A supply chain risk early warning method and system
By establishing a risk assessment matrix and a judgment matrix, and calculating the weights and scores of risk factors, the problem of inaccurate supply chain risk assessment in the absence of historical data is solved, and more accurate risk warnings are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU HUMMINGBIRD NOTE TECHNOLOGY CO LTD
- Filing Date
- 2026-03-19
- Publication Date
- 2026-06-19
AI Technical Summary
Existing technologies suffer from poor accuracy in supply chain risk assessment and early warning when historical data is lacking. Objective assessment methods rely on massive amounts of data, while subjective assessment methods are greatly influenced by the personal experience of experts, leading to inconsistent assessment results.
By establishing risk assessment matrices and risk judgment matrices corresponding to multiple assessment experts, the weights of each risk factor are determined, and the risk score is calculated using the weighted geometric average method to generate a comprehensive risk score and supply chain risk early warning information.
In the absence of historical data, it enables accurate assessment and early warning of supply chain risks. By integrating the experience and judgment of different experts, it improves the accuracy of assessment results and avoids the influence of single expert preferences and knowledge levels.
Smart Images
Figure CN122243199A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of supply chain risk early warning technology, specifically relating to a supply chain risk early warning method and system. Background Technology
[0002] Supply Chain Risk Management (SCRM) is crucial for ensuring the stability and reliability of the supply chain. Supply chain risk assessment and early warning are core components of SCRM. It analyzes risk factors, assesses the likelihood and impact of risks, and thus provides a basis for enterprises to formulate risk management strategies.
[0003] Existing supply chain risk early warning methods are mainly divided into two categories: objective assessment and subjective assessment. Objective assessment methods rely on massive amounts of historical data and use probability theory and mathematical statistics to conduct quantitative assessment and early warning, such as Fault Tree Analysis (FTA) or Event Tree Analysis (ETA). These methods require massive amounts of historical data as a basis, so in practice, objective assessment methods are often difficult to apply to the assessment and early warning of supply chain risks.
[0004] Subjective assessment methods rely on the experience and judgment of assessment experts to conduct qualitative risk assessments and early warnings, such as the Delphi method or expert scoring method. However, subjective assessment methods are often influenced by the individual experience, risk appetite, knowledge level, and analytical methods of the assessment experts, leading to significant differences in the results and consequently, poor accuracy in supply chain risk assessment and early warning.
[0005] Therefore, how to provide an effective solution to accurately assess and warn of supply chain risks when historical data is lacking has become a pressing problem to be solved in existing technologies. Summary of the Invention
[0006] The purpose of this invention is to provide a supply chain risk early warning method and system to solve the above-mentioned problems existing in the prior art.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a supply chain risk early warning method, characterized in that it includes: Based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, a risk assessment matrix corresponding to each assessment expert is established. For any risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results to obtain the risk judgment matrix corresponding to each assessment expert. Based on the risk judgment matrix corresponding to each assessment expert, the weight of each risk factor corresponding to each assessment expert is determined, and the weight vector corresponding to each assessment expert is obtained. Based on the risk assessment matrix and weight vector of each assessment expert, the risk score of each assessment expert for each risk factor is determined by weighted geometric average. Based on the risk scores of each assessment expert for each risk factor and the expert weights corresponding to each assessment expert, the comprehensive risk score of each risk factor is determined. Supply chain risk warning information is generated based on the comprehensive risk score of each risk factor, and the supply chain risk warning information includes the comprehensive risk score of each risk factor.
[0008] Based on the above-disclosed content, this invention establishes a risk assessment matrix corresponding to each assessment expert by assessing the membership of each risk factor in the supply chain to each risk level based on the assessments of multiple assessment experts. For any risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results to obtain the risk judgment matrix corresponding to each assessment expert. Based on the risk judgment matrix corresponding to each assessment expert, the weight of each risk factor corresponding to each assessment expert is determined, resulting in a weight vector corresponding to each assessment expert. Based on the risk assessment matrix and weight vector corresponding to each assessment expert, a weighted geometric average is used to determine the risk score value of each assessment expert for each risk factor. Based on the risk score value of each assessment expert for each risk factor and the expert weight corresponding to each assessment expert, a comprehensive risk score value for each risk factor is determined. Based on the comprehensive risk score value of each risk factor, supply chain risk early warning information is generated, which includes the comprehensive risk score value of each risk factor. In this way, supply chain risk assessment and early warning can be achieved even in the absence of massive historical data. At the same time, the introduction of expert weights allows for a comprehensive analysis of the degree to which each risk factor in the supply chain assessed by multiple experts belongs to each risk level. This avoids the problem of poor accuracy in supply chain risk assessment and early warning due to the influence of a single assessment expert's personal experience, risk preference, knowledge level, and analytical methods. By combining the experience and judgment of different experts, the assessment results are more in line with the actual situation, and the supply chain risk assessment and early warning are more accurate.
[0009] In one possible design, based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, a risk assessment matrix corresponding to each assessment expert is established, including: Based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, an initial risk assessment matrix corresponding to each assessment expert is established. The initial risk assessment matrix corresponding to each assessment expert is normalized to obtain the risk assessment matrix corresponding to each assessment expert.
[0010] In one possible design, for any risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results, including the following steps: Step 1. For any risk assessment matrix, compare the m-th risk factor with the n-th risk factor based on the evaluation criteria set by the corresponding assessment experts to obtain the comparison result between the m-th risk factor and the n-th risk factor, where the initial values of m and n are both 1; Step 2. Increment n by 1, and re-compare the m-th risk factor with the n-th risk factor based on the established evaluation criteria; Step 3. When n=i, increment m by 1 and n=1, then return to Step 1 until m=i and n=i, to obtain the pairwise comparison results of all risk factors, where i represents the total number of risk factors; Step 4. Assign values based on the pairwise comparison results of all risk factors to obtain the pairwise risk factor comparison values of all risk factors; Step 5. Construct a matrix based on the pairwise comparison values of all risk factors to obtain the risk assessment matrix.
[0011] In one possible design, the pairwise risk factor comparison value of two identical risk factors is 0.5, and the sum of the risk factor comparison values of the m-th risk factor and the n-th risk factor, as well as the risk factor comparison values of the n-th risk factor and the m-th risk factor, is 1.
[0012] In one possible design, the weight of each risk factor corresponding to each assessment expert is determined based on the risk judgment matrix corresponding to each assessment expert, including: The weight of each risk factor corresponding to each assessment expert is determined according to the following formula: w_k=(1+b_k1+b_k2+……+b_ki) / (i×(i 2 -1)); Where w_k represents the weight of the k-th risk factor corresponding to the assessment expert, b_ki represents the data in the k-th row and i-th column of the risk judgment matrix, and i represents the total number of risk factors.
[0013] In one possible design, each risk level includes 5 risk levels.
[0014] In a possible design, the risk factors in the supply chain include supply risk, production risk, logistics risk, and / or market risk.
[0015] Secondly, the present invention provides a supply chain risk early warning system, comprising: Establish a unit to build a risk assessment matrix corresponding to each assessment expert, based on the degree of membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts. The comparison unit is used to compare all risk factors pairwise for any risk assessment matrix, and to establish a risk judgment matrix based on the comparison results, thereby obtaining the risk judgment matrix corresponding to each assessment expert. The first determining unit is used to determine the weight of each risk factor corresponding to each assessment expert based on the risk judgment matrix corresponding to each assessment expert, and to obtain the weight vector corresponding to each assessment expert. The second determining unit is used to determine the risk score value of each assessment expert for each risk factor by weighted geometric average based on the risk assessment matrix and weight vector of each assessment expert. The third determining unit is used to determine the comprehensive risk score of each risk factor based on the risk score of each assessment expert for each risk factor and the expert weight corresponding to each assessment expert. The generation unit is used to generate supply chain risk warning information based on the comprehensive risk score of each risk factor, wherein the supply chain risk warning information includes the comprehensive risk score of each risk factor.
[0016] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the supply chain risk warning method as described in the first aspect or any possible design of the first aspect.
[0017] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the supply chain risk warning method described in the first aspect or any possible design of the first aspect.
[0018] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the supply chain risk warning method as described in the first aspect or any possible design of the first aspect.
[0019] Beneficial effects: This invention enables supply chain risk assessment and early warning even in the absence of massive historical data. It also introduces expert weights to comprehensively analyze the membership of various risk factors in the supply chain to different risk levels assessed by multiple experts. This avoids the problem of poor accuracy in supply chain risk assessment and early warning due to limitations imposed by a single expert's personal experience, risk preference, knowledge level, and analytical methods. By integrating the experience and judgment of different experts, the assessment results are more consistent with reality, making supply chain risk assessment and early warning more accurate and facilitating practical application and promotion. Attached Figure Description
[0020] Figure 1 A flowchart illustrating the supply chain risk warning method provided in this application embodiment; Figure 2 A block diagram of the supply chain risk early warning system provided in this application embodiment; Figure 3 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.
[0022] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.
[0023] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.
[0024] It should be understood that specific details are provided in the following description to facilitate a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other instances, well-known processes, structures, and techniques may be shown without unnecessary details to avoid obscuring the exemplary embodiments.
[0025] To achieve supply chain risk assessment and early warning, embodiments of this application provide a supply chain risk early warning method and system, which can achieve accurate assessment and early warning of supply chain risks.
[0026] The supply chain risk early warning method provided in this application can be applied to user terminals or servers. It is understood that the execution entity described does not constitute a limitation on the embodiments of this application.
[0027] The supply chain risk warning method provided in the embodiments of this application will be described in detail below.
[0028] like Figure 1 The diagram shown is a flowchart of a supply chain risk warning method provided in the first aspect of the present application. The supply chain risk warning method may include, but is not limited to, the following steps S101-S106.
[0029] Step S101. Based on the degree of membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, establish a risk assessment matrix corresponding to each assessment expert.
[0030] The various risk factors in the supply chain can be set according to actual conditions. These risk factors may include, but are not limited to, supply risk, production risk, logistics risk, and / or market risk. In one or more embodiments, supply risk may be further subdivided into, but is not limited to, supplier financial condition risk and supplier reputation risk; production risk may be further subdivided into, but is not limited to, supplier capacity risk and supplier product quality risk; logistics risk may be further subdivided into, but is not limited to, logistics cost risk and logistics delay risk; and market risk may be further subdivided into, but is not limited to, material price fluctuation risk and market competition risk.
[0031] Risk levels can also be divided according to the actual situation. For example, there can be 5 risk levels: very low risk, low risk, moderate risk, high risk, and very high risk.
[0032] The membership degree of a risk factor to a risk level can refer to the probability that a risk factor will cause a corresponding risk level. The greater the probability that a risk factor will cause a corresponding risk level, the greater the value of the membership degree of the risk factor to the risk level can be.
[0033] In one or more embodiments, when establishing the risk assessment matrix corresponding to each assessment expert, an initial risk assessment matrix corresponding to each assessment expert can be established first based on the membership degree of each risk factor in the supply chain to each risk level assessed by multiple assessment experts. Then, the initial risk assessment matrix corresponding to each assessment expert is normalized to obtain the risk assessment matrix corresponding to each assessment expert.
[0034] For example, there are 3 assessment experts. The supply chain contains 3 risk factors: Risk Factor 1, Risk Factor 2, and Risk Factor 3. All risk levels are classified as Risk Level 1, Risk Level 2, and Risk Level 3. As shown in Table 1, in the supply chain assessed by expert A, Risk Factor 1 has a membership degree of 0 to Risk Level 1, 0.3 to Risk Level 2, and 0.2 to Risk Level 3. Risk Factor 2 has a membership degree of 0.5 to Risk Level 1, 0.3 to Risk Level 2, and 0.2 to Risk Level 3. Risk Factor 3 has a membership degree of 0 to Risk Level 1, 0 to Risk Level 2, and 0.4 to Risk Level 3.
[0035] Table 1
[0036] Based on Table 1 above, an initial risk assessment matrix can be established for assessment expert A. By normalizing the initial risk assessment matrix corresponding to assessment expert A, we can obtain the risk assessment matrix corresponding to assessment expert A. .
[0037] Step S102. For any risk assessment matrix, compare all risk factors pairwise, and establish a risk judgment matrix based on the comparison results to obtain the risk judgment matrix corresponding to each assessment expert.
[0038] In one or more embodiments, for any risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results. This may include, but is not limited to, the following steps: Step 1. For any risk assessment matrix, compare the m-th risk factor with the n-th risk factor based on the evaluation criteria set by the corresponding assessment experts to obtain the comparison result between the m-th risk factor and the n-th risk factor, where the initial values of m and n are both 1; Step 2. Increment n by 1, and re-compare the m-th risk factor with the n-th risk factor based on the established evaluation criteria; Step 3. When n=i, increment m by 1 and n=1, then return to Step 1 until m=i and n=i, to obtain the pairwise comparison results of all risk factors, where i represents the total number of risk factors; Step 4. Assign values based on the pairwise comparison results of all risk factors to obtain the pairwise risk factor comparison values of all risk factors; Step 5. Construct a matrix based on the pairwise comparison values of all risk factors to obtain the risk assessment matrix.
[0039] When comparing the m-th risk factor with the n-th risk factor, the evaluation criteria set by the assessment experts can be the probability of risk occurrence (comparing the likelihood of the two risk factors occurring), the degree of risk impact (comparing the degree of impact of the two risk factors on the supply chain after they occur), or the controllability of risk (comparing the controllability of the two risk factors).
[0040] After comparison, values can be assigned based on the pairwise comparison results of all risk factors to obtain the pairwise risk factor comparison values of all risk factors. Among them, the pairwise risk factor comparison value of two identical risk factors is 0.5, and the sum of the risk factor comparison values of the m-th risk factor and the n-th risk factor, as well as the sum of the risk factor comparison values of the n-th risk factor and the m-th risk factor, is 1.
[0041] For example, comparing the m-th risk factor with the n-th risk factor based on the degree of risk impact, if the degree of risk impact of the m-th risk factor is slightly greater than that of the n-th risk factor, then the risk factor comparison value between the m-th and n-th risk factors can be taken as 0.7, and the risk factor comparison value between the n-th and m-th risk factors would be 0.3; if the degree of risk impact of the m-th risk factor is much greater than that of the n-th risk factor, then the risk factor comparison value between the m-th and n-th risk factors can be taken as 0.9, and the risk factor comparison value between the n-th and m-th risk factors would be 0.1; if the degree of risk impact of the m-th risk factor is approximately equal to that of the n-th risk factor, then the risk factor comparison value between the m-th and n-th risk factors can be taken as 0.5, and the risk factor comparison value between the n-th and m-th risk factors would also be 0.5.
[0042] For example, if the risk factor comparison value between the first risk factor and the first risk factor is B11, the risk factor comparison value between the first risk factor and the second risk factor is B12, the risk factor comparison value between the first risk factor and the third risk factor is B13, the risk factor comparison value between the second risk factor and the second risk factor is B22, the risk factor comparison value between the second risk factor and the third risk factor is B23, the risk factor comparison value between the second risk factor and the first risk factor is B21, the risk factor comparison value between the third risk factor and the first risk factor is B31, the risk factor comparison value between the third risk factor and the second risk factor is B32, and the risk factor comparison value between the third risk factor and the third risk factor is B33, then a 3×3 risk judgment matrix can be constructed based on B11, B12, B13, B21, B22, B23, B31, B32, and B33. .
[0043] Step S103. Based on the risk judgment matrix corresponding to each assessment expert, determine the weight of each risk factor corresponding to each assessment expert, and obtain the weight vector corresponding to each assessment expert.
[0044] In one or more embodiments, the weight of each risk factor corresponding to the assessment expert can be determined according to the following formula: w_k=(1+b_k1+b_k2+……+b_ki) / (i×(i 2 -1)); Where w_k represents the weight of the k-th risk factor corresponding to the assessment expert, b_ki represents the data in the k-th row and i-th column of the risk judgment matrix, and i represents the total number of risk factors.
[0045] After obtaining the weight of each risk factor for each assessment expert, the weights of all risk factors for each assessment expert can be combined to obtain the weight vector for each assessment expert.
[0046] For example, if the weight of the first risk factor corresponding to expert A is 0.6, the weight of the second risk factor corresponding to expert A is 0.8, and the weight of the third risk factor corresponding to expert A is 0.3, then the weight vector corresponding to expert A can be represented as (0.6, 0.8, 0.3).
[0047] Step S104. Based on the risk assessment matrix and weight vector of each assessment expert, determine the risk score of each assessment expert for each risk factor by weighted geometric average.
[0048] Specifically, for a risk assessment matrix corresponding to a certain assessment expert, multiple values of the same risk factor (in the same row) in the risk assessment matrix can be averaged and weighted with the weights of the corresponding risk factors in the weight vector to obtain the risk score value of that assessment expert for each risk factor.
[0049] Step S105. Based on the risk scores of each assessment expert for each risk factor and the expert weights corresponding to each assessment expert, determine the comprehensive risk score of each risk factor.
[0050] Specifically, the comprehensive risk score of each risk factor can be obtained by weighting the risk scores of each assessment expert for each risk factor and the expert weights corresponding to each assessment expert.
[0051] Step S106. Generate supply chain risk early warning information based on the comprehensive risk score of each risk factor. The supply chain risk early warning information includes the comprehensive risk score of each risk factor.
[0052] In one or more embodiments, a comprehensive risk score range can be set for each risk factor based on experience to characterize whether it is likely to cause supply chain risk. If the comprehensive risk score value of a certain risk factor exceeds the corresponding comprehensive risk score range, it indicates that supply chain risk may be caused by that risk factor. In this way, it is very convenient and quick to identify whether one or more risk factors will cause supply chain risk based on the comprehensive risk score value of each risk factor, thereby realizing the assessment and early warning of supply chain risk.
[0053] In summary, this invention establishes a risk assessment matrix corresponding to each assessment expert by determining the membership of each risk factor in the supply chain to each risk level based on assessments by multiple assessment experts. For any given risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results, resulting in a risk judgment matrix corresponding to each assessment expert. The weight of each risk factor corresponding to each assessment expert is determined based on their respective risk judgment matrices, resulting in a weight vector for each assessment expert. Based on the risk assessment matrices and weight vectors of each assessment expert, a weighted geometric average is used to determine the risk score for each risk factor by each assessment expert. Based on the risk score for each risk factor and the expert weights of each assessment expert, a comprehensive risk score for each risk factor is determined. Supply chain risk warning information is generated based on the comprehensive risk score for each risk factor, and this information includes the comprehensive risk score for each risk factor. In this way, supply chain risk assessment and early warning can be achieved even in the absence of massive historical data. At the same time, the introduction of expert weights allows for a comprehensive analysis of the degree to which each risk factor in the supply chain assessed by multiple experts belongs to each risk level. This avoids the problem of poor accuracy in supply chain risk assessment and early warning due to the influence of a single assessment expert's personal experience, risk preference, knowledge level, and analytical methods. By combining the experience and judgment of different experts, the assessment results are more in line with the actual situation, and the supply chain risk assessment and early warning are more accurate, making it easier to apply and promote in practice.
[0054] Please see Figure 2 The second aspect of this application provides a supply chain risk early warning system, which includes: Establish a unit to build a risk assessment matrix corresponding to each assessment expert, based on the degree of membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts. The comparison unit is used to compare all risk factors pairwise for any risk assessment matrix, and to establish a risk judgment matrix based on the comparison results, thereby obtaining the risk judgment matrix corresponding to each assessment expert. The first determining unit is used to determine the weight of each risk factor corresponding to each assessment expert based on the risk judgment matrix corresponding to each assessment expert, and to obtain the weight vector corresponding to each assessment expert. The second determining unit is used to determine the risk score value of each assessment expert for each risk factor by weighted geometric average based on the risk assessment matrix and weight vector of each assessment expert. The third determining unit is used to determine the comprehensive risk score of each risk factor based on the risk score of each assessment expert for each risk factor and the expert weight corresponding to each assessment expert. The generation unit is used to generate supply chain risk warning information based on the comprehensive risk score of each risk factor, wherein the supply chain risk warning information includes the comprehensive risk score of each risk factor.
[0055] The working process, working details and technical effects of the supply chain risk early warning system provided in the second aspect of this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.
[0056] Please see Figure 3 The third aspect of this application provides an electronic device, including a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the supply chain risk warning method as described in the first aspect of the application.
[0057] Specifically, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or last-in-first-out (FILO) memory, etc.; the processor may not be limited to microprocessors of the STM32F105 series, ARM (Advanced RISC Machines), x86 architecture processors, or processors with integrated NPU (neural-network processing units); the transceiver may be, but is not limited to, WiFi (Wireless Fidelity) wireless transceivers, Bluetooth wireless transceivers, General Packet Radio Service (GPRS) wireless transceivers, ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard), 3G transceivers, 4G transceivers, and / or 5G transceivers, etc.
[0058] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions containing the supply chain risk warning method described in the first aspect of the embodiment. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the supply chain risk warning method as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
[0059] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the supply chain risk warning method as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
[0060] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A supply chain risk early warning method, characterized in that, include: Based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, a risk assessment matrix corresponding to each assessment expert is established. For any risk assessment matrix, all risk factors are compared pairwise, and a risk judgment matrix is established based on the comparison results to obtain the risk judgment matrix corresponding to each assessment expert. Based on the risk judgment matrix corresponding to each assessment expert, the weight of each risk factor corresponding to each assessment expert is determined, and the weight vector corresponding to each assessment expert is obtained. Based on the risk assessment matrix and weight vector of each assessment expert, the risk score of each assessment expert for each risk factor is determined by weighted geometric average. Based on the risk scores of each assessment expert for each risk factor and the expert weights corresponding to each assessment expert, the comprehensive risk score of each risk factor is determined. Supply chain risk warning information is generated based on the comprehensive risk score of each risk factor, and the supply chain risk warning information includes the comprehensive risk score of each risk factor.
2. The supply chain risk early warning method according to claim 1, characterized in that, Based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, a risk assessment matrix corresponding to each assessment expert is established, including: Based on the membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts, an initial risk assessment matrix corresponding to each assessment expert is established. The initial risk assessment matrix corresponding to each assessment expert is normalized to obtain the risk assessment matrix corresponding to each assessment expert.
3. The supply chain risk early warning method according to claim 1, characterized in that, For any risk assessment matrix, compare all risk factors pairwise, and build a risk judgment matrix based on the comparison results, including the following steps: Step 1. For any risk assessment matrix, compare the m-th risk factor with the n-th risk factor based on the evaluation criteria set by the corresponding assessment experts to obtain the comparison result between the m-th risk factor and the n-th risk factor, where the initial values of m and n are both 1; Step 2. Increment n by 1, and re-compare the m-th risk factor with the n-th risk factor based on the established evaluation criteria; Step 3. When n=i, increment m by 1 and n=1, then return to Step 1 until m=i and n=i, to obtain the pairwise comparison results of all risk factors, where i represents the total number of risk factors; Step 4. Assign values based on the pairwise comparison results of all risk factors to obtain the pairwise risk factor comparison values of all risk factors; Step 5. Construct a matrix based on the pairwise comparison values of all risk factors to obtain the risk assessment matrix.
4. The supply chain risk early warning method according to claim 3, characterized in that, The pairwise risk factor comparison value of two identical risk factors is 0.5, and the sum of the risk factor comparison values of the m-th risk factor and the n-th risk factor, as well as the sum of the risk factor comparison values of the n-th risk factor and the m-th risk factor, is 1.
5. The supply chain risk early warning method according to claim 3, characterized in that, Based on the risk judgment matrix corresponding to each assessment expert, the weight of each risk factor corresponding to each assessment expert is determined, including: The weight of each risk factor corresponding to each assessment expert is determined according to the following formula: w_k=(1+b_k1+b_k2+......+b_ki) / (i×(i 2 -1)); Where w_k represents the weight of the k-th risk factor corresponding to the assessment expert, b_ki represents the data in the k-th row and i-th column of the risk judgment matrix, and i represents the total number of risk factors.
6. The supply chain risk early warning method according to claim 1, characterized in that, Each risk level includes 5 risk levels.
7. The supply chain risk early warning method according to claim 1, characterized in that, Risk factors in the supply chain include supply risk, production risk, logistics risk, and / or market risk.
8. A supply chain risk early warning system, characterized in that, include: Establish a unit to build a risk assessment matrix corresponding to each assessment expert, based on the degree of membership of each risk factor in the supply chain to each risk level as assessed by multiple assessment experts. The comparison unit is used to compare all risk factors pairwise for any risk assessment matrix, and to establish a risk judgment matrix based on the comparison results, thereby obtaining the risk judgment matrix corresponding to each assessment expert. The first determining unit is used to determine the weight of each risk factor corresponding to each assessment expert based on the risk judgment matrix corresponding to each assessment expert, and to obtain the weight vector corresponding to each assessment expert. The second determining unit is used to determine the risk score value of each assessment expert for each risk factor by weighted geometric average based on the risk assessment matrix and weight vector of each assessment expert. The third determining unit is used to determine the comprehensive risk score of each risk factor based on the risk score of each assessment expert for each risk factor and the expert weight corresponding to each assessment expert. The generation unit is used to generate supply chain risk warning information based on the comprehensive risk score of each risk factor, wherein the supply chain risk warning information includes the comprehensive risk score of each risk factor.
9. An electronic device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the supply chain risk warning method as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the supply chain risk early warning method as described in any one of claims 1 to 7.