnel, third-party or others that
are, for example, listed in, or subject to, a technical item or
detail provided.
[1257] In embodiments, a CTO digital twin 8310 may be configured to
provide a CTO, or other technology, development, and/or engineering
department personnel, with information that is unique to the CTO
digital twin 8310 and thus can provide insights and perspectives on
technical performance that are unique to the CTO digital twin 8310,
based at least in part on the CTO digital twin 8310 make making use
of real time production, development and operational data based on
both real world and simulated activity.
[1258] In embodiments, the CTO digital twin 8310 may be configured
to manage operational planning, based at least in part by
leveraging predictive analytics for development planning, and
supply chain management in order to increase company efficacy while
optimizing operating expenses. In embodiments, the CTO digital twin
8310 may be configured to obtain and depict oversight activity that
includes, but is not limited to, internal controls design, testing,
and reporting while directing listed actions the appropriate
personnel.
[1259] In embodiments, a CTO digital twin 8310 may be configured to
depict, aggregate, merge, analyze, prepare, report and distribute
material relating to a technical strategy, plan, activity or
initiative. For example, the CTO digital twin 8310 may be
associated with a plurality of databases or other repositories of
technical materials, summaries and reports and analytics, including
such materials, summaries and reports and analytics related to
prior technical activity and results (e.g., bug testing), each of
which may be further associated with third-party technical or
economic data, including competitor product data and/or technical
benchmarks.
[1260] In embodiments, a CTO digital twin 8310 may be configured to
depict, aggregate, merge, analyze, prepare, report and distribute
material relating to technical reporting, ratings, rankings,
technical trend data, or other data related to company technology,
development, and/or engineering. A CTO digital twin 8310 may link
to, interact with, and be associated with external data sources,
and able to upload, download, aggregate external data sources,
including with the EMP's internal data, and analyze such data, as
described herein. Data analysis, machine learning, AI processing,
and other analysis may be coordinated between the CTO digital twin
8310 and an analytics team based at least in part on using the
intelligence services system 8010. This cooperation and interaction
may include assisting with seeding technology, development, and/or
engineering-related data elements and domains in the enterprise
data store 8012 for use in modeling, machine learning, and AI
processing to identify the optimal technical strategy, or some
other technology, development, and/or engineering-relating metric
or aspect, as well as identification of the optimal data
measurement parameters on which to base judgement of a technology
initiative, development initiative, and/or engineering endeavor's
success. Examples of data sources 8020 that may be connected to,
associated with, and/or accessed from the CTO digital twin 8310 may
include, but are not limited to, a sensor system 8022, a sales
database 8024 that is updated with sales figures in real time, a
technology, development, and/or engineering platform, news websites
8048, a technical database that tracks costs of the business, an
org chart 8034, a workflow management system 8036, customer
databases 8040 that store customer data, and/or third-party data
sources 8038 that store third-party data.
[1261] In embodiments, a CTO digital twin 8310 may aggregate data
sources and types, creating new data types, summaries and reports
that are not available elsewhere. This may reduce reliance upon the
need of multiple third-party providers and current solutions. This
may, among other benefits and improvements, reduce expenses
associated with acquiring data needed for sound technical decision
making.
[1262] In embodiments, a CTO digital twin 8310 may be configured to
monitor technical performance, including real time monitoring,
based at least in part on use of the monitoring agent of the client
application 8052, as described herein, that is associated with the
CTO digital twin 8310. The monitoring agent may report on such
activities to the EMP 8000 for presentation in a user interface
that is associated with the CTO digital twin 8310. In response, the
EMP 8000 may train an executive agent (which may include one or
more machine-learned models) to handle and process such
notifications when they next arrive, and escalate and/or alert the
CTO when such notifications are of an urgent nature, for example,
an identification of a new technical bug or a security patch that
is urgently needed. In embodiments, the CTO digital twin 8310 may
generate technical performance alerts based on performance trends.
This may allow a CTO to optimize initiatives in real-time without
having to manually request such real-time technical performance
data; the CTO digital twin 8310 may automatically present such
information and related/necessary alerts as configured by the
organization, CTO, or some other interested party.
[1263] In embodiments, a CTO digital twin 8310 may be configured to
report on the performance of the technology, development, and/or
engineering department, personnel of the technology, development,
and/or engineering department, technology, development, and/or
engineering activities, technology, development, and/or engineering
content, technology, development, and/or engineering platforms,
technology, development, and/or engineering partners, or some other
aspect of management within a CTO's responsibilities. Reporting may
be to the CEO, the technology, development, and/or engineering
department, to other executives of an organization (e.g., the CIO),
or to outside third parties.
[1264] In embodiments, a CTO digital twin 8310 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to industry best practices,
benchmarks, or some other requirement or standard. For example, the
CTO digital twin 8310 may be in communication with another
enterprise digital twin, such as a CIO digital twin 8312, through
which the technical team can keep the CIO apprised of changes as
they occur.
[1265] In embodiments, the client application 8052 that executes
the CTO digital twin 8310 may be configured with an executive agent
that reports a CTO's behaviors and preferences (or other
technology, development, and/or engineering personnel's behaviors
and preferences) to the executive agent system 8008, as described
herein, and the executive agent system 8008 may train the executive
agent on how the CTO or other technology, development, and/or
engineering personnel respond to certain situations and adjust its
operation based at least in part on the data collection, analysis,
machine learning and A.I. techniques, as described herein.
[1266] References to features and functions of the EMP and digital
twins in this example of a CTO digital twin 8310 should be
understood to apply to other departments and digital twins, and
their respective projects and workflows, except where context
indicates otherwise.
[1267] In embodiments, a Chief Information Officer (CIO) digital
twin 8312 may be a digital twin configured for the CIO of an
enterprise, or analogous executive tasked with overseeing the
intelligence, information, data, knowledge, and/or IT operations of
the enterprise. In embodiments, a CIO digital twin 8312 depicts a
real time representation of an organization's information assets
and workflows including data relating to data security, network
security and enterprise knowledge. The real time representation may
be based at least in part on real-time operations data that tracks
the performance of an organization's information infrastructure,
including internal information assets, customer-facing
technologies, and information assets provided and/or serviced by
third parties, such as cloud computing service providers. For
example, a CIO digital twin 8312 may receive real time information
regarding the performance of a network, such as an intranet used by
an organization, APIs that are accessed by the enterprise, APIs
that are exposed by the enterprise, software that is running on the
enterprises software, or the like. The information may be
aggregated and presented to a CIO in order to provide him an
overview of the general performance of the computing infrastructure
of the enterprise. For example, the CIO digital twin may indicate
whether there are any network outages occurring, whether there are
any security risks detected in the enterprises network, whether any
software systems are operating improperly, and may other scenarios.
In embodiments, the CIO digital twin 8312 may present a user
interface that allows a user (e.g., the CIO) to select particular
network assets to review in greater detail, such as an asset the
real time operations data indicates is experiencing an operational
failure or other issue. Such real time operations data related to
IT and other information asset performance may allow the CIO to
better track the performance and needs of an organization's
information and IT infrastructure and better enable him to
troubleshoot issues, simulate solutions, select appropriate
information and IT management actions, and maintain the
organization's information and IT infrastructure.
[1268] In embodiments, a CIO digital twin 8312 may provide data,
analytics, summary, and/or information and IT reporting including,
but not limited to, real-time, historical, aggregated, comparison,
and/or forecasted information (e.g., real-time, historical,
simulated, and/or forecasted performance data related to company
information and IT assets, third-party assets, and the like). A CIO
empowered by a CIO digital twin 8312 may be better able to maintain
and evolve information and IT assets through continuous monitoring
using the CIO digital twin 8312. A CIO digital twin 8312 may assist
in virtual monitoring and testing in a virtual environment to test
implementations, changes, reconfigurations, the introduction and/or
removal of components and other assets, and the like. In
embodiments, the CIO digital twin may work in connection with the
EMP 8000 to provide simulations, predictions, statistical
summaries, decision support based on analytics, machine learning,
and/or other AI and learning-type processing of inputs (e.g.,
performance data, sensor data, and the like).
[1269] In embodiments, the types of data that may populate a CIO
digital twin 8312 may include, but are not limited to, information
and IT asset performance and specification data, interoperability
and compatibility data, cybersecurity data,
uptime/downtime/operational performance data, asset
aging/vintage/timing data, resource utilization, results of AI
and/or machine learning modeling (e.g., IT performance
simulations), or some other type of data relevant to the operations
of the CIO.
[1270] In embodiments, a CIO digital twin 8312 may be configured to
interface with the collaboration suite 8006 to specify and provide
a set of collaboration tools that may be leveraged by the
technology, development, and/or engineering department and
associated parties. The collaboration tools may include video
conferencing tools, "in-twin" collaboration tools, whiteboard
tools, presentation tools, word processing tools, spreadsheet
tools, and the like, as described herein. Collaboration and
communication rules may be configured based at least in part on
using the AI reporting tool, as described herein. Collaboration and
communication tools and associated rules may be configured to use
company-, industry- and domain-specific taxonomies and lexicons
when representing entities, states and flows within the CIO digital
twin 8312.
[1271] In embodiments, the CIO digital twin 8312 may be configured
to provide simulations of an organization's information and IT
activities including, but not limited to network utilization,
disaster planning, IT asset selection, maintenance protocols,
downtime planning, and the like that is simulated under a plurality
of hypothetical IT environments and scenarios that might impact
performance, such as a security breach, IT asset failure,
information failure, network congestion, or other activity or
event. Real time operations data, such as that available through
the EMP, as described herein, may be incorporated into simulated
information or IT Infrastructure scenarios for the purposes of
running operational simulations. The simulations, analytics and/or
modeling performed by the EMP 100 with respect to a CIO digital
twin 8312 may be used to reduce testing time, design time, or some
other type of IT cost. The simulations, analytics and/or modeling
performed by the CIO digital twin 8312 may be used to create and
structure IT assets, networks, and guide development and testing
plans. The simulations, analytics and/or modeling performed by the
CIO digital twin 8312 may be used to evaluate network security,
performance, and other features. The CIO equipped with digital twin
8312 may quickly identify optimal asset configurations to maximize
operational performance.
[1272] In embodiments, a CIO digital twin 8312 may be configured to
provide a user (e.g., the CIO) with information that is unique to
the CIO digital twin 8312 and thus can provide insights and
perspectives on information and IT asset performance that are
unique to the CIO digital twin 8312, based at least in part on the
CIO digital twin 8312 make making use of real time production,
development and operational data based on both real world and
simulated activity. In embodiments, the CIO digital twin 8312 may
be configured to manage operational planning, based at least in
part by leveraging predictive analytics for development planning.
In embodiments, a CIO digital twin 8312 may be configured to store,
aggregate, merge, analyze, prepare, report and distribute material
relating to an information and/or IT strategy, scenario, event,
plan, activity or initiative. For example, the CIO digital twin
8312 may be associated with a plurality of databases or other
repositories of information, materials, summaries and reports and
analytics, including such materials, summaries and reports and
analytics related to prior events, activity and results (e.g., a
system outage).
[1273] In embodiments, a CIO digital twin 8312 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to information and/or IT reporting, ratings,
rankings, information, knowledge and IT trend data, or other data
related to company information and/or IT assets and infrastructure.
A CIO digital twin 8312 may link to, interact with, and be
associated with external data sources, such that the CIO digital
twin 8312 may upload, download, aggregate external data sources,
and/or analyze such enterprise data.
[1274] In embodiments, a CIO digital twin 8312 may be configured to
monitor IT performance, including in real time, based at least in
part on use of the monitoring agent of the client application 8052,
as described herein, that is associated with the CIO digital twin
8312. The monitoring agent may report on such activities to the EMP
8000 for presentation in a user interface that is associated with
the CIO digital twin 8312. In response, the EMP 8000 may train an
executive agent (which may include one or more machine-learned
models) to handle and process such notifications when they next
arrive and escalate and/or alert the CIO when such notifications
are urgent.
[1275] In embodiments, a CIO digital twin 8312 may be configured to
report on the performance of an organization's IT assets, network,
or some other aspect of management within a CIO's responsibilities.
In embodiments, the client application 8052 that executes the CIO
digital twin 8312 may be configured with an executive agent that
reports a CIO's behaviors and preferences to the executive agent
system 8008, and the executive agent system 8008 may train the
executive agent on how the CIO or other personnel respond to
certain IT situations and adjust its operation based at least in
part on the data collection, analysis, machine learning and A.I.
techniques described throughout the disclosure.
[1276] References to features and functions of the EMP and digital
twins in this example of a marketing department and a CIO digital
twin 8312 should be understood to apply to other departments and
digital twins, and their respective projects and workflows, except
where context indicates otherwise.
[1277] In embodiments, a general counsel (GC) digital twin 8314 may
be an executive digital twin configured for the general counsel
(GC) of an enterprise, or an analogous executive tasked with
overseeing the legal department and/or outside counsel of the
enterprise. A GC digital twin 8314 may provide functionality
including, but not limited to, management of legal personnel,
partners and outside counsel, oversight of legal budgets and
resources, compliance, management of contracting and litigation,
management of internal policies, intellectual property, employment
law, tax law, privacy law, reporting, and regulatory analysis.
[1278] In embodiments, the types of data that may populate and/or
be utilized by a GC digital twin 8314 may include, but are not
limited to, budgetary data (e.g., external legal spend, internal
legal spend, ancillary legal costs, and the like), regulatory data
(e.g., regulatory requirements, regulatory actions taken, and the
like); contract and licensing data (e.g., in progress negotiations,
current contract obligations, past contract obligations, and the
like); compliance data (e.g., compliance requirements, compliance
actions taken, and the like, litigation data (e.g. potential
litigations sources, pending litigations, past litigations,
settlement agreements, and the like), employment data (e.g.,
employment contracts, employee complaints, employee stock options,
and the like), intellectual property data (e.g., filed patent
applications, patent dockets, issued patents, trademark
applications, trademark docket data, registered trademarks, and the
like), tax data, privacy data, regulatory data, analytic results of
AI and/or machine learning modeling; prediction data;
recommendation data, or some other type of data relevant to the
operations of the GC and/or legal department.
[1279] In embodiments, a GC digital twin 8314 may be configured
based at least in part on using the collaboration suite 8006 to
specify and provide a set of collaboration tools that may be
leveraged by the legal department and associated parties. The
collaboration tools may include video conferencing tools, "in-twin"
collaboration tools, whiteboard tools, presentation tools, word
processing tools, spreadsheet tools, and the like, as described
herein. Collaboration and communication rules may be configured
based at least in part on using the AI reporting tool, as described
herein. Collaboration and communication tools and associated rules
may be configured to use company-, industry- and domain-specific
taxonomies and lexicons when representing entities, states and
flows within the GC digital twin 8314, such as ones related to
particular bodies of law, regulation, jurisdiction, or practice
area, such as ones related to corporate law, commercial law,
bankruptcy law, the law of secured transactions, banking law,
customs law, export control regulations, maritime law, trade law,
international treaties, securities law, contracts law,
environmental law, international law, privacy law, data privacy
law, patent law, civil and criminal procedure, trademark law,
copyright law, trade secret law, unfair competition law, law of
torts, property law, advertising law, and many others.
[1280] In embodiments, a GC digital twin 8314 may be configured to
research, create, track and issue reports on a legal department
budget including, but not limited to, an overall department budget,
a budget for a specific project, such as "U.S. patent filings," or
group of projects, a budget for a specific litigation, a budget for
a third-party vendor, such as outside counsel, or some other type
of legal budget. A GC digital twin 8314 may be configured to
create, track, provide research, and report on financial data
related to material under review or supervisions of the legal
department including, but not limited to, licensing revenues,
licensing expenditures, or some other type of financial data
related to legal department review and responsibilities. In
embodiments, he GC digital twin 8314 may interact with and share
such licensing revenue and/or budget data and reporting with other
executive twins, as described herein, including, but not limited
to, a CFO digital twin 8304, CEO digital twin, COO digital twin,
CTO digital twin, and the like. In embodiments, the GC digital twin
8314 may include intelligence, based at least in part on the data
analytics, machine learning and A.I. processes, as described
herein, to read legal contracts, licenses, budgets and related
summaries and data in order to identify key departments, personnel,
third-party or others that are, for example, listed in, or subject
to, or impacted by a license and/or budget line item and who
therefore may have an interest in such material. License and/or
budget material pertaining to a given party may be abstracted and
summarized for presentation independent from the entirety of the
budget, and formatted and presented automatically, or at the
direction of a user, to the party that is the subject of the budget
item. In a simplified example, a GC may have license(s) under her
department's review which have line items, schedules, appendices
and the like detailing licensing revenues that will be owed to the
organization over a prescribed timeframe. The GC may use the GC
digital twin 8314 to consolidate, summarize and/or share such
financial data derived, or to be derived, from licensing revenues
with another executive in an organization, such as the CFO (e.g.,
via a CFO digital twin) and/or CEO (e.g., via a CEO digital twin).
The data shared may indicate the licensing revenues to be obtained
in a given financial quarter to assist the CFO and others in
maintaining an accurate and current summary of projected quarterly
revenues.
[1281] In embodiments, a GC digital twin 8314 may be configured to
track and report on inbound (e.g., settlement or litigation
revenue) and outbound billing (e.g., outside counsel costs) related
to the legal department. The billing department, personnel,
processes and systems may interact with the GC digital twin 8314 to
present, store, analyze, reconcile and/or report on billing
activities related to parties with whom the legal department is
contracting, such as outside counsel, consultants, research
services, online entities, or others. In embodiments, a GC digital
twin 8314 may be configured to research, track, monitor, store,
analyze, create and distribute legal content, and automatically
report on such activity to a user interface associated with the GC
digital twin 8314. Such activities might include storing data so
that the GC digital twin 8314 may detect a state change, for
example, a new court filing in a litigation, a communication
received from outside counsel, a new license draft from opposing
counsel, a draft patent application, a notice from the United
States Patent and Trademark Office, or some other type of new or
updated material. The GC digital twin 8314 may also detect activity
among a class of entities that are monitored or that are specified
for monitoring in the GC digital twin 8314, such as particular
courts, regulatory or legislative bodies or some other type of
entity. In embodiments, a GC digital twin 8314 may be configured to
research, track, monitor, store, and analyze content of various
legal related platforms, and automatically report on such activity
to a user interface associated with the GC digital twin 8314. Such
platforms may include, but are not limited to, bar or other legal
associations, courts, legal search platforms, social media, legal
blogs, press releases, or some other type of legal platform-related
material or activity.
[1282] In embodiments, a GC digital twin 8314 may be configured to
store, aggregate, merge, analyze, prepare, report and distribute
material relating to a legal strategy, legal documents, litigation,
legal recommendations or some other legal activity. For example,
the GC digital twin 8314 may be associated with a plurality of
databases or other repositories of legal materials, contracts,
licenses, intellectual property (e.g., patent filings), summaries
and reports and analytics. A GC digital twin 8314 may link to,
interact with, and be associated with external data sources, and
able to upload, download, aggregate external data sources,
including with the EMP's internal data, and analyze such data, as
described herein. Data analysis, machine learning, AI processing,
and other analysis may be coordinated between the GC digital twin
8314 and an analytics team based at least in part on using the
intelligence services system 8010. This cooperation and interaction
may include assisting with seeding data elements and domains in the
enterprise data store 8012 for use in modeling, machine learning,
and AI processing to identify the optimal and/or relevant legal
content, legal documents, parties associated with a legal activity
(e.g., a litigation), as well as identification of the optimal data
measurement parameters on which to base judgement of a legal
endeavor's success (e.g., licensing revenue, staying within a
stated budget for the use of outside counsel, and the like).
Examples of data sources 8020 that may be connected to, associated
with, and/or accessed from the GC digital twin 8314 may include,
but are not limited to, a legal research platform, legal websites,
news websites 8048, a financial database 8030, contracts database,
an HR database 8046, a workflow management system 8036, and/or
third-party data sources 8038 that store third-party data.
[1283] In embodiments, a GC digital twin 8314 may be configured to
assist in the development of a new legal endeavor, such as pursuit
of a new contract, review of a new law or regulation impacting a
business, litigation or arbitration, or some other legal activity.
For example, the GC digital twin 8314 may identify an internal and
external partner (e.g., outside counsel) team for a legal action.
For example, individuals who are ideal candidates to assist with a
legal action may be identified based at least in part on experience
and expertise data that is stored within or in association with the
GC digital twin 8314. For example, the GC may be initiating
negotiations of a joint development agreement between entities that
are located in the United States and Taiwan and may need to obtain
outside Taiwanese counsel. Using the GC digital twin 8314, the GC
may be presented with details of prior outside counsel used in
Taiwan for similar projects. In another example, if the GC digital
twin 8314 does not locate details of prior outside counsel used in
Taiwan for similar projects, the GC digital twin 8314 may scan,
research, collect and summarize information from public or other
sources on highly rated, recommended or other Taiwanese outside
counsel that may be appropriate, based on skills, experience and
the like, to work on the joint development agreement project.
[1284] In embodiments, the GC digital twin 8314 may identify legal
project goals and record, monitor and track the project's
performance relative to those goals and present, in real-time, the
tracking of the project to the GC within a user interface that is
associated with the GC digital twin 8314. For example, the GC
digital twin 8314 may include a clickable dashboard that, when
clicked, illustrates the status of a set of legal projects. In some
embodiments, the dashboard may include timelines for each project
and a relative status of each project with respect to its
timeline.
[1285] In embodiments, a GC digital twin 8314 may be configured to
report on the performance of the legal department, personnel of the
legal department, legal actions, legal content, legal platforms,
legal partners, or some other aspect of a GC's management.
Reporting may be to the GC, the legal department, to other
executives of an organization (e.g., the CEO), or to outside third
parties (e.g., outside counsel, legal notices, press releases, and
the like). Reporting and the content of reporting may be shared by
the GC digital twin 8314 with other executive digital twins, for
example, data related to regulation compliance, ongoing litigation,
or some other legal activity. The reporting functionality of the GC
digital twin 8314 may also be used for populating required data for
formal reporting requirements such as shareholder statements,
annual reports, SEC filings, and the like. Templates of common
reporting formats may be stored and associated with the GC digital
twin 8314 to automate the presentation of data and analytics
according to pre-defined formats, styles and system requirements.
In some embodiments, the GC digital twin may be configured to
leverage an executive agent 8364 trained on behalf of the GC to
create and disseminate the reports.
[1286] In embodiments, a GC digital twin 8314 may be configured to
monitor, store, aggregate, merge, analyze, prepare, report and
distribute material relating to regulatory activity, such as
government regulations, regulatory compliance, legislation, court
opinions, industry best practices or some other requirement or
standard. For example, the GC digital twin 8314 may keep the GC
apprised of new regulation or regulation changes as they occur. The
GC may set parameters of the GC digital twin 8314 regarding the
legal domains, subject matter areas, jurisdictions, or some other
parameter, that are of interest to the GC that the GC digital twin
8314 should monitor.
[1287] In embodiments, a GC digital twin 8314 may leverage an
executive agent 8364 that is trained on user's (e.g., GC) behaviors
and preferences (or other legal personnel's behaviors and
preferences). In embodiments, the client application 8052 hosting
the GC digital twin 8314 may track the user's actions relating to
various events, notifications, alerts, or the like and may report
the tracked events using the expert agent system 8008, as described
herein. In response, the expert agent system 8008 may learn how the
GC or other legal personnel respond to certain situations and may
train an execute agent 8364 on behalf of the user (e.g., GC), such
that the executive agent 8364 may respond to similar situations
once deployed.
[1288] References to features and functions of the EMP and digital
twins in this example of a legal department and a GC digital twin
8314 should be understood to apply to other departments and digital
twins, and their respective projects and workflows, except where
context indicates otherwise.
[1289] In embodiments, a Chief Human Resources Officer (CHRO)
digital twin 8316 (or HR digital twin 8316) is an executive digital
twin configured for a human resources executive (e.g., a CHRO) of
an enterprise or analogous executive tasked with overseeing the
human resources HR aspects of the enterprise, such as a Chief
People Officer (CPO), a chief talent officer, a head of human
resources, a director of human resources, or the like. In
embodiments, the CHRO digital twin 8316 may depict different
HR-related states of the enterprise, such as states relating to
human capital management, workforce management, risk management,
and the management of payroll, recruitment, regulatory compliance,
employee performance, benefits, employee relations, time and
attendance, training and development, compensation, onboarding,
offboarding, succession planning, and the like. In embodiments, the
CHRO digital twin 8316 may initially depict the various states at a
lower granularity level. A user that is viewing the CHRO digital
twin 8316 may select a state to drill down into the selected state
and view the selected state at a higher level of granularity.
[1290] In embodiments, the types of data that may be depicted in
CHRO digital twin 8316 may include, but are not limited to:
individual employee data, key performance indicators by business
unit, key performance indicators by individual employee, risk
management data, regulatory compliance data (e.g., OSHA and EPA
compliance data), safety data, diversity data, benefits data (e.g.,
medical, dental, vision, and health savings accounts (HSA))
compensation data, compensation comparison data, compensation trend
data, payroll data, overtime data, recruitment data, employee
referrals data, applicant data, applicant screening data, applicant
reference data, applicant background check data, offer data, time
and attendance data, employee relations data, employee complaints
data, onboarding data, offboarding data, employee training and
development data, employee turnover rate data, voluntary employee
turnover rate data, new hire turnover rate data, high performer
turnover rate data, turnover rate by performance rating data,
headcount and/or headcount planning data (e.g., headcount to plan
percentage), promotion rate data, succession plan data,
organizational levels data, span of control data, employee survey
data, cost to move employees below midpoint data, comparative ratio
data, simulation data, decision support data from AI and/or machine
learning systems, prediction data from AI and/or machine learning
systems, classification data from AI and/or machine learning
systems, detection and/or identification data from AI and/or
machine learning systems, and the like.
[1291] In embodiments, a CHRO digital twin 8316 may depict a data
item with an icon indicating whether the data item is at a normal
state, a suboptimal state, a critical state, or an alarm state. In
embodiments, the icons may be different colors, fonts, symbols,
codes or the like. For example, a CHRO digital twin 8316 may depict
high performer turnover rate data with an orange icon indicating
that the high performer turnover rate is at a critical level.
Continuing the example, an HR executive may be enabled to escalate
the high performer turnover rate data to another executive, such as
the CEO, via the CHRO digital twin 8316. In embodiments, a CHRO
digital twin 8316 may automatically highlight data items that are
at suboptimal, critical, or alarm state.
[1292] In embodiments, a CHRO digital twin 8316 may be configured
to provide an "in-twin" collaboration suite having tools that may
facilitate communication and collaboration between enterprise
stakeholders. In embodiments, the "in-twin" collaboration tools may
include an interface enabling a user to escalate and/or deescalate
data sets to another user associated with the enterprise. In
embodiments, the interface may be configured to enable a user to
send a message with the data set, generate a request or assign a
task related to the data set, and/or schedule an event associated
with the data set. In embodiments, AI and/or machine learning could
be leveraged to suggest message content, suggest event scheduling,
suggest a request or task, and/or suggest a request or task
assignee. For example, an HR executive could escalate a data set
related to employee training to the GC with a predictive text
message about employee training and a calendar request at a time
determined by AI and/or machine learning to attend a meeting
related to employee training. In embodiments, the "in twin"
collaboration tools include digital twin conferences. In
embodiments, the "in twin" collaboration tools may include an
"in-twin" messaging system and/or an "in-twin" video conferencing
system for enabling enterprise stakeholders to communicate. In
embodiments, a machine learning and/or AI system may be leveraged
for automatically generating and/or assigning tasks from these
communications. In embodiments, the "in-twin" videoconferencing
system supports subchats. In embodiments, the subchats may be
created via a "drag-and-drop" action in the user interface. In
embodiments, the "in-twin" videoconferencing system may leverage
machine learning and/or AI to make suggestions to optimize a user's
lighting, audio, camera placement, and the like. In embodiments,
the "in twin" videoconferencing system leverages machine learning
and/or AI to automatically disable the video feed upon the
detection of an inappropriate activity in the video feed. In
embodiments, the "in twin" collaboration suite includes an
"in-twin" stakeholder approval system for collecting approval on
actions from other enterprise stakeholders. In embodiments,
"in-twin" collaboration tools may include an AI-driven translation
system configured to intelligently translate communications amongst
enterprise stakeholders to achieve maximum understanding by the
user of the digital twin, wherein the AI driven translation system
is configured to translate from a first language to a second
language (e.g., translate English into a foreign language) and is
also configured to translate terminology or jargon such that it is
consumable by the user. These features described in connection with
the CHRO digital twin 8316 may be deployed with other types of
digital twins described herein, including ones for other
executives, including to facilitate collaboration among different
types of executives, such as for enterprise control tower
activities, such as monitoring operations, development activities,
or other aspects of the enterprise across locations, departments,
and functions. Collaboration and communication tools and associated
rules may be configured to use company-, industry- and
domain-specific taxonomies and lexicons when representing entities,
states and flows within the CHRO digital twin 8316, such as ones
relating to health and safety of workers, ones related to education
and training, ones related to performance indicators, ones related
to worker attributes (including psychographic, demographic and
similar factors), and many others.
[1293] In embodiments, a CHRO digital twin 8316 may be configured
to identify, interview, select, hire, and onboard new employees. In
some of these embodiments, the CHRO digital twin 8316 may be
configured to research, track, and report on applicant data,
including, but not limited to, employee referral data, applicant
education data, applicant testing data, applicant experience data,
applicant reference data, applicant screening data, applicant
background check data, applicant interview data, job application
data, applicant resume data, applicant cover letters, applicant
offer data, and the like. The CHRO digital twin 8316 may interact
with and share such applicant data and reporting with other
executive digital twins, as described herein. The CHRO digital twin
8316 may include machine learning, AI, and/or other intelligence
such as analytics, to process job applications, resumes, cover
letters, applicant reference materials, applicant screening data,
applicant interview data, and the like in order to identify and
select potential new employees and/or to identify other executives
or enterprise stakeholders that may be interested in such
information.
[1294] In embodiments, the EMP 8000 may obtain HR-relevant data
from the enterprise's human resources management software (e.g.,
via an API), human capital software, workforce management software,
payroll software, applicant tracking software, accounting software,
employee applicant software, publicly disclosed financial
statements, third-party reports, tax filings, social media
software, job listing websites, recruitment software, and the
like.
[1295] In embodiments, a CHRO digital twin 8316 may provide an
interface for an HR executive to perform one or more HR-related
workflows. For example, the CHRO digital twin 8316 may provide an
interface for an HR-executive to perform, supervise, or monitor
workflows, the entities involved in the workflows, and attributes
thereof, such as onboarding workflows, offboarding workflows,
dismissal workflows, decision documentation workflows, succession
planning workflows, candidate assessment workflows, candidate
screening workflows, compliance workflows, disciplinary workflows,
review workflows, interview workflows, offer workflows, employee
training workflows, and many others.
[1296] In embodiments, a CHRO digital twin 8316 may leverage an
executive agent 8364 that is trained on a user's (e.g., an HR
executive's) actions (e.g., behaviors, responses, interactions and
preferences) using the expert agent system 8008 in response to
events and situations encountered by the user (e.g., alerts,
notifications, escalations, delegations, presentations of data,
events, and the like). In some of these embodiments, the client
application 8052 hosting the CHRO digital twin 8316 may report
actions taken by the user in response to various events encountered
by the user via the CHRO digital twin 8316. For example, the client
application 8052 may identify events such as a request to authorize
a new hire, a request to terminate an employee, or a notification
indicating that employee turnover has reached a critical threshold.
In this example, the client application 8052 may record and report
the actions taken by the user in response to such events and may
report the actions in relation to the identified events to the
expert agent system 8008, as well as any other features that are
relevant to the event. In response, the expert agent system 8008
may train an executive agent 8364 on behalf of the user, such that
the executive agent may perform or recommend actions to the user
when similar events are encountered in the future.
[1297] References to features and functions of the EMP and digital
twins in this example of a human resources department and a CHRO
digital twin 8316 should be understood to apply to other
departments and digital twins, and their respective projects and
workflows, except where context indicates otherwise.
[1298] In embodiments, the executive digital twins may link to,
interact with, integrate with and/or be used by a number of
different applications. For example, the executive digital twins
may be used in automated AI-reporting tools 8360, collaboration
tools 8362, in connection with executive agents 8364, in board
meeting tools 8366, for training modules 8368, and for planning
tools 8370.
[1299] In embodiments, AI reporting tools 8360 assist users to
report one or more states to another user. For example, a
subordinate may need to report an identified issue to a
higher-ranking member of the enterprise (e.g., CTO may wish to
report an issue that needs to be addressed to the CEO). In
embodiments, the AI reporting tool 8360 may be configured to
receive a request to report a state from a client device 8050. In
embodiments, the AI-reporting tool 8360 may identify the
appropriate recipients of the reported state based on the type of
request, the role of the user that issued the request and the
organizational structure of the entity. In some embodiments, the
AI-reporting tool may determine the role of the user and the
recipients of the report from the organizational digital twin of
the enterprise. In some embodiments, the AI-reporting tool 8360 may
determine whether the intended recipients of a notification have
access rights to the data being shared from the executive digital
twin. For example, if the CFO is reporting to the CEO, it is likely
that the CEO has access to all the enterprise's data and will not
be precluded from receiving the report. Conversely, if the CFO
wishes to delegate the handling of an issue via the AI-reporting
tool to an employee in her business unit, the recipient may not
have access to such data. In this scenario, the AI-reporting tool
8360 may notify the requesting user (e.g., the CFO) that certain
types of data may not be shared with the subordinate employee and
may determine a manner by which the issue may be reported to the
subordinate without sharing the non-accessible data. Upon
determining that a user has access rights to view a particular
state of data, the AI-reporting tool 8360 may generate a report
that is for the intended recipient. In embodiments, the
AI-reporting tool may leverage the NLP services of the intelligence
system to generate the report. In some embodiments, the
AI-reporting tool 8360 may leverage an executive agent 8364 to
determine when to report a state and the appropriate recipients of
the reported state. In these embodiments, the executive agent 8364
may be trained on interactions of the user with the client
application 8052 and digital twins that were previously presented
to the user.
[1300] In some embodiments, the AI-reporting tool 8360 may be
configured to monitor one or more user-defined key performance
indicators (KPIs). Examples of KPIs of an enterprise may include,
but are not limited to, with respect to systems, facilities,
processes, functions, or workforce units: uptime (e.g., of an
assembly line or other manufacturing system), capacity utilization,
on-standard operating efficiency, overall effectiveness, downtime,
amount of unscheduled downtime, setup time, an amount of inventory
turns, inventory accuracy, quality metrics relating to products and
services, first-pass yield amounts for the enterprise, an amount of
rework required, days-sales-outstanding (DSOs), an amount of scrap
or waste produced, throughput, changeover, maintenance percentage,
yield per system or unit, overall yield, industry reviews, industry
ratings, customer reviews, customer ratings, editorial reviews,
awards, social media and website attention metrics, search engine
performance metrics, safety metrics, health metrics, environmental
impact metrics, political metrics, certification and testing
metrics, regulatory metrics, social impact metrics, financial and
investment metrics, corporate bond ratings, trade association
metrics, union metrics, lobbying organization ratings, advertising
performance metrics, referral metrics, and many others. Additional
or alternative KPI metrics may be defined by a user. Examples of
these KPI metrics may include an amount or percentage of failed
audits, a number or percentage of deliveries that are on-time/late,
a number of customer returns, a number of employee training hours,
employee turnover percentage, number of reportable health or safety
incidents, revenue per employee, profit per employee, schedule
attainment metrics, total cycle time, and the like.
[1301] In embodiments, the collaboration tools 8362 include various
tools that allow collaboration between executives of the
enterprise. In embodiments, the collaboration tools include
digital-twin enabled video conferencing. In these embodiments, the
EMP 8000 may present participants in the video conference with the
requested view of an enterprise digital twin. For example, during a
Board meeting, a CTO proposing an update to the machinery or
equipment in a facility may present an environment digital twin of
the facility where the updates to the machinery or equipment would
be made. In this example, the CTO may illustrate the results of
simulations performed in the facility without the updates and with
the updates. The simulation may illustrate how the update may
benefit the enterprise using a number of selected metrics (e.g.,
throughput, profits, employee safety, or the like). Collaboration
and communication tools and associated rules may be configured to
use company-, industry- and domain-specific taxonomies and lexicons
when representing entities, states and flows within the digital
twin.
[1302] In embodiments, executive agents 8364 are expert agents that
are trained to perform tasks on behalf of executive users. As
discussed, in some embodiments, a client application may monitor
the user of the client application by a user when using the client
application 8052. In these embodiments, the client application 8052
may monitor the states of an executive digital twin that the user
drills down into, the states that the user reports to a superior
and/or delegates to a team member in her respective business unit,
decisions that are made, and the like. As the user uses the client
application 8052, the expert agent system 8008 may train one or
more machine-learned models on behalf of the particular user, such
that the models may be leveraged by an executive agent 8364 to
perform tasks on behalf of or recommend actions to the user.
[1303] In embodiments, Board meeting tools 8366 are tools that are
used to prepare for, to access within and/or to follow-up on board
and similar meetings, such as Board of Directors, Board of
Trustees, shareholder meetings, annual meetings, investor meetings,
and other important meetings. References to Board meetings herein
should be understood to encompass these and other important
meetings that require executive preparation, attendance and/or
attention. In embodiments, Board meeting tools 8366 may allow
different users to present one or more states of an enterprise
digital twins within the context of a Board report or Board
meeting. For example, a user (e.g., a COO) may share a simulation
of a proposed logistics solution from the COO digital twin 8366
with one or more devices (e.g., a device in the Board room and/or
devices of participants accessing the Board meeting remotely). In
embodiments, a Board meeting tool 8366 may limit access to certain
types of data based on time, scope, and permissions. For example, a
Board meeting tool 8366 may require that all geolocations that
board members be registered before a Board meeting (e.g., Board
room, designated home offices for those joining by phone or video,
and the like), such that some or all of the data depicted in a
digital twin that is being presented can only be viewed on a device
that is at one of the registered geolocations and/or only for a
defined duration, such as from a few hours before through a few
hours after a meeting, or only during the meeting. Similarly, in
embodiments, the Board meeting tools 8366 may limit access to some
or all of the data shared in a presented digital twin to particular
times (e.g., during the Board meeting or the day of the Board
meeting). Other examples of board meeting tools 8366 are discussed
throughout the application.
[1304] In embodiments, training modules 8368 may include software
tools that are used to train a user. In embodiments, the training
modules 8368 may leverage digital twins to improve executive
training for an enterprise. For example, a training module 8368 may
provide real-world examples that are based on the data collected
from the enterprise. The training module 8368 may present the user
with different scenarios via an executive digital twin 8368 and the
user may take actions. Based on the actions, the training module
8368 may request a simulation from the EMP 8000, which in turn
returns the results to the user. In this way, the user may be
trained on scenarios that are based on the actual enterprise of the
user.
[1305] In embodiments, planning tools 8370 are software tools that
leverage digital twins to assist users to make plans for the
enterprise. In embodiments, a planning tool 8370 may be configured
to provide a graphical user interface that allows an executive to
make plans (e.g., budgets, defining KPIs, etc.). In some
embodiments, the planning tool 8370 may be configured to request a
simulation from the IMP 8000 given the parameters set in the
created plan. In response, the EMP 8000 may return the results of
the simulation and the user can determine whether to adjust the
plan.
[1306] In this way, the user may iteratively refine the plan to
achieve one or more objectives. In embodiments, an executive agent
8362 may monitor the track the actions taken while the plan is
being refined by the user so that the expert agent system 8008 may
train the executive agent 8362 to generate or recommend plans to
the user in the future.
[1307] The enterprise digital twins may be leveraged and/or
interface with other software applications without departing from
the scope of the disclosure.
[1308] FIG. 84 illustrates an example implementation of the EMP
8000. In this example, the EMP 8000 is in communication with a
plurality of client applications 8052 and a set of enterprise
assets 8400. In the example, the EMP 8000 receives enterprise data
from a set of enterprise entities 8400, such as a sensor system
8022, physical entities 8402, digital entities 8404, computational
entities 8406, and/or network entities 8408 belonging to and/or
associated with the enterprise. In embodiments, the enterprise data
may relate to environments, processes, and/or a condition of the
enterprise. For example, a sensor system 8022 may be deployed
within an enterprise facility (e.g., manufacturing facility,
warehouse, distribution center, logistics facility, transportation
facility, office building, customer location, retail location,
agricultural facility, natural resource extraction facility, or the
like) of the enterprise, whereby the sensor system 8022 provides
sensor readings (e.g., vibration data, location data, motion data,
temperature data, pressure data, or the like) relating to the
facility in general or a piece of machinery, equipment, or other
physical or workforce asset within the facility. Within the
facility, a number of physical assets (e.g., robots, autonomous
vehicles, smart equipment, personnel and the like) or other
entities may output data streams relating to the operation of the
assets or other entities. Additionally or alternatively, the
enterprise may include a number of digital assets (e.g., CRM, ERP,
databases, or the like) that provide data streams relating to
sales, costs, human resources or the like. The network entities may
provide networking-related data, including bandwidth, API requests,
throughput, detected cyber-attacks, or the like. The computational
entities may provide data relating to a computing infrastructure of
an enterprise. In some embodiments, the enterprise management
system 8000 may receive data from other sources as well, including
third-party data 8038 from third-party data providers. Taken in
combination, the data from the enterprise assets 8400 and/or other
data sources may provide information relating to the status of the
industrial facility and the machinery contained therein, the state
of various processes (e.g., industrial processes, sales workflows,
hiring processes, logistics workflows, and the like), the
efficiencies of the processes, the financial health of the
enterprise, and the like.
[1309] In embodiments, the enterprise entities may communicate
directly with the EMP 8000 via a communication network.
Additionally or alternatively, one or more of the enterprise assets
may stream data to a local data collection system 8420 that
collects and stores enterprise data locally. In some embodiments,
the local data collection system 8420 may provide the collected
data to an edge intelligence system 8422 of the enterprise.
[1310] In embodiments, the edge intelligence system 8422 may be
executed by an edge device 8042 configured to receive data, such as
from the local data collection systems 8420, a local sensor system
8022, or other enterprise entities 8400 that are located in or near
a physical location of the entities (e.g., at an industrial
facility) and may perform one or more edge-related processes
relating to the received data. The edge device may be a
pre-configured and/or substantially self- or automatically
configuring computing device, such as an "edge intelligence in a
box" device. An edge-related process may refer to a process that is
performed at an edge device in order to store sensor data, reduce
bandwidth on a communication network, and/or reduce the
computational resources required at a backend system. Examples of
edge processes can include data filtering, signal filtering, data
processing, compression, encoding, quick-predictions,
quick-notifications, emergency alarming, and the like, and may
include creation of automated smart data bands. For example, the
edge intelligence system 8422 may determine whether to transmit a
subset of the data to the EMP 8000 or to store the subset of the
data locally until it is explicitly requested from the EMP 8000. In
another example, the edge intelligence system 8422 may be
configured to compress data streams (e.g., sensor data streams) to
improve data throughput of high-volume data streams (e.g.,
vibration data). In some embodiments, the edge intelligence system
8422 may be configured to analyze the high-volume data to determine
whether to compress or stream a raw data stream. In some
embodiments, the local data collection system 8420 and the edge
intelligence system 8422 may be embodied in edge devices 8042 of
the enterprise. In some embodiments, the edge intelligence system
8422 may communicate data to the EMP 8000. In some of these
embodiments, the edge intelligence system 8422 communicates data to
the EMP 8000 via a network enhancement system 8424.
[1311] In embodiments, the network enhancement system 8424 may be
configured to optimize flow of data transmitted from one or both of
the edge intelligence system 8422 and the local data collection
system 8420 and received by the EMP 8000. For example, a local data
collection system 8420 may be configured to collect data from one
or more real world environments, entities, ecosystems, and/or
processes, which may be analyzed by a connected edge intelligence
system 8422. In this example, the edge intelligence system 8422 may
transmit the collected data to the network enhancement system 8424,
which may optimize transmission of the data to the EMP 8000 for
processing and implementation by the EMP 8000. The EMP 8000 may
store, analyze, or otherwise process the transmitted data to the
client applications 8052, such that the client applications 8052
may update enterprise digital twins (e.g., role-based digital
twins, environment digital twins, cohort digital twins, and the
like) that are hosted by the client applications 8052.
[1312] In embodiments, the network enhancement system 8424 may
include one or more signal amplifiers, signal repeaters, digital
filters, analog filters, digital-to-analog converters,
analog-to-digital converter and/or antennae configured to optimize
the flow of data. In some embodiments, the network enhancement
system may include a wireless repeater system such as is disclosed
by U.S. Pat. No. 7,623,826 to Pergal, the entirety of which is
hereby incorporated by reference. The network enhancement system
8424 may optimize the flow of data by, for example, filtering data,
repeating data transmission, amplifying data transmission,
adjusting one or more sampling rates and/or transmission rates, and
implementing one or more data communication protocols.
[1313] In embodiments, the network enhancement system 8424 may
include one or more processors configured to perform digital signal
processing to optimize the flow of data. The one or more processors
may implement optimization algorithms to optimize the flow of data.
The one or more processors may determine one or more optimal paths
in a network, the network enhancement system 8424 transmitting the
data along the one or more optimal paths. The network enhancement
system 8424 may be configured to implement a software filter via
the one or more processors. The software filter may filter data
before transmission to the EMP 8000, for example to lower network
bandwidth consumed by data transmission. The one or more processors
may determine that portions of data are relevant only to one or
more intended recipients, such as digital twins, executive agents,
collaboration suites, or other components of the EMP 8000 and
determine optimal paths based upon intended recipients of the
portions of data.
[1314] In embodiments, the network enhancement system 8424 may be
configured to optimize data flow between a plurality of nodes over
a plurality of data paths. In some embodiments, the network
enhancement system 8424 may transmit a first portion of data over a
first path of the plurality of data paths and a second portion of
data over a second path of the plurality of data paths. The network
enhancement system 8424 may determine that one or more data paths,
such as the first data path, the second data path, other data
paths, are advantageous for transmission of one or more portions of
data. The network enhancement system 8424 may make determinations
of advantageous data paths based upon one or more networking
variables, such as one or more types of data being transmitted, one
or more protocols being suitable for transmission, present and/or
anticipated network congestion, timing of data transmission,
present and/or anticipated volumes of data being or to be
transmitted, and the like. Protocols suitable for transmission may
include transmission control protocol (TCP), user datagram protocol
(UDP), and the like. In some embodiments, the network enhancement
system may be configured to implement a method for data
communication such as is disclosed by U.S. Pat. No. 9,979,664 to Ho
et al., the entirety of which is hereby incorporated by
reference.
[1315] The EMP 8000 receives enterprise data (e.g., directly or via
the network enhancement system 8424, an edge intelligence system
8422, a local data collection system 8420 or from any other data
source). In embodiments, the digital twin system 8004 may structure
and/or store the enterprise data in one or more digital twin
databases (e.g., graph databases, relational databases, SQL
databases, distributed databases, blockchains, caches, servers,
and/or the like). In embodiments, the client application 8052
requests an enterprise digital twin 8410 from the EMP 8000. In
response, the digital twin system 8004 may generate and serve the
requested enterprise digital twin 8410 (e.g., a role-based digital
twin, executive digital twin, environment digital twin, process
digital twin, cohort digital twins, or the like) to the client
application 8052, whereby the enterprise digital twin 8410 may
include the enterprise data and/or data that was derived from the
enterprise data (e.g., by the intelligence services system). The
client application 8052 may provide an interface for the user of
the client application 8052 to interact with the requested digital
twin 8410. For example, the user may delegate tasks relating to a
depicted state to subordinates and/or may notify a superior of a
depicted state via the digital twin interface. In another example,
the user may drill down into a particular state and may initiate a
corrective action via the digital twin interface. In some
embodiments, the client application 8052 may allow the user to
share the digital twin 8410 (or a portion thereof) within a
collaboration tool 8414 or access collaboration features of a
collaboration tool 8414 within the twin 8410. For example, the
client application 8052 may allow the user to share a depicted
state of the digital twin 8410 into a board meeting collaboration
tool Additionally or alternatively, an expert agent 8364 may
monitor the interactions of the user with the digital twin and may
report the interactions to the expert agent system 8008 of the EMP.
In embodiments, the expert agent system 8008 may receive the
interactions and may train the expert agent 8364 based on the
interactions with the digital twin, as well as outcomes stemming
from the expert agent. For example, the expert agent may be trained
to identify situations where the user delegates tasks or notifies a
superior.
[1316] The executive digital twins discussed with respect to FIG.
71 are provided for example and not intended to limit the scope of
the disclosure. Additional and/or alternative data types may be
included in a respective type of executive digital twin.
[1317] FIG. 73 illustrates an example method 8510 for configuring
and serving an enterprise digital twin. In embodiments, the method
may be executed by the digital twin system 8004. The method may be
performed with respect to different types of enterprise digital
twins, including role-based digital twins (e.g., executive digital
twins), cohort digital twins, environment digital twins, process
digital twins, and/or the like.
[1318] At 8512, the structural views for a particular type of
digital twin are selected. In embodiments, the structural views can
be stored in a graph database (representing interconnected data) or
in a geospatial database (representing coordinates of actual
facilities).
[1319] At 8514, associated transactional data for the digital twin
is selected. In embodiments, a combination of interaction data and
transaction data is selected at grain that is suitable for the
dynamic interaction within the digital twin is selected. This
selection process may involve dynamic configuration of the
structure, functions and features of a data mart or other
summarization system and/or may work dynamically using typically
high-performance database storage mechanisms (such as columnar
databases or in memory databases).
[1320] At 8516, embellishment and/or augmentation data for the
digital twin is selected. In embodiments, embellishment data are
the associated attributes that can be tied to elements within the
executive digital twin. For example, in generating an environment
digital twin of a facility, embellishment or augmentation data may
include the ages of machinery or other assets in the facility, the
names of key third-party suppliers that could replace items with
supply chain deliveries, the inputs or outputs of process flows
that occur within the facility, identities of managers, indicators
of states and flows, and many others. In an abstract executive
digital twin the embellishment data may include social media data,
for example sentiment analytics that can be associated with the
customer hierarchical views.
[1321] At 8518, a representation medium for the digital twin is
selected. In embodiments, the final representation can be
multi-faceted, this can include a range of devices from simple
mobile phone-based devices and touchscreen tablets to
special-purpose devices and/or immersive AR/VR headsets, among many
others. The representation medium impacts the volume and nature of
data that is preferably selected in the earlier steps. In
embodiments, selection of a representation medium is provided as a
feedback indicator to the data and networking pipeline, such that
filtering and data path selection can be undertaken with awareness
of end device and other capabilities and requirements of the
representation medium. This may occur automatically, such as by an
agent that is trained to provide context-sensitive feedback based
on a training set of outcomes.
[1322] At 8520, the perspective views are constructed. In
embodiments, the perspective builder 8110 generates a level and
nature of data that allows for different types of user to interact
with the digital twin while gaining the appropriate level of
perspective. For example, with a CEO-level view the CEO may require
the context of third-party alternatives, market forces, and current
strategic initiatives. In this example, the perspective builder
8110 takes these considerations into account in producing the level
of digital twin appropriate for the CEO, furthermore this will
impact the data selection process as different grains of data are
appropriate for the different views. These different perspectives
can be simultaneously interacted with various roles allowing the
executive to provide their guidance on the same topic while seeing
and interaction with information relevant to their specific
needs.
[1323] At 8522, user notifications are enabled. In embodiments,
notifications within the digital twin are controlled by the grain
of the data selected and the required perspective. For example, a
CTO level view requires notifications of various technology changes
and technology market forces, the CTO digital twin is constantly
being overlaid with these notifications that are structurally
associated with the relevant part of the digital environment
abstract or concrete. For example, in an organizational chart the
CTO could be seeing the implementation options for new technology
to provide more efficient communication between organizational
units in strategic planning exercise to acquire a new company.
Simultaneously the CFO is seeing the financial impacts of these
various options, and the CEO is being notified of decisions that
might impact the future market opportunities regarding the upcoming
company acquisition.
[1324] The method is provided for example only. Additional and/or
alternative methods may be performed to generate and serve digital
twins without departing from the scope of the disclosure.
[1325] The method of FIG. 73 is provided for example and not
intended to limit the scope of the disclosure. The method may
include additional or alternative operations.
[1326] FIG. 74 illustrates an example set of operations of a method
8600 for configuring an organizational digital twin. In
embodiments, the method may be executed at least in part by the
digital twin system 8004. It is appreciated that the method may be
executed by other suitable computing systems without departing from
the scope of the disclosure.
[1327] At 8610 an organizational chart of an enterprise is
determined. In embodiments, a user may upload the organizational
chart via a GUI displayed to the user. In some embodiments, the
digital twin system 8004 or a connected component may crawl one or
more websites (e.g., the enterprise website, a social networking
website, or the like) and may parse the crawled website(s) to
determine the organizational chart.
[1328] At 8612, the organizational framework of the enterprise is
updated based on user input. In embodiments, a user may define
roles within the enterprise to individuals listed in the
organizational chart, grant access rights to different roles and/or
individuals, grant permissions to individuals and/or roles, and may
define relationships between roles and/or individuals. In
embodiments, the relationships may represent reporting structures,
teams, business units, and the like.
[1329] At 8614, an organizational digital twin of the enterprise is
generated and deployed. In embodiments, the digital twin system
8004 may generate the organizational digital twin by connecting
data from the enterprise to the organizational chart. This may
include information relating to the individuals, such as birthdate,
social security or tax id, role, relationships, citizenship,
employment status, salary, stock holdings, title, current status,
goals or targets, and the like. Once deployed, the organizational
chart may be continuously updated from one or more enterprise data
sources. In embodiments, the organizational digital twin may be
leveraged to determine the roles of individuals within an
organization and/or the reporting structure of the digital
twin.
[1330] The method of FIG. 74 is provided for example and not
intended to limit the scope of the disclosure. The method may
include additional or alternative operations.
[1331] FIG. 75 illustrates an example set of operations of a method
8700 for generating an executive digital twin. In embodiments, the
method may be executed at least in part by the digital twin system
8004. It is appreciated that the method may be executed by other
suitable computing systems without departing from the scope of the
disclosure.
[1332] At 8710, a request for an executive digital twin is received
from a user. In embodiments, the digital twin system 8004 may
receive a request for an executive digital twin from a user device
associated with a user, such as a mobile device, a personal
computer, a VR device, or the like. The request may indicate an
identity of the user and/or a role of the user.
[1333] At 8712, a role of the user is determined. In embodiments,
the digital twin system 8004 may determine a role of the user from
the request and/or from an organizational digital twin of an
enterprise associated with the user. In embodiments, the
organizational digital twin may indicate the role of the user, the
permissions of the user, the access rights of the user,
restrictions of the user, and a reporting structure of the
user.
[1334] At 8714, a configuration of the executive digital twin is
determined based on the role of the user. In embodiments, the
configuration of the executive digital twin indicates a set of
states that re to be depicted in the executive digital twin and a
granularity of the digital twin. In embodiments, the configuration
of the executive digital twin is stored in a configuration file in
the digital twin data store associated with the enterprise. The
configuration file may define the initial states of the digital
twin and the granularities of the states.
[1335] At 8716, a digital twin is generated based on one or more
data sources corresponding to the enterprise. In embodiments, the
digital twin system 8004 may determine the appropriate perspective
for the requested digital twin based on the configuration of the
digital twin and any access rights or restrictions of the user. In
embodiments, the restrictions may include data restrictions,
interaction restrictions, depth of data restrictions, usage
restrictions, length of visibility restrictions, that the user may
have. In some embodiments, generating the requested digital twin
may include identifying the appropriate data sources for the
digital twin given the perspective and obtaining any data that
initially parameterizes the executive digital twin from the data
sources.
[1336] At 8718, the executive digital twin is served to a user
device of the user. In embodiments, the digital twin system 8004
may provide a file (e.g., a JSON file) containing the executive
digital twin data and any data structures or visual elements that
are needed to depict the executive digital twin by the user device.
In embodiments, the digital twin system 8004 may also stream one or
more real-time data or near-real time data streams to the user
device (e.g., via a data bus), such that the executive digital twin
may be updated with fresh data as the user interacts with the
executive digital twin. The user may then interact with the digital
twin. For example, the user may delegate tasks via the executive
digital twin, request simulations via the executive digital twin,
drill down into or zoom out of states depicted in the executive
digital twin, report states to a supervisor via the executive
digital twin, and/or the like.
[1337] The method of FIG. 75 is provided for example and not
intended to limit the scope of the disclosure. The method may
include additional or alternative operations.
Artificial Intelligence and Neural Network Embodiments
[1338] Referring to FIGS. 76 through 103, in embodiments of the
present disclosure, including ones involving artificial
intelligence 1160, expert systems, self-organization, machine
learning, automation (including robotic process automation, remote
control, autonomous operation, automated configuration, and the
like), adaptive intelligence and adaptive intelligent systems,
prediction, classification, optimization, and the like, may benefit
from the use of a neural network or other artificial intelligence
system, such as a neural net trained for pattern recognition, for
classification of one or more parameters, characteristics, or
phenomena, for support of autonomous control, and other purposes.
References to artificial intelligence, neural network or neural net
throughout this disclosure should be understood to encompass a wide
range of different types of neural networks, machine learning
systems, artificial intelligence systems, and the like, such as
feed forward neural networks, radial basis function neural
networks, self-organizing neural networks (e.g., Kohonen
self-organizing neural networks), recurrent neural networks,
modular neural networks, artificial neural networks, physical
neural networks, multi-layered neural networks, convolutional
neural networks, hybrids of neural networks with other expert
systems (e.g., hybrid fuzzy logic--neural network systems),
Autoencoder neural networks, probabilistic neural networks, time
delay neural networks, convolutional neural networks, regulatory
feedback neural networks, radial basis function neural networks,
recurrent neural networks, Hopfield neural networks, Boltzmann
machine neural networks, self-organizing map (SOM) neural networks,
learning vector quantization (LVQ) neural networks, fully recurrent
neural networks, simple recurrent neural networks, echo state
neural networks, long short-term memory neural networks,
bi-directional neural networks, hierarchical neural networks,
stochastic neural networks, genetic scale RNN neural networks,
committee of machines neural networks, associative neural networks,
physical neural networks, instantaneously trained neural networks,
spiking neural networks, neocognition neural networks, dynamic
neural networks, cascading neural networks, neuro-fuzzy neural
networks, compositional pattern-producing neural networks, memory
neural networks, hierarchical temporal memory neural networks, deep
feed forward neural networks, gated recurrent unit (GCU) neural
networks, auto encoder neural networks, variational auto encoder
neural networks, de-noising auto encoder neural networks, sparse
auto-encoder neural networks, Markov chain neural networks,
restricted Boltzmann machine neural networks, deep belief neural
networks, deep convolutional neural networks, de-convolutional
neural networks, deep convolutional inverse graphics neural
networks, generative adversarial neural networks, liquid state
machine neural networks, extreme learning machine neural networks,
echo state neural networks, deep residual neural networks, support
vector machine neural networks, neural Turing machine neural
networks, and/or holographic associative memory neural networks, or
hybrids or combinations of the foregoing, or combinations with
other expert systems, such as rule-based systems, model-based
systems (including ones based on physical models, statistical
models, flow-based models, biological models, biomimetic models,
and the like).
[1339] The foregoing neural networks may have a variety of nodes or
neurons, which may perform a variety of functions on inputs, such
as inputs received from sensors or other data sources, including
other nodes. Functions may involve weights, features, feature
vectors, and the like. Neurons may include perceptron, neurons that
mimic biological functions (such as of the human senses of touch,
vision, taste, hearing, and smell), and the like. Continuous
neurons, such as with sigmoidal activation, may be used in the
context of various forms of neural net, such as where back
propagation is involved.
[1340] In many embodiments, an expert system or neural network may
be trained, such as by a human operator or supervisor, or based on
a data set, model, or the like. Training may include presenting the
neural network with one or more training data sets that represent
values (including the many types described throughout this
disclosure), as well as one or more indicators of an outcome, such
as an outcome of a process, an outcome of a calculation, an outcome
of an event, an outcome of an activity, or the like. Training may
include training in optimization, such as training a neural network
to optimize one or more systems based on one or more optimization
approaches, such as Bayesian approaches, parametric Bayes
classifier approaches, k-nearest-neighbor classifier approaches,
iterative approaches, interpolation approaches, Pareto optimization
approaches, algorithmic approaches, and the like. Feedback may be
provided in a process of variation and selection, such as with a
genetic algorithm that evolves one or more solutions based on
feedback through a series of rounds.
[1341] In embodiments, a plurality of neural networks may be
deployed in a cloud platform that receives data streams and other
inputs collected (such as by mobile data collectors) in one or more
environments and transmitted to the cloud platform over one or more
networks, including using network coding to provide efficient
transmission. In the cloud platform, optionally using massively
parallel computational capability, a plurality of different neural
networks of various types (including modular forms,
structure-adaptive forms, hybrids, and the like) may be used to
undertake prediction, classification, control functions, and
provide other outputs as described in connection with expert
systems disclosed throughout this disclosure. The different neural
networks may be structured to compete with each other (optionally
including use evolutionary algorithms, genetic algorithms, or the
like), such that an appropriate type of neural network, with
appropriate input sets, weights, node types and functions, and the
like, may be selected, such as by an expert system, for a specific
task involved in a given context, workflow, environment process,
system, or the like.
[1342] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed forward neural network, which moves information in one
direction, such as from a data input, like a source of data about
an individual, through a series of neurons or nodes, to an output.
Data may move from the input nodes to the output nodes, optionally
passing through one or more hidden nodes, without loops. In
embodiments, feed forward neural networks may be constructed with
various types of units, such as binary McCulloch-Pitts neurons, the
simplest of which is a perceptron.
[1343] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, which may be preferred
in some situations involving interpolation in a multi-dimensional
space (such as where interpolation is helpful in optimizing a
multi-dimensional function, such as for optimizing a data
marketplace as described here, optimizing the efficiency or output
of a power generation system, a factory system, or the like, or
other situation involving multiple dimensions. In embodiments, each
neuron in the RBF neural network stores an example from a training
set as a "prototype." Linearity involved in the functioning of this
neural network offers RBF the advantage of not typically suffering
from problems with local minima or maxima.
[1344] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, such as one that
employs a distance criterion with respect to a center (e.g., a
Gaussian function). A radial basis function may be applied as a
replacement for a hidden layer, such as a sigmoidal hidden layer
transfer, in a multi-layer perceptron. An RBF network may have two
layers, such as where an input is mapped onto each RBF in a hidden
layer. In embodiments, an output layer may comprise a linear
combination of hidden layer values representing, for example, a
mean predicted output. The output layer value may provide an output
that may be the same as or similar to that of a regression model in
statistics. In classification problems, the output layer may be a
sigmoid function of a linear combination of hidden layer values,
representing a posterior probability. Performance in both cases may
be often improved by shrinkage techniques, such as ridge regression
in classical statistics. This corresponds to a prior belief in
small parameter values (and therefore smooth output functions) in a
Bayesian framework. RBF networks may avoid local minima, because
the only parameters that are adjusted in the learning process are
the linear mapping from hidden layer to output layer. Linearity
ensures that the error surface may be quadratic and therefore has a
single minimum. In regression problems, this can be found in one
matrix operation. In classification problems, the fixed
non-linearity introduced by the sigmoid output function may be
handled using an iteratively. Re-weighted least squares function or
the like.
[1345] In embodiments, RBF networks may use kernel methods such as
support vector machines (SVM) and Gaussian processes (where the RBF
may be the kernel function). A non-linear kernel function may be
used to project the input data into a space where the learning
problem can be solved using a linear model.
[1346] In embodiments, an RBF neural network may include an input
layer, a hidden layer and a summation layer. In the input layer,
one neuron appears in the input layer for each predictor variable.
In the case of categorical variables, N-1 neurons are used, where N
is the number of categories. The input neurons may, in embodiments,
standardize the value ranges by subtracting the median and dividing
by the interquartile range. The input neurons may then feed the
values to each of the neurons in the hidden layer. In the hidden
layer, a variable number of neurons may be used (determined by the
training process). Each neuron may consist of a radial basis
function that may be centered on a point with as many dimensions as
a number of predictor variables. The spread (e.g., radius) of the
RBF function may be different for each dimension. The centers and
spreads may be determined by training. When presented with a vector
of input values from the input layer, a hidden neuron may compute a
Euclidean distance of the test case from the neuron's center point
and then apply the RBF kernel function to this distance, such as
using the spread values. The resulting value may then be passed to
the summation layer. In the summation layer, the value coming out
of a neuron in the hidden layer may be multiplied by a weight
associated with the neuron and may add to the weighted values of
other neurons. This sum becomes the output. For classification
problems, one output may be produced (with a separate set of
weights and summation units) for each target category. The value
output for a category is the probability that the case being
evaluated has that category. In training of an RBF, various
parameters may be determined, such as the number of neurons in a
hidden layer, the coordinates of the center of each hidden-layer
function, the spread of each function in each dimension, and the
weights applied to outputs as they pass to the summation layer.
Training may be used by clustering algorithms (such as k-means
clustering), by evolutionary approaches, and the like.
[1347] In embodiments, a recurrent neural network may have a
time-varying, real-valued (more than just zero or one) activation
(output). Each connection may have a modifiable real-valued weight.
Some of the nodes are called labeled nodes, some output nodes, and
other hidden nodes. For supervised learning in discrete time
settings, training sequences of real-valued input vectors may
become sequences of activations of the input nodes, one input
vector at a time. At each time step, each non-input unit may
compute its current activation as a nonlinear function of the
weighted sum of the activations of all units from which it receives
connections. The system can explicitly activate (independent of
incoming signals) some output units at certain time steps.
[1348] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing neural network, such as a Kohonen self-organizing
neural network, such as for visualization of views of data, such as
low-dimensional views of high-dimensional data. The self-organizing
neural network may apply competitive learning to a set of input
data, such as from one or more sensors or other data inputs from or
associated with an individual. In embodiments, the self-organizing
neural network may be used to identify structures in data, such as
unlabeled data, such as in data from various unstructured sources,
such as social media sources about an individual, where sources of
the data are unknown (such as where data comes from various unknown
or uncertain sources). The self-organizing neural network may
organize structures or patterns in the data, such that they can be
recognized, analyzed, and labeled, such as identifying structures
as corresponding to individuals, disease conditions, health states,
activity states, and the like.
[1349] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
recurrent neural network, which may allow for a bi-directional flow
of data, such as where connected units (e.g., neurons or nodes)
form a directed cycle. Such a network may be used to model or
exhibit dynamic temporal behavior, such as involved in dynamic
systems, such as a wide variety of the disease conditions, health
states, and biological systems described throughout this
disclosure, such as a body experiencing multiple different diseases
or health conditions, or the like, where dynamic system behavior
involves complex interactions that an observer may desire to
understand, diagnose, predict, control, treat and/or optimize. For
example, the recurrent neural network may be used to anticipate the
state (such as a maintenance state, a health state, a disease
state, or the like), of an individual, such as one interacting with
a system, performing an action, or the like. In embodiments, the
recurrent neural network may use internal memory to process a
sequence of inputs, such as from other nodes and/or from sensors
and other data inputs from an environment, of the various types
described herein, such as a social network, a home or work
environment, a health care environment, a recreational or sports
environment, or the like. In embodiments, the recurrent neural
network may also be used for pattern recognition, such as for
recognizing a person based on a biomarker, a face, a voice or sound
signature, a heat signature, a set of feature vectors in an image,
a chemical signature, or the like. In a non-limiting example, a
recurrent neural network may recognize a change or shift in a state
of a human by learning to classify the shift or change from a
training data set consisting of a stream of data from unstructured
data sources, such as social media sources.
[1350] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
modular neural network, which may comprise a series of independent
neural networks (such as ones of various types described herein)
that are moderated by an intermediary. Each of the independent
neural networks in the modular neural network may work with
separate inputs, accomplishing subtasks that make up the task the
modular network as a whole is intended to perform. For example, a
modular neural network may comprise a recurrent neural network for
pattern recognition, such as to recognize what type of person,
condition, state, or the like is being sensed by one or more
sensors that are provided as input channels to the modular network
and an RBF neural network for optimizing a system, protocol, or the
like, once understood. The intermediary may accept inputs of each
of the individual neural networks, process them, and create output
for the modular neural network, such an appropriate control
parameter, a prediction of state, or the like.
[1351] Combinations among any of the pairs, triplets, or larger
combinations, of the various neural network types described herein,
are encompassed by the present disclosure. This may include
combinations where an expert system uses one neural network for
recognizing a pattern (e.g., a pattern indicating a problem or
fault condition) and a different neural network for self-organizing
an activity or work flow based on the recognized pattern (such as
providing an output governing autonomous control of a system in
response to the recognized condition or pattern). This may also
include combinations where an expert system uses one neural network
for classifying an item (e.g., identifying a machine, a component,
or an operational mode) and a different neural network for
predicting a state of the item (e.g., a fault state, an operational
state, an anticipated state, a maintenance state, a. predicted
state, or the like). Modular neural networks may also include
situations where an expert system uses one neural network for
determining a state or context (such as a state of a machine, a
process, a work flow, a storage system, a network, a data
collector, or the like) and a different neural network for
self-organizing a process involving the state or context (e.g., a
data storage process, a network coding process, a network selection
process, a data processing process, or other process described
herein).
[1352] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
physical neural network where one or more hardware elements may be
used to perform or simulate neural behavior. One or more hardware
nodes may be configured to stream output data resulting from the
activity of the neural net. Hardware nodes, which may comprise one
or more chips, microprocessors, integrated circuits, programmable
logic controllers, application-specific integrated circuits,
field-programmable gate arrays, or the like, may be provided to
optimize the speed, input/output efficiency, energy efficiency,
signal to noise ratio, or other parameter of some part of a neural
net of any of the types described herein. Hardware nodes may
include hardware for acceleration of calculations (such as
dedicated processors for performing basic or more sophisticated
calculations on input data to provide outputs, dedicated processors
for filtering or compressing data, dedicated processors for
de-compressing data, dedicated processors for compression of
specific file or data types (e.g., for handling image data, video
streams, acoustic signals, vibration data, thermal images, heat
maps, or the like), and the like. A physical neural network may be
embodied in a data collector, edge intelligence system, adaptive
intelligent system, mobile data collector, IoT monitoring system,
or other system described herein, including one that may be
reconfigured by switching or routing inputs in varying
configurations, such as to provide different neural net
configurations within the system for handling different types of
inputs (with the switching and configuration optionally under
control of an expert system, which may include a software-based
neural net located on the data collector or remotely). A physical,
or at least partially physical, neural network may include physical
hardware nodes located in a storage system, such as for storing
data within machine, a product, or the like, such as for
accelerating input/output functions to one or more storage elements
that supply data to or take data from the neural net. A physical,
or at least partially physical, neural network may include physical
hardware nodes located in a network, such as for transmitting data
within, to or from an environment, such as for accelerating
input/output functions to one or more network nodes in the net,
accelerating relay functions, or the like. In embodiments, of a
physical neural network, an electrically adjustable resistance
material may be used for emulating the function of a neural
synapse. In embodiments, the physical hardware emulates the
neurons, and software emulates the neural network between the
neurons. In embodiments, neural networks complement conventional
algorithmic computers. They may be trained to perform appropriate
functions without the need for any instructions, such as
classification functions, optimization functions, pattern
recognition functions, control functions, selection functions,
evolution functions, and others.
[1353] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
multilayered feed forward neural network, such as for complex
pattern classification of one or more items, phenomena, modes,
states, or the like. In embodiments, a multilayered feed forward
neural network may be trained by an optimization technical, such as
a genetic algorithm, such as to explore a large and complex space
of options to find an optimum, or near-optimum, global solution.
For example, one or more genetic algorithms may be used to train a
multilayered feed forward neural network to classify complex
phenomena, such as to recognize complex operational modes or states
of individuals, such as modes involving complex interactions among
entities (including interference effects, amplifying effects, and
the like), modes involving non-linear phenomena, such as impacts of
interaction of protocols, which may make analysis of symptoms or
diagnosis of conditions of entities difficult, modes involving
critical risks, such as where multiple, simultaneous conditions
occur, making root cause analysis difficult, and others. In
embodiments, a multilayered feed forward neural network may be used
to classify results from monitoring unstructured data, such as form
social media.
[1354] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed-forward, back-propagation multi-layer perceptron (MLP) neural
network, such as for handling one or more remote sensing
applications, such as for taking inputs from sensors distributed
throughout various human-inhabited environments, including home and
work environments, business environments, and the like. In
embodiments, the MLP neural network may be used for classification
of physical environments. This may include fuzzy
classification.
[1355] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
structure-adaptive neural network, where the structure of a neural
network may be adapted, such as based on a rule, a sensed
condition, a contextual parameter, or the like. For example, if a
neural network does not converge on a solution, such as classifying
an item or arriving at a prediction, when acting on a set of inputs
after some amount of training, the neural network may be modified,
such as from a feed forward neural network to a recurrent neural
network, such as by switching data paths between some subset of
nodes from unidirectional to bi-directional data paths. The
structure adaptation may occur under control of an expert system,
such as to trigger adaptation upon occurrence of a trigger, rule or
event, such as recognizing occurrence of a threshold (such as an
absence of a convergence to a solution within a given amount of
time) or recognizing a phenomenon as requiring different or
additional structure (such as recognizing that a system may be
varying dynamically or in a non-linear fashion).
[1356] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
autoencoder, autoassociator or Diabolo neural network, which may be
similar to a multilayer perceptron (MLP) neural network, such as
where there may be an input layer, an output layer and one or more
hidden layers connecting them. However, the output layer in the
auto-encoder may have the same number of units as the input layer,
where the purpose of the MLP neural network may be to reconstruct
its own inputs (rather than just emitting a target value).
Therefore, the auto encoders are may operate as an unsupervised
learning model. An auto encoder may be used, for example, for
unsupervised learning of efficient codings, such as for
dimensionality reduction, for learning generative models of data,
and the like. In embodiments, an auto-encoding neural network may
be used to self-learn an efficient network coding for transmission
of data from or about an individual over one or more networks,
which may include social networks. In embodiments, an auto-encoding
neural network may be used to self-learn an efficient storage
approach for the storage of streams of analog sensor data from an
environment.
[1357] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
probabilistic neural network (PNN), which, in embodiments, may
comprise a multi-layer (e.g., four-layer) feed forward neural
network, where layers may include input layers, hidden layers,
pattern/summation layers and an output layer. In an embodiment of a
PNN algorithm, a parent probability distribution function (PDF) of
each class may be approximated, such as by a Parzen window and/or a
non-parametric function. Then, using the PDF of each class, the
class probability of a new input may be estimated, and Bayes' rule
may be employed, such as to allocate it to the class with the
highest posterior probability. A PNN may embody a Bayesian network
and may use a statistical algorithm or analytic technique, such as
Kernel Fisher discriminant analysis technique. The PNN may be used
for classification and pattern recognition in any of a wide range
of embodiments disclosed herein. In one non-limiting example, a
probabilistic neural network may be used to predict a fault
condition of a product or system based on a collection of data
inputs from sensors and instruments for the engine.
[1358] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
time delay neural network (TDNN), which may comprise a feed forward
architecture for sequential data that recognizes features
independent of sequence position. In embodiments, to account for
time shifts in data, delays are added to one or more inputs, or
between one or more nodes, so that multiple data points (from
distinct points in time) are analyzed together. A time delay neural
network may form part of a larger pattern recognition system, such
as using a perceptron network. In embodiments, a TDNN may be
trained with supervised learning, such as where connection weights
are trained with back propagation or under feedback. In
embodiments, a TDNN may be used to process sensor data from
distinct streams, where time delays are used to align the data
streams in time, such as to help understand patterns that involve
the understanding of the various streams.
[1359] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
convolutional neural network (referred to in some cases as a CNN, a
ConvNet, a shift invariant neural network, or a space invariant
neural network), wherein the units are connected in a pattern
similar to the visual cortex of the human brain. Neurons may
respond to stimuli in a restricted region of space, referred to as
a receptive field. Receptive fields may partially overlap, such
that they collectively cover the entire (e.g., visual) field. Node
responses can be calculated mathematically, such as by a
convolution operation, such as using. Multilayer perceptrons that
use minimal preprocessing. A convolutional neural network may be
used for recognition within images and video streams, such as for
recognizing an individual, recognizing a marker of a disease
condition, or the like. This may include recognizing an individual
in a crowd, such as using a camera system disposed on a mobile data
collector, such as on a drone or mobile robot. In embodiments, a
convolutional neural network may be used to provide a
recommendation based on data inputs, including sensor inputs and
other contextual information. In embodiments, a convolutional
neural network may be used for processing inputs, such as for
natural language processing of instructions provided by one or more
parties involved in a workflow in an environment. In embodiments, a
convolutional neural network may be deployed with a large number of
neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4,
5, 6 or more) layers, and with many (e.g., millions) of parameters.
A convolutional neural net may use one or more convolutional
nets.
[1360] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
regulatory feedback network, such as for recognizing emergent
phenomena (such as new types of conditions not previously
understood in an individual or population of individuals).
[1361] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing map (SOM), involving unsupervised learning. A set
of neurons may learn to map points in an input space to coordinates
in an output space. The input space can have different dimensions
and topology from the output space, and the SOM may preserve these
while mapping phenomena into groups.
[1362] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
learning vector quantization neural net (LVQ). Prototypical
representatives of the classes may parameterize, together with an
appropriate distance measure, in a distance-based classification
scheme.
[1363] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
echo state network (ESN), which may comprise a recurrent neural
network with a sparsely connected, random hidden layer. The weights
of output neurons may be changed (e.g., the weights may be trained
based on feedback). In embodiments, an ESN may be used to handle
time series patterns, such as, in an example, recognizing a pattern
of progression of a process.
[1364] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
Bi-directional, recurrent neural network (BRNN), such as using a
finite sequence of values (e.g., voltage values from a sensor) to
predict or label each element of the sequence based on both the
past and the future context of the element. This may be done by
adding the outputs of two RNNs, such as one processing the sequence
from left to right, the other one from right to left. The combined
outputs are the predictions of target signals, such as ones
provided by a teacher or supervisor. A bi-directional RNN may be
combined with a long short-term memory RNN.
[1365] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical RNN that connects elements in various ways to
decompose hierarchical behavior, such as into useful subprograms.
In embodiments, a hierarchical RNN may be used to manage one or
more hierarchical templates for data collection in a social
network, a value chain environment, or the like.
[1366] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
stochastic neural network, which may introduce random variations
into the network. Such random variations can be viewed as a form of
statistical sampling, such as Monte Carlo sampling or other
statistical sampling techniques.
[1367] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
genetic scale recurrent neural network. In such embodiments, an RNN
(often a LSTM) may be used where a series may be decomposed into a
number of scales where every scale informs the primary length
between two consecutive points. A first order scale consists of a
normal RNN, a second order consists of all points separated by two
indices and so on. The Nth order RNN connects the first and last
node. The outputs from all the various scales may be treated as a
committee of members, and the associated scores may be used
genetically for the next iteration.
[1368] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
committee of machines (CoM), comprising a collection of different
neural networks that together "vote" on a given example. Because
neural networks may suffer from local minima, starting with the
same architecture and training, but using randomly different
initial weights often gives different results. A CoM tends to
stabilize the result.
[1369] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
associative neural network (ASNN), such as involving an extension
of a committee of machines that combines multiple feed forward
neural networks and a k-nearest neighbor technique. It may use the
correlation between ensemble responses as a measure of distance
amid the analyzed cases for the kNN. This corrects the bias of the
neural network ensemble. An associative neural network may have a
memory that can coincide with a training set. If new data become
available, the network instantly improves its predictive ability
and provides data approximation (self-learns) without retraining.
Another important feature of ASNN may be the possibility to
interpret neural network results by analysis of correlations
between data cases in the space of models.
[1370] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
instantaneously trained neural network (ITNN), where the weights of
the hidden and the output layers are mapped directly from training
vector data.
[1371] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
spiking neural network, which may explicitly consider the timing of
inputs. The network input and output may be represented as a series
of spikes (such as a delta function or more complex shapes). SNNs
can process information in the time domain (e.g., signals that vary
over time, such as signals involving dynamic behavior of an
individual, a disease condition, a health condition, or the like).
They may be implemented as recurrent networks.
[1372] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
dynamic neural network that addresses nonlinear multivariate
behavior and includes learning of time-dependent behavior, such as
transient phenomena and delay effects. Transients may include
behavior of progressing states.
[1373] In embodiments, cascade correlation may be used as an
architecture and supervised learning algorithm, supplementing
adjustment of the weights in a network of fixed topology.
Cascade-correlation may begin with a minimal network, then
automatically trains and add new hidden units one by one, creating
a multi-layer structure. Once anew hidden unit has been added to
the network, its input-side weights may be frozen. This unit then
becomes a permanent feature-detector in the network, available for
producing outputs or for creating other, more complex feature
detectors. The cascade-correlation architecture may learn quickly,
determine its own size and topology, and retain the structures it
has built even if the training set changes and requires no
back-propagation.
[1374] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
neuro-fuzzy network, such as involving a fuzzy interference system
in the body of an artificial neural network. Depending on the type,
several layers may simulate the processes involved in a fuzzy
inference, such as fuzzification, inference, aggregation and
defuzzification. Embedding a fuzzy system in a general structure of
a neural net as the benefit of using available training methods to
find the parameters of a fuzzy system.
[1375] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
compositional pattern-producing network (CPPN), such as a variation
of an associative neural network (ANN) that differs the set of
activation functions and how they are applied. While typical ANNs
often contain only sigmoid functions (and sometimes Gaussian
functions. PPNs can include both types of functions and many
others. Furthermore, CPPNs may be applied across the entire space
of possible inputs, so that they can represent a complete image.
Since they are compositions of functions, CPPNs in effect encode
images at infinite resolution and can be sampled for a particular
display at whatever resolution may be optimal. This type of network
can add new patterns without re-training. In embodiments, methods
and systems described herein that involve an expert system or
self-organization capability may use a one-shot associative memory
network, such as by creating a specific memory structure, which
assigns each new pattern to an orthogonal plane using adjacently
connected hierarchical arrays.
[1376] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical temporal memory (HTM) neural network, such as
involving the structural and algorithmic properties of the
neocortex. HTM may use a biomimetic model, such as based on
memory-prediction. HTM may be used to discover and infer the
high-level causes of observed input patterns and sequences.
[1377] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
holographic associative memory (HAM) neural network, which may
comprise an analog, correlation-based, associative,
stimulus-response system. Information may be mapped onto the phase
orientation of complex numbers. The memory may be effective for
associative memory tasks, generalization and pattern recognition
with changeable attention.
[1378] The foregoing neural networks may have a variety of nodes or
neurons, which may perform a variety of functions on inputs, such
as inputs received from sensors or other data sources, including
other nodes. Functions may involve weights, features, feature
vectors, and the like. Neurons may include perceptrons, neurons
that mimic biological functions (such as of the human senses of
touch, vision, taste, hearing, and smell), and the like. Continuous
neurons, such as with sigmoidal activation, may be used in the
context of various forms of neural net, such as where back
propagation is involved.
[1379] In many embodiments, an expert system or neural network may
be trained, such as by a human operator or supervisor, or based on
a data set, model, or the like. Training may include presenting the
neural network with one or more training data sets that represent
values, such as sensor data, event data, parameter data, and other
types of data (including the many types described throughout this
disclosure), as well as one or more indicators of an outcome, such
as an outcome of a process, an outcome of a calculation, an outcome
of an event, an outcome of an activity, or the like. Training may
include training in optimization, such as training a neural network
to optimize one or more systems based on one or more optimization
approaches, such as Bayesian approaches, parametric Bayes
classifier approaches, k-nearest-neighbor classifier approaches,
iterative approaches, interpolation approaches, Pareto optimization
approaches, algorithmic approaches, and the like. Feedback may be
provided in a process of variation and selection, such as with a
genetic algorithm that evolves one or more solutions based on
feedback through a series of rounds.
[1380] In embodiments, a plurality of neural networks may be
deployed in a cloud platform that receives data streams and other
inputs collected (such as by mobile data collectors) in one or more
industrial environments and transmitted to the cloud platform over
one or more networks, including using network coding to provide
efficient transmission. In the cloud platform, optionally using
massively parallel computational capability, a plurality of
different neural networks of several types (including modular
forms, structure-adaptive forms, hybrids, and the like) may be used
to undertake prediction, classification, control functions, and
provide other outputs as described in connection with expert
systems disclosed throughout this disclosure. The different neural
networks may be structured to compete with each other (optionally
including the use of evolutionary algorithms, genetic algorithms,
or the like), such that an appropriate type of neural network, with
appropriate input sets, weights, node types and functions, and the
like, may be selected, such as by an expert system, for a specific
task involved in a given context, workflow, environment process,
system, or the like.
[1381] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feed forward neural network, which moves information in one
direction, such as from a data input, like an analog sensor located
on or proximal to an industrial machine, through a series of
neurons or nodes, to an output. Data may move from the input nodes
to the output nodes, optionally passing through one or more hidden
nodes, without loops. In embodiments, feedforward neural networks
may be constructed with various types of units, such as binary
McCulloch-Pitts neurons, the simplest of which is a perceptron.
[1382] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, which may be preferred
in some situations involving interpolation in a multi-dimensional
space (such as where interpolation is helpful in optimizing a
multi-dimensional function, such as for optimizing a data
marketplace as described here, optimizing the efficiency or output
of a power generation system, a factory system, or the like, or
other situation involving multiple dimensions). In embodiments,
each neuron in the RBF neural network stores an example from a
training set as a "prototype." Linearity involved in the
functioning of this neural network offers RBF the advantage of not
typically suffering from problems with local minima or maxima.
[1383] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
radial basis function (RBF) neural network, such as one that
employs a distance criterion with respect to a center (e.g., a
Gaussian function). A radial basis function may be applied as a
replacement for a hidden layer (such as a sigmoidal hidden layer
transfer) in a multi-layer perceptron. An RBF network may have two
layers, such as the case where an input is mapped onto each RBF in
a hidden layer. In embodiments, an output layer may comprise a
linear combination of hidden layer values representing, for
example, a mean predicted output. The output layer value may
provide an output that is the same as or similar to that of a
regression model in statistics. In classification problems, the
output layer may be a sigmoid function of a linear combination of
hidden layer values, representing a posterior probability.
Performance in both cases is often improved by shrinkage
techniques, such as ridge regression in classical statistics. This
corresponds to a prior belief in small parameter values (and
therefore smooth output functions) in a Bayesian framework. RBF
networks may avoid local minima, because the only parameters that
are adjusted in the learning process are the linear mapping from
hidden layer to output layer. Linearity ensures that the error
surface is quadratic and therefore has a single minimum. In
regression problems, this can be found in one matrix operation. In
classification problems, the fixed non-linearity introduced by the
sigmoid output function may be handled using an iteratively
re-weighted least squares function or the like.
[1384] RBF networks may use kernel methods such as support vector
machines (SVM) and Gaussian processes (where the RBF is the kernel
function). A non-linear kernel function may be used to project the
input data into a space where the learning problem can be solved
using a linear model.
[1385] In embodiments, an RBF neural network may include an input
layer, a hidden layer, and a summation layer. In the input layer,
one neuron appears in the input layer for each predictor variable.
In the case of categorical variables, N-1 neurons are used, where N
is the number of categories. The input neurons may, in embodiments,
standardize the value ranges by subtracting the median and dividing
by the interquartile range. The input neurons may then feed the
values to each of the neurons in the hidden layer. In the hidden
layer, a variable number of neurons may be used (determined by the
training process). Each neuron may consist of a radial basis
function that is centered on a point with as many dimensions as a
number of predictor variables. The spread (e.g., radius) of the RBF
function may be different for each dimension. The centers and
spreads may be determined by training. When presented with a vector
of input values from the input layer, a hidden neuron may compute a
Euclidean distance of the test case from the neuron's center point
and then apply the RBF kernel function to this distance, such as
using the spread values. The resulting value may then be passed to
the summation layer. In the summation layer, the value coming out
of a neuron in the hidden layer may be multiplied by a weight
associated with the neuron and may add to the weighted values of
other neurons. This sum becomes the output. For classification
problems, one output is produced (with a separate set of weights
and summation units) for each target category. The value output for
a category is the probability that the case being evaluated has
that category. In training of an RBF, various parameters may be
determined, such as the number of neurons in a hidden layer, the
coordinates of the center of each hidden-layer function, the spread
of each function in each dimension, and the weights applied to
outputs as they pass to the summation layer. Training may be used
by clustering algorithms (such as k-means clustering), by
evolutionary approaches, and the like.
[1386] In embodiments, a recurrent neural network may have a
time-varying, real-valued (more than just zero or one) activation
(output). Each connection may have a modifiable real-valued weight.
Some of the nodes are called labeled nodes, some output nodes, and
other hidden nodes. For supervised learning in discrete time
settings, training sequences of real-valued input vectors may
become sequences of activations of the input nodes, one input
vector at a time. At each time step, each non-input unit may
compute its current activation as a nonlinear function of the
weighted sum of the activations of all units from which it receives
connections. The system can explicitly activate (independent of
incoming signals) some output units at certain time steps.
[1387] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing neural network, such as a Kohonen self-organizing
neural network, such as for visualization of views of data, such as
low-dimensional views of high-dimensional data. The self-organizing
neural network may apply competitive learning to a set of input
data, such as from one or more sensors or other data inputs from or
associated with an industrial machine. In embodiments, the
self-organizing neural network may be used to identify structures
in data, such as unlabeled data, such as in data sensed from a
range of vibration, acoustic, or other analog sensors in an
industrial environment, where sources of the data are unknown (such
as where vibrations may be coming from any of a range of unknown
sources). The self-organizing neural network may organize
structures or patterns in the data, such that they can be
recognized, analyzed, and labeled, such as identifying structures
as corresponding to vibrations induced by the movement of a floor,
or acoustic signals created by high frequency rotation of a shaft
of a somewhat distant machine.
[1388] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
recurrent neural network, which may allow for a bi-directional flow
of data, such as where connected units (e.g., neurons or nodes)
form a directed cycle. Such a network may be used to model or
exhibit dynamic temporal behavior, such as those involved in
dynamic systems including a wide variety of the industrial machines
and devices described throughout this disclosure, such as a power
generation machine operating at variable speeds or frequencies in
variable conditions with variable inputs, a robotic manufacturing
system, a refining system, or the like, where dynamic system
behavior involves complex interactions that an operator may desire
to understand, predict, control and/or optimize. For example, the
recurrent neural network may be used to anticipate the state (such
as a maintenance state, a fault state, an operational state, or the
like), of an industrial machine, such as one performing a dynamic
process or action. In embodiments, the recurrent neural network may
use internal memory to process a sequence of inputs, such as from
other nodes and/or from sensors and other data inputs from the
industrial environment, of the various types described herein. In
embodiments, the recurrent neural network may also be used for
pattern recognition, such as for recognizing an industrial machine
based on a sound signature, a heat signature, a set of feature
vectors in an image, a chemical signature, or the like. In a
non-limiting example, a recurrent neural network may recognize a
shift in an operational mode of a turbine, a generator, a motor, a
compressor, or the like (such as a gear shift) by learning to
classify the shift from a training data set consisting of a stream
of data from tri-axial vibration sensors and/or acoustic sensors
applied to one or more of such machines.
[1389] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
modular neural network, which may comprise a series of independent
neural networks (such as ones of various types described herein)
that are moderated by an intermediary. Each of the independent
neural networks in the modular neural network may work with
separate inputs, accomplishing subtasks that make up the task the
modular network as a whole is intended to perform. For example, a
modular neural network may comprise a recurrent neural network for
pattern recognition, such as to recognize what type of industrial
machine is being sensed by one or more sensors that are provided as
input channels to the modular network and an RBF neural network for
optimizing the behavior of the machine once understood. The
intermediary may accept inputs of each of the individual neural
networks, process them, and create output for the modular neural
network, such an appropriate control parameter, a prediction of
state, or the like.
[1390] Combinations among any of the pairs, triplets, or larger
combinations, of the various neural network types described herein,
are encompassed by the present disclosure. This may include
combinations where an expert system uses one neural network for
recognizing a pattern (e.g., a pattern indicating a problem or
fault condition) and a different neural network for self-organizing
an activity or work flow based on the recognized pattern (such as
providing an output governing autonomous control of a system in
response to the recognized condition or pattern). This may also
include combinations where an expert system uses one neural network
for classifying an item (e.g., identifying a machine, a component,
or an operational mode) and a different neural network for
predicting a state of the item (e.g., a fault state, an operational
state, an anticipated state, a maintenance state, or the like).
Modular neural networks may also include situations where an expert
system uses one neural network for determining a state or context
(such as a state of a machine, a process, a work flow, a
marketplace, a storage system, a network, a data collector, or the
like) and a different neural network for self-organizing a process
involving the state or context (e.g., a data storage process, a
network coding process, a network selection process, a data
marketplace process, a power generation process, a manufacturing
process, a refining process, a digging process, a boring process,
or other process described herein).
[1391] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
physical neural network where one or more hardware elements are
used to perform or simulate neural behavior. In embodiments, one or
more hardware neurons may be configured to stream voltage values
that represent analog vibration sensor data voltage values, to
calculate velocity information from analog sensor inputs
representing acoustic, vibration or other data, to calculation
acceleration information from sensor inputs representing acoustic,
vibration, or other data, or the like. One or more hardware nodes
may be configured to stream output data resulting from the activity
of the neural net. Hardware nodes, which may comprise one or more
chips, microprocessors, integrated circuits, programmable logic
controllers, application-specific integrated circuits,
field-programmable gate arrays, or the like, may be provided to
optimize the speed, input/output efficiency, energy efficiency,
signal to noise ratio, or other parameter of some part of a neural
net of any of the types described herein. Hardware nodes may
include hardware for acceleration of calculations (such as
dedicated processors for performing basic or more sophisticated
calculations on input data to provide outputs, dedicated processors
for filtering or compressing data, dedicated processors for
decompressing data, dedicated processors for compression of
specific file or data types (e.g., for handling image data, video
streams, acoustic signals, vibration data, thermal images, heat
maps, or the like), and the like. A physical neural network may be
embodied in a data collector, such as a mobile data collector
described herein, including one that may be reconfigured by
switching or routing inputs in varying configurations, such as to
provide different neural net configurations within the data
collector for handling different types of inputs (with the
switching and configuration optionally under control of an expert
system, which may include a software-based neural net located on
the data collector or remotely). A physical, or at least partially
physical, neural network may include physical hardware nodes
located in a storage system, such as for storing data within an
industrial machine or in an industrial environment, such as for
accelerating input/output functions to one or more storage elements
that supply data to or take data from the neural net. A physical,
or at least partially physical, neural network may include physical
hardware nodes located in a network, such as for transmitting data
within, to or from an industrial environment, such as for
accelerating input/output functions to one or more network nodes in
the net, accelerating relay functions, or the like. In embodiments,
of a physical neural network, an electrically adjustable resistance
material may be used for emulating the function of a neural
synapse. In embodiments, the physical hardware emulates the
neurons, and software emulates the neural network between the
neurons. In embodiments, neural networks complement conventional
algorithmic computers. They are versatile and can be trained to
perform appropriate functions without the need for any
instructions, such as classification functions, optimization
functions, pattern recognition functions, control functions,
selection functions, evolution functions, and others.
[1392] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
multilayered feed forward neural network, such as for complex
pattern classification of one or more items, phenomena, modes,
states, or the like. In embodiments, a multilayered feedforward
neural network may be trained by an optimization technique, such as
a genetic algorithm, such as to explore a large and complex space
of options to find an optimum, or near-optimum, global solution.
For example, one or more genetic algorithms may be used to train a
multilayered feedforward neural network to classify complex
phenomena, such as to recognize complex operational modes of
industrial machines, such as modes involving complex interactions
among machines (including interference effects, resonance effects,
and the like), modes involving non-linear phenomena, such as
impacts of variable speed shafts, which may make analysis of
vibration and other signals difficult, modes involving critical
faults, such as where multiple, simultaneous faults occur, making
root cause analysis difficult, and others. In embodiments, a
multilayered feed forward neural network may be used to classify
results from ultrasonic monitoring or acoustic monitoring of an
industrial machine, such as monitoring an interior set of
components within a housing, such as motor components, pumps,
valves, fluid handling components, and many others, such as in
refrigeration systems, refining systems, reactor systems, catalytic
systems, and others.
[1393] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
feedforward, back-propagation multi-layer perceptron (MLP) neural
network, such as for handling one or more remote sensing
applications, such as for taking inputs from sensors distributed
throughout various industrial environments. In embodiments, the MLP
neural network may be used for classification of physical
environments, such as mining environments, exploration
environments, drilling environments, and the like, including
classification of geological structures (including underground
features and above ground features), classification of materials
(including fluids, minerals, metals, and the like), and other
problems. This may include fuzzy classification.
[1394] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
structure-adaptive neural network, where the structure of a neural
network is adapted, such as based on a rule, a sensed condition, a
contextual parameter, or the like. For example, if a neural network
does not converge on a solution, such as classifying an item or
arriving at a prediction, when acting on a set of inputs after some
amount of training, the neural network may be modified, such as
from a feedforward neural network to a recurrent neural network,
such as by switching data paths between some subset of nodes from
unidirectional to bi-directional data paths. The structure
adaptation may occur under control of an expert system, such as to
trigger adaptation upon occurrence of a trigger, rule or event,
such as recognizing occurrence of a threshold (such as an absence
of a convergence to a solution within a given amount of time) or
recognizing a phenomenon as requiring different or additional
structure (such as recognizing that a system is varying dynamically
or in a non-linear fashion). In one non-limiting example, an expert
system may switch from a simple neural network structure like a
feedforward neural network to a more complex neural network
structure like a recurrent neural network, a convolutional neural
network, or the like upon receiving an indication that a
continuously variable transmission is being used to drive a
generator, turbine, or the like in a system being analyzed.
[1395] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
autoencoder, autoassociator or Diabolo neural network, which may be
similar to a multilayer perceptron ("MLP") neural network, such as
where there may be an input layer, an output layer and one or more
hidden layers connecting them. However, the output layer in the
auto-encoder may have the same number of units as the input layer,
where the purpose of the MLP neural network is to reconstruct its
own inputs (rather than just emitting a target value). Therefore,
the auto encoders may operate as an unsupervised learning model. An
auto encoder may be used, for example, for unsupervised learning of
efficient codings, such as for dimensionality reduction, for
learning generative models of data, and the like. In embodiments,
an auto-encoding neural network may be used to self-learn an
efficient network coding for transmission of analog sensor data
from an industrial machine over one or more networks. In
embodiments, an auto-encoding neural network may be used to
self-learn an efficient storage approach for storage of streams of
analog sensor data from an industrial environment.
[1396] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
probabilistic neural network ("PNN"), which, in embodiments, may
comprise a multi-layer (e.g., four-layer) feedforward neural
network, where layers may include input layers, hidden layers,
pattern/summation layers and an output layer. In an embodiment of a
PNN algorithm, a parent probability distribution function (PDF) of
each class may be approximated, such as by a Parzen window and/or a
non-parametric function. Then, using the PDF of each class, the
class probability of a new input is estimated, and Bayes' rule may
be employed, such as to allocate it to the class with the highest
posterior probability. A PNN may embody a Bayesian network and may
use a statistical algorithm or analytic technique, such as Kernel
Fisher discriminant analysis technique. The PNN may be used for
classification and pattern recognition in any of a wide range of
embodiments disclosed herein. In one non-limiting example, a
probabilistic neural network may be used to predict a fault
condition of an engine based on a collection of data inputs from
sensors and instruments for the engine.
[1397] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
time delay neural network (TDNN), which may comprise a feedforward
architecture for sequential data that recognizes features
independent of sequence position. In embodiments, to account for
time shifts in data, delays are added to one or more inputs, or
between one or more nodes, so that multiple data points (from
distinct points in time) are analyzed together. A time delay neural
network may form part of a larger pattern recognition system, such
as using a perceptron network. In embodiments, a TDNN may be
trained with supervised learning, such as where connection weights
are trained with back propagation or under feedback. In
embodiments, a TDNN may be used to process sensor data from
distinct streams, such as a stream of velocity data, a stream of
acceleration data, a stream of temperature data, a stream of
pressure data, and the like, where time delays are used to align
the data streams in time, such as to help understand patterns that
involve understanding of the various streams (e.g., where increases
in pressure and acceleration occur as an industrial machine
overheats).
[1398] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
convolutional neural network (referred to in some cases as a CNN, a
ConvNet, a shift invariant neural network, or a space invariant
neural network), wherein the units are connected in a pattern
similar to the visual cortex of the human brain. Neurons may
respond to stimuli in a restricted region of space, referred to as
a receptive field. Receptive fields may partially overlap, such
that they collectively cover the entire (e.g., visual) field. Node
responses can be calculated mathematically, such as by a
convolution operation, such as using multilayer perceptrons that
use minimal preprocessing. A convolutional neural network may be
used for recognition within images and video streams, such as for
recognizing a type of machine in a large environment using a camera
system disposed on a mobile data collector, such as on a drone or
mobile robot. In embodiments, a convolutional neural network may be
used to provide a recommendation based on data inputs, including
sensor inputs and other contextual information, such as
recommending a route for a mobile data collector. In embodiments, a
convolutional neural network may be used for processing inputs,
such as for natural language processing of instructions provided by
one or more parties involved in a workflow in an environment. In
embodiments, a convolutional neural network may be deployed with a
large number of neurons (e.g., 100,000, 500,000 or more), with
multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g.,
millions) parameters. A convolutional neural net may use one or
more convolutional nets.
[1399] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
regulatory feedback network, such as for recognizing emergent
phenomena (such as new types of faults not previously understood in
an industrial environment).
[1400] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
self-organizing map ("SOM"), involving unsupervised learning. A set
of neurons may learn to map points in an input space to coordinates
in an output space. The input space can have different dimensions
and topology from the output space, and the SOM may preserve these
while mapping phenomena into groups.
[1401] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
learning vector quantization neural net ("LVQ"). Prototypical
representatives of the classes may parameterize, together with an
appropriate distance measure, in a distance-based classification
scheme.
[1402] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
echo state network ("ESN"), which may comprise a recurrent neural
network with a sparsely connected, random hidden layer. The weights
of output neurons may be changed (e.g., the weights may be trained
based on feedback). In embodiments, an ESN may be used to handle
time series patterns, such as, in an example, recognizing a pattern
of events associated with a gear shift in an industrial turbine,
generator, or the like.
[1403] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
bi-directional, recurrent neural network ("BRNN"), such as using a
finite sequence of values (e.g., voltage values from a sensor) to
predict or label each element of the sequence based on both the
past and the future context of the element. This may be done by
adding the outputs of two RNNs, such as one processing the sequence
from left to right, the other one from right to left. The combined
outputs are the predictions of target signals, such as those
provided by a teacher or supervisor. A bi-directional RNN may be
combined with a long short-term memory RNN.
[1404] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical RNN that connects elements in various ways to
decompose hierarchical behavior, such as into useful subprograms.
In embodiments, a hierarchical RNN may be used to manage one or
more hierarchical templates for data collection in an industrial
environment.
[1405] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
stochastic neural network, which may introduce random variations
into the network. Such random variations can be viewed as a form of
statistical sampling, such as Monte Carlo sampling.
[1406] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
genetic scale recurrent neural network. In such embodiments, a RNN
(often a LSTM) is used where a series is decomposed into a number
of scales where every scale informs the primary length between two
consecutive points. A first order scale consists of a normal RNN, a
second order consists of all points separated by two indices and so
on. The Nth order RNN connects the first and last node. The outputs
from all the various scales may be treated as a committee of
members, and the associated scores may be used genetically for the
next iteration.
[1407] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
committee of machines ("CoM"), comprising a collection of different
neural networks that together "vote" on a given example. Because
neural networks may suffer from local minima, starting with the
same architecture and training, but using randomly different
initial weights often gives different results. A CoM tends to
stabilize the result.
[1408] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
associative neural network ("ASNN"), such as involving an extension
of committee of machines that combines multiple feed forward neural
networks and a k-nearest neighbor technique. It may use the
correlation between ensemble responses as a measure of distance
amid the analyzed cases for the kNN. This corrects the bias of the
neural network ensemble. An associative neural network may have a
memory that can coincide with a training set. If new data become
available, the network instantly improves its predictive ability
and provides data approximation (self-learns) without retraining.
Another important feature of ASNN is the possibility to interpret
neural network results by analysis of correlations between data
cases in the space of models.
[1409] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use an
instantaneously trained neural network ("ITNN"), where the weights
of the hidden and the output layers are mapped directly from
training vector data.
[1410] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
spiking neural network, which may explicitly consider the timing of
inputs. The network input and output may be represented as a series
of spikes (such as a delta function or more complex shapes). SNNs
can process information in the time domain (e.g., signals that vary
over time, such as signals involving dynamic behavior of industrial
machines). They are often implemented as recurrent networks.
[1411] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
dynamic neural network that addresses nonlinear multivariate
behavior and includes learning of time-dependent behavior, such as
transient phenomena and delay effects. Transients may include
behavior of shifting industrial components, such as variable speeds
of rotating shafts or other rotating components.
[1412] In embodiments, cascade correlation may be used as an
architecture and supervised learning algorithm, supplementing
adjustment of the weights in a network of fixed topology.
Cascade-correlation may begin with a minimal network, then
automatically trains and adds new hidden units one by one, creating
a multi-layer structure. Once anew hidden unit has been added to
the network, its input-side weights may be frozen. This unit then
becomes a permanent feature-detector in the network, available for
producing outputs or for creating other, more complex feature
detectors. The cascade-correlation architecture may learn quickly,
determine its own size and topology, and retain the structures it
has built even if the training set changes and requires no
back-propagation.
[1413] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
neuro-fuzzy network, such as involving a fuzzy inference system in
the body of an artificial neural network. Depending on the type,
several layers may simulate the processes involved in a fuzzy
inference, such as fuzzification, inference, aggregation and
defuzzification. Embedding a fuzzy system in a general structure of
a neural net as the benefit of using available training methods to
find the parameters of a fuzzy system.
[1414] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
compositional pattern-producing network ("CPPN"), such as a
variation of an associative neural network ("ANN") that differs the
set of activation functions and how they are applied. While typical
ANNs often contain only sigmoid functions (and sometimes Gaussian
functions), CPPNs can include both types of functions and many
others. Furthermore, CPPNs may be applied across the entire space
of possible inputs, so that they can represent a complete image.
Since they are compositions of functions, CPPNs in effect encode
images at infinite resolution and can be sampled for a particular
display at whatever resolution is optimal.
[1415] This type of network can add new patterns without
re-training. In embodiments, methods and systems described herein
that involve an expert system or self-organization capability may
use a one-shot associative memory network, such as by creating a
specific memory structure, which assigns each new pattern to an
orthogonal plane using adjacently connected hierarchical
arrays.
[1416] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
hierarchical temporal memory ("HTM") neural network, such as
involving the structural and algorithmic properties of the
neocortex. HTM may use a biomimetic model based on
memory-prediction theory. HTM may be used to discover and infer the
high-level causes of observed input patterns and sequences.
[1417] In embodiments, methods and systems described herein that
involve an expert system or self-organization capability may use a
holographic associative memory ("HAM") neural network, which may
comprise an analog, correlation-based, associative,
stimulus-response system. Information may be mapped onto the phase
orientation of complex numbers. The memory is effective for
associative memory tasks, generalization and pattern recognition
with changeable attention.
[1418] In embodiments, various embodiments involving network coding
may be used to code transmission data among network nodes in neural
net, such as where nodes are located in one or more data collectors
or machines in an industrial environment.
[1419] In embodiments of the present disclosure, a method is
provided for configuring role-based digital twins, comprising:
receiving, by a processing system having one or more processors, an
organizational definition of an enterprise, wherein the
organizational definition defines a set of roles within the
enterprise; generating, by the processing system, an organizational
digital twin of the enterprise based on the organizational
definition, wherein the organizational digital twin is a digital
representation of an organizational structure of the enterprise;
determining, by the processing system, a set of relationships
between different roles within the set of roles based on the
organizational definition; determining, by the processing system, a
set of settings for a role from the set of roles based on the
determined set of relationships; linking an identity of a
respective individual to the role; determining, by the processing
system, a configuration of a presentation layer of a role-based
digital twin corresponding to the role based on the settings of the
role that is linked to the identity, wherein the configuration of
the presentation layer defines a set of states that is depicted in
the role-based digital twin associated with the role; determining,
by the processing system, a set of data sources that provide data
corresponding to the set of states, wherein each data source
provides one or more respective types of data; and configuring one
or more data structures that is received from the one or more data
sources, wherein the one or more data structures are configured to
provide data used to populate one or more of the set of states in
the role-based digital twin.
[1420] In embodiments, an organizational definition may further
identify a set of physical assets of the enterprise.
[1421] In embodiments, determining a set of relationships may
include parsing the organizational definition to identify a
reporting structure and one or more business units of the
enterprise.
[1422] In embodiments, a set of relationships may be inferred from
a reporting structure and a business unit.
[1423] In embodiments, a set of identities may be linked to a set
of roles, wherein each identity corresponds to a respective role
from the set of roles.
[1424] In embodiments, a role-based digital twin may integrate with
an enterprise resource planning system that operates on the
organizational digital twin that represents a set of roles in the
enterprise, such that changes in an enterprise resource planning
system are automatically reflected in the organizational digital
twin.
[1425] In embodiments, an organizational structure may include
hierarchical components, which may be embodied in a graph data
structure.
[1426] In embodiments, a set of settings for the set of roles may
include role-based permission settings.
[1427] In embodiments, a role-based permission setting may be based
on hierarchical components defined in the organizational
definition.
[1428] In embodiments, a set of settings for a set of roles may
include role-based preference settings.
[1429] In embodiments, a role-based preference setting may be
configured based on a set of role-specific templates.
[1430] In embodiments, a set of templates may include at least one
of a CEO template, a COO template, a CFO template, a counsel
template, a board member template, a CTO template, a chief
marketing officer template, an information technology manager
template, a chief information officer template, a chief data
officer template, an investor template, a customer template, a
vendor template, a supplier template, an engineering manager
template, a project manager template, an operations manager
template, a sales manager template, a salesperson template, a
service manager template, a maintenance operator template, and a
business development template.
[1431] In embodiments, a set of settings for the set of roles may
include role-based taxonomy settings.
[1432] In embodiments, a taxonomy setting may identify a taxonomy
that is used to characterize data that is presented in a role-based
digital twin, such that the data is presented in a taxonomy that is
linked to the role corresponding to the role-based digital
twin.
[1433] In embodiments, a set of taxonomies includes at least one of
a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy,
a board member taxonomy, a CTO taxonomy, a chief marketing officer
taxonomy, an information technology manager taxonomy, a chief
information officer taxonomy, a chief data officer taxonomy, an
investor taxonomy, a customer taxonomy, a vendor taxonomy, a
supplier taxonomy, an engineering manager taxonomy, a project
manager taxonomy, an operations manager taxonomy, a sales manager
taxonomy, a salesperson taxonomy, a service manager taxonomy, a
maintenance operator taxonomy, and a business development
taxonomy.
[1434] In embodiments, at least one role of the set of roles may be
selected from among a CEO role, a COO role, a CFO role, a counsel
role, a board member role, a CTO role, an information technology
manager role, a chief information officer role, a chief data
officer role, a human resources manager role, an investor role, an
engineering manager role, an accountant role, an auditor role, a
resource planning role, a public relations manager role, a project
manager role, an operations manager role, a research and
development role, an engineer role, including but not limited to
mechanical engineer, electrical engineer, semiconductor engineer,
chemical engineer, computer science engineer, data science
engineer, network engineer, or some other type of engineer, and a
business development role.
[1435] In embodiments, at least one role may be selected from among
a factory manager role, a factory operations role, a factory worker
role, a power plant manager role, a power plant operations role, a
power plant worker role, an equipment service role, and an
equipment maintenance operator role.
[1436] In embodiments, at least one role may be selected from among
a market maker role, a market analyst role, an exchange manager
role, a broker-dealer role, a trading role, a reconciliation role,
a contract counterparty role, an exchange rate setting role, a
market orchestration role, a market configuration role, and a
contract configuration role.
[1437] In embodiments, at least one role may be selected from among
a chief marketing officer role, a product development role, a
supply chain manager role, a product design role, a marketing
analyst role, a product manager role, a competitive analyst role, a
customer service representative role, a procurement operator, an
inbound logistics operator, an outbound logistics operator, a
customer role, a supplier role, a vendor role, a demand management
role, a marketing manager role, a sales manager role, a service
manager role, a demand forecasting role, a retail manager role, a
warehouse manager role, a salesperson role, and a distribution
center manager role.
[1438] In embodiments of the present disclosure, a method is
provided for training an expert agent, comprising; receiving
digital twin data from a set of data sources, the digital twin data
including: sensor data that is received from a set of sensors that
monitor a set of monitored physical entities associated with the
enterprise, the sensor data transported by a set of network
entities; enterprise data streams generated by a set of enterprise
assets, wherein the enterprise assets include at least one of
physical entities associated with the enterprise and digital
entities associated with the enterprise; structuring the digital
twin data into a set of digital twin data structures that are
configured to serve a plurality of different role-based digital
twins; receiving a request for a role-based digital twin from a
client application, wherein the role-based digital twin is
configured with respect to a defined role within the enterprise;
determining a subset of the structured digital twin data to
corresponds to a set of states that are depicted in the role-based
digital twin; providing the subset of the structured digital twin
data to the client application; receiving expert agent training
data sets from the client application, each expert agent training
data set indicating a respective action taken by a user using the
client application and one or more features that correspond to the
respective action; and training an expert agent on behalf of the
user based on the expert agent training data sets, wherein the
expert agent is configured to determine actions to be performed on
behalf of the user, wherein the determined actions are either
recommended to the user or automatically performed on behalf of the
user.
[1439] In embodiments, a defined role may be selected from among a
CEO role, a COO role, a CFO role, a counsel role, a board member
role, a CTO role, an information technology manager role, a chief
information officer role, a chief data officer role, an investor
role, an engineering manager role, a project manager role, an
operations manager role, and a business development role.
[1440] In embodiments, a defined role may be selected from among a
factory manager role, a factory operations role, a factory worker
role, a power plant manager role, a power plant operations role, a
power plant worker role, an equipment service role, and an
equipment maintenance operator role.
[1441] In embodiments, a defined role may be selected from among a
market maker role, an exchange manager role, a broker-dealer role,
a trading role, a reconciliation role, a contract counterparty
role, an exchange rate setting role, a market orchestration role, a
market configuration role, and a contract configuration role.
[1442] In embodiments, a defined role may be selected from among a
chief marketing officer role, a product development role, a supply
chain manager role, a customer role, a supplier role, a vendor
role, a demand management role, a marketing manager role, a sales
manager role, a service manager role, a demand forecasting role, a
retail manager role, a warehouse manager role, a salesperson role,
and a distribution center manager role.
[1443] In embodiments, an expert agent training data may include
interactions training data that indicates a set of interactions
with a set of experts by the user during performance of the
role.
[1444] In embodiments, a set of interactions used to train the
expert agent may include interactions of the user with the physical
entities, interactions of the user with the role-based digital
twin, interactions of the user with the sensor data as depicted in
the role-based digital twin, interactions of the experts with the
data streams generated by the physical entities, interactions of
the experts with one or more computational entities, interactions
of the user with one or more network entities, or some other type
of interaction.
[1445] In embodiments, an expert agent may be trained to determine
an action selected from the group comprising: selection of a tool,
selection of a task, selection of a dimension, setting of a
parameter, selection of an object, selection of a workflow,
triggering of a workflow, ordering of a process, ordering of a
workflow, cessation of a workflow, selection of a data set,
selection of a design choice, creation of a set of design choices,
identification of a failure mode, identification of a fault,
identification of an operating mode, identification of a problem,
selection of a human resource, selection of a workforce resource,
providing an instruction to a human resource, and providing an
instruction to a workforce resource.
[1446] In embodiments, an executive may be trained on a training
set of outcomes resulting from the actions taken by the
executive.
[1447] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1448] In embodiments, an expert agent may be trained to perform an
action selected from among determining an architecture for a
system, reporting on a status, reporting on an event, reporting on
a context, reporting on a condition, determining a model,
configuring a model, populating a model, designing a system,
designing a process, designing an apparatus, engineering a system,
engineering a device, engineering a process, engineering a product,
maintaining a system, maintaining a device, maintaining a process,
maintaining a network, maintaining a computational resource,
maintaining equipment, maintaining hardware, repairing a system,
repairing a device, repairing a process, repairing a network,
repairing a computational resource, repairing equipment, repairing
hardware, assembling a system, assembling a device, assembling a
process, assembling a network, assembling a computational resource,
assembling equipment, assembling hardware, setting a price,
physically securing a system, physically securing a device,
physically securing a process, physically securing a network,
physically securing a computational resource, physically securing
equipment, physically securing hardware, cyber-securing a system,
cyber-securing a device, cyber-securing a process, cyber-securing a
network, cyber-securing a computational resource, cyber-securing
equipment, cyber-securing hardware, detecting a threat, detecting a
fault, tuning a system, tuning a device, tuning a process, tuning a
network, tuning a computational resource, tuning equipment, tuning
hardware, optimizing a system, optimizing a device, optimizing a
process, optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[1449] In embodiments, an expert agent is at least one of trained
and configured via feedback from at least one expert in the defined
role regarding a set of outputs of expert agent.
[1450] In embodiments, a set of outputs of the expert agent upon
which the expert provides feedback may include at least one of a
recommendation, a classification, a prediction, a control
instruction, an input selection, a protocol selection, a
communication, an alert, a target selection for a communication, a
data storage selection, a computational selection, a configuration,
an event detection, and a forecast.
[1451] In embodiments, feedback of the at least one expert may be
solicited to train the expert agent to replicate the expertise of
the expert in the role.
[1452] In embodiments, a feedback of the at least one expert may be
used to modify the set of inputs to the expert agent and/or used to
identify and characterize at least one error by the expert
agent.
[1453] In embodiments, a report on a set of errors may be provided
to a user of the expert agent to enable reconfiguring of the expert
agent based on the feedback from the expert.
[1454] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[1455] In embodiments, an expert agent may be trained learn upon a
training set of outcomes and to provide at least one of training
and guidance to an individual who is responsible for performing the
defined role.
[1456] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1457] In embodiments of the present disclosure, a method is
provided taking an information technology architecture that
supports a digital twin of a set of physical and digital entities,
the architecture including: a set of sensors that provide sensor
data about the set of physical entities; a set of data streams
generated by at least a subset of the set of physical and digital
entities; a set of computational entities for processing data and a
set of network entities for transporting data that is derived from
the set of sensors and the set of data streams; a set of data
processing systems for extracting, transforming and loading the
data that is transported by the network entities into a set of
resources that are sources for the digital twin; and integrating an
artificial intelligence system with the information technology
architecture, wherein the artificial intelligence system is
configured to operate as a double of an expert worker for a defined
role of the enterprise.
[1458] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by a specific expert worker during performance of the
defined role.
[1459] In embodiments, a set of interactions may be used to train
the artificial intelligence system may include interactions of the
expert with the physical entities, wherein the set of interactions
used to train the artificial intelligence system includes
interactions of the expert with the digital twin.
[1460] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the sensor data, wherein the set of interactions used
to train the artificial intelligence system includes interactions
of the expert with the data streams generated by the physical
entities.
[1461] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the computational entities, wherein the set of
interactions used to train the artificial intelligence system may
include interactions of the expert with the network entities.
[1462] In embodiments, a set of interactions may be parsed to
identify a chain of reasoning of the expert worker upon a set of
information and the chain of reasoning is embodied in the
configuration of the artificial intelligence system.
[1463] In embodiments, an artificial intelligence system may be
trained based on the set interactions to determine an action
selected from: selection of a tool, selection of a task, selection
of a dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[1464] In embodiments, a chain of reasoning may be parsed to
identify a type of reasoning of the expert worker and the type of
reasoning is used as a basis for configuration of the artificial
intelligence system.
[1465] In embodiments, a chain of reasoning may be a deductive
chain of reasoning from a set of data.
[1466] In embodiments, a chain of reasoning may be an inductive
chain of reasoning, a classification chain of reasoning, a
predictive chain of reasoning, an iterative chain of reasoning, a
trial-and-error chain of reasoning, a Bayesian chain of reasoning,
a scientific method chain of reasoning, or some other reasoning
method or system.
[1467] In embodiments, an artificial intelligence system may be
trained on a training set to perform an action selected from among
determining an architecture for a system, reporting on a status,
reporting on an event, reporting on a context, reporting on a
condition, determining a model, configuring a model, populating a
model, designing a system, designing a process, designing an
apparatus, engineering a system, engineering a device, engineering
a process, engineering a product, maintaining a system, maintaining
a device, maintaining a process, maintaining a network, maintaining
a computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[1468] In embodiments, a training set of interactions may be parsed
to identify a type of processing of the expert worker upon a set of
information and the type of processing is embodied in the
configuration of the artificial intelligence system.
[1469] In embodiments, a type of processing may use visual
processing of the expert worker and the artificial intelligence
system is configured to operate on image or video information.
[1470] In embodiments, a type of processing may use audio
processing of the expert worker and the artificial intelligence
system may be configured to operate on audio information.
[1471] In embodiments, a type of processing may use touch
processing of the expert worker and the artificial intelligence
system may be configured to operate on physical sensor
information.
[1472] In embodiments, a type of processing may use olfactory
processing of the expert worker and the artificial intelligence
system may be configured to operate on chemical sensing
information.
[1473] In embodiments, a type of processing may use textual
information processing of the expert worker and the artificial
intelligence system may be configured to operate on text
information.
[1474] In embodiments, a type of processing may use motion
processing of the expert worker and the artificial intelligence
system may be configured to operate on motion information.
[1475] In embodiments, a type of processing may use taste
processing of the expert worker and the artificial intelligence
system may be configured to operate on chemical information.
[1476] In embodiments, a type of processing may use mathematical
processing of the expert worker and the artificial intelligence
system may be configured to operate mathematically on available
data.
[1477] In embodiments, a type of processing may use executive
manager processing of the expert worker and the artificial
intelligence system may be configured to provide executive decision
support.
[1478] In embodiments, a type of processing may use creative
processing of the expert worker and the artificial intelligence
system may be configured to provide a set of alternative
options.
[1479] In embodiments, a type of processing may use analytic
processing of the expert worker to select among a set of available
choices and the artificial intelligence system may be configured to
provide a recommendation among a set of choices.
[1480] In embodiments, an artificial intelligence system may be
trained on a training set of outcomes.
[1481] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1482] In embodiments, an artificial intelligence system may be at
least one of trained and configured via feedback from the specific
expert worker regarding a set of outputs of the artificial
intelligence system.
[1483] In embodiments, a set of outputs of the artificial
intelligence system upon which the expert provides feedback may
include at least one of a recommendation, a classification, a
prediction, a control instruction, an input selection, a protocol
selection, a communication, an alert, a target selection for a
communication, a data storage selection, a computational selection,
a configuration, an event detection, and a forecast.
[1484] In embodiments, a feedback of the expert may be solicited to
train the artificial intelligence system to replicate the expertise
of the expert in the role, used to modify the set of inputs to the
artificial intelligence system, and or used to identify and
characterize at least one error by the artificial intelligence
system.
[1485] In embodiments, a report on a set of errors may be provided
to a manager associated with the artificial intelligence system to
enable reconfiguring of the artificial intelligence system based on
the feedback from the expert.
[1486] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[1487] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
another worker to enable the other worker to perform the defined
role.
[1488] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[1489] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1490] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
another worker to enable the other worker to perform the defined
role.
[1491] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[1492] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1493] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to the
expert worker to enable the expert worker to perform the defined
role.
[1494] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[1495] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1496] In embodiments, outcomes may be compared between a set of
actions of the expert worker and a set of outputs of the artificial
intelligence system.
[1497] In embodiments, a comparison may be used to train the expert
worker.
[1498] In embodiments, a comparison may be used to improve the
artificial intelligence system.
[1499] In embodiments, a defined role of the expert worker may be
selected from among a CEO role, a COO role, a CFO role, a counsel
role, a board member role, a CTO role, a chief marketing officer
role, an information technology manager role, a chief information
officer role, a chief data officer role, an investor role, a
customer role, a vendor role, a supplier role, an engineering
manager role, a project manager role, an operations manager role, a
sales manager role, a salesperson role, a service manager role, a
maintenance operator role, and a business development role.
[1500] In embodiments, computational entities and the network
entities may be integrated as a converged computational and network
entity.
[1501] In embodiments of the present disclosure, a method is
provided for maintaining an information technology architecture
that supports a digital twin of a set of physical entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical entities; a set of
computational entities for processing data and a set of network
entities for transporting data that is derived from the set of
sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; and integrating an artificial
intelligence system with the information technology architecture,
wherein the artificial intelligence system is configured to operate
as a double of an expert worker for a defined role of the
enterprise and wherein an electronic account associated with the
expert worker is awarded with a benefit for training the artificial
intelligence system.
[1502] In embodiments, a benefit may be a reward based on the
outcomes of the use of the artificial intelligence system, a reward
based on the productivity of the artificial intelligence system
and/or a reward based on a measure of the expertise of the
artificial intelligence system.
[1503] In embodiments, a benefit may be a share of revenue or
profit generated by the work of the artificial intelligence system
and/or a reward that is tracked via a distributed ledger on a
blockchain that captures information associated with a set of
actions and events involving the artificial intelligence
system.
[1504] In embodiments, a reward may be administered via a smart
contract operating on the blockchain.
[1505] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by a specific expert worker during performance of the
defined role.
[1506] In embodiments, a set of interactions may be used to train
the artificial intelligence system includes interactions of the
expert with the physical entities, used to train the artificial
intelligence system includes interactions of the expert with the
digital twin and/or used to train the artificial intelligence
system includes interactions of the expert with the sensor
data.
[1507] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
expert with the data streams generated by the physical entities,
interactions of the expert with the computational entities, and/or
interactions of the expert with the network entities.
[1508] In embodiments, an artificial intelligence system may be
trained based on the interactions to determine an action selected
from: selection of a tool, selection of a task, selection of a
dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[1509] In embodiments, a training set of interactions may be parsed
to identify a chain of reasoning of the expert worker upon a set of
information and the chain of reasoning is embodied in the
configuration of the artificial intelligence system.
[1510] In embodiments, a chain of reasoning may be parsed to
identify a type of reasoning of the expert worker and the type of
reasoning is used as a basis for configuration of the artificial
intelligence system.
[1511] In embodiments, a chain of reasoning may be a deductive
chain of reasoning from a set of data.
[1512] In embodiments, an artificial intelligence system may be
trained to perform an action selected from: determining an
architecture for a system, reporting on a status, reporting on an
event, reporting on a context, reporting on a condition,
determining a model, configuring a model, populating a model,
designing a system, designing a process, designing an apparatus,
engineering a system, engineering a device, engineering a process,
engineering a product, maintaining a system, maintaining a device,
maintaining a process, maintaining a network, maintaining a
computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[1513] In embodiments of the present disclosure, a method is
provided for taking an information technology architecture that
supports a digital twin of a set of physical entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical entities; a set of
computational entities for processing data and a set of network
entities for transporting data that is derived from the set of
sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; and integrating an artificial
intelligence system with the information technology architecture,
wherein the artificial intelligence system is configured to operate
as a double of a defined workforce involving a defined set of roles
of the enterprise.
[1514] In embodiments, an artificial intelligence system may be
trained upon a training set of data that includes a set of
interactions by members of the defined workforce during performance
of the defined set of roles.
[1515] In embodiments, a set of interactions used to train the
artificial intelligence system may include interactions of the
workforce with the physical entities, interactions of the workforce
with the digital twin, interactions of the workforce with the
sensor data, interactions of the workforce with the data streams
generated by the physical entities, interactions of the workforce
with the computational entities, and/or interactions of the
workforce with the network entities.
[1516] In embodiments, a training set of interactions may be parsed
to identify a chain of operations of the workforce upon a set of
information and the chain of reasoning may be embodied in the
configuration of the artificial intelligence system.
[1517] In embodiments, a training set of interactions may be parsed
to identify a type of processing of the workforce upon a set of
information and the type of processing may be embodied in the
configuration of the artificial intelligence system.
[1518] In embodiments, an artificial intelligence system may be
trained based on the interactions to determine an action selected
from: selection of a tool, selection of a task, selection of a
dimension, setting of a parameter, selection of an object,
selection of a workflow, triggering of a workflow, ordering of a
process, ordering of a workflow, cessation of a workflow, selection
of a data set, selection of a design choice, creation of a set of
design choices, identification of a failure mode, identification of
a fault, identification of an operating mode, identification of a
problem, selection of a human resource, selection of a workforce
resource, providing an instruction to a human resource, and
providing an instruction to a workforce resource.
[1519] In embodiments, an artificial intelligence system may be
trained on a training set of outcomes.
[1520] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1521] In embodiments, an artificial intelligence system may be at
least one of trained and configured via feedback from members of
the workforce regarding a set of outputs of the artificial
intelligence system.
[1522] In embodiments, a set of outputs of the artificial
intelligence system upon which the workforce members provide
feedback may include at least one of a recommendation, a
classification, a prediction, a control instruction, an input
selection, a protocol selection, a communication, an alert, a
target selection for a communication, a data storage selection, a
computational selection, a configuration, an event detection, and a
forecast.
[1523] In embodiments, a feedback of the workforce members may be
solicited to train the artificial intelligence system to replicate
the operation of the workforce in the defined set of roles.
[1524] In embodiments, a feedback of the workforce members may be
used to modify the set of inputs to the artificial intelligence
system.
[1525] In embodiments, a feedback of the workforce members may be
used to identify and characterize at least one error by the
artificial intelligence system.
[1526] In embodiments, a report on a set of errors may be provided
to a manager of the artificial intelligence system to enable
reconfiguring of the artificial intelligence system based on the
feedback.
[1527] In embodiments, reconfiguring the artificial intelligence
system may include at least one of removing an input that is the
source of the error, reconfiguring a set of nodes of the artificial
intelligence system, reconfiguring a set of weights of the
artificial intelligence system, reconfiguring a set of outputs of
the artificial intelligence system, reconfiguring a processing flow
within the artificial intelligence system, and augmenting the set
of inputs to the artificial intelligence system.
[1528] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to
enable the other worker to perform a role within the defined set of
roles of the workforce.
[1529] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[1530] In embodiments, a training set of outcomes may include data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome.
[1531] In embodiments, an artificial intelligence system may be
trained to perform an action selected from among determining an
architecture for a system, reporting on a status, reporting on an
event, reporting on a context, reporting on a condition,
determining a model, configuring a model, populating a model,
designing a system, designing a process, designing an apparatus,
engineering a system, engineering a device, engineering a process,
engineering a product, maintaining a system, maintaining a device,
maintaining a process, maintaining a network, maintaining a
computational resource, maintaining equipment, maintaining
hardware, repairing a system, repairing a device, repairing a
process, repairing a network, repairing a computational resource,
repairing equipment, repairing hardware, assembling a system,
assembling a device, assembling a process, assembling a network,
assembling a computational resource, assembling equipment,
assembling hardware, setting a price, physically securing a system,
physically securing a device, physically securing a process,
physically securing a network, physically securing a computational
resource, physically securing equipment, physically securing
hardware, cyber-securing a system, cyber-securing a device,
cyber-securing a process, cyber-securing a network, cyber-securing
a computational resource, cyber-securing equipment, cyber-securing
hardware, detecting a threat, detecting a fault, tuning a system,
tuning a device, tuning a process, tuning a network, tuning a
computational resource, tuning equipment, tuning hardware,
optimizing a system, optimizing a device, optimizing a process,
optimizing a network, optimizing a computational resource,
optimizing equipment, optimizing hardware, monitoring a system,
monitoring a device, monitoring a process, monitoring a network,
monitoring a computational resource, monitoring equipment,
monitoring hardware, configuring a system, configuring a device,
configuring a process, configuring a network, configuring a
computational resource, configuring equipment, and configuring
hardware.
[1532] In embodiments, an artificial intelligence system may be
configured to provide at least one of training and guidance to the
workforce to enable the workforce to perform the defined role.
[1533] In embodiments, an artificial intelligence system may learn
on a training set of outcomes to enhance the training and
guidance.
[1534] In embodiments, a training set of outcomes may include. data
relating to at least one of a financial outcome, an operational
outcome, a fault outcome, a success outcome, a performance
indicator outcome, an output outcome, a consumption outcome, an
energy utilization outcome, a resource utilization outcome, a cost
outcome, a profit outcome, a revenue outcome, a sales outcome, and
a production outcome
[1535] In embodiments, outcomes may be compared between a set of
actions of the workforce and a set of outputs of the artificial
intelligence system, wherein the comparison is used to train the
workforce and/or is used to improve the artificial intelligence
system.
[1536] In embodiments, at least one role within the set of roles of
the workforce may be selected from among a CEO role, a COO role, a
CFO role, a counsel role, a board member role, a CTO role, an
information technology manager role, a chief information officer
role, a chief data officer role, an investor role, an engineering
manager role, a project manager role, an operations manager role,
and a business development role.
[1537] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, or
some other kind of workforce.
[1538] In embodiments, a workforce may be a brokering workforce for
a marketplace, a trading workforce for a marketplace, a trade
reconciliation workforce for a marketplace, a transactional
execution workforce for a marketplace, or some other kind of
workforce.
[1539] In embodiments, computational entities and the network
entities may be integrated as a converged computational and network
entity.
[1540] In embodiments of the present disclosure, a method is
provided for configuring a digital twin of a workforce, comprising:
representing an enterprise organizational structure in a digital
twin of an enterprise; parsing the structure to infer relationships
among a set of roles within the organizational structure, the
relationships and the roles defining a workforce of the enterprise;
and configuring the presentation layer of a digital twin to
represent the enterprise as a set of workforces having a set of
attributes and relationships.
[1541] In embodiments, a digital twin may integrate with an
enterprise resource planning system that operates on a data
structure representing a set of roles in the enterprise, such that
changes in the enterprise resource planning system are
automatically reflected in the digital twin.
[1542] In embodiments, an organizational structure may include
hierarchical components.
[1543] In embodiments, hierarchical components may be embodied in a
graph data structure.
[1544] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, or some other type of workforce.
[1545] In embodiments, a workforce may be a network operations
workforce responsible for operating a network for an industrial
production environment, wherein the workforce is a supply chain
management workforce, a demand planning workforce, a logistics
planning workforce, a vendor management workforce, a brokering
workforce for a marketplace, a trading workforce for a marketplace,
a trade reconciliation workforce for a marketplace, a transactional
execution workforce for a marketplace, or some other type of
workforce.
[1546] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[1547] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[1548] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[1549] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[1550] In embodiments, a digital twin may represent a
recommendation for training for the workforce, a recommendation for
augmentation of the workforce, a recommendation for configuration
of a set of operations involving the workforce, a recommendation
for configuration of the workforce, or some other kind of
recommendation.
[1551] In embodiments of the present disclosure, a method is
provided for providing a digital twin of a workforce, comprising:
maintaining an information technology architecture that supports a
digital twin of a set of physical and digital entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical and digital entities; a
set of computational entities for processing data and a set of
network entities for transporting data that is derived from the set
of sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; representing an enterprise
organizational structure in a digital twin of an enterprise;
parsing the structure to infer relationships among a set of roles
within the organizational structure, the relationships and the
roles defining a workforce of the enterprise; integrating an
artificial intelligence system with the information technology
architecture, wherein the artificial intelligence system is
configured to operate as a double of a set of workers for a set of
defined roles of the enterprise and configuring the presentation
layer of a digital twin to represent the enterprise as a set of
workforces having a set of attributes and relationships, wherein
the attributes and relationships include human worker attributes
and relationships and artificial intelligence double attributes and
relationships.
[1552] In embodiments, a digital twin may integrate with an
enterprise resource planning system that operates on a data
structure representing a set of roles in the enterprise, such that
changes in the enterprise resource planning system are
automatically reflected in the digital twin.
[1553] In embodiments, an organizational structure may include
hierarchical components.
[1554] In embodiments, hierarchical components may be embodied in a
graph data structure.
[1555] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, a
brokering workforce, a trading workforce, a trade reconciliation
workforce, a transactional execution workforce, or some other type
of workforce.
[1556] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[1557] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[1558] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[1559] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[1560] In embodiments, a digital twin may represent a
recommendation for training for the workforce, a recommendation for
augmentation of the workforce, a recommendation for configuration
of a set of operations involving the workforce, a recommendation
for configuration of the workforce, a set of capacities and
competencies of a set of workers and a set of doubles, and/or a set
of mixed workgroups of human workers and artificial intelligence
doubles.
[1561] In embodiments of the present disclosure, a method is
provided for serving digital twins comprising: receiving, by a
processing system of a digital twin system, a request for a digital
twin from a user device of a user associated with an enterprise,
the enterprise deploying a sensor system to monitor one or more
facilities of the enterprise; determining, by the processing
system, a workforce role of the user with respect to the
enterprise; generating, by the processing system, a role-based
digital twin corresponding to the workforce role of the user based
on a perspective view corresponding to the workforce role of the
user, wherein the role-based digital twin depicts one or more
states and/or entities that are related to the enterprise;
providing, by the processing system, the role-based digital twin to
the user device, wherein providing the role-based digital twin:
identifying, by the processing system, a set of data types that are
used to populate the at least one of the states and/or entities of
the role-based digital twin, wherein the set of data types include
one or more sensor data feeds that are received from the sensor
system deployed by the enterprise; and connecting, by the
processing system, the one or more sensor data streams to the
role-based digital twin.
[1562] In embodiments, generating a role-based digital twin may
include determining the perspective view corresponding to the
workforce role of the user based on the workforce role of the user
and a set of data types that are relevant to the workforce role of
the user.
[1563] In embodiments, determining the perspective view
corresponding to the workforce role of the user may include
determining an appropriate granularity level for each of the data
types.
[1564] In embodiments, an appropriate granularity level for at
least one of the data types may be defined in a default
configuration corresponding to the workforce role.
[1565] In embodiments, an appropriate granularity level for at
least one of the data types may be determined based on previous
interactions of the user with the role-based digital twin.
[1566] In embodiments, a sensor system may include an edge device
that receives sensor data from a set of sensors within the sensor
system and generates the sensor data stream that is provided to the
digital twin system via a network.
[1567] In embodiments, an edge device may receive sensor data from
the set of sensors and selectively compresses the sensor data based
on values indicated in the sensor data to obtain the sensor data
stream.
[1568] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
and routing the sensor data stream to the user device that is
presenting the role-based digital twin to the user.
[1569] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify one or more fault
conditions corresponding to an object being monitored by the sensor
system; and routing an indicator of the fault condition to the user
device that is presenting the role-based digital twin to the
user.
[1570] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify a recommendation
corresponding to the workforce role of the user; and routing an
indicator of the recommendation to the user device that is
presenting the role-based digital twin to the user.
[1571] In embodiments, connecting the one or more sensor streams
may include: receiving the sensor data stream from the edge device;
analyzing the sensor data stream to identify a recommendation
corresponding to the workforce role of the user; and routing an
indicator of the recommendation to the user device that is
presenting the role-based digital twin to the user.
[1572] In embodiments, a workforce may be a factory operations
workforce, a plant operations workforce, a resource extraction
operations workforce, a network operations workforce responsible
for operating a network for an industrial production environment, a
supply chain management workforce, a demand planning workforce, a
logistics planning workforce, a vendor management workforce, or
some other type of workforce.
[1573] In embodiments, at least one workforce role may be selected
from among a CEO role, a COO role, a CFO role, a counsel role, a
board member role, a CTO role, an information technology manager
role, a chief information officer role, a chief data officer role,
an investor role, an engineering manager role, a project manager
role, an operations manager role, and a business development
role.
[1574] In embodiments, at least one workforce role may be selected
from among a factory manager role, a factory operations role, a
factory worker role, a power plant manager role, a power plant
operations role, a power plant worker role, an equipment service
role, and an equipment maintenance operator role.
[1575] In embodiments, at least one workforce role may be selected
from among a market maker role, an exchange manager role, a
broker-dealer role, a trading role, a reconciliation role, a
contract counterparty role, an exchange rate setting role, a market
orchestration role, a market configuration role, and a contract
configuration role.
[1576] In embodiments, at least one workforce role may be selected
from among a chief marketing officer role, a product development
role, a supply chain manager role, a customer role, a supplier
role, a vendor role, a demand management role, a marketing manager
role, a sales manager role, a service manager role, a demand
forecasting role, a retail manager role, a warehouse manager role,
a salesperson role, and a distribution center manager role.
[1577] In embodiments of the present disclosure, a method is
provided for providing a digital twin of a workforce, comprising:
maintaining an information technology architecture that supports a
digital twin of a set of physical and digital entities, the
architecture including: a set of sensors that provide sensor data
about the set of physical entities; a set of data streams generated
by at least a subset of the set of physical and digital entities; a
set of computational entities for processing data and a set of
network entities for transporting data that is derived from the set
of sensors and the set of data streams; a set of data processing
systems for extracting, transforming and loading the data that is
transported by the network entities into a set of resources that
are sources for the digital twin; representing an enterprise
organizational structure in a digital twin of an enterprise;
parsing the structure to infer relationships among a set of roles
within the organizational structure, the relationships and the
roles defining a workforce of the enterprise; determining a set of
parameters with which the digital twin is configured based on the
inferred set of relationships; and configuring the presentation
layer of a digital twin based on the set of parameters.
Software and Networking Capabilities
[1578] While only a few embodiments of the present disclosure have
been shown and described, it will be obvious to those skilled in
the art that many changes and modifications may be made thereunto
without departing from the spirit and scope of the present
disclosure as described in the following claims. All patent
applications and patents, both foreign and domestic, and all other
publications referenced herein are incorporated herein in their
entireties to the full extent permitted by law.
[1579] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software,
program codes, and/or instructions on a processor. The present
disclosure may be implemented as a method on the machine, as a
system or apparatus as part of or in relation to the machine, or as
a computer program product embodied in a computer readable medium
executing on one or more of the machines. In embodiments, the
processor may be part of a server, cloud server, client, network
infrastructure, mobile computing platform, stationary computing
platform, or other computing platforms. A processor may be any kind
of computational or processing device capable of executing program
instructions, codes, binary instructions and the like, including a
central processing unit (CPU), a general processing unit (GPU), a
logic board, a chip (e.g., a graphics chip, a video processing
chip, a data compression chip, or the like), a chipset, a
controller, a system-on-chip (e.g., an RF system on chip, an AI
system on chip, a video processing system on chip, an
organ-on-chip, a quantum algorithm system on chip, or others), an
integrated circuit, an application specific integrated circuit
(ASIC), a field programmable gate array (FPGA), a complex
programmable logic device (CPLD), an approximate computing
processor, a quantum computing processor, a parallel computing
processor, a neural network processor, or other type of processor.
The processor may be or may include a signal processor, digital
processor, data processor, embedded processor, microprocessor or
any variant such as a co-processor (math co-processor, graphic
co-processor, communication co-processor, video co-processor, AI
co-processor, and the like) and the like that may directly or
indirectly facilitate execution of program code or program
instructions stored thereon. In addition, the processor may enable
execution of multiple programs, threads, and codes. The threads may
be executed simultaneously to enhance the performance of the
processor and to facilitate simultaneous operations of the
application. By way of implementation, methods, program codes,
program instructions and the like described herein may be
implemented in one or more threads. The thread may spawn other
threads that may have assigned priorities associated with them; the
processor may execute these threads based on priority or any other
order based on instructions provided in the program code. The
processor, or any machine utilizing one, may include non-transitory
memory that stores methods, codes, instructions and programs as
described herein and elsewhere. The processor may access a
non-transitory storage medium through an interface that may store
methods, codes, and instructions as described herein and elsewhere.
The storage medium associated with the processor for storing
methods, programs, codes, program instructions or other type of
instructions capable of being executed by the computing or
processing device may include but may not be limited to one or more
of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache,
network-attached storage, server-based storage, and the like.
[1580] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (sometimes called a die).
[1581] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, switch,
infrastructure-as-a-service, platform-as-a-service, or other such
computer and/or networking hardware or system. The software may be
associated with a server that may include a file server, print
server, domain server, internet server, intranet server, cloud
server, infrastructure-as-a-service server, platform-as-a-service
server, web server, and other variants such as secondary server,
host server, distributed server, failover server, backup server,
server farm, and the like. The server may include one or more of
memories, processors, computer readable media, storage media, ports
(physical and virtual), communication devices, and interfaces
capable of accessing other servers, clients, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs, or codes as described herein and elsewhere may be
executed by the server. In addition, other devices required for
execution of methods as described in this application may be
considered as a part of the infrastructure associated with the
server.
[1582] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers, social networks, and the like.
Additionally, this coupling and/or connection may facilitate remote
execution of programs across the network. The networking of some or
all of these devices may facilitate parallel processing of a
program or method at one or more locations without deviating from
the scope of the disclosure. In addition, any of the devices
attached to the server through an interface may include at least
one storage medium capable of storing methods, programs, code
and/or instructions. A central repository may provide program
instructions to be executed on different devices. In this
implementation, the remote repository may act as a storage medium
for program code, instructions, and programs.
[1583] The software program may be associated with a client that
may include a file client, print client, domain client, internet
client, intranet client and other variants such as secondary
client, host client, distributed client and the like. The client
may include one or more of memories, processors, computer readable
media, storage media, ports (physical and virtual), communication
devices, and interfaces capable of accessing other clients,
servers, machines, and devices through a wired or a wireless
medium, and the like. The methods, programs, or codes as described
herein and elsewhere may be executed by the client. In addition,
other devices required for the execution of methods as described in
this application may be considered as a part of the infrastructure
associated with the client.
[1584] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
programs across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more locations without deviating from the scope of the
disclosure. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[1585] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM and
the like. The processes, methods, program codes, instructions
described herein and elsewhere may be executed by one or more of
the network infrastructural elements. The methods and systems
described herein may be adapted for use with any kind of private,
community, or hybrid cloud computing network or cloud computing
environment, including those which involve features of software as
a service (SaaS), platform as a service (PaaS), and/or
infrastructure as a service (IaaS).
[1586] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network with
multiple cells. The cellular network may either be frequency
division multiple access (FDMA) network or code division multiple
access (CDMA) network. The cellular network may include mobile
devices, cell sites, base stations, repeaters, antennas, towers,
and the like. The cell network may be a GSM, GPRS, 3G, 4G, 5G, LTE,
EVDO, mesh, or other network types.
[1587] The methods, program codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic book readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as flash memory, buffer, RAM, ROM
and one or more computing devices. The computing devices associated
with mobile devices may be enabled to execute program codes,
methods, and instructions stored thereon. Alternatively, the mobile
devices may be configured to execute instructions in collaboration
with other devices. The mobile devices may communicate with base
stations interfaced with servers and configured to execute program
codes. The mobile devices may communicate on a peer-to-peer
network, mesh network, or other communications network. The program
code may be stored on the storage medium associated with the server
and executed by a computing device embedded within the server. The
base station may include a computing device and a storage medium.
The storage device may store program codes and instructions
executed by the computing devices associated with the base
station.
[1588] The computer software, program codes, and/or instructions
may be stored and/or accessed on machine readable media that may
include: computer components, devices, and recording media that
retain digital data used for computing for some interval of time;
semiconductor storage known as random access memory (RAM); mass
storage typically for more permanent storage, such as optical
discs, forms of magnetic storage like hard disks, tapes, drums,
cards and other types; processor registers, cache memory, volatile
memory, non-volatile memory; optical storage such as CD, DVD;
removable media such as flash memory (e.g., USB sticks or keys),
floppy disks, magnetic tape, paper tape, punch cards, standalone
RAM disks, Zip drives, removable mass storage, off-line, and the
like; other computer memory such as dynamic memory, static memory,
read/write storage, mutable storage, read only, random access,
sequential access, location addressable, file addressable, content
addressable, network attached storage, storage area network, bar
codes, magnetic ink, network-attached storage, network storage,
NVME-accessible storage, PCIE connected storage, distributed
storage, and the like.
[1589] The methods and systems described herein may transform
physical and/or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[1590] The elements described and depicted herein, including in
flow charts and block diagrams throughout the figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable code using a processor capable of executing
program instructions stored thereon as a monolithic software
structure, as standalone software modules, or as modules that
employ external routines, code, services, and so forth, or any
combination of these, and all such implementations may be within
the scope of the present disclosure. Examples of such machines may
include, but may not be limited to, personal digital assistants,
laptops, personal computers, mobile phones, other handheld
computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices,
artificial intelligence, computing devices, networking equipment,
servers, routers and the like. Furthermore, the elements depicted
in the flow chart and block diagrams or any other logical component
may be implemented on a machine capable of executing program
instructions. Thus, while the foregoing drawings and descriptions
set forth functional aspects of the disclosed systems, no
particular arrangement of software for implementing these
functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it will be appreciated that the various steps identified
and described above may be varied, and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[1591] The methods and/or processes described above, and steps
associated therewith, may be realized in hardware, software or any
combination of hardware and software suitable for a particular
application. The hardware may include a general-purpose computer
and/or dedicated computing device or specific computing device or
particular aspect or component of a specific computing device. The
processes may be realized in one or more microprocessors,
microcontrollers, embedded microcontrollers, programmable digital
signal processors or other programmable devices, along with
internal and/or external memory. The processes may also, or
instead, be embodied in an application specific integrated circuit,
a programmable gate array, programmable array logic, or any other
device or combination of devices that may be configured to process
electronic signals. It will further be appreciated that one or more
of the processes may be realized as a computer executable code
capable of being executed on a machine-readable medium.
[1592] The computer executable code may be created using a
structured programming language such as C, an object oriented
programming language such as C++, or any other high-level or
low-level programming language (including assembly languages,
hardware description languages, and database programming languages
and technologies) that may be stored, compiled or interpreted to
run on one of the above devices, as well as heterogeneous
combinations of processors, processor architectures, or
combinations of different hardware and software, or any other
machine capable of executing program instructions. Computer
software may employ virtualization, virtual machines, containers,
dock facilities, portainers, and other capabilities.
[1593] In embodiments, a value chain system that controls one or
more processes to mitigate waste comprising: a machine learning
system that trains machine-learned models that output waste
mitigation decisions based on training data sets that each
respectively defines one or more features of a respective process
and an outcome relating to an amount of a type of waste generated
by the respective process; an artificial intelligence system that
receives a request for a waste mitigation decision and determines
the waste mitigation decision based on one or more of the
machine-learned models and the request; and a digital twin system
that generates a digital twin of a process that incorporates the
waste mitigation decision and executes a simulation based on the
digital twin. In embodiments, the digital twin system outputs a
graphical representation of the digital twin to a display, whereby
a user views the simulation via the display.
[1594] In embodiments, the digital twin system outputs a graphical
representation of the digital twin in a graphical user interface,
whereby a user edits the process via the graphical user
interface.
[1595] In embodiments, the digital twin system outputs a simulation
outcome of the simulation to the machine learning system and the
machine learning system reinforces the one or more machine learned
models based on the simulation outcome. In embodiments, the request
received by the artificial intelligence system includes one or more
process features of the process. In embodiments, the process
features include one or more of: a type of object being the
physical objects, dimensions of the physical objects, masses of the
physical objects, and shipping methods of the physical objects. In
embodiments, a waste mitigation system adjusts the process in
accordance with the waste mitigation decision and provides outcome
data relating to the waste mitigation decision to the machine
learning system, and the machine learning system reinforces the one
or more models that provided the waste mitigation decision based on
the outcome data. In embodiments, a value chain system that
controls one or more processes to mitigate wastewater resulting
from the one or more processes comprising: a machine learning
system that trains machine-learned models that output wastewater
mitigation decisions based on training data sets that each
respectively defines one or more features of a respective process
that generates wastewater and an outcome relating to an amount of
wastewater generated by the respective process; an artificial
intelligence system that receives a request for a wastewater
mitigation decision and determines the wastewater mitigation
decision based on one or more of the machine-learned models and the
request; and a digital twin system that generates a digital twin of
a process that incorporates the wastewater mitigation decision and
executes a simulation based on the digital twin.
[1596] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide coordinated intelligence
for a set of demand management applications and a set of supply
chain applications for a category of goods.
[1597] In embodiments, coordinated intelligence comprises
artificial intelligence capabilities.
[1598] In embodiments, the artificial intelligence system
facilitates coordinated intelligence for the set of demand
management applications or the set of supply chain applications, or
both for a category of goods by processing data that is available
in any of a plurality of data sources including processes, bill of
materials, weather, traffic, design specification, customer
complaint logs, customer reviews, Enterprise Resource Planning
(ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle
Management (SLM) System, Product Lifecycle Management (PLM)
System.
[1599] In embodiments the set of adaptive intelligence systems
provide user access to artificial intelligence applications for
coordinating intelligence for the sets of applications. In
embodiments, the user interface presents a set of artificial
intelligence systems responsive to the category of goods. In
embodiments, the user interface facilitates configuring the set of
adaptive intelligence systems with at least one artificial
intelligence system.
[1600] In embodiments, the at least one artificial intelligence
system is a hybrid artificial intelligence system. In embodiments,
the at least one artificial intelligence system comprises a hybrid
neural network.
[1601] In embodiments, the set of adaptive intelligence systems
provides a set of capabilities that facilitate development and
deployment of intelligence for at least one function selected from
a list of functions consisting of supply chain application
automation, demand management application automation, machine
learning, artificial intelligence, intelligent transactions,
intelligent operations, remote control, analytics, monitoring,
reporting, state management, event management, and process
management. In embodiments, coordinated intelligence comprises
artificial intelligence capabilities that operate on or responsive
to data collected by or produced by other systems of an adaptive
intelligence systems layer. In embodiments, the coordinated
intelligence comprises artificial intelligence capabilities that
provide coordinated intelligence for a specific operator and/or
enterprise that participates in the supply chain for the category
of goods. In embodiments, the coordinated intelligence includes a
portion of a set of artificial intelligence systems that employs a
neural network that processes at least one of demand management
application outputs and supply chain application outputs to provide
the coordinated intelligence.
[1602] In embodiments, the coordinated intelligence is configured
through the user interface for at least two demand management
applications selected from the list consisting of a demand planning
application, a demand prediction application, a sales application,
a future demand aggregation application, a marketing application,
an advertising application, an e-commerce application, a marketing
analytics application, a customer relationship management
application, a search engine optimization application, a sales
management application, an advertising network application, a
behavioral tracking application, a marketing analytics application,
a location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service. In embodiments, coordinated intelligence is
configured through the user interface for at least two supply chain
applications selected from the list consisting of a goods timing
management application, a goods quantity management application, a
logistics management application, a shipping application, a
delivery application, an order for goods management application,
and an order for components management application.
[1603] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide coordinated intelligence
comprising artificial intelligence capabilities for a set of demand
management applications and a set of supply chain applications for
a category of goods. In embodiments, the artificial intelligence
system facilitates coordinated intelligence for the set of demand
management applications or the set of supply chain applications, or
both for a category of goods by processing data that is available
in any of a plurality of data sources including processes, bill of
materials, weather, traffic, design specification, customer
complaint logs, customer reviews, Enterprise Resource Planning
(ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle
Management (SLM) System, Product Lifecycle Management (PLM)
System.
[1604] In embodiments, the set of adaptive intelligence systems
provide user access to the artificial intelligence capabilities for
coordinating intelligence for the sets of applications.
[1605] In embodiments, the user interface presents a set of
artificial intelligence systems responsive to the category of
goods. In embodiments, the user interface facilitates configuring
the set of adaptive intelligence systems with at least one
artificial intelligence system. In embodiments, the at least one
artificial intelligence system is a hybrid artificial intelligence
system. In embodiments, the at least one artificial intelligence
system comprises a hybrid neural network. In embodiments,
coordinated intelligence-based artificial intelligence capabilities
operate on or responsive to data collected by or produced by other
systems of an adaptive intelligence systems layer.
[1606] In embodiments, coordinated intelligence-based artificial
intelligence capabilities provide coordinated intelligence for a
specific operator and/or enterprise that participates in the supply
chain for the category of goods.
[1607] In embodiments, the coordinated intelligence-based
artificial intelligence capabilities employ a neural network that
processes at least one of demand management application outputs and
supply chain application outputs to provide the coordinated
intelligence. In embodiments, coordinated intelligence-based
artificial intelligence is configured through the user interface
for at least two demand management applications selected from the
list consisting of a demand planning application, a demand
prediction application, a sales application, a future demand
aggregation application, a marketing application, an advertising
application, an e-commerce application, a marketing analytics
application, a customer relationship management application, a
search engine optimization application, a sales management
application, an advertising network application, a behavioral
tracking application, a marketing analytics application, a
location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service. In embodiments, coordinated intelligence-based
artificial intelligence is configured through the user interface
for at least two supply chain applications selected from the list
consisting of a goods timing management application, a goods
quantity management application, a logistics management
application, a shipping application, a delivery application, an
order for goods management application, and an order for components
management application.
[1608] In embodiments, an information technology system having a
management platform with a set of artificial intelligence systems
as part of a set of adaptive intelligence systems that provide
coordinated intelligence for a set of demand management
applications and a set of supply chain applications for a category
of goods so that the at least one supply chain application produces
results that address at least one aspect of supply for at least one
of the goods in the category of goods determined by at least one of
the demand management applications.
[1609] In embodiments, the artificial intelligence systems provide
coordinated intelligence for the set of demand management
applications or the set of supply chain applications, or both for a
category of goods by processing data that is available in any of a
plurality of data sources including processes, bill of materials,
weather, traffic, design specification, customer complaint logs,
customer reviews, Enterprise Resource Planning (ERP) System,
Customer Relationship Management (CRM) System, Customer Experience
Management (CEM) System, Service Lifecycle Management (SLM) System,
Product Lifecycle Management (PLM) System.
[1610] In embodiments, the set of adaptive intelligence systems
provide user access to the set of artificial intelligence systems
for use with the sets of applications. In embodiments, the user
interface presents a set of artificial intelligence systems
responsive to the category of goods. In embodiments, the user
interface facilitates configuring the set of adaptive intelligence
systems with at least one artificial intelligence system. In
embodiments, the at least one artificial intelligence system is a
hybrid artificial intelligence system. In embodiments, the at least
one artificial intelligence system comprises a hybrid neural
network. In embodiments, the set of artificial intelligence systems
provides coordinated intelligence operates on or responsive to data
collected by or produced by other systems of an adaptive
intelligence systems layer. In embodiments, the set of artificial
intelligence systems provides coordinated intelligence for a
specific operator and/or enterprise that participates in the supply
chain for the category of goods.
[1611] In embodiments, the set of artificial intelligence systems
provides coordinated intelligence employs a neural network that
processes at least one of demand management application outputs and
supply chain application outputs to provide the coordinated
intelligence. In embodiments, the set of artificial intelligence
systems is configured through the user interface for at least two
demand management applications selected from the list consisting of
a demand planning application, a demand prediction application, a
sales application, a future demand aggregation application, a
marketing application, an advertising application, an e-commerce
application, a marketing analytics application, a customer
relationship management application, a search engine optimization
application, a sales management application, an advertising network
application, a behavioral tracking application, a marketing
analytics application, a location-based product or
service-targeting application, a collaborative filtering
application, a recommendation engine for a product or service. In
embodiments, the set of artificial intelligence systems is
configured through the user interface for at least two supply chain
applications selected from the list consisting of a goods timing
management application, a goods quantity management application, a
logistics management application, a shipping application, a
delivery application, an order for goods management application,
and an order for components management application.
[1612] In embodiments, an information technology system having a
management platform with a user interface that provides a hybrid
set of adaptive intelligence systems that provide coordinated
intelligence for a set of demand management applications and a set
of supply chain applications for a category of goods, wherein at
least one type of artificial intelligence system is used with
respect to a set of demand management applications and at least one
other type of artificial intelligence system is used with respect
to a set of supply chain applications.
[1613] In embodiments, the hybrid set of adaptive intelligence
systems includes a plurality of distinct artificial intelligence
systems. In embodiments, the hybrid set of adaptive intelligence
systems includes a plurality of neural network-based systems. In
embodiments, the hybrid set of adaptive intelligence systems
provides coordinated intelligence for the set of demand management
applications or the set of supply chain applications, or both for a
category of goods by processing data that is available in any of a
plurality of data sources including processes, bill of materials,
weather, traffic, design specification, customer complaint logs,
customer reviews, Enterprise Resource Planning (ERP) System,
Customer Relationship Management (CRM) System, Customer Experience
Management (CEM) System, Service Lifecycle Management (SLM) System,
Product Lifecycle Management (PLM) System. In embodiments, the
hybrid set of adaptive intelligence systems provides user access to
the set of artificial intelligence systems for use with the sets of
applications.
[1614] In embodiments, the user interface presents a hybrid set of
adaptive intelligence systems responsive to the category of goods.
In embodiments, the user interface facilitates configuring the
hybrid set of adaptive intelligence systems with at least one
artificial intelligence system.
[1615] In embodiments, the at least one artificial intelligence
system is a hybrid artificial intelligence system. In embodiments,
the at least one artificial intelligence system comprises a hybrid
neural network. In embodiments, the hybrid set of adaptive
intelligence systems that provides coordinated intelligence
operates on or responsive to data collected by or produced by other
systems of an adaptive intelligence systems layer. In embodiments,
the hybrid set of adaptive intelligence systems provides
coordinated intelligence for a specific operator and/or enterprise
that participates in the supply chain for the category of goods. In
embodiments, the hybrid set of adaptive intelligence systems
provides coordinated intelligence employs a neural network that
processes at least one of demand management application outputs and
supply chain application outputs to provide the coordinated
intelligence. In embodiments, the hybrid set of adaptive
intelligence systems is configured through the user interface for
at least two demand management applications selected from the list
consisting of a demand planning application, a demand prediction
application, a sales application, a future demand aggregation
application, a marketing application, an advertising application,
an e-commerce application, a marketing analytics application, a
customer relationship management application, a search engine
optimization application, a sales management application, an
advertising network application, a behavioral tracking application,
a marketing analytics application, a location-based product or
service-targeting application, a collaborative filtering
application, a recommendation engine for a product or service. In
embodiments, the hybrid set of adaptive intelligence systems is
configured through the user interface for at least two supply chain
applications selected from the list consisting of a goods timing
management application, a goods quantity management application, a
logistics management application, a shipping application, a
delivery application, an order for goods management application,
and an order for components management application. In embodiments,
the at least one type of artificial intelligence system used with
respect to a set of demand management applications is a machine
learning-based system and wherein the at least one other type of
artificial intelligence system is a neural network-based
system.
[1616] In embodiments, the at least one type of artificial
intelligence system used with respect to a set of demand management
application is a first type of neural network-based system and
wherein the at least one other type of artificial intelligence
system is a second type of neural network-based system.
[1617] In embodiments, the at least one type of artificial
intelligence system configured/selected/accessed through the user
interface for use with respect to a set of demand management
applications is a hybrid neural-network that applies a first type
of neural network with respect to a first application of the set of
demand management applications and a second type of neural network
with respect to a second application of the set of demand
management applications. In embodiments, the at least one other
type of artificial intelligence system used with respect to a set
of supply chain applications is a hybrid neural-network that
applies a first type of neural network with respect to a first
application of the set of supply chain applications and a second
type of neural network with respect to a second application of the
set of supply chain applications.
[1618] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide hybrid artificial
intelligence capabilities for coordinated intelligence for a set of
demand management applications and a set of supply chain
applications for a category of goods, wherein at least one type of
artificial intelligence system is used with respect to a set of
demand management applications and at least one other type of
artificial intelligence system is used with respect to a set of
supply chain applications.
[1619] In embodiments, the hybrid artificial intelligence
capabilities include a plurality of distinct artificial
intelligence systems.
[1620] In embodiments, the hybrid artificial intelligence
capabilities include a plurality of neural network-based
systems.
[1621] In embodiments, the hybrid artificial intelligence
capabilities provide coordinated intelligence for the set of demand
management applications or the set of supply chain applications, or
both for a category of goods by processing data that is available
in any of a plurality of data sources including processes, bill of
materials, weather, traffic, design specification, customer
complaint logs, customer reviews, Enterprise Resource Planning
(ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle
Management (SLM) System, Product Lifecycle Management (PLM)
System.
[1622] In embodiments, the hybrid artificial intelligence
capabilities provide user access to the set of artificial
intelligence systems for use with the sets of applications. In
embodiments, the user interface presents hybrid artificial
intelligence capabilities responsive to the category of goods. In
embodiments, the user interface facilitates configuring the hybrid
artificial intelligence capabilities with at least one artificial
intelligence system. In embodiments, the at least one artificial
intelligence system is a hybrid artificial intelligence system. In
embodiments, the at least one artificial intelligence system
comprises a hybrid neural network. In embodiments, the hybrid
artificial intelligence capabilities that provide coordinated
intelligence operates on or responsive to data collected by or
produced by other systems of an adaptive intelligence systems
layer. In embodiments, the hybrid artificial intelligence
capabilities provide coordinated intelligence for a specific
operator and/or enterprise that participates in the supply chain
for the category of goods.
[1623] In embodiments, the hybrid artificial intelligence
capabilities provide coordinated intelligence employs a neural
network that processes at least one of demand management
application outputs and supply chain application outputs to provide
the coordinated intelligence. In embodiments, the hybrid artificial
intelligence capabilities are configured through the user interface
for at least two demand management applications selected from the
list consisting of a demand planning application, a demand
prediction application, a sales application, a future demand
aggregation application, a marketing application, an advertising
application, an e-commerce application, a marketing analytics
application, a customer relationship management application, a
search engine optimization application, a sales management
application, an advertising network application, a behavioral
tracking application, a marketing analytics application, a
location-based product or service-targeting application, a
collaborative filtering application, a recommendation engine for a
product or service. In embodiments, the hybrid artificial
intelligence capabilities are configured through the user interface
for at least two supply chain applications selected from the list
consisting of a goods timing management application, a goods
quantity management application, a logistics management
application, a shipping application, a delivery application, an
order for goods management application, and an order for components
management application. In embodiments, the at least one type of
artificial intelligence system used with respect to a set of demand
management applications is a machine learning-based system and
wherein the at least one other type of artificial intelligence
system is a neural network-based system. In embodiments, the at
least one type of artificial intelligence system used with respect
to a set of demand management application is a first type of neural
network-based system and wherein the at least one other type of
artificial intelligence system is a second type of neural
network-based system. In embodiments, the at least one type of
artificial intelligence system configured/selected/accessed through
the user interface for use with respect to a set of demand
management applications is a hybrid neural-network that applies a
first type of neural network with respect to a first application of
the set of demand management applications and a second type of
neural network with respect to a second application of the set of
demand management applications. In embodiments, the at least one
other type of artificial intelligence system used with respect to a
set of supply chain applications is a hybrid neural-network that
applies a first type of neural network with respect to a first
application of the set of supply chain applications and a second
type of neural network with respect to a second application of the
set of supply chain applications.
[1624] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of predictions for
a coordinated set of demand management applications and supply
chain applications for a category of goods.
[1625] In embodiments, the set of predictions includes a least one
prediction of an impact on a supply chain application based on a
current state of a coordinated demand management application. In
embodiments, the set of predictions is a set of predictions of
adjustments in supply required to meet demand. In embodiments, the
set of predictions includes at least one prediction of change in
demand that impacts supply. In embodiments, the set of predictions
includes at least one prediction of change in supply that impacts
at least one of the sets of demand management applications. In
embodiments, the at least one of the sets of demand management
applications is a promotion application for at least one good in
the category of goods. In embodiments, the set of predictions
includes a likelihood that a supply of a good in the category of
goods will not meet demand.
[1626] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems to provide a set of artificial
intelligence capabilities that facilitate providing a set of
predictions for a coordinated set of demand management applications
and supply chain applications for a category of goods. In
embodiments, the set of predictions includes a least one prediction
of an impact on a supply chain application based on a current state
of a coordinated demand management application.
[1627] In embodiments, the set of predictions is a set of
predictions of adjustments in supply required to meet demand. In
embodiments, the set of predictions includes at least one
prediction of change in demand that impacts supply. In embodiments,
the set of predictions includes at least one prediction of change
in supply that impacts at least one of the sets of demand
management applications. In embodiments, the at least one of the
set of demand management applications is a promotion application
for at least one good in the category of goods. In embodiments, the
set of predictions includes a likelihood that a supply of a good in
the category of goods will not meet demand.
[1628] In embodiments, an information technology system having a
management platform with a set of adaptive intelligence systems
that apply artificial intelligence to provide a set of predictions
for a coordinated set of demand management applications and supply
chain applications for a category of goods. In embodiments, the set
of predictions includes a least one prediction of an impact on a
supply chain application based on a current state of a coordinated
demand management application. In embodiments, the set of
predictions is a set of predictions of adjustments in supply
required to meet demand. In embodiments, the set of predictions
includes at least one prediction of change in demand that impacts
supply. In embodiments, the set of predictions includes at least
one prediction of change in supply that impacts at least one of the
set of demand management applications.
[1629] In embodiments, the at least one of the set of demand
management applications is a promotion application for at least one
good in the category of goods. In embodiments, the set of
predictions includes a likelihood that a supply of a good in the
category of goods will not meet demand.
[1630] In embodiments, an information technology system having a
management platform with a set of adaptive artificial intelligence
systems that provide a set of predictions for a coordinated set of
demand management applications and supply chain applications for a
category of goods. In embodiments, the set of predictions includes
a least one prediction of an impact on a supply chain application
based on a current state of a coordinated demand management
application. In embodiments, the set of predictions is a set of
predictions of adjustments in supply required to meet demand. In
embodiments, the set of predictions includes at least one
prediction of change in demand that impacts supply. In embodiments,
the set of predictions includes at least one prediction of change
in supply that impacts at least one of the set of demand management
applications. In embodiments, the at least one of the set of demand
management applications is a promotion application for at least one
good in the category of goods. In embodiments, the set of
predictions includes a likelihood that a supply of a good in the
category of goods will not meet demand.
[1631] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of predictions for
a coordinated set of demand management applications and supply
chain applications for a category of goods by applying artificial
intelligence for coordinating the set of demand management
applications and supply chain applications.
[1632] In embodiments, the set of predictions includes a least one
prediction of an impact on a supply chain application based on a
current state of a coordinated demand management application. In
embodiments, the set of predictions is a set of predictions of
adjustments in supply required to meet demand. In embodiments, the
set of predictions includes at least one prediction of change in
demand that impacts supply. In embodiments, the set of predictions
includes at least one prediction of change in supply that impacts
at least one of the set of demand management applications. In
embodiments, the at least one of the set of demand management
applications is a promotion application for at least one good in
the category of goods. In embodiments, the set of predictions
includes a likelihood that a supply of a good in the category of
goods will not meet demand.
[1633] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of predictions of
outcomes from operating a supply chain with a coordinated set of
demand management applications and supply chain applications for a
category of goods. In embodiments, the set of predictions includes
a least one prediction of an impact on a supply chain application
based on a current state of a coordinated demand management
application.
[1634] In embodiments, the set of predictions is a set of
predictions of adjustments in supply required to meet demand. In
embodiments, the set of predictions includes at least one
prediction of change in demand that impacts supply. In embodiments,
the set of predictions includes at least one prediction of change
in supply that impacts at least one of the set of demand management
applications. In embodiments, the at least one of the set of demand
management applications is a promotion application for at least one
good in the category of goods. In embodiments, the set of
predictions includes a likelihood that a supply of a good in the
category of goods will not meet demand. In embodiments, an
information technology system having a management platform with a
user interface that provides a set of adaptive intelligence systems
that provide a set of classifications for a coordinated set of
demand management applications and supply chain applications for a
category of goods. In embodiments, the set of classifications
comprises at least one neural network adapted to classify
information associated with the category of goods. In embodiments,
the at least one neural network is a multilayered feed forward
neural network. In embodiments, the user interface facilitates
access to artificial intelligence classification capabilities
adapted for use with the coordinated set of demand management
applications and supply chain applications.
[1635] In embodiments, the set of classifications comprises a set
of classifications of artificial intelligence capabilities to
facilitate user application of the artificial intelligence
capabilities for the category of goods. In embodiments, an
information technology system having a management platform with a
set of adaptive intelligence systems that provide a set of
artificial intelligence capabilities for performing classifications
for a coordinated set of demand management applications and supply
chain applications for a category of goods.
[1636] In embodiments, the set of artificial intelligence
capabilities comprises at least one neural network adapted to
classify information associated with the category of goods. In
embodiments the at least one neural network is a multilayered feed
forward neural network. In embodiments, the user interface
facilitates access to artificial intelligence classification
capabilities in the set of artificial intelligence capabilities
adapted for use with the coordinated set of demand management
applications and supply chain applications. In embodiments,
performing classifications comprises classifying available adaptive
intelligence systems as one of suitable for use with a demand
management application and suitable for use with a supply chain
application. In embodiments, performing classifications comprises
classifying discovered value chain entities as one of demand
centric and supply centric.
[1637] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of classifications
for a coordinated set of demand management applications and supply
chain applications for a category of goods by applying artificial
intelligence capabilities for coordinating the set of demand
management applications and supply chain applications.
[1638] In embodiments, applying artificial intelligence
capabilities comprises applying at least one neural network adapted
to classify information associated with the category of goods.
[1639] In embodiments, the at least one neural network is a
multilayered feed forward neural network.
[1640] In embodiments, the user interface facilitates determining
the coordinated set of demand management applications and supply
chain applications to which the artificial intelligence
capabilities apply. In embodiments, applying artificial
intelligence capabilities comprises classifying available adaptive
intelligence systems as one of suitable for use with a demand
management application and suitable for use with a supply chain
application.
[1641] In embodiments, applying artificial intelligence
capabilities comprises classifying discovered value chain entities
as one of demand centric and supply centric.
[1642] An information technology system having a management
platform with a user interface that provides a set of adaptive
intelligence systems that provide a set of classifications of
outcomes from operating a supply chain with a coordinated set of
demand management applications and supply chain applications for a
category of goods.
[1643] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of automated
control signals for a coordinated set of demand management
applications and supply chain applications for a category of goods.
In embodiments, the set of automated control signals comprises at
least one control signal for execution of a supply chain
application in the coordinated set of demand management
applications and supply chain applications.
[1644] In embodiments, the set of automated control signals
comprises at least one control signal for execution of a demand
management application in the coordinated set of demand management
applications and supply chain applications. In embodiments, the
automated control signals control timing of demand management
applications based on goods supply status. In embodiments, in the
adaptive intelligence systems apply machine learning to outcomes of
supply to automatically adapt a set of demand management
application control signals.
[1645] In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of demand management to automatically
adapt a set of supply chain application control signals. In
embodiments, the set of adaptive intelligence systems determine
aspects of a value chain that impact automated control of the
coordinated set of demand management applications and supply chain
applications for a category of goods.
[1646] In embodiments, the set of adaptive intelligence systems
determines at least one range of application control values within
which control can be automated. In embodiments, the at least one
range is a supply rate. In embodiments, the at least one range is a
supply timing rate. In embodiments, the at least one range is a mix
of goods in the category of goods.
[1647] In embodiments, an information technology system having a
management platform with a set of adaptive intelligence systems
that apply artificial intelligence to provide a set of automated
control signals for a coordinated set of demand management
applications and supply chain applications for a category of
goods.
[1648] In embodiments, the set of automated control signals
comprises at least one control signal for execution of a supply
chain application in the coordinated set of demand management
applications and supply chain applications. In embodiments, the set
of automated control signals comprises at least one control signal
for execution of a demand management application in the coordinated
set of demand management applications and supply chain
applications. In embodiments, the automated control signals control
timing of demand management applications based on goods supply
status. In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of supply to automatically adapt a set
of demand management application control signals.
[1649] In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of demand management to automatically
adapt a set of supply chain application control signals. In
embodiments, the set of adaptive intelligence systems determine
aspects of a value chain that impact automated control of the
coordinated set of demand management applications and supply chain
applications for a category of goods. In embodiments, the set of
adaptive intelligence systems determines at least one range of
application control values within which control can be automated.
In embodiments, the at least one range is a supply rate. In
embodiments, the at least one range is a supply timing rate. In
embodiments, the at least one range is a mix of goods in the
category of goods.
[1650] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of automated
control signals for a coordinated set of demand management
applications and supply chain applications for a category of goods
by applying artificial intelligence for coordinating the set of
demand management applications and supply chain applications.
[1651] In embodiments, the set of automated control signals
comprises at least one control signal for execution of a supply
chain application in the coordinated set of demand management
applications and supply chain applications. In embodiments, the set
of automated control signals comprises at least one control signal
for execution of a demand management application in the coordinated
set of demand management applications and supply chain
applications. In embodiments, the automated control signals control
timing of demand management applications based on goods supply
status. In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of supply to automatically adapt a set
of demand management application control signals.
[1652] In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of demand management to automatically
adapt a set of supply chain application control signals. In
embodiments, the set of adaptive intelligence systems determine
aspects of a value chain that impact automated control of the
coordinated set of demand management applications and supply chain
applications for a category of goods.
[1653] In embodiments, the set of adaptive intelligence systems
determines at least one range of application control values within
which control can be automated. In embodiments, the at least one
range is a supply rate. In embodiments, the at least one range is a
supply timing rate. In embodiments, the at least one range is a mix
of goods in the category of goods.
[1654] In embodiments, an information technology system having a
management platform with a user interface that provides a set of
adaptive intelligence systems that provide a set of automated
control signals that are responsive to outcomes from operating a
supply chain with a coordinated set of demand management
applications and supply chain applications for a category of goods.
In embodiments, the set of automated control signals comprises at
least one control signal for execution of a supply chain
application in the coordinated set of demand management
applications and supply chain applications. In embodiments, the set
of automated control signals comprises at least one control signal
for execution of a demand management application in the coordinated
set of demand management applications and supply chain
applications. In embodiments, the automated control signals control
timing of demand management applications based on goods supply
status. In embodiments, the adaptive intelligence systems apply
machine learning to outcomes of supply to automatically adapt a set
of demand management application control signals. In embodiments,
the adaptive intelligence systems apply machine learning to
outcomes of demand management to automatically adapt a set of
supply chain application control signals.
[1655] In embodiments, the set of adaptive intelligence systems
determine aspects of a value chain that impact automated control of
the coordinated set of demand management applications and supply
chain applications for a category of goods. In embodiments, the set
of adaptive intelligence systems determines at least one range of
application control values within which control can be automated.
In embodiments, the at least one range is a supply rate.
[1656] In embodiments, the at least one range is a supply timing
rate. In embodiments, the at least one range is a mix of goods in
the category of goods.
[1657] In embodiments, an information technology system having an
artificial intelligence/machine learning system for learning on a
training set of outcomes, parameters, and data collected from a set
of information routing activities in a set of value chain networks
and for providing an information routing recommendation based on
current status information for a selected value chain network. In
embodiments, the artificial intelligence/machine learning system
trains on transaction types within the value chain network. In
embodiments, the information routing recommendation is based on a
transaction type within the value chain network for which
information is being routed. In embodiments the information routing
recommendation is based on a type of information being routed
within the value chain network. In embodiments, the information
routing recommendation is based on network types within the value
chain network. In embodiments, the information routing
recommendation is based on compatibility of information being
routed with a network routing protocol for at least one candidate
route within the value chain network. In embodiments, the
information routing recommendation is based on detected network
conditions. In embodiments, the information routing recommendation
is based on an existence of edge intelligence. In embodiments, the
information routing recommendation is based on an availability of
networking resources. In embodiments, the information routing
recommendation is based on network storage resources.
[1658] In embodiments, the information routing recommendation is
based on detection of network resources/entities.
[1659] In embodiments, the information routing recommendation is
based on goals of routing. In embodiments, the goals of routing
comprise a measure of quality of service (QoS). In embodiments, the
goals of routing comprise a measure reliability. In embodiments,
the measure of reliability is a transmission failure rate. In
embodiments, the goals of routing comprise a measure of latency
associated with a candidate route. In embodiments, the goals of
routing are based on information availability within the selected
value chain network. In embodiments, the goals of routing are based
on information persistence within the selected value chain network.
In embodiments, the artificial intelligence/machine learning system
develops an understanding of parameters of routing that impact
information value. In embodiments, the artificial
intelligence/machine learning system facilitates recommending
routes that maintain information value. In embodiments, the
artificial intelligence/machine learning system is configured to
develop an understanding of timing of information supply versus
information demand.
[1660] In embodiments, the artificial intelligence/machine learning
system develops an understanding of needs for coordination of
information delivery. In embodiments, coordination of information
delivery includes ensuring delivery of an item of information to a
first node before delivering the item of information to a second
node in the selected value chain network. In embodiments, the
artificial intelligence/machine learning system is selected from a
list of systems consisting of decision trees, k-nearest neighbor,
linear regression, k-means clustering, deep learning neural
network, random forest, logistic regression, naive Bayes, learning
vector quantization, support vector machines, linear discriminant
analysis, boosting, principal component analysis, hybrid of k-means
and linear regression. In embodiments, the routing recommendations
are based on a topology of the selected value chain network. In
embodiments, the routing recommendations are adapted based on
availability of edge intelligence in the selected value chain
network. In embodiments, the routing recommendations are adapted
based on location and availability of network storage resources in
the selected value chain network.
[1661] In embodiments, an information technology system having an
artificial intelligence/machine learning system for learning on a
training set of outcomes, parameters, and data collected from a set
of data sources relating to a set of value chain entities and
activities to recognize a problem state in a portion of a value
chain network using computing resources that are local to a set of
value chain network entities that are experiencing the problem. In
embodiments, to recognize a problem state is based on variances in
outcomes over time from a portion of the value chain network. In
embodiments, variances in outcomes over time indicate a problem
state. In embodiments, the artificial intelligence/machine learning
system determines an acceptable range of outcome variance. In
embodiments, the acceptable range is a standard deviation of the
outcomes.
[1662] In embodiments, the artificial intelligence/machine learning
system determines a problem state threshold for at least one
measure of the value chain network. In embodiments, to recognize a
problem state comprises detecting at least one measure of the value
chain that is greater than the problem state threshold.
[1663] In embodiments, to recognize a problem state is based on
variances in start/end times of scheduled value chain network
entity activities. In embodiments, to recognize a problem state is
based on variances in at least one of production time, production
quality, production rate, production start time, production
resource availability or trends thereof. In embodiments, to
recognize a problem state is based on variances in a measure of
shipping supply. In embodiments, a problem state is a duration of
time for transfer from one mode of transport to another greater
than a problem state threshold variance in quality testing. In
embodiments, the machine learning/artificial intelligence systems
predict a correlated pain point further along the supply chain due
to a detected pain point.
[1664] In embodiments, the predicted correlated pain point is a
least one of a risk and/or need for overtime.
[1665] In embodiments, the predicted correlated pain point is a
least one of risk and/or need for expedited shipping.
[1666] In embodiments, the predicted correlated pain point is a
least one of risk and/or need for discounted goods prices.
[1667] In embodiments, the machine learning/artificial intelligence
systems determines a problem state based on a detected stress level
of humans along the supply chain. In embodiments, the detected
stress level of humans is based on data received from at least one
physiological wearable detector. In embodiments, the machine
learning/artificial intelligence system uses natural language
processing to identify phrases in digital communications within
and/or among value chain entities that indicate candidate problem
states. In embodiments, the machine learning/artificial
intelligence system processes outcomes, parameters, and data
collected from a set of data sources relating to a set of value
chain entities and activities to detect at least one pain point
selected from the list of pain points consisting of late shipment,
damaged container, damaged goods, wrong goods, customs delay,
unpaid duties, weather event, damaged infrastructure, blocked
waterway, incompatible infrastructure, congested port, congested
handling infrastructure, congested roadway, congested distribution
center, rejected goods, returned goods, waste material, wasted
energy, wasted labor force, untrained workforce, poor customer
service, empty transport vehicle on return route, excessive fuel
prices, excessive tariffs.
[1668] In embodiments, an information technology system having a
set of artificial intelligence systems operating on information
from a monitoring set of network-connected value chain network
entities to enable automated coordination of a set of value chain
network activities involving a set of products of an
enterprise.
[1669] In embodiments, the monitoring set of network connected
value chain entities is generated by a value chain monitoring
system. In embodiments, activity data for the monitoring set is
captured by a collection and management systems. In embodiments,
the automate coordination is performed at least in part through an
application programming connectivity facility. In embodiments, the
application programming connectivity facility automates access to
the monitored activity information. In embodiments, compromising a
plurality of interconnected entities that each perform several
activities for completing the value chain. In embodiments, the
artificial intelligence-type systems comprise at least one of a
machine learning system, an expert system, a self-organizing
system. In embodiments, the automated coordination comprises
configuring activity workflows with an artificial intelligence
system. In embodiments, the automated coordination involves
automating value chain network activities that produce the product.
In embodiments, the automated coordination involves configuring
resources required for a workflow of an activity. In embodiments,
the set of artificial intelligence systems further determine
relationships among value change network entities and activities.
In embodiments, the automated coordination is based on inputs used
by the activities. In embodiments, the automated coordination is
based on results produced by the activities. In embodiments, the
set of artificial intelligence systems comprises at least one
hybrid artificial intelligence system. In embodiments, the
automated coordination comprises generation of automated control
signals. In embodiments, the automated coordination comprises
semi-sentient problem recognition. In embodiments, the
semi-sentient problem recognition is based on structured content.
In embodiments, the semi-sentient problem recognition is based on
unstructured content. In embodiments, the semi-sentient problem
recognition is based on measures of human emotion.
[1670] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the machine learning model is
configured to prioritize collection and receipt of sensor data
related to simulations of the digital replica of the value chain
entity. In embodiments, the machine learning model is configured to
learn which types of sensor data are relevant to dynamics of the
value chain entity and simulation thereof.
[1671] In embodiments, the machine learning model is configured to
make suggestions to a user of the information technology system
regarding potential changes to the plurality of sensors that would
improve simulation of the value chain entity via the digital twin
system. In embodiments, the machine learning model is configured to
prioritize collection and transmission of sensor data that are
relevant to dynamics of the value chain entity and simulation
thereof.
[1672] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the machine learning model is
configured to determine which types of the sensor data are to be
included in the simulation data used to create the digital replica
of the value chain entity by the digital twin system, the machine
learning model determining which types of the sensor data are to be
included based on one or both of a modeling goal and a quality of
the type of sensor data.
[1673] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the artificial intelligence system
includes a model interpretability system, the model
interpretability system being configured to facilitate human
understanding of training and outputs of the machine learning
model.
[1674] In embodiments, the model interpretability system includes
one or more of linear regression, logistic regression, a
generalized linear model (GLM), a generalized additive model (GAM),
a decision tree, a decision rule, RuleFit, Naive Bayes Classifier,
a K-nearest neighbors algorithm, a partial dependence plot,
individual conditional expectation (ICE), an accumulated local
effects (ALE) plot, feature interaction, permutation feature
importance, a global surrogate model, a local surrogate (LIME)
model, scoped rules, i.e. anchors, Shapley values, Shapley additive
explanations (SHAP), feature visualization, and network
dissection.
[1675] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the artificial intelligence system
includes a model interpretability system, the model
interpretability system being configured to facilitate human
understanding of training and outputs of the machine learning
model; and wherein the model dataset visualization system provides
human-readable analysis related to distribution of values across
features of the simulation data.
[1676] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the artificial intelligence system
includes an embedded model interpretability system, the embedded
model interpretability system being configured to facilitate human
understanding of training and outputs of the machine learning
model, and the embedded model interpretability system including a
model dataset visualization system. In embodiments, the embedded
model interpretability system implements a Bayesian case model that
facilitates human understanding of the cognition of the machine
learning model. In embodiments, the embedded model interpretability
system implements a glass box model that facilitates human
understanding of the cognition of the machine learning model. In
embodiments, the glass box model is a Gaussian process model.
[1677] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data;
[1678] wherein the artificial intelligence system includes a model
interpretability system, the model interpretability system being
configured to facilitate human understanding of training and
outputs of the machine learning model; and wherein the model
interpretability system is configured to implement Testing with
Concept Activation Vectors (TCAV) functionality, whereby the model
interpretability facilitates learning of human-interpretable
concepts by the machine learning model.
[1679] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the digital replica of the value chain
entity is configured to accept a modeling command from a user of
the information technology system to facilitate simulation of the
value chain entity, the digital twin system being configured to
adjust the digital replica in response to the modeling command; and
wherein the machine learning model is configured to predict the
modeling command and suggest the predicted modeling command to the
user based on the sensor data, prior modeling commands, and
simulation of the value chain entity.
[1680] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the machine learning model is
configured to determine which instances of the sensor data to
include in transformation into the simulation data based on
training of the machine learning model.
[1681] In embodiments, an information technology system leveraging
digital twins in a value chain having a plurality of value chain
entities, the information technology system comprising: a plurality
of sensors positioned in, on, and/or near a value chain entity of
the value chain entities and configured to collect sensor data
related to the value chain entity, the sensor data being
substantially real-time sensor data; and an adaptive intelligence
system connected to the plurality of sensors and configured to
receive the sensor data from the plurality of sensors, the adaptive
intelligence system including: an artificial intelligence system
configured to input the sensor data into a machine learning model,
the sensor data being used as training data for the machine
learning model, the machine learning model being configured to
transform the sensor data into simulation data; and a digital twin
system configured to create a digital replica of the value chain
entity based on the simulation data, the digital replica of the
value chain entity providing for substantially real-time
representation of the value chain entity and providing for
simulation of a possible future state of the value chain entity via
the simulation data; wherein the machine learning model is
configured to provide a plurality of sets of hypothetical
simulation data to the digital twin system based on training of the
machine learning model, the digital twin system being configured to
create a set of hypothetical digital replicas of the value chain
entity, each of the hypothetical digital replicas being based on a
set of hypothetical simulation data and the simulation data; and
wherein the machine learning model is configured to evaluate
performance of each hypothetical digital replica of the set of
hypothetical digital replicas, determine which hypothetical digital
replicas perform better than others of the set of hypothetical
digital replicas, and determine why the hypothetical digital
replicas that perform better than the others of the set of
hypothetical digital replicas perform better than the others of the
set of hypothetical digital replicas.
[1682] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
user interface that provides a set of unified views for a set of
demand management information and supply chain information for a
category of goods.
[1683] In embodiments, the user interface includes a voice operated
assistant. In the embodiments, the user interface includes a
digital twin for presenting a visual representation of a set of
attributes of a set of value chain network entities.
[1684] In embodiments, the user interface includes an interface for
configuring the adaptive intelligence facilities.
[1685] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a peer-to-peer
network system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of adaptive
intelligence facilities for automating a set of capabilities of the
platform includes an edge intelligence system deployed in a supply
chain infrastructure facility operated by the enterprise.
[1686] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger.
[1687] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses graph
database representing a set of hierarchical relationships of value
chain network entities. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
an Internet of Things monitoring system. In embodiments, the set of
monitoring facilities for monitoring the value chain network
entities includes a sensor system deployed in an infrastructure
facility operated by an enterprise. In embodiments, the set of
applications includes a set of applications of at least two types
from among a set of supply chain management application, demand
management applications, intelligent product applications and
enterprise resource management applications. In embodiments, the
set of applications includes an asset management application.
[1688] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1689] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1690] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities. In
embodiments, the set of applications involves a set selected from
the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1691] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
unified database that supports a set of applications of at least
two types from among a set of demand management applications, a set
of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1692] In embodiments, the unified database that supports a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods is a
distributed database.
[1693] In embodiments, the unified database that supports a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods uses a
graph database architecture.
[1694] In embodiments, the set of demand management applications
includes a demand prediction application. In embodiments, the set
of demand management applications includes a demand aggregation
application. In embodiments, the set of demand management
applications includes a demand activation application. In
embodiments, the set of supply chain management applications
includes a vendor search application. In embodiments, the set of
supply chain management applications includes a route configuration
application. In embodiments, the set of supply chain management
applications includes a logistics scheduling application.
[1695] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a peer-to-peer
network system deployed in a supply chain infrastructure facility
operated by the enterprise.
[1696] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes an
edge intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1697] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain.
[1698] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities.
[1699] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1700] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application. In embodiments, the value chain
network entities are selected from the group consisting of
products, suppliers, producers, manufacturers, retailers,
businesses, owners, operators, operating facilities, customers,
consumers, workers, mobile devices, wearable devices, distributors,
resellers, supply chain infrastructure facilities, supply chain
processes, logistics processes, reverse logistics processes, demand
prediction processes, demand management processes, demand
aggregation processes, machines, ships, barges, warehouses,
maritime ports, airports, airways, waterways, roadways, railways,
bridges, tunnels, online retailers, ecommerce sites, demand
factors, supply factors, delivery systems, floating assets, points
of origin, points of destination, points of storage, points of use,
networks, information technology systems, software platforms,
distribution centers, fulfillment centers, containers, container
handling facilities, customs, export control, border control,
drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, and port infrastructure facilities.
In embodiments, the platform manages a set of demand factors, a set
of supply factors and a set of supply chain infrastructure
facilities. In embodiments, the supply factors are factors selected
from the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety factors.
In embodiments, the demand factors are factors selected from the
group consisting of product availability, product pricing, delivery
timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need for maintenance, need for update, need for
repair, need for consumable, taste, preference, inferred need,
inferred want, group demand, individual demand, family demand,
business demand, need for workflow, need for process, need for
procedure, need for treatment, need for improvement, need for
diagnosis, compatibility to system, compatibility to product,
compatibility to style, compatibility to brand, demographic,
psychographic, geolocation, indoor location, destination, route,
home location, visit location, workplace location, business
location, personality, mood, emotion, customer behavior, business
type, business activity, personal activity, wealth, income,
purchasing history, shopping history, search history, engagement
history, clickstream history, website history, online navigation
history, group behavior, family behavior, family membership,
customer identity, group identity, business identity, customer
profile, business profile, group profile, family profile, declared
interest, and inferred interest factors. In embodiments, the supply
chain infrastructure facilities are facilities selected from the
group consisting of ship, container ship, boat, barge, maritime
port, crane, container, container handling, shipyard, maritime
dock, warehouse, distribution, fulfillment, fueling, refueling,
nuclear refueling, waste removal, food supply, beverage supply,
drone, robot, autonomous vehicle, aircraft, automotive, truck,
train, lift, forklift, hauling facilities, conveyor, loading dock,
waterway, bridge, tunnel, airport, depot, vehicle station, train
station, weigh station, inspection, roadway, railway, highway,
customs house, and border control facilities. In embodiments, the
set of applications involves a set selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1701] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
unified set of data collection systems that support a set of
applications of at least two types from among a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods. In
embodiments, the unified set of data collection systems includes a
set of crowdsourcing data collection systems. In embodiments, the
unified set of data collection systems includes a set of Internet
of Things data collection systems. In embodiments, the unified set
of data collection systems includes a set of self-configuring
sensor systems. In embodiments, the unified set of data collection
systems includes a set of data collection systems that interact
with a network-connected product. In embodiments, the unified set
of data collection systems includes a set of mobile data collectors
deployed in a set of value chain network environments operated by
an enterprise.
[1702] In embodiments, the unified set of data collection systems
includes a set of edge intelligence systems deployed in a set of
value chain network environments operated by an enterprise. In
embodiments, the set of interfaces includes a demand management
interface and a supply chain management interface. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes a
5G network system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1703] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes an Internet of Things system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications. In
embodiments, the set of applications includes an asset management
application.
[1704] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1705] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1706] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities. In
embodiments, the set of applications involves a set selected from
the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1707] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications
integrated with the platform for enabling an enterprise user of the
platform to manage a set of value chain network entities from a
point of origin to a point of customer use; and
[1708] a unified set of Internet of Things systems that provide
coordinated monitoring of a set of applications of at least two
types from among a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1709] In embodiments, the unified set of Internet of Things
systems includes a set of smart home Internet of Things devices to
enable monitoring of a set of demand factors and a set of Internet
of Things devices deployed in proximity to a set of supply chain
infrastructure facilities to enable monitoring of a set of supply
factors.
[1710] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1711] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1712] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities.
[1713] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, the unified set of Internet of Things
systems includes a set of workplace Internet of Things devices to
enable monitoring of a set of demand factors for a set of business
customers and a set of Internet of Things devices deployed in
proximity to a set of supply chain infrastructure facilities to
enable monitoring of a set of supply factors. In embodiments, the
value chain network entities are selected from the group consisting
of products, suppliers, producers, manufacturers, retailers,
businesses, owners, operators, operating facilities, customers,
consumers, workers, mobile devices, wearable devices, distributors,
resellers, supply chain infrastructure facilities, supply chain
processes, logistics processes, reverse logistics processes, demand
prediction processes, demand management processes, demand
aggregation processes, machines, ships, barges, warehouses,
maritime ports, airports, airways, waterways, roadways, railways,
bridges, tunnels, online retailers, ecommerce sites, demand
factors, supply factors, delivery systems, floating assets, points
of origin, points of destination, points of storage, points of use,
networks, information technology systems, software platforms,
distribution centers, fulfillment centers, containers, container
handling facilities, customs, export control, border control,
drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, and port infrastructure facilities.
In embodiments, the supply factors are factors selected from the
group consisting of Component availability, material availability,
component location, material location, component pricing, material
pricing, taxation, tariff, impost, duty, import regulation, export
regulation, border control, trade regulation, customs, navigation,
traffic, congestion, vehicle capacity, ship capacity, container
capacity, package capacity, vehicle availability, ship
availability, container availability, package availability, vehicle
location, ship location, container location, port location, port
availability, port capacity, storage availability, storage
capacity, warehouse availability, warehouse capacity, fulfillment
center location, fulfillment center availability, fulfillment
center capacity, asset owner identity, system compatibility, worker
availability, worker competency, worker location, goods pricing,
fuel pricing, energy pricing, route availability, route distance,
route cost, and route safety factors.
[1714] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities.
[1715] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, the unified set of Internet of Things
systems includes a set of Internet of Things devices to monitor a
set of consumer goods stores to enable monitoring of a set of
demand factors for a set of consumers and a set of Internet of
Things devices deployed in proximity to a set of supply chain
infrastructure facilities to enable monitoring of a set of supply
factors. In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities. In embodiments, the
supply factors are factors selected from the group consisting of
Component availability, material availability, component location,
material location, component pricing, material pricing, taxation,
tariff, impost, duty, import regulation, export regulation, border
control, trade regulation, customs, navigation, traffic,
congestion, vehicle capacity, ship capacity, container capacity,
package capacity, vehicle availability, ship availability,
container availability, package availability, vehicle location,
ship location, container location, port location, port
availability, port capacity, storage availability, storage
capacity, warehouse availability, warehouse capacity, fulfillment
center location, fulfillment center availability, fulfillment
center capacity, asset owner identity, system compatibility, worker
availability, worker competency, worker location, goods pricing,
fuel pricing, energy pricing, route availability, route distance,
route cost, and route safety factors. In embodiments, the demand
factors are factors selected from the group consisting of product
availability, product pricing, delivery timing, need for refill,
need for replacement, manufacturer recall, need for upgrade, need
for maintenance, need for update, need for repair, need for
consumable, taste, preference, inferred need, inferred want, group
demand, individual demand, family demand, business demand, need for
workflow, need for process, need for procedure, need for treatment,
need for improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities. In
embodiments, the set of applications involves a set selected from
the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of interfaces includes a demand management
interface and a supply chain management interface. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes a
5G network system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1716] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes an Internet of Things system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1717] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger.
[1718] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses graph
database representing a set of hierarchical relationships of value
chain network entities. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
an Internet of Things monitoring system. In embodiments, the set of
monitoring facilities for monitoring the value chain network
entities includes a sensor system deployed in an infrastructure
facility operated by an enterprise. In embodiments, the set of
applications includes a set of applications of at least two types
from among a set of supply chain management applications, demand
management applications, intelligent product applications and
enterprise resource management applications. In embodiments the set
of applications includes an asset management application.
[1719] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1720] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1721] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities. In
embodiments, the set of applications involves a set selected from
the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1722] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and for
a set of applications of at least two types from among a set of
supply chain applications, a set of demand management applications,
a set of intelligent product applications and a set of enterprise
resource management applications and having a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system. In embodiments, the set of supply
chain applications and demand management applications is selected
from the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of supply chain applications and demand
management applications is selected from the group consisting of
inventory management, demand prediction, demand aggregation,
pricing, blockchain, smart contract, positioning, placement,
promotion, analytics, finance, trading, arbitrage, customer
identity management, store planning, shelf-planning, customer route
planning, customer route analytics, commerce, ecommerce, payments,
customer relationship management, sales, marketing, advertising,
bidding, customer monitoring, customer process monitoring, customer
relationship monitoring, collaborative filtering, customer
profiling, customer feedback, similarity analytics, customer
clustering, product clustering, seasonality factor analytics,
customer behavior tracking, customer behavior analytics, product
design, product configuration, A/B testing, product variation
analytics, augmented reality, virtual reality, mixed reality,
customer demand profiling, customer mood, emotion or affect
detection, customer mood, emotion of affect analytics, business
entity profiling, customer enterprise profiling, demand matching,
location-based targeting, location-based offering, point of sale
interface, point of use interface, search, advertisement, entity
discovery, entity search, enterprise resource planning, workforce
management, customer digital twin, product pricing, product
bundling, product and service bundling, product assortment, upsell
offer configuration, customer feedback engagement, and customer
survey applications. In embodiments, the set of supply chain
applications and demand management applications is selected from
the group consisting of supply chain, asset management, risk
management, inventory management, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
supply chain digital twin, vendor profiling, supplier profiling,
manufacturer profiling, logistics entity profiling, enterprise
profiling, worker profiling, workforce profiling, component supply
policy management, warehousing, distribution, fulfillment, shipping
fleet management, vehicle fleet management, workforce management,
maritime fleet management, navigation, routing, shipping
management, opportunity matching, search, entity discovery, entity
search, distribution, delivery, and enterprise resource planning
applications. In embodiments, the set of supply chain applications
and demand management applications is selected from the group
consisting of asset management, risk management, inventory
management, blockchain, smart contract, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, compatibility testing, compatibility
management, incident management, predictive maintenance,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, waste reduction, augmented
reality, virtual reality, mixed reality, product design, product
configuration, product updating, product maintenance, product
support, product testing, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, product digital twin, opportunity matching, search,
advertisement, entity discovery, entity search, variation,
simulation, user interface, application programming interface,
connectivity management, natural language interface, voice/speech
interface, robotic interface, touch interface, haptic interface,
vision system interface, and enterprise resource planning
applications.
[1723] The system of claim 1, wherein the set of supply chain
applications and demand management applications is selected from
the group consisting of operations, finance, asset management,
supply chain management, demand management, human resource
management, product management, risk management, regulatory and
compliance management, inventory management, infrastructure
management, facilities management, analytics, trading, tax,
identity management, vendor management, process management, project
management, operations management, customer relationship
management, workforce management, incident management, research and
development, sales management, marketing management, fleet
management, opportunity analytics, decision support, strategic
planning, forecasting, resource management, and property management
applications.
[1724] In embodiments, the machine vision system includes an
artificial intelligence system that is trained to recognize a type
of value chain asset based on a labeled data set of images of such
type of value chain assets.
[1725] In embodiments, the digital twin presents an indicator of
the type of asset based on the output of the artificial
intelligence system. In embodiments, the machine vision system
includes an artificial intelligence system that is trained to
recognize a type of activity involving a set of value chain
entities based on a labeled data set of images of such type of
activity. In embodiments, the digital twin presents an indicator of
the type of activity based on the output of the artificial
intelligence system. In embodiments, the machine vision system
includes an artificial intelligence system that is trained to
recognize a safety hazard involving a value chain entity based on a
training data set that includes a set of images of value chain
network activities and a set of value chain network safety
outcomes. In embodiments, the digital twin presents an indicator of
the hazard based on the output of the artificial intelligence
system. In embodiments, the machine vision system includes an
artificial intelligence system that is trained to predict a delay
based on a training data set that includes a set of images of value
chain network activities and a set of value chain network timing
outcomes. In embodiments, the digital twin presents an indicator of
a likelihood of delay based on the output of the artificial
intelligence system. In embodiments, the set of interfaces includes
a demand management interface and a supply chain management
interface.
[1726] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a 5G network system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the
enterprise.
[1727] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes an
edge intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1728] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise.
[1729] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
smart contract system for automating a set of interactions among a
set of value chain network entities.
[1730] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed data architecture. In embodiments, the set of data
storage facilities for storing data collected and handled by the
platform uses a blockchain. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses a distributed ledger.
[1731] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses graph
database representing a set of hierarchical relationships of value
chain network entities.
[1732] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1733] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application. In embodiments, the value chain
network entities are selected from the group consisting of
products, suppliers, producers, manufacturers, retailers,
businesses, owners, operators, operating facilities, customers,
consumers, workers, mobile devices, wearable devices, distributors,
resellers, supply chain infrastructure facilities, supply chain
processes, logistics processes, reverse logistics processes, demand
prediction processes, demand management processes, demand
aggregation processes, machines, ships, barges, warehouses,
maritime ports, airports, airways, waterways, roadways, railways,
bridges, tunnels, online retailers, ecommerce sites, demand
factors, supply factors, delivery systems, floating assets, points
of origin, points of destination, points of storage, points of use,
networks, information technology systems, software platforms,
distribution centers, fulfillment centers, containers, container
handling facilities, customs, export control, border control,
drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, and port infrastructure facilities.
In embodiments, the platform manages a set of demand factors, a set
of supply factors and a set of supply chain infrastructure
facilities. In embodiments, the supply factors are factors selected
from the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety factors.
In embodiments, the demand factors are factors selected from the
group consisting of product availability, product pricing, delivery
timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need for maintenance, need for update, need for
repair, need for consumable, taste, preference, inferred need,
inferred want, group demand, individual demand, family demand,
business demand, need for workflow, need for process, need for
procedure, need for treatment, need for improvement, need for
diagnosis, compatibility to system, compatibility to product,
compatibility to style, compatibility to brand, demographic,
psychographic, geolocation, indoor location, destination, route,
home location, visit location, workplace location, business
location, personality, mood, emotion, customer behavior, business
type, business activity, personal activity, wealth, income,
purchasing history, shopping history, search history, engagement
history, clickstream history, website history, online navigation
history, group behavior, family behavior, family membership,
customer identity, group identity, business identity, customer
profile, business profile, group profile, family profile, declared
interest, and inferred interest factors. In embodiments, the supply
chain infrastructure facilities are facilities selected from the
group consisting of ship, container ship, boat, barge, maritime
port, crane, container, container handling, shipyard, maritime
dock, warehouse, distribution, fulfillment, fueling, refueling,
nuclear refueling, waste removal, food supply, beverage supply,
drone, robot, autonomous vehicle, aircraft, automotive, truck,
train, lift, forklift, hauling facilities, conveyor, loading dock,
waterway, bridge, tunnel, airport, depot, vehicle station, train
station, weigh station, inspection, roadway, railway, highway,
customs house, and border control facilities.
[1734] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[1735] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
unified set of adaptive edge computing systems that provide
coordinated edge computation for a set of applications of at least
two types from among a set of demand management applications, a set
of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods. In embodiments, the unified
set of adaptive edge computing systems that provide coordinated
edge computation includes systems selected from the group
consisting of image classification systems, video compression
systems, analog-to-digital transformation systems,
digital-to-analog transformation systems, RF filtering systems,
motion prediction systems, object type recognition systems, point
cloud processing systems, analog signal processing systems,
multiplexing systems, data filtering systems, statistical signal
processing systems, signal filtering systems, signal processing
systems, protocol selection systems, storage configuration systems,
power management systems, clustering systems, variation systems,
machine learning systems, event prediction systems, autonomous
control systems, robotic control systems, robotic process
automation systems, data visualization systems, data normalization
systems, data cleansing system, data deduplication systems,
graph-based data storage systems, object-oriented data storage
systems, self-configuration systems, self-healing systems,
handshake negotiation systems, entity discovery systems,
cybersecurity systems, biometric systems, natural language
processing systems, sound processing systems, ultrasound processing
systems, artificial intelligence systems, rules engine systems,
workflow automation systems, opportunity discovery systems,
physical modeling systems, testing systems, diagnostic systems,
software image propagation systems, peer-to-peer network
configuration systems, RF spectrum management systems, network
resource management systems, storage management systems, data
management systems, intrusion detection systems, firewall systems,
virtualization systems, digital twin systems, Internet of Things
monitoring systems, routing systems, switching systems, indoor
location systems, and geolocation systems. In embodiments, the
interface is a user interface for a command center dashboard by
which an enterprise orchestrates a set of value chain entities
related to a type of product. In embodiments, the interface is a
user interface of a local management system located in an
environment that hosts a set of value chain entities. In
embodiments, the local management system user interface facilitates
configuration of a set of network connections for the adaptive edge
computing systems. In embodiments, the local management system user
interface facilitates configuration of a set of data storage
resources for the adaptive edge computing systems.
[1736] In embodiments, the local management system user interface
facilitates configuration of a set of data integration capabilities
for the adaptive edge computing systems.
[1737] In embodiments, the local management system user interface
facilitates configuration of a set of machine learning input
resources for the adaptive edge computing systems. In embodiments,
the local management system user interface facilitates
configuration of a set of power resources that support the adaptive
edge computing systems.
[1738] In embodiments, the local management system user interface
facilitates configuration of a set of workflows that are managed by
the adaptive edge computing systems. In embodiments, the interface
is a user interface of a mobile computing device that has a network
connection to the adaptive edge computing systems. In embodiments,
the interface is an application programming interface. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive edge computing systems and a
cloud-based artificial intelligence system. In embodiments, the
application programming interface facilitates exchange of data
between the adaptive edge computing systems and a real-time
operating system of a cloud data management platform. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive edge computing systems and a
computational facility of a cloud data management platform. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive edge computing systems and a
set of environmental sensors that collect data about an environment
that hosts a set of value chain network entities.
[1739] In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of sensors that collect data about a product. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive edge computing systems and a
set of sensors that collect data published by an intelligent
product. In embodiments, the application programming interface
facilitates exchange of data between the adaptive edge computing
systems and a set of sensors that collect data published by a set
of Internet of Things systems that are disposed in an environment
that hosts a set of value chain network entities. In embodiments,
the set of demand management applications, supply chain
applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of interfaces includes a demand management
interface and a supply chain management interface. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes a
5G network system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes an Internet of
Things system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of network
connectivity facilities for enabling a set of value chain network
entities to connect to the platform includes a cognitive networking
system deployed in a supply chain infrastructure facility operated
by the enterprise. In embodiments, the set of network connectivity
facilities for enabling a set of value chain network entities to
connect to the platform includes a peer-to-peer network system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes an edge intelligence system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities.
[1740] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1741] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application.
[1742] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1743] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities.
[1744] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1745] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[1746] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities. In embodiments, the set of
applications involves a set selected from the group consisting of
supply chain, asset management, risk management, inventory
management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1747] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
unified set of adaptive intelligence systems that provide
coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1748] In embodiments, the unified set of adaptive intelligence
systems includes systems selected from the group consisting of
artificial intelligence systems, neural networks, deep learning
systems, model-based systems, expert systems, machine learning
systems, rule-based systems, opportunity miners, robotic process
automation systems, data transformation systems, data extraction
systems, data loading systems, genetic programming systems, image
classification systems, video compression systems,
analog-to-digital transformation systems, digital-to-analog
transformation systems, signal analysis systems, RF filtering
systems, motion prediction systems, object type recognition
systems, point cloud processing systems, analog signal processing
systems, signal multiplexing systems, data fusion systems, sensor
fusion systems, data filtering systems, statistical signal
processing systems, signal filtering systems, signal processing
systems, protocol selection systems, storage configuration systems,
power management systems, clustering systems, variation systems,
machine learning systems, event prediction systems, autonomous
control systems, robotic control systems, robotic process
automation systems, data visualization systems, data normalization
systems, data cleansing systems, data deduplication systems,
graph-based data storage systems, intelligent agent systems,
object-oriented data storage systems, self-configuration systems,
self-healing systems, self-organizing systems, self-organizing map
systems, cost-based routing systems, handshake negotiation systems,
entity discovery systems, cybersecurity systems, biometric systems,
natural language processing systems, speech processing systems,
voice recognition systems, sound processing systems, ultrasound
processing systems, artificial intelligence systems, rules engine
systems, workflow automation systems, opportunity discovery
systems, physical modeling systems, testing systems, diagnostic
systems, software image propagation systems, peer-to-peer network
configuration systems, RF spectrum management systems, network
resource management systems, storage management systems, data
management systems, intrusion detection systems, firewall systems,
virtualization systems, digital twin systems, Internet of Things
monitoring systems, routing systems, switching systems, indoor
location systems, geolocation systems, parsing systems, semantic
filtering systems, machine vision systems, fuzzy logic systems,
recommendation systems, and dialog management systems. In
embodiments, the unified set of adaptive intelligent systems
includes a set of artificial intelligence systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
neural networks. In embodiments, the unified set of adaptive
intelligent system includes a set of deep learning systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of model-based systems. In embodiments, the unified
set of adaptive intelligent system includes a set of expert
systems. In embodiments, the unified set of adaptive intelligent
system includes a set of machine learning systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
rule-based systems. In embodiments, the unified set of adaptive
intelligent system includes a set of opportunity miners. In
embodiments, the unified set of adaptive intelligent system
includes a set of robotic process automation systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of data transformation systems. In embodiments, the
unified set of adaptive intelligent system includes a set of data
extraction systems. In embodiments, the unified set of adaptive
intelligent system includes a set of data loading systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of genetic programming systems. In embodiments, the
unified set of adaptive intelligent system includes a set of image
classification systems. In embodiments, the unified set of adaptive
intelligent system includes a set of video compression systems.
[1749] In embodiments, the unified set of adaptive intelligent
system includes a set of analog-to-digital transformation systems.
In embodiments, the unified set of adaptive intelligent system
includes a set of digital-to-analog transformation systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of signal analysis systems. In embodiments, the
unified set of adaptive intelligent system includes a set of RF
filtering systems. In embodiments, the unified set of adaptive
intelligent system includes a set of motion prediction systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of object type recognition systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
point cloud processing systems. In embodiments, the unified set of
adaptive intelligent system includes a set of analog signal
processing systems. In embodiments, the unified set of adaptive
intelligent system includes a set of signal multiplexing systems.
In embodiments, the unified set of adaptive intelligent system
includes a set of data fusion systems.
[1750] In embodiments, the unified set of adaptive intelligent
system includes a set of sensor fusion systems.
[1751] In embodiments, the unified set of adaptive intelligent
system includes a set of data filtering systems.
[1752] In embodiments, the unified set of adaptive intelligent
system includes a set of statistical signal processing systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of signal filtering systems.
[1753] In embodiments, the unified set of adaptive intelligent
system includes a set of signal processing systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
protocol selection systems. In embodiments, the unified set of
adaptive intelligent system includes a set of storage configuration
systems. In embodiments, the unified set of adaptive intelligent
system includes a set of power management systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
clustering systems. In embodiments, the unified set of adaptive
intelligent system includes a set of variation systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of machine learning systems.
[1754] In embodiments, the unified set of adaptive intelligent
system includes a set of event prediction systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
autonomous control systems. In embodiments, the unified set of
adaptive intelligent system includes a set of robotic control
systems. In embodiments, the unified set of adaptive intelligent
system includes a set of robotic process automation systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of data visualization systems. In embodiments, the
unified set of adaptive intelligent system includes a set of data
normalization systems. In embodiments, the unified set of adaptive
intelligent system includes a set of data cleansing systems.
[1755] In embodiments, the unified set of adaptive intelligent
system includes a set of data deduplication systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of graph-based data storage systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
intelligent agent systems. In embodiments, the unified set of
adaptive intelligent system includes a set of object-oriented data
storage systems. In embodiments, the unified set of adaptive
intelligent system includes a set of self-configuration systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of self-healing systems. In embodiments, the unified
set of adaptive intelligent system includes a set of
self-organizing systems.
[1756] In embodiments, the unified set of adaptive intelligent
system includes a set of self-organizing map systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of cost-based routing systems. In embodiments, the
unified set of adaptive intelligent system includes a set of
handshake negotiation systems. In embodiments, the unified set of
adaptive intelligent system includes a set of entity discovery
systems. In embodiments, the unified set of adaptive intelligent
system includes a set of cybersecurity systems. In embodiments, the
unified set of adaptive intelligent system includes a set of
biometric systems. In embodiments, the unified set of adaptive
intelligent system includes a set of natural language processing
systems. In embodiments, the unified set of adaptive intelligent
system includes a set of speech processing systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
voice recognition systems.
[1757] In embodiments, the unified set of adaptive intelligent
system includes a set of sound processing systems.
[1758] In embodiments, the unified set of adaptive intelligent
system includes a set of ultrasound processing systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of artificial intelligence systems.
[1759] In embodiments, the unified set of adaptive intelligent
system includes a set of rules engine systems.
[1760] In the embodiments, the unified set of adaptive intelligent
system includes a set of workflow automation systems.
[1761] In embodiments, the unified set of adaptive intelligent
system includes a set of opportunity discovery systems.
[1762] In embodiments, the unified set of adaptive intelligent
system includes a set of physical modeling systems.
[1763] In embodiments, the unified set of adaptive intelligent
system includes a set of testing systems.
[1764] In embodiments, the unified set of adaptive intelligent
system includes a set of diagnostic systems.
[1765] In embodiments, the unified set of adaptive intelligent
system includes a set of software image propagation systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of peer-to-peer network configuration systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of RF spectrum management systems. In embodiments,
the unified set of adaptive intelligent system includes a set of
network resource management systems. In embodiments, the unified
set of adaptive intelligent system includes a set of storage
management systems. In embodiments, the unified set of adaptive
intelligent system includes a set of data management systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of intrusion detection systems. In embodiments, the
unified set of adaptive intelligent system includes a set of
firewall systems. In embodiments, the unified set of adaptive
intelligent system includes a set of virtualization systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of digital twin systems.
[1766] In embodiments, the unified set of adaptive intelligent
system includes a set of Internet of Things monitoring systems. In
embodiments, the unified set of adaptive intelligent system
includes a set of routing systems.
[1767] In embodiments, the unified set of adaptive intelligent
system includes a set of switching systems.
[1768] In embodiments, the unified set of adaptive intelligent
system includes a set of indoor location systems.
[1769] In embodiments, the unified set of adaptive intelligent
system includes a set of geolocation systems.
[1770] In embodiments, the unified set of adaptive intelligent
system includes a set of parsing systems.
[1771] In embodiments, the unified set of adaptive intelligent
system includes a set of semantic filtering systems.
[1772] In embodiments, the unified set of adaptive intelligent
system includes a set of machine vision systems.
[1773] In embodiments, the unified set of adaptive intelligent
system includes a set of fuzzy logic systems.
[1774] In embodiments, the unified set of adaptive intelligent
system includes a set of recommendation systems.
[1775] In embodiments, the unified set of adaptive intelligent
system includes a set of dialog management systems.
[1776] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the interface is a user interface for a command center
dashboard by which an enterprise orchestrates a set of value chain
entities related to a type of product. In embodiments, the
interface is a user interface of a local management system located
in an environment that hosts a set of value chain entities.
[1777] In embodiments, the local management system user interface
facilitates configuration of a set of network connections for the
adaptive intelligence systems. In embodiments, the local management
system user interface facilitates configuration of a set of data
storage resources for the adaptive intelligence systems.
[1778] In embodiments, the local management system user interface
facilitates configuration of a set of data integration capabilities
for the adaptive intelligence systems. In embodiments, the local
management system user interface facilitates configuration of a set
of machine learning input resources for the adaptive intelligence
systems.
[1779] In embodiments, the local management system user interface
facilitates configuration of a set of power resources that support
the adaptive intelligence systems. In embodiments, the local
management system user interface facilitates configuration of a set
of workflows that are managed by the adaptive intelligence
systems.
[1780] In embodiments, the interface is a user interface of a
mobile computing device that has a network connection to the
adaptive intelligence systems. In embodiments, the interface is an
application programming interface.
[1781] In embodiments, the application programming interface
facilitates exchange of data between the adaptive intelligence
systems and a cloud-based artificial intelligence system. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive intelligence systems and a
real-time operating system of a cloud data management platform. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive intelligence systems and a
computational facility of a cloud data management platform. In
embodiments, the application programming interface facilitates
exchange of data between the adaptive intelligence systems and a
set of environmental sensors that collect data about an environment
that hosts a set of value chain network entities. In embodiments,
the application programming interface facilitates exchange of data
between the adaptive intelligence systems and a set of sensors that
collect data about a product. In embodiments, the application
programming interface facilitates exchange of data between the
adaptive intelligence systems and a set of sensors that collect
data published by an intelligent product. In embodiments, the
application programming interface facilitates exchange of data
between the adaptive intelligence systems and a set of sensors that
collect data published by a set of Internet of Things systems that
are disposed in an environment that hosts a set of value chain
network entities. In embodiments, the set of demand management
applications, supply chain applications, intelligent product
applications and enterprise resource management applications are
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, the set of network connectivity
facilities for enabling a set of value chain network entities to
connect to the platform includes a 5G network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the
enterprise.
[1782] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes an
edge intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1783] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise.
[1784] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
smart contract system for automating a set of interactions among a
set of value chain network entities.
[1785] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed data architecture. In embodiments, the set of data
storage facilities for storing data collected and handled by the
platform uses a blockchain. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses a distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications.
[1786] In embodiments, the set of applications includes an asset
management application. In embodiments, the value chain network
entities are selected from the group consisting of products,
suppliers, producers, manufacturers, retailers, businesses, owners,
operators, operating facilities, customers, consumers, workers,
mobile devices, wearable devices, distributors, resellers, supply
chain infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1787] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1788] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities.
[1789] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, an information technology system
including a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of project management facilities that
provide automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods. In embodiments, the set of
project management facilities are configured to manage a set of
projects selected from the set of procurement projects, logistics
projects, reverse logistics projects, fulfillment projects,
distribution projects, warehousing projects, inventory management
projects, product design projects, product management projects,
shipping projects, maritime projects, loading or unloading
projects, packing projects, purchasing projects, marketing
projects, sales projects, analytics projects, demand management
projects, demand planning projects, and resource planning
projects.
[1790] In embodiments, the project management facilities are
configured to manage a set of procurement projects. In embodiments,
the project management facilities are configured to manage a set of
logistics projects. In embodiments, the project management
facilities are configured to manage a set of reverse logistics
projects. In embodiments, the project management facilities are
configured to manage a set of fulfillment projects. In embodiments,
the project management facilities are configured to manage a set of
distribution projects. In embodiments, the project management
facilities are configured to manage a set of warehousing
projects.
[1791] In embodiments, the project management facilities are
configured to manage a set of inventory management projects. In
embodiments, the project management facilities are configured to
manage a set of product design projects. In embodiments, the
project management facilities are configured to manage a set of
product management projects. In embodiments, the project management
facilities are configured to manage a set of shipping projects. In
embodiments, the project management facilities are configured to
manage a set of maritime projects. In embodiments, the project
management facilities are configured to manage a set of loading or
unloading projects. In embodiments, the project management
facilities are configured to manage a set of packing projects. In
embodiments, the project management facilities are configured to
manage a set of purchasing projects.
[1792] In embodiments, the project management facilities are
configured to manage a set of marketing projects.
[1793] In embodiments, the project management facilities are
configured to manage a set of sales projects.
[1794] In embodiments, the project management facilities are
configured to manage a set of analytics projects.
[1795] In embodiments, the project management facilities are
configured to manage a set of demand management projects. In
embodiments, the project management facilities are configured to
manage a set of demand planning projects. In embodiments, the
project management facilities are configured to manage a set of
resource planning projects. In embodiments, the set of interfaces
includes a demand management interface and a supply chain
management interface. In embodiments, the set of demand management
applications, supply chain applications, intelligent product
applications and enterprise resource management applications are
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, the set of network connectivity
facilities for enabling a set of value chain network entities to
connect to the platform includes a 5G network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities.
[1796] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed data architecture. In embodiments, the set of data
storage facilities for storing data collected and handled by the
platform uses a blockchain. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses a distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications. In
embodiments, the set of applications includes an asset management
application. In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities. In embodiments, the
platform manages a set of demand factors, a set of supply factors
and a set of supply chain infrastructure facilities. In
embodiments, the supply factors are factors selected from the group
consisting of Component availability, material availability,
component location, material location, component pricing, material
pricing, taxation, tariff, impost, duty, import regulation, export
regulation, border control, trade regulation, customs, navigation,
traffic, congestion, vehicle capacity, ship capacity, container
capacity, package capacity, vehicle availability, ship
availability, container availability, package availability, vehicle
location, ship location, container location, port location, port
availability, port capacity, storage availability, storage
capacity, warehouse availability, warehouse capacity, fulfillment
center location, fulfillment center availability, fulfillment
center capacity, asset owner identity, system compatibility, worker
availability, worker competency, worker location, goods pricing,
fuel pricing, energy pricing, route availability, route distance,
route cost, and route safety factors. In embodiments, the demand
factors are factors selected from the group consisting of product
availability, product pricing, delivery timing, need for refill,
need for replacement, manufacturer recall, need for upgrade, need
for maintenance, need for update, need for repair, need for
consumable, taste, preference, inferred need, inferred want, group
demand, individual demand, family demand, business demand, need for
workflow, need for process, need for procedure, need for treatment,
need for improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities. In
embodiments, the set of applications involves a set selected from
the group consisting of supply chain, asset management, risk
management, inventory management, demand management, demand
prediction, demand aggregation, pricing, positioning, placement,
promotion, blockchain, smart contract, infrastructure management,
facility management, analytics, finance, trading, tax, regulatory,
identity management, commerce, ecommerce, payments, security,
safety, vendor management, process management, compatibility
testing, compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1797] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
set of facilities that provide automated recommendations for a set
of value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1798] In embodiments, the set of facilities that provide automated
recommendations for a set of value chain process tasks provide
recommendations involving activities selected from the group
consisting of product configuration activities, product selection
activities for a customer, supplier selection activities, shipper
selection activities, route selection activities, factory selection
activities, product assortment activities, product management
activities, logistics activities, reverse logistics activities,
artificial intelligence configuration activities, maintenance
activities, product support activities, product recommendation
activities. In embodiments, the automated recommendations relate to
a set of product configuration activities. In embodiments, the
automated recommendations relate to a set of product selection
activities for a customer. In embodiments, the automated
recommendations relate to a set of supplier selection activities.
In embodiments, the automated recommendations relate to a set of
shipper selection activities. In embodiments, the automated
recommendations relate to a set of route selection activities. In
embodiments, the automated recommendations relate to a set of
factory selection activities. In embodiments, the automated
recommendations relate to a set of product assortment activities.
In embodiments, the automated recommendations relate to a set of
product management activities. In embodiments, the automated
recommendations relate to a set of logistics activities. In
embodiments, the automated recommendations relate to a set of
reverse logistics activities. In embodiments, the automated
recommendations relate to a set of artificial intelligence
configuration activities. In embodiments, the automated
recommendations relate to a set of maintenance activities. In
embodiments, the automated recommendations relate to a set of
product support activities. In embodiments, the automated
recommendations relate to a set of product recommendation
activities. In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a peer-to-peer
network system deployed in a supply chain infrastructure facility
operated by the enterprise. In embodiments, the set of adaptive
intelligence facilities for automating a set of capabilities of the
platform includes an edge intelligence system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes a robotic
process automation system. In embodiments, the set of adaptive
intelligence facilities for automating a set of capabilities of the
platform includes a self-configuring data collection system
deployed in a supply chain infrastructure facility operated by the
enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a digital twin system representing attributes of value
chain network entity controlled by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a smart contract system for
automating a set of interactions among a set of value chain network
entities. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed data architecture. In embodiments, the set of data
storage facilities for storing data collected and handled by the
platform uses a blockchain. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses a distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities.
[1799] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1800] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application.
[1801] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1802] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities.
[1803] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1804] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[1805] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[1806] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[1807] In embodiments, an information technology system including a
cloud-based management platform for a value chain network with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities that are coordinated
for monitoring and management of a set of value chain network
entities; and a set of applications for enabling an enterprise to
manage a set of value chain network entities from a point of origin
to a point of customer use; wherein a set of routing facilities
generate a set of routing instructions for routing information
among a set of nodes in the value chain network based on current
status information for the value chain network.
[1808] In embodiments, the set of routing facilities that generate
a set of routing instructions for routing information among a set
of nodes in the value chain network use a routing system selected
from the group consisting of priority-based routing, master
controller routing, least cost routing, rule-based routing,
genetically programmed routing, random linear network coding
routing, traffic-based routing, spectrum-based routing, RF
condition-based routing, energy-based routing, latency-sensitive
routing, protocol compatibility based routing, dynamic spectrum
access routing, peer-to-peer negotiated routing, and queue-based
routing. In embodiments, the routing includes priority-based
routing. In embodiments, the routing includes master controller
routing. In embodiments, the routing includes least cost routing.
In embodiments, the routing includes rule-based routing. In
embodiments, the routing includes genetically programmed routing.
In embodiments, the routing includes random linear network coding
routing. In embodiments, the routing includes traffic-based
routing. In embodiments, the routing includes spectrum-based
routing. In embodiments, the routing includes RF condition-based
routing. In embodiments, the routing includes energy-based routing.
In embodiments, the routing includes latency-sensitive routing. In
embodiments, the routing includes protocol compatibility-based
routing. In embodiments, the routing includes dynamic spectrum
access routing. In embodiments, the routing includes peer-to-peer
negotiated routing.
[1809] In embodiments, the routing includes queue-based routing. In
embodiments, the status information for the value chain network is
selected from the group consisting of traffic status, congestion
status, bandwidth status, operating status, workflow progress
status, incident status, damage status, safety status, power
availability status, worker status, data availability status,
predicted system status, shipment location status, shipment timing
status, delivery status, anticipated delivery status, environmental
condition status, system diagnostic status, system fault status,
cybersecurity status, compliance status, demand status, supply
status, price status, volatility status, need status, interest
status, aggregate status for a group or population, and individual
status. In embodiments, the status information involves traffic
status. In embodiments, the status information involves congestion
status. In embodiments, the status information involves bandwidth
status. In embodiments, the status information involves operating
status. In embodiments, the status information involves workflow
progress status. In embodiments, the status information involves
incident status. In embodiments, the status information involves
damage status. In embodiments, the status information involves
safety status. In embodiments, the status information involves
power availability status. In embodiments, the status information
involves worker status. In embodiments, the status information
involves data availability status. In embodiments, the status
information involves predicted system status. In embodiments, the
status information involves shipment location status. In
embodiments, the status information involves shipment timing
status. In embodiments, the status information involves delivery
status. In embodiments, the status information involves anticipated
delivery status. In embodiments, the status information involves
environmental condition status. In embodiments, the status
information involves system diagnostic status. In embodiments, the
status information involves system fault status. In embodiments,
the status information involves cybersecurity status. In
embodiments, the status information involves compliance status.
[1810] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1811] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger.
[1812] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses graph
database representing a set of hierarchical relationships of value
chain network entities.
[1813] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1814] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application. In embodiments, the value chain
network entities are selected from the group consisting of
products, suppliers, producers, manufacturers, retailers,
businesses, owners, operators, operating facilities, customers,
consumers, workers, mobile devices, wearable devices, distributors,
resellers, supply chain infrastructure facilities, supply chain
processes, logistics processes, reverse logistics processes, demand
prediction processes, demand management processes, demand
aggregation processes, machines, ships, barges, warehouses,
maritime ports, airports, airways, waterways, roadways, railways,
bridges, tunnels, online retailers, ecommerce sites, demand
factors, supply factors, delivery systems, floating assets, points
of origin, points of destination, points of storage, points of use,
networks, information technology systems, software platforms,
distribution centers, fulfillment centers, containers, container
handling facilities, customs, export control, border control,
drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, and port infrastructure facilities.
In embodiments, the platform manages a set of demand factors, a set
of supply factors and a set of supply chain infrastructure
facilities. In embodiments, the supply factors are factors selected
from the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety factors.
In embodiments, the demand factors are factors selected from the
group consisting of product availability, product pricing, delivery
timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need for maintenance, need for update, need for
repair, need for consumable, taste, preference, inferred need,
inferred want, group demand, individual demand, family demand,
business demand, need for workflow, need for process, need for
procedure, need for treatment, need for improvement, need for
diagnosis, compatibility to system, compatibility to product,
compatibility to style, compatibility to brand, demographic,
psychographic, geolocation, indoor location, destination, route,
home location, visit location, workplace location, business
location, personality, mood, emotion, customer behavior, business
type, business activity, personal activity, wealth, income,
purchasing history, shopping history, search history, engagement
history, clickstream history, website history, online navigation
history, group behavior, family behavior, family membership,
customer identity, group identity, business identity, customer
profile, business profile, group profile, family profile, declared
interest, and inferred interest factors. In embodiments, the supply
chain infrastructure facilities are facilities selected from the
group consisting of ship, container ship, boat, barge, maritime
port, crane, container, container handling, shipyard, maritime
dock, warehouse, distribution, fulfillment, fueling, refueling,
nuclear refueling, waste removal, food supply, beverage supply,
drone, robot, autonomous vehicle, aircraft, automotive, truck,
train, lift, forklift, hauling facilities, conveyor, loading dock,
waterway, bridge, tunnel, airport, depot, vehicle station, train
station, weigh station, inspection, roadway, railway, highway,
customs house, and border control facilities. In embodiments, the
set of applications involves a set selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1815] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
dashboard for managing a set of digital twins, wherein at least one
digital twin represents a set of supply chain entities, workflows
and assets and at least one other digital twin represents a set of
demand management entities and workflows. In embodiments, the
dashboard for managing a set of digital twins, wherein at least one
digital twin represents a set of supply chain entities and
workflows and at least one other digital twin represents a set of
demand management entities and workflows.
[1816] In embodiments, the entities and workflows relate to a set
of products of an enterprise. In embodiments, the entities and
workflows relate to a set of suppliers of an enterprise. In
embodiments, the entities and workflows relate to a set of
producers of a set of products. In embodiments, the entities and
workflows relate to a set of manufacturers of a set of products. In
embodiments, the entities and workflows relate to a set of
retailers of a line of products. In embodiments, the entities and
workflows relate to a set of businesses involved in an ecosystem
for a category of products. In embodiments, the entities and
workflows relate to a set of owners of a set of assets involved in
a value chain for a set of products. In embodiments, the entities
and workflows relate to a set of operators of a set of assets
involved in a value chain for a set of products. In embodiments,
the entities and workflows relate to a set of operating facilities.
In embodiments, the entities and workflows relate to a set of
customers. In embodiments, the entities and workflows relate to a
set of consumers. In embodiments, the entities and workflows relate
to a set of workers. In embodiments, the entities and workflows
relate to a set of mobile devices. In embodiments, the entities and
workflows relate to a set of wearable devices. In embodiments, the
entities and workflows relate to a set of distributors. In
embodiments, the entities and workflows relate to a set of
resellers. In embodiments, the entities and workflows relate to a
set of supply chain infrastructure facilities.
[1817] In embodiments, the entities and workflows relate to a set
of supply chain processes. In embodiments, the entities and
workflows relate to a set of logistics processes. In embodiments,
the entities and workflows relate to a set of reverse logistics
processes. In embodiments, the entities and workflows relate to a
set of demand prediction processes. In embodiments, the entities
and workflows relate to a set of demand management processes.
[1818] In embodiments, the entities and workflows relate to a set
of demand aggregation processes. In embodiments, the entities and
workflows relate to a set of machines. In embodiments, the entities
and workflows relate to a set of ships. In embodiments, the
entities and workflows relate to a set of barges. In embodiments,
the entities and workflows relate to a set of warehouses. In
embodiments, the entities and workflows relate to a set of maritime
ports. In embodiments, the entities and workflows relate to a set
of airports. In embodiments, the entities and workflows relate to a
set of airways. In embodiments the entities and workflows relate to
a set of waterways. In embodiments, the entities and workflows
relate to a set of roadways. In embodiments, the entities and
workflows relate to a set of railways. In embodiments, the entities
and workflows relate to a set of bridges. In embodiments, the
entities and workflows relate to a set of tunnels. In embodiments,
the entities and workflows relate to a set of online retailers. In
embodiments, the entities and workflows relate to a set of
ecommerce sites. In embodiments, the entities and workflows relate
to a set of demand factors. In embodiments, the entities and
workflows relate to a set of supply factors. In embodiments, the
entities and workflows relate to a set of delivery systems. In
embodiments, the entities and workflows relate to a set of floating
assets. In embodiments, the entities and workflows relate to a set
of points of origin. In embodiments, the entities and workflows
relate to a set of points of destination. In embodiments, the
entities and workflows relate to a set of points of storage. In
embodiments the entities and workflows relate to a set of points of
product usage. In embodiments the entities and workflows relate to
a set of networks. In embodiments, the entities and workflows
relate to a set of information technology systems. In embodiments,
the entities and workflows relate to a set of software platforms.
In embodiments, the entities and workflows relate to a set of
distribution centers. In embodiments, the entities and workflows
relate to a set of fulfillment centers. In embodiments, the
entities and workflows relate to a set of containers.
[1819] In embodiments, the entities and workflows relate to a set
of container handling facilities. In embodiments, the entities and
workflows relate to a set of customs. In embodiments, the entities
and workflows relate to a set of export control. In embodiments,
the entities and workflows relate to a set of border control. In
embodiments, the entities and workflows relate to a set of drones.
In embodiments, the entities and workflows relate to a set of
robots. In embodiments, the entities and workflows relate to a set
of autonomous vehicles. In embodiments, the entities and workflows
relate to a set of hauling facilities. In embodiments, the entities
and workflows relate to a set of drones, robots and autonomous
vehicles. In embodiments, the entities and workflows relate to a
set of waterways. In embodiments, the entities and workflows relate
to a set of port infrastructure facilities.
[1820] In embodiments, the set of digital twins is selected from
the set of distribution twins, warehousing twins, port
infrastructure twins, shipping facility twins, operating facility
twins, customer twins, worker twins, wearable device twins,
portable device twins, mobile device twins, process twins, machine
twins, asset twins, product twins, point of origin twins, point of
destination twins, supply factor twins, maritime facility twins,
floating asset twins, shipyard twins, fulfillment twins, delivery
system twins, demand factors twins, retailer twins, ecommerce
twins, online twins, waterway twins, roadway twins, roadway twins,
railway twins, air facility twins, aircraft twins, ship twins,
vehicle twins, train twins, autonomous vehicle twins, robotic
system twins, drone twins, and logistics factor twins.
[1821] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1822] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise.
[1823] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
smart contract system for automating a set of interactions among a
set of value chain network entities.
[1824] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed data architecture. In embodiments, the set of data
storage facilities for storing data collected and handled by the
platform uses a blockchain. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses a distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications.
[1825] In embodiments, the set of applications includes an asset
management application. In embodiments, the value chain network
entities are selected from the group consisting of products,
suppliers, producers, manufacturers, retailers, businesses, owners,
operators, operating facilities, customers, consumers, workers,
mobile devices, wearable devices, distributors, resellers, supply
chain infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities. In embodiments, the
platform manages a set of demand factors, a set of supply factors
and a set of supply chain infrastructure facilities.
[1826] In embodiments, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1827] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors.
[1828] In embodiments, the supply chain infrastructure facilities
are facilities selected from the group consisting of ship,
container ship, boat, barge, maritime port, crane, container,
container handling, shipyard, maritime dock, warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling,
waste removal, food supply, beverage supply, drone, robot,
autonomous vehicle, aircraft, automotive, truck, train, lift,
forklift, hauling facilities, conveyor, loading dock, waterway,
bridge, tunnel, airport, depot, vehicle station, train station,
weigh station, inspection, roadway, railway, highway, customs
house, and border control facilities.
[1829] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[1830] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services. In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1831] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a 5G network system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the
enterprise.
[1832] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes an
edge intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1833] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications. In
embodiments, the set of applications includes an asset management
application. In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities. In embodiments, the
platform manages a set of demand factors, a set of supply factors
and a set of supply chain infrastructure facilities.
[1834] In embodiment, the supply factors are factors selected from
the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety factors.
In embodiment, the demand factors are factors selected from the
group consisting of product availability, product pricing, delivery
timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need for maintenance, need for update, need for
repair, need for consumable, taste, preference, inferred need,
inferred want, group demand, individual demand, family demand,
business demand, need for workflow, need for process, need for
procedure, need for treatment, need for improvement, need for
diagnosis, compatibility to system, compatibility to product,
compatibility to style, compatibility to brand, demographic,
psychographic, geolocation, indoor location, destination, route,
home location, visit location, workplace location, business
location, personality, mood, emotion, customer behavior, business
type, business activity, personal activity, wealth, income,
purchasing history, shopping history, search history, engagement
history, clickstream history, website history, online navigation
history, group behavior, family behavior, family membership,
customer identity, group identity, business identity, customer
profile, business profile, group profile, family profile, declared
interest, and inferred interest factors. In embodiments, the supply
chain infrastructure facilities are facilities selected from the
group consisting of ship, container ship, boat, barge, maritime
port, crane, container, container handling, shipyard, maritime
dock, warehouse, distribution, fulfillment, fueling, refueling,
nuclear refueling, waste removal, food supply, beverage supply,
drone, robot, autonomous vehicle, aircraft, automotive, truck,
train, lift, forklift, hauling facilities, conveyor, loading dock,
waterway, bridge, tunnel, airport, depot, vehicle station, train
station, weigh station, inspection, roadway, railway, highway,
customs house, and border control facilities. In embodiments, the
set of applications involves a set selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1835] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; a set of applications for
enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of social network sources that provide information with respect
to supply chain entities and demand management entities.
[1836] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications.
[1837] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a 5G network system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the
enterprise.
[1838] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes an
edge intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1839] In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
robotic process automation system. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a self-configuring data
collection system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture. In embodiments, the set of data storage facilities
for storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities. In embodiments, the
set of monitoring facilities for monitoring the value chain network
entities includes an Internet of Things monitoring system. In
embodiments, the set of monitoring facilities for monitoring the
value chain network entities includes a sensor system deployed in
an infrastructure facility operated by an enterprise. In
embodiments, the set of applications includes a set of applications
of at least two types from among a set of supply chain management
applications, demand management applications, intelligent product
applications and enterprise resource management applications. In
embodiments, the set of applications includes an asset management
application.
[1840] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1841] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1842] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities.
[1843] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, an information technology system
including a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are coordinated for monitoring and
management of a set of value chain network entities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, the set of interfaces includes
a demand management interface and a supply chain management
interface. In embodiments, the set of demand management
applications, supply chain applications, intelligent product
applications and enterprise resource management applications are
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, the set of network connectivity
facilities for enabling a set of value chain network entities to
connect to the platform includes a 5G network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes an Internet of Things system deployed in a supply
chain infrastructure facility operated by the enterprise. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a cognitive networking system deployed in a
supply chain infrastructure facility operated by the
enterprise.
[1844] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture.
[1845] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities.
[1846] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1847] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application. In embodiments, the value chain
network entities are selected from the group consisting of
products, suppliers, producers, manufacturers, retailers,
businesses, owners, operators, operating facilities, customers,
consumers, workers, mobile devices, wearable devices, distributors,
resellers, supply chain infrastructure facilities, supply chain
processes, logistics processes, reverse logistics processes, demand
prediction processes, demand management processes, demand
aggregation processes, machines, ships, barges, warehouses,
maritime ports, airports, airways, waterways, roadways, railways,
bridges, tunnels, online retailers, ecommerce sites, demand
factors, supply factors, delivery systems, floating assets, points
of origin, points of destination, points of storage, points of use,
networks, information technology systems, software platforms,
distribution centers, fulfillment centers, containers, container
handling facilities, customs, export control, border control,
drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways, and port infrastructure facilities.
In embodiments, the platform manages a set of demand factors, a set
of supply factors and a set of supply chain infrastructure
facilities. In embodiments, the supply factors are factors selected
from the group consisting of Component availability, material
availability, component location, material location, component
pricing, material pricing, taxation, tariff, impost, duty, import
regulation, export regulation, border control, trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship
capacity, container capacity, package capacity, vehicle
availability, ship availability, container availability, package
availability, vehicle location, ship location, container location,
port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety factors.
In embodiments, the demand factors are factors selected from the
group consisting of product availability, product pricing, delivery
timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need for maintenance, need for update, need for
repair, need for consumable, taste, preference, inferred need,
inferred want, group demand, individual demand, family demand,
business demand, need for workflow, need for process, need for
procedure, need for treatment, need for improvement, need for
diagnosis, compatibility to system, compatibility to product,
compatibility to style, compatibility to brand, demographic,
psychographic, geolocation, indoor location, destination, route,
home location, visit location, workplace location, business
location, personality, mood, emotion, customer behavior, business
type, business activity, personal activity, wealth, income,
purchasing history, shopping history, search history, engagement
history, clickstream history, website history, online navigation
history, group behavior, family behavior, family membership,
customer identity, group identity, business identity, customer
profile, business profile, group profile, family profile, declared
interest, and inferred interest factors. In embodiments, the supply
chain infrastructure facilities are facilities selected from the
group consisting of ship, container ship, boat, barge, maritime
port, crane, container, container handling, shipyard, maritime
dock, warehouse, distribution, fulfillment, fueling, refueling,
nuclear refueling, waste removal, food supply, beverage supply,
drone, robot, autonomous vehicle, aircraft, automotive, truck,
train, lift, forklift, hauling facilities, conveyor, loading dock,
waterway, bridge, tunnel, airport, depot, vehicle station, train
station, weigh station, inspection, roadway, railway, highway,
customs house, and border control facilities.
[1848] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications. In embodiments, an information technology system
including a cloud-based management platform with a micro-services
architecture; and a set of interfaces, network connectivity
facilities, adaptive intelligence facilities, data storage
facilities, and monitoring facilities; and a set of applications
for enabling an enterprise to manage a set of value chain network
entities from a point of origin to a point of customer use.
[1849] In embodiments, the set of interfaces includes a demand
management interface and a supply chain management interface. In
embodiments, the set of demand management applications, supply
chain applications, intelligent product applications and enterprise
resource management applications are selected from the group
consisting of supply chain, asset management, risk management,
inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion,
blockchain, smart contract, infrastructure management, facility
management, analytics, finance, trading, tax, regulatory, identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process management, compatibility testing,
compatibility management, infrastructure testing, incident
management, predictive maintenance, logistics, monitoring, remote
control, automation, self-configuration, self-healing,
self-organization, logistics, reverse logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer
profiling, entity profiling, enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
product design, product configuration, product updating, product
maintenance, product support, product testing, warehousing,
distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating, kit maintenance, kit modification, kit
management, shipping fleet management, vehicle fleet management,
workforce management, maritime fleet management, navigation,
routing, shipping management, opportunity matching, search,
advertisement, entity discovery, entity search, distribution,
delivery, and enterprise resource planning applications. In
embodiments, the set of network connectivity facilities for
enabling a set of value chain network entities to connect to the
platform includes a 5G network system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of network connectivity facilities for enabling a set of
value chain network entities to connect to the platform includes an
Internet of Things system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
network connectivity facilities for enabling a set of value chain
network entities to connect to the platform includes a cognitive
networking system deployed in a supply chain infrastructure
facility operated by the enterprise.
[1850] In embodiments, the set of network connectivity facilities
for enabling a set of value chain network entities to connect to
the platform includes a peer-to-peer network system deployed in a
supply chain infrastructure facility operated by the enterprise. In
embodiments, the set of adaptive intelligence facilities for
automating a set of capabilities of the platform includes an edge
intelligence system deployed in a supply chain infrastructure
facility operated by the enterprise. In embodiments, the set of
adaptive intelligence facilities for automating a set of
capabilities of the platform includes a robotic process automation
system. In embodiments, the set of adaptive intelligence facilities
for automating a set of capabilities of the platform includes a
self-configuring data collection system deployed in a supply chain
infrastructure facility operated by the enterprise. In embodiments,
the set of adaptive intelligence facilities for automating a set of
capabilities of the platform includes a digital twin system
representing attributes of value chain network entity controlled by
the enterprise. In embodiments, the set of adaptive intelligence
facilities for automating a set of capabilities of the platform
includes a smart contract system for automating a set of
interactions among a set of value chain network entities. In
embodiments, the set of data storage facilities for storing data
collected and handled by the platform uses a distributed data
architecture.
[1851] In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
blockchain. In embodiments, the set of data storage facilities for
storing data collected and handled by the platform uses a
distributed ledger. In embodiments, the set of data storage
facilities for storing data collected and handled by the platform
uses graph database representing a set of hierarchical
relationships of value chain network entities.
[1852] In embodiments, the set of monitoring facilities for
monitoring the value chain network entities includes an Internet of
Things monitoring system. In embodiments, the set of monitoring
facilities for monitoring the value chain network entities includes
a sensor system deployed in an infrastructure facility operated by
an enterprise.
[1853] In embodiments, the set of applications includes a set of
applications of at least two types from among a set of supply chain
management applications, demand management applications,
intelligent product applications and enterprise resource management
applications. In embodiments, the set of applications includes an
asset management application.
[1854] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1855] In embodiments, the platform manages a set of demand
factors, a set of supply factors and a set of supply chain
infrastructure facilities. In embodiments, the supply factors are
factors selected from the group consisting of Component
availability, material availability, component location, material
location, component pricing, material pricing, taxation, tariff,
impost, duty, import regulation, export regulation, border control,
trade regulation, customs, navigation, traffic, congestion, vehicle
capacity, ship capacity, container capacity, package capacity,
vehicle availability, ship availability, container availability,
package availability, vehicle location, ship location, container
location, port location, port availability, port capacity, storage
availability, storage capacity, warehouse availability, warehouse
capacity, fulfillment center location, fulfillment center
availability, fulfillment center capacity, asset owner identity,
system compatibility, worker availability, worker competency,
worker location, goods pricing, fuel pricing, energy pricing, route
availability, route distance, route cost, and route safety
factors.
[1856] In embodiments, the demand factors are factors selected from
the group consisting of product availability, product pricing,
delivery timing, need for refill, need for replacement,
manufacturer recall, need for upgrade, need for maintenance, need
for update, need for repair, need for consumable, taste,
preference, inferred need, inferred want, group demand, individual
demand, family demand, business demand, need for workflow, need for
process, need for procedure, need for treatment, need for
improvement, need for diagnosis, compatibility to system,
compatibility to product, compatibility to style, compatibility to
brand, demographic, psychographic, geolocation, indoor location,
destination, route, home location, visit location, workplace
location, business location, personality, mood, emotion, customer
behavior, business type, business activity, personal activity,
wealth, income, purchasing history, shopping history, search
history, engagement history, clickstream history, website history,
online navigation history, group behavior, family behavior, family
membership, customer identity, group identity, business identity,
customer profile, business profile, group profile, family profile,
declared interest, and inferred interest factors. In embodiments,
the supply chain infrastructure facilities are facilities selected
from the group consisting of ship, container ship, boat, barge,
maritime port, crane, container, container handling, shipyard,
maritime dock, warehouse, distribution, fulfillment, fueling,
refueling, nuclear refueling, waste removal, food supply, beverage
supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift, hauling facilities, conveyor, loading
dock, waterway, bridge, tunnel, airport, depot, vehicle station,
train station, weigh station, inspection, roadway, railway,
highway, customs house, and border control facilities.
[1857] In embodiments, the set of applications involves a set
selected from the group consisting of supply chain, asset
management, risk management, inventory management, demand
management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract,
infrastructure management, facility management, analytics, finance,
trading, tax, regulatory, identity management, commerce, ecommerce,
payments, security, safety, vendor management, process management,
compatibility testing, compatibility management, infrastructure
testing, incident management, predictive maintenance, logistics,
monitoring, remote control, automation, self-configuration,
self-healing, self-organization, logistics, reverse logistics,
waste reduction, augmented reality, virtual reality, mixed reality,
demand customer profiling, entity profiling, enterprise profiling,
worker profiling, workforce profiling, component supply policy
management, product design, product configuration, product
updating, product maintenance, product support, product testing,
warehousing, distribution, fulfillment, kit configuration, kit
deployment, kit support, kit updating, kit maintenance, kit
modification, kit management, shipping fleet management, vehicle
fleet management, workforce management, maritime fleet management,
navigation, routing, shipping management, opportunity matching,
search, advertisement, entity discovery, entity search,
distribution, delivery, and enterprise resource planning
applications.
[1858] In embodiments, an information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities that are coordinated for monitoring and management of a
set of value chain network entities; and a set of robotic process
automation systems for automating a set of processes in a value
chain network, wherein the robotic process automation systems learn
on a training set of data involving a set of user interactions with
a set of interfaces of a set of software systems that are used to
monitor and manage the value chain network entities.
[1859] In embodiments, the value chain network entities are
selected from the group consisting of products, suppliers,
producers, manufacturers, retailers, businesses, owners, operators,
operating facilities, customers, consumers, workers, mobile
devices, wearable devices, distributors, resellers, supply chain
infrastructure facilities, supply chain processes, logistics
processes, reverse logistics processes, demand prediction
processes, demand management processes, demand aggregation
processes, machines, ships, barges, warehouses, maritime ports,
airports, airways, waterways, roadways, railways, bridges, tunnels,
online retailers, ecommerce sites, demand factors, supply factors,
delivery systems, floating assets, points of origin, points of
destination, points of storage, points of use, networks,
information technology systems, software platforms, distribution
centers, fulfillment centers, containers, container handling
facilities, customs, export control, border control, drones,
robots, autonomous vehicles, hauling facilities, drones/robots/AVs,
waterways, and port infrastructure facilities.
[1860] In embodiments, the process automated by the robotic process
automation system involves selection of a vendor for a component.
In embodiments, the process automated by the robotic process
automation system involves selection of a vendor for a finished
goods order. In embodiments, the process automated by the robotic
process automation system involves selection of a variation of a
product for marketing. In embodiments, the process automated by the
robotic process automation system involves selection of an
assortment of goods for a shelf.
[1861] In embodiments, the process automated by the robotic process
automation system involves determination of a price for a finished
good. In embodiments, the process automated by the robotic process
automation system involves configuration of a service offer related
to a product. In embodiments, the process automated by the robotic
process automation system involves configuration of product bundle.
In embodiments, the process automated by the robotic process
automation system involves configuration of a product kit. In
embodiments, the process automated by the robotic process
automation system involves configuration of a product package. In
embodiments, the process automated by the robotic process
automation system involves configuration of a product display. In
embodiments, the process automated by the robotic process
automation system involves configuration of a product image. In
embodiments, the process automated by the robotic process
automation system involves configuration of a product description.
In embodiments, the process automated by the robotic process
automation system involves configuration of a website navigation
path related to a product. In embodiments, the process automated by
the robotic process automation system involves determination of an
inventory level for a product. In embodiments, the process
automated by the robotic process automation system involves
selection of a logistics type. In embodiments, the process
automated by the robotic process automation system involves
configuration of a schedule for product delivery. In embodiments,
the process automated by the robotic process automation system
involves configuration of a logistics schedule. In embodiments, the
process automated by the robotic process automation system involves
configuration of a set of inputs for machine learning. In
embodiments, the process automated by the robotic process
automation system involves preparation of product documentation. In
embodiments, the process automated by the robotic process
automation system involves preparation of required disclosures
about a product. In embodiments, the process automated by the
robotic process automation system involves configuration of a
product for a set of local requirements. In embodiments, the
process automated by the robotic process automation system involves
configuration of a set of products for compatibility. In
embodiments, the process automated by the robotic process
automation system involves configuration of a request for
proposals. In embodiments, the process automated by the robotic
process automation system involves ordering of equipment for a
warehouse.
[1862] In embodiments, the process automated by the robotic process
automation system involves ordering of equipment for a fulfillment
center. In embodiments, the process automated by the robotic
process automation system involves classification of a product
defect in an image. In embodiments, the process automated by the
robotic process automation system involves inspection of a product
in an image. In embodiments, the process automated by the robotic
process automation system involves inspection of product quality
data from a set of sensors. In embodiments, the process automated
by the robotic process automation system involves inspection of
data from a set of onboard diagnostics on a. product. In
embodiments, the process automated by the robotic process
automation system involves inspection of diagnostic data from an
Internet of Things system. In embodiments, the process automated by
the robotic process automation system involves review of sensor
data from environmental sensors in a set of supply chain
environments. In embodiments, the process automated by the robotic
process automation system involves selection of inputs for a
digital twin. In embodiments, the process automated by the robotic
process automation system involves selection of outputs from a
digital twin. In embodiments, the process automated by the robotic
process automation system involves selection of visual elements for
presentation in a digital twin. In embodiments, the process
automated by the robotic process automation system involves
diagnosis of sources of delay in a supply chain. In embodiments,
the process automated by the robotic process automation system
involves diagnosis of sources of scarcity in a supply chain. In
embodiments, wherein the process automated by the robotic process
automation system involves diagnosis of sources of congestion in a
supply chain. In embodiments, the process automated by the robotic
process automation system involves diagnosis of sources of cost
overruns in a supply chain. In embodiments, the process automated
by the robotic process automation system involves diagnosis of
sources of product defects in a supply chain.
[1863] In embodiments, the process automated by the robotic process
automation system involves prediction of maintenance requirements
in supply chain infrastructure.
[1864] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods. In embodiments, a value chain
network information technology system including a cloud-based
management platform with a micro-services architecture, a set of
interfaces, network connectivity facilities, adaptive intelligence
facilities, data storage facilities, and monitoring facilities;
[1865] a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a unified database that supports a set
of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods. In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities;
[1866] a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a unified set of data collection systems
that support a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods. In embodiments, a value chain
network information technology system including a cloud-based
management platform with a micro-services architecture, a set of
interfaces, network connectivity facilities, adaptive intelligence
facilities, data storage facilities, and monitoring facilities; a
set of applications for enabling an enterprise to manage a set of
value chain network entities from a point of origin to a point of
customer use; and a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods. In embodiments, a
value chain network information technology system including a
cloud-based management platform with a micro-services architecture,
a set of interfaces, network connectivity facilities, adaptive
intelligence facilities, data storage facilities, and monitoring
facilities;
[1867] a set of applications for enabling an enterprise to manage a
set of value chain network entities from a point of origin to a
point of customer use; and a set of supply chain applications for
management of a set of demand management applications; and a
machine vision system and a digital twin system, wherein the
machine vision system feeds data to the digital twin system. In
embodiments, a value chain network information technology system
including a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities; a set of applications for enabling an
enterprise to manage a set of value chain network entities from a
point of origin to a point of customer use; and a unified set of
adaptive edge computing systems that provide coordinated edge
computation for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1868] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1869] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods. In
embodiments, a value chain network information technology system
including a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities; a set of applications for enabling an
enterprise to manage a set of value chain network entities from a
point of origin to a point of customer use; and a set of project
management facilities that provide automated recommendations for a
set of value chain project management tasks based on processing
current status information and a set of outcomes for a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods. In
embodiments, a value chain network information technology system
including a cloud-based management platform with a micro-services
architecture, a set of interfaces, network connectivity facilities,
adaptive intelligence facilities, data storage facilities, and
monitoring facilities; a set of applications for enabling an
enterprise to manage a set of value chain network entities from a
point of origin to a point of customer use; and a set of facilities
that provide automated recommendations for a set of value chain
process tasks based on processing current status information and a
set of outcomes for a set of demand management applications, a set
of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods. In embodiments, a value chain
network information technology system including a cloud-based
management platform with a micro-services architecture, a set of
interfaces, network connectivity facilities, adaptive intelligence
facilities, data storage facilities, and monitoring facilities; a
set of applications for enabling an enterprise to manage a set of
value chain network entities from a point of origin to a point of
customer use, wherein a set of routing facilities generate a set of
routing instructions for routing information among a set of nodes
in the value chain network based on current status information for
the value chain network.
[1870] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows.
[1871] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
applications of the application layer use a common set of services
among a set of data processing services, data collection services,
and data storage services. In embodiments, a value chain network
information technology system including a cloud-based management
platform with a micro-services architecture, a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of Internet of Things resources that collect
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a cloud-based management
platform with a micro-services architecture, a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a cloud-based management
platform with a micro-services architecture, a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[1872] In embodiments, a value chain network information technology
system including a cloud-based management platform with a
micro-services architecture, a set of interfaces, network
connectivity facilities, adaptive intelligence facilities, data
storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities. In embodiments a, value chain network
information technology system including a cloud-based management
platform with a micro-services architecture, a set of interfaces,
network connectivity facilities, adaptive intelligence facilities,
data storage facilities, and monitoring facilities; a set of
applications for enabling an enterprise to manage a set of value
chain network entities from a point of origin to a point of
customer use; and a machine learning/artificial intelligence system
configured to generate recommendations for placing an additional
sensor/and or camera on and/or in proximity to a value chain entity
and wherein data from the additional sensor and/or camera feeds
into a digital twin that represents a set of value chain entities.
In embodiments, a value chain network information technology system
including a user interface that provides a set of unified views for
a set of demand management information and supply chain information
for a category of goods; and a unified database that supports a set
of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1873] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a unified set of data
collection systems that support a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1874] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a unified set of Internet
of Things systems that provide coordinated monitoring of a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1875] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; a set of supply chain
applications for management of a set of demand management
applications; and
[1876] a machine vision system and a digital twin system, wherein
the machine vision system feeds data to the digital twin system. In
embodiments, a value chain network information technology system
including a user interface that provides a set of unified views for
a set of demand management information and supply chain information
for a category of goods; and a unified set of adaptive edge
computing systems that provide coordinated edge computation for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1877] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a unified set of robotic
process automation systems that provide coordinated automation
among at least two types of applications from among a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1878] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a unified set of adaptive
intelligence systems that provide coordinated intelligence for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1879] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of project
management facilities that provide automated recommendations for a
set of value chain project management tasks based on processing
current status information and a set of outcomes for a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1880] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of facilities that
provide automated recommendations for a set of value chain process
tasks based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1881] In embodiments, value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a management platform for
a value chain network, wherein a set of routing facilities generate
a set of routing instructions for routing information among a set
of nodes in the value chain network based on current status
information for the value chain network.
[1882] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a dashboard for managing a
set of digital twins, wherein at least one digital twin represents
a set of supply chain entities, workflows and assets and at least
one other digital twin represents a set of demand management
entities and workflows.
[1883] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the applications of the application layer use
a common set of services among a set of data processing services,
data collection services, and data storage services.
[1884] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of Internet
of Things resources that collect information with respect to supply
chain entities and demand management entities.
[1885] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of social
network sources that provide information with respect to supply
chain entities and demand management entities.
[1886] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1887] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1888] In embodiments, a value chain network information technology
system including a user interface that provides a set of unified
views for a set of demand management information and supply chain
information for a category of goods; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities.
[1889] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of data collection systems that support a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1890] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of Internet of Things systems that provide coordinated
monitoring of a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1891] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; a set of
supply chain applications for management of a set of demand
management applications; and a machine vision system and a digital
twin system, wherein the machine vision system feeds data to the
digital twin system.
[1892] In embodiments, value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of adaptive edge computing systems that provide
coordinated edge computation for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1893] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of robotic process automation systems that provide
coordinated automation among at least two types of applications
from among a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods. In embodiments, a value chain network information
technology system including a unified database that supports a set
of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a unified set of adaptive intelligence systems that
provide coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1894] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1895] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1896] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
management platform for a value chain network, wherein a set of
routing facilities generate a set of routing instructions for
routing information among a set of nodes in the value chain network
based on current status information for the value chain
network.
[1897] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
dashboard for managing a set of digital twins, wherein at least one
digital twin represents a set of supply chain entities, workflows
and assets and at least one other digital twin represents a set of
demand management entities and workflows.
[1898] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services.
[1899] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
Internet of Things resources that collect information with respect
to supply chain entities and demand management entities.
[1900] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
social network sources that provide information with respect to
supply chain entities and demand management entities.
[1901] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1902] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1903] In embodiments, a value chain network information technology
system including a unified database that supports a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
machine learning/artificial intelligence system configured to
generate recommendations for placing an additional sensor/and or
camera on and/or in proximity to a value chain entity and wherein
data from the additional sensor and/or camera feeds into a digital
twin that represents a set of value chain entities.
[1904] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1905] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; a set of supply chain applications for management of a
set of demand management applications; and
[1906] a machine vision system and a digital twin system, wherein
the machine vision system feeds data to the digital twin
system.
[1907] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a unified set of adaptive edge computing systems that
provide coordinated edge computation for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1908] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a unified set of robotic process automation systems
that provide coordinated automation among at least two types of
applications from among a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1909] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a unified set of adaptive intelligence systems that
provide coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1910] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of project management facilities that provide
automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1911] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1912] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network.
[1913] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows.
[1914] In embodiments a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
applications of the application layer use a common set of services
among a set of data processing services, data collection services,
and data storage services.
[1915] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of Internet of Things resources that collect
information with respect to supply chain entities and demand
management entities.
[1916] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities.
[1917] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[1918] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[1919] In embodiments, a value chain network information technology
system including a unified set of data collection systems that
support a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a machine learning/artificial intelligence system
configured to generate recommendations for placing an additional
sensor/and or camera on and/or in proximity to a value chain entity
and wherein data from the additional sensor and/or camera feeds
into a digital twin that represents a set of value chain
entities.
[1920] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; a set of supply
chain applications for management of a set of demand management
applications; and a machine vision system and a digital twin
system, wherein the machine vision system feeds data to the digital
twin system.
[1921] In embodiments, value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a unified set
of adaptive edge computing systems that provide coordinated edge
computation for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1922] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and
[1923] a unified set of robotic process automation systems that
provide coordinated automation among at least two types of
applications from among a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1924] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a unified set
of adaptive intelligence systems that provide coordinated
intelligence for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1925] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1926] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1927] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a management
platform for a value chain network, wherein a set of routing
facilities generate a set of routing instructions for routing
information among a set of nodes in the value chain network based
on current status information for the value chain network.
[1928] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a dashboard
for managing a set of digital twins, wherein at least one digital
twin represents a set of supply chain entities, workflows and
assets and at least one other digital twin represents a set of
demand management entities and workflows.
[1929] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services.
[1930] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
Internet of Things resources that collect information with respect
to supply chain entities and demand management entities.
[1931] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
social network sources that provide information with respect to
supply chain entities and demand management entities.
[1932] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1933] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1934] In embodiments, a value chain network information technology
system including a unified set of Internet of Things systems that
provide coordinated monitoring of a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities.
[1935] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a unified set of adaptive edge
computing systems that provide coordinated edge computation for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1936] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a unified set of robotic
process automation systems that provide coordinated automation
among at least two types of applications from among a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1937] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a unified set of adaptive
intelligence systems that provide coordinated intelligence for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods.
[1938] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of project management
facilities that provide automated recommendations for a set of
value chain project management tasks based on processing current
status information and a set of outcomes for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods.
[1939] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of facilities that
provide automated recommendations for a set of value chain process
tasks based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1940] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a management platform for a
value chain network, wherein a set of routing facilities generate a
set of routing instructions for routing information among a set of
nodes in the value chain network based on current status
information for the value chain network.
[1941] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a dashboard for managing a set
of digital twins, wherein at least one digital twin represents a
set of supply chain entities, workflows and assets and at least one
other digital twin represents a set of demand management entities
and workflows.
[1942] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of microservices layers
including an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the applications of the application layer use a common set of
services among a set of data processing services, data collection
services, and data storage services.
[1943] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of microservices layers
including an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the microservice layers include a data collection layer that
collects information from a set of Internet of Things resources
that collect information with respect to supply chain entities and
demand management entities.
[1944] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of microservices layers
including an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the microservice layers include a data collection layer that
collects information from a set of social network sources that
provide information with respect to supply chain entities and
demand management entities.
[1945] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and a set of microservices layers
including an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the microservice layers include a data collection layer that
collects information from a set of crowdsourcing resources that
provide information with respect to supply chain entities and
demand management entities.
[1946] In embodiments, a value chain network information technology
system including a set of supply chain applications for management
of a set of demand management applications, a machine vision system
and a digital twin system, wherein the machine vision system feeds
data to the digital twin system; and
[1947] a set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of supply chain applications for management of a
set of demand management applications, a machine vision system and
a digital twin system, wherein the machine vision system feeds data
to the digital twin system; and a machine learning/artificial
intelligence system configured to generate recommendations for
placing an additional sensor/and or camera on and/or in proximity
to a value chain entity and wherein data from the additional sensor
and/or camera feeds into a digital twin that represents a set of
value chain entities.
[1948] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of robotic process automation systems that provide
coordinated automation among at least two types of applications
from among a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods.
[1949] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
unified set of adaptive intelligence systems that provide
coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1950] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1951] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1952] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
management platform for a value chain network, wherein a set of
routing facilities generate a set of routing instructions for
routing information among a set of nodes in the value chain network
based on current status information for the value chain
network.
[1953] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
dashboard for managing a set of digital twins, wherein at least one
digital twin represents a set of supply chain entities, workflows
and assets and at least one other digital twin represents a set of
demand management entities and workflows.
[1954] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services.
[1955] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
Internet of Things resources that collect information with respect
to supply chain entities and demand management entities.
[1956] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
social network sources that provide information with respect to
supply chain entities and demand management entities.
[1957] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1958] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1959] In embodiments, a value chain network information technology
system including a unified set of adaptive edge computing systems
that provide coordinated edge computation for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
machine learning/artificial intelligence system configured to
generate recommendations for placing an additional sensor/and or
camera on and/or in proximity to a value chain entity and wherein
data from the additional sensor and/or camera feeds into a digital
twin that represents a set of value chain entities. In embodiments,
a value chain network information technology system including a
unified set of robotic process automation systems that provide
coordinated automation among at least two types of applications
from among a set of demand management applications, a set of supply
chain applications, a set of intelligent product applications and a
set of enterprise resource management applications for a category
of goods; and a unified set of adaptive intelligence systems that
provide coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1960] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1961] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1962] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a management
platform for a value chain network, wherein a set of routing
facilities generate a set of routing instructions for routing
information among a set of nodes in the value chain network based
on current status information for the value chain network.
[1963] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a dashboard
for managing a set of digital twins, wherein at least one digital
twin represents a set of supply chain entities, workflows and
assets and at least one other digital twin represents a set of
demand management entities and workflows. In embodiments, a value
chain network information technology system including a unified set
of robotic process automation systems that provide coordinated
automation among at least two types of applications from among a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services.
[1964] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
Internet of Things resources that collect information with respect
to supply chain entities and demand management entities.
[1965] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
social network sources that provide information with respect to
supply chain entities and demand management entities.
[1966] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1967] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1968] In embodiments, a value chain network information technology
system including a unified set of robotic process automation
systems that provide coordinated automation among at least two
types of applications from among a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities.
[1969] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods.
[1970] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods.
[1971] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
management platform for a value chain network, wherein a set of
routing facilities generate a set of routing instructions for
routing information among a set of nodes in the value chain network
based on current status information for the value chain
network.
[1972] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
dashboard for managing a set of digital twins, wherein at least one
digital twin represents a set of supply chain entities, workflows
and assets and at least one other digital twin represents a set of
demand management entities and workflows.
[1973] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services.
[1974] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
Internet of Things resources that collect information with respect
to supply chain entities and demand management entities.
[1975] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
social network sources that provide information with respect to
supply chain entities and demand management entities.
[1976] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1977] In embodiments, a value chain network information technology
system including a unified set of adaptive intelligence systems
that provide coordinated intelligence for a set of demand
management applications, a set of supply chain applications, a set
of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a unified set of adaptive intelligence systems that
provide coordinated intelligence for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities. In embodiments, a value
chain network information technology system including a set of
project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a set of facilities that
provide automated recommendations for a set of value chain process
tasks based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods. In embodiments, a value chain
network information technology system including a set of project
management facilities that provide automated recommendations for a
set of value chain project management tasks based on processing
current status information and a set of outcomes for a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a
management platform for a value chain network, wherein a set of
routing facilities generate a set of routing instructions for
routing information among a set of nodes in the value chain network
based on current status information for the value chain network. In
embodiments, a value chain network information technology system
including a set of project management facilities that provide
automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a dashboard for managing
a set of digital twins, wherein at least one digital twin
represents a set of supply chain entities, workflows and assets and
at least one other digital twin represents a set of demand
management entities and workflows. In embodiments, a value chain
network information technology system including a set of project
management facilities that provide automated recommendations for a
set of value chain project management tasks based on processing
current status information and a set of outcomes for a set of
demand management applications, a set of supply chain applications,
a set of intelligent product applications and a set of enterprise
resource management applications for a category of goods; and a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services. In
embodiments, a value chain network information technology system
including a set of project management facilities that provide
automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of Internet
of Things resources that collect information with respect to supply
chain entities and demand management entities. In embodiments, a
value chain network information technology system including a set
of project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of social
network sources that provide information with respect to supply
chain entities and demand management entities. In embodiments, a
value chain network information technology system including a set
of project management facilities that provide automated
recommendations for a set of value chain project management tasks
based on processing current status information and a set of
outcomes for a set of demand management applications, a set of
supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1978] In embodiments, a value chain network information technology
system including a set of project management facilities that
provide automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of project management facilities that provide
automated recommendations for a set of value chain project
management tasks based on processing current status information and
a set of outcomes for a set of demand management applications, a
set of supply chain applications, a set of intelligent product
applications and a set of enterprise resource management
applications for a category of goods; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities. In embodiments, a value
chain network information technology system including a set of
facilities that provide automated recommendations for a set of
value chain process tasks based on processing current status
information and a set of outcomes for a set of demand management
applications, a set of supply chain applications, a set of
intelligent product applications and a set of enterprise resource
management applications for a category of goods; and a management
platform for a value chain network, wherein a set of routing
facilities generate a set of routing instructions for routing
information among a set of nodes in the value chain network based
on current status information for the value chain network. In
embodiments, a value chain network information technology system
including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a dashboard for managing a set of digital twins, wherein
at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows.
[1979] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services.
[1980] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management
entities.
[1981] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of social network sources that provide information with respect
to supply chain entities and demand management entities.
[1982] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management
entities.
[1983] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management
entities.
[1984] In embodiments, a value chain network information technology
system including a set of facilities that provide automated
recommendations for a set of value chain process tasks based on
processing current status information and a set of outcomes for a
set of demand management applications, a set of supply chain
applications, a set of intelligent product applications and a set
of enterprise resource management applications for a category of
goods; and a machine learning/artificial intelligence system
configured to generate recommendations for placing an additional
sensor/and or camera on and/or in proximity to a value chain entity
and wherein data from the additional sensor and/or camera feeds
into a digital twin that represents a set of value chain
entities.
[1985] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a dashboard for managing a set of digital
twins, wherein at least one digital twin represents a set of supply
chain entities, workflows and assets and at least one other digital
twin represents a set of demand management entities and
workflows.
[1986] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
applications of the application layer use a common set of services
among a set of data processing services, data collection services,
and data storage services.
[1987] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of Internet of Things resources that collect
information with respect to supply chain entities and demand
management entities.
[1988] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities.
[1989] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[1990] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities.
[1991] In embodiments, a value chain network information technology
system including a management platform for a value chain network,
wherein a set of routing facilities generate a set of routing
instructions for routing information among a set of nodes in the
value chain network based on current status information for the
value chain network; and a machine learning/artificial intelligence
system configured to generate recommendations for placing an
additional sensor/and or camera on and/or in proximity to a value
chain entity and wherein data from the additional sensor and/or
camera feeds into a digital twin that represents a set of value
chain entities.
[1992] In embodiments, a value chain network information technology
system including a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services.
[1993] In embodiments, a value chain network information technology
system including a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management
entities.
[1994] In embodiments, a value chain network information technology
system including a dashboard for managing a set of digital twins,
wherein at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of social network sources that provide information with respect
to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a dashboard for managing a set of digital twins, wherein
at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a dashboard for managing a set of digital twins, wherein
at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a dashboard for managing a set of digital twins, wherein
at least one digital twin represents a set of supply chain
entities, workflows and assets and at least one other digital twin
represents a set of demand management entities and workflows; and a
machine learning/artificial intelligence system configured to
generate recommendations for placing an additional sensor/and or
camera on and/or in proximity to a value chain entity and wherein
data from the additional sensor and/or camera feeds into a digital
twin that represents a set of value chain entities. In embodiments,
a value chain network information technology system including a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the applications of the application
layer use a common set of services among a set of data processing
services, data collection services, and data storage services; and
a set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the applications of the application layer use
a common set of services among a set of data processing services,
data collection services, and data storage services; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the applications of the
application layer use a common set of services among a set of data
processing services, data collection services, and data storage
services; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the applications of the application layer use
a common set of services among a set of data processing services,
data collection services, and data storage services; and a machine
learning/artificial intelligence system configured to generate
recommendations for placing an additional sensor/and or camera on
and/or in proximity to a value chain entity and wherein data from
the additional sensor and/or camera feeds into a digital twin that
represents a set of value chain entities.
[1995] In embodiments, a value chain network information technology
system including a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of Internet of Things resources that collect
information with respect to supply chain entities and demand
management entities; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of Internet
of Things resources that collect information with respect to supply
chain entities and demand management entities; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management entities;
and a set of microservices layers including an application layer
supporting at least one supply chain application and at least one
demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of crowdsourcing resources that provide information with
respect to supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of Internet of Things resources that collect information with
respect to supply chain entities and demand management entities;
and a machine learning/artificial intelligence system configured to
generate recommendations for placing an additional sensor/and or
camera on and/or in proximity to a value chain entity and wherein
data from the additional sensor and/or camera feeds into a digital
twin that represents a set of value chain entities.
[1996] In embodiments, a value chain network information technology
system including a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of social network sources that provide
information with respect to supply chain entities and demand
management entities; and a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities. In embodiments, a value chain network
information technology system including a set of microservices
layers including an application layer supporting at least one
supply chain application and at least one demand management
application, wherein the microservice layers include a data
collection layer that collects information from a set of social
network sources that provide information with respect to supply
chain entities and demand management entities; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities. In
embodiments, a value chain network information technology system
including a set of microservices layers including an application
layer supporting at least one supply chain application and at least
one demand management application, wherein the microservice layers
include a data collection layer that collects information from a
set of social network sources that provide information with respect
to supply chain entities and demand management entities; and a
machine learning/artificial intelligence system configured to
generate recommendations for placing an additional sensor/and or
camera on and/or in proximity to a value chain entity and wherein
data from the additional sensor and/or camera feeds into a digital
twin that represents a set of value chain entities. In embodiments,
a value chain network information technology system including a set
of microservices layers including an application layer supporting
at least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities; and a set of
microservices layers including an application layer supporting at
least one supply chain application and at least one demand
management application, wherein the microservice layers include a
data collection layer that collects information from a set of
crowdsourcing resources that provide information with respect to
supply chain entities and demand management entities.
[1997] In embodiments, a value chain network information technology
system including a set of microservices layers including an
application layer supporting at least one supply chain application
and at least one demand management application, wherein the
microservice layers include a data collection layer that collects
information from a set of crowdsourcing resources that provide
information with respect to supply chain entities and demand
management entities; and a machine learning/artificial intelligence
system configured to generate recommendations for placing an
additional sensor/and or camera on and/or in proximity to a value
chain entity and wherein data from the additional sensor and/or
camera feeds into a digital twin that represents a set of value
chain entities. In embodiments, a value chain network information
technology system including a set of microservices layers including
an application layer supporting at least one supply chain
application and at least one demand management application, wherein
the microservice layers include a data collection layer that
collects information from a set of crowdsourcing resources that
provide information with respect to supply chain entities and
demand management entities; and a machine learning/artificial
intelligence system configured to generate recommendations for
placing an additional sensor/and or camera on and/or in proximity
to a value chain entity and wherein data from the additional sensor
and/or camera feeds into a digital twin that represents a set of
value chain entities.
[1998] In embodiments, a value chain network management platform,
comprising: a machine learning system that trains one or more
machine-learned models to output an e-commerce recommendation to a
value chain network customer using training data comprising product
features and outcomes; and an artificial intelligence system that
receives a request for a recommendation from an e-commerce system,
generates the recommendation based on the one or more
machine-learned models and the request, and leverages one or more
product digital twins and one or more customer profile digital
twins to execute a simulation based on the one or more customer
profile digital twins and one or more product digital twins. In
embodiments, the machine learning system integrates with a model
interpretability system, and wherein the model interpretability
system is configured to implement Testing with Concept Activation
Vectors (TCAV) functionality, whereby the model interpretability
facilitates learning of human-interpretable concepts by the
machine-learned model. In embodiments, the one or more
machine-learned models are trained and/or retrained using
simulation data from one or more simulations involving one or more
customer profile digital twins.
[1999] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to determine an advertising decision using
training data comprising advertising features and outcomes; and an
artificial intelligence system that receives a request for an
advertising-related decision from an advertising system, determines
a decision based on the one or more machine-learned models and the
request, and leverages one or more advertisement digital twins and
one or more customer digital twins to execute a simulation based on
the one or more customer digital twins and the one or more
advertisement digital twins. In embodiments, the machine learning
system integrates with a model interpretability system, and wherein
the model interpretability system is configured to implement
Testing with Concept Activation Vectors (TCAV) functionality,
whereby the model interpretability facilitates learning of
human-interpretable concepts by the machine learning model. In
embodiments, the one or more machine-learned models are trained
and/or retrained using simulation data from one or more simulations
involving one or more customer digital twins.
[2000] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to determine an advertising decision using
training data comprising advertising features and outcomes; and an
artificial intelligence system that receives a request for an
advertising-related decision from an advertising system, determines
a decision based on the one or more machine-learned models and the
request, and leverages one or more advertisement digital twins and
one or more customer profile digital twins to execute a simulation
based on the one or more customer profile digital twins and the one
or more advertisement digital twins. In embodiments, the machine
learning system integrates with a model interpretability system,
and wherein the model interpretability system is configured to
implement Testing with Concept Activation Vectors (TCAV)
functionality, whereby the model interpretability facilitates
learning of human-interpretable concepts by the machine learning
model. In embodiments, the value chain network management platform
of claim 1, wherein the one or more machine-learned models are
trained and/or retrained using simulation data from one or more
simulations involving one or more customer profile digital
twins.
[2001] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to determine a demand management decision
using training data comprising demand features and outcomes; and an
artificial intelligence system that receives a request for a demand
management decision from a demand management system, determines a
decision based on the one or more machine-learned models and the
request, and leverages one or more customer digital twins to
execute a simulation based on the one or more customer digital
twins and the demand management decision.
[2002] In embodiments, the machine learning system integrates with
a model interpretability system, and wherein the model
interpretability system is configured to implement Testing with
Concept Activation Vectors (TCAV) functionality, whereby the model
interpretability facilitates learning of human-interpretable
concepts by the machine learning model. In embodiments, the one or
more machine-learned models are trained and/or retrained using
simulation data from one or more simulations involving one or more
customer digital twins.
[2003] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to determine a demand management decision
using training data comprising demand features and outcomes; and an
artificial intelligence system that receives a request for a demand
management decision from a demand management system, determines a
decision based on the one or more machine-learned models and the
request, and leverages one or more customer profile digital twins
to execute a simulation based on the one or more customer profile
digital twins and the demand management decision.
[2004] In embodiments, the machine learning system integrates with
a model interpretability system, and wherein the model
interpretability system is configured to implement Testing with
Concept Activation Vectors (TCAV) functionality, whereby the model
interpretability facilitates learning of human-interpretable
concepts by the machine learning model. In embodiment the one or
more machine-learned models are trained and/or retrained using
simulation data from one or more simulations involving one or more
customer profile digital twins.
[2005] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to determine a demand management decision
using training data comprising demand features and outcomes; and an
artificial intelligence system that receives a request for a demand
management decision from a demand management system, determines a
decision based on the one or more machine-learned models and the
request, and leverages one or more household demand digital twins
to execute a simulation based on the one or more household demand
digital twins and the demand management decision.
[2006] In embodiments, the one or more machine-learned models are
trained and/or retrained using simulation data from one or more
simulations involving one or more household demand digital
twins.
[2007] In embodiments, a value chain network management platform
comprising: a machine learning system that trains one or more
machine-learned models to output and risk management decision using
training data comprising component features and outcomes; and an
artificial intelligence system that receives a request for a risk
management decision from a risk management system, determines a
decision based on the one or more machine-learned models and the
request, and leverages a set of component digital twins
representing product components to execute one or more simulations
based on the component digital twins. In embodiments, the component
digital twins are arranged and interact in a product configuration.
In embodiments, the risk management decision relates to the
condition of the component. In embodiments, the one or more
machine-learned models are trained and/or retrained using
simulation data from one or more simulations involving one or more
components.
[2008] In embodiments, an information technology system comprising:
a value chain network management platform having an asset
management application associated with one or more ships; a data
handling layer of the management platform including data sources
containing information used to populate a training set based on a
set of maritime activities of one or more of the ships and one of
design outcomes, parameters, and data associated with the one or
more of the ships; an artificial intelligence system that is
configured to learn on the training set collected from the data
sources, that simulates one or more design attributes of one or
more of the ships, and that generates one or more sets of design
recommendations based on the training set collected from the data
sources;
[2009] a digital twin system included in the value chain network
management platform that provides for visualization of a digital
twin of one or more of the ships including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations.
[2010] In embodiments, one or more of the ships include one or more
container ships, and wherein the digital twin system further
provides for visualization of the digital twin of one or more of
the container ships including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the container ships. In embodiments, one or more of
the container ships are moored to a component of port
infrastructure. In embodiments, wherein one or more of the ships
are connected to a barge.
[2011] In embodiments, the digital twin is configured to provide
further visualization of a navigation course relative to a planned
course and one or more of the sets of recommendations from the
artificial intelligence system for a change in the navigation
course associated with one or more of the ships. In embodiments,
the digital twin is configured to provide further visualization of
an engine performance of one or more of the ships and one or more
of the sets of recommendations from the artificial intelligence
system for a change in the engine performance. In embodiments, the
visualization of the engine performance includes an emissions
profile of one or more of the ships. In embodiments, the digital
twin is configured to provide further visualization of a hull
integrity of one or more of the ships and one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of a hull of one or more of the ships. In
embodiments, the digital twin is configured to provide further
visualization of in-situ hydrodynamic changes to a portion of a
hull disposed below a water line of one or more of the ships and
one or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of one or more of the ships. In embodiments, the
digital twin is configured to determine a schedule for the change
to the hydrodynamic surface of the hull disposed below the
waterline of one or more of the ships to improve fuel efficiency
based on known routes of travel and weather patterns. In
embodiments, the digital twin is configured to provide further
visualization of in-situ aerodynamic changes to a portion of a hull
disposed above a water line of one or more of the ships and one or
more of the sets of recommendations from the artificial
intelligence system for a change in an aerodynamic surface to
change performance of one or more of the ships. In embodiments, the
digital twin is configured to determine a schedule for the change
to the aerodynamic surface disposed above the waterline of one or
more of the ships to improve fuel efficiency using known routes of
travel and historical weather patterns. In embodiments, the digital
twin is configured to provide further visualization of extendable
buoyant members from a hull of one or more of the ships to improve
stability during certain maneuvers and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the extendable buoyant members to change performance of
one or more of the ships. In embodiments, the digital twin is
configured to provide further visualization of a plurality of
inspection points on one or more of the ships and maintenance
histories associated with those inspection points. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide further
visualization of the plurality of inspection points on the ship
affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide details of a
ledger of activity associated with the visualization of the
plurality of inspection points on one or more of the ships affected
by travel within a geofenced area and maintenance histories
associated with those inspection points.
[2012] In embodiments, the digital twin is configured to provide
for visualization for a first user of one of a navigation course
and an engine performance of one more of the ships within a first
geofenced area and for visualization for a second user of one of
the navigation course and the engine performance of one or more the
ships within a second different geofenced area and where transit
between the first and second geofenced areas motivates a handoff of
one or more of the ships visualized by the digital twin of one or
more of the ships between the first user and the second user. In
embodiments, the digital twin is configured to at least partially
represent one or more of the ships associated with an event
investigation and to at least partially detail a timeline of the
event investigation and associated ships. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the associated ships. In
embodiments, the digital twin is configured to at least partially
represent one or more of the ships associated with a legal
proceeding and to at least partially detail at least a portion of a
timeline pertinent to the legal proceeding and associated ships. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated ships. In embodiments, the digital twin is configured to
at least partially represent one or more of the ships associated
with a casualty forecast and to at least partially detail at least
a portion of a timeline pertinent to the casualty report and
associated ships. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated ships to reduce exposure relative to a
set of previous casualty forecasts. In embodiments, the data
collected by a value chain network management platform facilitates
identifying theft at or misuse of physical items one of the ships
by correlating data between a set of data collectors for one or
more physical items in one of the ships and the digital twin
detailing one or more of the physical items associated with one of
the ships for the at least one of the port infrastructure facility
and the set of operators. In embodiments, the digital twin details
the one or more physical items associated with one of the ships for
at least one operator that includes a view of expected states of at
least a portion of the one or more physical items. In embodiments,
the artificial intelligence system determines a set of geofence
parameters, and wherein the digital twin provides further
visualization of at least one geofence that integrates
representation of one or more of the ships with a representation of
a maritime environment adjacent to the geofence. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change of one of the attributes of one or more of the ships. In
embodiments, one or more of the ships are capable of carrying
cargo, wherein the artificial intelligence system determines a set
of geofence parameters, and wherein the digital twin provides
further visualization of at least one geofence that integrates
representation of one or more of the ships capable of carrying
cargo with a representation of a maritime environment. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of one or
more of the ships capable of carrying cargo. In embodiments, the
maritime activities include the forward speed of one or more of the
ships relative to water and weather conditions based on the
parameters associated with energy consumption of the propulsion
units on one or more of the ships.
[2013] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of design outcomes, parameters, and data collected
from data sources relating to a set of shipping activities to train
an artificial intelligence system to simulate attributes of a
container ship and generate a set of recommendations of changes to
the attributes using a digital twin of the container ship.
[2014] In embodiments, the container ship is moored to port
infrastructure installed on or adjacent to land.
[2015] In embodiments, the shipping activities include the forward
speed of the container ship relative to water and weather
conditions based on the parameters associated with energy
consumption of propulsion units on the container ship. In
embodiments, further comprising an asset management application
associated with one or more maritime facilities connected to the
container ship. In embodiments, the asset management application is
associated with one or more ships connected to barges. In
embodiments, the digital twin of the container ship provides for
visualization of a navigation course of the container ship.
[2016] In embodiments, the digital twin of the container ship
provides for visualization of an engine performance of the
container ship. In embodiments, the digital twin of the container
ship provides for visualization of a hull integrity of the
container ship. In embodiments, the digital twin of the container
ship provides for visualization of in-situ hydrodynamic changes to
a portion of a hull disposed below a water line of the container
ship. In embodiments, the digital twin of the container ship
determines a schedule of the in-situ hydrodynamic changes to the
portion of the hull disposed below the waterline of the container
ship to improve fuel efficiency using known routes of travel and
historical weather patterns. In embodiments, the digital twin of
the container ship provides for visualization of in-situ
aerodynamic changes to a portion of a hull disposed above a water
line of the container ship.
[2017] In embodiments, the digital twin of the container ship
determines a schedule of in-situ aerodynamic changes to the portion
of the hull disposed above the waterline of the container ship to
improve fuel efficiency using known routes of travel and historical
weather patterns. In embodiments, the digital twin of the container
ship provides for visualization of extendable buoyant members from
a hull of the container ship to improve stability during certain
maneuvers of the container ship. In embodiments, the digital twin
of the container ship provides for visualization of extendable
buoyant members from a hull of the container ship to improve
stability during certain maneuvers of the container ship. In
embodiments, the digital twin of the container ship provides for
visualization of a plurality of inspection points on the container
ship and maintenance histories associated with those inspection
points. In embodiments, the digital twin of the container ship
provides for the visualization of the plurality of inspection
points on the container ship affected by travel within a geofenced
area and maintenance histories associated with those inspection
points when maintenance follows travel through the geofenced
area.
[2018] In embodiments, the digital twin of the container ship
provides for details of a ledger of activity associated with the
visualization of the plurality of inspection points on the
container ship affected by travel within a geofenced area and
maintenance histories associated with those inspection points when
maintenance follows travel through the geofenced area. In
embodiments, the digital twin of the container ship provides for
visualization for a first user of one of a navigation course of the
container ship and an engine performance of the container ship
within a first geofenced area and for visualization for a second
user of one of the navigation course of the container ship and the
engine performance of the container ship within a second geofenced
area and where transit between the first and second geofenced areas
motivates a handoff of the digital twin of the container ship
between the first user and the second user.
[2019] In embodiments, an information technology system comprising:
a value chain network management platform having an asset
management application associated with one or more barges; a data
handling layer of the management platform including data sources
containing information used to populate a training set based on a
set of maritime activities of one or more of the barges and one of
design outcomes, parameters, and data associated with the one or
more of the barges; an artificial intelligence system that is
configured to learn on the training set collected from the data
sources, that simulates one or more design attributes of one or
more of the barges, and that generates one or more sets of design
recommendations based on the training set collected from the data
sources; a digital twin system included in the value chain network
management platform that provides for visualization of a digital
twin of one or more of the barges including detail generated by the
artificial intelligence system of one or more of the design
attributes in combination with the one or more sets of design
recommendations.
[2020] In embodiments, the digital twin system further provides for
visualization of the digital twin of one or more of the barges
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with the barges. In
embodiments, one of the barges is connected to a ship. In
embodiments, the digital twin is configured to provide for
visualization of a navigation course of one of the barges relative
to a planned course of one of the barges and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the navigation course of one of the barges. In
embodiments, the digital twin is configured to provide for
visualization of a hull integrity of one of the barges relative to
a planned course of one of the barges and one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the hull of one of the barges. In
embodiments, the digital twin is configured to provide for
visualization of in-situ hydrodynamic changes to a portion of a
hull disposed below a water line of one or more of the barges and
one or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of one or more of the barges. In embodiments,
the digital twin is configured to determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the
waterline of one or more of the barges to improve fuel efficiency
based on known routes of travel and weather patterns.
[2021] In embodiments, the digital twin is configured to provide
visualizations of extendable buoyant members from a hull of one or
more of the barges to improve stability during certain maneuvers of
one or more of the barges and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the extendable buoyant members to change performance of
one or more of the barges. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points on one or more of the barges and maintenance histories
associated with those inspection points. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide for
visualizations of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the plurality of
inspection points.
[2022] In embodiments, the digital twin is configured to provide
details of a ledger of activity associated with the visualization
of the plurality of inspection points on one or more of the barges
affected by travel within a geofenced area and maintenance
histories associated with those inspection points.
[2023] In embodiments, the digital twin is configured to provide
for visualization for a first user of one of a navigation course of
one or more of the barges within a first geofenced area and for
visualization for a second user of one of the navigation course of
one or more of the barges within a second different geofenced area
and where transit between the first and second geofenced areas
motivates a handoff of the digital twin of one or more of the
barges between the first user and the second user. In embodiments,
the digital twin is configured to at least partially represent one
or more of the barges associated with an event investigation and to
at least partially detail a timeline of the event investigation and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges. In
embodiments, the digital twin is configured to at least partially
represent one or more of the barges associated with a legal
proceeding and to at least partially detail at least a portion of a
timeline pertinent to the legal proceeding and associated barges.
In embodiments, the digital twin is also configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated barges. In embodiment, the digital twin is configured to
at least partially represent one or more of the barges associated
with a casualty forecast and to at least partially detail at least
a portion of a timeline pertinent to the casualty report and
associated barges. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated barges to reduce exposure relative to
a set of previous casualty forecasts. In embodiments, the data
collected by a value chain network management platform facilitates
identifying theft at or misuse of physical items on one of the
barges by correlating data between a set of data collectors for one
or more physical items on one of the barges and the digital twin
detailing the one or more physical items on one of the barges for
at least one of a port infrastructure facility and a set of
operators.
[2024] In embodiments, the digital twin details the one or more
physical items on of the barges for at least one operator that
includes a view of expected states of at least a portion of the one
or more physical items. In embodiments, the artificial intelligence
system determines a set of geofence parameters, and wherein the
digital twin provides further visualization of at least one
geofence that integrates representation of one or more of the
barges with a representation of a maritime environment adjacent to
the geofence. In embodiments, the digital twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the set of one or more of the barges. In embodiments,
the asset management application is associated with one or more
ships connected to one of the barges.
[2025] In embodiments, the data handling layer of the management
platform includes data sources containing information used to
populate the training set based on a set of maritime activities of
one or more of the barges underway and each connected to a ship and
one of design outcomes, parameters, and data associated with the
one or more of the barges and its associated ship. In embodiments,
the artificial intelligence system is configured to learn on the
training set collected from the data sources and to simulate one or
more design attributes of one or more of the barges each connected
to a ship. In embodiments, the digital twin system provides for
visualization of a digital twin of one or more of the barges and
each of the ships to which they are connected.
[2026] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of design outcomes, parameters, and data collected
from data sources relating to a set of shipping activities to train
an artificial intelligence system to simulate attributes of a barge
and generate a set of recommendations of changes to the attributes
using a digital twin of the barge. In embodiments, the digital twin
system further provides for visualization of the digital twin of
one or more of the barges including one or more of the attributes
in combination with one or more of the sets of recommendations of
changes to the attributes associated with the barges. In
embodiments, one of the barges is connected to a ship. In
embodiments, the digital twin is configured to provide for
visualization of a navigation course of one of the barges relative
to a planned course of one of the barges and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the navigation course of one of the barges. In
embodiments, the digital twin is configured to provide for
visualization of a hull integrity of one of the barges relative to
a planned course of one of the barges and one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the hull of one of the barges. In
embodiments, the digital twin is configured to provide for
visualization of in-situ hydrodynamic changes to a portion of a
hull disposed below a water line of one or more of the barges and
one or more of the sets of recommendations from the artificial
intelligence system for a change in a hydrodynamic surface to
change performance of one or more of the barges. In embodiments,
the digital twin is configured to determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the
waterline of one or more of the barges to improve fuel efficiency
based on known routes of travel and weather patterns.
[2027] In embodiments, the digital twin is configured to provide
visualizations of extendable buoyant members from a hull of one or
more of the barges to improve stability during certain maneuvers of
one or more of the barges and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the extendable buoyant members to change performance of
one or more of the barges. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points on one or more of the barges and maintenance histories
associated with those inspection points. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points.
[2028] In embodiments, the digital twin is configured to provide
for visualizations of the plurality of inspection points on one or
more of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points. In
embodiments, the digital twin is also configured to provide one or
more of the sets of recommendations from the artificial
intelligence system for a change in maintenance of the plurality of
inspection points. In embodiments, the digital twin is configured
to provide details of a ledger of activity associated with the
visualization of the plurality of inspection points on one or more
of the barges affected by travel within a geofenced area and
maintenance histories associated with those inspection points. In
embodiments, the digital twin is configured to provide for
visualization for a first user of one of a navigation course of one
or more of the barges within a first geofenced area and for
visualization for a second user of one of the navigation course of
one or more of the barges within a second different geofenced area
and where transit between the first and second geofenced areas
motivates a handoff of the digital twin of one or more of the
barges between the first user and the second user. In embodiments,
the digital twin is configured to at least partially represent one
or more of the barges associated with an event investigation and to
at least partially detail a timeline of the event investigation and
associated maritime assets. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges. In
embodiments, the digital twin is configured to at least partially
represent one or more of the barges associated with a legal
proceeding and to at least partially detail at least a portion of a
timeline pertinent to the legal proceeding and associated barges.
In embodiments, the digital twin is also configured to provide one
or more of the sets of recommendations from the artificial
intelligence system for a change of one of the attributes of the
associated barges. In embodiments, the digital twin is configured
to at least partially represent one or more of the barges
associated with a casualty forecast and to at least partially
detail at least a portion of a timeline pertinent to the casualty
report and associated barges. In embodiments, the digital twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the associated barges to reduce
exposure relative to a set of previous casualty forecasts.
[2029] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft at or misuse of
physical items on one of the barges by correlating data between a
set of data collectors for one or more physical items on one of the
barges and the digital twin detailing the one or more physical
items on one of the barges for at least one of a port
infrastructure facility and a set of operators. In embodiments, the
digital twin details the one or more physical items on of the
barges for at least one operator that includes a view of expected
states of at least a portion of the one or more physical items. In
embodiments, the artificial intelligence system determines a set of
geofence parameters, and wherein the digital twin provides further
visualization of at least one geofence that integrates
representation of one or more of the barges with a representation
of a maritime environment adjacent to the geofence.
[2030] In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the set of one or more of the barges.
[2031] In embodiments, the asset management application is
associated with one or more ships connected to one of the barges.
In embodiments, the data handling layer of the management platform
includes data sources containing information used to populate the
training set based on a set of maritime activities of one or more
of the barges underway and each connected to a ship and one of
design outcomes, parameters, and data associated with the one or
more of the barges and its associated ship. In embodiments, the
artificial intelligence system is configured to learn on the
training set collected from the data sources and to simulate one or
more design attributes of one or more of the barges each connected
to a ship. In embodiments, the digital twin system provides for
visualization of a digital twin of one or more of the barges and
each of the ships to which they are connected.
[2032] In embodiments, an information technology system comprising:
a value chain network management platform having an asset
management application associated with port infrastructure; a data
handling layer of the management platform including data sources
containing information used to populate a training set based on a
set of maritime activities around the port infrastructure and one
of design outcomes, parameters, and data associated with the port
infrastructure; an artificial intelligence system that is
configured to learn on the training set collected from the data
sources, that simulates one or more attributes of the port
infrastructure, and that generates one or more sets of
recommendations for a change in the one or more attributes based on
the training set collected from the data sources; a digital twin
system included in the value chain network management platform that
provides for visualization of a digital twin of the port
infrastructure including detail generated by the artificial
intelligence system of one or more of the attributes in combination
with the one or more sets of recommendations. In embodiments, the
digital twin system further provides for visualization of the
digital twin of one or more of container ships in the port
infrastructure including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with one or more of the container ships. In embodiments,
the digital twin system further provides for visualization of the
digital twin of one or more of barges in the port infrastructure
including one or more of the attributes in combination with one or
more of the sets of recommendations associated with one or more of
the barges. In embodiments, the port infrastructure includes one or
more moored navigation units deployed on water. In embodiments, the
port infrastructure includes one or more ships each connected to a
barge. In embodiments, the port infrastructure is associated with a
real-world maritime port, and wherein the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world maritime port including
one or more of the attributes in combination with one or more of
the sets of recommendations associated with the components of the
real-world maritime port.
[2033] In embodiments, the port infrastructure is associated with a
real-world shipyard, and wherein the digital twin system further
provides for visualization of the digital twin of one or more of
the components of the real-world shipyard including one or more of
the attributes in combination with one or more of the sets of
recommendations associated with the components of the real-world
shipyard. In embodiments, the digital twin is configured to provide
for visualization of an engine performance of the port
infrastructure and one or more of the sets of recommendations from
the artificial intelligence system for a change in the engine
performance installed in the port infrastructure. In embodiments,
the visualization of an engine performance includes an emissions
profile.
[2034] In embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points on the port
infrastructure and maintenance histories associated with those
inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide for visualizations of the plurality
of inspection points on the port infrastructure includes within a
geofenced area and maintenance histories associated with those
inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on the port infrastructure includes within a geofenced area
and maintenance histories associated with those inspection points.
In embodiments, the digital twin is configured to at least
partially represent the port infrastructure associated with an
event investigation and to at least partially detail a timeline of
the event investigation. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated port infrastructure. In embodiments,
the digital twin is configured to at least partially represent the
port infrastructure associated with a legal proceeding and to at
least partially detail at least a portion of a timeline pertinent
to the legal proceeding. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated port infrastructure. In embodiments,
the digital twin is configured to at least partially represent the
port infrastructure associated with a casualty forecast and to at
least partially detail at least a portion of a timeline pertinent
to the casualty report and the port infrastructure. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change of one of the attributes of the associated port
infrastructure to reduce exposure relative to a set of previous
casualty forecasts. In embodiments, the data collected by a value
chain network management platform facilitates identifying theft at
or misuse at the port infrastructure by correlating data between a
set of data collectors for one or more physical items at the port
infrastructure and the digital twin detailing the one or more
physical items of the port infrastructure for the at least one of a
facility at the port infrastructure and the set of operators.
[2035] In embodiments, the digital twin details the one or more
physical items at the port infrastructure for at least one operator
that includes a view of expected states of at least a portion of
the one or more physical items.
[2036] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft at or misuse of
one or more physical items at the port infrastructure by
correlating data between a set of data collectors for the one or
more physical items and the digital twin detailing the one or more
physical items at the port infrastructure includes for the at least
one of a facility at the port infrastructure and the set of
operators.
[2037] In embodiments, the digital twin details the one or more
physical items at the port infrastructure for at least one operator
that includes a view of expected states of at least a portion of
the one or more physical items.
[2038] In embodiments, the artificial intelligence system
determines a set of geofence parameters, and wherein the digital
twin provides further visualization of at least one geofence that
integrates representation of at least a portion of the port
infrastructure with a representation of a maritime environment
adjacent to the geofence.
[2039] In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the port infrastructure.
[2040] In embodiments, one or more components of the port
infrastructure are installed on land. In embodiments, the one or
more components of the port infrastructure include one or more
moored navigation units deployed on water.
[2041] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of design outcomes, parameters, and data collected
from data sources relating to a set of shipping activities to train
an artificial intelligence system to simulate design attributes of
a port infrastructure facility and generate a set of design
recommendations using a digital twin of the port infrastructure
facility.
[2042] In embodiments, the digital twin system further provides for
visualization of the digital twin of the port infrastructure
facility including one or more of the attributes in combination
with one or more of the sets of recommendations of changes to the
attributes associated with the port infrastructure facility. In
embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points on the port
infrastructure facility and maintenance histories associated with
those inspection points. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points. In
embodiments, the digital twin is configured to provide details of a
ledger of activity associated with the visualization of the
plurality of inspection points on the port infrastructure facility
within a geofenced area and maintenance histories associated with
those inspection points.
[2043] In embodiments, the digital twin is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with an event investigation and to at least
partially detail a timeline of the event investigation and
associated with the port infrastructure facility. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change of one of the attributes of the port infrastructure
facility. In embodiments, the digital twin is configured to at
least partially represent at least a portion of the port
infrastructure facility associated with a legal proceeding and to
at least partially detail at least a portion of a timeline
pertinent to the legal proceeding and associated with the port
infrastructure facility. In embodiments, the digital twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change of one of the
attributes of the associated port infrastructure facility. In
embodiments, the digital twin is configured to at least partially
represent at least a portion of the port infrastructure facility
associated with a casualty forecast and to at least partially
detail at least a portion of a timeline pertinent to the casualty
report and associated port infrastructure facility. In embodiments,
the digital twin is also configured to provide one or more of the
sets of recommendations from the artificial intelligence system for
a change of one of the attributes of at least a portion of the port
infrastructure facility to reduce exposure relative to a set of
previous casualty forecasts. In embodiments, the data collected by
a value chain network management platform facilitates identifying
theft at or misuse of physical items in at least a portion of the
port infrastructure facility by correlating data between a set of
data collectors for one or more physical items in at least a
portion of the port infrastructure facility and the digital twin
detailing the one or more physical items in at least a portion of
the port infrastructure facility for at least one of the port
infrastructure facility and a set of operators. In embodiments, the
digital twin details the one or more physical items in the port
infrastructure facility for at least one operator that includes a
view of expected states of at least a portion of the one or more
physical items. In embodiments, the artificial intelligence system
determines a set of geofence parameters, and wherein the digital
twin provides further visualization of at least one geofence that
integrates representation of at least a portion of the port
infrastructure facility with a representation of a maritime
environment adjacent to the geofence. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of at least a portion of the port
infrastructure facility.
[2044] In embodiments, an information technology system comprising:
a value chain network management platform having an asset
management application associated with maritime assets involved in
a maritime event; a data handling layer of the management platform
including data sources containing information used to populate a
training set based on a set of maritime activities of the maritime
assets involved in the maritime event and one of design outcomes,
parameters, and data associated with the maritime assets involved
in the maritime event; an artificial intelligence system that is
configured to learn on the training set collected from the data
sources, that simulates one or more design attributes of the
maritime assets involved in a maritime event, and that generates
one or more sets of design recommendations based on the training
set collected from the data sources; a digital twin system included
in the value chain network management platform that provides for
visualization of a digital twin of the maritime assets involved in
a maritime event including detail generated by the artificial
intelligence system of one or more of the design attributes in
combination with the one or more sets of design recommendations
applicable to at least one of the maritime assets involved in the
maritime event.
[2045] In embodiments, the maritime assets include one or more
container ships involved in the maritime event, and wherein the
digital twin system further provides for visualization of the
digital twin of one or more of the container ships including one or
more of the attributes in combination with one or more of the sets
of recommendations associated with the container ships. In
embodiments, the maritime assets include one or more barges
involved in the maritime event, and wherein the digital twin system
further provides for visualization of the digital twin of one or
more of the barges including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the barges. In embodiments, the maritime assets
include one or more components of port infrastructure involved in
the maritime event, and wherein the digital twin system further
provides for visualization of the digital twin of one or more of
the components of port infrastructure including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of port
infrastructure. In embodiments, the maritime assets are associated
with a real-world maritime port, and wherein the digital twin
system further provides for visualization of the digital twin of
one or more of the components of the real-world maritime port
involved in the maritime event including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of the real-world
maritime port. In embodiments, the maritime assets are associated
with a real-world shipyard, and wherein the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world shipyard involved in the
maritime event including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of the real-world shipyard. In
embodiments, the digital twin of one or more of the maritime assets
is a floating asset twin associated with a ship. In embodiments,
the floating asset twin is configured to provide for visualization
of a navigation course of the ship involved in the maritime event
relative to a planned course of the ship and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the navigation course of the ship.
[2046] In embodiments, the floating asset twin is configured to
provide for visualization of an engine performance of the ship
involved in the maritime event and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the engine performance of the ship. In embodiments, the
visualization of an engine performance includes an emissions
profile of the ship. In embodiments, the floating asset twin is
configured to provide for visualization of a hull integrity of the
ship involved in the maritime event and one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the hull of the ship.
[2047] In embodiments, the floating asset twin is configured to
provide visualizations of a plurality of inspection points on the
ship involved in the maritime event and maintenance histories
associated with those inspection points.
[2048] In embodiments, the floating asset twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of the
plurality of inspection points associated with the maritime event.
In embodiments, the floating asset twin is configured to provide
for visualizations of the plurality of inspection points on the
ship affected by travel within a geofenced area and maintenance
histories associated with those inspection points. In embodiments,
the floating asset twin is also configured to provide one or more
of the sets of recommendations from the artificial intelligence
system for a change in maintenance of the plurality of inspection
points associated with the maritime event. In embodiments, the
floating asset twin is configured to provide details of a ledger of
activity associated with the visualization of the plurality of
inspection points on the ship involved in the maritime event within
a geofenced area and maintenance histories associated with those
inspection points. In embodiments, the artificial intelligence
system determines a set of geofence parameters, and wherein the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets involved in the maritime event with a representation of a
maritime environment adjacent to the geofence. In embodiments, the
digital twin is also configured to provide one or more of the sets
of recommendations from the artificial intelligence system for a
change of one of the attributes of the set of maritime assets
involved in the maritime event.
[2049] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of maritime event outcomes, parameters, and data
collected from data sources to train an artificial intelligence
system to use a digital twin to facilitate investigation of a
maritime event.
[2050] In embodiments, the maritime event outcomes are associated
with a real-world shipyard, and wherein the digital twin is
configured to detail at least a portion of the real-world shipyard
to facilitate investigation of the maritime event. In embodiments,
the maritime event outcomes are associated with a real-world
maritime port, and wherein the digital twin is configured to detail
at least a portion of the real-world maritime port to facilitate
investigation of the maritime event. In embodiments, the maritime
event outcomes are associated with one or more container ships, and
wherein the digital twin is configured to detail one or more of the
container ships to facilitate investigation of the maritime event.
In embodiments, the maritime event outcomes are associated with one
or more barges, and wherein the digital twin is configured to
detail one or more of the barges to facilitate investigation of the
maritime event. In embodiments, the maritime event outcomes are
associated with at least a portion of port infrastructure, and
wherein the digital twin is configured to detail at least a portion
of the of port infrastructure to facilitate investigation of the
maritime event. In embodiments, the digital twin is configured to
at least partially represent activity of one or more maritime value
chain network entities during a timeline associated with the
maritime event.
[2051] In embodiments, the one or more maritime value chain network
entities are associated with a legal proceeding and wherein the
digital twin is further configured to at least partially represent
activity of one or more maritime value chain network entities
during a timeline associated with the legal proceeding. In
embodiments, the one or more maritime value chain network entities
are associated with a legal proceeding and wherein the digital twin
is further configured to at least partially represent activity of
one or more maritime value chain network entities during a timeline
associated with the legal proceeding. In embodiments, the one or
more maritime value chain network entities are associated with a
casualty forecast and wherein the digital twin is further
configured to at least partially represent activity of one or more
maritime value chain network entities during a timeline associated
with the casualty forecast. In embodiments, one or more of the
maritime value chain network entities is a port infrastructure
facility, wherein the data collected by the value chain network
management platform facilitates identifying theft or misuse of one
or more physical items of the port infrastructure facility by
correlating data between a set of data collectors for one or more
of the physical items in the port infrastructure facility and the
digital twin detailing one or more of the physical items of the
port infrastructure facility for the at least one of the port
infrastructure facility and the set of operators to further
facilitate investigation of the maritime event.
[2052] In embodiments, the maritime event includes a container ship
that is moored to port infrastructure installed on or adjacent to
land. In embodiments, the maritime event includes at least a
container ship having a forward speed relative to water and weather
conditions and parameters associated with energy consumption of
propulsion units on the container ship. In embodiments, the
maritime event includes one or more ships connected to barges.
[2053] In embodiments, the maritime event includes one or more
ships, and wherein the digital twin provides for visualization of a
navigation course of one or more of the ships during the maritime
event. In embodiments, the maritime event includes one or more
ships, and wherein the digital twin provides for visualization of
an engine performance of one or more of the ships during the
maritime event. In embodiments, the maritime event includes one or
more ships, and wherein the digital twin provides for visualization
of a hull integrity of one or more of the ships involved in the
maritime event. In embodiments, the maritime event includes one or
more ships, and wherein the digital twin provides for visualization
of a plurality of inspection points associated with one or more of
the ships and maintenance histories associated with those
inspection points. In embodiments, the digital twin further
provides for the visualization of the plurality of inspection
points associated with one or more of the ships within a geofenced
area related to the maritime event and maintenance histories
associated with those inspection points. In embodiments, the
digital twin further provides for details of a ledger of activity
associated with the visualization of the plurality of inspection
points associated with one or more of the ships within a geofenced
area related to the maritime event and maintenance histories
associated with those inspection points.
[2054] In embodiments, an information technology system comprising:
a value chain network management platform having an asset
management application associated with maritime assets involved in
a maritime legal proceeding; a data handling layer of the
management platform including data sources containing information
used to populate a training set based on a set of maritime
activities of the maritime assets involved in the maritime legal
proceeding and one of parameters and data associated with the
maritime assets involved in the maritime legal proceeding; an
artificial intelligence system that is configured to learn on the
training set collected from the data sources, that simulates one or
more attributes of one or more of the maritime assets involved in
the maritime legal proceeding, and that generates one or more sets
of recommendations for a change in the one or more attributes based
on the training set collected from the data sources; a digital twin
system included in the value chain network management platform that
provides for visualization of a digital twin of one or more of the
maritime assets involved in the maritime legal proceeding including
detail generated by the artificial intelligence system of one or
more of the attributes in combination with the one or more sets of
recommendations.
[2055] In embodiments, the maritime assets include one or more
container ships involved in the maritime legal proceeding, and
wherein the digital twin system further provides for visualization
of the digital twin of one or more of the container ships including
one or more of the attributes in combination with one or more of
the sets of recommendations associated with the container ships. In
embodiments, the maritime assets include one or more barges
involved in the maritime legal proceeding, and wherein the digital
twin system further provides for visualization of the digital twin
of one or more of the barges including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the barges. In embodiments, the
maritime assets include one or more components of port
infrastructure involved in the maritime legal proceeding, and
wherein the digital twin system further provides for visualization
of the digital twin of one or more of the components of port
infrastructure including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of port infrastructure. In
embodiments, the maritime assets are associated with a real-world
maritime port, and wherein the digital twin system further provides
for visualization of the digital twin of one or more of the
components of the real-world maritime port involved in the maritime
legal proceeding including one or more of the attributes in
combination with one or more of the sets of recommendations
associated with the components of the real-world maritime port. In
embodiments, the maritime assets are associated with a real-world
shipyard, and wherein the digital twin system further provides for
visualization of the digital twin of one or more of the components
of the real-world shipyard involved in the maritime legal
proceeding including one or more of the attributes in combination
with one or more of the sets of recommendations associated with the
components of the real-world shipyard. In embodiments, the digital
twin of one or more of the maritime assets is a floating asset twin
associated with a ship. In embodiments, the floating asset twin is
configured to provide for visualization of a navigation course of
the ship involved in the maritime legal proceeding relative to a
planned course of the ship and one or more of the sets of
recommendations from the artificial intelligence system for a
change in the navigation course of the ship.
[2056] In embodiments, the floating asset twin is configured to
provide for visualization of an engine performance of the ship
involved in the maritime legal proceeding and one or more of the
sets of recommendations from the artificial intelligence system for
a change in the engine performance of the ship. In embodiments, the
visualization of an engine performance includes an emissions
profile of the ship. In embodiments, the floating asset twin is
configured to provide for visualization of a hull integrity of the
ship involved in the maritime legal proceeding and one or more of
the sets of recommendations from the artificial intelligence system
for a change in maintenance of the hull of the ship. In
embodiments, the floating asset twin is configured to provide
visualizations of a plurality of inspection points on the ship
involved in the maritime legal proceeding and maintenance histories
associated with those inspection points. In embodiments, the
floating asset twin is also configured to provide one or more of
the sets of recommendations from the artificial intelligence system
for a change in maintenance of the plurality of inspection points
associated with the maritime event. In embodiments, the floating
asset twin is configured to provide for visualizations of the
plurality of inspection points on the ship affected by travel
within a geofenced area and maintenance histories associated with
those inspection points. In embodiments, the floating asset twin is
also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change in maintenance of the plurality of inspection points
associated with the maritime event.
[2057] In embodiments, the floating asset twin is configured to
provide details of a ledger of activity associated with the
visualization of the plurality of inspection points on the ship
involved in the maritime legal proceeding within a geofenced area
and maintenance histories associated with those inspection points.
In embodiments, the artificial intelligence system determines a set
of geofence parameters, and wherein the digital twin provides
further visualization of at least one geofence that integrates
representation of a set of the maritime assets involved in the
maritime legal proceeding with a representation of a maritime
environment adjacent to the geofence. In embodiments, the digital
twin is also configured to provide one or more of the sets of
recommendations from the artificial intelligence system for a
change of one of the attributes of the set of maritime assets
involved in the maritime legal proceeding.
[2058] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of maritime legal outcomes, parameters, and data
collected from data sources to train an artificial intelligence
system to use a digital twin to generate a recommendation relating
to a maritime legal proceeding.
[2059] In embodiments, the maritime legal outcomes are associated
with a real-world shipyard, and wherein the digital twin is
configured to detail at least a portion of the real-world shipyard
associated with the maritime legal proceeding. In embodiments, the
maritime legal outcomes are associated with a real-world maritime
port, and wherein the digital twin is configured to detail at least
a portion of the real-world maritime port associated with the
maritime legal proceeding. In embodiments, the maritime legal
outcomes are associated with one or more container ships, and
wherein the digital twin is configured to detail at least a portion
of the one or more container ships associated with the maritime
legal proceeding. In embodiments, the maritime legal outcomes are
associated with one or more barges, and wherein the digital twin is
configured to detail at least a portion of the one or more barges
associated with the maritime legal proceeding. In embodiments, the
maritime legal outcomes are associated with at least a portion of
port infrastructure, and wherein the digital twin is configured to
detail at least a portion of the port infrastructure associated
with the maritime legal proceeding. In embodiments, the digital
twin is configured to at least partially represent activity of one
or more maritime value chain network entities during a timeline
associated with the maritime legal proceeding. In embodiments, one
or more of the maritime value chain network entities is a port
infrastructure facility, wherein the data collected by the value
chain network management platform facilitates identifying theft or
misuse of one or more physical items of the port infrastructure
facility relating to the maritime legal proceeding by correlating
data between a set of data collectors for one or more of the
physical items in the port infrastructure facility, wherein the
digital twin is configured to further detail one or more of the
physical items of the port infrastructure facility for the at least
one of the port infrastructure facility and the set of operators.
In embodiments the maritime legal proceeding includes a situation
involving a container ship that is moored to port infrastructure
installed on or adjacent to land. In embodiments, the maritime
legal proceeding includes a situation involving a container ship
having a forward speed relative to water and weather conditions and
parameters associated with energy consumption of propulsion units
on the container ship. In embodiments, the maritime legal
proceeding includes a situation involving one or more ships
connected to barges. In embodiments, the maritime legal proceeding
includes a situation involving one or more ships, and wherein the
digital twin provides for visualization of a navigation course of
one or more of the ships relevant to the maritime legal proceeding.
In embodiments, the maritime legal proceeding includes a situation
involving one or more ships, and wherein the digital twin provides
for visualization of an engine performance of one or more of the
ships relevant to the maritime legal proceeding. In embodiments,
wherein the maritime legal proceeding includes a situation
involving one or more ships, and wherein the digital twin provides
for visualization of a hull integrity of one or more of the ships
relevant to the maritime legal proceeding. In embodiments, the
maritime legal proceeding includes a situation involving one or
more ships, and wherein the digital twin provides for visualization
of a plurality of inspection points associated with one or more of
the ships and maintenance histories associated with those
inspection points. In embodiments, the digital twin further
provides for the visualization of the plurality of inspection
points associated with one or more of the ships within a geofenced
area relevant to the maritime legal proceeding and maintenance
histories associated with those inspection points.
[2060] In embodiments, the digital twin further provides for
details of a ledger of activity associated with the visualization
of the plurality of inspection points associated with one or more
of the ships within a geofenced area relevant to the maritime legal
proceeding and maintenance histories associated with those
inspection points.
[2061] In embodiments, an information technology system comprising;
a value chain network management platform having an asset
management application associated with maritime assets; a data
handling layer of the management platform including data sources
containing information used to populate a training set based on a
set of maritime activities of one or more of the maritime assets
involved in a loss event and one of outcomes, parameters, and data
associated with the one or more maritime assets experiencing the
loss event; an artificial intelligence system that is configured to
learn on the training set collected from the data sources, that
simulates one or more attributes of one or more of the maritime
assets, and that generates one or more sets of casualty forecasts
based on the training set collected from the data sources; a
digital twin system included in the value chain network management
platform that provides for visualization of one or more digital
twins associated with one or more of the maritime assets involved
in the loss event including detail generated by the artificial
intelligence system of at least a portion of one of the sets of
casualty forecasts. In embodiments, the maritime assets include one
or more container ships associated with at least a portion of one
of the sets of casualty forecasts, and wherein the digital twin
system further provides for visualization of the digital twin of
one or more of the container ships including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the container ships. In
embodiments, the maritime assets include one or more barges with at
least a portion of one of the sets of casualty forecasts, and
wherein the digital twin system further provides for visualization
of the digital twin of one or more of the barges including one or
more of the attributes in combination with one or more of the sets
of recommendations associated with the barges. In embodiments, the
maritime assets include one or more components of port
infrastructure with at least a portion of one of the sets of
casualty forecasts, and wherein the digital twin system further
provides for visualization of the digital twin of one or more of
the components of port infrastructure including one or more of the
attributes in combination with one or more of the sets of
recommendations associated with the components of port
infrastructure associated with the sets of casualty forecasts. In
embodiments, the maritime assets are associated with a real-world
maritime port, and wherein the digital twin system further provides
for visualization of the digital twin of one or more of the
components of the real-world maritime port associated at least a
portion of one of the sets of casualty forecasts including one or
more of the attributes in combination with one or more of the sets
of recommendations associated with the components of the real-world
maritime port. In embodiments, the maritime assets are associated
with a real-world shipyard, and wherein the digital twin system
further provides for visualization of the digital twin of one or
more of the components of the real-world shipyard associated at
least a portion of one of the sets of casualty forecasts including
one or more of the attributes in combination with one or more of
the sets of recommendations associated with the components of the
real-world shipyard. In embodiments, the digital twin of one or
more of the maritime assets is a floating asset twin associated
with a ship associated with at least a portion of one of the sets
of casualty forecasts. In embodiments, the floating asset twin is
configured to provide for visualization of a navigation course of
the ship associated at least a portion of one of the sets of
casualty forecasts relative to a planned course of the ship and one
or more of the sets of recommendations from the artificial
intelligence system for a change in the navigation course of the
ship. In embodiments, the floating asset twin is configured to
provide for visualization of an engine performance of the ship
associated at least a portion of one of the sets of casualty
forecasts and one or more of the sets of recommendations from the
artificial intelligence system for a change in the engine
performance of the ship. In embodiments, the visualization of an
engine performance includes an emissions profile of the ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a hull integrity of the ship associated at least a
portion of one of the sets of casualty forecasts and one or more of
the sets of recommendations from the artificial intelligence system
for a change in maintenance of the hull of the ship. In
embodiments, the floating asset twin is configured to provide
visualizations of a plurality of inspection points on the ship
associated with at least a portion of one of the sets of casualty
forecasts and maintenance histories associated with those
inspection points. In embodiments, the floating asset twin is also
configured to provide one or more of the sets of recommendations
from the artificial intelligence system for a change in maintenance
of the plurality of inspection points associated with the maritime
event. In embodiments, the floating asset twin is configured to
provide for visualizations of the plurality of inspection points on
the ship affected by travel within a geofenced area and maintenance
histories associated with those inspection points.
[2062] In embodiments, the floating asset twin is also configured
to provide one or more of the sets of recommendations from the
artificial intelligence system for a change in maintenance of the
plurality of inspection points associated with the maritime event.
In embodiments, the floating asset twin is configured to provide
details of a ledger of activity associated with the visualization
of the plurality of inspection points on the ship associated at
least a portion of one of the sets of casualty forecasts within a
geofenced area and maintenance histories associated with those
inspection points. In embodiments, the artificial intelligence
system determines a set of geofence parameters, and wherein the
digital twin provides further visualization of at least one
geofence that integrates representation of a set of the maritime
assets associated at least a portion of one of the sets of casualty
forecasts with a representation of a maritime environment adjacent
to the geofence.
[2063] In embodiments, the digital twin is also configured to
provide one or more of the sets of recommendations from the
artificial intelligence system for a change of one of the
attributes of the set of maritime assets associated with at least a
portion of one of the sets of casualty forecasts.
[2064] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of maritime outcomes, parameters, and data collected
from data sources to train an artificial intelligence system to use
a digital twin to predict and display a casualty forecast for a set
of maritime assets. In embodiments, the set of maritime assets
includes a real-world shipyard, and wherein the digital twin is
configured to detail at least a portion of the real-world shipyard
associated with the casualty forecast.
[2065] In embodiments, the set of maritime assets includes a
real-world maritime port, and wherein the digital twin is
configured to detail at least a portion of the real-world maritime
port associated with the casualty forecast.
[2066] In embodiments, the set of maritime assets includes one or
more container ships, and wherein the digital twin is configured to
detail at least a portion of the one or more container ships
associated with the casualty forecast.
[2067] In embodiments, the set of maritime assets includes one or
more barges, and wherein the digital twin is configured to detail
at least a portion of the one or more barges associated with the
casualty forecast. In embodiments, the set of maritime assets
includes at least a portion of port infrastructure, and wherein the
digital twin is configured to detail at least a portion of the port
infrastructure associated with the casualty forecast. In
embodiments the digital twin is configured to at least partially
represent activity of the set of maritime assets during a timeline
associated with the casualty forecast. In embodiments, the set of
maritime assets includes a port infrastructure facility, wherein
the data collected by the value chain network management platform
facilitates identifying theft or misuse of one or more physical
items of the port infrastructure facility relating to the casualty
forecast by correlating data between a set of data collectors for
one or more of the physical items in the port infrastructure
facility, wherein the digital twin is configured to further detail
one or more of the physical items of the port infrastructure
facility for the at least one of the port infrastructure facility
and the set of operators. In embodiments, the set of maritime
assets includes a container ship that is moored to port
infrastructure installed on or adjacent to land. In embodiments,
the set of maritime assets includes one or more ships connected to
barges. In embodiments, the set of maritime assets includes one or
more ships, and wherein the digital twin provides for visualization
of a navigation course of one or more of the ships relevant to the
casualty forecast. In embodiments, the set of maritime assets
includes one or more ships, and wherein the digital twin provides
for visualization of an engine performance of one or more of the
ships relevant to the casualty forecast. In embodiments, the set of
maritime assets includes one or more ships, and wherein the digital
twin provides for visualization of a hull integrity of one or more
the ships relevant to the casualty forecast. In embodiments, the
set of maritime assets includes one or more ships, and wherein the
digital twin provides for visualization of a plurality of
inspection points associated with one or more of the ships and
maintenance histories associated with those inspection points
relevant to the casualty forecast. In embodiments, the digital twin
further provides for the visualization of the plurality of
inspection points associated with one or more of the ships within a
geofenced area relevant to the casualty forecast and maintenance
histories associated with those inspection points.
[2068] In embodiments, the digital twin further provides for
details of a ledger of activity associated with the visualization
of the plurality of inspection points associated with one or more
of the ships within a geofenced area relevant to the casualty
forecast and maintenance histories associated with those inspection
points.
[2069] In embodiments, an information technology system comprising:
a value chain network management platform for identifying theft or
misuse of a port infrastructure facility by correlating data
between a set of data collectors for the physical item and a set of
digital twins for at least one of the port infrastructure facility
and a set of operators.
[2070] In embodiments, the set of digital twins of the port
infrastructure facility includes one or more of the attributes in
combination with one or more of the sets of recommendations of
changes to attributes associated with the port infrastructure
facility. In embodiments, the set of digital twins is configured to
provide visualizations of a plurality of inspection points on the
port infrastructure facility and maintenance histories associated
with those inspection points. In embodiments, the set of digital
twins is configured to provide details of a ledger of activity
associated with the visualization of the plurality of inspection
points on the port infrastructure facility within a geofenced area
and maintenance histories associated with those inspection points.
In embodiments, the set of digital twins is configured to at least
partially represent at least a portion of the port infrastructure
facility associated with an event investigation and to at least
partially detail a timeline of the event investigation and
associated with the port infrastructure facility. In embodiments,
the set of digital twins is configured to at least partially
represent at least a portion of the port infrastructure facility
associated with a legal proceeding and to at least partially detail
at least a portion of a timeline pertinent to the legal proceeding
and associated with the port infrastructure facility.
[2071] In embodiments, the set of digital twins is configured to at
least partially represent at least a portion of the port
infrastructure facility associated with a casualty forecast and to
at least partially detail at least a portion of a timeline
pertinent to the casualty report and associated port infrastructure
facility. In embodiments, the digital twin details the one or more
physical items in the port infrastructure facility for at least one
operator that includes a view of expected states of at least a
portion of the one or more physical items. In embodiments, the set
of digital twins provides further visualization of at least one
geofence that integrates representation of at least a portion of
the port infrastructure facility with a representation of a
maritime environment adjacent to the geofence.
[2072] In embodiments, an information technology system comprising:
a value chain network management platform identifying theft or
misuse of a shipyard facility by correlating data between a set of
data collectors for the physical item and a set of digital twins
for at least one of the shipyard facility and a set of operators.
In embodiments, the set of digital twins for at least one of the
shipyard facilities and a set of operators includes one or more of
the attributes in combination with one or more of the sets of
recommendations of changes to attributes associated with the
shipyard facility. In embodiments, the set of digital twins is
configured to provide visualizations of a plurality of inspection
points on in the shipyard facility and maintenance histories
associated with those inspection points. In embodiments, the set of
digital twins is configured to provide details of a ledger of
activity associated with the visualization of the plurality of
inspection points on the shipyard facility within a geofenced area
and maintenance histories associated with those inspection points.
In embodiments, the set of digital twins is configured to at least
partially represent at least a portion of the shipyard facility
associated with an event investigation and to at least partially
detail a timeline of the event investigation and associated with
the port infrastructure facility.
[2073] In embodiments, the set of digital twins is configured to at
least partially represent at least a portion of the shipyard
facility associated with a legal proceeding and to at least
partially detail at least a portion of a timeline pertinent to the
legal proceeding and associated with the shipyard facility. In
embodiments, the set of digital twins is configured to at least
partially represent at least a portion of the shipyard facility
associated with a casualty forecast and to at least partially
detail at least a portion of a timeline pertinent to the casualty
report and associated shipyard facility. In embodiments, the
digital twin details the one or more physical items in the shipyard
facility for at least one operator that includes a view of expected
states of at least a portion of the one or more physical items. In
embodiments, the set of digital twins provides further
visualization of at least one geofence that integrates
representation of at least a portion of the shipyard facility with
a representation of a maritime environment adjacent to the
geofence.
[2074] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of maritime outcomes, parameters, and data collected
from data sources to train an artificial intelligence system to
determine a set of geofence parameters and represent at least one
geofence in a digital twin that integrates representation of a set
of maritime entities with a representation of a maritime
environment. In embodiments, the set of maritime entities is
associated with a real-world shipyard, and wherein the digital twin
is configured to represent the real-world shipyard and geofence
parameters include a location within the real-world shipyard. In
embodiments, the set of maritime entities is associated with a
real-world maritime port, and wherein the digital twin is
configured to represent the real-world maritime port and geofence
parameters include a location within the real-world maritime port.
In embodiments, the set of maritime entities is associated with one
or more container ships, and wherein the digital twin is configured
to represent the one or more container ships relative to the
geofence parameters. In embodiments, the set of maritime entities
is associated with one or more container barges, and wherein the
digital twin is configured to represent the one or more barges
relative to the geofence parameters. In embodiments, the set of
maritime entities is associated with an event investigation and
wherein the digital twin is configured to at least partially
represent the set of maritime entities as it at least one of
interacted during a timeline associated with the event
investigation or is predicted to act based on a suggestion
associated with the event investigation.
[2075] In embodiments, the set of maritime entities is associated
with a legal proceeding and wherein the digital twin is configured
to at least partially represent the set of maritime entities as it
at least one of interacted during a timeline associated with the
legal proceeding or is predicted to act based on a suggestion
associated with the legal proceeding. In embodiments, the data
collected by the value chain network management platform relates to
a casualty report, wherein the digital twin of the set of maritime
entities is configured to simulate possibilities of a loss relevant
to the casualty report based on the data collected by the value
chain network management platform.
[2076] In embodiments, the data collected by a value chain network
management platform facilitates identifying theft or misuse of
physical items contained on the set of maritime entities by
correlating data between a set of data collectors for one or more
physical items on the set of maritime entities and the digital twin
detailing the one or more physical items associated with the set of
maritime entities for the at least one of a port infrastructure
facility and a set of operators. In embodiments, the set of
maritime entities is a container ship that is moored to port
infrastructure installed on or adjacent to land. In embodiments,
data collected by a value chain network management platform is
based on at least a ship having a forward speed relative to water
and weather conditions and parameters associated with energy
consumption of propulsion units on the ship.
[2077] In embodiments, further comprising an asset management
application associated with the value chain network management
platform and one or more maritime entities connected to a ship. In
embodiments, the asset management application is associated with
one or more ships connected to barges. In embodiments, the set of
maritime entities includes one or more ships, and wherein the
digital twin provides for visualization of a navigation course of
one or more of the ships. In embodiments, the set of maritime
entities includes one or more ships, and wherein the digital twin
provides for visualization of an engine performance of one or more
of the ships. In embodiments, the set of maritime entities includes
one or more ships, and wherein the digital twin provides for
visualization of a hull integrity of one or more of the ships. In
embodiments, the digital twin provides for visualization of a
plurality of inspection points on the set of the maritime entities
and maintenance histories associated with those inspection points.
In embodiments, the digital twin further provides for the
visualization of the plurality of inspection points on the set of
the maritime entities within the geofenced parameters and
maintenance histories associated with those inspection points. In
embodiments, the digital twin further provides for details of a
ledger of activity associated with the visualization of the
plurality of inspection points on the maritime entities within the
geofenced parameters and maintenance histories associated with
those inspection points. In embodiments, the training set of
maritime outcomes, parameters, and data collected from the data
sources is related to a set of shipping activities.
[2078] In embodiments, an information technology system comprising:
a value chain network management platform for learning on a
training set of maritime outcomes, parameters, and data collected
from data sources relating to a set of shipping activities to train
an artificial intelligence system to determine a set of geofence
parameters and represent at least one geofence in a digital twin
that integrates representation of a set of maritime entities with a
representation of a maritime environment. In embodiments, the set
of maritime entities is associated with a real-world shipyard, and
wherein the digital twin is configured to represent the real-world
shipyard, its associated set of the shipping activities and
geofence parameters include a location within the real-world
shipyard.
[2079] In embodiments, the set of maritime entities is associated
with a real-world maritime port, and wherein the digital twin is
configured to represent the real-world maritime port, its
associated set of the shipping activities and geofence parameters
include a location within the real-world maritime port. In
embodiments, the set of maritime entities is associated with one or
more container ships, and wherein the digital twin is configured to
represent the one or more container ships and its associated set of
the shipping activities relative to the geofence parameters.
[2080] In embodiments, the set of maritime entities is associated
with one or more container barges, and wherein the digital twin is
configured to represent the one or more barges and its associated
set of the shipping activities relative to the geofence parameters.
In embodiments, the set of maritime entities is associated with an
event investigation, and wherein the digital twin is configured to
at least partially represent the set of maritime entities and its
associated set of the shipping activities at least partially
detailed on a timeline associated with the event investigation. In
embodiments, the set of maritime entities is associated with a
legal proceeding and wherein the digital twin is configured to at
least partially represent the set of maritime entities as it at
least one of interacted during a timeline associated with the legal
proceeding or is predicted to act based on a suggestion associated
with the legal proceeding. In embodiments, the data collected by
the value chain network management platform relates to a casualty
report, wherein the digital twin of the set of maritime entities is
configured to simulate possibilities of a loss relevant to the
casualty report based on the data collected by the value chain
network management platform. In embodiments, the data collected by
a value chain network management platform facilitates identifying
theft or misuse of physical items contained on the set of maritime
entities by correlating data between a set of data collectors for
one or more physical items on the set of maritime entities and the
digital twin detailing the one or more physical items associated
with the set of maritime entities for the at least one of a port
infrastructure facility and a set of operators. In embodiments, the
set of maritime entities is a container ship that is moored to port
infrastructure installed on or adjacent to land. In embodiments,
data collected by a value chain network management platform is
based on at least a ship having a forward speed relative to water
and weather conditions and parameters associated with energy
consumption of propulsion units on the ship.
[2081] In embodiments, further comprising an asset management
application associated with the value chain network management
platform and one or more maritime entities connected to a ship. In
embodiments, the asset management application is associated with
one or more ships connected to barges. In embodiments, the set of
maritime entities includes one or more ships, and wherein the
digital twin provides for visualization of a navigation course of
one or more of the ships.
[2082] In embodiments, the set of maritime entities includes one or
more ships, and wherein the digital twin provides for visualization
of an engine performance of one or more of the ships. In
embodiments, the set of maritime entities includes one or more
ships, and wherein the digital twin provides for visualization of a
hull integrity of one or more of the ships. In embodiments, the
digital twin provides for visualization of a plurality of
inspection points on the set of the maritime entities and one of
maintenance histories and the set of shipping activities associated
with those inspection points. In embodiments, the digital twin
further provides for the visualization of the plurality of
inspection points on the set of the maritime entities within the
geofenced parameters and one of maintenance histories and the set
of shipping activities associated with those inspection points. In
embodiments, the digital twin further provides for details of a
ledger of activity associated with the visualization of the
plurality of inspection points on the maritime entities within the
geofenced parameters and one of maintenance histories and the set
of shipping activities associated with those inspection points.
[2083] In embodiments, an information technology system comprising:
a value chain network management platform generating a digital twin
representing a real-world maritime port. In embodiments, the
digital twin representing the real-world maritime port includes one
or more container ships. In embodiments, the digital twin
representing the real-world maritime port includes one or more
barges. In embodiments, the digital twin representing the
real-world maritime port includes one or more components of the
port infrastructure installed on or adjacent to land.
[2084] In embodiments, the digital twin representing the real-world
maritime port also includes a container ship moored to a component
of the port infrastructure. In embodiments, the digital twin
representing the real-world maritime port includes include one or
more moored navigation units deployed on water. In embodiments,
digital twin representing the real-world maritime port includes
include one or more ships connected to barges. In embodiments, the
digital twin representing the real-world maritime port includes a
ship. In embodiments, the digital twin is configured to provide for
visualization of a navigation course of the ship in a simulated
maritime port based on the real-world maritime port. In
embodiments, the digital twin is configured to provide for
visualization of an engine performance of the ship including an
emissions profile as the ship moves around the real-world maritime
port. In embodiments, the digital twin is configured to provide for
visualization of a hull of the ship as it moves through the
real-world maritime port on a path having a water depth, wherein
the digital twin is configured to further provide for visualization
of a proximity of a portion of the hull to a portion of a seafloor
in the real-word shipyard. In embodiments, the digital twin
displays suggestions from an artificial intelligence system that
generates a portion of a maintenance schedule to maintain the water
depth through the real-world maritime port based on at least a
combination of a portion of actual activity in the real-world
maritime port and simulations provided by the digital twin of the
real-world maritime port. In embodiments, the digital twin is
configured to provide visualizations of a plurality of inspection
points in the real-world maritime port and maintenance histories
associated with those inspection points.
[2085] In embodiments, the digital twin is configured to provide
for visualizations of the plurality of inspection points in the
real-world maritime port and maintenance histories associated with
those inspection points when within a geofenced area. In
embodiments, the digital twin is configured to provide details of a
ledger of activity associated with the visualization of the
plurality of inspection points and maintenance histories associated
with those inspection points within a geofenced of the real-world
maritime port. In embodiments, the digital twin is configured to
provide for further visualization for a first user of one of a
navigation course of a ship and an engine performance of the ship
within a first geofenced area of the real-world maritime port and
for further visualization for a second user of one of the
navigation course of the ship and the engine performance of the
ship within a second different geofenced area in the real-world
maritime port and where transit between the first and second
geofenced areas motivates a handoff of the ship between the first
user and the second user as depicted by the digital twin
representing the real-world maritime port including the ship.
[2086] In embodiments, an information technology system comprising:
a value chain network management platform for generating a digital
twin representing a real-world shipyard. In embodiments, the
digital twin representing the real-world shipyard includes one or
more container ships. In embodiments, the digital twin representing
the real-world shipyard includes one or more barges. In
embodiments, the digital twin representing the real-world shipyard
includes one or more components of the port infrastructure
installed on or adjacent to land.
[2087] In embodiments, the digital twin representing the real-world
shipyard also includes a container ship moored to a component of
the port infrastructure. In embodiments, the digital twin
representing the real-world shipyard includes include one or more
moored navigation units deployed on water. In embodiments, the
digital twin representing the real-world shipyard includes include
one or more ships connected to barges.
[2088] In embodiments, the digital twin representing the real-world
shipyard includes a ship. In embodiments, the digital twin is
configured to provide for visualization of a navigation course of
the ship in a simulated shipyard based on the real-world shipyard.
In embodiments, the digital twin is configured to provide for
visualization of an engine performance of the ship including an
emissions profile as the ship moves around the real-world shipyard.
In embodiments, the digital twin is configured to provide for
visualization of a hull of the ship as it moves through the
real-world shipyard on a path having a water depth, wherein the
digital twin is configured to further provide for visualization of
a proximity of a portion of the hull to a portion of a seafloor in
the real-word shipyard. In embodiments, the digital twin displays
suggestions from an artificial intelligence system that generates a
portion of a maintenance schedule to maintain the water depth
through the real-world shipyard based on at least a combination of
a portion of actual activity in the real-world shipyard and
simulations provided by the digital twin of the real-world
shipyard. In embodiments, the digital twin is configured to provide
visualizations of a plurality of inspection points in the
real-world shipyard and maintenance histories associated with those
inspection points.
[2089] In embodiments, the digital twin is configured to provide
for visualizations of the plurality of inspection points in the
real-world shipyard and maintenance histories associated with those
inspection points when within a geofenced area. In embodiments, the
digital twin is configured to provide details of a ledger of
activity associated with the visualization of the plurality of
inspection points and maintenance histories associated with those
inspection points within a geofenced of the real-world shipyard. In
embodiments, the digital twin is configured to provide for further
visualization for a first user of one of a navigation course of a
ship and an engine performance of the ship within a first geofenced
area of the real-world shipyard and for further visualization for a
second user of one of the navigation course of the ship and the
engine performance of the ship within a second different geofenced
area in the real-world shipyard and where transit between the first
and second geofenced areas motivates a handoff of the ship between
the first user and the second user as depicted by the digital twin
representing the real-world shipyard including the ship.
[2090] In embodiments, an information technology system comprising:
a set of intelligent systems for automatically populating a digital
twin of a maritime value chain network entity based on data
collected by a value chain network management platform. In
embodiments, the maritime value chain network entity is associated
with a real-world shipyard, and wherein the digital twin is
configured to represent the real-world shipyard. In embodiments,
the maritime value chain network entity is associated with a
real-world maritime port, and wherein the digital twin is
configured to represent the real-world maritime port. In
embodiments, the maritime value chain network entity is associated
with a container ship, and wherein the digital twin is configured
to represent the container ship.
[2091] In embodiments, the maritime value chain network entity is
associated with a barge, and wherein the digital twin is configured
to represent the barge. In embodiments, the maritime value chain
network entity is associated with port infrastructure, and wherein
the digital twin is configured to represent one or more components
of the port infrastructure. In embodiments, the maritime value
chain network entity is associated with an event investigation and
wherein the digital twin is configured to at least partially
represent the maritime value chain network entity as it interacted
during a timeline associated with the event investigation. In
embodiments, the maritime value chain network entity is associated
with a legal proceeding and wherein the digital twin is configured
to at least partially represent the maritime value chain network
entity. In embodiments, the data collected by a value chain network
management platform relates to a casualty report, wherein the
digital twin of the maritime value chain network entity is
configured to simulate possibilities of a loss relevant to the
casualty report based on the data collected by a value chain
network management platform. In embodiments, the maritime value
chain network entity is a port infrastructure facility, wherein the
data collected by a value chain network management platform
facilitates identifying theft or misuse of the port infrastructure
facility by correlating data between a set of data collectors for
one or more physical items in the port infrastructure facility and
the digital twin detailing the one or more physical items of the
port infrastructure facility for the at least one of the port
infrastructure facility and the set of operators. In embodiments,
the maritime value chain network entity is a container ship that is
moored to port infrastructure installed on or adjacent to land. In
embodiments, data collected by a value chain network management
platform is based on at least a container ship having a forward
speed relative to water and weather conditions and parameters
associated with energy consumption of propulsion units on the
container ship.
[2092] In embodiments, further comprising an asset management
application associated with the value chain network management
platform and one or more maritime facilities connected to a
container ship. In embodiments, the asset management application is
associated with one or more ships connected to barges. In
embodiments, the maritime value chain network entity is one or more
ships, and wherein the digital twin provides for visualization of a
navigation course of one or more of the ships. In embodiments, the
maritime value chain network entity is one or more ships, and
wherein the digital twin provides for visualization of an engine
performance of one or more of the ships. In embodiments, the
maritime value chain network entity is one or more ships, and
wherein the digital twin provides for visualization of a hull
integrity of one or more of the ships. In embodiments, the digital
twin provides for visualization of a plurality of inspection points
on the maritime value chain network entity and maintenance
histories associated with those inspection points. In embodiments,
the digital twin further provides for the visualization of the
plurality of inspection points on the maritime value chain network
entity within a geofenced area and maintenance histories associated
with those inspection points. In embodiments, the digital twin
further provides for details of a ledger of activity associated
with the visualization of the plurality of inspection points on the
maritime value chain network entity within a geofenced area and
maintenance histories associated with those inspection points.
[2093] Thus, in one aspect, methods described above and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described above may include any of
the hardware and/or software described above. All such permutations
and combinations are intended to fall within the scope of the
present disclosure.
[2094] While the disclosure has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present disclosure is not to be limited by the foregoing
examples, but is to be understood in the broadest sense allowable
by law.
[2095] The use of the terms "a" and "an" and "the" and similar
referents in the context of describing the disclosure (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising," "with,"
"including," and "containing" are to be construed as open-ended
terms (i.e., meaning "including, but not limited to,") unless
otherwise noted. Recitations of ranges of values herein are merely
intended to serve as a shorthand method of referring individually
to each separate value falling within the range, unless otherwise
indicated herein, and each separate value is incorporated into the
specification as if it were individually recited herein. All
methods described herein can be performed in any suitable order
unless otherwise indicated herein or otherwise clearly contradicted
by context. The use of any and all examples, or exemplary language
(e.g., "such as") provided herein, is intended merely to better
illuminate the disclosure and does not pose a limitation on the
scope of the disclosure unless otherwise claimed. The term "set"
may include a set with a single member. No language in the
specification should be construed as indicating any non-claimed
element as essential to the practice of the disclosure.
[2096] While the foregoing written description enables one skilled
to make and use what is considered presently to be the best mode
thereof, those skilled in the art will understand and appreciate
the existence of variations, combinations, and equivalents of the
specific embodiment, method, and examples herein. The disclosure
should therefore not be limited by the above described embodiment,
method, and examples, but by all embodiments and methods within the
scope and spirit of the disclosure.
[2097] All documents referenced herein are hereby incorporated by
reference as if fully set forth herein.
* * * * *