U.S. patent application number 16/616279 was filed with the patent office on 2020-05-07 for systems and methods for interfaces to a supply chain management system.
This patent application is currently assigned to JABIL INC.. The applicant listed for this patent is JABIL INC.. Invention is credited to Mudit BAJAJ, Paul DOCHERTY, Andrew JOYNER, Ancha KOTESWARARAO, Erin MORRIS, Ross VALENTINE.
Application Number | 20200143314 16/616279 |
Document ID | / |
Family ID | 64395871 |
Filed Date | 2020-05-07 |
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United States Patent
Application |
20200143314 |
Kind Code |
A1 |
BAJAJ; Mudit ; et
al. |
May 7, 2020 |
SYSTEMS AND METHODS FOR INTERFACES TO A SUPPLY CHAIN MANAGEMENT
SYSTEM
Abstract
Apparatus, system and method for supply chain management (SCM)
system processing. A SCM operating platform is operatively coupled
to SCM modules for collecting, storing, distributing and processing
SCM data to determine statistical opportunities and risk in a SCM
hierarchy. SCM risk processing may be utilized to determine risk
values that are dependent upon SCM attributes. Multiple SCM risk
processing results may be produced for further drill-down by a
user. SCM network nodes, their relation and status may further be
produced for fast and efficient status determination.
Inventors: |
BAJAJ; Mudit; (St.
Petersburg, FL) ; JOYNER; Andrew; (St. Petersburg,
FL) ; VALENTINE; Ross; (St. Petersburg, FL) ;
MORRIS; Erin; (St. Petersburg, FL) ; DOCHERTY;
Paul; (St. Petersburg, FL) ; KOTESWARARAO; Ancha;
(St. Petersburg, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
JABIL INC. |
St. Petersburg |
FL |
US |
|
|
Assignee: |
JABIL INC.
St. Petersburg
FL
|
Family ID: |
64395871 |
Appl. No.: |
16/616279 |
Filed: |
May 22, 2018 |
PCT Filed: |
May 22, 2018 |
PCT NO: |
PCT/US2018/033806 |
371 Date: |
November 22, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62509665 |
May 22, 2017 |
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62509660 |
May 22, 2017 |
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62509669 |
May 22, 2017 |
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62509675 |
May 22, 2017 |
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62509653 |
May 22, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/904 20190101;
G06Q 10/0635 20130101; G06Q 10/06315 20130101; G06Q 10/087
20130101; G06N 20/00 20190101; G06Q 50/28 20130101; G06Q 10/0637
20130101; G06Q 10/063114 20130101; G06Q 10/06375 20130101 |
International
Class: |
G06Q 10/08 20060101
G06Q010/08; G06F 16/904 20060101 G06F016/904 |
Claims
1. A supply chain management interface for managing a supply chain
comprising a plurality of supply chain nodes, comprising: a
plurality of data inputs capable of receiving primary hardware and
software data from at least one supply chain node in a supply chain
computer network upon indication by at least one processor; a
plurality of rules stored in at least one memory element associated
with the at least one processor and capable of performing
operations on the primary hardware and software data to produce
secondary data upon direction from the at least one processor; and
a plurality of data outputs capable of at least: interfacing with a
user to provide the secondary data comprised of at least widgets
including stock language and unique analytics data in accordance
with the primary hardware and software data and at least third
party supply chain data, to ones of the plurality of application
inputs for interfacing to a user.
Description
RELATED APPLICATIONS
[0001] The present application claims the benefit of priority to
International Application No. PCT/US2018/033806, filed May 22,
2018, entitled "Systems and Methods for Interfaces to a Supply
Chain Management System, which claims priority to, is related to,
and incorporates by reference, U.S. provisional application No.
62/509,665, filed May 22, 2017, entitled "Systems and Methods for
Interfaces to a Supply Chain Management System"; U.S. provisional
application No. 62/509,660, filed May 22, 2017, entitled "Systems
and Methods for Risk Processing of Supply Chain Management System
Data"; U.S. provisional application No. 62/509,669, filed May 22,
2017, entitled "Systems and Methods Optimized Design of a Supply
Chain"; U.S. provisional application No. 62/509,675, filed May 22,
2017, entitled Systems and Methods for Assessment and Visualization
of Supply Chain Management System Data; U.S. provisional
application No. 62/509,653, filed May 22, 2017, entitled Systems
and Methods for Providing Diagnostics for a Supply Chain; U.S.
patent application Ser. No. 14/523,642, filed Oct. 24, 2014, to
Valentine, et al., titled "Systems and Methods for Risk Processing
and Visualization of Supply Chain Management System Data," which
claims priority to U.S. provisional patent application Ser. No.
61/895,636, to Valentine, et al., titled "Power Supply With
Balanced Current Sharing," filed Oct. 28, 2013, U.S. provisional
patent application Ser. No. 61/895,665, to Joyner et al., titled
"System and Method for Managing Supply Chain Risk," filed Oct. 25,
2013, and U.S. provisional patent application Ser. No. 61/896,251
to McLellan et al., titled "Method for Identifying and Presenting
Risk Mitigation Opportunities in a Supply Chain," filed Oct. 28,
2013. Each of these is incorporated by reference in their
respective entireties herein.
BACKGROUND
Field of the Disclosure
[0002] The present disclosure relates to supply chain management
(SCM) system processing. More specifically, the present disclosure
is related to processing SCM data to reduce cost, optimize data
processing and networked communications, improving flexibility, and
identifying and mitigating risk in a supply chain. Furthermore, the
SCM data may be structured using visualization, analytics and
frameworks.
Background of the Disclosure
[0003] Supply chains have become increasingly complex, and product
companies are faced with numerous challenges such as globalization,
shortening product lifecycles, high mix product offerings and
countless supply chain procurement models. In addition, challenging
economic conditions have placed additional pressure on companies to
reduce cost to maximize margin or profit. Focus areas of supply
chain-centric companies include reducing cost in the supply chain,
maximizing flexibility across the supply chain, and mitigating
risks in the supply chain to prevent lost revenue.
[0004] Supply chain risk, or the likelihood of supply chain
disruptions, is emerging as a key challenge to SCM. The ability to
identify which supplier has a greater potential of a disruption is
an important first step in managing the frequency and impact of
these disruptions that often significantly impact a supply chain.
Currently, supply chain risk management approaches seek to measure
either supplier attributes or the supply chain structure, where the
findings are used to compare suppliers and predict disruption. The
results are then used to prepare proper mitigation and response
strategies associated with these suppliers. Ideally, such risk
management and assessment would be performed during the design of a
supply chain for a product or line of products, but design tools
and data analysis to allow for such design capabilities are not
available in the known art.
[0005] Rather than the data- and algorithm-centric supply chain
design and risk analysis discussed above, supply chain risk
management is instead most often a formal, largely manual process
that involves identifying potential losses, understanding the
likelihood of potential losses, assigning significance to these
losses, and taking steps to proactively prevent these losses. A
conventional example of such an approach is the purchasing risk and
mitigation (PRAM) methodology developed by the Dow Chemical Company
to measure supply chain risks and its impacts. This approach
examines supply market risk, supplier risk, organization risk and
supply strategy risk as factors for supply chain analysis.
Generally speaking, this approach is based on the belief that
supplier problems account for the large majority of shutdowns and
supply chain failures.
[0006] Such conventional systems are needlessly complicated and
somewhat disorganized in that multiple layers of classification
risks are utilized and, too often, the systems focus mainly on
proactively endeavoring to predict disruptive events instead of
analyzing and processing underlying root causes and large-scale
accumulated data to assess potential disruptions. Further, these
conventional systems fail to provide tools to aid in the design of
a supply chain at the outset to address potential breakdown and
disruption, and they also give little insight or visibility into
the actual supply chain over its entirety. Thus, what is needed is
an efficient, simplified SCM processing system for aiding in the
design of the supply chain, and thereby maximizing opportunities to
address potential supply chain risks at the outset and during the
life cycle of a supply chain.
[0007] Moreover, conventional supply chain management has
historically been based on various assumptions that may prove
incorrect. By way of example, it has generally been understood that
the highest risk in the supply chain resides with suppliers with
whom the highest spend occurs--however, the most significant risk
in a supply chain may actually reside with small suppliers,
particularly if language barriers reside between the supplier and
the supply chain manager, or with sole source suppliers, or in
relation to suppliers highly likely to be subject to catastrophic
events, such as earthquakes, for example. Further, it has typically
been the case that increased inventory results in improved delivery
performance--however, this, too, may prove to be an incorrect
assumption upon analysis of large-scale data over time and across
multiple suppliers, at least in that this assumption is true only
if an inventory buffer is placed on the correct part or parts, and
at the correct service level. Needless to say, such information
would be difficult to glean absent automated review of large-scale
data over time, and without visibility across an entire supply
chain.
[0008] Yet further, present supply chain management fails to
account for much of the available large-scale data information. By
way of example, social media or other third party data sources may
be highly indicative of supply chain needs or prospective
disruptions. For example, if a provider expresses a desire for
increased inventory levels, but social media expresses a largely
negative customer sentiment, sales are likely to fall and the
increased inventory levels will likely not be necessary. Similarly,
large scale data inclusive of third party data may indicate that a
supplier previously deemed high risk, such as due to the threat of
earthquake, is actually lower risk because that supplier has not
been hit with an earthquake over magnitude 5 for that last 20
years, and earthquakes of less than magnitude 5 have only a minimal
probability of affecting the supply chain in a certain vertical. As
such, large scale data, such as may include social media or other
third party data, may complement supply chain management in ways
not provided by conventional supply chain management.
[0009] By way of further example, conventional systems often deem
certain events, such as significant geopolitical events, to pose a
very high risk to the supply chain. However, large scale data
analysis, such as from the inception of the design of many supply
chains in a given vertical and from end-to-end of such supply
chains throughout their respective life cycles, may reveal that
this supposition has generally not been the case--rather, the
supply chain risk may instead be revealed as far more dependent on
sole source items and the size and language spoken by certain
suppliers than on geopolitical events, by way of non-limiting
example.
SUMMARY OF THE DISCLOSURE
[0010] Disclosed is an apparatus, system and method for supply
chain management (SCM) system processing. A SCM operating platform
is operatively coupled to SCM modules for collecting, storing,
distributing and processing SCM data to determine statistical
opportunities and risk in a SCM hierarchy. SCM risk processing may
be utilized to determine risk values that are dependent upon SCM
attributes. Multiple SCM risk processing results may be produced
for further drill-down by a user. SCM network nodes, their relation
and status may further be produced for fast and efficient status
determination.
[0011] More particularly, a supply chain management operating
platform is disclosed for managing a supply chain that includes a
plurality of supply chain nodes. The platform, and its associated
system and method, may include a plurality of data inputs capable
of receiving primary hardware and software data from at least one
third party data source and at least one supply chain node upon
indication by at least one processor. The platform and its
associated system and method may also include a plurality of rules
stored in at least one memory element associated with at least one
processor and capable of performing operations on the primary
hardware and software data to produce secondary data upon direction
from the processor(s). The platform and its associated system and
method may also include a plurality of data outputs capable of at
least one of interfacing with a plurality of application inputs,
and capable of providing the secondary data, comprised of at least
one of supply chain risk data, supply chain management data, and
supply chain analytics, to ones of the plurality of application
inputs for interfacing to a user; and interfacing with the user to
provide the secondary data comprised of at least one of supply
chain risk data, supply chain management data, and supply chain
analytics.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The present invention is illustrated by way of example and
not limitation in the figures of the accompanying drawings, in
which like references indicate similar elements and in which:
[0013] FIG. 1 illustrates a computer system for transmitting and
processing data, and particularly supply chain management (SCM)
data under an exemplary embodiment;
[0014] FIG. 2 illustrates an exemplary processing device suitable
for use in the embodiment of FIG. 1 for processing and presenting
SCM data;
[0015] FIG. 3A illustrates an exemplary SCM platform comprising a
plurality of plug-in applications/modules, including a control
tower module, a network optimization module, a supply chain
analytics module, a supplier radar module, and a supply/demand
processing module under one embodiment;
[0016] FIG. 3B illustrates the SCM platform utilizing extended
plug-in applications/modules under another exemplary
embodiment;
[0017] FIG. 4 illustrates exemplary data points and variables
modules operatively coupled to a SCM platform under one
embodiment;
[0018] FIGS. 5A-5F illustrate logical processing outcomes for a
variety of exemplary embodiments;
[0019] FIG. 6 illustrates an exemplary automation process suitable
for utilization in the embodiment of FIG. 1;
[0020] FIG. 7 illustrates an exemplary data visualization example
for actionable-measurable-proactive (AMP) SCM processing;
[0021] FIG. 8A illustrates a further data visualization and
"one-click" report generation under one embodiment;
[0022] FIG. 8B illustrates a functional action input module
associated with report generation from the data visualization of
FIG. 8A;
[0023] FIG. 9 illustrates an exemplary data table providing for
attribute naming, attribute description and applicable weight
attribution for SCM processing;
[0024] FIG. 10 illustrates an exemplary risk assembly detail for
commodities/parts, wherein part and supplier attributes are
processed to determine an overall risk;
[0025] FIG. 11 illustrates an exemplary risk part detail for
commodities/parts, wherein various attributes are processed
together with attribute weights and selection scores to calculate a
weighted risk score;
[0026] FIG. 12 illustrates an exemplary data visualization heat map
for various assemblies and associated parts, wherein specific
assemblies and/or parts are presented as color-coded objects to
indicate a level of risk;
[0027] FIG. 13 illustrates an exemplary cross-source processing
configuration where a same part, as well as suitable part
alternatives are processed and presented to a user;
[0028] FIG. 14 illustrates a resultant risk trend processing for
processing and displaying a mean and standard deviation of risk
over time;
[0029] FIG. 15 illustrates an exemplary visualization for a
company's entire supply chain network including assembly plants,
parts, suppliers and manufacturers under one embodiment;
[0030] FIG. 16 illustrates an exemplary embodiment wherein the
selection of a SCM node automatically causes the system to display
all upstream and downstream nodes;
[0031] FIG. 17 illustrates another exemplary embodiment wherein the
selection of a SCM node automatically causes the system to display
all upstream and downstream nodes;
[0032] FIG. 18 illustrates an exemplary screenshot of a network
optimizer under one exemplary embodiment;
[0033] FIG. 19 illustrates a screenshot of an exemplary system
dashboard for globally displaying the status of various SCM
attributes under one embodiment;
[0034] FIG. 20A illustrates a screenshot of an interactive map for
displaying various attributes for SCM nodes under one
embodiment;
[0035] FIG. 20B illustrates a screenshot in which a plurality of
suppliers located about the same geographical location are visually
clustered into a bubble;
[0036] FIG. 21 illustrates a screenshot of an interactive node
network diagram under one exemplary embodiment;
[0037] FIG. 22 illustrates a screenshot of a data visualization
benchmark for various attributes under one exemplary
embodiment;
[0038] FIG. 23 illustrates a screenshot of an exemplary risk-based
heat map, together with attribute values under one exemplary
embodiment;
[0039] FIG. 24 illustrates a screenshot of a report generation
module for an associated risk-based heat map under one exemplary
embodiment;
[0040] FIG. 25 illustrates a screenshot of an exemplary geographic
impact report produced from the report generation module under one
exemplary embodiment;
[0041] FIG. 26 illustrates a screenshot of an exemplary risk
attribute score average report produced from the report generation
module under one exemplary embodiment;
[0042] FIG. 27 illustrates a screenshot of a screenshot of an
exemplary risk attribute score distribution report produced from
the report generation module under one exemplary embodiment;
[0043] FIG. 28 illustrates a screenshot of a screenshot of an
exemplary risk attribute score standard deviation report produced
from the report generation module under one exemplary
embodiment;
[0044] FIG. 29 illustrates an exemplary risk attribute part detail
report produced from the report generation module under one
exemplary embodiment;
[0045] FIG. 30 illustrates a screenshot of an exemplary AMP SCM
opportunity bubble chart produced from an analytics module under
one exemplary embodiment;
[0046] FIG. 31 illustrates a screenshot of a sell-through chart
produced by the status reports module under one exemplary
embodiment;
[0047] FIG. 32 illustrates a screenshot of an inventory chart
processed and produced by the status reports module under one
exemplary embodiment;
[0048] FIG. 33 illustrates a screenshot of a current supply chain
model split processed and produced by the status reports module
under one exemplary embodiment;
[0049] FIG. 34 illustrates a screenshot of an interactive map
visualizing nodes produced by the supplier radar module under one
exemplary embodiment;
[0050] FIG. 35 illustrates a screenshot of a geographic impact
report and interactive map visualizing nodes generated by the
supplier radar supply and demand module under one exemplary
embodiment;
[0051] FIG. 36 illustrates a screenshot of an interactive map
visualizing nodes and geographic impact report generated by the
supply and demand supplier radar module under one exemplary
embodiment;
[0052] FIG. 37 illustrates a screenshot of a critical shortage
summary report generated by the supply and demand module under one
exemplary embodiment;
[0053] FIG. 38 illustrates a screenshot of an exemplary radar
module;
[0054] FIG. 39 illustrates an exemplary interface;
[0055] FIG. 40 illustrates an exemplary interface;
[0056] FIG. 41 illustrates an exemplary interface;
[0057] FIG. 42 illustrates an exemplary interface;
[0058] FIG. 43 illustrates an exemplary interface;
[0059] FIG. 44 illustrates an exemplary interface;
[0060] FIG. 45 illustrates an exemplary interface; and
[0061] FIG. 46 illustrates an exemplary interface.
DETAILED DESCRIPTION
[0062] The figures and descriptions provided herein may have been
simplified to illustrate aspects that are relevant for a clear
understanding of the herein described devices, systems, and
methods, while eliminating, for the purpose of clarity, other
aspects that may be found in typical devices, systems, and methods.
Those of ordinary skill may recognize that other elements and/or
operations may be desirable and/or necessary to implement the
devices, systems, and methods described herein. Because such
elements and operations are well known in the art, and because they
do not facilitate a better understanding of the present disclosure,
a discussion of such elements and operations may not be provided
herein. However, the present disclosure is deemed to inherently
include all such elements, variations, and modifications to the
described aspects that would be known to those of ordinary skill in
the art.
[0063] The terminology used herein is for the purpose of describing
particular example embodiments only and is not intended to be
limiting. As used herein, the singular forms "a", "an" and "the"
may be intended to include the plural forms as well, unless the
context clearly indicates otherwise. The terms "comprises,"
"comprising," "including," and "having," are inclusive and
therefore specify the presence of stated features, integers, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof. The
method steps, processes, and operations described herein are not to
be construed as necessarily requiring their performance in the
particular order discussed or illustrated, unless specifically
identified as an order of performance. It is also to be understood
that additional or alternative steps may be employed.
[0064] When an element or layer is referred to as being "on",
"engaged to", "connected to" or "coupled to" another element or
layer, it may be directly on, engaged, connected or coupled to the
other element or layer, or intervening elements or layers may be
present. In contrast, when an element is referred to as being
"directly on," "directly engaged to", "directly connected to" or
"directly coupled to" another element or layer, there may be no
intervening elements or layers present. Other words used to
describe the relationship between elements should be interpreted in
a like fashion (e.g., "between" versus "directly between,"
"adjacent" versus "directly adjacent," etc.). As used herein, the
term "and/or" includes any and all combinations of one or more of
the associated listed items.
[0065] Although the terms first, second, third, etc., may be used
herein to describe various elements, components, regions, layers
and/or sections, these elements, components, regions, layers and/or
sections should not be limited by these terms. These terms may be
only used to distinguish one element, component, region, layer or
section from another element, component, region, layer or section.
Terms such as "first," "second," and other numerical terms when
used herein do not imply a sequence or order unless clearly
indicated by the context. Thus, a first element, component, region,
layer or section discussed below could be termed a second element,
component, region, layer or section without departing from the
teachings of the exemplary embodiments.
[0066] Computer-implemented platforms, engines, systems and methods
of use are disclosed herein that provide networked access to a
plurality of types of digital content, including but not limited to
video, image, text, audio, metadata, algorithms, interactive and
document content, and that track, deliver, manipulate, transform
and report the accessed content. Described embodiments of these
platforms, engines, systems and methods are intended to be
exemplary and not limiting. As such, it is contemplated that the
herein described systems and methods may be adapted to provide many
types of server and cloud-based valuations, interactions, data
exchanges, and the like, and may be extended to provide
enhancements and/or additions to the exemplary platforms, engines,
systems and methods described. The disclosure is thus intended to
include all such extensions.
[0067] Furthermore, it will be understood that the terms "module"
or "engine", as used herein does not limit the functionality to
particular physical modules, but may include any number of
tangibly-embodied software and/or hardware components having a
transformative effect on at least a portion of a system. In
general, a computer program product in accordance with one
embodiment comprises a tangible computer usable medium (e.g.,
standard RAM, an optical disc, a USB drive, or the like) having
computer-readable program code embodied therein, wherein the
computer-readable program code is adapted to be executed by a
processor (working in connection with an operating system) to
implement one or more functions and methods as described below. In
this regard, the program code may be implemented in any desired
language, and may be implemented as machine code, assembly code,
byte code, interpretable source code or the like (e.g., via C, C++,
C #, Java, Actionscript, Objective-C, Javascript, CSS, XML,
etc.).
[0068] Turning to FIG. 1, an exemplary computer system is disclosed
in an embodiment. In this example, computer system 100 is
configured as a SCM processing system, wherein primary processing
node 101 is configured to contain an SCM platform for processing
data from other nodes (104, 107), which will be described in
further detail below. In one embodiment, primary node 101 comprises
one or more servers 102 operatively coupled to one or more
terminals 103. Primary node 101 is communicatively coupled to
network 112, which in turn is operatively coupled to supply chain
nodes 104, 107. Nodes 104, 107 may be configured as standalone
nodes or, preferably, as network nodes, where each node 104, 107
comprises network servers 105, 108 and terminals 106, 109,
respectively.
[0069] As will be explained in the embodiments discussed below,
nodes 104, 107 may be configured as assembly nodes, part nodes,
supplier nodes, manufacturer nodes and/or any other suitable supply
chain node. Each of these nodes may be configured to collect,
store, and process relevant supply chain-related data and transmit
the SCM data to primary node 101 via network 112. Primary node 101
may further be communicatively coupled to one or more data services
110, 111 which may be associated with governmental, monetary,
economic, meteorological, etc., data services. Services 110, 111
may be third-party services configured to provide general
environmental data relating to SCM, such as interest rate data,
tax/tariff data, weather data, trade data, currency exchange data,
and the like, to further assist in SCM processing. Primary node 101
may be "spread" across multiple nodes, rather than comprising a
single node, may access data at any one or more of a plurality of
layers from nodes 104, 107, and may be capable of applying a
selectable one or more algorithms, applications, calculations, or
reporting in relation to any one or more data layers from nodes
104, 107.
[0070] FIG. 2 is an exemplary embodiment of a computing device 200
which may function as a computer terminal (e.g., 103), and may be a
desktop computer, laptop, tablet computer, smart phone, or the
like. Actual devices may include greater or fewer components and/or
modules than those explicitly depicted in FIG. 2. Device 200 may
include a central processing unit (CPU) 201 (which may include one
or more computer readable storage mediums), a memory controller
202, one or more processors 203, a peripherals interface 1204, RF
circuitry 205, audio circuitry 206, a speaker 221, a microphone
222, and an input/output (I/O) subsystem 223 having display
controller 218, control circuitry for one or more sensors 216 and
input device control 214. These components may communicate over one
or more communication buses or signal lines in device 200. It
should be appreciated that device 200 is only one example of a
multifunction device 200, and that device 200 may have more or
fewer components than shown, may combine two or more components, or
a may have a different configuration or arrangement of the
components. The various components shown in FIG. 2 may be
implemented in hardware or a combination of hardware and
tangibly-embodied, non-transitory software, including one or more
signal processing and/or application specific integrated
circuits.
[0071] Data communication with device 200 may occur via a direct
wired link or data communication through wireless, such as RF,
interface 205, or through any other data interface allowing for the
receipt of data in digital form. Decoder 213 is capable of
providing data decoding or transcoding capabilities for received
media, and may also be enabled to provide encoding capabilities as
well, depending on the needs of the designer. Memory 208 may also
include high-speed random access memory (RAM) and may also include
non-volatile memory, such as one or more magnetic disk storage
devices, flash memory devices, or other non-volatile solid-state
memory devices. Access to memory 208 by other components of the
device 200, such as processor 203, decoder 213 and peripherals
interface 204, may be controlled by the memory controller 202.
Peripherals interface 204 couples the input and output peripherals
of the device to the processor 203 and memory 208. The one or more
processors 203 run or execute various software programs and/or sets
of instructions stored in memory 208 to perform various functions
for the device 200 and to process data including SCM data. In some
embodiments, the peripherals interface 204, processor(s) 203,
decoder 213 and memory controller 202 may be implemented on a
single chip, such as a chip 201. In some other embodiments, they
may be implemented on separate chips.
[0072] The RF (radio frequency) circuitry 205 receives and sends RF
signals, also known as electromagnetic signals. The RF circuitry
205 converts electrical signals to/from electromagnetic signals and
communicates with communications networks and other communications
devices via the electromagnetic signals. The RF circuitry 205 may
include well-known circuitry for performing these functions,
including but not limited to an antenna system, an RF transceiver,
one or more amplifiers, a tuner, one or more oscillators, a digital
signal processor, a CODEC chipset, a subscriber identity module
(SIM) card, memory, and so forth. RF circuitry 205 may communicate
with networks, such as the Internet, also referred to as the World
Wide Web (WWW), an intranet and/or a wireless network, such as a
cellular telephone network, a wireless local area network (LAN)
and/or a metropolitan area network (MAN), and other devices by
wireless communication. The wireless communication may use any of a
plurality of communications standards, protocols and technologies,
including but not limited to Global System for Mobile
Communications (GSM), Enhanced Data GSM Environment (EDGE),
high-speed downlink packet access (HSDPA), wideband code division
multiple access (W-CDMA), code division multiple access (CDMA),
time division multiple access (TDMA), BLE, Bluetooth, Wireless
Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g
and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX,
a protocol for email (e.g., Internet message access protocol (IMAP)
and/or post office protocol (POP)), instant messaging (e.g.,
extensible messaging and presence protocol (XMPP), Session
Initiation Protocol for Instant Messaging and Presence Leveraging
Extensions (SIMPLE), and/or Instant Messaging and Presence Service
(IMPS)), and/or Short Message Service (SMS)), or any other suitable
communication protocol, including communication protocols not yet
developed as of the filing date of this document.
[0073] Audio circuitry 206, speaker 221, and microphone 222 may
provide an audio interface between a user and the device 200. Audio
circuitry 1206 may receive audio data from the peripherals
interface 204, converts the audio data to an electrical signal, and
transmits the electrical signal to speaker 221. The speaker 221
converts the electrical signal to human-audible sound waves. Audio
circuitry 206 also receives electrical signals converted by the
microphone 221 from sound waves, which may include audio. The audio
circuitry 206 converts the electrical signal to audio data and
transmits the audio data to the peripherals interface 204 for
processing. Audio data may be retrieved from and/or transmitted to
memory 208 and/or the RF circuitry 205 by peripherals interface
204. In some embodiments, audio circuitry 206 also includes a
headset jack for providing an interface between the audio circuitry
206 and removable audio input/output peripherals, such as
output-only headphones or a headset with both output (e.g., a
headphone for one or both ears) and input (e.g., a microphone).
[0074] I/O subsystem 223 couples input/output peripherals on the
device 200, such as touch screen 215 and other input/control
devices 217, to the peripherals interface 204. The I/O subsystem
223 may include a display controller 218 and one or more input
controllers 220 for other input or control devices. The one or more
input controllers 220 receive/send electrical signals from/to other
input or control devices 217. The other input/control devices 217
may include physical buttons (e.g., push buttons, rocker buttons,
etc.), dials, slider switches, joysticks, click wheels, and so
forth. In some alternate embodiments, input controller(s) 220 may
be coupled to any (or none) of the following: a keyboard, infrared
port, USB port, and a pointer device such as a mouse, an up/down
button for volume control of the speaker 221 and/or the microphone
222. Touch screen 215 may also be used to implement virtual or soft
buttons and one or more soft keyboards.
[0075] Touch screen 215 provides an input interface and an output
interface between the device and a user. The display controller 218
receives and/or sends electrical signals from/to the touch screen
215. Touch screen 215 displays visual output to the user. The
visual output may include graphics, text, icons, video, and any
combination thereof (collectively termed "graphics"). In some
embodiments, some or all of the visual output may correspond to
user-interface objects. Touch screen 215 has a touch-sensitive
surface, sensor or set of sensors that accepts input from the user
based on haptic and/or tactile contact. Touch screen 215 and
display controller 218 (along with any associated modules and/or
sets of instructions in memory 208) detect contact (and any
movement or breaking of the contact) on the touch screen 215 and
converts the detected contact into interaction with user-interface
objects (e.g., one or more soft keys, icons, web pages or images)
that are displayed on the touch screen. In an exemplary embodiment,
a point of contact between a touch screen 215 and the user
corresponds to a finger of the user. Touch screen 215 may use LCD
(liquid crystal display) technology, or LPD (light emitting polymer
display) technology, although other display technologies may be
used in other embodiments. Touch screen 215 and display controller
218 may detect contact and any movement or breaking thereof using
any of a plurality of touch sensing technologies now known or later
developed, including but not limited to capacitive, resistive,
infrared, and surface acoustic wave technologies, as well as other
proximity sensor arrays or other elements for determining one or
more points of contact with a touch screen 215.
[0076] Device 200 may also include one or more sensors 216 such as
optical sensors that comprise charge-coupled device (CCD) or
complementary metal-oxide semiconductor (CMOS) phototransistors.
The optical sensor may capture still images or video, where the
sensor is operated in conjunction with touch screen display 215.
Device 200 may also include one or more accelerometers 207, which
may be operatively coupled to peripherals interface 1204.
Alternately, the accelerometer 207 may be coupled to an input
controller 214 in the I/O subsystem 211. The accelerometer is
preferably configured to output accelerometer data in the x, y, and
z axes.
[0077] In one embodiment, the software components stored in memory
208 may include an operating system 209, a communication module
210, a text/graphics module 211, a Global Positioning System (GPS)
module 212, audio decoder 1213 and applications 214. Operating
system 209 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, Windows, or an
embedded operating system such as VxWorks) includes various
software components and/or drivers for controlling and managing
general system tasks (e.g., memory management, storage device
control, power management, etc.) and facilitates communication
between various hardware and software components. A SCM processing
platform may be integrated as part of operating system 209, or all
or some of the disclosed portions of SCM processing may occur
within the one or more applications 214. Communication module 210
facilitates communication with other devices over one or more
external ports and also includes various software components for
handling data received by the RF circuitry 205. An external port
(e.g., Universal Serial Bus (USB), Firewire, etc.) may be provided
and adapted for coupling directly to other devices or indirectly
over a network (e.g., the Internet, wireless LAN, etc.).
[0078] Text/graphics module 211 includes various known software
components for rendering and displaying graphics on a screen and/or
touch screen 215, including components for changing the intensity
of graphics that are displayed. As used herein, the term "graphics"
includes any object that can be displayed to a user, including
without limitation text, web pages, icons (such as user-interface
objects including soft keys), digital images, videos, animations
and the like. Additionally, soft keyboards may be provided for
entering text in various applications requiring text input. GPS
module 212 determines the location of the device and provides this
information for use in various applications. Applications 214 may
include various modules, including address books/contact list,
email, instant messaging, video conferencing, media player,
widgets, instant messaging, camera/image management, and the like.
Examples of other applications include word processing
applications, JAVA-enabled applications, encryption, digital rights
management, voice recognition, and voice replication. Under one
embodiment, a 3D object may have access to any or all of features
in memory 208.
[0079] Turning to FIG. 3A, a SCM operating platform 307 is
disclosed, wherein platform 307 may reside at a primary node 101.
Platform 307 may be configured to perform and/or control SCM data
processing on data received from external nodes 104, 107 and other
data sources 110, 111. Platform 307 is operatively coupled to
control module 302, which may be configured to process, connect and
visualize nodes and their respective geographic locations. Network
optimization module 303 processes SCM data to determine which nodes
and links meet or exceed predetermined risk thresholds and
determines new nodes and/or links that may be added, deleted and/or
substituted to establish more efficient network optimization.
[0080] Supply chain analytics module/engine 304 may be configured
to process incoming supply chain data and forward results to
platform 307 for storage, distribution to other modules and/or for
further processing. Supplier radar module 305 may be configured to
process SCM data to determine supplier geographic impact and/or
geographical risk. Supply/demand processing module 306 may be
configured to receive and process supply and demand data for
determining supply/demand values for various nodes. Each of modules
302-306 may share data between themselves via platform 307.
Platform 307 may further be configured to generate visualizations,
such as media, charts, graphs, node trees, and the like, for
inspection and/or follow-up action by a user.
[0081] The platform of FIG. 3A is configured to utilize extensive
data across many primary and secondary nodes, advanced analytics,
logic and visualization to convert extensive, voluminous
unstructured data into an easy-to-action, prioritized list of tasks
for improved SCM functionality. One advantageous effect of the
platform is that it is effective in identifying actual and
potential opportunities of improvement, such as based on analysis
of extended historical data of similar or related supply chains.
These opportunities are designed to streamline and optimize SCM by
generating better SCM terms, models and implementation of optimal
parameter settings. The techniques described herein, and their
advantageous effects are sometimes referred to as "actionable
measurable proactive" (AMP) processing techniques.
[0082] FIG. 3B illustrates, at the primary node 101 of a data
exchange diagram, platform 307. In the illustration, platform 307
may provide a plurality of rules and processes, such as the
aforementioned analytics, exception management, risk management,
and visualization techniques, that may be applied by one or more
modules. That is, access to the rules and processes provided by the
platform may be available to the aforementioned modules. Thus,
these applications, also referred to herein as "apps" or modules,
may be "thin client", wherein the processes reside entirely within
the platform's processing and are accessed by the app; "thick
client," wherein the processes reside entirely within the app's
processing; or partially thin client, wherein processing and rule
application is shared between the app and the platform.
[0083] Data inputs for the one of more modules, also referred to in
the pertinent art as "data hooks" for "apps," may be associated
with the platform 307, and thus may obtain data that is made
available by the platform, such as may be obtained from hardware or
software outputs provided from nodes 104, 107 and/or sources 110,
111. As illustrated, data may be received in platform modules for
risk management 311, analytics 312, information visualization 313
and exception management 314. The data may be provided in the form
of network optimization data 321, supply chain analytics data 322,
design/engineering/technology data 315, consumer intelligence data
316, supplier data 317, procurement data 318, operations data 321,
and supply and demand data 319, by way of non-limiting example.
Output data from any given app may be provided through
visualization rules unique to the app and within the app, or via
the platform, such as within a discreet display aspect for a given
app within the platform. Output data from any given app may be
provided, such as through visualization rules unique to the app,
within the app, or via the platform, such as within a discreet
display aspect, such as a drop down, top line, or side line menu,
for a given app within the platform.
[0084] Moreover, primary data employed by the platform and its
associated apps may be atypical of that employed by conventional
SCM systems. For example, customer intelligence data may include
social media trends and/or third party data feeds in relation to a
supply chain, or for all supply chains for similar devices, device
lines, or for device lines including the same or a similar part.
Secondary data derived from the third party data sources for a
device, for example, allows for secondary data to be derived
therefrom in relation to inventory stock, the need for alternate
sourcing, and the like. For example, a negative overall indication
on a device, as indicated by social media data drawn from one or
more networked social media locations, would indicate a need for
decreased inventory (since a negative consumer impression likely
indicates an upcoming decrease in sales), notwithstanding any
request by the seller of the device to the contrary. This need for
decreased inventory may also dictate modifications for the
presently disclosed SCM of the approach to other aspects of the
supply chain, such as parts needed across multiple customers, the
need to de-risk with multiple sources for parts, the need to ship
present inventory in a certain timeframe, and the like. This same
data may be mined for other purposes, such as to assess
geopolitical, weather, and like events.
[0085] The disclosure thus provides a SCM operating platform 307
suitable for receiving base data from the supply chain, and/or from
a data store, and/or from third party networked sources, and
applying thereto a plurality of rules, algorithms and processes to
produce secondary data. This secondary data may be made available
within the platform, and/or may be made available to one or more
apps, to provide indications to the user based on the applied
rules, algorithms and processes. Therefore, the disclosure makes
use of significant amounts of data across what may be thousands of
supply chain nodes for a single device line to allow for supply
chain management, risk management, supply chain monitoring, and
supply chain modification, in real time. Moreover, based on the
significant data available to the platform, the platform and/or its
interfaced apps may "learn" from certain of the data received, such
as trend data fail point data, or the like, in order to modify the
aforementioned rules, algorithms and processes, in real time and
for subsequent application.
[0086] Because the apps disclosed make use of the data, rules,
algorithms, and processes provided by the platform, any number of
different component apps may be provided. Apps may interface with
the platform solely to obtain data, and may thereafter apply unique
app-based rules, algorithms and processes to the received data; or
apps may make use of the data and some or all of the rules,
learning algorithms, and processes of the platform and may solely
or most significantly provide variations in the visualizations
regarding the secondary data produced. Those skilled in the art
will thus appreciate, in light of the instant disclosure, that
various of the apps and data discussed herein throughout are
exemplary only, and thus various other apps, data input, and data
output may be provided without departing from the spirit or scope
of the invention.
[0087] Turning now to FIG. 4, an embodiment is illustrated for a
materials system utilizing the platform 307 of FIG. 3. As SCM data
is entered into the system, various data points, variables and
loads are entered into the SCM system database for processing
and/or distribution to any of the various modules described herein.
For each node, a hierarchy structure 401 is determined, which may
comprise one or more sites 401A, customer groups 401B, customers
401C, region 401D, division 401E and sector 401E. It is understood
by those skilled in the art that the hierarchical structure data
points may include additional, other, data points, or may contain
fewer data points as the case may be.
[0088] Other entries in the embodiment of FIG. 4 include part
number 402, which may comprise unique customer material component
numbers for each part. Stock on hand 403 may comprise data relating
to a current quantity of each component in stock by ownership. For
example, quantity data may be segregated among manufacturers,
suppliers and customers. It may be understood by those skilled in
the art, in light of the instant disclosure, that other entries and
segregations are contemplated by the present disclosure. For
example, data may also be segregated among types, such as Raw, Work
In Process (WIP) and Finished Goods (FG). Data may likewise be
segregated by location, such as by Warehouse, Manufacturing Line,
Test, Packout, Shipping, etc., or by using any other methodology
that may be contemplated by the skilled artisan in view of the
discussion provided herein.
[0089] Unit price 404 may contain data relating to a cost per
component. The cost may be determined via a materials cost, labor
cost, or some combination. ABC classification 405 may comprise a
classification value of procurement frequency (e.g., every 7 days,
14 days, 28 days, etc.).
[0090] ABC Analysis is a term used to define an inventory
categorization technique often used in materials management. It is
also known as Selective Inventory Control. Policies based on ABC
analysis are typically structured such that "A" items are processed
under very tight control and accurate records, "B" items are
processed under less tightly controlled and good records, and "C"
items are processed under the simplest controls possible and
minimal records. ABC analysis provides a mechanism for identifying
items that will have a significant impact on overall inventory
cost, while also providing a mechanism for identifying different
categories of stock that will require different management and
controls. ABC analysis suggests that inventories of an organization
are not of equal value. Thus, the inventory is grouped into three
categories (A, B, and C) in order of their estimated importance.
Accordingly, "A" items are very important for an organization.
Because of the high value of these "A" items, frequent value
analysis is required. In addition to that, an organization needs to
choose an appropriate order pattern (e.g. "just-in-time") to avoid
excess capacity. "B" items are important, but less important than
"A" items and more important than "C" items (marginally important).
Accordingly, "B" items may be intergroup items. ABC type
classifications within the system may help dictate how often
materials are procured. By way of non-limiting example, to limit
the value of inventory holding and risk, A Classes may be
predominantly ordered once per week, B Classes bi-weekly, and C
Classes monthly.
[0091] MOQ 406 may comprise data relating to a component minimum
order quantity for a predetermined time period. This value may be
advantageous in determining, for example, a minimum order quantity
that must be procured over a predetermined time period. Multiple
407 may comprise data relating to component multiple quantities,
such as multiples for demand more than the MOQ value. System lead
time 408 may comprise data relating to a system period of time
required to release a purchase order prior to receiving
components.
[0092] Continuing with the example of FIG. 4, supplier 417 may
comprise identity data relating to a component source supplier.
Sourcing Application Tool (SAT) lead time 409 may comprise a
supplier quoted period of time required to release a purchase order
prior to receiving components. Safety stock 410 may comprise
component and FG buffer stock data relating to a buffer stock
quantity that will be excluded from available stock until a
shortage status is detected. Safety lead time 411 may comprise
component buffer lead time data that may be utilized to recommend a
component be delivered in an on-time or earlier-than-expected
manner. Quota percentage 412 may comprise data relating to a
percentage of supply which should be allocated to each component
source supplier. Supply 414 may comprise data relating to supply
quantity per component over a predetermined (e.g., 90-day) time
period. 90-day demand 416 may comprise data relating to customer
demand quantity per component over a 90-day time period.
Manufacturers of one or more components may also be tracked
separately from suppliers. In some cases, a supplier may act as a
distributor by stocking parts from different manufacturers to make
them quickly available, but typically at a higher price. Here,
supply chain models, such as consigned material and/or vendor
managed processes, may be used to assist in identifying and
potentially offsetting the extra cost.
[0093] Utilizing the exemplary platform illustrated in FIG. 3 and
FIG. 4, a number of advantageous SCM processing determinations may
be made. In one example, optimal values or actions may be generated
based on predetermined logic. For example, a MOQ may be determined
to be 10,000 units, while an optimal MOQ quantity is calculated to
be 6,000 units. Calculating an area of improvement based on
predefined logic, the reduction of current MOQ may be calculated to
be 4,000 units (10,000-6,000). Data values calculated for
improvement may be determined according to predefined logic,
wherein for the exemplary MOQ improvement of 4,000 units, a unit
value multiplier of $1 would yield an improvement "opportunity"
value of $4,000. As used herein, the phrase "opportunity value" may
be used to indicate a particular area of data, such as an item
source, a replacement part, an inventory level, or the like, that
provides an opportunity to improve an indicated area of the supply
chain, such as de-risking, lowering costs, increasing available
sources, optimizing inventory levels, or the like.
[0094] In addition, one or more opportunity thresholds may be set
for each component, and a resulting prioritization may be
determined. For example, the system may be configured to only list
components with an opportunity value greater than $1,000, where
opportunities are sorted in a descending value. Ownership of each
component may be assigned, where the system may notify users
associated with an ownership entity. Each component may be assigned
to multiple users or a single user, and largest opportunities may
be identified and notified first. Owners may assign actions, add
comments, and potentially escalate SCM data. For example, owners
may advise which actions have been taken or escalate data for
resolution, etc. When all options and/or system negotiations are
completed or exhausted, the system may manually or automatically
close a SCM task associated with the data.
[0095] In the field of SCM processing, various data points have
been used to improve a supply chain. However, the present
applicants have identified a number of data areas that are
relatively efficient to obtain and process. These data areas are
opportunity value areas that have potentially been overlooked by
conventional approaches, but have been found to be useful in
determining better days in inventory, inventory turn and cash flow,
among others. One data area includes MOQ, which provides
opportunities to reduce MOQ to an optimal quantity using logic
based on order frequency, multiple quantities and demand profiles.
Another data area includes safety stock, which provides
opportunities to reduce safety stock levels using logic based on
order frequency and demand profiles to an optimal safety stock
(buffer quantity). Yet another data point includes lead time, which
may provide opportunities to reduce procurement lead times with
higher system parameters versus an active quotes database.
[0096] A still further data point includes safety lead time, which
may provide opportunities to reduce safety lead times based on
removal of system parameters and/or reducing excessive parameters.
Excess inventory data points may also provide opportunities to
reduce owned excess inventory based on a rolling measurement and
highlight supplier returns privileges. Supply not required data
points may also provide opportunity to reduce, divert, cancel,
etc., material arriving within a certain period which is not
required to meet customer demand. Of course the aforementioned data
points are not exclusive and may be combined with other data points
discussed in the present disclosure or other data points known in
the art.
[0097] Thus, for example and as further illustrated with regard to
FIGS. 5A-5F, 6-7, and 8A and B, described below, derived secondary
data may be provided to indicate, for example, a recommended buffer
for an inventoried part. A risk calculation, as discussed in more
detail below with regard to FIGS. 9-12, may indicate that a
particular part is a high risk part (such as because it is from a
small, sole source, foreign supplier). Further, as is often the
case with a high risk part, the indication may be that the part is
relatively inexpensive in relation to other parts for a given
device. Consequently, the presently disclosed SCM platform 307,
notwithstanding a calculation that the optimal procurement time may
be 14 days, may derive secondary data from the combinations of the
optimal procurement secondary data, the risk associated with the
part, and the cost of the part, that a 28 day buffer should be
ordered for the part at each of the next two 14 day procurement
windows--thereby increasing the buffer for this key, high risk part
using the learning algorithms of the platform 307. That is, the
disclosed embodiments may perform balancing of input primary and
derived secondary data to arrive at a solution that is optimal when
considering a wide range of factors, but which is not necessarily
optimal for any given factor.
[0098] FIGS. 5A-5F illustrates various examples of data processing
under various embodiments. FIG. 5A provides an exemplary process
based on MOQ data points. In this example, the logic is to process
an ABC classification, indicating how often a component is
procured. For example, an "A" class part may be procured every 7
days, class "B" 14 days and class "C" 28 days. The MOQ and multiple
(Pack Size) data points are then processed in the system over a
predetermined time period (e.g., 90 days). Referring to FIG. 5A,
the system calculates a 7 day-of-service (DOS) quantity of 5,911
based on a daily forward looking demand of 844. If the system is
configured to have a DOS threshold of 7 days, the respective
multiple quantity of 1,000 may be rounded up to greater than the 7
DOS quantity, which results in a new MOQ of 6,000 (6.times.
multiple qty. 1,000). Here, the supplier may be notified to reduce
MOQ to 6,000, as the purchaser will not want to purchase more than
7 DOS for a class "A" part. Furthermore, if a multiple quantity is
a genuine pack size, then the purchaser may be able to purchase 6
units instead of 10. Since the unit reduction is calculated to be
4,000 (10,000-6,000), the reduction value may be multiplied by the
unit value to determine a total opportunity value. This calculation
in turn may be used by the system to effect monthly/quarterly
ending inventory values. Such a configuration advantageously
improves end-of-life situation and reduce liability within a supply
chain.
[0099] Turning to FIG. 5B, the exemplary embodiment provides an
illustrative process based on safety stock data points. In this
example, the system determines if a safety stock is available, and,
if one is available, a similar ABC and daily demand logic described
above is applied. The daily demand quantity is used to calculate
what seven, fourteen and 28 DOSs should be, and, if the part safety
stock quantity is greater than this value, then a new safety stock
quantity is established. In one example, class "A" part of 5,911
quantity is determined to have a 7 DOS. Accordingly, the system
automatically sets a new SS quantity at 5,911. One reason for this
is that, for class "A" parts, a location should not be holding more
than 7 days safety stock, (unless otherwise configured by the
system), as this pulls the full order book from suppliers early and
may affect ending inventory values. In the example of FIG. 5B, the
same process may be repeated for "B" and "C" classes for 14 and 28
day time periods, respectively.
[0100] FIG. 5C illustrates an exemplary embodiment using lead time
data points. Lead time data is more straightforward to process,
where the system simply takes a SAT quote for the part and supplier
combination. If the system finds a quote and the lead time is less
than what is entered in the system, additional processing steps may
be taken. First, daily demand quantity is calculated, and, based on
this, the system uses the difference between the current lead time
and the SAT lead time to calculate an opportunity value. As an
example, assuming a unit cost is $1, and a lead time reduction is
84 days in SAP versus 70 days in SAT (14 days), the 14 day
reduction may be processed with a daily demand value such that
844.times.$1=$844.times.14 days=$11,822.
[0101] In FIG. 5D, an exemplary embodiment is provided using safety
lead time data points. Similar to lead time, safety lead time is
straightforward for the system to process, where the system looks
for the removal of the full SLT if the SLT indicator is set. In
this case, the system calculates a daily demand quantity, and,
based on this, it uses a reduction of a current SLT to calculate an
opportunity value. For example, assuming a unit cost is $1, and the
SLT is 10 days in SAP, the daily demand value (844.times.$1=$844)
is multiplied by the reduction of 10 days, which results in
$8,444.
[0102] In FIG. 5E, an exemplary embodiment is illustrated for the
system utilizing excess stock data points. Here the owned stock on
hand is compared to the next 90 days of demand. If the stock is
greater than the specified demand, then the remaining quantity is
then used as an excess quantity. As such, an opportunity value may
be calculated, based on a product of the standard unit cost. For
example, if 150,000 units of stock is on hand, and the next 90 days
demand (which may include past demand) is only 76,000, the system
would process the data such that 150,000-76,000=74,000. Again
assuming a unit cost of $1, the opportunity value may be determined
to be $74,000. In one embodiment, the system may also highlight is
the part/unit has potential supplier returns privileges in place
via a connection to SAT.
[0103] In FIG. 5F, a supply not required data point processing
embodiment is shown. In this example, certain supplier purchase
orders are not required to meet current quarter demands. Here, the
opportunity value is not necessarily limited the purchase order
quantity (e.g., use exception); the system calculates a quantity
arriving within a quarter which is greater that a needed quantity.
For example, assuming a unit cost is $1 and the stock on hand is
100,000, the demand unit quarter end is 12,000 and a supply unit
QTR end is 50,000. Here the system would process the data points as
(100,000+50,000)-120,000=30,000 (or $30,000 that is not required,
representing $30,000 of opportunity)
[0104] In addition to the examples provided in FIGS. 5A-F, other
variables may be utilized by the system for optimization. For
example, master production schedule (MPS) tactical rules may be
employed to generate a scorecard format in order to identify areas
of concern and opportunity. By using a plurality of variables as
inputs, an MPS may be configured to generate a set of outputs for
decision making within the system. Inputs may include any of the
data points disclosed herein, as well as forecast demand,
production costs, inventory money, customer needs, inventory
progress, supply, lot size, production lead time, and capacity.
Inputs may be automatically generated by the system by linking one
or more departments at a node with a production department. For
instance, when a sale is recorded, the forecast demand may be
automatically shifted to meet the new demand. Inputs may also be
inputted manually from forecasts that have also been calculated
manually. Outputs may include amounts to be produced, staffing
levels, quantity available to promise, and projected available
balance. Outputs may be used to create a Material Requirements
Planning (MRP) schedule.
[0105] Other variables may include product lead-time stack data,
where procurement and manufacturing lead times are stacked (which
may also include safety lead times) to an end product level to
identify areas of concern and opportunity. Supply chain models may
also be used to optimize inventory. For example, if a sub-optimal
supply chain model is not in place with current suppliers,
arrangements with customers may be processed to improve the supply
chain model, which in turn could allow for identification and/or
quantification for potential inventory reduction or inventory
avoidance opportunities. Supplier payment term data may also be
cross-referenced to identify potential extended payment terms to
produce better cash flow.
[0106] Generally speaking, certain features and processes described
herein are based on a "plan-do-check-act" (PDCA) methodology, where
the PDCA cycle may be thought of as a checklist of multiple stages
to solve SCM issues. The AMP methodology described above may
effectively be used to identify opportunities, and, when no
suitable opportunities are available, cycle the system to flag the
lack of opportunity and move to another suitable area. The AMP
categories should be arranged to prioritize opportunities to
highlight the best ones, allowing the user to concentrate on areas
having the greatest impact.
[0107] By automating the AMP process, a system may quickly and
efficiently identify opportunities. In FIG. 6, an exemplary block
diagram of an automatic AMP process is illustrated, where a supply
chain dashboard and AMP scorecard for SCM data is generated by the
system in 601 and forwarded to automated subscription 602. In
certain instances when a process cannot be automated, a manual run
and export function 603 may be provided. SCM data may then be
processed in a supply chain development manager (SCDM)
module/global planning manager (GPM) module that may be part of the
system platform. The modules allow for business team analytics and
review, where part ownership is assigned and used to provide one or
more summary/detail reports issued at predetermined times (e.g.,
weekly). Once the system has reviewed the relevant data, a process
owner utilizing the system may drive action for subsequent
negotiation/implementation 609. In instances where unresolved
issues arise, an escalation process may flag the issue for higher
level system review. As processes are completed (or left
unresolved), the system closes the current process.
[0108] In addition to data processing, the SCM platform system
advantageously packages processed data to be uniquely visualized on
a user's screen. In the example of FIG. 7, an exemplary bubble
chart 700 is illustrated, where a total opportunity visualization
is provided using MOQ, lead time and safety stock data points.
Here, various opportunities identified in the system relating to
MOQ using any of the techniques described herein. The various
identified opportunities are visualized in the system as "bubbles"
of varying size 702, where the size of the bubble is dependent upon
the size of the opportunity. In this example, MOQ opportunity 701
is identified as having the largest opportunity ($2 M). The
remaining bubbles in the exemplary illustration, as well as in
certain other examples disclosed herein, may, of course, represent
other opportunities available.
[0109] Similarly, lead time opportunities identified by the system
are visualized 704, where lead-time opportunity 703 is identified
as the largest opportunity ($1 M). Likewise, safety stock
opportunities 706 are identified and opportunity 705 is identified
as the largest opportunity ($5 M). As each of the largest
opportunities are identified (701, 703, 705), they are linked to
total opportunity bubble 707 which visualizes a total opportunity
value ($8,550,323). The system may be configured such that, as
other opportunities (i.e., opportunities other than the largest)
are selected, the total opportunity bubble 707 automatically
recalculates the total opportunity value for immediate review by a
user. Such a configuration is particularly advantageous for
analyzing primary and secondary opportunities quickly and
efficiently.
[0110] The bubble data visualization of FIG. 7 may be
advantageously configured to provide immediate analytics generated
from one or more modules in the system. Turning to FIG. 8A, an
exemplary embodiment is provided where opportunity bubble 701 is
selected, which in turn launches analytics window 801 comprising
graphical 803 and textual 802 representations of the underlying
data. In this example, graphical representation 803 comprises a
chart illustrating a dollar value opportunity trend spanning a
predetermined time period. Textual representation 802 comprises a
table, indicating a site location (Ste), part name, ABC code, MOQ,
multiple quantity value, reduction value and opportunity value,
similar to the embodiments discussed above in connection with FIGS.
5A-F. In chart 802, the component opportunities making up the total
MOQ opportunity may be simultaneously viewed to determine greater
details surrounding the opportunity.
[0111] FIG. 8B illustrates an embodiment, where, if a system
component is selected in section 802, a functionality window 804 is
provided for assigning ownership, comments, entering actions and
escalation to components of 802. In this example, window 804
enables entry of ownership ("owner") for a part number, and an
"assigned by" and assignment date entry for each area (safety
stock, MOQ). Comments may be entered into window 804 as shown,
together with an action drop-down menu allowing automated action
entries such as "not started", "started", "achieved",
"unachievable" and "in escalation". For the escalation drop-down
menu, escalation system managers may be assigned via the interface
for further action.
[0112] As part of the embodiments disclosed herein, the system is
further enabled to process and calculate risk(s), and various other
factors and related factors, within supply chains, automatically
and based on real time data from a variety of sources. Generally
speaking, supply chain risks may emanate from geographic risk and
attribute-based risk, among others. For geographic risks,
manufacturing locations are registered within the system for parts
purchased so that when an area becomes volatile because of
socio-political, geographic, (macro-) economic, and/or
weather-related disruption, related variables may be processed to
determine an effect on, or risk to, a supply chain.
[0113] For attribute risk, the system may be configured to
calculate a risk-in-supply chain (risk attribute) value, where a
risk attribute value is based on a framework that analyzes various
different risk categories of the supply chain. FIG. 9 illustrates
an exemplary embodiment in which attributes 901-906, along with
their respective descriptions 907, are assigned risk weight values
908 to calculate risk. In one embodiment, a total risk attribute
score may be based on a 1-5 scale, with 1 representing the least
risk and 5 representing the most risk. The weights may be applied
to stress attributes that may be more important than others in
calculating supply chain risk. After multiplying a score in each
category by the associated weight, scores for each part on an end
product or assembly may be added up and divided by the number of
parts to determine a risk attribute score for each assembly. Next,
scores for each assembly for a customer are added up and then
divided by the number of assemblies to determine a risk attribute
score for the customer.
[0114] In the embodiment of FIG. 9, the example illustrates six
attributes: alternative sourcing 901, part change risk 902, part
manufacturing risk 903, lead time 904, spend leverage 905 and
strategic status 906. It should be understood by those skilled in
the art that additional attributes or fewer attributes may be
utilized by the system. Moreover, in the illustration, the
attributes are weighted by a weighting algorithm (applied at
platform 307 or one of its associated apps) at 36%, 19%, 9%, 19%,
19%, and 0%, respectively, although those skilled in the art will
appreciate that these weightings may be varied. Additional
attributes may comprise defects per million, lot return rate,
corrective action count, inventory performance, environmental and
regulatory items, security programs (e.g., Customs-Trade
Partnership Against Terrorism (C-TPAT)), supplier financial status,
supplier audit results and component life cycle stages, among
others. It is also worth noting that attributes and weighting may
be dependent upon data availability, i.e., algorithms and selected
attributes may be modified based on availability, and the data
selection and/or aspects of the applied algorithm may be controlled
automatically and in real time by platform, and/or control may
include or be exclusively indicated by manual inputs of data or
aspects of the algorithms.
[0115] An exemplary risk attribute score detail is provided in the
embodiment of FIG. 10. The embodiment of FIG. 10 illustrates an
exemplary report for an assembly, where the report is based on each
of the parts making up the assembly and the associated risk
attribute scores. In FIG. 10, a part level report is provided which
provides additional detail by part to show risk attribute score
specifics. Such a report may break out each category score so that
a user may see where, and to what extent, risk exists, and
potential courses of action that may be taken to lessen the risk.
As is illustrated in FIG. 10, the platform 307, and/or the
individual app, may receive primary data and generate therefrom
secondary data, such as calculation of the risk attribute score. In
the example shown, the primary and secondary data used to generate
the risk attribute score is the same as that of FIG. 9, although
those skilled in the art will appreciate that other primary and/or
derived secondary data may be used in supply chain risk
calculations.
[0116] For example, and as illustrated in FIG. 11, if an
alternative sourcing category contains a high risk attribute score,
a user may investigate the parts where the user requires purchase
from only one manufacturer ("sourced parts") which may be causing a
high risk attribute score. The user may configure the system to
enable or suggest other manufacturers as suppliers which will lower
the risk attribute score by diversifying the supply base. Long lead
times may increase a risk attribute score as well. As such, a user
may configure the system to communicate or order suppliers to lower
lead times so that a manufacturer may react quicker to demand
changes if the parts can be bought and received in a shorter time
period. In either case, the risk attribute system module allows a
manufacturer to be a proactive party in the chain and suggest
alternative sourcing using an extended supply base to lessen the
amount of sole source parts and reduce lead times for a customer.
Of course, it is understood by those skilled in the art that the
risk attribute calculations disclosed herein may be applied to any
aspect or attribute of a supply chain system including parts,
suppliers, manufacturers, geography, and so forth.
[0117] FIG. 11 additionally serves to illustrate that a weighting
algorithm may be employed with regard to primary and/or secondary
data to arrive at requested information, and further that such
weighting algorithm may be adjusted over time, such as by platform
307, based on "learning" that occurs upon application of the
algorithm. For example, if actual risk is repeatedly indicated as
higher than a generated risk attribute score, the weighting
algorithm shown in FIG. 11 may be adjusted, and/or the primary and
secondary data that is used in the score calculation may be
modified, etc., in real time by the platform 307 (and/or by the app
making use of the primary data, secondary data, and algorithms
provided by the platform 307).
[0118] The risk attribute processing may subsequently be utilized
by, and/or may utilize, the SCM platform 307 to generate a heat map
to visualize risk attribute scores and their impact easily and
quickly for a user. In certain embodiments, heat maps may allow the
display of multiple variables, such as revenues and risk. In the
illustrative example of FIG. 12, a heat map 1200 is shown for a
plurality of assemblies (Assy 101-105), where each assembly is
associated with one or more parts. Thus, assembly Assy 101
comprises five primary parts ("parts 201-205"), Assy 102 comprises
"parts 206-210", Assy 103 comprises "parts 211-215 and so on.
[0119] In one embodiment, assemblies or parts with higher revenue
over a predetermined time period (e.g., 90 days) are visualized
with bigger boxes compared to assemblies and/or parts having lower
revenue. In addition to size, the heat map may color code boxes to
reflect risk attribute scores. The color codes may be configured to
show green for low risk, yellow for medium-low risk, orange for
medium-high risk and red for high risk. As shown in FIG. 12, the
risk attribute module may be configured to utilize a nested "heat
map" format for inserting one set of heat maps into a higher level
heat map. Thus, the heat maps for parts may be simultaneously
visualized with their associated assembly.
[0120] During operation, a user may select the top X assemblies and
parts for visualization. In one embodiment, the risk attribute
module automatically determines the top X assemblies by multiplying
an assembly risk attribute score by a planned revenue value over a
predetermined time period (e.g., 90 days). Assemblies with the
highest results may be displayed for further analysis. The same
calculation may be also used to determine which component parts are
displayed inside an assembly heat map. In the example of FIG. 12,
the risk attribute module displays the top 5 component parts with
the highest risk. A user may select any of the displayed assemblies
or parts to drill down and receive reports, such as those
illustrated in FIGS. 10-11. A user may also select any of the boxes
(not just on part numbers) to enlarge the selected box and any
nested boxes.
[0121] As can be appreciated by those skilled in the art, the risk
attribute module not only displays supply chain risk, but also
helps to reduce it by providing a list of alternative parts for
circumstances where a customer has a single manufacturer from which
purchases are obtained. For such sole source parts, the module
checks one manufacturer's part number (MPN) against an approved
manufacturer list (AML) from other customers to see if another
customer (or associated manufacturer) may approve the purchase of
the MPN and/or other comparable parts from other manufacturers. Of
course, multi-sourcing may de-risk the supply chain, but may also
increase the pricing of the subject parts (at least in that best
pricing may be available only upon sole-source contracting).
[0122] This technique may be referred to herein as "cross source
opportunity" processing and is powerful because of the potentially
large size of a supply chain. If the system finds the same MPN as
well as alternatives, they may be automatically listed as
illustrated in FIG. 13. Accordingly, a user may forward or
otherwise present these to a customer to see if they are approved
as viable alternatives to allow the option of purchasing from more
manufacturers in order to lower a supply chain risk.
[0123] Risk attribute processing results may further be used by the
platform to show trends over time, as well as a current risk
attribute score distribution. Such trends may be reported upon
certain triggers, and/or may be tracked in order to allow automated
or manual modifications to algorithms and processes of an app or
the platform 307. Because there are a plurality of aspects for
improving the supply chain risk for a customer or assembly (thus
lowering the average risk and lowering a variation of risk), a mean
and standard deviation as illustrated in FIG. 14 may be trended
over time. The data for the risk attribute module may be collected
from the network via customers and suppliers, and may further be
obtained from manufacturer nodes (e.g., 104, 107). The data is
collected in the module and processed to determine risk attribute
scores and trend them to further determine action needed to reduce
risk for customers. Under one embodiment, the risk attribute data
and calculations may be automatically processed on predetermined
time intervals, such as weekly, monthly and/or quarterly.
[0124] In addition to the processing described above, node
processing may be conducted in the SCM platform to advantageously
reflect node SCM relationships and conditions. In one embodiment, a
node tree is provided to specify a SCM structure and end-to-end
supply chains. In one embodiment, processed nodes are associated
with data attributes such as metadata, and nodes are linked in the
node tree with node connector indicia indicating a relationship or
SCM status between nodes. For example, node connectors may be color
coded to identify nodes and connections having supply chain issues
(e.g., red), supply chain opportunities (e.g., green), both issues
and opportunities (e.g., yellow) and neutral (e.g., white)
indicating that threshold issue or opportunity does not exist. The
visualization may contain interactive and dynamic filtering
capabilities to allow users to track upstream and/or downstream
nodes from any node in the supply chain.
[0125] An app may be provided in accordance with this node-based
processing, as shown with greater particularity in the exemplary
embodiments of FIGS. 15-17. Of note, although the visual
presentation and information provided by the node processing app
illustrated herein may differ from that provided by the exemplary
risk-scoring app discussed above, the same primary and/or secondary
data provided by platform 307 may be accessible to both the
node-based and risk-scoring apps, as discussed herein.
[0126] Supply chains, and particularly those in the field of
high-tech manufacturing, can be very complex, and, from a data
standpoint may be made up of hundreds of thousands of records and
data points. The node network interactive data visualization
disclosed herein advantageously allows a customer to see the entire
supply chain in a single depiction. Using such a depiction,
non-supply chain professionals from any level may quickly and
efficiently determine important aspects of a supply chain.
[0127] An exemplary node network is illustrated in FIG. 15, where
primary (root) company node 1500 is connected in a tree node
fashion with assembly nodes, part nodes, supplier nodes and
manufacturer nodes as shown. Each node of the supply chain in FIG.
15 may be considered as a separate level. These nodes may be
configured such that different supply chains will contain different
structures or networks. It is understood by those skilled in the
art that the example of FIG. 15 is merely exemplary, and that the
nodes and node layers may be arranged in a myriad of ways and
structures, and may include additional or fewer layers from those
depicted in the example.
[0128] Thus, exemplary node structures may be arranged for various
nodes:
Example 11
[0129] Raw Material
Mfg..fwdarw.Supplier.fwdarw.Component.fwdarw.Assembly.fwdarw.Customer
Example 21
[0130] Mfg. Plant.fwdarw.Distribution.fwdarw.Customer.fwdarw.End
Consumer
Example 3
[0131] Supplier.fwdarw.Vendor Hub.fwdarw.Mfg. Plant.fwdarw.Customer
Hub.fwdarw.End Consumer
[0132] As shown in FIG. 15, each supply chain node is linked by a
connection. These connections may be one-to-one, one-to-many and/or
many-to-many. The visualization makes it possible to display every
node in a given supply chain in a single graphic which allows a
user to understand the overall activity and complexity within a
supply chain, as well as its overall health. Likewise, displayed
nodes may be limited by a user or by the app, and/or by number or
by node type, by way of non-limiting example. The exemplary
embodiment allows a user to quickly relate to patters being
depicted in the node tree visualization. For example, certain nodes
may be quickly identified as having high concentrations of demand
flowing through them. Nodes may also be identified having existing
overall risk and/or opportunity in certain parts of the supply
chain. As mentioned previously, a single holistic visualization may
allow a company to make quick and efficient assessments of the
health of the supply chain, rather than relying on multiple
different screens or charts covering hundreds of thousands of data
records and/or data points.
[0133] Another advantageous effect of the node tree is the apparent
structure of the supply chain is determined quickly. The
visualization makes use of data which defines how the supply chain
is structured. For example, manufacturers may be linked (e.g., via
tags) to suppliers via approved manufacturers lists. Parts may
similarly be linked to assemblies via bill of materials (product
structures. The complexity of the visualization may be simplified
by quickly focusing the node tree to a defined number of nodes. As
the nodes and underlying metadata are linked, the selection of one
or more nodes may automatically instruct the system to present only
the nodes/layers associated with a selected node. This in turn
permits focused attention on the nodes that are most relevant
(e.g., high demand volume nodes). In one embodiment a predetermined
number of "top" nodes may be displayed for each node parent (e.g.,
based on the top 10 highest demand volume nodes).
[0134] As each node carries pre-calculated data attributes
(metadata), the data attributes may be dynamically categorized
based on predetermined thresholds. The attributes may further be
categorized and color coded as discussed above. For example
processed attributes showing issues may be displayed in red,
attributes showing opportunities may be displayed as green and
neutral attributes (i.e., neither an issue nor an opportunity) may
be displayed as white. As such, the overall health of the supply
chain may be determined.
[0135] In one exemplary embodiment, an assembly or product
determined to carry a high risk would be highlighted as a red node,
indicating it is an area of concern meriting a corrective action.
In another embodiment a component part containing a large amount of
excess inventory would be highlighted as a red node indicating it
is an area of concern meriting a corrective action. In another
exemplary embodiment, a supplier determined to be a candidate to be
moved into a supply chain postponement model (e.g., Supplier
Managed Inventory Program) may be highlighted as a green node,
since the representative node is indicative of an improvement
opportunity.
[0136] The visualization is preferably interactive, allowing data
attributes for each node to be drilled down. Dynamic filtering may
further be applied to display upstream and downstream nodes by
selecting any single node in the supply chain. In the exemplary
embodiment of FIG. 16, a selection of supplier node 1600 may cause
the system to automatically apply filtering to only display
upstream and downstream nodes having dependency on selected node
1600. In the exemplary embodiment of FIG. 17, selection of assembly
node 1700 may cause the system to highlight upstream and downstream
supply chain nodes.
[0137] As can be appreciated by those skilled in the art, the
disclosed configurations advantageously provide users with the
ability to review end-to-end supply chains and supply chain
portions without requiring specialized knowledge. The unique data
visualization helps users to truly understand the supply chain
network and is relatable for all types of users to identify overall
status issues and opportunities. This in turn allows for improved
productivity by allowing users to spend time crafting and taking
actions instead of analyzing complex data and identifying
opportunities/issues. The visualizations further provide
standardized definition of issues and opportunities through an
entire organization. Drill-down capabilities provide an
action-oriented, fact-based analysis with supporting data. The
disclosed node network configurations provide a differentiated
capability that helps customers understand issues and opportunities
that can have meaningful impact on bottom-line performance.
[0138] The figures discussed throughout provide illustrative
screenshots of system and system platform operation disclosed
herein. The system screen layout may contain a plurality of
workspace and navigation areas. A cross-function pane may be
provided to present functional areas of a business which are
included in the platform. User roles may be assigned in the system
to control which functional areas may be made visible to different
user groups. A tool pan may likewise be provided for tools that are
available within each functional area of the business. User roles
may control which tools are visible to different user groups. A
main content pane may be provided as a main workspace of the
platform which contains data and informational content. In
embodiments, a news feed/main page/landing page may also be
provided, such as for a chronological timeline of events, facts,
occurrences, status, risks, or the like which may be particularly
relevant to the user and/or relevant to the particular
design/product/similar designs or products. "Pings" may contain
alerts such as new critical shortages. In one embodiment, a social
data interface may be provided for real-time information from
sources such as Twitter. Through various interfaces, data may be
filtered by geography, organization level or both.
[0139] Turning to FIG. 18, an exemplary screenshot is provided of a
network optimizer module function, which provides an active user
interface for an exemplary global manufacturing footprint through a
total landed cost analysis. The illustration shows a plurality of
charts may be provided, such as total landed cost, freight and
inventory financing expense. Product, market, mechanical sourcing
and transportation costs may further be provided, along with a
"scenario" processing screen for displaying options for individual
global markets (EU, IN, CN, US). Data points such as annual volume,
freight in/out, value add, cost of capital and other log expenses
may be conveniently charted for presentation and/or other action.
Economic indicators including currency, revenue and acro-economic
indicators may be provided as well.
[0140] FIG. 19 illustrates an exemplary health check screen shot
that may be configured as part of a supply chain analytics module.
As shown in the main content pane of FIG. 19, the system may be
configured to process and visualize various data points as a
dashboard, where, in this example, demand, inventory and risk
attribute scoring are provided in a relative data format (29%, 33%
and 3.3, respectively). Flexibility and opportunity processing and
visualization is provided in the example as bar charts, indicating
time/value determinations over a variety of predetermined time
periods (<6 weeks, <8 weeks, <12 weeks, >12 weeks).
Opportunity processing and visualization generates a bar chart
indicating lead time, safety stock and MOQ valuations determined in
the system. Sourcing options may also be provided to determine
sourcing arrangements for the visualized output.
[0141] The health check allows users to quickly assess the health
of a customer supply chain over a plurality of key performance
indicators (KPI). For demand, the demand KPI may focus on service
level and/or delivery performance to a customer. The raw data may
be processed via the platform and subsequently displayed in the
dashboard to indicate a service level. For inventory, the inventory
KPI may focus on the inventory position and breakdown. The
dashboard indicator may display a proportion of excess and obsolete
inventory versus a total inventory. For risk attribute, the risk
attribute KPI displays a total supply chain risk score for a
customer as discussed herein. A sourcing KPI may display a
breakdown of BOM/parts/supplier ownership versus a customer. A
flexibility KPI may display a proportion of total demand flowing
through lead time thresholds. An opportunity KPI may display a
breakdown of potential opportunities to improve flexibility or
reduce cost in a supply chain.
[0142] FIG. 20A illustrates an exemplary simplified interactive map
screenshot, which allows users to access nodes such as customer
nodes, manufacturing nodes and supplier nodes. A graphic overlay on
the node geographical location may provide processed data results
for the node. Exemplary attributes that may be displayed include,
but are not limited to, demand, service level, inventory, excess,
obsolete inventory, AMP opportunity, safety stock, risk attribute
score and critical shortages. A supplier location count may also be
provided to quickly access numbers of suppliers available at a
given location.
[0143] For example, as illustrated in FIG. 20B, a plurality of
suppliers located about the same geographical location may be
visually clustered into a shape, such as a bubble, for manipulation
by a user on a map interface, for example. Each cluster, which may
contain more than one bubble, may be populated with the number of
suppliers based on the level of view such that the number of
suppliers may be easily ascertainable by a user. For example, as
illustrated in FIG. 20B, the map view presented clusters 15
suppliers in the center of the Macau into a single bubble, while
also allowing for several smaller clusters which may be readily
discernable by the user a separate clusters given the level of map
view. In this way, a user may quickly and easily determine at least
the general geographic concentration of suppliers in a particular
area.
[0144] FIG. 21 illustrates an exemplary screenshot of a node
network diagram, similar to those disclosed above in connection
with FIGS. 15-17. FIG. 22 illustrates an exemplary screenshot of
benchmark results that advantageously allow users to access one or
more reference points for supply chain metrics and may compare
results against similar size and complexity customers in the
system. Benchmarking may be performed using only that data
accessible, pursuant to authorization, to that customer; using data
across multiple clients of the manager of multiple customers;
and/or using third party data, such as may be purchased from third
parties at networked locations. Further, in the particular example
of FIG. 22, days of supply, risk attribute score and supply chain
model processing result benchmarks are presented (41.56, 3.39 and
34%, respectively) against similar benchmarks obtained from other
customers ("DEMO CUST 1"), wherein it can be seen that the
benchmarks are all above similarly-situated customers (24.28, 3.38
and 7%), thus indicating that potential issues may need to be
addressed in the system.
[0145] FIG. 23 illustrates an exemplary screenshot of a
system-generated heat map, similar to the embodiment discussed
above in connection with FIG. 12. In one embodiment, the risk
attribute scores for a collection of parts are provided. As
discussed above, the heat map boxes may be configured such that box
sizes are provided according to an attribute (e.g., revenue) and
color coded to indicate a status of the part (e.g., high/medium/low
risk). As each box is selected, a pop-up window or overlay may
generate analytic results for the selection. Thus, in the example
of FIG. 23, selection of PART-COAEHDE automatically launches a
window or overlay to identify a related assembly (PART-CDAEHDE),
revenue impact ($699,850.75) and risk attribute score (3.42).
Similar functions for other parts are available as shown in the
figure.
[0146] In addition to providing a heat map, automated reports may
be generated for the items of interest within the heat map as shown
in FIG. 24. Exemplary reports may include, but are not limited to,
geographic risk attribute, cross source options, geographic impact,
risk attribute part detail, risk attribute score average, risk
attribute score distribution and risk attribute score standard
deviation. In the exemplary illustration of FIG. 26, a geographical
impact report is selected, generated and displayed in the system to
provide locations, manufacturer revenue impact and spend values for
a selected heat map part of interest. In the exemplary illustration
of FIG. 26, a risk attribute score average chart is processed and
displayed in the system to show risk attribute score averages over
a predetermined time period (e.g., weekly) for a selected heat map
part of interest. FIG. 27 illustrates an exemplary risk attribute
score distribution over a predetermined time period. In addition to
displaying and processing a current risk attribute score
distribution, the system may be configured to store and process
previous distributions (e.g., 13 weeks ago) and compare the two in
one chart. FIG. 28 illustrates an exemplary risk attribute score
standard deviation over a predetermined time period (e.g.,
weekly)
[0147] FIG. 29 illustrates a detail report, similar to the
discussion above in connection with FIG. 11. The report provides
part detail analytics (e.g., site, part, part description,
commodity), commercial analytics (e.g., spend leverage) component
analytics (e.g., alternative sourcing, lead time, part change risk,
part manufacturing risk), supplier performance (e.g., defects per
million, inventory performance), and a total risk score.
[0148] In accordance with the foregoing, factors that are likely to
cause failure of certain supply chain attributes, such as on time
delivery (OTD), end of life (EoL), or days of supply (DoS), may be
"cascaded" to indicate the likely eventual effects on that user's
supply chain. The results of this analysis may be provided in a
guided user interface, as discussed further below.
[0149] Further, each node indicated at the interface may have a
"fixed" structure or a "flexible" structure, i.e., may be
modifiable, either by simulation or reality, with respect to
certain attributes in real time. Further, the interface may
flexibly display the nodes, such as by allowing for a hierarchical
level by level drill down, a check/uncheck of hierarchical layers
for display to simplify presentation and make data more
understandable, level-by-level "highlights" to show metrics/risk
profile per level, level-by-level sensitivity settings, historical
drill-downs, and/or a variable node size indicative of certain
attributes, such as demand or other selectable effects on supply
chain, by way of example.
[0150] Turning to FIG. 30, an exemplary screen shot is provided for
supply chain analytics which shows one example of processing and
identifying supply chain opportunities. Here, an AMP opportunity is
defined in reference to a lead time and MOQ, where specific data
points may be processed and presented for each attribute. The
system may calculate and present a specific opportunity value for
lead time supply chain attributes ($424,380), together with MOQ
opportunity value ($951,931). As discussed above in connection with
FIG. 7, an opportunity bubble chart may be simultaneously
presented, containing the same and/or related attributes (lead
time, MOQ), for further analysis. As discussed above, the
individual bubbles of the bubble chart may be selected/rearranged
to present alternate and/or additional opportunity values, which
may be automatically recalculated and presented in the lead time
and MOQ boxes shown in FIG. 30.
[0151] FIG. 31 illustrates an exemplary screen shot for status
reports, which process and display data points and metrics for
analysis. In the example of FIG. 31, status reports may be
generated for any metric including sell thru, inventory, supply
chain model, order status and service level. In the example, sell
thru status is displayed in dollars over predetermined periods of
time (e.g., week), where COGS and master schedule metrics are
displayed as a bar graph. The system may also be configured to
overlay average COGS and MS averages onto the graph to provide a
quick analysis of system performance on these data point.
[0152] Continuing with FIG. 32, an exemplary inventory status
report is illustrated, where inventory and days of supply are
processed and displayed over a predetermined period of time (e.g.,
week). FIG. 33 illustrates an exemplary supply chain model chart
indicating a percentage of units, parts, assemblies, etc. that are
in the supply chain model (inSCM) and out of the supply chain model
(OutSCM) over a predetermined period of time (e.g., week).
[0153] FIG. 34 illustrates an exemplary interactive map that may be
displayed as part of the supply radar module. Here, different nodes
may be simultaneously displayed, including customer nodes,
manufacturing nodes and supplier nodes. The system may be
configured to display a global sourcing footprint. In one
embodiment, geographic areas containing a large concentration of,
e.g., supplier, may be configured to cluster the locations into a
bubble, where the cluster may contain a count of the units
(suppliers) included in the cluster. To view which units
(suppliers) make up the cluster, the cluster bubble may be selected
and zoomed to expand the cluster. The map may be toggled between a
normal map view and/or a satellite view. The exemplary interactive
map of FIG. 34 may be customized to provide maps pertaining to
various attributes including, but not limited to, demand, service
level, inventory, excess, obsolete inventory, AMP opportunity,
safety stock, risk attribute score and critical shortages. FIG. 35
illustrates another exemplary interactive map display, similar to
the display in FIG. 34, except that the system configures the map
in terms of demand, along with a total value ($26,334,422).
[0154] As part of the supplier radar module, status reports for
suppliers may be generated as shown in the exemplary screenshot of
FIG. 36. The status reports may include, but are not limited to,
geographical impact reports and geographic risk attribute scores by
manufacturer. In the example of FIG. 36 an exemplary geographical
impact report is illustrated, showing impacts of manufacturers at
given locations based on revenue and spend. The geographical impact
report advantageously allows users to process revenue impact and
spend by supplier and/or geographic location. Such information may
be very useful in times of supply chain disruptions.
[0155] FIG. 37 illustrates an exemplary screenshot of a critical
shortages summary which may be obtained from status reports
generated by the supply and demand module. In one embodiment,
supplementary information and data relating to critical shortages
may be obtained from 3rd party database sources (e.g., 110, 111 of
FIG. 1) and/or may even be obtained from news feeds or social media
as discussed above. Status reports may further be configured to
provide critical shortage detail, supply and demand summaries and
service level reports.
[0156] As an additional part of the supplier radar module, conflict
material reports regarding suppliers may be generated as shown in
the exemplary screenshot of FIG. 38. Such information may be
presented to allow for better control of those natural resources
whose systematic exploitation and trade in a context of conflict
contribute to, benefit from or result in the commission of serious
violations of human rights, violations of international
humanitarian law or violations amounting to crimes under
international law. Such information may be very useful when the
origin of supply chain materials is called into question.
[0157] As discussed throughout, in order to aid the user's
visualization of the supply chain and the risks resident therein,
presentation aspects may be provided. Such presentation interface
aspects may include, for example, a user's landing page or initial
page to access the disclosed SaaS. By way of example, word widgets
may be provided, such as in banner format, scrolling format, pop up
format, or the like, in which consistent verbiage explaining
aspects of the supply chain is provided, but into which the
analytics engine 304 referenced herein places numbers particular to
the supply chain of that given user. Further, alerts may be
provided on a main interface page, such as in a pop up, audio,
scrolling ticker, or like format, wherein the user may have
previously requested alerts regarding the topics displayed.
[0158] Also provided may be ready access to one or more current or
prior simulations and/or recommendation models. In a simulation
presentation window, the user may be able to "experiment off-line",
such as wherein the user may readily modify different factors just
to see what effect varying those factors would have on the outcome
from the supply chain if changed. Further, predictive trends may be
provided, such as in the simulation display, wherein trends and a
predetermined timeframe, such as 12 months or 24 months, may be
provided, such as in conjunction with projections, predictions,
simulations, and/or recommendations.
[0159] Further provided within this simulation and/or predictive
window may be information unique to a given supply chain for a
particular user. For example, if the user's risk modeling would
improve significantly in the event a given part were assigned a one
day lead time, and available data indicates to the analytics engine
304 that that part may be 3-D printed and the user has a 3-D
printer on site, the analytics engine 304 may recommend that the
user build the part on site using the 3-D printer in order to
greatly improve the user's risk model.
[0160] Alerts provided by the user may, as referenced, be
preselected by the user. Further, such alerts may form part of an
event risk, such as may reside in a dash board on, for example, the
aforementioned user landing page. For example, the user may request
an alert in the event there is an earthquake within 250 miles of a
supplier's facility that supplies part to that user. If an
earthquake does occur, the user alert may indicate that the user
has 2 manufacturers within 250 miles, and in combination those
manufacturers provide 28 parts that would affect one product and 3
customers of that user. Further, the analytics engine 304 may
understand, from previously gained data, that an earthquake of
magnitude less than 5.5 is unlikely to cause any effect in the
supply chain. As such, the event risk dashboard or window may not
provide the requested alert to the user if the earthquake assessed
has a magnitude of less than 5.5, at least due to the
extraordinarily high likelihood that such a smaller earthquake
would have no effect on the supply chain based on historical
data.
[0161] An event risk may also be associated with alternative
information within an event alert. For example, an event alert may
also include cross source or multisource data, and the effect on
supply chain risk that proactively switching to a different source
might effect. Further, communications, such as instant messaging,
may be provided within the event risk alert window, such that, upon
receipt of an alert such as the earthquake alert referenced above,
affected manufacturers may be contacted so that they can directly
provide a damage assessment to the user. Such communications may be
stored so that a historical record of who, what, when, where, how,
and whether communications occurred may be maintained.
[0162] Needless to say, the analytics engine 304 may automate the
alerts discussed herein, such as by performing an information
crawl, such as a Web crawl, at relevant time frames, such as every
3 minutes, in order to assess the occurrence of events worldwide.
Further, rather than simply gain events worldwide, or select
particular areas of interest by name, a user may graphically engage
the user interface to "draw" areas of particular interest to the
user. The user may be enabled to draw one or more such areas.
[0163] All of the foregoing may be used to provide an impact score
in the event an occurrence of interest happens. That is, the
historical impact of such an event may be assessed based on
existing data. The impact of an event may be modified if comments
are received from affected parties, such as indicating that the
parties are not impacted by the event. Likewise, typical "domino
effects" may be assessed based on historical data. Accordingly, it
may be assessed that a particular event is likely to affect only
manufacturers within a 50 mile radius of the particular event, but
a comment from the only manufacturer within 50 miles of the event
that that manufacturer's okay may cause the impact score to go to
zero once the event occurs. Thereby, using the data available to
the analytics engine, any event may be tied to any effects of that
event on any outcome. For example, the analytics engine may
recognize that fish oil from Vancouver should not be employed in a
manufacturing process within 6 months of the occurrence of a
nuclear meltdown on the coast of Japan.
[0164] As such, the presentation, such as the landing page or front
page, of the user interface may be modifiable by and/or otherwise
unique to the user, such as to add what the user most wishes to see
or remove that which is not of interest to the user. Of course,
those features most interesting to the user may vary over time. For
example, if the user is concerned only regarding information on
lead times, the user may include only that information, and
simulations, recommendations, and alerts that relate to that
information, on the user's landing page. As such, the dash-boarding
provided in accordance with the embodiments may enable guided
analytics for the user, i.e., analytics that are related with
particularity to those aspects of a final product most important to
the user.
[0165] This guided analytics, enabled by the disclosed analytics
engine 304, may allow not only for personalized supply chain
analysis unique to the user, as underlaid by analytics on big data,
but additionally may provide to the user information not known to
the user to be relevant to the aspects of interest to the user. For
example, a side-by-side comparison that includes a particular
feature indicated by the user as of-interest to the user maybe
provided. Such a side-by-side comparison may compare current status
to recommended status, current status to various simulations, the
user's situation to the user's competitors, cross-sources or
multi-sources for particular parts having high impact on risk
scores, or the like. And, as discussed throughout, this information
may be requested by the user, or may be pushed to the user, based
on assessments made by the analytics engine 304 or indications
provided by the user. In the event pushed information is provided,
this information may include recommendations or simulations that
the analytic engine deems necessary for the user to see, corrective
action to user requests or user provided information, or content,
providers, manufacturers, parts, or the like, that may be of
interest to the user based on the user's express preferences and
prior interactions with the user interface.
[0166] Further, the information may be provided by user requests,
but may also be pushed to the user based on achievement of
automated thresholds. For example, a user may request an alert to
the extent an earthquake occurs within 50 miles of one of the users
supply chain facilities, but data analysis by the analytics engine
may indicate that no earthquake below a magnitude of 5.2 has ever
caused damage to a manufacturing facility in the last 24 months.
Consequently, the analytic engine may not alert a user even if an
earthquake has occurred within 50 miles of one of the users supply
chain facilities if the magnitude of that earthquake is below
magnitude 5.2. That is, the analytics engine may, through a
learning process, apply an automated threshold based on analytics
of existing big data to modify a user's request to optimize the
usefulness of the data provided to the user.
[0167] In sum, certain of the embodiments may provide a
personalized supply chain interface to which users of varying
administrative levels may have differing access to see
content/analytics/simulations/recommendations that user wishes to
or needs to (in the judgment of analytics engine 304) see. These
analytics/recommendations/goals that should be important to the
user may be based on available "big data" analytics across large
numbers of relevant supply chains, products, parts, manufacturers,
and suppliers. These analytics may include what competitors are
doing to succeed and where they are outdoing the user, such that
recommended analytics/recommendations/targets/goals to enhance the
competitiveness of the user's supply chain may be provided by
analytics engine 304 based on this high volume data. These
recommendations to enhance competitiveness may be provided as a
"side-by-side", and needless to say may be anonymized when
presented.
[0168] These recommendations may be akin to existing models of
content recommendations and/or targeted advertising (i.e., Google,
Amazon, etc.), but for supply chain analytics. That is, the
analytics engine 304, working across the high volume data, may
allow for "partnering" between the SaaS provider and entities
offering supply chain or product development--related
goods/services to allow for offering of those goods/services
responsive to the user's perceived or recommended interests, i.e.,
"targeted" offerings. This content may be "pushed" or "pulled", as
will be understood to the skilled artisan. Thereby, the embodiments
may provide a guided supply chain analytics interface.
[0169] By way of example, and as shown in FIG. 39, a user's main
page interfaces may include a variety of specialized widgets. Such
widgets may include stock or standard text or aspects, but may plug
in data relevant to the user's needs, wants, products, and the
like, in real-time, thereby populating each widget on the main page
as unique to that user.
[0170] Moreover and as shown in FIG. 40, the user may have a
variety of main pages, each keyed aspects of that user's online
presence within a rules module resident at analytics engine 304.
For example and as shown, the information presented may vary by
product attributes or part attributes that are important to that
user, by the administrative level of that user (such as executive
or administrative), or the like. That is, product manufacturing
flexibility may be important to that user, and as such, the user
may "follow" that attribute. Consequently, the user may receive an
indication, such as in the manner of a social network, of other
users or supply chains, either anonymously or otherwise, to whom
that particular attribute is important, such as within that
industry, that industry vertical, or in relation to that product or
a similar product. Accordingly, each such person following the
flexibility attribute may receive the same or similar widgets, such
as upon drill down into that attribute, with the exception that the
data populated into those widgets may vary from user to user.
[0171] Needless to say, given that certain attributes may be
selected by the user as more important than others, the user may
receive recommendations in relation to one or multiple attributes.
Such recommendations may additionally include comparisons or
simulations, such as in relation to cross sourced parts, improved
attributes such as lead time, or the like, as referenced above and
as generated by analytics engine 304. An explicit indication of
these recommendations in relation to a given risk attribute as
shown in FIG. 41.
[0172] Yet further, a user may request, create, edit, or receive
one or more alerts relevant to topics of interest to that user. For
example, an alert may be requested for any environmental event that
might affect apart within that user's product. As shown in FIG. 42,
such an alert request may be created by or edited by the user.
[0173] Further, and as shown in FIGS. 43 and 44, the alerts
available for the user to set up may be highly varied. By way of
non-limiting example and as shown, the user may name an alert or
otherwise describe it, may select why that alert is important to
the user, may indicate the impact of that alert, should it occur,
to the user or may ask the engine to automatically assess the
impact on the user or the user's products, may select dates, time
frames, data limits, impact limits, or the like for that alert, and
so on.
[0174] By way of non-limiting example, FIG. 45 illustrates a
specific alert message that may be available through the
embodiments. The alert has complied with a user's requested and/or
automated triggers to illustrate an alert of an occurrence that is
impactful towards the user's product or product line. In the
illustration, the alert relates to an environmental event in a
particular geography, and may have a significant impact on demand
for parts made in or shipped from that geography. Not only does the
analytics engine 304 assess the impact as shown, but further
indicates the impact across multiple parts, multiple customers,
multiple manufacturers and the like, and yet further estimates
likely recovery based on the occurrence of prior similar events as
recorded in the data store associated with the analytics engine
304. Of note, although the illustrated alert relates to a given
geographic site, an alert could also be designed for a product,
parts within the product, manufacturers, suppliers, geographic
regions such as countries, and the like.
[0175] Further and as shown in FIG. 46, alerts may have assigned
thereto, either by the user or automatically, a priority based on
any of the various risk attributes in the supply chain. For
example, an alert that indicates an impact on a significant
manufacturer may be critical, whereas another alert that may be
relevant to less than 0.1% of a product lines demand may be deemed
informational only. Such variability in alerts may be indicated by,
for example, colors, text, node size or shape, or the like.
Further, and as shown in FIG. 46, different sites relevant to the
supply chain may also be shown on an alert map and may be assigned
a differing node types, such as to indicate different node
functionality. For example, manufacturers as compared to suppliers,
suppliers as compared to raw materials, and the like, may be
assigned nodes of different shapes, colors, or the like.
[0176] The exemplary embodiments discussed herein, by virtue of the
processing and networked nature of platform 307 and its associated
apps, may provide typical data services, in conjunction with the
specific features discussed herein. By way of non-limiting example,
reports may be made available, such as for download, and data
outputs in various formats/file types, and using various
visualizations, may be available. Moreover, certain of the aspects
discussed herein may be modified in mobile-device based
embodiments, such as to ease processing needs and/or to fit
modified displays.
[0177] In the foregoing Detailed Description, it can be seen that
various features are grouped together in a single embodiment for
the purpose of streamlining the disclosure. This method of
disclosure is not to be interpreted as reflecting an intention that
the claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter lies in less than all features of a single
disclosed embodiment. Thus the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
* * * * *