U.S. patent application number 17/580472 was filed with the patent office on 2022-05-12 for systems and methods for optimized design of a supply chain.
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 | 20220147891 17/580472 |
Document ID | / |
Family ID | 1000006096247 |
Filed Date | 2022-05-12 |
United States Patent
Application |
20220147891 |
Kind Code |
A1 |
BAJAJ; Mudit ; et
al. |
May 12, 2022 |
SYSTEMS AND METHODS FOR OPTIMIZED DESIGN OF A SUPPLY CHAIN
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: |
1000006096247 |
Appl. No.: |
17/580472 |
Filed: |
January 20, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16616248 |
Nov 22, 2019 |
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PCT/US2018/033803 |
May 22, 2018 |
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17580472 |
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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: |
G06F 16/904 20190101;
G06Q 10/0635 20130101; G06Q 10/0637 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 design platform for designing 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 second supply chain accessible over
a computer network, and capable of receiving design data regarding
a prospective at least one of the plurality of supply chain nodes,
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 comparative operations on
the primary hardware data, the software data, and the design data
to produce secondary data upon direction from the at least one
processor; and a plurality of data outputs capable of: providing to
a user interface of the secondary data comprised of at least a
substantially optimized one of the design for the plurality of
supply chain nodes based on the comparative application of at least
ones of the plurality of rules; and providing the secondary data to
a user via the user interface.
Description
RELATED APPLICATIONS
[0001] The present application is a Continuation Application of
U.S. application Ser. No. 16/616,248, filed Nov. 22, 2019, entitled
"Systems and Methods for Optimized Design of a Supply Chain, which
claims the benefit of priority to International Application No.
PCT/US2018/033803, filed May 22, 2018, entitled "Systems and
Methods for Optimized Design of a Supply Chain" which claims
priority to, is related to, and incorporates by reference, U.S.
provisional application No. 62/509,669, filed May 22, 2017,
entitled "Systems and Methods Optimized Design of a Supply Chain";
U.S. provisional application No. 62/509,660, filed May 22, 2017,
entitled "Systems and Methods for Risk Processing of Supply Chain
Management System Data"; 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., entitled "Systems and Methods
for Risk Processing and Visualization of Supply Chain Management
System Data," which claims priority to U.S. provisional patent
application Ser. No. 61/895,636, to Valentine, et al., 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
may be operatively coupled to SCM modules for collecting, storing,
distributing and processing SCM data to determine statistical
opportunities and risk in a SCM hierarchy. SCM risk processing may
be utilized to determine risk values that are dependent upon SCM
attributes. Multiple SCM risk processing results may be produced
for further drill-down by a user. SCM network nodes, their relation
and status may further be produced for fast and efficient status
determination.
[0011] More particularly, a supply chain management operating
platform is disclosed for managing a supply chain that includes a
plurality of supply chain nodes. The platform, and its associated
system and method, may include a plurality of data inputs capable
of receiving primary hardware and software data from at least one
third party data source and at least one supply chain node upon
indication by at least one processor. The platform and its
associated system and method may also include a plurality of rules
stored in at least one memory element associated with at least one
processor and capable of performing operations on the primary
hardware and software data to produce secondary data upon direction
from the processor(s). The platform and its associated system and
method may also include a plurality of data outputs capable of at
least one of interfacing with a plurality of application inputs,
and capable of providing the secondary data, comprised of at least
one of supply chain risk data, supply chain management data, and
supply chain analytics, to ones of the plurality of application
inputs for interfacing to a user; and interfacing with the user to
provide the secondary data comprised of at least one of supply
chain risk data, supply chain management data, and supply chain
analytics.
[0012] Further disclosed is a supply chain design and/or redesign
platform for designing a supply chain comprising a plurality of
supply chain nodes. The platform may comprise a plurality of data
inputs capable of receiving primary hardware and software data from
at least one second supply chain accessible over a computer
network, and capable of receiving design data regarding a
prospective at least one of the plurality of supply chain nodes,
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 comparative operations on
the primary hardware data, the software data, and the design data
to produce secondary data upon direction from the at least one
processor; and a plurality of data outputs. The data outputs may be
capable of: providing to a user interface of the secondary data
comprised of at least a substantially optimized one of the design
for the plurality of supply chain nodes based on the comparative
application of at least ones of the plurality of rules; and
providing the secondary data to a user via the user interface.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The present invention is illustrated by way of example and
not limitation in the figures of the accompanying drawings, in
which like references indicate similar elements and in which:
[0014] FIG. 1 illustrates a computer system for transmitting and
processing data, and particularly supply chain management (SCM)
data under an exemplary embodiment;
[0015] FIG. 2 illustrates an exemplary processing device suitable
for use in the embodiment of FIG. 1 for processing and presenting
SCM data;
[0016] FIG. 3A illustrates an exemplary SCM platform
[0017] FIG. 3B illustrates the SCM platform utilizing extended
plug-in applications/modules under another exemplary
embodiment;
[0018] FIG. 4 illustrates exemplary data points and variables
modules operatively coupled to a SCM platform under one
embodiment;
[0019] FIGS. 5A-5F illustrate logical processing outcomes for a
variety of exemplary embodiments;
[0020] FIG. 6 illustrates an exemplary automation process suitable
for utilization in the embodiment of FIG. 1;
[0021] FIG. 7 illustrates an exemplary data visualization example
for actionable-measurable-proactive SCM processing;
[0022] FIG. 8A illustrates a further data visualization and
"one-click" report generation under one embodiment;
[0023] FIG. 8B illustrates a functional action input module
associated with report generation from the data visualization of
FIG. 8A;
[0024] FIG. 9 illustrates an exemplary data table providing for
attribute naming, attribute description and applicable weight
attribution for SCM processing;
[0025] FIG. 10 illustrates an exemplary risk assembly detail for
commodities/parts, wherein part and supplier attributes are
processed to determine an overall risk;
[0026] FIG. 11 illustrates an exemplary risk part detail for
commodities/parts, wherein various attributes are processed
together with attribute weights and selection scores to calculate a
weighted risk score;
[0027] FIG. 12 illustrates an exemplary data visualization heat map
for various assemblies and associated parts, wherein specific
assemblies and/or parts are presented as color-coded objects to
indicate a level of risk;
[0028] FIG. 13 illustrates an exemplary interface for a network
optimizer under one exemplary embodiment;
[0029] FIG. 14 illustrates an exemplary computing system under the
embodiments;
[0030] FIG. 15 illustrates an exemplary interface;
[0031] FIG. 16 illustrates an exemplary interface;
[0032] FIG. 17 illustrates an exemplary interface;
[0033] FIG. 18 illustrates an exemplary interface;
[0034] FIG. 19 illustrates an exemplary interface;
[0035] FIG. 20 illustrates an exemplary interface;
[0036] FIG. 21 illustrates an exemplary interface;
[0037] FIG. 22 illustrates an exemplary interface;
[0038] FIG. 23 illustrates an exemplary interface;
[0039] FIG. 24 illustrates an exemplary interface;
[0040] FIG. 25 illustrates an exemplary interface;
[0041] FIG. 26 illustrates an exemplary interface;
[0042] FIG. 27 illustrates an exemplary interface;
[0043] FIG. 28 illustrates an exemplary interface;
[0044] FIG. 29 illustrates an exemplary interface;
[0045] FIG. 30 illustrates an exemplary interface;
[0046] FIG. 31 illustrates an exemplary interface;
[0047] FIG. 32 illustrates an exemplary interface;
[0048] FIG. 33 illustrates an exemplary interface;
[0049] FIG. 34 illustrates an exemplary interface;
[0050] FIG. 35 illustrates an exemplary interface;; and
[0051] FIG. 36 illustrates an exemplary interface.
DETAILED DESCRIPTION
[0052] 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.
[0053] 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.
[0054] 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.
[0055] 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.
[0056] 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.
[0057] 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.).
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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).
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.).
[0068] 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.
[0069] 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.
[0070] 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. 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.
[0071] 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.
[0072] 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.
[0073] 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. 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.).
[0080] 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.
[0081] 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.
[0082] 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.
[0083] Utilizing the exemplary platform illustrated in FIGS. 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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)
[0094] 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.
[0095] 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.
[0096] 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 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.
[0097] In FIG. 6, an exemplary block diagram of an automatic AMP
process is illustrated, where a supply chain dashboard and
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.
[0098] 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 ($2M). The
remaining bubbles in the exemplary illustration, as well as in
certain other examples disclosed herein, may, of course, represent
other opportunities available.
[0099] Similarly, lead time opportunities identified by the system
are visualized 704, where lead-time opportunity 703 is identified
as the largest opportunity ($1M). Likewise, safety stock
opportunities 706 are identified and opportunity 705 is identified
as the largest opportunity ($5M). 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.
[0100] 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.
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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.
[0105] 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.
[0106] 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.
[0107] 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).
[0108] 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.
[0109] 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.
[0110] 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.
[0111] Turning to FIG. 13, an exemplary screenshot is provided of a
network optimizer module, which provides an active user interface
for an exemplary global manufacturing footprint through a total
landed cost analysis. The illustration shows a plurality of charts
may be provided, such as total landed cost, freight and inventory
financing expense. Product, market, mechanical sourcing and
transportation costs may further be provided, along with a
"scenario" processing screen for displaying options for individual
global markets (EU, IN, CN, US). Data points such as annual volume,
freight in/out, value add, cost of capital and other log expenses
may be conveniently charted for presentation and/or other action.
Economic indicators including currency, revenue and acro-economic
indicators may be provided as well. More particularly, FIG. 13
illustrates that a supply chain may be optimized, such as through
the use of analytics on large-volume data, and such as either at
the design phase or at the operational phase.
[0112] In short and as referenced throughout, various aspects of
the supply chain, such as manufacturers, resellers, parts
providers, and customers of parts and final products, may desire
insight into the entirety of the supply chain, or at least aspects
thereof relevant to that contributor to the supply chain, rather
than the known insight into only discrete aspects of the supply
chain immediately relevant to that contributor to the supply chain.
Consequently, in certain embodiments software as a service, such as
with administrative limitations to access as will be understood by
those skilled in the pertinent arts, may provide insight into the
overall supply chain, or more broad aspects thereof, than is known
in the art. This insight may be provided at both the design phase
for a product or supply chain, such as using analytics applied to
voluminous data of existing relevant supply chains as compared to
the supply chain of interest, and at the operational phase of the
supply chain, such as to provide diagnostics on the supply
chain.
[0113] For example, as product life cycles shrink, the supply chain
may necessarily need to become faster, lower in cost, lower and
risk, and lower in failure rate, particularly in certain verticals,
and this may be reflected in competitive supply chains before it is
executed in a supply chain of interest. In a more specific example,
the ability to drive lead time for part acquisition to or near
zero, such as through the use of 3D printing, if being performed by
competitors, must necessarily be accounted for in the SCM or supply
chain design for a supply chain of interest.
[0114] The foregoing aspects may require significant data-driven
aspects of a software as a service for supply chain management.
These aspects may include, by way of non-limiting example:
visibility into the entire supply chain, including all companies,
all resellers, all parts, and so on; analytics capable of analyzing
big data in order to assess and balance speed, cost, risk,
likelihood of failure, recommended replacements and modifications,
and to enable the waiting and balancing of the foregoing factors;
and an ability to collaborate and dictate action in the supply
chain based on the analytics, such as wherein the collaboration may
be in the form of multi-point communications that occur
automatically or manually, and such as wherein the actions may be
subject to manual triggers or may trigger automatically.
Accordingly, to meet future supply chain analysis needs, the supply
chain and many or all aspects thereof may be analytically
productized, and this may include the people and places that form
part of the supply chain.
[0115] In order to optimize a supply chain at either the design or
the operational (i.e., redesign) phase, needs must be assessed and
modifications recommended as early as possible in the supply chain
management process. That is, necessary future changes in the supply
chain to improve speed and costs, and to lower risk and failure
rates, must be assessed in real time. This improves the operation
and inter-operation of all automated aspects of the supply chain,
i.e., the management of network resources, computing processing
power, the need for manual interaction with automated processes,
including ordering processes, and the like. The draw on the
aforementioned resources, among other resources, is thus minimized
through the use of certain of the embodiments.
[0116] By way of illustration and as shown in the attached figures
and the discussion below, a user may be enabled to create a design
or redesign, such as by accessing a "create a new design" interface
through the use of the disclosed aspects; although it will be
appreciated that much of the discussion herein may be applied to an
existing supply chain in order to allow for optimization. This may
be enabled by a supply chain analytics engine 1703 within platform
307, as is shown in FIG. 14. Analytics engine may be, include, be
included within, or be distinct from the supply chain analytics
module 304 discussed above. The analytics engine 1703 may include
an optimizer 1705 that may perform the functionality described in
the Figures below, including optimization of computing resources
and networking related to SCM such as may be indicated by analysis
by a rules engine 1707 that applies at least a comparator 1709
having therein comparative algorithms, such as those discussed
below, 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 to the supply chain of interest.
The large volume data may be, include, be included within, or be
distinct from analytics data 322, discussed above.
[0117] As illustrated in FIGS. 15-26, the design of a supply chain
to be run through comparator 1709 may include, by way of
non-limiting example, a launch date, a project name, various
aspects of the design, and "preferences". More particularly,
preferences may be prioritized objectives for the design, such as
wherein a user may rank the design objectives. Accordingly, the
wealth of data 1711 available to the disclosed algorithms 1707 may
be subjected to comparative analytics 1709 (which may be
anonymized), and the output from these analytics may be weighted,
prioritized, or otherwise ranked in accordance with the preferences
input by the user.
[0118] Yet further, any data input by the user, such as current
parts for aspects of a products design, may be subjected to the
analytics 1709 in order to make a completeness assessment in light
of the entered preferences/priorities; that is, once data is
entered to data store 1711 by the user and preferences are entered
by the user to the optimizer 1705, automated searching may be
performed, such as using a comparative algorithm 1707 to the supply
chains/part lists of similar product supply chains in the data
store 1711, to assess the completeness of the design and the
completeness of a supply chain that may be provided in accordance
with the design. If additional data or input is necessary, the
disclosed algorithms may, such as in an automated manner and such
as based on pre-entered or user-input algorithms/rules to rules
engine 1707 or based on "learning" that occurs in accordance with
previously input learning algorithms, make a qualitative and
quantitative assessment of the user's input. In accordance with
this assessment, additional information may be requested from the
user, or the user may be instructed to obtain additional
information to the analytics engine 1703 as deemed necessary by the
algorithms of the rules engine 1707.
[0119] In the known art, it is typical that research and
development is performed to design a product, and only thereafter
is the supply chain that will enable the creation of the product
assessed. The disclosed design for supply chain may integrate
supply chain design with product design, as referenced above, such
that product design may be tweaked or modified as necessary in
order to both optimize the final designed product and optimize the
supply chain used to create the final product. This dual
optimization provided by optimizer 1705 may indicate modifications
to both product design and the supply chain, as discussed, based on
the priorities input by the user to the disclosed algorithms. By
way of example, an input product design may be optimized using the
disclosed analytic algorithms, and the optimized product design
best suited for an optimized supply chain may be output to the user
based on the user's input objectives/preferences.
[0120] Further provided to the user by optimizer 1705 may be a
compare and contrast of the user's original input design and its
impact on the supply chain, as compared to the optimized design and
optimized supply chain. Thereby, the user may be enabled to
interact, such as through the graphical user interface shown and
discussed herein and using known computing peripherals, with the
comparison in order to assess whether the automated modifications
made in accordance with the analytics have provided an improvement
or a detriment to the original product design and supply chain in
light of the user's objectives. This comparison may be provided by
any known methods, including a side-by-side simultaneous graphical
illustration, such as may include user interactivity to enable a
user to make modifications categorically within the side-by-side
comparison to assess effects of those modifications on the
side-by-side comparison; one or more of a series of split windows,
hierarchical drill down menus, or the like; or by any other known
methodology.
[0121] As discussed, analytics are algorithmically applied, such as
both based on pre-stored, user input, and/or learning algorithms,
in order to provide the disclosed optimizations. These analytics
may review a variety of variables indicated by data from data store
1711, including but not limited to lead time, alternate parts,
product and part lifecycle, supplier and reseller alignments,
product and part risk assessment, and the like, wherein each of the
foregoing may be weighted or otherwise balanced in accordance with
the user's input objectives, in order to provide a recommended
solution of the supply chain for the user's input product based on
all of the foregoing and other factors, such as manpower,
geographic, and environmental effects, by way of example. Moreover,
this recommended solution may typically stem not only from the
user's input data, but from data gained from numerous similar and
dissimilar products, the parts used to make those products, the
suppliers from which those parts are obtained, the manpower and
geography necessary to make those parts, and the like, all of which
data resides in data store 1711. Thereby, the disclosed analytics
may provide optimized suppliers, parts, source and assembly
locations, and the like, which allows for a significant ramping of
speed to market for an input product design. Moreover, and as
disclosed herein, a subscriber to the SaaS system set forth herein
may be enabled to access very significant and or anonymized content
in order to optimize a design in light of the supply chain based on
the performance of several, dozens, or hundreds of other relevant
supply chains.
[0122] It goes without saying that the analytics 1709 discussed
herein, particularly when engaged in "learning", may change as data
is accumulated by data store 1711 from particular verticals and/or
across similar or dissimilar products. By way of example, to the
extent the comparison discussed herein is generated repeatedly to
include a particular part or parts, and the algorithmic estimation
of performance of the supply chain in light of that modification in
the comparison repeatedly proves to underperform the estimated
comparison, the disclosed learning algorithm may assess that the
particular part or parts common among the failed analytic
estimations are the root cause of the failure. Thereby, the
recommended use of that part or those parts in future designs may
be automatically minimized.
[0123] Needless to say, the aforementioned performance assessment
may not only be globally performed by the analytics engine 1703
disclosed, but may be performed on a case-by-case basis. That is,
once the user selects a solution (such as may be recommended by the
analytics engine in light of the input user preferences) the design
for supply chain may convert to a full SCM platform 307 that tracks
the progress of the selected solution, and which continues to
compares to the initial user input, final executed user solution,
and or other available ongoing solutions to assess the performance
of the selected solution.
[0124] As referenced above and discussed further below with
reference to the figures, a predetermined set of prioritized
objectives may be made available by the analytics engine 1703 to
the user through a graphical user interface. By way of non-limiting
example and as shown, these objectives (which may be prioritized by
the user) may include price, preferred suppliers, risk, time to
market, and flexibility, and/or user-entered "free form"
objectives. These objectives may be placed into a simple sort for
analytics purposes, i.e., they may be ranked according to user
ranking input, they may be weighted based on learnings performed by
the analytics algorithms, and/or they may be structured based on
other information provided by the user, such as input goals or
objectives. It goes without saying that numerous additional
objectives to those shown and discussed throughout may be added
without departing from the disclosed scope of the embodiments, and
further that added optional objectives may be interdependent on
rankings of the objectives provided by the user. That is, by way of
non-limiting example, if the user prioritizes risk as the number
one supply chain design objective, the user may be provided with
numerous other factors that either relate to or are subsets of
risk, or that are generally important to users who prioritize risk
as a main objective according to data store 1711.
[0125] To the extent, such as rather than a simple rank sorting of
objectives, weighting of objectives is applied by the disclosed
analytics engine 1703, such weighting may be obtained via a variety
of methods. The weightings may be, for example, algebraically
obtained, such as wherein a first ranked priority receives a 50%
weight, a second ranked priority a 20% weight, a third ranked
priority a 15% weight, a fourth ranked priority a 10% weight, and
the last and fifth ranked priority a 5% weight. Additionally and
alternatively, weightings may vary dependent upon the objectives
provided by the user based on the users input goals or requests, or
the weightings may vary over time as data is accumulated and made
available to the analytics engine.
[0126] Also referenced throughout is a data completeness check. The
data completeness checks discussed herein may vary from the
simplistic to the complex. For example, data completeness may be
complex and may interrelate to the input user objectives; that is,
dependent on the objective selected and/or ranked by the user,
certain additional or particularly complete data may be necessary.
On the other hand, data completeness checks may be relatively
simple, such as wherein input data for a particular part or parts
may be compared to a known part naming convention, and if a letter
is provided for a given part were a number is expected by the
system, the user input data may be flagged for modification. It
goes without saying that the foregoing data error is provided only
by way of example, and it will be understood in light of such
examples that other data completeness checks and remedies may be
performed by the disclosed analytics engine 1703.
[0127] As such, data quality analytics may be applied by the
analytics engine 1703 to the data supplied by the user. For
example, the aforementioned standard naming conventions may allow
for an automated comparison of user input to standard conventions,
and, to the extent an error is assessed, comparison of that error
to a list of common errors that are generally made in naming a
part, a supplier, a location, or the like, may be performed. To the
extent the subject error falls within the list of common errors, a
correction may then automatically be made, or the user may be asked
if the user wishes to make a suggested modification to the data.
For example, a standard naming convention may indicate that a
particular supplier is referred to as "Supply AXE", but the user
may have entered "Supplier AX". When this error is flagged, the
entered Supplier AX may be compared to a list of common errors,
which list may indicate that most users who enter Supplier AX
actually intend to refer to Supply AXE. Corrective action may then
be taken, either automatically or with user permission.
[0128] Accordingly, "heuristic fuzzy logic searching" may be
enabled by the analytics engine 1703, in which a string search to a
corresponding database within or communicatively associated with
the analytics engine 1703 may be performed. Such heuristic fuzzy
logic string searching may allow for the application of heuristic
fuzzy logic to data entry errors, which may allow for assessment of
unique or common errors, and which may allow for a request of user
input if an error is suspected. Further, such heuristic fuzzy logic
searching may allow for the generation of a confidence threshold by
the analytics engine 1703. As such, if the analytics engine deems,
based on the heuristic fuzzy logic search, that the engine is only
20% certain of what the user likely meant, a correction may not
automatically be made but instead the user may be asked if a
correction to the heuristic fuzzy logic search results is desired.
Or the analytics engine may deem it undesirable to even make the
correction recommendation to the user due to the likelihood that
the suggested correction is not what the user desired based on the
limited 20% certainty threshold in the foregoing example.
[0129] Moreover and as referenced throughout, the analytics engine
1703 may "learn" as corrections are suggested and/or made. For
example, if a confidence threshold initially indicates that it is
20% likely that a particular correction is what a user wants, but
then nine consecutive users say yes that is a correction they wish
to make, the confidence threshold may successively increase, such
as to a point where the correction to the data may be made
automatically by the analytics engine without asking the user.
[0130] In relation to the foregoing supply chain design and
optimization, FIG. 15 illustrates a screen shot user interface
suitable to access the supply chain analytics engine 1703. As
illustrated, the interface allows the user to engage in data
manipulation to create, compare, and view existing designs or new
designs in relation to performance of those designs in the supply
chain.
[0131] Yet more particularly, FIG. 16 illustrates various aspects
of automated design assessment. For example, a design roadmap is
illustrated. While the design roadmap may initially be entered by
the user, the analytics engine 1703 may, either automatically or
manually via user input, modify the roadmap as needed to account
for, by way of non-limiting example, availability of new parts,
generation of new or additional data, ongoing manufacturing
performance, comparative performance of other designs, and the
like.
[0132] Further illustrated in FIG. 16 is an assessment of the data
completeness for the state of the design as entered by the user.
That is, until the data completeness reaches 100%, additional user
input is indicated as needed in order to complete the design
sufficiently so that a full assessment of supply chain performance
of that design may be generated by the engine 1703. Further
illustrated are a plurality of interfaces to various different
designs, such as may be for the same or different products, and
additionally the ability to compare those designs as to the supply
chain performance of each design.
[0133] By way of additional example, FIG. 17 illustrates design
comparisons for supply chain performance of various designs for the
same or a similar product. As illustrated and given particular user
inputs, different design attributes--such as risk score, lead time,
sourcing, supplier alignment, preferred suppliers, and part
lifecycle--may be compared for each of different designs such that
the user may make judgments regarding those different designs as to
which will perform best in light of the prospective supply chain.
Needless to say, stored data 1711 from other supply chain designs
may be used by the design engine to suggest design modifications or
to make design recommendations, such as by combining various
different designs to arrive at an optimal design.
[0134] FIG. 18 illustrates that one or more designers may associate
commentary or documents with a particular design or designs.
Further, designers may exchange messages within the design window
for a given design, as is also illustrated in the example provided
in FIG. 18.
[0135] A supply chain analysis for a single design is illustrated
in the example of FIG. 19. As shown, various design attributes may
be assessed as to their respective impact on the supply chain. This
may be reflected from the supply chain analytics 1707, which may be
uniquely associated with a particular design or vertical and which
may, such as in a summary format, provide analysis of that design's
likely performance with respect to the supply chain. Of course,
recommendations to improve this performance may also be made
available from the assessments by the engine 1703.
[0136] FIG. 20 illustrates the exemplary creation of a new design.
As shown, the design may have a name, description, and one or more
documents, such as drawings, images, part lists, and the like,
associated therewith. FIG. 21 illustrates the association of
milestones with a new design, such as via a drill down within the
creation of a new design. As shown, milestones may be designed to
have target dates, achieved dates, specific information, and
comments associated therewith, by way of non-limiting example.
[0137] A new design may have preferences for the design and
priorities for the design associated therewith, as discussed above
and as illustrated in the exemplary interface of FIG. 22. As shown,
a user may be given a list of prospective objectives, or may input
different objectives important to that user, and may rank those
objectives based on the priority to the user. Further, the user may
be enabled to select other supply chain preferences, such as parts,
preferred suppliers, preferred manufacturing locations or
manufacturers, and the like. By way of example, and as illustrated
in FIG. 23, the user may also be enabled to upload or update the
bill of materials, i.e., the parts, that are associated with a
given product design and compare the bill of materials across
different revisions.
[0138] As referenced throughout, the various aspects of design
creation may have associated therewith required data, and the
design engine may automatically check this data for completeness of
both the data and the supply chain design. This is illustrated in
the example of FIG. 24. As shown, data completeness inadequacies or
errors may be assessed by the design engine, and provided to the
user.
[0139] As illustrated in FIG. 25, the provision of these data
completeness errors to the user may also include the performance,
by the design engine, of a logical and/or heuristic fuzzy logic
matching of the data entered by the user to the existing data 1711
accessible to the design and analytics engine 1703. This heuristic
fuzzy logic may allow for a matching of incorrectly entered part
numbers to correct part numbers, misspelled or unknown objectives
and preferences to available data (such as prior known objectives
and preferences), suggested corrections to milestone dates, and the
like. The logic applied by the matching engine for a new design may
or may not ask the user, such as subject to a certain correctness
threshold, whether the user wishes to make a change. This
application of logical matching may be controlled by one or more
business rules, such as may be associated with the rules engine
1707.
[0140] FIG. 26 illustrates an additional user interface to the
rules engine 1707. As shown, the user may be enabled to activate or
deactivate some or all business rules typically associated with a
new design for optimized supply chain performance. Moreover, new
business rules may be entered by the user to the rules engine 1707,
and may be received and understood by the rules engine, such as by
using semantical and/or logical matching as discussed above.
[0141] FIG. 27 illustrates an exemplary health check screen shot
that may be configured as part of a supply chain analytics engine
1703. As shown in the main content pane of FIG. 27, the system may
be configured to process and visualize various data points as a
dashboard, where, in this example, demand, inventory and risk
attribute scoring are provided in a relative data format (29%, 33%
and 3.3, respectively). Flexibility and opportunity processing and
visualization is provided in the example as bar charts, indicating
time/value determinations over a variety of predetermined time
periods (<6 weeks, <8 weeks, <12 weeks, >12 weeks).
Opportunity processing and visualization generates a bar chart
indicating lead time, safety stock and MOQ valuations determined in
the system. Sourcing options may also be provided to determine
sourcing arrangements for the visualized output.
[0142] The health check allows users to quickly assess the health
of a customer supply chain over a plurality of key performance
indicators (KPI). For demand, the demand KPI may focus on service
level and/or delivery performance to a customer. The raw data may
be processed via the platform and subsequently displayed in the
dashboard to indicate a service level. For inventory, the inventory
KPI may focus on the inventory position and breakdown. The
dashboard indicator may display a proportion of excess and obsolete
inventory versus a total inventory. For risk attribute, the risk
attribute KPI displays a total supply chain risk score for a
customer as discussed herein. A sourcing KPI may display a
breakdown of BOM/parts/supplier ownership versus a customer. A
flexibility KPI may display a proportion of total demand flowing
through lead time thresholds. An opportunity KPI may display a
breakdown of potential opportunities to improve flexibility or
reduce cost in a supply chain.
[0143] FIG. 28 illustrates an exemplary screenshot of a node
network diagram, similar to those disclosed above. FIG. 29
illustrates an exemplary screenshot of benchmark results that
advantageously allow users to access one or more reference points
for supply chain metrics and may compare results against similar
size and complexity customers in the system. Benchmarking may be
performed using only that data accessible, pursuant to
authorization, to that customer; using data across multiple clients
of the manager of multiple customers; and/or using third party
data, such as may be purchased from third parties at networked
locations. Further, in the particular example of FIG. 29, days of
supply, risk attribute score and supply chain model processing
result benchmarks are presented (41.56, 3.39 and 34%, respectively)
against similar benchmarks obtained from other customers ("DEMO
CUST 1"), wherein it can be seen that the benchmarks are all above
similarly-situated customers (24.28, 3.38 and 7%), thus indicating
that potential issues may need to be addressed in the system.
[0144] Turning to FIG. 30, an exemplary screen shot is provided for
supply chain analytics which shows one example of processing and
identifying supply chain opportunities. Here, an opportunity is
defined in reference to a lead time and MOQ, where specific data
points may be processed and presented for each attribute. The
system may calculate and present a specific opportunity value for
lead time supply chain attributes ($424,380), together with MOQ
opportunity value ($951,931). As discussed above in connection with
FIG. 7, an opportunity bubble chart may be simultaneously
presented, containing the same and/or related attributes (lead
time, MOQ), for further analysis. As discussed above, the
individual bubbles of the bubble chart may be selected/rearranged
to present alternate and/or additional opportunity values, which
may be automatically recalculated and presented in the lead time
and MOQ boxes shown in FIG. 30.
[0145] FIG. 31 illustrates an exemplary screen shot for status
reports, which process and display data points and metrics for
analysis. In the example of FIG. 31, status reports may be
generated for any metric including sell thru, inventory, supply
chain model, order status and service level. In the example, sell
thru status is displayed in dollars over predetermined periods of
time (e.g., week), where COGS and master schedule metrics are
displayed as a bar graph. The system may also be configured to
overlay average COGS and MS averages onto the graph to provide a
quick analysis of system performance on these data point.
[0146] Continuing with FIG. 32, an exemplary inventory status
report is illustrated, where inventory and days of supply are
processed and displayed over a predetermined period of time (e.g.,
week). FIG. 33 illustrates an exemplary supply chain model chart
indicating a percentage of units, parts, assemblies, etc. that are
in the supply chain model (inSCM) and out of the supply chain model
(OutSCM) over a predetermined period of time (e.g., week).
[0147] Status reports for suppliers may be generated as shown in
the exemplary screenshot of FIG. 34. The status reports may
include, but are not limited to, geographical impact reports and
geographic risk attribute scores by manufacturer. In the example of
FIG. 34 an exemplary geographical impact report is illustrated,
showing impacts of manufacturers at given locations based on
revenue and spend. The geographical impact report advantageously
allows users to process revenue impact and spend by supplier and/or
geographic location. Such information may be very useful in times
of supply chain disruptions.
[0148] FIG. 35 illustrates an exemplary screenshot of a critical
shortages summary which may be obtained from status reports. In one
embodiment, supplementary information and data relating to critical
shortages may be obtained from 3rd party database sources (e.g.,
110, 111 of FIG. 1) and/or may even be obtained from news feeds or
social media as discussed above.
[0149] Additional reports may be generated as shown in the
exemplary screenshot of FIG. 36. Such information may be presented
to allow for better control of those natural resources whose
systematic exploitation and trade in a context of conflict
contribute to, benefit from or result in the commission of serious
violations of human rights, violations of international
humanitarian law or violations amounting to crimes under
international law. Such information may be very useful when the
origin of supply chain materials is called into question.
[0150] 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.
[0151] 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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