Method and system for managing supply chain risk

Abstract

A method for managing supply chain risk is provided, the method including: providing a plurality of nodes for a supply chain network, the plurality of nodes corresponding respectively to a plurality of supplier entities, the plurality of nodes grouped into a plurality of tiers across the supply chain network; determining, for each of the plurality of nodes, an overall risk score associated with the node, the overall risk score determined based on an internal risk score and one or more common risk scores associated with the node and based on transaction data associated with one or more supplier entities of the plurality of supplier entities corresponding respectively to one or more adjacent nodes, the one or more common risk scores each being determined between the node and one or more adjacent nodes, the one or more adjacent nodes each being located in a tier immediately adjacent to the tier in which the node is located of the plurality of tiers; and forming a supply chain network based on the overall risk scores associated with the plurality of nodes. A corresponding system for managing supply chain risk is also provided.

Description

[Technical Field]

[0001] The present invention relates generally to methods and systems for managing supply chain risk. [Background technology]

[0002] Assessing supply chain risk becomes complex as risks dynamically change in terms of their magnitude and / or location within the supply chain. Considering dynamic risk, for example, evaluating a loan application from a supplier or buyer that is part of a complex supply chain is a tedious process to ensure that approval of the loan application does not result in late payments and defaults. Furthermore, risks within the company itself and / or its impact may increase over time, which may create other risks in the supply chain. For illustrative purposes only, Figures 1A and 1B show schematic diagrams of a supply chain network at a first time instance (t=T) and a second time instance (t=T+Δ), respectively. For example, as illustrated, risk may originate from one entity (e.g., a company) as shown in Figure 1A and be distributed to other entities as shown in Figure 1B.

[0003] Additionally, the presence of multiple entities in a supply chain (e.g., Company Co1, Company Co2, and Company Co3) can make risk analysis and assessment even more complex because data from these companies may be stored and managed independently, for example, in a variety of different data formats for various purposes. Furthermore, manual assessments can make the process tedious and prone to error. For example, manual assessments may involve multiple factors or aspects (e.g., economic, environmental, political, etc.) and therefore require perspectives or insights that are subject to subjectivity, bias, or blind spots.

[0004] Therefore, there is a need to provide a method and system for managing supply chain risk that seeks to overcome, or at least ameliorate, one or more problems associated with conventional methods and systems for managing supply chain risk, and in particular, to increase or improve the efficiency and effectiveness of supply chain management, such as an automated system for managing supply chain risk with increased efficiency and effectiveness. It is against this background that the present invention has been developed. Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a method of managing supply chain risk using at least one processor, the method comprising: providing a plurality of nodes for a supply chain network corresponding respectively to a plurality of supplier entities, the plurality of nodes being grouped into a plurality of tiers across the supply chain network; For each of a plurality of nodes, determining an overall risk score associated with the node, the overall risk score being determined based on the internal risk score and one or more common risk scores associated with the node, and based on transaction data associated with one or more supplier entities among a plurality of supplier entities corresponding to one or more adjacent nodes, respectively, wherein the one or more common risk scores are each determined between the node and one or more adjacent nodes, and the one or more adjacent nodes are each located in a tier immediately adjacent to the tier in which the node is located among the plurality of tiers; forming a supply chain network based on the aggregate risk scores associated with the plurality of nodes.

[0006] According to a second aspect of the present invention, there is provided a system for managing supply chain risk, the system comprising at least one memory and at least one processor communicatively coupled to the at least one memory, the at least one processor being configured to: provide a plurality of nodes for a supply chain network corresponding respectively to a plurality of supplier entities, the plurality of nodes grouped into a plurality of tiers across the supply chain network; determine for each of the plurality of nodes an overall risk score associated with the node, the overall risk score being determined based on an internal risk score and one or more common risk scores associated with the node, and also based on transaction data associated with one or more supplier entities among the plurality of supplier entities corresponding respectively to one or more adjacent nodes, the one or more common risk scores being each determined between the node and one or more adjacent nodes, the one or more adjacent nodes each being located in a tier immediately adjacent to the tier in which the node is located among the plurality of tiers; and form a supply chain network based on the overall risk scores associated with the plurality of nodes.

[0007] According to a third aspect of the present invention, there is provided a computer program product embodied in one or more non-transitory computer-readable storage media and comprising instructions executable by at least one processor to perform a method for managing supply chain risk according to the above-mentioned first aspect of the present invention. [Brief explanation of the drawings]

[0008] Embodiments of the present invention will be better understood and readily apparent to those skilled in the art from a reading of the following description, by way of example only, in conjunction with the drawings in which: [Figure 1A] 1A and 1B are schematic diagrams illustrating a supply chain network at a first time instance (t=T) and a second time instance (t=T+Δ), respectively. [Figure 1B]1A and 1B are schematic diagrams illustrating a supply chain network at a first time instance (t=T) and a second time instance (t=T+Δ), respectively. [Figure 2] FIG. 1 is a schematic flow diagram illustrating a method for managing supply chain risk, according to various embodiments of the present invention. [Figure 3] FIG. 1 is a schematic block diagram illustrating a system for managing supply chain risk, according to various embodiments of the present invention. [Figure 4] FIG. 1 is a schematic block diagram illustrating an exemplary computer system that may be used to realize or implement a system for managing supply chain risk, according to various embodiments of the present invention. [Figure 5A] 1 is a schematic diagram illustrating a conventional method of managing supply chain risk, in which the risk of entities in a supply chain is assessed solely based on internal data associated with the entities. [Figure 5B] FIG. 1 is a schematic diagram illustrating a method of managing supply chain risk according to various exemplary embodiments of the present invention, in which the risk of an entity in a supply chain is assessed not only based on internal data associated with the entity, but also based on data associated with other entities in the supply chain. [Figure 6] 1 is a schematic diagram outlining exemplary interactions between a loan management system and various entities or stakeholders, along with exemplary types of information exchanged therebetween, according to various exemplary embodiments of the present invention. [Figure 7A] 1 is a schematic diagram illustrating a loan management system, including exemplary components thereof, in accordance with various exemplary embodiments of the present invention; [Figure 7B] 2 is a schematic flow diagram illustrating exemplary interactions and data flows between a loan management system and a sponsor, according to various exemplary embodiments of the present invention. [Figure 8A] 3A-3C illustrate exemplary parameters of various transaction data, according to various exemplary embodiments of the present invention. [Figure 8B]3A-3C illustrate exemplary parameters of various transaction data, according to various exemplary embodiments of the present invention. [Figure 8C] 3A-3C illustrate exemplary parameters of various transaction data, according to various exemplary embodiments of the present invention. [Figure 8D] 3A-3C illustrate exemplary parameters of various transaction data, according to various exemplary embodiments of the present invention. [Figure 9] FIG. 1 is a schematic diagram illustrating a representation of a supply chain network including nodes and edges, in accordance with various exemplary embodiments of the present invention. [Figure 10] 10 is a table illustrating exemplary calculations resulting from an analysis of transaction data, according to various exemplary embodiments of the present invention. [Figure 11] FIG. 1 is a schematic flow diagram illustrating a method for creating or generating a supply chain network, according to various exemplary embodiments of the present invention. [Figure 12A] 12 is a schematic diagram illustrating a method of creating or generating the supply chain network shown in FIG. 11, according to various exemplary embodiments of the present invention. [Figure 12B] 12 is a schematic diagram illustrating a method of creating or generating the supply chain network shown in FIG. 11, according to various exemplary embodiments of the present invention. [Figure 13] FIG. 1 is a schematic diagram illustrating a subgraph evaluation process (sometimes referred to as a high-risk node evaluation process), according to various exemplary embodiments of the present invention. [Figure 14] FIG. 10 is a schematic flow diagram illustrating a subgraph evaluation process, in accordance with various exemplary embodiments of the present invention; [Figure 15] FIG. 1 is a schematic flow diagram illustrating a method for managing supply chain risk, according to various exemplary embodiments of the present invention. [Figure 16] FIG. 1 is a schematic diagram illustrating risk assessment using multiple nodes, according to various exemplary embodiments of the present invention. [Figure 17] FIG. 1 is a schematic flow diagram illustrating a method for enterprise risk assessment, according to various exemplary embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0009] Various embodiments of the present invention provide methods and systems for managing supply chain risk. For example, as described in the Background section, assessing or managing supply chain risk becomes complex as risks dynamically change with respect to their magnitude and / or location within the supply chain. Furthermore, risks to the enterprise itself and / or its impact may increase over time, which may lead to other risks in the supply chain, such as those illustrated in FIGS. 1A and 1B. In addition, manual assessment or management of supply chain risk is inefficient and ineffective (e.g., time-consuming, tedious, and error-prone). Therefore, there is a need to provide methods and systems for managing supply chain risk that attempt to overcome, or at least ameliorate, one or more problems associated with conventional methods and systems for managing supply chain risk, and in particular, to enhance or improve the efficiency and effectiveness of supply chain management, such as an automated system for managing supply chain risk with increased efficiency and effectiveness.

[0010] 2 illustrates a flow diagram of a method 200 for managing supply chain risk using at least one processor according to various embodiments of the present invention. The method 200 includes providing (at 202) a plurality of nodes for a supply chain network corresponding respectively to a plurality of supplier entities, the plurality of nodes grouped into a plurality of tiers across the supply chain network; determining (at 204) for each of the plurality of nodes an overall risk score associated with the node, the overall risk score being determined based on an internal risk score and one or more common risk scores associated with the node and based on transaction data associated with one or more supplier entities of the plurality of supplier entities corresponding respectively to one or more adjacent nodes, the one or more common risk scores being respectively determined between the node and one or more adjacent nodes, the one or more adjacent nodes each being located in a tier immediately adjacent to the tier in which the node is located of the plurality of tiers; and forming (at 206) the supply chain network based on the overall risk scores associated with the plurality of nodes.

[0011] In various embodiments, a supplier entity in a supply chain refers to any entity that functions or operates as a supplier of products and / or services (including portions or components thereof) within a supply chain, such as, but not limited to, a producer / manufacturer, service provider, vendor, warehouse, shipping company, distribution center, retailer, etc. Accordingly, those skilled in the art will recognize that a supplier entity may be, for example, an individual, a company, or an organization. Additionally, those skilled in the art will recognize that a supplier entity need not necessarily function or operate solely as a supplier within a supply chain, so long as the supplier entity at least partially functions or operates as a supplier within the supply chain. Accordingly, multiple nodes may be provided within a supply chain network (sometimes referred to herein as a supplier network), each corresponding to multiple supplier entities.

[0012] In various embodiments, supply chain risk refers to any risk along a supply chain that may adversely affect the production and / or delivery of products and / or services to consumers or buyers (e.g., final products and / or services to end consumers), such as the consumer not receiving the products and / or services as originally agreed or expected, or not receiving them in a timely manner (i.e., delays). Those skilled in the art will recognize that there are a wide range of possible risks in a supply chain, including, but not limited to, supplier delays, supplier bankruptcy, supplier fraud, supplier accidents, defective products or services, etc. Thus, those skilled in the art will recognize that the present invention is not limited to any particular type of supply chain risk.

[0013] In various embodiments, the internal risk associated with a node refers to a risk associated with the supplier entity corresponding to the node originating from the supplier entity, such as, but not limited to, the supplier entity's financial risk (e.g., based on its credit rating), the supplier entity's operational risk (e.g., based on its operational (e.g., production) reliability rating), the supplier entity's sustainability risk (e.g., based on its ESG (environmental, social, and governance) rating), or a combination thereof. Those skilled in the art will recognize that there are various types of possible risks originating from a supplier entity, and the present invention is not limited to any particular type of risk originating from a supplier entity.

[0014] Thus, method 200 for managing supply chain risk has been found to advantageously enhance or improve efficiency and effectiveness in managing supply chain risk. In particular, by determining an overall risk score for a node that takes into account not only the internal risks associated with the node but also the common risks between the node and its neighboring nodes, the impact of risk dispersion or propagation from one supplier entity to another supplier entity or entities within the supply chain can be advantageously captured, resulting in a more accurate and actionable risk assessment or evaluation in the supply chain. Additionally, such overall risk score associated with the node is determined automatically based on transaction data. Thus, method 200 can enhance or improve efficiency and effectiveness in supply chain management, including managing supply chain risk in an automated and dynamic manner with increased efficiency and effectiveness. These and / or other advantages or technical effects will become more apparent to those skilled in the art as method 200 for managing supply chain risk and a corresponding system for managing supply chain risk are described in more detail according to various embodiments and exemplary embodiments of the present invention.

[0015] In various embodiments, for each of one or more adjacent nodes, the common risk score associated with the node between the node and the adjacent node is determined based on a forward risk propagation model if the adjacent node is upstream relative to the node, and based on a reverse risk propagation model if the adjacent node is downstream relative to the node, where the forward risk propagation model and the reverse risk propagation model each represent the risk of the adjacent node relative to the node.

[0016] In various embodiments, the forward risk propagation model and the backward risk propagation model are each based on a risk transferability parameter that provides an indication of the likelihood of risk transfer from adjacent nodes to the node.

[0017] In various embodiments, the forward risk propagation model and the backward risk propagation model are each further based on a trust parameter of the node with respect to neighboring nodes, which provides an indication of the node's trustworthiness with respect to the neighboring nodes.

[0018] In various embodiments, the risk transferability parameter and the trust parameter are determined based on transaction data associated with supplier entities corresponding to adjacent nodes and / or transaction data associated with supplier entities corresponding to the node.

[0019] In various embodiments, providing (at 202) a plurality of nodes for the supply chain network corresponding to each of the plurality of supplier entities described above is based on transaction data associated with one or more of the plurality of supplier entities and / or based on predetermined supplier entity identity data including entity identity information for one or more of the plurality of supplier entities. For example, all of the plurality of nodes may be determined based on transaction data associated with the plurality of supplier entities, all of the plurality of nodes may be determined based on predetermined supplier entity identity data including entity identity information for the plurality of supplier entities, or a subset of the plurality of nodes may be determined based on transaction data associated with a corresponding subset of the plurality of supplier entities, and a remaining subset of the plurality of nodes may be determined based on predetermined supplier entity identity data including entity identity information for a corresponding remaining subset of the plurality of supplier entities.

[0020] In various embodiments, transaction data associated with a supplier entity of the plurality of supplier entities relates to a transaction involving the supplier entity and includes entity identification information, product and / or service information, and a status of the transaction and / or a rating associated with the transaction.

[0021] In various embodiments, forming the supply chain network (at 206) as described above includes determining whether to link a node in a tier of the plurality of tiers to an adjacent node in an immediately adjacent tier of the plurality of tiers based on an overall risk score associated with the node in the tier and an overall risk score associated with an adjacent node in an immediately adjacent tier of the plurality of tiers.

[0022] In various embodiments, a node in a tier is linked with an adjacent node in an immediately adjacent tier if the overall risk score associated with the node in the tier and the overall risk score associated with the adjacent node in the immediately adjacent tier of tiers meet a predetermined risk score condition. In various embodiments, the predetermined risk score condition may be based on a predetermined risk score threshold. For example, if a low overall risk score indicates low overall risk, the predetermined risk score condition for a node may be met if the overall risk score associated with the node is less than (or equal to) a predetermined risk score threshold.

[0023] In various embodiments, the above-described determination of whether to link a node in a stratum with an adjacent node in a directly adjacent stratum is further based on the stratum pool size of the stratum if the overall risk score associated with the node in the stratum does not satisfy a predetermined risk score condition. For example, if the overall risk score associated with the node is higher than a predetermined risk score threshold, the predetermined risk score condition for the node may not be satisfied.

[0024] In various embodiments, the above-described determination of whether to link a node in a stratum with an adjacent node in a directly adjacent stratum is further based on the relative stratum pool size between the stratum and the directly adjacent stratum if the overall risk score associated with the node in the stratum does not satisfy a predetermined risk score condition. For example, the relative stratum pool size between the stratum and the directly adjacent stratum may be the ratio between the stratum pool size of the stratum and the stratum pool size of the directly adjacent stratum.

[0025] In various embodiments, the method 200 further includes selecting a supply chain path within the supply chain network based on the overall risk score associated with the plurality of nodes.

[0026] In various embodiments, selecting the supply chain route includes, for each of a plurality of candidate supply chain routes in the supply chain network, determining a route risk score associated with the candidate supply chain route based on the overall risk scores associated with nodes along the candidate supply chain route, and selecting the candidate supply chain route from among the plurality of candidate supply chain routes as the selected supply chain route associated with the supply chain route that satisfies a predetermined route risk score condition. For example, the predetermined route risk score condition may be that the supply chain route among the plurality of candidate supply chain routes has the lowest route risk score or is within a predetermined route risk score range.

[0027] 3 illustrates a schematic block diagram of a system 300 for managing supply chain risk according to various embodiments of the present invention, such as corresponding to the method 200 for managing supply chain risk as described above with reference to FIG. 2. The system 300 comprises at least one memory 302 and at least one processor 304 communicatively coupled to the at least one memory 302, the at least one processor 304 being configured to: provide a plurality of nodes for a supply chain network, each corresponding to a plurality of supplier entities, the plurality of nodes being grouped into a plurality of tiers across the supply chain network; determine, for each of the plurality of nodes, an overall risk score associated with the node, the overall risk score being determined based on an internal risk score and one or more common risk scores associated with the node and based on transaction data associated with one or more supplier entities among the plurality of supplier entities corresponding to one or more adjacent nodes, respectively; determine the overall risk score between the node and one or more adjacent nodes, each of the one or more adjacent nodes being located in a tier immediately adjacent to the tier in which the node is located among the plurality of tiers; and form a supply chain network based on the overall risk scores associated with the plurality of nodes.

[0028] Those skilled in the art will recognize that the at least one processor 304 may be configured to perform various functions or operations through a set of instructions (e.g., software modules) executable by the at least one processor 304 to perform the various functions or operations. Thus, as shown in Figure 3, the system 300 may include a node module (or node circuitry) 306 configured to perform the provision of a plurality of nodes to the supply chain network, each corresponding to a plurality of supplier entities, as described above, a risk score determination module (or risk score determination circuitry) 308 configured to perform the determination of an overall risk score associated with the node for each of the plurality of nodes, as described above, and a supply chain network formation module (or supply chain network formation circuitry) 310 configured to perform the formation of the supply chain network based on the overall risk scores associated with the plurality of nodes, as described above.

[0029] Those skilled in the art will recognize that the above modules need not necessarily be separate modules, and that two or more modules may be implemented by or as a single functional module (e.g., a circuit or software program), as desired or appropriate, without departing from the scope of the present invention. For example, two or more of the node module 306, the risk score determination module 308, and the supply chain network formation module 310 may be implemented (e.g., compiled together) as a single executable software program (e.g., referred to as a software application, or simply an "app") that may be stored in at least one memory 302 and executable by at least one processor 304 to perform various functions / operations as described herein in accordance with various embodiments of the present invention.

[0030] In various embodiments, the system 300 for managing supply chain risk corresponds to the method 200 for managing supply chain risk as described above with reference to FIG. 2 according to various embodiments, and thus, various functions or operations configured to be performed by the at least one processor 304 may correspond to various steps or operations of the method 200 for managing supply chain risk as described above according to various embodiments, and thus, for the sake of clarity and brevity, need not be repeated with respect to the system 300 for managing supply chain risk. In other words, various embodiments described herein in the context of a method are equally valid for the corresponding system, and vice versa.

[0031] For example, in various embodiments, the at least one memory 302 may store a node module 306, a risk score determination module 308, and / or a supply chain network formation module 310, each of which corresponds to various steps (or operations or functions) of the method 200 for managing supply chain risk as described herein according to various embodiments, executable by the at least one processor 304 to perform the corresponding function or operation as described herein.

[0032] A computing system, controller, microcontroller, or any other system providing processing capabilities may be provided in accordance with various embodiments of the present disclosure. Such a system may be considered to include one or more processors and one or more computer-readable storage media. For example, the supply chain risk management system 300 described above may include at least one processor (or controller) 304 and at least one computer-readable storage medium (or memory) 302 used in various processes performed therein, such as those described herein. The memory or computer-readable storage medium used in various embodiments may be volatile memory, such as DRAM (dynamic random access memory), or non-volatile memory, such as PROM (programmable read-only memory), EPROM (erasable PROM), EEPROM (electrically erasable PROM), or flash memory, such as floating gate memory, charge trap memory, MRAM (magnetoresistive random access memory), or PCRAM (phase change random access memory).

[0033] In various embodiments, a “circuit” may be understood as any type of logic-implementing entity, which may be application-specific circuitry or a processor that executes software stored in memory, firmware, or any combination thereof. Thus, in one embodiment, a “circuit” may be a hard-wired logic circuit or a programmable logic circuit, such as a programmable processor, for example, a microprocessor (e.g., a complex instruction set computer (CISC) processor or a reduced instruction set computer (RISC) processor). A “circuit” may also be a processor that executes software, for example, any type of computer program, for example, a computer program that uses virtual machine code (e.g., Java). Any other type of implementation of the respective functions may also be understood as a “circuit” according to various embodiments. Similarly, a “module” may be part of a system according to various embodiments, may encompass a “circuit” as described above, or may be understood as any type of logic-implementing entity.

[0034] Some portions of this disclosure are presented explicitly or implicitly in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic, or optical signals, capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0035] Unless specifically stated otherwise, and as will become apparent below, it will be recognized that throughout this specification, descriptions or discussions utilizing terms such as "providing," "determining," "forming," "selecting," and the like refer to the actions and processes of a computer system or similar electronic device that manipulates and converts data represented as physical quantities within a computer system into other data similarly represented as physical quantities within a computer system or other information storage, transmission, or display device.

[0036] This specification also discloses systems (e.g., which may also be embodied as devices or apparatuses), such as supply chain risk management system 300, for performing the operations / functions of the various methods described herein. Such systems may be specially constructed for the required purposes, or may comprise a general-purpose computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose machines may be used with computer programs in accordance with the teachings herein. Alternatively, it may be appropriate to construct more specialized apparatuses to perform the various method steps.

[0037] Additionally, this specification also discloses, at least implicitly, computer programs or software / functional modules, in that it will be apparent to one skilled in the art that individual steps of the various methods described herein may be implemented by computer code. The computer program is not intended to be limited to any particular programming language and implementation. It will be recognized that various programming languages ​​and coding thereof may be used to implement the teachings of the disclosure contained herein. Furthermore, the computer program is not intended to be limited to any particular control flow. There are many other variations of computer programs that may use different control flows without departing from the scope of the present invention. Those skilled in the art will recognize that the various modules described herein (e.g., node module 306, risk score determination module 308, and / or supply chain network formation module 310) may be software modules implemented by a computer program or instruction set executable by a computer processor to perform the required functions, or may be hardware modules, which are functional hardware units designed to perform the required functions. It will also be recognized that a combination of hardware and software modules may be implemented.

[0038] Furthermore, one or more of the computer programs / modules or method steps described herein may be performed in parallel rather than sequentially. Such computer programs may be stored on any computer-readable medium. The computer-readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a general-purpose computer. The computer program, when loaded into and executed on such a general-purpose computer, effectively provides an apparatus for performing the method steps described herein.

[0039] In various embodiments, a computer program product is provided that is embodied in one or more computer-readable storage media (non-transitory computer-readable storage media) that includes instructions executable by one or more computer processors (e.g., node module 306, risk score determination module 308, and / or supply chain network formation module 310) to implement the method 200 for managing supply chain risk as described above with reference to Figure 2 according to various embodiments. Accordingly, the various computer programs or modules described herein may be stored in a computer program product that is receivable by a system, such as the system 300 for managing supply chain risk as shown in Figure 3, for execution by at least one processor 304 of the system 300 to perform various functions.

[0040] The software or functional modules described herein may also be implemented as hardware modules. More specifically, in the hardware sense, a module is a functional hardware unit designed to be used with other components or modules. For example, a module may be implemented using discrete electronic components or may form part of an entire electronic circuit, such as an application-specific integrated circuit (ASIC). Many other possibilities exist. Those skilled in the art will recognize that the software or functional modules described herein may also be implemented as a combination of hardware and software modules.

[0041] In various embodiments, the system 300 for managing supply chain risk may be realized by any computer system (e.g., a desktop or portable computer system) including at least one processor and at least one memory, such as, by way of example only and not limitation, the exemplary computer system 400 as shown schematically in FIG. 4 . The various methods / steps or functional modules may be implemented as software, such as a computer program, that executes within the computer system 400 and instructs the computer system 400 (and in particular the one or more processors therein) to perform various functions or operations as described herein according to various embodiments. The computer system 400 may include a system unit 402, input devices such as a keyboard and / or touchscreen 404 and a mouse 406, and multiple output devices such as a display device 408. The system unit 402 may be connected to a computer network 412 via a suitable transceiver device 414 to provide access to, for example, the Internet or other network systems such as a local area network (LAN) or a wide area network (WAN). The system unit 402 may include a processor 418 that executes various instructions, a random access memory (RAM) 420, and a read-only memory (ROM) 422. The system unit 402 may further include a number of input / output (I / O) interfaces, for example, an I / O interface 424 to a display device 408 and an I / O interface 426 to a keyboard and / or touch screen 404. The components of the system unit 402 typically communicate via an interconnected bus 428 in a manner known to those skilled in the art.

[0042] Those skilled in the art will recognize that the terminology used herein is for the purpose of describing various embodiments only and is not intended to limit the invention. As used herein, the singular forms "a," "an," and "the," shall include the plural forms unless the context clearly dictates otherwise. Furthermore, it will be understood that the terms "comprises" and / or "comprising," as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0043] Any reference herein to an element or feature using a designation such as "first," "second," etc. does not limit the quantity or order of such elements or features unless stated otherwise or unless otherwise required by context. For example, such designations may be used herein as a convenient way of distinguishing between two or more elements or multiple instances of an element. Thus, reference to a first and a second element does not necessarily imply that only two elements can be used or that the first element must precede the second element. Additionally, a phrase referring to "at least one of" a list of items refers to any single item therein or any combination of two or more items therein.

[0044] In order to make the present invention readily understandable and practically implementable, various illustrative embodiments of the present invention are described below by way of example only and not by way of limitation. However, those skilled in the art will recognize that the present invention may be embodied in a variety of different forms or configurations and should not be construed as being limited to the illustrative embodiments described below. Rather, these illustrative embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0045] In particular, to facilitate a better understanding of the present invention, and without limitation or loss of generality, various exemplary embodiments of the present invention may be described, for illustrative purposes only, with respect to an exemplary application of a system for managing supply chain risk in loan applications, which may also be referred to as a loan management system. However, those skilled in the art will recognize that the present invention is not limited to such a particular application, and that methods and systems for managing supply chain risk may be implemented in other types of applications as desired or appropriate, so long as supply chain risk management is required or desired.

[0046] For example, as described in the Background section, assessing or managing supply chain risk becomes complex as risks dynamically change in terms of their magnitude and / or location within the supply chain. Furthermore, risks to a company itself and / or its impact may increase over time, which may lead to other risks in the supply chain, such as those illustrated in Figures 1A and 1B. Additionally, manual assessment or management of supply chain risk is inefficient and ineffective (e.g., time-consuming, tedious, and error-prone). Additionally, the presence of multiple entities (e.g., Company Co1, Company Co2, and Company Co3) in the supply chain can further complicate risk analysis and assessment, as data from these companies may be stored in various different data formats and managed independently for various purposes. Furthermore, manual assessments can be tedious and error-prone processes. For example, manual assessments may involve multiple factors or aspects (e.g., economic, environmental, political, etc.) and, therefore, require perspectives or insights that are subjective, biased, or prone to blind spots.

[0047] In contrast, various exemplary embodiments provide a system for managing supply chain risk that is configured to assess the overall risk of an entity (e.g., a company) in a supply chain based on internal data associated with the entity and transaction data associated with other entities (adjacent entities) in the supply chain, thereby advantageously capturing the impact of risk dispersion or propagation from one entity to one or more other entities in the supply chain, resulting in a more accurate and actionable risk assessment or evaluation. In various exemplary embodiments, the internal data associated with the entity is utilized to determine risks associated with entities originating from the entity, such as, but not limited to, the supplier entity's financial risk (e.g., based on its credit rating as internal data), the supplier entity's operational risk (e.g., based on its operational (e.g., production) reliability rating as internal data), the supplier entity's sustainability risk (e.g., based on its ESG (environmental, social, and governance) rating as internal data), or combinations thereof.

[0048] As one illustration, Figure 5A shows a schematic diagram of a conventional method of managing supply chain risk in which the risk of an entity in a supply chain is assessed based solely on internal data associated with the entity, and Figure 5B shows a schematic diagram of a method of managing supply chain risk according to various exemplary embodiments of the present invention in which the risk of an entity in a supply chain is assessed not only based on internal data associated with the entity (e.g., corresponding to internal risks associated with the entity) but also based on transaction data associated with other entities (adjacent entities) in the supply chain (e.g., corresponding to common risks between the entity and other entities). Thus, according to various exemplary embodiments, a total risk associated with an entity in a supply chain is determined, including the internal risks and common risks associated with the entity.

[0049] Thus, various exemplary embodiments generate a supply chain network (or supplier network) based on an estimation of common risk (or dynamic risk) through analysis of transaction data of entities within the supply chain network. In various exemplary embodiments, the transaction data may relate to any transaction conducted between entities in the course of operations or business, such as, but not limited to, financial transactions, transportation transactions, order transactions, and manufacturing transactions. Those skilled in the art will recognize that a wide range of transactions may be conducted between entities in the course of operations or business, and the present invention is not limited to any particular type or category of transaction. In various exemplary embodiments, these point-to-point transactions are aggregated together so that risk assessment is not limited to only one entity or two trading entities (e.g., buyer and seller), but involves all related entities providing services and / or products (including portions thereof) within the supply chain. Furthermore, because transaction data is utilized, specific entities can be assessed instead of simply assessing the general category or industry to which the entity generally belongs.

[0050] Various exemplary embodiments further provide for modeling a common risk (or dynamic risk) associated with a first entity between the first entity and the second entity based on an internal risk of the second entity (e.g., the second company), a risk transferability factor or parameter providing an indication of risk transferability (or transferability) from the second entity to the first entity (e.g., the first company), and a reliability factor or parameter providing the reliability of the first entity vis-à-vis the second entity. Thus, in various exemplary embodiments, the risk model is based on the common risk.

[0051] In various exemplary embodiments, the common risk may be represented as or determined based on a forward and backward risk diffusion (or propagation) model that evaluates, for example, forward and backward (or downstream and upstream) risk diffusion, regardless of the single or directional flow of products or services within the supply chain. Thus, various exemplary embodiments advantageously model risk distribution within a supply chain network as a diffusion or propagation flow. In various exemplary embodiments, the forward risk propagation model and the backward risk propagation model are each based on a risk transferability parameter and a trust parameter. Thus, a common risk score associated with a first entity between a first entity and a second entity may be determined based on the second entity's internal risk score, a risk transferability factor from the second entity to the first entity, and the first entity's trust factor relative to the second entity. A total or composite risk score for the first entity may then be determined based on the first entity's internal risk score and one or more common risk scores between the first entity and one or more second entities. Illustratively, this risk model, according to various exemplary embodiments of the present invention, can be used to adjudicate loan approvals based on the quality of the transaction data and the reliability and confidence in the supplier network data.

[0052] Various exemplary embodiments further generate suggested or recommended supply chain paths, such as best or optimal supply chain paths (or routes) that minimize risk in the supply chain network. For example, the generated supply chain network allows for the selection of paths that extend not only between sellers and buyers but also among their connections. In various exemplary embodiments, the supply chain path with the lowest path risk score may be selected because different paths may have different risks.

[0053] Accordingly, various illustrative embodiments assess risk by determining common risks in a supply chain network by using transaction data. For example, various illustrative embodiments may perform a pre-assessment prior to creating a supply chain network and performing a multi-path analysis to assess risk. Accordingly, various illustrative embodiments may provide one or more of the following features: Risk assessment by applying supply chain networks / models Use of transactional data (e.g., manufacturing, logistics, or financial data) to generate the structure of a supply chain network Using transaction data to model dynamic (or common) risks in supply chain networks Assessment of risks associated with nodes prior to creating / linking the nodes to the supply chain network Use of blockchain technology to improve data integrity and security Financing options that incorporate sustainability metrics

[0054] An exemplary application of a system for managing supply chain risk in loan applications (loan management system) will now be described, for illustrative purposes only, in accordance with various exemplary embodiments. FIG. 6 provides a schematic overview of exemplary interactions between a loan management system 610 and various entities or sponsors 620, 630, 640, along with exemplary types of information exchanged therebetween, in accordance with various exemplary embodiments of the present invention. In various exemplary embodiments, a buyer 620 is a consumer of products and / or services, e.g., as an end consumer (i.e., an end-user) or as a supplier 620 that is part of a supply chain (e.g., both supplying and consuming). A supplier 620 is a provider of products and / or services (or portions thereof) in the supply chain. A funder 640 is a provider of financial products and / or services, such as a bank or non-banking company that offers and grants loans. Data providers 630 are providers of transaction data 631, such as logistics service providers that can provide transportation transaction data, e-commerce companies that can provide sales (or order) transaction data, and sales transaction data aggregators that can provide sales (or order) transaction data.

[0055] In various exemplary embodiments, the loan management system 610 is configured to generate a supply chain network using transaction data provided by data providers 630, assess risk by modeling dynamic risk (or common risk) of entities (e.g., companies), such as buyer companies and / or supplier companies 620, including companies that are part of the supply chain, match funding from funders 640, and provide loan offers to buyers and / or suppliers 620 based on funding options provided by funders 640.

[0056] 7A is a schematic diagram illustrating a loan management system 610, including exemplary components thereof, according to various exemplary embodiments of the present invention. The loan pre-processing module 710 may be configured to detect whether the loan applicant is within a supply chain network (or supplier network) and whether information regarding internal and common risks or transaction data is available to determine or update the overall risk associated with the loan applicant. For example, if a pre-determination of the overall risk has not been made or if the determined overall risk has expired (e.g., more than one year), the loan pre-processing module 710 may obtain the requested transaction data through the data request and processing module 720. The data request and processing module 720 may be configured to send a data request 612 to a data provider 630 and receive corresponding transaction data 631 from the data provider 630. The network creation module 730 may be configured to process or convert the transaction data 631 into supply chain (SC) data elements (e.g., corresponding to nodes in a supply chain network) and determine whether to add (or link) the SC data elements within the supply chain network. The risk assessment module 740 may be configured to analyze the buyer and supplier data, including key transaction data and other information about the entity (e.g., business), such as entity identification information, year of establishment, type of entity (e.g., nature of business), type of products / services offered, etc. The risk assessment module 740 may be configured to analyze the buyer and supplier data to determine an overall risk score associated with the loan applicant, and use the overall risk score to assess the risk of granting the loan to the loan applicant.The loan evaluation module 760 may be configured to process the funding information 641 from the funder 640, the overall risk score from the risk assessment module 740, and the loan request 621 to propose or recommend one or more funding options as one or more loan offers 611 among a plurality of funding options 642 in the funding option database 751 obtained from the funder 640 via the funding information search module 750. In particular, the funding information search module 750 searches for and receives available funding options 642 from the funder 640.

[0057] For better understanding, FIG. 7B shows a schematic flow diagram illustrating the interactions and data flow between the loan management system 610 and the sponsors 620, 630, and 640 according to various exemplary embodiments of the present invention. For example, a loan request or application 621 may include a loan amount and a projected loan release date. A funding query 752 may include a query for available funding (e.g., current funding and future funding) from the funding provider 640. The funding information 641 may include the loan available amount, the loan availability date, an interest rate range value, and the loan term. A data request 612 may include information (e.g., parameters) for the loan management system 610 to request desired transaction data from the data provider 630, such as for assessing risk associated with entities in the supply chain network 731. In various exemplary embodiments, the transaction data 631 includes raw transaction data for analysis to form the supply chain network 731. A matching loan 613 may be a loan specified in the funding information 641 that matches the loan application 621. For example, a match may occur when the requested loan amount and other terms specified in the loan application 621 are within the range of amounts and other terms provided in the funding information 641. The funding options 642 may include loan payment terms. A loan (i.e., loan offer) 611 that includes funding options may be a matching loan with a specified interest rate and payment terms.

[0058] In various exemplary embodiments, transactional data 631 provides point-to-point links between entities (e.g., companies) and is created from actual operations. This link may serve as a building block of a supply chain network. Thus, for example, when transactional data 631 is captured as it is generated, risk assessments can be performed in real time and with up-to-date information, as opposed to using annual or quarterly financial reports to perform the assessment.

[0059] In various exemplary embodiments, transaction data 631 may relate to any transaction conducted between entities in the course of operations or business and may be obtained from various data sources, such as, but not limited to, company ledgers showing logistics transportation transactions, e-commerce order transactions, financial transactions, and manufacturing transactions (e.g., product quality audits). In various exemplary embodiments, transaction data 631 associated with an entity relates to transactions involving the entity and includes entity identification information, product and / or service information (e.g., the products and / or services involved in the transaction), and the status of the transaction and / or a rating associated with the transaction (e.g., whether the product and / or service was successfully delivered or the quality rating of the product and / or service provided). For example, transaction data 631 may include transacting company identification information, product and / or service information, transaction date, value or cost of the product and / or service, quality rating, etc. In various exemplary embodiments, transaction data 631 may be obtained manually or automatically from one or more data providers 630, such as via a corresponding application programming interface (API).

[0060] For illustrative purposes, FIG. 8A shows exemplary parameters for financial transaction data 810 according to various exemplary embodiments, including a date parameter 811, an account ID parameter 812, a transaction ID parameter 813, a transaction company ID parameter 814, an account type parameter 815, a credit value parameter 816, a payment type parameter 817 (e.g., installment or cash), a transaction details parameter 818 (e.g., product information such as product name and number of units), and a status parameter 819 (e.g., completed or delayed).

[0061] 8B shows example parameters for shipping transaction data 820 according to various example embodiments, including date parameter 821, shipping company ID parameter 822, receiving company parameter 823, transaction details parameter (e.g., shipping cost details, e.g., product information such as product name, number of units, and their price) 824, and status parameter 825 (e.g., delivery status), etc. For example, shipping transaction data 820 may correspond to a bill of lading providing details of shipping costs from a sending company to a receiving company.

[0062] 8C shows example parameters for sales order transaction data 830 according to various exemplary embodiments, including date parameter 831, vendor ID parameter 832, order ID parameter 833, buyer ID parameter 834, transaction details parameter 835 (e.g., order details, e.g., product information such as product name and number of units), order quantity parameter 836, and status parameter 837. For example, sales order transaction data 830 may correspond to a sales order transaction of a supplier company with a customer company.

[0063] 8D shows example parameters for manufacturing transaction data 840 according to various example embodiments, including date parameter 841, ID parameter 842 of the inspection company that inspects the supplied product, ID parameter 843 of the customer that consumes the supplier's product, supplier ID parameter 844, transaction details parameter 845 (e.g., inspected product information such as product name and number of units), and rating parameter 846 (e.g., product quality rating). For example, manufacturing transaction data 840 may correspond to a manufacturing transaction that provides information of products inspected by an inspection company, such as production data and quantity.

[0064] FIG. 9 illustrates a schematic representation of a supply chain network 900 comprising nodes 910, 920 and edges 930, 940, in accordance with various exemplary embodiments of the present invention. Entities (e.g., companies) are represented as nodes, and connections indicating their relationships (e.g., transactions between them) are represented as edges. For example, company information (e.g., company profiles, credit risk scores, aggregate information) may be stored in or associated with nodes 910, 920. Edges 930, 940 represent connections or links between two companies. Arrow directions indicate the flow of products and / or services (e.g., between nodes 910, 920, an arrow directional from node 920 to node 910 may indicate the flow of products and / or services supplied by node 920 to node 910, and an arrow directional from node 910 to node 920 may indicate the flow of products and / or services returned by node 910 to node 920). In various exemplary embodiments, transaction data is processed and analyzed during implementation or formation of supply chain network 900. The results of the analysis may then be provided as input parameters to edge functions corresponding to edges 930, 940, as described below.

[0065] In various exemplary embodiments, the edge functions represent risk dynamics, such as changes in risk intensity and / or risk propagation. Because the risk model according to various exemplary embodiments considers bidirectional (i.e., forward and reverse) risk propagation, risk origins may also be identified. Accordingly, various exemplary embodiments provide a risk model for a common risk modeled by two edge functions: a forward risk propagation function and a reverse risk propagation function. In various exemplary embodiments, the forward risk propagation function may be defined as E(start=C02 920, end=C01 910), and the reverse risk propagation function may be defined as E(start=C01 910, end=C02 920). In various exemplary embodiments, the forward risk propagation function may be any function configured to represent or model risk propagation from a second entity to a first entity, whereby the second entity is upstream with respect to the first entity. Similarly, a backward risk propagation function may be any function configured to represent or model risk propagation from a second entity to a first entity, whereby the second entity is downstream with respect to the first entity. In FIG. 9 , for example, node 910 corresponds to company Co1 (buyer) and node 920 corresponds to company Co2 (seller). In this example, with respect to node 910, a forward risk propagation function may correspond to edge 930, and a node of the backward risk propagation function may correspond to edge 940. Illustratively, forward risk propagation is in the same direction as the flow of products and / or services, which may be visually indicated. On the other hand, backward risk propagation is in the opposite direction of the flow of products and / or services, which may not generally be visually indicated.

[0066] By way of example only and not by way of limitation, for illustrative purposes, assume that supply chain network 900 includes two companies (e.g., Co1 910 and Co2 920) as shown in FIG. 9 . In various exemplary embodiments, internal risk is first assessed or determined at the company (or internal) level of the two companies. Then, common risk between these two companies is assessed or determined. When assessing common risk, as a result of the forward and backward risk propagation model, the risk of Co1 910 is affected by the risk of Co2 920, and vice versa. For example, consider the case where Co1 910 has a good internal risk score, but the impact of the common risk with Co2 920 is poor, and the overall risk of Co1 910 may be assessed or determined to be poor.

[0067] Thus, the edge function E(·) may be modeled as a diffusion flow that includes forward and backward risk propagation. In various exemplary embodiments, the forward and backward risk propagation may each be based on a risk transferability factor that represents the risk of adjacent nodes (e.g., coming from adjacent nodes) to the node in question and provides an indication of the risk transferability from adjacent nodes to the node, and a reliability factor that provides an indication of the node's reliability to its adjacent nodes. By way of example only and not by way of limitation, a forward risk propagation model or function may be described by the following equation: E(start=Co2, end=Co1) = (risk of Co2) × (risk transferability factor from Co2 to Co1) × (1 - reliability of Co1 for Co2) (Equation 1)

[0068] Similarly, the backward risk propagation model or function may be described by the following equation: E(start=Co1, end=Co2) = (Co1's risk) × (risk transfer factor from Co1 to Co2) × (1 - Co2's reliability towards Co1) (Equation 2)

[0069] Therefore, the two risk propagation models described above may be used to determine the common risk between two companies. For example, for Co1 910, the common risk with Co2 is the result of evaluating the forward risk propagation model, E(start=Co2, end=Co1), and for Co2 920, it is the result of evaluating the backward propagation flow, E(start=Co1, end=Co2). For example, if Co1 910 is connected to other adjacent nodes (e.g., companies), then for each of these other adjacent companies, the common risk between Co1 910 and the other adjacent companies is also evaluated in the same or similar manner as between Co1 910 and Co2 920. Therefore, the selection of the forward or backward risk propagation model may depend on the direction of the flow of products or services as shown in FIG. 9.

[0070] For example, in the forward risk propagation model of Equation 1, the risk of Co2 920 may be the internal risk of Co2 920 and may be determined based on its credit risk, which may be estimated, for example, as a probability of default using financial transaction data associated with Co2 920. The transferability factor from Co2 920 to Co1 910 may be a risk transferability parameter that may be, for example, a value between 0 and 1 (inclusive) that provides an indication of the transferability of risk from Co2 920 to Co1 910 (e.g., the fractional amount of risk from Co2 920 that is transferred to Co1 910). The reliability factor of Co1 910 relative to Co2 920 may be a trust parameter that provides an indication of the reliability of Co1 910 relative to Co2 920. In various exemplary embodiments, the reliability factor may be a value between 0 and 1 (inclusive) (e.g., as determined by Co1 910). For example, as can be seen from equations (1) and (2), in various exemplary embodiments, the trust factor is inversely proportional to or inversely correlated with risk, i.e., the higher the trust factor, the lower the risk, and vice versa. For example, the trust factor of Col 910 relative to Co2 920 may be set based on the reputation of Co2 920 based on previous transactions between Col 910 and Co2 920.

[0071] In various exemplary embodiments, a total or overall risk score for Co1 910 may be determined or calculated by combining (e.g., summing) all of Co1 910's internal risk scores and Co1 910's common risk scores with neighboring entities connected to Co1 910 (e.g., neighboring nodes, which are nodes located in tiers of the supply chain network immediately adjacent to the tier in which Co1 910 is located). In the exemplary supply chain network 900 shown in Figure 9 and described above, there is only one neighboring node, so only one common risk score between Co1 910 and Co2 920 may be determined for inclusion in Co1 910's overall risk score.

[0072] In various exemplary embodiments, a risk transferability factor (or parameter) from a second entity (e.g., Co2 920) to a first entity (e.g., Co1 910) may be determined based on transaction data. By way of example only and not by way of limitation, the risk transferability factor from the second entity to the first entity may be determined based on the quantity or number of units of parts supplied to the first entity (e.g., the number of parts supplied to the first entity relative to the total number of parts supplied by the second entity) and / or based on the first entity's revenue (e.g., revenue from the first entity relative to the second entity's total revenue). For example, the second entity's financial transactions (e.g., 810 shown in FIG. 8A ) may be analyzed to determine a revenue value from the first entity's credit value parameter (e.g., 816), which may be found in the trading company parameters (e.g., 814), and the financial transactions of other entities may be analyzed to calculate a total revenue, taking into account payment terms such as those defined as payment type parameters (e.g., 817) and transaction details parameters (e.g., 818). Similarly, a risk transferability factor based on the number of parts supplies can be derived from a shipping transaction (e.g., 820 shown in FIG. 8B ) of a second entity by analyzing the shipping cost details (e.g., 824) and calculating the number of products delivered to the other entity (e.g., also found in 823) by considering the number of products or their equivalent delivered to the first entity, which may be found in the receiving entity parameters (e.g., 823), as well as the status of 825 (e.g., if some products were returned). Similar logic may be applied with respect to order transactions 830 (e.g., shown in FIG. 8C ), manufacturing transactions 840 (e.g., shown in FIG. 8D ), or other types of transaction data. Those skilled in the art will recognize that the risk transferability factor may be determined based on transaction data, as appropriate or desired, so long as it provides an indication of the risk transferability from the second entity to the first entity. Therefore, those skilled in the art will recognize that the present invention is not limited to the above-described examples of determining a risk transferability factor.

[0073] When determining the risk transferability factor, there may be cases where information from the second entity is incomplete (e.g., not all financial transactions are shared by the second entity), which may be the case with the first entity or Co1 or other entities. In such cases, financial transactions from the first entity (as well as other entities that may be connected to the second entity) that involve the second entity as a supplier but are not present in the second entity's financial transactions may be utilized in the determination. For example, this is an advantage of creating a supplier network that has the ability to combine information from the first and second entities. In various exemplary embodiments, the risk transferability factor may be determined by first determining a sub-risk transferability factor for each of one or more types of transaction data (e.g., transaction data 810, 820, 830, and / or 840). The risk transferability factor may then be determined based on the sub-risk transferability factors determined for multiple types of transaction data, such as by selecting the highest sub-risk transferability factor or averaging the sub-risk transferability factors.

[0074] In various exemplary embodiments, a trust factor (or parameter) of a first entity (e.g., Co1 910) relative to a second entity (e.g., Co2 920) may also be determined based on transaction data. By way of example and not limitation, a trust factor of a first entity relative to a second entity may be determined based on the impression or reputation of the second entity based on previous transactions with the first entity. For example, one way to objectively quantify trust is to analyze specific or selected parameters in transaction data. For example, based on a status parameter (e.g., 825) in a shipping transaction (e.g., 820 in FIG. 8B ), the ratio between the number of incidents (late delivery, return due to defects, etc.) related to the second entity and the total number of incidents for all suppliers of the first entity may be calculated as an indicator of uncertainty for forward risk propagation. Conversely, a status parameter 837 in an order transaction (e.g., 830 in FIG. 8C ) may be used to determine a trust factor for reverse risk propagation. For example, the number of cancelled transactions from the first entity may be compared to the total number of cancelled transactions faced by the second entity. Similarly, product quality parameters (e.g., 846) in a manufacturing transaction (e.g., 840 in FIG. 8D ) may be used to calculate the confidence level. Those skilled in the art will recognize that the confidence factor may be determined based on transaction data, as appropriate or desired, so long as it provides an indication (a quantitative indicator) of the first entity's trustworthiness relative to the second entity. Therefore, those skilled in the art will recognize that the present invention is not limited to the above-described examples of determining the confidence factor.

[0075] In various exemplary embodiments, the reliability factor may be determined by first determining a sub-reliability factor for each of one or more types of transaction data (e.g., transaction data 810, 820, 830, and / or 840). The reliability factor may then be determined based on the sub-reliability factors determined for the multiple types of transaction data, such as by selecting the lowest sub-reliability factor or averaging the sub-reliability factors.

[0076] In various exemplary embodiments, determining or calculating a common risk score between entities may be applied at various stages of risk assessment. For example, it may be performed when evaluating whether a low-ranked entity should be added to the supply chain network. This corresponds to the stage in supply chain network creation where nodes representing entities are first evaluated to screen the entities to ensure that low-ranked entities are not inadvertently added to the supply chain network. Another exemplary stage for performing a common risk assessment is when assessing enterprise risk in a created supply chain network, for example, to determine the best connected supply chain path or route.

[0077] In various exemplary embodiments, various data elements in the transaction data may be analyzed, processed, and integrated into a supply chain network. As described above, a wide range of transactions may occur between entities during the course of operations or business, and those skilled in the art will recognize that the present invention is not limited to any particular type or category of transactions. For example, exemplary transaction data may include custom data or international transactions, sales tax information, domestic sales transactions, and both international and domestic payment data. Data sources may also relate to information related to green financing, emissions, energy usage, or the provision of green loans for production and distribution facilities and resources. Analysis of the transaction data may include information regarding supply, delivery, supplier diversity, connectivity, and risk.

[0078] By way of example only and not by way of limitation, FIG. 10 illustrates a table depicting exemplary calculations resulting from an analysis of transaction data. For example, consider a first entity (e.g., Co1 910) as a buyer and a second entity (e.g., Co2 920) as a supplier. Inventory value growth rate 1001 may refer to the rate of change over time (e.g., annualized rate) of products supplied from a second entity to a first entity, which may be derived from order transactions (e.g., 830 in FIG. 8C ). Defect return rate 1002 may refer to the number of defects compared to the total number of products delivered, which may be determined from shipping transactions (e.g., 820 in FIG. 8B ) based on status parameters (e.g., 825) over a particular period (e.g., one year). The lead time may be calculated based on the difference between the date and time information (e.g., at 821) from a transportation transaction (e.g., 820 in FIG. 8B) related to the transportation delivery and the date and time information (e.g., 831) from the order transaction (e.g., 830 in FIG. 8C) corresponding to the transportation transaction (e.g., 820). The variability of lead time 1003 may then be calculated based on the variability, i.e., squared standard deviation, of the lead times of the various transportation and order transactions. The inventory quantity may be calculated based on the total number of products ordered in the order information (e.g., 835) for the same product, the products delivered in the shipping charge details (e.g., 824) of the transportation transaction (e.g., 820), or the devices purchased in the transaction details (e.g., 818) of the financial transaction (e.g., 810). The variability of inventory quantity 1004 may then be determined by calculating the variability of these inventory quantities over a period of time (e.g., one year). The shipping cost variability 1005 may similarly be derived by calculating the shipping cost variability provided in the shipping cost details (e.g., 824) of the shipping transaction 820 as the total cost of the product shipped from the second entity to the first entity. The number of direct connections 1006 may correspond to the number of suppliers and customers connected to an entity (e.g., the first entity if an overall risk score for the first entity is being determined). The number of direct connections may be further measured as the number of supplier connections and as the number of customer connections.The number of distinct connections 1007 may be determined by considering the location or address of an entity (e.g., at a country level) and comparing it to other locations or addresses of neighboring entities directly connected to it. For example, the presence of a connection 1008 to an upstream supplier may be a binary value (e.g., yes / no) indicating whether the entity is connected to the supplier. The number of paths from an upstream supplier 1009 may refer to the number of possible paths that include a second entity leading to a first entity. The average number of connection points from an upstream supplier 1010 may be determined by counting the number of companies that include a second entity between the enterprise and the upstream supplier for each possible path and averaging these numbers by the number of paths. The number of points away from upstream high-risk enterprises (assumed to be in the same industry) 1011 may be determined by determining the closest enterprises to the enterprise such that the path between the enterprises includes the second entity and counting the number of enterprises in between. For example, the closest companies can be determined by a shortest path algorithm, such as Dijkstra's algorithm, and other graph-based search algorithms with the constraint that the second entity is included because the risk transferability factor takes the second entity into account. Similarly, the number of points away from upstream high-risk companies (different companies) may be considered at 1012. A safety margin, such as one or two tier levels, may be considered and the number of high-risk companies may be counted at 1013.

[0079] For example, the values ​​calculated under the supply categories (e.g., 1001 and 1002) and the values ​​calculated under the delivery categories (e.g., 1003, 1004, and 1005) shown in FIG. 10 may be utilized as input parameters for determining an entity's reliability factor. For example, as the number of inventory received from the second entity (1001) increases and the defect return rate (1002) decreases, the reliability factor also increases. Similarly, if the lead time variability (1003), inventory quantity variability (1004), or delivery cost variability (1005) decreases, the reliability factor may also increase. For example, multiple of these determined reliability factor values ​​may be aggregated by setting appropriate weights to calculate a final reliability factor. In various exemplary embodiments, heuristics based on statistical analysis or machine learning algorithms may be applied to determine appropriate weights for the determined reliability factor values.

[0080] As an example, the risk transferability factor may be implicitly determined based on possible routes in the supplier network, which is determined based on values ​​under the diversity, connectivity, and risk categories shown in FIG. 10 . For example, when indirectly determining risk transferability from a second entity to a first entity, the number of direct connections 1006 between the first entity and the second entity may be determined to estimate the risk transferability factor, which may be utilized when transaction data between the second entity and the first entity is unavailable or unreliable. If the first entity and the second entity are in the same area affected by a particular risk (1007), the risk transferability is higher, regardless of whether both are affected as represented by forward and backward risk propagation. For example, the values ​​calculated under the connectivity and risk categories shown in FIG. 10 may consider not only the relationship between the first entity and the second entity, but also other upstream companies and other companies with high values ​​for internal risk. In particular, by considering possible paths in 1011, 1012, and 1013, the risk transferability is inversely proportional to the number of points separated by 1011 and 1012, but directly proportional to the value of 1013. Similarly, a number of these determined risk transferability values ​​may be aggregated by setting appropriate weights to calculate a final transferability factor.

[0081] 8A-8D are examples that represent transaction data 631 that may be analyzed. For example, identifying information for the companies involved in a transaction, such as 812 and 814 from financial transaction data 810, 822 and 823 from transportation transaction data 820, 832 and 834 from sales order transaction data 830, and 843 and 844 from manufacturing transaction data 840, may be extracted and utilized to create a supply chain network (e.g., to provide corresponding nodes in the supply chain network).

[0082] FIG. 11 illustrates a schematic flow diagram of a system-implemented method 1100 for creating or generating a supply chain network, according to various exemplary embodiments of the present invention. At 1110, the system may create a pool of suppliers (i.e., corresponding nodes) 1211 from the supplier transaction data or a prepared or predetermined list. In this regard, multiple nodes for the supply chain network corresponding to each of a plurality of supplier entities may be provided based on transaction data associated with one or more of the plurality of supplier entities and / or based on predetermined supplier entity identification data (e.g., corresponding to the prepared or predetermined list of suppliers described above) including entity identification information for one or more of the plurality of supplier entities. If tier information (e.g., which suppliers are in which tier) is known, this step may be skipped and the system may proceed to 1120. At 1120, the system may cluster suppliers for each tier, i.e., group multiple suppliers (corresponding nodes) into multiple tiers across the supply chain network. Pools 1221, 1222, 1223 for each tier may be created based on tier information such as products and / or services provided by suppliers. For better understanding, Figure 12A shows a schematic diagram illustrating the process at 1100 and 1120. If the tier information is not known, suppliers may be grouped based on transaction details such as based on the same or similar products / services supplied at 818, products / services in shipping cost details 824, products / services delivered at order information 835, and inspected product information at 845.

[0083] At 1130, the system may calculate or determine an overall risk score (based on internal and common risk scores) for each supplier based on transaction data (e.g., financial transaction data, sales order transaction data, and green or sustainability-related transaction data). For example, node 1A may be determined to have a good overall risk score (e.g., meeting a predetermined risk score condition), and node 1B may be determined to have a low overall risk score (e.g., not meeting a predetermined risk score condition). At 1140, the system may generate a supplier hierarchy or connection by using each supplier's calculated overall risk score. For example, in various exemplary embodiments, suppliers with low ratings are not selected to be included (or linked) in the supply chain network at this stage due to their high risk. For better understanding, FIG. 12B shows a schematic diagram illustrating the processes at 1130 and 1140. For example, at 1140, all possible links may be candidates for the supply chain hierarchy, but certain links (e.g., 1B → 2E) may be omitted because nodes 1B and 2E have low calculated overall risk scores. In this regard, connecting these nodes in the supply chain network may result in diversification of risk within the supply chain network.

[0084] In various exemplary embodiments, for low-ranked suppliers, a method is implemented to further evaluate whether to link the corresponding node to adjacent nodes in the supply chain network. In this regard, a separate (and temporary) subgraph may be created that includes direct connections to adjacent nodes to evaluate its impact on the supply chain network. Thus, the subgraph evaluation may be used to determine whether to add a low-ranked node, and thus the subgraph evaluation may also be referred to as a high-risk node evaluation process.

[0085] At 1150, the system may propose candidate sets of prioritized suppliers as a hierarchy of the supply chain (e.g., the structure of the supply chain and links between suppliers). For example, with reference to FIG. 12B , the candidate sets may include Set 1 (Path 1A-2D-3I), Set 2 (Path 1A-2D-3F), and Set 3 (Path 1A-2C-3F), etc. Candidate criteria may be pre-selected by a user (e.g., a customer) from a list of criteria, such as financial criteria (e.g., credit score, no default record), environmental criteria (e.g., low carbon emissions, energy-efficient resource use), social criteria, or a combination of these criteria.

[0086] At 1160, in various exemplary embodiments, a user of the system may decide which candidate set to use (e.g., corresponding to selecting a candidate supply chain path from among multiple candidate supply chain paths as a selected supply chain path). For example, a bank may choose set 3 because it allows for the selection of suppliers with higher risk but higher interest rates. As another example, the selected candidate sets may have the same rating but different green marks, so that greener suppliers are selected or preferred.

[0087] As described above, at 1140, a subgraph evaluation may be performed for a low-rated supplier so that the system can evaluate whether to add such supplier to the supply chain network. For example, the subgraph evaluation may be performed based on the tier pool size of the tier in which such low-rated supplier is located. In this regard, for example, if there are too few suppliers in the tier pool (e.g., below a predetermined minimum threshold), the added risk of a significant bottleneck may outweigh the impact of adding a low-rated supplier to the supplier chain network. By doing so, the system can set up and form a more appropriate supply chain network.

[0088] FIG. 13 shows a schematic diagram illustrating a subgraph evaluation process (sometimes referred to as a high-risk node evaluation process) according to various exemplary embodiments of the present invention. For example, even if the number of paths from tier n-1 to tier n+1 is 12, it is not possible to determine whether there is a significant bottleneck by simply checking the total number of paths, assuming that node 7D is the only supplier of tier n. In this regard, various exemplary embodiments analyze the subgraph and compare tier pool sizes. In various exemplary embodiments, the system may perform the following process: (1) check the current tier pool size of tier n, where a low-ranked or high-risk node (e.g., node 7E) is located; and (2) compare the tier pool sizes of tiers n-1 and n (i.e., the immediately downstream tier) and tier pool sizes of tiers n+1 and n (i.e., the immediately upstream tier). Therefore, this subgraph evaluation process is applicable when a low-ranked company (e.g., node 7E) is deemed to be added to avoid a bottleneck.

[0089] FIG. 14 shows a schematic flow diagram of a subgraph evaluation process (or high-risk node evaluation process) 1400 according to various exemplary embodiments of the present invention. Process 1400 may be performed starting with tier 1 and iteratively down to tier nMax-1, where nMax corresponds to the lowest tier (or highest tier number n). At 1412, the tier's pool size is compared to a predetermined minimum number of suppliers (minN) for each tier, such as 1, the lowest number. If the tier's pool size is determined to be less than or equal to minN, the node in question may be added or accepted as being linked within the supply chain network. Otherwise, at 1413 and 1414, it is determined whether the relative tier sizes of tier n and tier n-1 (e.g., the ratio of the tier size of tier n to tier n-1) are less than a predetermined value (e.g., 0.5 as an exemplary ideal ratio). In this regard, if it is determined that the relative tier sizes of tiers n and n-1 are less than a predetermined value, which means that the number of companies in tier n-1 is significantly greater than that of tier n, the node in question may be added or accepted as one to be linked within the supply chain network to avoid bottlenecks resulting from a small number of suppliers in tier n. Otherwise, at 1415 and 1416, it is determined whether the relative tier sizes of tier n and tier n+1 (e.g., the ratio of the tier size of tier n to tier n+1) are less than a predetermined value (e.g., 0.5 as an exemplary ideal ratio). In this regard, if it is determined that the relative tier sizes of tier n and tier n+1 are less than the predetermined value, the node in question may be added or accepted as one to be linked within the supply chain network. Otherwise, at 1417, the node in question is not added or accepted as one to be linked within the supply chain network.

[0090] In various exemplary embodiments, a system for generating a supply chain network, such as that described with reference to FIG. 11 , is configured to process transaction data, provide nodes, determine risk scores, and form the supply chain network. Because risks generally exist within a supply chain, various factors, which may be internal or external to supplier entities, may cause these risks at different times. These risks may affect the supply chain network, for example, causing various supplier entities to be adversely affected and closing or going out of business. In various exemplary embodiments, during the update phase, the system is configured to continuously receive transaction data and / or supplier entity identity data, including entity identity information for one or more supplier entities. For example, if an existing supplier entity (or corresponding node) in the supply chain network is a going concern or has an undesirable business performance, ranging from a good rating to a low rating as a result of determining its overall risk score, such supplier entity (or corresponding node) may be removed as part of the supply chain network. In that case, edges connected to this supplier entity (or corresponding node) are also correspondingly removed, and the overall risk scores of adjacent nodes (in directly adjacent tiers) are updated. On the other hand, a new supplier entity (or corresponding node) may be added to the supply chain network, for example, as a result of processing transaction data and determining that the new supplier has a good overall risk score. In various exemplary embodiments, updates to a supplier entity's internal risk score or new transaction data between entities in the supply chain network may modify the supply chain network. In this regard, in various exemplary embodiments, whenever updates to a supplier entity's internal risk score or new transaction data is received, the system may determine the overall risk of each supplier entity in the supply chain network and form an updated supply chain network.

[0091] 15 illustrates a schematic flow diagram of a method 1500 for managing supply chain risk, according to various exemplary embodiments of the present invention. For example, the flow diagram summarizes an overall process that includes analyzing transactional data (e.g., financial data) from within an enterprise at 1550, analyzing the transactional data to determine doing business with customers and suppliers at 1551, estimating internal and shared risks at 1552, generating a supply chain network at 1553, and applying risk scores from the supply chain network to find matches, such as to match loan applications with funders, at 1554.

[0092] 16 shows a schematic diagram illustrating risk assessment using multiple nodes, according to various exemplary embodiments of the present invention. As shown, the risk scores of nodes 2D and 3F change after considering the common risks contributed by adjacent or connecting nodes, such as node 2C affecting node 3F, and node 3H affecting node 2D.

[0093] FIG. 17 illustrates a schematic flow diagram of a method 1700 for enterprise risk assessment, according to various exemplary embodiments of the present invention. At 1731, internal risk is first assessed at the enterprise level based on internal financial, operational, and sustainability-related risks associated with the enterprise. At 1732, for each neighboring enterprise (in the immediately adjacent tier), common risks between the enterprise and the neighboring enterprise are assessed based on the forward and backward risk propagation models as described above in accordance with various exemplary embodiments. For example, common risks associated with an enterprise between an enterprise and its neighboring enterprise are assessed based on the neighboring enterprise's internal risks and the neighboring enterprise's risks to the enterprise. For example, in FIG. 16, common risks associated with node 2D are assessed by considering the pairing of node 2D with neighboring nodes 3F, 3H, and 3I, respectively, as forward propagation, and the pairing of node 2D with neighboring node 1A, respectively, as backward propagation. A similar assessment may be performed to determine common risks associated with node 2C by considering the pairing of node 2C with neighboring nodes 3F, 3H, and 1A, respectively. For example, before being updated by common risks, nodes 3F and 2D have good internal risk scores, as shown in FIG. 16. However, after being updated by common risks, due to the influence of common risks according to various exemplary embodiments, as shown in FIG. 16, these nodes are assessed to have low overall or total risk scores. This demonstrates the impact of risk propagation: node 3H influences node 2D, which in turn influences nodes 2C and 2D. As another example, assume that at a first time instance (e.g., t=t0), the internal risk of node 2D is good, but the overall risk associated with node 2D is bad due to the influence of common risks. At a second time instance (e.g., t=t1) following the first time instance, the internal risk of node 2D may have worsened (e.g., their financial performance may have been adversely affected by the previous poor overall risk). In such a case, for example, even if the common risks are improving (e.g., getting better), the overall risk of node 2D may remain bad.

[0094] In some cases, a company may need more than one supplier because they require a larger volume or need to diversify for some reason. In this case, multiple routes can be evaluated and ordered simultaneously based on their total or composite risk score. The route that belongs to the band with the lower total or composite risk score is selected.

[0095] In various exemplary embodiments, after common risks have been evaluated or considered, an overall risk score may be aggregated across multiple nodes along the supply chain path at 1733. For example, when evaluating the risk of a buyer buying a product from entity 1A, multiple potential supply chain paths may be evaluated. For example, with reference to FIG. 16 , the path 3I → 2D → 1A → Buyer may be one potential supply chain path, and another potential supply chain path may be 3H → 2C → 1A → Buyer. With multiple potential supply chain paths, a path risk score for the potential supply chain path may be determined by first aggregating, for each potential supply chain path, the overall risk score of each company (or node) in the potential supply chain path. For example, aggregation may be performed by adding the overall risk scores at each node in the potential supply chain path, with or without weights at one or more nodes. For example, the potential supply chain path with the lowest risk score may be selected at 1734.

[0096] Thus, various exemplary embodiments provide a supply chain network management method that includes analyzing transaction data from enterprises such as banks, logistics service providers, e-commerce, etc. to identify suppliers, customers, products, and transaction types; using results from the transaction data analysis to estimate internal risks of suppliers and customers, as well as common risks between suppliers and customers; and generating a supply chain network based on the estimated risks.

[0097] In various exemplary embodiments, a method for estimating common risk is provided by assessing the transferability of an internal risk of a second company to a first company and assessing a trustworthiness factor of the first company to the second company.

[0098] In various exemplary embodiments, a method for selecting a supplier is provided by assessing risk by evaluating multiple paths within a supplier network and aggregating internal and common risks.

[0099] Thus, methods and systems for managing supply chain risk according to various exemplary embodiments of the present invention can advantageously provide improvements to sourcing and procurement solutions. For example, aggregated transaction data may be analyzed as described above to provide an engine for evaluating suppliers by taking into account not only financial aspects but also other factors, such as operational data and compliance requirements. Using methods and systems for managing supply chain risk according to various exemplary embodiments of the present invention, for example, a higher confidence and success rate in finding suppliers and partners that match buyer specifications can be found. Consequently, in financial applications, for example, avoiding risks in the supply chain network can result in more accurate financial decisions made by financial providers.

[0100] Although embodiments of the present invention have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention as defined by the appended claims. The scope of the present invention is thus indicated by the appended claims, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced.

Claims

1. 1. A method for managing supply chain risk using at least one processor, comprising: providing a plurality of nodes for a supply chain network corresponding respectively to a plurality of supplier entities, the plurality of nodes being grouped into a plurality of tiers across the supply chain network; determining, for each of the plurality of nodes, an overall risk score associated with the node, the overall risk score being determined based on an internal risk score and one or more common risk scores associated with the node, and based on transaction data associated with one or more supplier entities of the plurality of supplier entities corresponding to one or more adjacent nodes, respectively, the one or more common risk scores being determined between the node and the one or more adjacent nodes, each of the one or more adjacent nodes being located in a tier of the plurality of tiers immediately adjacent to the tier in which the node is located; forming the supply chain network based on the overall risk scores associated with the plurality of nodes.

2. 2. The method of claim 1, wherein for each of the one or more adjacent nodes, the common risk score associated with the node between the node and the adjacent node is determined based on a forward risk propagation model if the adjacent node is upstream with respect to the node, and based on a backward risk propagation model if the adjacent node is downstream with respect to the node, the forward risk propagation model and the backward risk propagation model each representing the risk of the adjacent node with respect to the node.

3. The method of claim 2 , wherein the forward risk propagation model and the backward risk propagation model are each based on a risk transferability parameter that provides an indication of risk transferability from the adjacent node to the node.

4. The method of claim 3 , wherein the forward risk propagation model and the backward risk propagation model are each further based on a trust parameter of the node with respect to the neighboring nodes, the trust parameter providing an indication of the node's trustworthiness with respect to the neighboring nodes.

5. 5. The method of claim 4, wherein the risk transferability parameter and the trust parameter are determined based on the transaction data associated with the supplier entity corresponding to the adjacent node and / or the transaction data associated with the supplier entity corresponding to the node.

6. 2. The method of claim 1, wherein providing the plurality of nodes for the supply chain network corresponding to each of the plurality of supplier entities is based on transaction data associated with one or more of the plurality of supplier entities and / or based on predetermined supplier entity identification data including entity identification information for one or more of the plurality of supplier entities.

7. 2. The method of claim 1, wherein the transaction data associated with a supplier entity of the plurality of supplier entities relates to a transaction involving the supplier entity and includes entity identification information, product and / or service information, and a status of the transaction and / or a rating associated with the transaction.

8. 2. The method of claim 1, wherein forming the supply chain network includes determining whether to link a node in a tier of the plurality of tiers with an adjacent node in an immediately adjacent tier of the plurality of tiers based on the overall risk score associated with the node in the tier and the overall risk score associated with the adjacent node in the immediately adjacent tier of the plurality of tiers.

9. 9. The method of claim 8, wherein the node in the tier is linked with the adjacent node in the immediately adjacent tier if the overall risk score associated with the node in the tier and the overall risk score associated with the adjacent node in the immediately adjacent tier of the plurality of tiers meet a predetermined risk score condition.

10. 10. The method of claim 9, wherein the determining whether to link the node in the stratum with the adjacent node in the immediately adjacent stratum is further based on a stratum pool size for the stratum if the overall risk score associated with the node in the stratum does not satisfy the predetermined risk score condition.

11. 11. The method of claim 10, wherein the determining whether to link the node in the stratum with the adjacent node in the immediately adjacent stratum is further based on relative stratum pool sizes between the stratum and the immediately adjacent stratum if the overall risk score associated with the node in the stratum does not satisfy the predetermined risk score condition.

12. The method of claim 1 , further comprising selecting a supply chain path within the supply chain network based on the overall risk score associated with the plurality of nodes.

13. selecting the supply chain path, For each of a plurality of potential supply chain routes within the supply chain network, determining a route risk score associated with the potential supply chain route based on the overall risk scores associated with nodes along the potential supply chain route; and selecting a candidate supply chain route from among the plurality of candidate supply chain routes as the selected supply chain route associated with the route risk score that satisfies a predetermined route risk score condition.

14. A system for managing supply chain risk, comprising: at least one memory; at least one processor communicatively coupled to the at least one memory, the at least one processor: providing a plurality of nodes for a supply chain network corresponding respectively to a plurality of supplier entities, the plurality of nodes being grouped into a plurality of tiers across the supply chain network; determining, for each of the plurality of nodes, an overall risk score associated with the node, the overall risk score being determined based on the internal risk score and one or more common risk scores associated with the node, and based on transaction data associated with one or more supplier entities of the plurality of supplier entities corresponding to one or more adjacent nodes, respectively, the one or more common risk scores being determined between the node and the one or more adjacent nodes, each of the one or more adjacent nodes being located in a tier of the plurality of tiers immediately adjacent to the tier in which the node is located; The system is configured to form the supply chain network based on the overall risk scores associated with the plurality of nodes.

15. 15. The system of claim 14, wherein for each of the one or more adjacent nodes, the common risk score associated with the node between the node and the adjacent node is determined based on a forward risk propagation model if the adjacent node is upstream relative to the node, and based on a reverse risk propagation model if the adjacent node is downstream relative to the node, the forward risk propagation model and the reverse risk propagation model each representing the risk of the adjacent node to the node.

16. 16. The system of claim 15, wherein the forward risk propagation model and the backward risk propagation model are each based on a risk transferability parameter that provides an indication of risk transferability from the adjacent node to the node.

17. 17. The system of claim 16, wherein the forward risk propagation model and the backward risk propagation model are each further based on a trust parameter of the node with respect to the neighboring nodes, the trust parameter providing an indication of the node's trustworthiness with respect to the neighboring nodes.

18. 20. The system of claim 17, wherein the risk transferability parameter and the trust parameter are determined based on the transaction data associated with the supplier entity corresponding to the adjacent node and / or the transaction data associated with the supplier entity corresponding to the node.

19. 15. The system of claim 14, wherein the providing of the plurality of nodes for the supply chain network corresponding to each of the plurality of supplier entities is based on transaction data associated with one or more of the plurality of supplier entities and / or based on predetermined supplier entity identification data including entity identification information for one or more of the plurality of supplier entities.

20. 15. The system of claim 14, wherein the transaction data associated with a supplier entity of the plurality of supplier entities relates to a transaction involving the supplier entity and includes entity identification information, product and / or service information, and a status of the transaction and / or a rating associated with the transaction.

21. 15. The system of claim 14, wherein forming the supply chain network includes determining whether to link a node in a tier of the plurality of tiers with an adjacent node in an immediately adjacent tier of the plurality of tiers based on the overall risk score associated with the node in the tier and the overall risk score associated with the adjacent node in the immediately adjacent tier of the plurality of tiers.

22. 22. The system of claim 21, wherein the node in the tier is linked with the adjacent node in the immediately adjacent tier if the overall risk score associated with the node in the tier and the overall risk score associated with the adjacent node in the immediately adjacent tier of the plurality of tiers meet a predetermined risk score condition.

23. 23. The system of claim 22, wherein the determining whether to link the node in the stratum with the adjacent node in the immediately adjacent stratum is further based on a stratum pool size for the stratum if the overall risk score associated with the node in the stratum does not satisfy the predetermined risk score condition.

24. 24. The system of claim 23, wherein the determining whether to link the node in the stratum with the adjacent node in the immediately adjacent stratum is further based on relative stratum pool sizes between the stratum and the immediately adjacent stratum if the overall risk score associated with the node in the stratum does not satisfy the predetermined risk score condition.

25. 15. The system of claim 14, wherein the at least one processor is further configured to select a supply chain path within the supply chain network based on the overall risk scores associated with the plurality of nodes.

26. selecting the supply chain path, For each of a plurality of potential supply chain routes within the supply chain network, determining a route risk score associated with the potential supply chain route based on the overall risk scores associated with nodes along the potential supply chain route; and selecting a candidate supply chain route from among the plurality of candidate supply chain routes as the selected supply chain route associated with the route risk score that satisfies a predetermined route risk score condition.

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