U.S. patent application number 17/683148 was filed with the patent office on 2022-06-16 for systems and methods for risk processing 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 | 20220188720 17/683148 |
Document ID | / |
Family ID | 1000006170183 |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220188720 |
Kind Code |
A1 |
BAJAJ; Mudit ; et
al. |
June 16, 2022 |
SYSTEMS AND METHODS FOR RISK PROCESSING 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: |
1000006170183 |
Appl. No.: |
17/683148 |
Filed: |
February 28, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16616257 |
Nov 22, 2019 |
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PCT/US2018/033804 |
May 22, 2018 |
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17683148 |
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62509669 |
May 22, 2017 |
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62509660 |
May 22, 2017 |
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62509665 |
May 22, 2017 |
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62509675 |
May 22, 2017 |
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62509653 |
May 22, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/0637 20130101;
G06F 16/904 20190101; G06Q 10/0635 20130101; G06Q 50/28 20130101;
G06Q 10/06315 20130101; G06N 20/00 20190101; G06Q 10/063114
20130101; G06Q 10/06375 20130101; G06Q 10/087 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06Q 10/08 20060101 G06Q010/08; G06F 16/904 20060101
G06F016/904; G06N 20/00 20060101 G06N020/00; G06Q 50/28 20060101
G06Q050/28 |
Claims
1. A supply chain risk manager for managing a supply chain
comprising a plurality of supply chain nodes, comprising: a
plurality of data inputs capable of receiving primary hardware and
software data from at least one supply chain node in a supply chain
computer network upon indication by at least one processor; a
plurality of rules stored in at least one memory element associated
with the at least one processor and capable of performing
operations on the primary hardware and software data to produce
secondary data upon direction from the at least one processor; and
a plurality of data outputs capable of at least one of: interfacing
with a plurality of application inputs, including at least risk in
a second supply chain, to provide the secondary data comprised of
at least cascaded supply chain risk data particular to attributes
of individual ones of the plurality of supply chain nodes and
cascaded from prior one of the plurality of supply chain nodes in
the supply chain; and interfacing with the user to provide the
secondary data.
Description
RELATED APPLICATIONS
[0001] The present application is a Continuation Application of
U.S. application Ser. No. 16/616,257, filed Nov. 22, 2019, entitled
"Systems and Methods for Risk Processing of Supply Chain Management
System Data", which claims the benefit of priority to International
Application No. PCT/US2018/033804, filed May 22, 2018, entitled
"Systems and Methods for Risk Processing of Supply Chain Management
System Data" which claims priority to, is related to, and
incorporates by reference, 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 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. 62/509,675, filed May 22,
2017, entitled Systems and Methods for Assessment and Visualization
of Supply Chain Management System Data; U.S. provisional
application No. 62/509,653, filed May 22, 2017, entitled Systems
and Methods for Providing Diagnostics for a Supply Chain; U.S.
patent application Ser. No. 14/523,642, filed Oct. 24, 2014, to
Valentine, et al., titled "Systems and Methods for Risk Processing
and Visualization of Supply Chain Management System Data," which
claims priority to U.S. provisional patent application Ser. No.
61/895,636, to Valentine, et al., titled "Power Supply With
Balanced Current Sharing," filed Oct. 28, 2013, U.S. provisional
patent application Ser. No. 61/895,665, to Joyner et al., titled
"System and Method for Managing Supply Chain Risk," filed Oct. 25,
2013, and U.S. provisional patent application Ser. No. 61/896,251
to McLellan et al., titled "Method for Identifying and Presenting
Risk Mitigation Opportunities in a Supply Chain," filed Oct. 28,
2013. Each of these is incorporated by reference in their
respective entireties herein.
BACKGROUND
Field of the Disclosure
[0002] The present disclosure relates to supply chain management
(SCM) system processing. More specifically, the present disclosure
is related to processing SCM data to reduce cost, optimize data
processing and networked communications, improving flexibility, and
identifying and mitigating risk in a supply chain. Furthermore, the
SCM data may be structured using visualization, analytics and
frameworks.
Background of the Disclosure
[0003] Supply chains have become increasingly complex, and product
companies are faced with numerous challenges such as globalization,
shortening product lifecycles, high mix product offerings and
countless supply chain procurement models. In addition, challenging
economic conditions have placed additional pressure on companies to
reduce cost to maximize margin or profit. Focus areas of supply
chain-centric companies include reducing cost in the supply chain,
maximizing flexibility across the supply chain, and mitigating
risks in the supply chain to prevent lost revenue.
[0004] Supply chain risk, or the likelihood of supply chain
disruptions, is emerging as a key challenge to SCM. The ability to
identify which supplier has a greater potential of a disruption is
an important first step in managing the frequency and impact of
these disruptions that often significantly impact a supply chain.
Currently, supply chain risk management approaches seek to measure
either supplier attributes or the supply chain structure, where the
findings are used to compare suppliers and predict disruption. The
results are then used to prepare proper mitigation and response
strategies associated with these suppliers. Ideally, such risk
management and assessment would be performed during the design of a
supply chain for a product or line of products, but design tools
and data analysis to allow for such design capabilities are not
available in the known art.
[0005] Rather than the data- and algorithm-centric supply chain
design and risk analysis discussed above, supply chain risk
management is instead most often a formal, largely manual process
that involves identifying potential losses, understanding the
likelihood of potential losses, assigning significance to these
losses, and taking steps to proactively prevent these losses. A
conventional example of such an approach is the purchasing risk and
mitigation (PRAM) methodology developed by the Dow Chemical Company
to measure supply chain risks and its impacts. This approach
examines supply market risk, supplier risk, organization risk and
supply strategy risk as factors for supply chain analysis.
Generally speaking, this approach is based on the belief that
supplier problems account for the large majority of shutdowns and
supply chain failures.
[0006] Such conventional systems are needlessly complicated and
somewhat disorganized in that multiple layers of classification
risks are utilized and, too often, the systems focus mainly on
proactively endeavoring to predict disruptive events instead of
analyzing and processing underlying root causes and large-scale
accumulated data to assess potential disruptions. Further, these
conventional systems fail to provide tools to aid in the design of
a supply chain at the outset to address potential breakdown and
disruption, and they also give little insight or visibility into
the actual supply chain over its entirety. Thus, what is needed is
an efficient, simplified SCM processing system for aiding in the
design of the supply chain, and thereby maximizing opportunities to
address potential supply chain risks at the outset and during the
life cycle of a supply chain.
[0007] Moreover, conventional supply chain management has
historically been based on various assumptions that may prove
incorrect. By way of example, it has generally been understood that
the highest risk in the supply chain resides with suppliers with
whom the highest spend occurs--however, the most significant risk
in a supply chain may actually reside with small suppliers,
particularly if language barriers reside between the supplier and
the supply chain manager, or with sole source suppliers, or in
relation to suppliers highly likely to be subject to catastrophic
events, such as earthquakes, for example. Further, it has typically
been the case that increased inventory results in improved delivery
performance--however, this, too, may prove to be an incorrect
assumption upon analysis of large-scale data over time and across
multiple suppliers, at least in that this assumption is true only
if an inventory buffer is placed on the correct part or parts, and
at the correct service level. Needless to say, such information
would be difficult to glean absent automated review of large-scale
data over time, and without visibility across an entire supply
chain.
[0008] Yet further, present supply chain management fails to
account for much of the available large-scale data information. By
way of example, social media or other third party data sources may
be highly indicative of supply chain needs or prospective
disruptions. For example, if a provider expresses a desire for
increased inventory levels, but social media expresses a largely
negative customer sentiment, sales are likely to fall and the
increased inventory levels will likely not be necessary. Similarly,
large scale data inclusive of third party data may indicate that a
supplier previously deemed high risk, such as due to the threat of
earthquake, is actually lower risk because that supplier has not
been hit with an earthquake over magnitude 5 for that last 20
years, and earthquakes of less than magnitude 5 have only a minimal
probability of affecting the supply chain in a certain vertical. As
such, large scale data, such as may include social media or other
third party data, may complement supply chain management in ways
not provided by conventional supply chain management.
[0009] By way of further example, conventional systems often deem
certain events, such as significant geopolitical events, to pose a
very high risk to the supply chain. However, large scale data
analysis, such as from the inception of the design of many supply
chains in a given vertical and from end-to-end of such supply
chains throughout their respective life cycles, may reveal that
this supposition has generally not been the case--rather, the
supply chain risk may instead be revealed as far more dependent on
sole source items and the size and language spoken by certain
suppliers than on geopolitical events, by way of non-limiting
example.
SUMMARY OF THE DISCLOSURE
[0010] Disclosed is an apparatus, system and method for supply
chain management (SCM) system processing. A SCM operating platform
is operatively coupled to SCM modules for collecting, storing,
distributing and processing SCM data to determine statistical
opportunities and risk in a SCM hierarchy. SCM risk processing may
be utilized to determine risk values that are dependent upon SCM
attributes. Multiple SCM risk processing results may be produced
for further drill-down by a user. SCM network nodes, their relation
and status may further be produced for fast and efficient status
determination.
[0011] More particularly, a supply chain management operating
platform is disclosed for managing a supply chain that includes a
plurality of supply chain nodes. The platform, and its associated
system and method, may include a plurality of data inputs capable
of receiving primary hardware and software data from at least one
third party data source and at least one supply chain node upon
indication by at least one processor. The platform and its
associated system and method may also include a plurality of rules
stored in at least one memory element associated with at least one
processor and capable of performing operations on the primary
hardware and software data to produce secondary data upon direction
from the processor(s). The platform and its associated system and
method may also include a plurality of data outputs capable of at
least one of interfacing with a plurality of application inputs,
and capable of providing the secondary data, comprised of at least
one of supply chain risk data, supply chain management data, and
supply chain analytics, to ones of the plurality of application
inputs for interfacing to a user; and interfacing with the user to
provide the secondary data comprised of at least one of supply
chain risk data, supply chain management data, and supply chain
analytics.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The present invention is illustrated by way of example and
not limitation in the figures of the accompanying drawings, in
which like references indicate similar elements and in which:
[0013] FIG. 1 illustrates a computer system for transmitting and
processing data, and particularly supply chain management (SCM)
data under an exemplary embodiment;
[0014] FIG. 2 illustrates an exemplary processing device suitable
for use in the embodiment of FIG. 1 for processing and presenting
SCM data;
[0015] FIG. 3A illustrates an exemplary SCM platform comprising a
plurality of plug-in applications/modules, including a control
tower module, a network optimization module, a supply chain
analytics module, a supplier radar module, and a supply/demand
processing module under one embodiment;
[0016] FIG. 3B illustrates the SCM platform utilizing extended
plug-in applications/modules under another exemplary
embodiment;
[0017] FIG. 4 illustrates exemplary data points and variables
modules operatively coupled to a SCM platform under one
embodiment;
[0018] FIGS. 5A-5F illustrate logical processing outcomes for a
variety of exemplary embodiments;
[0019] FIG. 6 illustrates an exemplary automation process suitable
for utilization in the embodiment of FIG. 1;
[0020] FIG. 7 illustrates an exemplary data visualization example
for actionable-measurable-proactive (AMP) SCM processing;
[0021] FIG. 8A illustrates a further data visualization and
"one-click" report generation under one embodiment;
[0022] FIG. 8B illustrates a functional action input module
associated with report generation from the data visualization of
FIG. 8A;
[0023] FIG. 9 illustrates an exemplary data table providing for
attribute naming, attribute description and applicable weight
attribution for SCM processing;
[0024] FIG. 10 illustrates an exemplary risk assembly detail for
commodities/parts, wherein part and supplier attributes are
processed to determine an overall risk;
[0025] FIG. 11 illustrates an exemplary risk part detail for
commodities/parts, wherein various attributes are processed
together with attribute weights and selection scores to calculate a
weighted risk score;
[0026] FIG. 12 illustrates an exemplary data visualization heat map
for various assemblies and associated parts, wherein specific
assemblies and/or parts are presented as color-coded objects to
indicate a level of risk;
[0027] FIG. 13 illustrates an exemplary cross-source processing
configuration where a same part, as well as suitable part
alternatives are processed and presented to a user;
[0028] FIG. 14 illustrates a resultant risk trend processing for
processing and displaying a mean and standard deviation of risk
over time;
[0029] FIG. 15 illustrates an exemplary analytics system for use in
the embodiments;
[0030] FIG. 16A illustrates an exemplary interface;
[0031] FIG. 16B illustrates an exemplary interface;
[0032] FIG. 17 illustrates another exemplary interface;
[0033] FIG. 18 illustrates an exemplary interface;
[0034] FIG. 19 illustrates a screenshot of an exemplary
interface;
[0035] FIG. 20 illustrates a screenshot of an interface;
[0036] FIG. 21 illustrates a screenshot of an interface;
[0037] FIG. 22 illustrates a screenshot of an interface;
[0038] FIG. 23 illustrates a screenshot of an exemplary
interface;
[0039] FIG. 24 illustrates a screenshot of an interface;
[0040] FIG. 25 illustrates a screenshot of an exemplary
interface;
[0041] FIG. 26 illustrates a screenshot of an exemplary
interface;
[0042] FIG. 27 illustrates a screenshot of an interface;
[0043] FIG. 28 illustrates a screenshot of an exemplary
interface;
[0044] FIG. 29 illustrates an exemplary interface;
[0045] FIG. 30 illustrates a screenshot of an exemplary
interface;
[0046] FIG. 31 illustrates a screenshot of an interface; and
[0047] FIG. 32 illustrates a screenshot of an interface.
DETAILED DESCRIPTION
[0048] 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.
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] 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.).
[0054] 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.
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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.
[0059] 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).
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.).
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] The disclosure thus provides a SCM operating platform 307
suitable for receiving base data from the supply chain, and/or from
a data store, and/or from third party networked sources, and
applying thereto a plurality of rules, algorithms and processes to
produce secondary data. This secondary data may be made available
within the platform, and/or may be made available to one or more
apps, to provide indications to the user based on the applied
rules, algorithms and processes. Therefore, the disclosure makes
use of significant amounts of data across what may be thousands of
supply chain nodes for a single device line to allow for supply
chain management, risk management, supply chain monitoring, and
supply chain modification, in real time. Moreover, based on the
significant data available to the platform, the platform and/or its
interfaced apps may "learn" from certain of the data received, such
as trend data fail point data, or the like, in order to modify the
aforementioned rules, algorithms and processes, in real time and
for subsequent application.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.).
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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)
[0090] In addition to the examples provided in FIGS. 5A-F, other
variables may be utilized by the system for optimization. For
example, master production schedule (MPS) tactical rules may be
employed to generate a scorecard format in order to identify areas
of concern and opportunity. By using a plurality of variables as
inputs, an MPS may be configured to generate a set of outputs for
decision making within the system. Inputs may include any of the
data points disclosed herein, as well as forecast demand,
production costs, inventory money, customer needs, inventory
progress, supply, lot size, production lead time, and capacity.
Inputs may be automatically generated by the system by linking one
or more departments at a node with a production department. For
instance, when a sale is recorded, the forecast demand may be
automatically shifted to meet the new demand. Inputs may also be
inputted manually from forecasts that have also been calculated
manually. Outputs may include amounts to be produced, staffing
levels, quantity available to promise, and projected available
balance. Outputs may be used to create a Material Requirements
Planning (MRP) schedule.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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. Additionally,
users may have the ability to select a country, country/radius,
country/city/radius, or draw an area to see the results of impact
on such areas of interest.
[0099] 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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).
[0104] 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.
[0105] 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.
[0106] During operation, a user may select the top X assemblies and
parts for visualization. In one embodiment, the risk attribute
module automatically determines the top X assemblies by multiplying
an assembly risk attribute score by a planned revenue value over a
predetermined time period (e.g., 90 days). Assemblies with the
highest results may be displayed for further analysis. The same
calculation may be also used to determine which component parts are
displayed inside an assembly heat map. In the example of FIG. 12,
the risk attribute module displays the top 5 component parts with
the highest risk. A user may select any of the displayed assemblies
or parts to drill down and receive reports, such as those
illustrated in FIGS. 10-11. A user may also select any of the boxes
(not just on part numbers) to enlarge the selected box and any
nested boxes.
[0107] As can be appreciated by those skilled in the art, the risk
attribute module not only displays supply chain risk, but also
helps to reduce it by providing a list of alternative parts for
circumstances where a customer has a single manufacturer from which
purchases are obtained. For such sole source parts, the module
checks one manufacturer's part number (MPN) against an approved
manufacturer list (AML) from other customers to see if another
customer (or associated manufacturer) may approve the purchase of
the MPN and/or other comparable parts from other manufacturers. Of
course, multi-sourcing may de-risk the supply chain, but may also
increase the pricing of the subject parts (at least in that best
pricing may be available only upon sole-source contracting).
[0108] This technique may be referred to herein as "cross source
opportunity" processing and is powerful because of the potentially
large size of a supply chain. If the system finds the same MPN as
well as alternatives, they may be automatically listed as
illustrated in FIG. 13. Accordingly, a user may forward or
otherwise present these to a customer to see if they are approved
as viable alternatives to allow the option of purchasing from more
manufacturers in order to lower a supply chain risk.
[0109] Risk attribute processing results may further be used by the
platform to show trends over time, as well as a current risk
attribute score distribution. Such trends may be reported upon
certain triggers, and/or may be tracked in order to allow automated
or manual modifications to algorithms and processes of an app or
the platform 307. Because there are a plurality of aspects for
improving the supply chain risk for a customer or assembly (thus
lowering the average risk and lowering a variation of risk), a mean
and standard deviation as illustrated in FIG. 14 may be trended
over time. The data for the risk attribute module may be collected
from the network via customers and suppliers, and may further be
obtained from manufacturer nodes (e.g., 104, 107). The data is
collected in the module and processed to determine risk attribute
scores and trend them to further determine action needed to reduce
risk for customers. Under one embodiment, the risk attribute data
and calculations may be automatically processed on predetermined
time intervals, such as weekly, monthly and/or quarterly.
[0110] By way of example and in order to execute analysis based on
the foregoing and other risk attributes across a supply chain, a
supply chain analytics engine 1703 may be provided within or in
association with platform 307, as is shown in FIG. 15. That is, the
analytics engine may be, include, be included within, or be
distinct from, the supply chain analytics module 304, and/or others
of modules 302-306, discussed above. The analytics engine 1703 may
include a risk attribute module 1705 that may perform the
functionality described in the Figures herein, and such as may
apply rules indicated for an risk attribute 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, risk data 311,
and/or other data discussed herein and above.
[0111] The diagnostics and analytics of analytics engine 1703 may
be applied on a node-by-node, part-by-part, line-by-line, or any
other basis, to a particular supply chain. That is, each aspect of
the supply chain may be treated as a node, part, manufacturer,
supplier, geography, or the like, regardless of the function of any
individual node or aspect within the supply chain. In that way, a
variety of comparative and cascading analytics may be assessed, and
accordingly recommendations may be cascaded through a supply
chain.
[0112] An application 214 may be provided as an aspect of platform
307 in accordance with this risk-based analysis, 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. 15. Of note, although the visual
presentation and information provided and as 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 risk-based analytic app 214 and other
applications discussed herein.
[0113] 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.
[0114] The analytics engine 1703 may employ the voluminous
accessible data 1711 to apply rules-based risk modeling to allow
for extensive risk management of risk in supply chain operation and
supply chain design. The comparative risk model algorithms 1709
that may be employed by the analytics engine1703 additional improve
computing processing speed and capabilities by providing a
hierarchical model structure. That is, categories in which risk may
occur in a supply chain design, such as five (5) risk categories,
may be hierarchically treated and provided within the compataive
algorithms 1709. Within each category, one or more attributes may
be provided that contribute to a scoring within the category. Of
note, each category may be scored, as may be each attribute within
each category to contribute to the category score, and these scores
may be weighted, such as in relation to the stated goals and
objectives of a user, as referenced further below. This weighted
scoring system may then provide a total risk score for the factor,
part, supplier, or the like for which the risk model was
applied.
[0115] A user, such as a user having only particular permissions,
such as an administrative user, may be enabled to select and/or
modify the categories, attributes, weighting, or the like, within a
risk model. Further, the analytics engine 1703 may recommend
certain aspects to the user to be employed in a risk model, such as
weightings, categories, attributes, or the like. Such
recommendations may be based on, for example, risk profiles of
competitors, presence in a particular industry vertical, or the
like. As will be understood to the skilled artisan, limitations may
be placed on the configurable nature of the risk model, such as the
requirement that the weighting of risk factors must total to 100%,
by way of non-limiting example.
[0116] Thereby, using the disclosed attribute-based risk, a user
may be provided with a mapping of what attributes of the supply
chain are causing different aspects of a risk score and/or a total
risk score. Thereby, those particular attributes may be addressed,
remedial action taken by the user, instructions provided directly
by the user to the supply chain design, or the analytics engine may
be asked to recommend remedial action to address detrimental
attributes that cause enhanced risk.
[0117] More particularly, the risk recommendations provided by the
analytics engine 1703 may reduce the risk on a per attribute, per
category, per part, per supplier, per product, per location, or per
event basis, by way of non-limiting example. These recommendations
may be included in reports, simulations, and trending models, such
as may include comparison of performance in the event that risk
recommendations were accepted, or were not accepted.
[0118] Further, the risk modeling and or recommendations discussed
herein may comprise one or more learning algorithms, such as
wherein learnings may be made from accumulated source report data,
risk rating of other sources or competitors, increase in
multi-sourcing to effect change in the risk score, and the like.
Further and by way of non-limiting example, the learnings may cause
modification to automated weightings assigned by the analytics
engine 1703, or aspects thereof, to one or more of the attributes.
For example, recommended weights may be created by cross-factoring
a particular customer and its required parts, relevant groups to
that customer, the product sector of that customer, the
manufacturer(s) for that customer and/or other customers serviced
by that manufacturer, historical data within a predetermined
timeframe (such as one year), assessed impact of events external to
the supply chain to typical users of the same or similar parts, and
the like. This accumulated information may then be employed to
create an ever-changing comparative rule set within the rules
engine 1707 that blends experiential knowledge and statistical
analysis with performance feedback of the level of correctness of
prior outputs.
[0119] It goes without saying that interrelationships may occur
between attributes, such as wherein one attribute may have more
significant impact than another, or wherein an effect on one
attribute may additionally affect one or more other attributes. For
example, in a particular risk model, if the analytics engine
assesses a risk factor of 5 (out of 5, by way of non-limiting
example) for a part lifecycle, and a risk factor of 2.5 for part
lead time, the analytics engine may conclude that the part
lifecycle is twice as important to that particular risk model than
the part lead time. This may then, of course, be factored into the
decision-making process and/or recommendations provided by the
analytics engine 1703. Moreover and as discussed above, new data
may provide feedback that is factored in to the conclusion drawn in
accordance with this analytics rule, such that the rule may be
modified as learning occurs. That is, feedback may allow for
judgment of the correlation between an attribute and actual risk
outcome for a given product, product design, supply chain design,
manufacturer, supplier, reseller, or the like. As such, some
attributes may prove to be irrelevant to a risk model over time,
such as only for a particular industrial vertical.
[0120] Of note, the attributes discussed herein are provided by way
of non-limiting example only. That is, other attributes may be
available to be applied in the risk model, such as the financial
stability of a supplier, reseller, or manufacturer. The attributes
applied as impacting the risk model may, of course, be gleaned from
particular data sets relevant to particular industries, products,
or product types, by way of non-limiting example. Nevertheless, the
analytics engine may always be subject to a user override, such as
wherein the user may always focus the risk model on those aspects
of the supply chain that the user deems most important.
[0121] Further and as referenced above, the risk attributes may not
only be used by the analytics engine 1703 to provide a risk model,
but may additionally be used for other purposes. For example, the
risk attributes may be used in a simulation, which may be related
or unrelated to risk modeling, and if a supply chain initial design
is subjected to simulation, the attributes may be optimized to
provide for a supply chain optimization.
[0122] In accordance with the ability to provide simulation, the
risk model may contribute to the making of recommendations for
optimization, as discussed throughout. More specifically, within
each attribute particular part or parts may be scored against the
overall risk model. Thereby, sensitivity to a particular part or
parts of the risk model may be assessed. Upon assessment of this
sensitivity to a particular part or parts, recommendations may be
provided to the user as to how to lower the overall risk score, the
risk score per category, or the risk score per attribute, by way of
non-limiting example. Further, such recommendations may or may not
offer trade-offs, i.e., may or may not offer recommendations that
might raise an overall risk score, a categorical risk score, or an
attribute risk score, dependent upon the effect on other risk
aspects.
[0123] Accordingly, an unlimited number of parts within a given
product, and a virtually unlimited number of nodes within a supply
chain, may be individually reviewed for risk, and may be subjected
to particular recommendations that would improve the risk profile.
The user may be provided with all such recommendations, or only
those recommendations at only that modeling level which the user
wishes to see. Further, the user may wish to see trends, which may
be used to predictively estimate parts or suppliers, for example,
that will provide better options to the user in a subsequent
timeframe, or current parts or suppliers that will become
detrimental to the risk model at a later point in time.
Alternatively, by way of non-limiting example, users can
collaborate in a recovery room about an event to speed resolution
of issues caused by it and to beat competitors to alternate
suppliers. As such, proactive movement within the supply chain to
lower risk, rather than historical reactive movement within the
supply chain, may be made available to the user through the use of
the disclosed embodiments.
[0124] As referenced, the analytics engine may provide
recommendations, which the user may or may not accept. As such,
whether or not the user accepts the recommendations, the outcome of
the recommended modifications v. the actual outcome within the
supply chain may be tracked using the embodiments. Further, even in
the event a recommended action causes increased risk, the
aforementioned comparative feedback may indicate that the risk
would have increased even more had a recommendation not been
adopted.
[0125] In order to aid the user's visualization of the supply chain
and the risks resident therein, presentation aspects may be
provided, such as on a user's landing page or on another initial
page to access the disclosed SaaS, i.e., to access at least the
output of analytics engine 1703. By way of example, "word widgets"
may be provided, such as in banner format, scrolling format, pop up
format, or the like, in which consistent verbiage explaining
aspects of the supply chain is provided, but into which the
analytics engine places numbers particular to the supply chain of
that given user. Further, alerts may be provided, such as in a pop
up, audio, scrolling ticker, or like format, wherein the user may
have previously requested alerts regarding the topics
displayed.
[0126] Also provided may be ready-access to one or more current or
prior simulations and/or recommendation models. In this simulation
presentation window, the user may be able to "experiment off-line",
such as wherein the user may readily modify different factors just
to see what the effect of those factors would be on the outcome
from the supply chain if changed. Further, predictive trends may be
provided, such as in the simulation display, wherein trends and a
predetermined timeframe, such as 12 months or 24 months, may be
provided, such as in conjunction with projections, predictions,
simulations, and/or recommendations.
[0127] Further provided within this simulation and/or predictive
window may be information, including recommendations and/or alerts,
unique to a given supply chain for a particular user. For example,
if the user's risk modeling would improve significantly in the
event a given part were assigned a one day lead time, and available
data indicates to the analytics engine that that part may be 3-D
printed and the user has a 3-D printer on site, the analytics
engine may recommend that the user build the part on site using the
3-D printer in order to greatly improve the user's risk model.
[0128] Alerts provided by the user may, as referenced, be
preselected by the user, such as to target those risk attributes or
aspects of the supply chain that the user deems most relevant to
supply chain performance. Further, these alerts may form part of
the afore-discussed event risk visualization, such as may reside in
a dashboard on, for example, the aforementioned user landing page.
For example, the user may request an alert in the event there is an
earthquake within 250 miles of a supplier's facility that supplies
part to that user. If an earthquake does occur, the user-alert may
indicate that the user has 2 manufacturers within 250 miles, and in
combination those manufacturers provide 28 parts that would affect
one product and 3 customers of that user. Further, the analytics
engine may understand, from previously gained data, that an
earthquake of magnitude less than 5.5 is unlikely to cause any
effect in the supply chain. As such, the event risk dashboard or
window may not provide the requested alert to the user if the
earthquake assessed has a magnitude of less than 5.5, at least due
to the extraordinarily high likelihood that such a smaller
earthquake would have no effect on the supply chain based on
historical data.
[0129] An event risk may also be associated with alternative
information within an event alert. For example, an event alert may
also include with it cross source or multisource data, and/or the
effect on supply chain risk that proactively switching to a
different source might effect. Further, communications, such as
instant messaging, may be provided within the event risk alert
window, such as, upon receipt of an alert (such as the earthquake
alert referenced above). For example, manufacturers in an affected
area may be automatically contacted so that they can directly
provide a damage assessment to the user. Such communications may be
stored so that a historical record of who, what, when, where, how,
and whether communications occurred may be maintained.
[0130] Needless to say, the analytics engine 1703 may automate the
alerts discussed herein, such as by performing an information
crawl, such as a Web crawl, at relevant/predetermined time frames,
such as every 3 minutes, in order to assess the occurrence of
events worldwide. Further, rather than simply assessing events
worldwide, or in select particular areas of interest (such as by
name, geography or part), a user may graphically engage the user
interface to "draw" areas of particular interest to the user. The
user may be enabled to draw one or more such areas. In this way,
the user has the ability to select country, country/radius,
country/city/radius or draw an area of interest to see the results
of impact on such areas.
[0131] All of the foregoing may be used to provide a risk impact
score in the event an occurrence of interest happens. That is, the
historical impact of such an event may be assessed based on
existing voluminous data 1711. Additionally, the impact of an event
may be modified if comments are received from affected parties,
such as indicating that the parties are not impacted by the event.
Likewise, typical historical "domino effects" may be assessed based
on the substantial historical data 1711. Accordingly, it may be
assessed that a particular event is likely to affect only
manufacturers within a 50 mile radius of the particular event, but
a comment from the only manufacturer within 50 miles of the event
indicating that the manufacturer is okay may cause the risk impact
score of that event (or of the relevant risk attribute) to go to
zero once (or before) the event occurs. Thereby, using the data
available to the analytics engine 1703, any event may be tied to
any effects of that event on any outcome. For example, the
analytics engine 1703 may recognize (based on algorithmic and
cascaded analysis of data 1711) that fish oil from Vancouver should
not be employed in a manufacturing process within 6 months of the
occurrence of a nuclear meltdown on the coast of Japan.
[0132] The analytics engine 1703 may rate key performance
indicators, such as in relation to risk attributes, such as subject
to or not subject to the input of the user's objectives or
selection of risk attributes, 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 key performance
indicator has a positive or no effect based on analytics of
existing data, a yellow KPI may have a neutral effect on the supply
chain, and a red KPI may be likely to have an adverse effect on the
supply chain.
[0133] KPI analysis may occur in light of the user's input
objectives and/or selected risk attributes. Further, KPI analysis
may have statistical algorithms applied thereto, such as the
removal of outlier data. For example, abnormal data spikes or dips,
such as in inventory levels, may be indicative of one-time
occurrences, and thus may be dismissed by the analytics engine 1703
and 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 spiked for a
particular part of a particular product in the middle of the 3rd
quarter of each year, such as in an 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 third quarter in the given
year. That is, the algorithms applied by the analytics engine 1703
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.
[0134] The algorithms within the analytics engine 1703 discussed
throughout may allow for simulations, such as those provided in the
comparative module 1709, that may simulate the outcome of
recommended modifications to the supply chain as compared to an
unmodified supply chain as 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.
[0135] More particularly, simulations provided by the analytics
engine 1703 may allow for the user to change aspects of product
design or supply chain design and be provided with an outcome 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 simulation, 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 (i.e.,
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.
[0136] Such simulations may further include predictive and/or
probabilistic 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 1703. Needless to say, in a similar
circumstance if a part supplier fails to meet criteria to be on 25%
of all part supplier's 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.
[0137] 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.
[0138] These probabilistic and/or predictive analyses may, for
example, provide part level predictions for on-time delivery. For
example, the analytics engine 1703 may assess, from the vast data
1711 available to the analytics engine 1703, that a particular
combination of three factors cause failure of on-time delivery of a
given part 80% of the time. Further, the analytics engine 1703 may
assess that a shortage of 3,000 of that same part for the user
would cause a catastrophic failure of the supply chain.
Consequently, occurrence of none, one, two, or all of the three
factors that would typically lead to an 80% failure of on-time
delivery may cause an indication by the analytics engine 1703 that
a shortfall in excess of 3,000 parts is likely to occur, and
consequently the user should immediately switch to a different part
or part supplier. Alternatively, the analytics engine 1703 may
indicate that such a shortfall is likely to occur in 30 to 90 days
after the probabilistic analysis has been performed, and as such it
may be recommended to the user that within the next 30 days the
user should switch to a different part or part supplier.
[0139] FIG. 16A is a screen shot illustrating a variety of
attributes for one or more aspects of a manufactured product or
manufacturing design. As illustrated, each attribute may comprise
one or more informational items or scores, such as an actual data
score for that attribute in relation to a supply chain aspect, such
as a risk score that may be indicative of the risk imparted to the
overall product by the risk stemming from that aspect, and/or such
as a weighting given to that attribute in an overall risk
profile.
[0140] By way of non-limiting example, attribute 1102 of FIG. 16A
comprises the lead time for the aspect of the product given
treatment in the illustrated attribute based risk profile. The
illustrated lead time may be displayed as the actual number of days
of lead time for that aspect, 132 days in the case of FIG. 16A, as
well as an indication of the risk to overall production caused by
that lead time, which, in the case of FIG. 16A, is a risk score of
4.2 on a 1 to 5 scale. However, it is also indicated by the
weighting illustrated in association with lead time 1102, that very
little weight is given to the lead time for that aspect in the
overall risk profile for the product design, and more particularly,
that only 2% weighting is assigned to the lead time for that
aspect.
[0141] FIG. 16B illustrates a user input having selected
recommendations based on lead time 1102, in FIG. 16A. Of note, it
was indicated in association with lead time 1102 in FIG. 16A that
four recommendations were available to improve lead time, and FIG.
16B illustrates, such as in a summary format, those four
recommendations. Of course, fewer or additional recommendations may
be provided upon accessing of any given attribute, and, should
numerous recommendations be available for a given attribute, a
scroll bar or multipage format may be provided in order to allow
for viewing of those recommendations. These recommendations may
include, by way of non-limiting example, a review of other products
that employ the same or a similar part, and may additionally
include alternative suppliers or the like. That is, recommendations
may result from the application of the risk algorithm rules, which
may be unique to each attribute. For example, lead time
recommendations may result from the application of lead time
guideline rules in rules engine of analytics engine 1703, and such
rules may be based on the vertical of the designed product, and
consequently may include various recommendations specifically to
reduce lead time using other manufacturing methods, other aspect
sources and so on; however, the spend leverage attribute 1104 may
have associated therewith an entirely different set of risk rules,
such as may include known methodologies of decreasing pricing,
aspect, replacement, or elimination, such as due to redundancy, in
a given product design, which may vary based on product vertical,
and so on.
[0142] More particularly and as illustrated in FIG. 17, the applied
risk model may be configurable by the user. As shown, a risk model
may be entered by any of various authorized users in relation to a
particular product line, product, or aspect of a product, and risk
models may be suitable for import or export, such as from other
computing programs. Further, new risk models may be created or
viewed, such as by accessing a risk model creation tool, such as
via a user input as illustrated in FIG. 17.
[0143] FIG. 18 illustrates the creation of and/or viewing of a
previously created risk model. As shown, risk model creation may
include a variety of factors, such as the five factors illustrated,
and may vary by product type, product vertical, or the like.
Further, a created risk model may not be limited as to what factors
are available, but rather factors may be selectable by a user and,
upon selection of factors, various sub-factors may automatically
populate based on the rules applied by the rules engine. In the
example shown in FIG. 18, each factor and/or sub-factor may receive
a weighting, and these weightings may make up the overall risk
model. Of note, irrespective of the number of factors or
sub-factors selected, it may be that the risk model must add to a
preselected percentage, such as 100%, or such as less than 100%,
such as 80%, if certain design factors are required for application
in certain verticals or to certain products by the rules engine.
That is, the percentage of the factors selected in the design of
the risk model may be affected by the required inclusion of other
factors having preassigned weights by the rules engine.
[0144] FIGS. 19 and 20 illustrate a rules-based presentation of
attribute based risk for a given product design and/or aspects
thereof. As shown in FIG. 19, multiple aspects of a risk model may
be presented, such as each in a summary format, simultaneously upon
request by a user. Both the number and type of summary risk
contributors may be selectable by the user, may be recommended by
the rules engine, or may be required by the rules engine, in
certain exemplary embodiments. Further, and as illustrated in FIG.
20, various data structures may be used to filter the risk
information displayed in FIG. 19, such as setting beginning and
ending dates during which the user wishes to be provided with
information.
[0145] By way of example, and as illustrated in FIG. 21, the
summary reports displayed may be varied, as may be particular drill
downs displayed to the user based on the presented summary. For
example, the four summary risk features presented in FIG. 21 may
include preferred sourcing or cross-sourcing. Accordingly, details
of this cross-sourcing may be available, such as via selection of a
drop-down menu.
[0146] Upon selection of the aforementioned drop-down menu,
cross-source report details may be displayed with greater
particularity than is displayed in FIG. 21. More particularly, FIG.
22 illustrates particular parts that may be used in the design,
what sources those parts are available from, and other pertinent
information regarding the specific part that may be subjected to
cross-sourcing.
[0147] Of course, other report types may be available so long as
data to populate said reports is available to the underlying
analytics engine 1703. By way of example, FIG. 23 illustrates a
product detail report in which a given part or parts within a
product may be displayed in association with the attributes of
those parts, such as the risk score for those parts, the category
of the parts, the lead time of the parts, the impact on demand of
the parts, the spend on the parts, and the like. Needless to say,
other information may be presented in association with product or
part detail, such as only component parts for certain aspects of an
overall product, cross-sourcing or substitute sourcing, or the
like.
[0148] As illustrated in the example of FIG. 24, graphical risk
information may also be presented based on manipulation of the data
by the analytics engine 1703. By way of non-limiting example, the
risk score for a given category of parts may be illustrated, such
as will allow for an assessment that the risk due to that part or
those parts is increasing or decreasing over time. Needless to say,
the illustration of such a trend requires application of rules by
the analytics engine 1703 to the underlying data 1711, and may
prove highly useful to a user in the event that risk is steadily
climbing in association with a certain part. Such an indication
might imply, for example, that a substitute part should be
used.
[0149] FIG. 25 illustrates a heat map associating given parts
within a product and the relevant attribute based risks and/or
total risk score of each of those parts. By way of non-limiting
example, one box may be associated with each part, and the sizes of
those boxes may be indicative of certain attributes of that part,
such as how many of the parts are used within each product, how
many of those parts are used overall in a product line, the
contribution of that part to the overall risk score, a weighting of
that part in contribution to the overall product, or the like.
Further, the boxes for each part may be coded in such a way as to
indicate to a user the contribution of that product to the overall
risk. For example, individual parts having high-risk may be coded
red, medium risk yellow, and low risk green. Alternatively,
individual parts, having little consequence to the risk of the
overall product may be coded green, and so on.
[0150] Such heat maps, such as including information in relation to
either specific part risk or overall product risk, may also be
applied by the rules engine to various additional data. For
example, FIG. 26 illustrates a heat map in association with
suppliers, such as for a particular product, component, part,
commodity, or the like.
[0151] FIG. 27 illustrates an exemplary screenshot of a generated
heat map, similar to the embodiments discussed above. In one
embodiment, the risk attribute scores for a collection of parts are
provided. As discussed above, the heat map boxes may be configured
such that box sizes are provided according to an attribute (e.g.,
revenue) and color coded to indicate a status of the part (e.g.,
high/medium/low risk). As each box is selected, a pop-up window or
overlay may generate analytic results for the selection. Thus, in
the example of FIG. 27, selection of PART-COAEHDE automatically
launches a window or overlay to identify a related assembly
(PART-CDAEHDE), revenue impact ($699,850.75) and risk attribute
score (3.42). Similar functions for other parts are available as
shown in the figure.
[0152] In addition to providing a heat map, automated reports may
be generated for the items of interest within the heat map, as
discussed above and as shown in FIG. 28. Exemplary reports may
include, but are not limited to, geographic risk attribute, cross
source options, geographic impact, risk attribute part detail, risk
attribute score average, risk attribute score distribution and risk
attribute score standard deviation. In the exemplary illustration
of FIG. 29, a geographical impact report is selected, generated and
displayed in the system to provide locations, manufacturer revenue
impact and spend values for a selected heat map part of interest.
In the exemplary illustration of FIG. 29, a risk attribute score
average chart is processed and displayed in the system to show risk
attribute score averages over a predetermined time period (e.g.,
weekly) for a selected heat map part of interest. FIG. 30
illustrates an exemplary risk attribute score distribution over a
predetermined time period. In addition to displaying and processing
a current risk attribute score distribution, the system may be
configured to store and process previous distributions (e.g., 13
weeks ago) and compare the two in one chart. FIG. 31 illustrates an
exemplary risk attribute score standard deviation over a
predetermined time period (e.g., weekly)
[0153] FIG. 32 illustrates a risk attribute part detail report,
similar to the discussion above in connection with FIG. 11. Here,
the risk attribute part detail report provides part detail
analytics (e.g., site, part, part description, commodity),
commercial analytics (e.g., spend leverage) component analytics
(e.g., alternative sourcing, lead time, part change risk, part
manufacturing risk), supplier performance (e.g., defects per
million, inventory performance), and a total risk attribute
score.
[0154] 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.
[0155] 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.
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