Supply chain risk mitigation strategy evaluation method based on multi-criterion decision model

Abstract

The invention provides a supply chain risk mitigation strategy evaluation method based on a multi-criterion decision model, and the method comprises the steps: carrying out the cross analysis of a supplier number weight adjustment coefficient and a component technical parameter, obtaining a technical risk coefficient, and carrying out the analysis of component replaceability data, obtaining a technical path dependency degree and supply source limitation, calculating comprehensive risk scores of the key components, and determining a risk priority sequence; formulating a multi-supplier configuration scheme based on a vulnerability assessment result, assessing supplier distribution and component compatibility, calculating a component interchange rate and a supplier coverage range, obtaining a standardized replacement power according to the interchange rate and the coverage range, and if the standardized replacement power is lower than a threshold value, increasing a supplier number weight; and analyzing supply interruption response time and replacement switching efficiency based on the component interchange rate, the supplier coverage range and the quantity weight, monitoring the supplier capacity utilization rate, measuring and calculating the production maintaining time length and the capacity recovery speed, and obtaining risk relief parameters after dynamic adjustment.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an evaluation method for supply chain risk mitigation strategies based on a multi-criteria decision-making model. Background Technology

[0002] Supply chain risk management, a crucial area of ​​business operations, directly impacts a company's stable development and market competitiveness. Especially in a globalized context, the complexity and uncertainty of supply chains make risk mitigation strategies a core issue for ensuring continuous operation. Effective risk management not only reduces the risk of supply chain disruptions but also enhances a company's responsiveness to unforeseen events, thus attracting significant attention in both academic and practical fields. However, current research and practical methods often struggle to fully capture the interactive effects between risk factors when facing dynamic changes in the supply chain. Particularly when assessing supply chain vulnerability, they neglect the deep connections between product characteristics and supply structures, resulting in a lack of targeted and flexible strategy adjustments. Many methods focus more on the dispersion of surface risks, failing to delve into the potential contradictions arising from changes in product complexity and supply dependence, thereby affecting the accuracy of overall risk assessment. Against this backdrop, supply chain risk management faces significant challenges, particularly in the interaction between product complexity and changes in key component characteristics, where component substitutability becomes a crucial factor influencing risk assessment. Decreased component substitutability, such as increased technological dependence or limited supply sources, directly leads to increased reliance on a single supplier within the supply chain. This reliance further exacerbates the weakening of standardization capabilities, meaning that while multi-supplier configurations can diversify risk, they may sacrifice the ease and consistency of component replacement, creating a dilemma in risk management. This contradiction not only affects the criticality assessment of individual components but also has a profound impact on the overall resilience of the supply chain. Therefore, how to dynamically balance the weighting conflict between the number of suppliers and component substitutability in component criticality scoring has become a key issue in evaluating supply chain risk mitigation strategies. Summary of the Invention

[0003] This invention provides a method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model, mainly including:

[0004] By acquiring key component substitute data, technical parameters, and compatibility through the component database, and combining historical supply record analysis, component dependencies and quality standards are collected. The initial substitute score value for each component is determined. Based on the initial substitute score value, supplier quantity weight data is determined. Supplier distribution information and dependence are extracted. Transportation vulnerability and inventory buffer data are obtained. The supplier quantity weight adjustment coefficient is obtained by combining geographical distribution and production capacity scale.

[0005] By cross-analyzing the supplier quantity weighting adjustment coefficient with the component technical parameters, the technical risk coefficient is obtained. Combined with the component substitutability data analysis, the technical path dependence and supply source restrictions are obtained. The comprehensive risk score of key components is calculated, and the risk priority ranking is determined.

[0006] Based on the technical risk coefficient and supply source constraints, the product complexity data is obtained by integrating component dependencies and quality standards. Combined with supplier dependence and inventory buffer data, multi-dimensional risk factors are formed. The mapping relationship between product complexity and supply chain stability is analyzed to obtain the supply chain vulnerability assessment results.

[0007] Based on the vulnerability assessment results, a multi-vendor configuration scheme is developed, the vendor distribution and component compatibility are evaluated, the component interchangeability rate and vendor coverage are calculated, and the standardized replacement force is obtained based on the interchangeability rate and coverage. If it is lower than the threshold, the vendor number weight is increased.

[0008] Based on component interchangeability, supplier coverage and quantity weighting, analyze supply interruption response time and substitution switching efficiency, monitor supplier capacity utilization, calculate production maintenance duration and capacity recovery speed, and obtain dynamically adjusted risk mitigation parameters.

[0009] Collect supplier risk change trends and supply chain operation data, analyze the mapping relationship between key component substitution changes and long-term supply disruption resilience, predict supply disruption scenarios based on production maintenance duration, determine risk management rules based on risk mitigation parameters, and obtain the latest risk control plan.

[0010] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0011] This invention discloses a supply chain risk mitigation strategy evaluation method based on a multi-criteria decision-making model. This method calculates component risk scores and supply chain vulnerability by analyzing multi-dimensional data such as component substitutability, supplier distribution, and technical parameters. Based on the evaluation results, this invention formulates a multi-supplier configuration scheme, assesses component interchangeability and supplier coverage, and derives a standardized substitution capability index. Simultaneously, it analyzes supply disruption response time and substitution switching efficiency, monitors supplier capacity, calculates production maintenance duration, and dynamically adjusts risk mitigation parameters. Furthermore, this invention analyzes the relationship between changes in component substitutability and long-term resilience to supply disruptions, predicts supply disruption scenarios, and formulates risk management rules. This method can comprehensively assess supply chain risks, optimize the supplier network, improve supply chain resilience, and effectively address potential supply disruption risks. Attached Figure Description

[0012] Figure 1 This is a flowchart of an evaluation method for supply chain risk mitigation strategies based on a multi-criteria decision-making model, according to the present invention.

[0013] Figure 2 This is a schematic diagram of a supply chain risk mitigation strategy evaluation method based on a multi-criteria decision-making model according to the present invention. Detailed Implementation

[0014] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0015] like Figure 1-2 This embodiment of a supply chain risk mitigation strategy evaluation method based on a multi-criteria decision-making model may specifically include:

[0016] S101. Obtain key component substitute data, technical parameters and compatibility through the component database, combine with historical supply record analysis, collect component dependencies and quality standards, determine the initial substitute score value for each component, determine supplier quantity weight data based on the initial substitute score value, extract supplier distribution information and dependence, obtain transportation vulnerability and inventory buffer data, and obtain supplier quantity weight adjustment coefficients by combining geographical distribution and production capacity scale.

[0017] Substitutability data, technical parameters, and compatibility information for key components are obtained from a component database. Compatibility scores are calculated based on the matching degree of component technical parameters. Component switchover success rate data is extracted from historical supply records. Combining the strength of dependencies between components and compliance with quality standards, weights are assigned to the compatibility score, switchover success rate, inverse of dependency strength, and compliance with quality standards, and then summed to obtain the initial substitutability score for each component. A corresponding supplier quantity weight benchmark value is determined based on the initial substitutability score for each component. If the initial substitutability score is higher than a preset threshold, a lower supplier quantity weight benchmark value is assigned; if it is lower than the preset threshold, a higher supplier quantity weight benchmark value is assigned. The geographical coordinates and production capacity data of each supplier are extracted from the supply database to calculate the supplier geographical distribution concentration.

[0018]

[0019] GC represents the geographical concentration of suppliers, n represents the total number of components, m_i represents the number of suppliers for component i, and A_ij represents the production capacity of supplier j for component i. This formula calculates the geographical concentration based on production capacity distribution; a smaller value indicates a more concentrated distribution. Data on transportation vulnerabilities and inventory buffer levels at each node in the transportation network are obtained. A supply chain risk coefficient is calculated by multiplying the geographical concentration by the number of transportation vulnerabilities. The supplier quantity weight baseline is multiplied by the supply chain risk coefficient, and then adjusted according to the ratio of production capacity share to inventory buffer level to obtain the supplier quantity weight adjustment coefficient.

[0020] For example, the calculation of component substitutability scores involves a comprehensive evaluation across multiple dimensions.

[0021] Specifically, the technical parameter matching degree reflects the similarity between the replacement component and the original component in terms of electrical characteristics, mechanical performance, etc. When an electronic component needs to be replaced, parameters such as operating voltage, temperature coefficient, and packaging form are compared; a higher matching degree indicates a lower replacement risk. The compatibility score assesses the interchangeability between components from the perspectives of interface standards and communication protocols. The component switchover success rate in historical supply records provides a reference for the actual replacement effect; these data reflect the success rate of past replacement cases.

[0022] In one possible implementation, dependency strength is determined by analyzing the number of connections and the scope of impact of a component within the overall system. Core components typically have high dependency strength, and their failure will affect the normal operation of multiple downstream components. Quality standard compliance examines whether alternative components meet industry certification requirements, such as ISO standards and CE certification. By assigning different weights to these factors and summing them, an initial score reflecting the overall feasibility of component replacement can be obtained.

[0023] It's important to note that there's an inverse relationship between the initial substitutability score and the supplier quantity weighting benchmark. When a component is highly substitutable, it means there are multiple viable alternatives in the market, reducing reliance on a single supplier and thus assigning a lower supplier quantity weighting. Conversely, components with weak substitutability require maintaining a larger number of suppliers to mitigate supply disruption risks. This mechanism ensures supply chain stability and flexibility. The calculation of geographical concentration takes into account the geographical dispersion of suppliers.

[0024] For example, if four out of five suppliers for a component are located in the same region, the geographical concentration is high, increasing the likelihood of regional risks. Transportation vulnerability data includes information on key nodes along transportation routes, such as ports, airports, and major highway hubs; disruptions to these nodes can severely impact supply chain continuity.

[0025] In one embodiment, the supply chain risk coefficient is measured by multiplying the geographical concentration by the number of transportation vulnerabilities. High concentration combined with multiple vulnerabilities indicates a greater systemic risk to the supply chain. Inventory buffer levels reflect the ability to cope with supply disruptions; higher inventory can mitigate the impact of supplier issues to some extent. Capacity share reflects the differences in supply capacity among suppliers, avoiding over-reliance on a single large supplier. By multiplying the supplier quantity weighting benchmark by the supply chain risk coefficient, and then adjusting it according to the ratio of capacity share to inventory buffer level, the final supplier quantity weighting adjustment coefficient comprehensively reflects the actual risk status of the supply chain.

[0026] S102. Cross-analyze the supplier quantity weighting coefficient with the component technical parameters to obtain the technology risk coefficient. Combine the component substitutability data analysis to obtain the technology path dependence and supply source restrictions. Calculate the comprehensive risk score of key components and determine the risk priority ranking.

[0027] The process involves acquiring supplier quantity weighting adjustment coefficients and component technical parameter data. Preset weight values ​​are assigned to the number of patents, manufacturing process complexity, and material scarcity within the technical parameters. A technology risk coefficient, reflecting the correlation between component technical difficulty and supply concentration, is obtained by multiplying the supplier quantity weighting adjustment coefficient by each technical parameter weight value and summing the results. Based on this technology risk coefficient, corresponding substitutability data is extracted from the component database. The ratio of the difference between the original and substitute components for each technical parameter to the original component's parameter value is calculated as the degree of difference. The ratio of the number of substitute components to the average degree of difference is used to obtain the technology path dependence. Simultaneously, the sum of the number of supplier countries and certification requirements for each substitute component is calculated to determine the supply source restriction index. A comprehensive risk score for key components is calculated by multiplying the technology risk coefficient by the path dependence weight, the technology path dependence by the dependence weight, and the supply source restriction index by the source weight. If the comprehensive risk score exceeds a preset threshold, the component is marked as high-risk. The components are then ranked from highest to lowest risk score to obtain a risk priority ranking.

[0028] For example, the calculation of the technology risk coefficient requires comprehensive consideration of multiple dimensions of technical parameter characteristics.

[0029] Specifically, the number of patents reflects the technological barrier of a component. A chip component with 50 core patents is significantly more technologically complex and harder to replace than a general-purpose component with only 5 patents. Manufacturing process complexity is measured by the number of production steps, precision requirements, and yield rate. Components produced using high-precision photolithography have higher process complexity than those produced using ordinary machining. Material scarcity examines the ease of obtaining raw materials; magnetic components made from rare-earth materials face greater supply risks than those made from iron-based materials.

[0030] In one possible implementation, the combination of supplier quantity weighting adjustment coefficients and the weights of these technical parameters reflects the amplifying effect of supply concentration on technical risk. When a technically complex component has only a few suppliers, the technical risk is further amplified. By multiplying the adjustment coefficients by the weights of each parameter and summing the results, the resulting technical risk coefficient can comprehensively reflect the overall technical risk level of the component.

[0031] It should be noted that the calculation of the difference in technical parameters involves the quantitative comparison of multiple specific parameters.

[0032] For example, if the original component operates at 2.4 GHz and the replacement component operates at 2.3 GHz, the frequency difference is 0.042. Similarly, parameters such as operating temperature range, power consumption, and size also need to have their corresponding differences calculated. The mean of these differences reflects the overall deviation of the replacement component from the original component. Technology path dependence is measured by the ratio of the number of replacement components to the mean difference. This calculation method reflects the mitigating effect of the abundance of alternative options on path dependence. When there are 10 replacement components on the market and the average difference is 0.05, the resulting technology path dependence is 200, indicating a large number of alternative options and small technological differences, resulting in a low degree of path dependence. Conversely, when there are only 2 replacement components and the average difference is 0.3, the dependence is only 6.67, indicating limited alternative options and large technological differences.

[0033] In one embodiment, the supply source constraint index is determined by statistically analyzing the geographical distribution and certification requirements of suppliers. If five suppliers are located in three different countries and need to meet three certification requirements—ISO 9001, IATF 16949, and AS 9100—then the supply source constraint index is 6. A higher index indicates greater complexity and potential constraints in the supply chain. The overall risk score is calculated using a weighted summation method, where path dependence weight, dependency weight, and source weight are determined based on industry characteristics and corporate strategy. Technology-intensive industries may assign higher weights to technology risk coefficients, while globalized supply chain companies may focus more on supply source constraints. By setting reasonable thresholds, high-risk components requiring focused attention can be identified.

[0034] S103. Based on the technical risk coefficient and supply source constraints, integrate component dependencies and quality standards to obtain product complexity data. Combine supplier dependence and inventory buffer data to form multi-dimensional risk factors. Analyze the mapping relationship between product complexity and supply chain stability to obtain supply chain vulnerability assessment results.

[0035] Based on the technology risk coefficient and supply source constraints, the product of the number of inter-component dependencies and the number of quality standard compliance items is calculated. This is achieved by multiplying the technology risk coefficient by the risk weight and the supply source constraint by the source weight, combined with the product of dependencies and quality standards, to obtain product complexity data. Based on the product complexity data, corresponding supplier dependency indicators and inventory buffer level data are retrieved from the supplier database. The ratio of inventory buffer level to supplier dependency is calculated as the supply assurance coefficient. A multi-dimensional risk factor matrix is ​​constructed using product complexity data as the first dimension, the supply assurance coefficient as the second dimension, and supplier dependency as the third dimension. By normalizing the values ​​of each dimension in the multi-dimensional risk factor matrix by their respective maximum values, the Pearson correlation coefficient between the product complexity dimension and the supply assurance coefficient dimension is calculated as the mapping strength. If the absolute value of the correlation coefficient exceeds a preset threshold, a strong mapping relationship is determined. The supply chain vulnerability assessment result is obtained by multiplying the weighted average of the normalized values ​​of the three dimensions by the mapping strength.

[0036] For example, the calculation of product complexity data fully considers the interactive effects of both technology and supply dimensions.

[0037] Specifically, the number of inter-component dependencies reflects the structural complexity of the product. When a core processor component has direct connections to 15 peripheral components, the number of dependencies is 15. The number of quality standard compliance items covers multiple aspects such as reliability testing, environmental adaptability, and electromagnetic compatibility; each industry standard met counts as one compliance item. The product of these two reflects the complexity of the product under the dual constraints of structure and quality.

[0038] In one possible implementation, technological risk coefficients and supply source constraints are incorporated into the complexity calculation through a weighted approach. The risk and source weights are set based on industry experience and historical data analysis; the risk weight for technology-intensive products is typically set at 0.6, while the source weight for products with a high degree of globalization in their supply chains can reach 0.4. This weighting mechanism allows for a more accurate assessment of the complexity of different product types.

[0039] It's important to note that the supplier dependency index query is based on a segmented mapping of product complexity data. The supplier database pre-establishes a correspondence between complexity and dependency; higher complexity products typically correspond to higher supplier dependency. Inventory buffer level data, measured in days, indicates the time existing inventory can sustain production in the event of a supply disruption. The supply assurance coefficient, calculated as the ratio of these two metrics, reflects the inventory's ability to mitigate supply risks. The construction of the multi-dimensional risk factor matrix integrates three key dimensions into a unified framework. Product complexity, as the first dimension, represents intrinsic technical characteristics; the supply assurance coefficient, as the second dimension, reflects risk mitigation capabilities; and supplier dependency, as the third dimension, reflects the degree of external dependence. This three-dimensional structure comprehensively characterizes the risk features of the supply chain.

[0040] In one embodiment, the normalization process employs a maximum value standardization method, unifying the values ​​of each dimension to a range of 0 to 1. The calculation of the Pearson correlation coefficient reveals the intrinsic link between product complexity and supply assurance capability. A correlation coefficient of -0.8 indicates that the more complex the product, the weaker its supply assurance capability, demonstrating a strong negative correlation. Identifying this correlation is crucial for understanding supply chain vulnerability. The supply chain vulnerability assessment results are derived by comprehensively considering the normalized values ​​of the three dimensions and the strength of the correlation. The weighted average of the three dimensions reflects the overall risk level, with the weights of each dimension determined based on the company's risk appetite and strategic priorities. When a strong correlation exists, the assessment results are amplified, as this implies a higher level of systemic risk. The assessment results are presented in a score range of 0 to 100, providing a quantitative basis for supply chain risk management decisions, enabling companies to identify the most vulnerable links and take targeted improvement measures.

[0041] S104. Based on the vulnerability assessment results, formulate a multi-vendor configuration scheme, evaluate the vendor distribution and component compatibility, calculate the component interchangeability rate and vendor coverage, obtain the standardized replacement force based on the interchangeability rate and coverage, and increase the vendor quantity weight if it is lower than the threshold.

[0042] Based on vulnerability assessment results, components are categorized according to risk scores. Supplier selection criteria are determined based on the technical parameters and quality requirements of high-risk components. Production capacity, certifications, and technical parameter ranges of each supplier are extracted from the supplier database. This data is matched with component technical requirements to obtain a candidate supplier set for each component, forming an initial multi-supplier configuration scheme. Using the geographical coordinates of each supplier in the initial multi-supplier configuration scheme, the sum of the negative logarithms of the proportion of suppliers in different geographical areas to the total number of suppliers is calculated as the supplier distribution entropy. The component database is used to retrieve the component technical parameter difference matrix. The number of component pairs with differences below the compatibility threshold is divided by the total number of components to obtain the component interchangeability rate. The supplier coverage is calculated using the ratio of the number of geographical areas covered by the supplier to the total number of target market areas. The standardized replacement force is calculated by multiplying the component interchangeability rate by the supplier coverage. If the standardized replacement force is below a preset threshold, the weight increase is determined according to the rule that the weight increases by 5% for every ten units increase in risk score. This corresponding increase is applied to the original supplier quantity weights to obtain new supplier quantity weights.

[0043] For example, vulnerability assessment results comprehensively reflect the risk status of the supply chain through multi-dimensional quantitative indicators.

[0044] Specifically, the risk score calculation comprehensively considers factors such as technological complexity, supply concentration, and geographical distribution, with each factor having a corresponding weighting coefficient. When a key chip component has extremely high technological complexity and can only be produced by two suppliers, its risk score will increase significantly. This quantitative assessment provides a scientific basis for subsequent supplier allocation decisions.

[0045] In one possible implementation, the supplier database encompasses comprehensive information about each supplier. Technical capabilities are recorded, including the types of components each supplier can produce, their technical parameter ranges, and process levels. Quality certification information includes the acquisition and validity period of industry standard certifications such as ISO 9001 and IATF 16949. Production capacity data details each supplier's monthly maximum capacity, current capacity utilization rate, and expansion potential. This systematic management of information makes the supplier matching process more efficient and accurate.

[0046] It's important to note that the core of the multi-dimensional comparison method lies in the standardization and weighting of parameters. For processor components, technical parameters such as operating frequency, power consumption, and temperature range need to be normalized to ensure comparability. Temperature range matching not only considers the coverage of numerical intervals but also assesses the supplier's product stability testing capabilities under extreme temperature conditions. By setting the importance weights for each parameter, a comprehensive matching score can be calculated. The calculation principle of supplier distribution entropy originates from the measurement of uncertainty in information theory. When suppliers are highly concentrated in a certain region, the proportion of suppliers in that region is close to 1, while the proportion in other regions is close to 0, resulting in a low entropy value, indicating a high risk of geographical concentration in the supply chain. Conversely, when suppliers are evenly distributed across multiple regions, the proportions in each region are similar, resulting in a higher entropy value and better dispersion of geographical risk in the supply chain. This quantitative method makes the assessment of geographical distribution more objective.

[0047] In one embodiment, the calculation of component technical parameter differences needs to consider the physical meaning and actual impact of the parameters. For voltage parameters, the difference between 5V and 5.5V is calculated to be 0.1, which is acceptable in most application scenarios. However, for precision instrument components, even small parameter differences can lead to a decrease in system performance. Therefore, the setting of the compatibility threshold needs to be combined with specific application scenarios and quality requirements. Standardized replacement capability, as a comprehensive indicator, reflects the supply chain's ability to cope with supply disruptions. Component interchangeability reflects technical flexibility; a high interchangeability rate means that when a component's supply is problematic, a replacement solution can be quickly found. Supplier coverage assesses supply assurance capabilities from a market service perspective; the wider the coverage, the better able it is to cope with supply demand fluctuations in different regions. The product of the two reflects the overall resilience of the supply chain. The linear relationship design of the weight adjustment mechanism ensures the predictability and consistency of risk response. This mechanism avoids the arbitrariness of subjective judgment, ensuring that components of different risk levels can obtain corresponding supplier resource allocations. By dynamically adjusting the supplier quantity weights, the optimal allocation of supply chain resources is achieved, ensuring the supply security of high-risk components while avoiding resource waste for low-risk components.

[0048] Based on the vulnerability assessment results, we extract supplier geographic distribution density and service radius data, analyze the technical interface matching degree and product specification correspondence of each supplier, establish a list of alternative components and a compatibility matrix, count the number of alternative suppliers for key components, calculate the success rate of interchangeability of a single component among different suppliers, thereby assessing the geographic coverage integrity and sufficiency of alternative solutions of the supplier network, and integrating the interchangeability success rate with geographic coverage data to form a standardized replacement capability index.

[0049] Based on the supply chain disruption risk value from the vulnerability assessment results, the latitude and longitude coordinates of each supplier's warehousing nodes and the corresponding component inventory levels are obtained. The Euclidean distance between each node and the customer's demand point is calculated. A distance-cost mapping table is constructed based on the distance value and transportation cost coefficient. The supplier nodes are regionalized using a K-means clustering algorithm, where the K value is determined according to the vulnerability risk level. The geographical density value and service radius value of each cluster center are obtained. For the geographical density value and service radius value, combined with the cost data in the distance-cost mapping table, the technical interface parameter set and product specification parameters of each supplier are extracted, including electrical interface type, data transmission protocol, physical size specifications, and performance index range. The comprehensive matching degree between different suppliers is calculated using cosine similarity. If the matching degree is greater than the threshold set according to the vulnerability level, the supplier pair is recorded as an interchangeable combination. A compatibility matrix containing supplier identifier, interface type, specification parameters, and matching degree value is constructed. Based on the interchangeable combinations in the compatibility matrix and the supply disruption probability in the vulnerability assessment, the real-time inventory levels and historical supply response time records of each supplier are read. The success rate of interchangeability for each component among different suppliers is calculated. Combining the transportation cost data and supply disruption probability in the distance-cost mapping table, a list of alternative components containing component numbers, a list of alternative suppliers, and risk-adjusted interchangeability rates is generated. Based on the list of alternative components and risk-adjusted interchangeability rate data, the distribution of the number of alternative suppliers in each geographical region is statistically analyzed. The regional coverage index is calculated as the ratio of the number of supplier nodes to the number of customer demand points in that region. The coverage index of each region, the risk-adjusted interchangeability rate of components, and the regional risk coefficient in the vulnerability assessment are integrated, and a standardized substitution power index value is obtained through a weighted average method.

[0050] In one possible implementation, vulnerability assessment results serve as the foundational data input for the entire supply chain replacement capability analysis. This data includes key information such as the historical outage frequency, recovery time, and risk level of each supplier. These assessment results directly influence the weighting of subsequent geographic distribution analysis. For example, if a manufacturing company discovers that its chip supplier A has experienced three supply disruptions in the past two years, with each recovery time exceeding 15 days, the system will mark this supplier as high-risk. This risk label will be directly passed to the geographic distribution density calculation.

[0051] Specifically, when acquiring supplier geographic location data, the system not only records the latitude and longitude coordinates of warehousing nodes but also assigns different reliability weights to each node based on vulnerability risk values. Even if a node of a high-risk supplier has a favorable geographical location, its influence in cluster analysis will be correspondingly reduced. The choice of the K-value in the K-means clustering algorithm is directly related to the risk level. When the overall supply chain vulnerability is high, the system increases the K value to create more supply area divisions, thereby diversifying the risk. This risk-based dynamic clustering method ensures that even if a supply disruption occurs in one area, other areas can still provide alternative solutions.

[0052] It should be noted that the calculation of technical interface matching involves a comprehensive evaluation of multiple dimensions of parameters. Electrical interface types include physical characteristics such as voltage level, current specification, and interface shape; data transmission protocols cover software-level compatibility such as communication rate, data format, and encryption methods; and product specifications focus on manufacturing standards such as dimensional tolerances, performance indicators, and environmental adaptability. The cosine similarity algorithm vectorizes these multidimensional parameters to calculate the degree of similarity between products from different suppliers in the technical space. When the similarity between two suppliers' products in these dimensions exceeds a threshold dynamically set according to vulnerability levels, the system determines that they are interchangeable.

[0053] In one embodiment, the compatibility matrix is ​​constructed with full consideration of the complexities of actual supply chain operations. The matrix not only records static technology matching information but also dynamically updates the real-time status of suppliers. When an electronics manufacturer needs to find alternative suppliers for capacitors, the system filters suppliers that meet the technology matching requirements from the compatibility matrix and calculates the actual procurement cost using a distance-cost mapping table. Suppliers that are closer but have insufficient inventory are assigned different replacement priorities compared to suppliers that are farther away but have sufficient inventory; this trade-off mechanism ensures the practicality of the alternatives.

[0054] Preferably, the calculation of the risk-adjusted swap ratio incorporates multiple dynamic factors. The system continuously monitors indicators such as inventory level trends, historical on-time delivery rates, and quality pass rates of various suppliers, and adjusts the original swap success rate based on the probability of supply disruption in the vulnerability assessment. This adjustment mechanism reflects the true state of supply chain risk, avoiding over-reliance on surface data and ignoring potential risks. The final generation of the standardized substitution capability indicator embodies the idea of ​​global optimization. By integrating regional coverage, risk-adjusted swap ratio, and regional risk coefficient, the system provides decision-makers with a comprehensive supply chain resilience assessment indicator. This indicator not only reflects current substitution capabilities but also predicts supply chain performance under different risk scenarios. Enterprises can use this to formulate more robust procurement strategies, find the optimal balance between cost control and risk prevention, thereby improving the risk resistance and operational efficiency of the entire supply chain network.

[0055] S105. Based on component interchangeability, supplier coverage and quantity weighting, analyze supply interruption response time and substitution switching efficiency, monitor supplier capacity utilization, calculate production maintenance time and capacity recovery speed, and obtain dynamically adjusted risk mitigation parameters.

[0056] The process involves acquiring component interchangeability data from various suppliers, calculating the substitution feasibility score for each component across different suppliers, determining the supply assurance coefficient for each component based on the supplier coverage matrix, and obtaining a component supply stability index by multiplying the quantity weights by the supply assurance coefficients. Based on the component supply stability index and real-time monitoring data of supplier capacity utilization, a moving average method is used to process recent supply interruption response time records, calculating the predicted supply interruption response time, where the predicted response time equals the historical average response time multiplied by the reciprocal of the current capacity utilization rate. Using the predicted supply interruption response time, a supplier substitution switching efficiency matrix is ​​constructed, where the matrix elements are the ratio of switching time to the predicted response time. If the ratio exceeds a preset switching efficiency threshold, the supplier pair is marked as an inefficient switching pair, forming a supplier switching priority sequence. Based on the supplier switching priority sequence, the product of each supplier's production maintenance duration and capacity recovery speed is calculated to obtain a supply chain resilience index. Combined with the values ​​in the switching efficiency matrix, a risk mitigation parameter is determined using a weighted average method. This parameter is dynamically adjusted according to changes in capacity utilization, enabling real-time management of supply chain risks.

[0057] For example, obtaining component interchangeability data involves a quantitative assessment of the feasibility of substituting various components in the supply chain with different suppliers.

[0058] Specifically, interchangeability reflects the ability of other suppliers to provide the same or similar components when one supplier experiences a supply disruption. This ability depends not only on the matching of technical specifications but also on the compatibility of quality standards, certification requirements, and manufacturing processes. By establishing a supplier coverage matrix, it is possible to clearly show how many suppliers cover each component, as well as the geographical distribution and production capacity of these suppliers.

[0059] In one possible implementation, the supply assurance coefficient calculation needs to comprehensively consider two dimensions: coverage and quantity weighting. High coverage means more alternative suppliers, while quantity weighting reflects the differences in supply capacity among suppliers. When the coverage is three suppliers with a supply capacity ratio of 5:3:2, a weighted calculation can yield a more accurate supply assurance coefficient, which better reflects the actual supply assurance capability than simple supplier quantity statistics. The prediction of supply disruption response time uses a moving average method to process historical data; this method can smooth short-term fluctuations and capture long-term trends.

[0060] It's important to note that capacity utilization plays a crucial role in response time forecasting. When a supplier's capacity utilization is near full capacity, its responsiveness to sudden demand decreases significantly. The predicted response time is calculated by multiplying the historical average response time by the inverse of the capacity utilization rate; this calculation method dynamically reflects the supplier's real-time responsiveness. The alternative switching efficiency matrix is ​​constructed based on the actual switching time and the predicted response time.

[0061] For example, if switching from supplier A to supplier B takes 10 days, and supplier B's predicted response time is 5 days, then the switching efficiency ratio is 2. The higher this ratio, the higher the switching cost and the lower the efficiency. By setting a switching efficiency threshold, supplier combinations with excessively high switching costs can be identified, thereby optimizing supplier switching strategies. Production maintenance duration reflects the time a supplier can continue production under existing inventory conditions, while capacity recovery speed measures the time required for a supplier to restore normal capacity after a supply disruption. The product of these two metrics forms the supply chain resilience index; the higher this index, the stronger the supply chain's ability to withstand risks. Combining the switching efficiency matrix values ​​with a pre-defined weighted average method to calculate risk mitigation parameters allows for a comprehensive assessment of the supply chain's risk status. This dynamic adjustment mechanism enables risk management strategies to be adjusted promptly as the supply chain status changes, improving the flexibility and responsiveness of supply chain management.

[0062] S106. Collect supplier risk change trends and supply chain operation data, analyze the mapping relationship between key component substitution changes and long-term supply disruption resistance, predict supply disruption scenarios based on production maintenance time, determine risk management rules based on risk mitigation parameters, and obtain the latest risk control plan.

[0063] The process involves acquiring supplier risk change trend data and supply chain operation data, calculating the risk change rate using time series methods, determining component substitutability indicators based on the feasibility scores of key components among different suppliers, and calculating the correlation between the substitutability indicators and historical supply disruption durations using the Pearson correlation coefficient to obtain a long-term supply disruption resilience mapping coefficient. The safety stock baseline is adjusted based on this mapping coefficient. Combining real-time monitored inventory levels and average daily consumption rates, the production maintenance duration is calculated by dividing inventory by the consumption rate. Monte Carlo simulations are used to generate supply disruption scenarios, with historical supply disruption frequency and duration distributions as inputs, and a probability distribution of the disruption scenarios as output. Based on the probability distribution of the disruption scenarios, the expected loss value for each scenario is calculated as the scenario risk value. If the scenario risk value exceeds the risk threshold determined based on historical loss data, the corresponding risk management rules are activated, including activating backup suppliers, increasing safety stock, and adjusting production plans, forming a tiered response management rule set. Using the rules in the management rule set, the rule execution order is determined based on the scenario risk value. Supplier switchover schedules, inventory replenishment plans, and capacity allocation parameters are integrated to form a new risk control scheme containing specific execution parameters and triggering conditions.

[0064] For example, the collection of supplier risk change trend data involves information collection from multiple dimensions, including fluctuations in the supplier's financial condition, changes in production capacity, quality stability indicators, and geopolitical risk levels.

[0065] Specifically, when calculating the rate of change of risk using time series methods, it is necessary to periodically sample historical data to identify upward or downward trends in risk indicators. The determination of component substitutability indicators is based on a comprehensive assessment of three key factors: technical specification matching, consistency of certification requirements, and supplier switching costs.

[0066] In one possible implementation, the Pearson correlation coefficient can quantify the strength of the association between substitutability indicators and historical supply disruption durations. A correlation coefficient close to 1 indicates that components with higher substitutability tend to have shorter supply disruption durations; a correlation coefficient close to -1 indicates a negative correlation. This correlation analysis provides the data foundation for subsequent mapping coefficient calculations, allowing long-term supply disruption resilience to be measured numerically. The long-term supply disruption resilience mapping coefficient plays a crucial role in safety stock adjustments.

[0067] It should be noted that components with higher mapping coefficients can have their safety stock baseline values ​​reduced accordingly, as these components have more alternatives when facing supply disruption risks. The calculation of production maintenance time is derived by dividing the current inventory level by the average daily consumption rate; this indicator directly reflects the number of days a company can maintain normal production in the event of a complete supply disruption. The application of Monte Carlo simulation in generating supply disruption scenarios is based on the probability distribution of historical data. During the simulation, the system randomly selects the time points of supply disruption occurrences from the historical supply disruption frequency distribution and the duration of the disruption from the duration distribution, generating a set of possible supply disruption scenarios through numerous repeated simulations. Each scenario includes the specific disruption time, duration, and range of affected components, forming a complete risk scenario description. The calculation of the scenario risk value comprehensively considers both the probability of supply disruption and the expected loss.

[0068] For example, if the probability of a critical component being unavailable for 30 days in a certain scenario is 0.1, and the expected loss is 1 million yuan, then the risk value for this scenario is 100,000 yuan. When the risk value exceeds a preset threshold, the system automatically triggers corresponding management rules. These rules include specific measures such as activating pre-certified backup suppliers, raising safety stock to emergency levels, and adjusting production plans to prioritize the consumption of readily replaceable components. The formation process of the risk control plan reflects a multi-layered decision-making logic. Based on different risk levels, the plan specifies the timing of supplier switching, the quantity and frequency of inventory replenishment, and the dynamic allocation ratio of production capacity across different product lines. This structured plan design enables enterprises to respond quickly to supply chain disruptions and effectively manage risks through preset parameters and triggering conditions.

[0069] It should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the disclosure of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model, characterized in that, The method includes: S1. Combine key component substitutability data, technical parameters, and compatibility to determine the initial substitutability score for each component. Based on the initial substitutability score, determine the supplier quantity weight data, obtain the supplier's transportation vulnerability and inventory buffer data, and combine geographical distribution and production capacity to obtain the supplier quantity weight adjustment coefficient. S2. The supplier quantity weighting adjustment coefficient is combined with the component technical parameter analysis to generate a technical risk coefficient. Combined with the component substitutability data analysis, the technical path dependence and supply source restrictions are analyzed to calculate the comprehensive risk score of key components and determine the risk priority ranking. S3. Based on the technical risk coefficient and supply source constraints, integrate component dependencies and quality standards to obtain product complexity data, combine supplier dependence and inventory buffer data to form multi-dimensional risk factors, analyze the mapping relationship between product complexity and supply chain stability, and obtain supply chain vulnerability assessment results. S4. Based on the vulnerability assessment results, develop a multi-vendor configuration plan, assess vendor distribution and component compatibility, and calculate component interchangeability and vendor coverage. S5. Analyze the supply interruption response time and substitution switching efficiency, monitor the supplier's capacity utilization rate, calculate the duration of production maintenance and capacity recovery speed, and obtain risk mitigation parameters. S6. Analyze the mapping relationship between the substitution changes of key components and the long-term resilience to supply disruptions, and generate risk control plans.

2. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, S1 includes: The alternative data, technical parameters and compatibility information of key components are obtained from the component database. The compatibility score is calculated based on the matching degree of the technical parameters of the components. The component switching success rate data is extracted from the historical supply records. The compatibility score, switching success rate, inverse of dependency strength and quality standard compliance are weighted and summed to obtain the initial alternative score of each component. The supplier quantity weight baseline value is determined based on the initial substitutability score of each component, and the supplier quantity weight adjustment coefficient is determined by combining the transportation vulnerability data of each node in the transportation network and the inventory buffer level.

3. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, The S2 includes: Obtain supplier quantity weight adjustment coefficient and component technical parameter data, assign preset weight values ​​according to the number of patents, manufacturing process complexity and material scarcity in the technical parameters, and sum the results by multiplying the supplier quantity weight adjustment coefficient and the weight values ​​of each technical parameter to obtain the technical risk coefficient. Extract substitutability data corresponding to the technical risk coefficient from the component database, calculate the proportion of the difference between the original component and the replacement component in each technical parameter to the parameter value of the original component, and obtain the degree of difference; The technology path dependence is obtained by the ratio of the number of alternative components to the mean of the difference. The supply source restriction index is obtained by summing the number of supplier countries and the number of certification requirements for each alternative component; The comprehensive risk score of the key component is obtained by multiplying the technology risk coefficient by a preset path dependence weight, the technology path dependence by a preset dependence weight, and the supply source restriction index by a preset source weight, and summing the products of the three items. Risk priority is determined based on the comprehensive risk score of key components.

4. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, The S3 includes: The product complexity data is obtained by multiplying the technology risk coefficient and risk weight, plus the product of supply source restrictions and source weight. Based on product complexity data, query the corresponding supplier dependence index and inventory buffer level data from the supplier database, and calculate the ratio of inventory buffer level to supplier dependence as the supply security coefficient. We use product complexity data as the first dimension, supply assurance coefficient as the second dimension, and supplier dependence as the third dimension. The values ​​of each dimension are normalized by dividing by their respective maximum values. The Pearson correlation coefficient between the product complexity dimension and the supply assurance coefficient dimension is calculated as the strength of the mapping relationship. The supply chain vulnerability assessment result is obtained by multiplying the weighted average of the normalized values ​​of the three dimensions with the strength of the mapping relationship.

5. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, The S4 includes: Based on the vulnerability assessment results, a set of candidate suppliers for each component is determined. The technical parameter differences between components are read from the component database. The number of component pairs with differences below the compatibility threshold is divided by the total number of components to obtain the component interchangeability rate. The supplier coverage is determined based on the geographical coordinates of each candidate supplier.

6. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, The S5 includes: The supply guarantee coefficient for each component is determined based on the supplier coverage area, and the component supply stability index is obtained by multiplying the supplier quantity weight by the supply guarantee coefficient. Based on the component supply stability index, the predicted value of supply interruption response time is calculated in combination with the supplier capacity utilization rate, and the risk mitigation parameters are determined by combining the production maintenance time and capacity recovery speed of each supplier.

7. The method for evaluating supply chain risk mitigation strategies based on a multi-criteria decision-making model according to claim 1, characterized in that, The S6 includes: The component substitutability index is determined based on the substitution feasibility score of key components among different suppliers. The correlation between the substitutability index and the duration of historical supply disruptions is calculated using the Pearson correlation coefficient to obtain the long-term supply disruption resilience mapping coefficient. The safety stock benchmark value is adjusted based on the long-term supply disruption capability mapping coefficient. Combined with the real-time monitored inventory level and average daily consumption rate, the production maintenance time is calculated by dividing the inventory by the consumption rate. Monte Carlo simulation of supply disruption scenarios is used. The input is the historical supply disruption frequency and duration distribution, and the output is the supply disruption scenario probability distribution. Calculate the scenario risk value based on the probability distribution of supply disruption scenarios, and generate a risk control plan based on the scenario risk value.

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