U.S. patent application number 16/616376 was filed with the patent office on 2020-04-23 for systems and methods for assessment and visualization of supply chain management system data.
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 | 20200126014 16/616376 |
Document ID | / |
Family ID | 64395871 |
Filed Date | 2020-04-23 |
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United States Patent
Application |
20200126014 |
Kind Code |
A1 |
BAJAJ; Mudit ; et
al. |
April 23, 2020 |
SYSTEMS AND METHODS FOR ASSESSMENT AND VISUALIZATION OF SUPPLY
CHAIN MANAGEMENT SYSTEM DATA
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.: |
16/616376 |
Filed: |
May 22, 2018 |
PCT Filed: |
May 22, 2018 |
PCT NO: |
PCT/US2018/033807 |
371 Date: |
November 22, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62509675 |
May 22, 2017 |
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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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62509653 |
May 22, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/28 20130101;
G06Q 10/063114 20130101; G06Q 10/0637 20130101; G06N 20/00
20190101; G06Q 10/06315 20130101; G06Q 10/0635 20130101; G06F
16/904 20190101; G06Q 10/06375 20130101; G06Q 10/087 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A supply chain management system for at least partially
individually managing a plurality of nodes in a supply chain
comprising the plurality of nodes, comprising: a plurality of data
inputs capable of receiving primary hardware and software data from
at least ones of the supply chain nodes at least two hierarchical
levels in the supply chain over at least one 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: interfacing with a plurality of application inputs, and
capable of providing the secondary data, comprised of at least one
of node-centric supply chain risk data, supply chain management
data, and supply chain analytics data individually particular to
the ones of the nodes; and interfacing with the user to provide the
secondary data as each of the data cascaded as supply chain risk
data, supply chain management data, and supply chain analytics as
between multiple ones of the ones of the nodes.
Description
RELATED APPLICATIONS
[0001] The present application claims the benefit of priority to
International Application No. PCT/US2018/033807, filed May 22,
2018, entitled "Systems and Methods for Assessment and
Visualization of Supply Chain Management System Data" which claims
priority to, is related to, and incorporates by reference, 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. 62/509,665, 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. 62/509,653, 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., entitled "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., entitled "Power Supply With
Balanced Current Sharing," filed Oct. 28, 2013, U.S. provisional
patent application Ser. No. 61/895,665, to Joyner et al., entitled
"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., entitled "Method for Identifying and Presenting
Risk Mitigation Opportunities in a Supply Chain," filed Oct. 28,
2013. Each of these are 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.
[0012] Further included may be a supply chain management system for
at least partially individually managing a plurality of nodes in a
supply chain comprising the plurality of supply chain nodes. The
system may include a plurality of data inputs capable of receiving
primary hardware and software data from at least ones of the supply
chain nodes at least two hierarchical levels in the supply chain
over at least one 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. The data outputs may be
capable of: interfacing with a plurality of application inputs, and
capable of providing the secondary data, comprised of at least one
of node-centric supply chain risk data, supply chain management
data, and supply chain analytics data individually particular to
the ones of the nodes; and interfacing with the user to provide the
secondary data as each of the data cascaded as supply chain risk
data, supply chain management data, and supply chain analytics as
between multiple ones of the ones of the nodes.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] 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:
[0014] FIG. 1 illustrates a computer system for transmitting and
processing data, and particularly supply chain management (SCM)
data under an exemplary embodiment;
[0015] FIG. 2 illustrates an exemplary processing device suitable
for use in the embodiment of FIG. 1 for processing and presenting
SCM data;
[0016] 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;
[0017] FIG. 3B illustrates the SCM platform utilizing extended
plug-in applications/modules under another exemplary
embodiment;
[0018] FIG. 4 illustrates exemplary data points and variables
modules operatively coupled to a SCM platform under one
embodiment;
[0019] FIGS. 5A-5F illustrate logical processing outcomes for a
variety of exemplary embodiments;
[0020] FIG. 6 illustrates an exemplary automation process suitable
for utilization in the embodiment of FIG. 1;
[0021] FIG. 7 illustrates an exemplary data visualization example
for actionable-measurable-proactive (AMP) SCM processing;
[0022] FIG. 8A illustrates a further data visualization and
"one-click" report generation under one embodiment;
[0023] FIG. 8B illustrates a functional action input module
associated with report generation from the data visualization of
FIG. 8A;
[0024] FIG. 9 illustrates an exemplary data table providing for
attribute naming, attribute description and applicable weight
attribution for SCM processing;
[0025] FIG. 10 illustrates an exemplary risk assembly detail for
commodities/parts, wherein part and supplier attributes are
processed to determine an overall risk;
[0026] 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;
[0027] 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;
[0028] FIG. 13 illustrates an exemplary analytics engine for use
with the embodiments;
[0029] FIG. 14 illustrates an exemplary interface;
[0030] FIG. 15 illustrates an exemplary interface;
[0031] FIG. 16 illustrates an exemplary interface;
[0032] FIG. 17 illustrates an exemplary interface;
[0033] FIG. 18 illustrates an exemplary interface;
[0034] FIG. 19 illustrates an exemplary interface;
[0035] FIG. 20 illustrates an exemplary interface;
[0036] FIG. 21 illustrates an exemplary interface;
[0037] FIG. 22 illustrates an exemplary interface;
[0038] FIG. 23 illustrates an exemplary interface;
[0039] FIG. 24 illustrates an exemplary interface;
[0040] FIG. 25 illustrates an exemplary interface;
[0041] FIG. 26 illustrates an exemplary interface;
[0042] FIG. 27 illustrates an exemplary interface;
[0043] FIG. 28 illustrates an exemplary interface;
[0044] FIG. 29 illustrates an exemplary interface;
[0045] FIG. 30 illustrates an exemplary interface;
[0046] FIG. 31 illustrates an exemplary interface;
[0047] FIG. 32 illustrates an exemplary interface; and
[0048] FIG. 33 illustrates an exemplary interface.
DETAILED DESCRIPTION
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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.
[0054] 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.).
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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.
[0060] 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).
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.).
[0065] 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.
[0066] 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.
[0067] Supply chain analytics module 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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. Further, the platform has
the capability to integrate with multiple systems within the
firewall of a customer's network and/or outside the firewall. These
systems may include, by way of non-limiting example, enterprise
resource planning (ERP) systems, Materials Requirement Planning
(MRP) systems, point solution systems, and proprietary data source
systems. 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. The disclosure herein may thus relate to a platform
that has the capability to profile, validate and/or monitor data
for its quality.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.).
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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)
[0091] 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 MRP schedule.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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).
[0105] 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.
[0106] 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.
[0107] In specific reference to the analytics processing described
above, node processing may be conducted in the SCM platform to
advantageously reflect node-by-node SCM relationships and
conditions, wherein each such node may be subjected to analytics.
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. 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.
[0108] 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.
[0109] In light of the aforementioned complexities in analyzing
independent nodes of the supply chain, customers desire insight
into the entire supply chain that is unavailable manually because
of these complexities and the volume of data these complexities
generate. Consequently, the disclosed software as a service may
provide overall supply chain insight on a node-by-node basis.
Needless to say, these and other disclosed aspects may require
significant data driven aspects of the software as a service for
supply chain management. More specifically, these data-driven
aspects may include, by way of non-limiting example, visibility
into the entire supply chain, including all companies, all
resellers, all parts, and so on; analytics capable of analyzing big
data in order to assess and balance speed, cost, risk, likelihood
of failure, recommended replacements and modifications, and to
enable the waiting and balancing of the foregoing factors; and an
ability to collaborate and dictate action in the supply chain based
on the analytics, such as wherein the collaboration may be in the
form of multi-point communications that occur automatically or
manually, and such as wherein the actions may be subject to manual
triggers or may trigger automatically. Accordingly, to meet future
supply chain analysis needs, the supply chain and all aspects
thereof may be analytically productized, and this may include the
people and places that form part of the supply chain.
[0110] By way of example, it is typical that inventory on hand or
on order for a supplier, reseller, or manufacture may exceed the
demand for those parts. It is also typical that said supplier,
reseller, or manufacture may retain these excess and obsolete (E
and O) parts in the hopes the parts are necessary to the same or
another product owner at a later point in time. Issues caused by
such E and O inventory may be further exacerbated by the fact that
the definition of E and O may differ as between the supplier,
reseller, or manufacture and the product owner/customer.
[0111] Accordingly, the disclosed analytics engine may include, for
example, algorithms whereby pattern recognition may be performed
across an entire supply chain, or within aspects thereof, to assess
patterns that typically lead to E and O. Once learned, these
recognized patterns may be applied to the product of a particular
customer, such as upon input from the customer at the inception of
a design for supply chain. These recognized patterns may also be
analytically applied to: a newly input product design, such as in
order to give a probability ratio that a part or parts within the
design may go E and O; similar products; the same or similar
verticals; and/or to any supply chain(s) using the same or similar
parts, by way of non-limiting example.
[0112] Such pattern recognition, for E and O and other end-to-end
aspects of a supply chain, may be performed by the analytics engine
to include part profiles for various parts, including, in certain
embodiments, directly competitive parts. Such profiles may include
the location of manufacture, generation or shipping of the parts,
the history of inclusion of the parts, and the like. Analytic
engine profiles may additionally include, for example, typical
and/or comparative responses from a particular part provider or
providers of that part to variations in demand.
[0113] In accordance with at least the foregoing factors,
end-to-end supply chain aspects, such as E and O, may be assessed
by the analytics engine before an event that will adversely affect
the supply chain occurs. By way of example, to the extent a
customer fears being affected by E and O, the analytics engine may
assess that the customer has modified demand for a particular part
by 50%; that the subject part is from a small province in China;
that the subject part is subject to a 90 day lead time; and/or
various other factors, such as based on large-volume data regarding
the use of that part. Based on this large-volume prior data stored
as part of the profile for that part, the analytics engine may
assess that, in accordance with the foregoing factors, that part
has an 83% chance to experience E and O in the next 60 days. That
is, the analytics engine may use particular variables uniquely in
relation to a part or parts, and these variables may change
dependent on the part, the use of the part, and other factors
related to the part. As referenced, the algorithmic application of
these variables by the analytics engine then allows for a
conclusion by the analytics engine, such as, in this example, a
percentage chance that E and O will occur in relation to that part
from that given supplier in a given time frame.
[0114] In accordance with the foregoing, the analytics engine may
allow for accommodations to be made or charges to be made as a
direct result of part consumption that leads to certain outcomes or
probabilities in a supply chain, such as E and O. By way of
example, in the known art, a customer may request that a given
manufacture order 10,000 of a particular part, but may use only
3000 of those parts in that customer's products. However, also in
the known art, to the extent that manufacture orders 500,000 of
that same part across multiple ones of its customers, there is
presently no known automated manner of assessing responsibility to
that manufacturer to the extent many of those parts become E and O.
In stark contrast to the known art, the disclosed embodiments may
allow the analytics engine to apportion responsibility to various
customers, such as in the aforementioned example in which one
customer is responsible for 7000 E and O parts, such as that the
customers may either bear financial responsibility for the E and O
rather than the manufacturer, or such that the manufacturer may
make more knowledgeable assessments of where accommodations have
been provided.
[0115] Specifically in relation to E and O, E and O may be assessed
and/or apportioned based on a particular confidence interval for a
given part. This confidence interval that E and O may occur may be
based by the analytics engine upon numerous analyzed factors, such
as those disclosed above, such as particularly time and money. That
is, the analytics engine may receive values based on pre-existing
knowledge for the various variables related to a given part, such
as including time and dollars, and may then generate and/or present
to a user a confidence interval that the part will become E and O.
The foregoing also allows for modification of the confidence
interval that E and O will occur with respect to a given part as
time moves on, which enables a snapshot, such as at the request of
a user, of likely E and O at any given moment in the supply chain
cycle. Due to the volume of data that is analyzed for such E and O
assessments, changes in data and or learning by the learning
algorithms disclosed may occur continuously or at preset periods.
For example, E and O updates may be performed daily, weekly, or
continuously.
[0116] Visibility into the end-to-end aspects of the supply chain
may be necessary at all times to insure the correct data to adjust
the supply chain, such as in the case of E and O discussed above,
is in the right hands when needed to enable adjustments to avoid
catastrophic occurrences. Thus, key data points should be assessed
to enable real time decision-making, either by the analytics engine
or by users. These key data points may vary, by example, based on
the size of the supply chain, which may vary based on the product
that is the outcome of the supply chain. For example, an airplane
has a very large supply chain with a great many parts, while a
plastic bottle has a very differently sized supply chain.
[0117] Ultimately, the supply chain may be, in essence, treated by
the analytics engine as a pipeline. The pipeline may be very wide,
such as wherein many suppliers at a particular level feeding a more
limited number of manufactures, or may be narrow, such as wherein a
limited number of suppliers feed a relatively equivalent number of
manufacturers. Accordingly, the supply chain is viewed by the
analytics engine level by level, and based upon what aspects of a
final outcome are built or what value is added at each level. This
level based structure may be provided as a rule-set within a rules
engine of the analytics engine.
[0118] More particularly, multiple tiers may appear within certain
supply chains, and each of these tiers maybe analyzed independently
by the analytics engine, and may be independently provided to the
user for viewing. As such, the probabilities and algorithms applied
by the analytics engine at each tier may cascade through additional
tiers, dependent on the number of additional tiers atop or below
the tier then-under analyses. Correspondingly, the processing
performed by the disclosed analytics engine provides a multi-level
probabilistic cascade for the algorithms applied, such as those
discussed herein. This is directly contrary to the known art, in
which each level of the supply chain maintains only its own
records, and, consequently, to the extent any probabilistic
analyses is done at a particular tier or level, that analyses is
not and cannot be cascaded through other tiers or levels.
[0119] This multi-tier and individual part breakdown is
particularly relevant for supply chains that include highly
numerous parts. In the known art, it has generally been assumed
that the most expensive parts to purchase or make were also the
highest impact parts for risk in the supply chain. However,
directly contrary to this thinking, the analytics engine may
frequently assess that the highest risk factor parts have no
relation to the cost of those parts. This is a result of the
probabilistic cascade that can occur even from the least expensive
parts through multiple tiers of the supply chain. It should be
noted that this probabilistic cascade relates not only to
particular parts, but also to customers, suppliers, resellers,
manufacturers, outcomes, and products, by way of non-limiting
example. Further, it should be noted that this cascade is not
unidirectional in all embodiments, but rather maybe vertically
and/or horizontally integrated, i.e., may be both "up and down"
and/or "left to right" along the continuum of a supply chain
"pipeline".
[0120] Further, in order to aid in the comprehensive nature of this
probabilistic cascade, the simulation and recommendations discussed
throughout may be provided both for design of supply chains and for
existing supply chains. Absent this cascade, which is heretofore
unknown in the art, true optimization efforts in the design and
execution of supply chains cannot be carried out.
[0121] The disclosed probabilistic cascade also allows the
analytics engine to expose cause and effect circumstances. For
example, consistent receipt of a single part on an average of one
day late in a product having 300 parts therein may ultimately
cascade to an average 9 day delay in shipping final product.
Needless to say, such a delay is significantly impactful to the
provider of the product, and as such, the cause of the delay,
namely the average one day delay, would be recommended by the
analytics engine for correction, such as by the recommendation of
cross-sourcing, multiple-sourcing, or alternative-sourcing of that
part.
[0122] The analytics engine may make certain of the foregoing
assessments by rating key performance indicators in accordance with
the referenced rule set, such as subject to or not subject to the
input of the user's objectives, and such as based on threshold
settings that may automatically alert the user to problems that may
affect, positively or detrimentally, the key performance
indicators. In certain examples, the effect of the key performance
indicators may be visually indicated, such as wherein a
green-colored key performance indicator on a display has a positive
or no effect based on analytics of existing data, while a yellow
KPI may have a possibility of a detrimental effect on the supply
chain, and a red KPI may be likely to have a substantial adverse
effect on the supply chain.
[0123] KPI analysis may, as referenced above, occur from end-to-end
in the supply chain in light of the user's input objectives.
Further, KPI analysis may have statistical algorithms applied
thereto, such as to provide for the removal of "outlier data" in
order to enhance analytic outcomes. For example, abnormal data
spikes or dips, such as in inventory levels, may be likely
indicative of one-time occurrences, and thus may be dismissed by
the analytics engine applying the KPI analysis. Further, rather
than dismissing some factors in the KPI analysis, pattern
recognition may be performed to assess, for example, repeated
variations under the same or similar circumstances. For example, if
inventory levels always spike for a particular part of a particular
product in the middle of the 3rd quarter of each year, such as in a
likely indication of increased product production in the 4th
quarter for an upcoming holiday season, the inventory KPI may be
assessed as normal notwithstanding yet another spike in inventory
in the middle of a 3rd quarter in the given year. That is, the
algorithms applied by the analytics engine may perform pattern
recognition to assess that this inventory spike is normal for this
part and/or product at that given time, and hence it may rate the
inventory KPI as not detrimental.
[0124] The algorithms within the analytics engine, as discussed
throughout, may perform and/or allow for simulations, such as those
provided to a comparator within the analytics engine, that may
simulate the outcome of recommended modifications to the supply
chain as compared to an unmodified supply chain (as may be input by
the user). Such simulated effects on a supply chain may be provided
to the user as a snapshot of a one-time modification, or may be
supplied to the user over a given horizon, such as the effect of a
recommended modification to the supply chain over a six-month
horizon.
[0125] More particularly, simulations provided by the analytics
engine may allow for the user to change aspects of product design,
supply chain design, and/or active supply chain and to be provided
with probabilistic, cascaded outcomes of the likely effects of
those modifications. Needless to say, numerous variables may be
available to the user for changing by the user in a given
manually-requested simulation (simulations may also be automated by
the analytics engine), and the user may toggle those variables in
real time and receive a modified simulated output. By way of
non-limiting example, variables may include service level (whether
or not part delivery target dates are hit), inventory levels, days
of supply, end of life proximity (as may be estimated across many
data sets of similar parts), and the like. The user may then modify
any of these variables and be provided with a simulated outcome of
that modification, or the user may modify a different part metric
and be able to see the effect of that modification on one or more
of the foregoing variables. By way of example, the user may change
an existing service level value, which may toggle the user's risk
score for that supply chain factor. Once that occurs, the user may
request that the analytics engine provide to the user a list of
part providers that are capable of hitting the modified service
level, such that the design risk score may be modified to that
desired by the user.
[0126] Such simulations may further include predictive modeling.
For example, part suppliers may come to understand, based on
simulations provided by the analytics engine to users, that a 10%
decrease in lead times for that part supplier would enable that
part supplier to receive 10% more business from users of the
analytics engine. Needless to say, in a similar circumstance, if a
part supplier fails to meet criteria to be on 25% of all supplier
listings for products using a given part supplied by that part
supplier, the part supplier may receive an indication of what
characteristics would need to be met by that part supplier to be
placed on a certain percentage of the lists that part supplier
presently fails to make.
[0127] Moreover, the predictive modeling based in the simulations
may be able to make assessments of user goals and interests based
on simulations or other interactions by the user in the user
interface. For example, if a user repeatedly views various
providers for a given part, it may be understood by the analytics
engine that that user is particularly interested in that part, and
consequently content, suppliers, or other products that include
that part may be recommended to the user for viewing by the user
through the user interface.
[0128] By way of illustration and as shown in the attached figures,
a supply chain analytics engine 1703 may be provided within or in
association with platform 307, as is shown in FIG. 13 (AAA). That
is, the analytics engine may be, include, be included within, or be
distinct from, the supply chain analytics module 304 discussed
above. The analytics engine 1703 may include a node-analytics
module 1705 that may perform the functionality described in the
Figures below, and such as may apply rules indicated for an
individual analysis by a rules engine 1707 that applies at least a
comparator 1709 having therein comparative algorithms, such as
those discussed herein, to the large volume data 1711 from other
supply chains, such as may include similar product verticals and
dissimilar verticals that integrate similar parts, and from the
nodes of the supply chain of interest. The large volume data may
be, include, be included within, or be distinct from analytics data
322, discussed above.
[0129] The diagnostics and analytics of analytics engine 1703 are
applied on a node-by-node basis to a particular supply chain. That
is, each aspect of the supply chain may be treated as a node,
regardless of the function of that node within the supply chain. In
that way, a variety of comparative and cascading analytics may be
assessed on each node, and accordingly recommendations may be
provided uniquely for each node, and each aspect of each node, in a
supply chain.
[0130] An application 214 may be provided as an aspect of platform
307 in accordance with this node-based processing, which
application may apply rules engine 1707 of analytics engine 1703,
as shown with greater particularity in the exemplary embodiments of
the figures herein, including FIG. 14. Of note, although the visual
presentation and information provided by the node processing
application illustrated herein may differ from the other
applications 214 discussed hereinabove, the same primary and/or
secondary data 1711 provided by platform 307 may be accessible to
both the node-based and other applications discussed herein.
[0131] This vast data store 1711 that is available to the analytics
engine may allow for, for example, global end to end visibility
throughout a supply chain for a particular product or group of
products, as is illustrated in FIGS. 15(A) and 16(B). As indicated
in FIGS. 15 and 16, various suppliers, manufacturers and customers
in the supply chain may be visually presented for a given product,
and summary information with available drill down detail may be
provided at each one of these "nodes within the supply chain."
Further available in association with each of these nodes may be a
"flowchart" of key elements, such as parts, suppliers,
manufacturers, geographies, or the like, such as from the inception
of the supply chain to its end upon sale of the product. The end to
end visibility throughout the supply chain and its nodes may be
enhanced by the providing of alerts, such as based on calculated
thresholds by the analytics engine 1703, of areas of enhanced risk
within the supply chain, as is illustrated in the exemplary
embodiment of FIG. 17 (C).
[0132] Such alerts of generally available end to end visibility
data 1711 may be based on calculated trends or analytics, by way of
non-limiting example, stored within, uploaded to, and/or "learned"
by analytics engine 1703. As illustrated in FIG. 18 (D), a "top 10"
of demands, such as for parts by site, by part type, by demand
trend, or the like, may be available to the user, such as via one
or more pop-up windows upon user access. Similarly, analytics for
particular attributes of the supply chain, such as obsoletion of a
part, and trending related thereto may likewise be accessible based
on the availability of the underlying data and action of the
analytics engine 1703, as illustrated in FIG. 19 (E). Such
analytics may be available via graphics, trend lines, or hard data,
as illustrated additionally with respect to FIG. 20 (F).
[0133] Due to the overwhelming number of aspects, nodes, and/or
parts that may contribute to a given supply chain, search
capabilities to search within the data through a search interface
may be provided to the user, as illustrated in FIG. 21 (G). Search
capabilities may relate to any one or more aspects of the supply
chain, such as parts, purchase orders, customers, shipments,
tracking numbers, serial numbers, and the like. Moreover, searches
may be hierarchically structured to enhance usability, such as
wherein a search for a particular part may include searches for
alternative parts, subparts, or the like, and such as wherein
searches may include additional pop-up information related to
parts, tracking, carriers, or the like, to ensure that the user has
accessed the desired information. Such enhanced search capabilities
are illustrated with respect to FIGS. 22 (H), 23 (I), and 24 (J),
by way of non-limiting example. Alternatives to free-form searches
may include, by way of non-limiting example, structured searches,
populating drop-downs, or the like, as will be understood by the
skilled artisan. A hierarchical search using structured data
drop-downs is illustrated, by way of non-limiting example, in FIG.
25 (K).
[0134] More particularly, an exemplary node network is illustrated
in FIG. 26 (15), wherein 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. 26 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. 26 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.
[0135] Thus, exemplary node structures may be arranged for various
nodes:
Example 11
[0136] Raw Material
Mfg..fwdarw.Supplier.fwdarw.Component.fwdarw.Assembly.fwdarw.Customer
Example 21
[0137] Mfg. Plant.fwdarw.Distribution.fwdarw.Customer.fwdarw.End
Consumer
Example 3
[0138] Supplier.fwdarw.Vendor Hub.fwdarw.*Mfg.
Plant.fwdarw.Customer Hub.fwdarw.End Consumer
[0139] As shown in FIG. 26, 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.
[0140] 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).
[0141] 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.
[0142] 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.
[0143] 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. 27 (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. 28 (17), selection of
assembly node 1700 may cause the system to highlight upstream and
downstream supply chain nodes.
[0144] 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.
[0145] 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.
[0146] FIG. 29 (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.
[0147] For example, as illustrated in FIG. 30 (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. 30, 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.
[0148] FIG. 31 (21) illustrates an exemplary screenshot of a node
network diagram, similar to those disclosed above. FIG. 32 (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. 32 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. 33 (35) illustrates
another exemplary interactive map display, similar to the display
in FIG. 32, except that the system configures the map in terms of
demand, along with a total value ($26,334,422).
[0149] 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.
[0150] 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.
* * * * *