Supply chain resilience evaluation method and device for customized production, equipment and medium

Abstract

The embodiment of the application provides a supply chain resilience evaluation method, device, equipment and medium for customized production. The method comprises the following steps: determining a capacity retention coefficient according to supply chain capacity data; determining a supply structure entropy coefficient according to the supply capacity parameters of different supply paths of each material; determining a product configuration entropy coefficient according to market share data of different product configurations; and determining a resilience evaluation index according to the capacity retention coefficient, the supply structure entropy coefficient and the product configuration entropy coefficient. Through the method, the capacity retention ability, the supply structure diversity and the product configuration flexibility of the supply chain are comprehensively considered, and the accuracy of the evaluation is improved.

Description

Technical Field

[0001] This application relates to the field of dynamic assessment technology of supply chain resilience, and in particular to a method, apparatus, equipment and medium for assessing supply chain resilience for customized production. Background Technology

[0002] As manufacturing shifts towards a large-scale personalized customization model, supply chain systems face unprecedented complexity and uncertainty. Supply chain disruption risks exhibit multidimensional characteristics. On the one hand, internal factors such as equipment failure and process disturbances may trigger chain reactions through process dependencies, leading to production line shutdowns. On the other hand, external shocks can directly affect production capacity and product diversity.

[0003] When supply chain resilience assessment methods in related technologies experience disruptions, the resilience assessment results become inaccurate, making it impossible to formulate targeted response strategies. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for assessing supply chain resilience for customized production. This method comprehensively assesses the supply chain in terms of capacity, supply side, and configuration side, thereby improving the comprehensiveness and accuracy of supply chain resilience assessment.

[0005] In a first aspect, embodiments of this application provide a supply chain resilience assessment method for customized production, comprising the following steps: acquiring supply chain capacity data, supply capacity parameters for different supply paths of each material, and market share data for different product configurations; wherein, the supply capacity parameters at least characterize the reliability or availability of each path.

[0006] Based on the supply chain capacity data, determine the capacity retention coefficient.

[0007] The supply structure entropy coefficient is determined based on the supply capacity parameters of different supply paths for each material.

[0008] Based on the market share data of different product configurations, the product configuration entropy coefficient is determined.

[0009] A resilience assessment index is determined based on the capacity retention coefficient, the supply structure entropy coefficient, and the product configuration entropy coefficient. This resilience assessment index is used to quantify the resilience level of the supply chain under different material disruption conditions.

[0010] In one possible implementation, the capacity retention coefficient is determined based on the supply chain capacity data, including: obtaining the material demand, current material inventory, in-transit arrival data, and supply rate of the set of materials required for production.

[0011] Based on the material demand, the current material inventory, the in-transit arrival data, and the supply rate, a production model is established to determine the supply chain capacity function, wherein the capacity function serves as a sequence representing the supply chain capacity data.

[0012] Based on the capacity function and the theoretical maximum capacity, the capacity maintenance coefficient is determined, wherein the theoretical maximum capacity refers to the maximum number of products that the supply chain can produce per unit time under ideal conditions where materials are unconstrained.

[0013] In one possible implementation, the capacity function of the supply chain is determined by establishing a production model, including at least one of the following: simulating a capacity decline trend based on a piecewise function, wherein when the current inventory of a certain material is lower than the installation threshold, the capacity function value corresponding to that material undergoes a discontinuous step decline.

[0014] Alternatively, a dynamic simulation can be used to model the production capacity recovery trajectory, wherein the simulation takes the current material inventory as the initial state, the material demand as the consumption rate, and drives the dynamic evolution of the production capacity function based on the supply rate and the in-transit arrival data.

[0015] In one possible implementation, determining the supply structure entropy coefficient based on the supply capacity parameters includes: for each material, performing normalization processing based on the supply capacity parameters of different supply paths for that material to determine the supply weight of each path.

[0016] Based on the supply weight, the supply entropy value of the material is calculated.

[0017] Based on the supply entropy value of each material and the corresponding material importance weight, the supply structure entropy coefficient is determined by weighted calculation.

[0018] In one possible implementation, the product configuration entropy coefficient is determined based on the market share data of different product configurations, including: determining the effective delivery weight of each product configuration by normalization based on the market share data of different product configurations and the configuration deliverability coefficient of the corresponding product configuration; the configuration deliverability coefficient characterizes whether the product configuration has delivery capability at time t.

[0019] Based on the effective delivery weight, the product configuration entropy coefficient is determined by calculating information entropy.

[0020] In one possible implementation, determining the resilience assessment index includes: based on the supply structure entropy coefficient and the product configuration entropy coefficient, introducing supply-side weights and configuration-side weights respectively, and calculating a diversity retention coefficient through weighted fusion; wherein the supply-side weights and configuration-side weights are used to adjust the contribution of the supply structure entropy coefficient and the product configuration entropy coefficient to the calculation of the diversity retention coefficient, respectively. The resilience assessment index is then determined based on the diversity retention coefficient and the capacity retention coefficient.

[0021] In one possible implementation, determining the resilience assessment index based on the diversity retention coefficient and the capacity retention coefficient includes: determining a time-varying resilience level index by coupling the diversity retention coefficient and the capacity retention coefficient.

[0022] Based on a preset evaluation period, the time-varying resilience level index is integrated over time and averaged, and the result is used as the resilience evaluation index.

[0023] Secondly, embodiments of this application provide a supply chain resilience assessment device for customized production, including: a data acquisition module for acquiring supply chain capacity data, supply capacity parameters of different supply paths for each material, and market share data of different product configurations; wherein the supply capacity parameters at least characterize the reliability or availability of each path.

[0024] The first determining module is used to determine the capacity retention coefficient based on the supply chain capacity data.

[0025] The second determining module is used to determine the supply structure entropy coefficient based on the supply capacity parameters of different supply paths for each material.

[0026] The third determining module is used to determine the product configuration entropy coefficient based on the market share data of the different product configurations.

[0027] The fourth determining module is used to determine a resilience assessment index based on the capacity maintenance coefficient, the supply structure entropy coefficient, and the product configuration entropy coefficient. The resilience assessment index is used to quantify the resilience level of the supply chain under different material disruption conditions.

[0028] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0029] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0030] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0031] The supply chain resilience assessment method, apparatus, equipment, and medium for customized production provided in this application embodiment determine the capacity retention coefficient based on supply chain capacity data, the supply structure entropy coefficient based on the supply capacity parameters of different supply paths for each material, the product configuration entropy coefficient based on the market share data of different product configurations, and the resilience assessment index based on the capacity retention coefficient, the supply structure entropy coefficient, and the product configuration entropy coefficient. This method comprehensively considers three key dimensions: the supply chain's capacity retention capability, the diversity of the supply structure, and the flexibility of product configuration, thereby improving the accuracy of the assessment. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0033] Figure 1 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 1 .

[0034] Figure 2 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 2 .

[0035] Figure 3 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 3 .

[0036] Figure 4 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 4 .

[0037] Figure 5 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 5 .

[0038] Figure 6 A schematic diagram of the supply chain resilience assessment device for customized production provided in this application.

[0039] Figure 7 A schematic diagram of the structure of the electronic device provided in this application.

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] First, the terms used in this application will be explained.

[0043] Supply chain resilience refers to a company's ability to maintain core functions, recover quickly, and adapt to new environments when its supply chain is subjected to external shocks (such as supplier disruptions, logistical bottlenecks, and demand fluctuations).

[0044] Capacity retention coefficient: The ratio of production line capacity to theoretical maximum capacity at a certain time t, used to measure the degree of decline in production capacity after an impact.

[0045] Diversity Preservation Coefficient: A comprehensive indicator based on supplier multipath structure entropy and product configuration entropy, used to quantify the level of supply chain's ability to maintain diversified supply and personalized manufacturing under shocks.

[0046] Resilience Assessment Index: A time-varying function that measures the overall resilience of the supply chain, taking into account the coupling of capacity and diversity.

[0047] Current research on supply chain disruption mechanisms faces the challenge of complex internal causes and significant propagation effects. In particular, under the model of large-scale personalized customization, fluctuations in the supply of raw materials at a single process point may generate a chain reaction through process dependence and gradually amplify, ultimately leading to large-scale production line shutdowns or quality fluctuations.

[0048] In related technologies, the accuracy of assessment results is low when static or unidirectional indicators such as production recovery time, additional costs, and inventory consumption are used to evaluate supply chain resilience, or when a single dimension is used to assess supply entropy resilience.

[0049] In view of this, this application provides a method, apparatus, equipment and medium for supply chain resilience assessment for customized production. The method comprehensively considers three key dimensions: the supply chain's capacity maintenance capability, the diversity of supply structure and the flexibility of product configuration. It integrates these dimensions through a mathematical model and finally outputs an intuitive resilience assessment index, which effectively improves the accuracy of resilience assessment and provides data support for supply chain risk management and decision-making.

[0050] The execution subject of the embodiments of this application can be an electronic device with processing capabilities, such as a computer, server, laptop computer, etc., and this application does not limit it.

[0051] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0052] Figure 1 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 1 .

[0053] like Figure 1 As shown, the method includes the following steps: S101, obtaining the supply chain capacity data required for the assessment, the supply capacity parameters of different supply paths for each material, and the market share data of different product configurations.

[0054] For example, the aforementioned supply chain capacity data refers to information related to key materials required for production, such as the required quantity of material i. Current material inventory In-transit arrival data Or obtain the estimated arrival time t and delivery rate. These data collectively form the basis of capacity analysis.

[0055] The aforementioned supply capacity parameter refers to the supply capacity parameters obtained for each material across all available supply paths (such as supplier A, supplier B, backup supplier C, etc.). This parameter at least characterizes the reliability (such as historical on-time delivery rate) or availability (such as current capacity utilization rate, quality pass rate, etc.) of each path. For example, the supply capacity parameter can be a value between 0 and 1, where 1 indicates complete reliability / availability.

[0056] In some implementations, supply capacity parameters can be obtained by constructing relevant capacity prediction models.

[0057] For example, the capacity prediction model performs dynamic analysis and calculation based on data such as current material inventory, historical delivery data, supplier capacity reports, and market risk intelligence to obtain the parameter values ​​involved in the model.

[0058] Specifically, the model can access historical transaction records, order fulfillment records, and quality inspection reports from an enterprise's internal systems, as well as external data such as logistics tracking information, supplier financial reports, and relevant data from industry databases.

[0059] After preprocessing the collected data, the raw data is transformed into features that the model can learn, and the labels required for supervised learning are defined.

[0060] Select a suitable machine learning model, train it on the above features, and output a supply capacity parameter, which is used to reflect the stability of the path.

[0061] To better reflect the status of the supply chain, the capacity prediction model adopts a timed or condition-triggered update mechanism.

[0062] For example, the model's predicted supply capacity parameters are compared with actual supply chain performance periodically (e.g., quarterly). When model performance (e.g., prediction accuracy) declines beyond a predetermined threshold, or when there are significant changes in the supply chain structure, a retraining or incremental learning process is triggered to update the model using a dataset containing recent data to maintain its prediction accuracy and timeliness.

[0063] The above-mentioned systematic model training process has enabled the transformation of supply capacity parameters from static empirical values ​​to dynamic intelligent prediction values.

[0064] Based on historical big data and machine learning algorithms, the bias of subjective human judgment is reduced, enabling parameters to more realistically reflect the potential risks and capabilities of the supply chain, thus improving the objectivity and accuracy of the parameters. Furthermore, regular model retraining and parameter updates ensure the continuous effectiveness and adaptability of the entire supply chain resilience assessment system, providing reliable data input for the calculation of the resilience assessment index.

[0065] The market share data mentioned above refers to the historical average or current expected sales share of the various product configurations (e.g., standard version, high-end version, customized version, etc.) produced by the company in the target market.

[0066] S102. Determine the capacity retention coefficient based on supply chain capacity data.

[0067] This step involves constructing a time-varying capacity model constrained by material supply, using the time-varying capacity model to calculate supply chain capacity data at different times, and then, by calculating the ratio of supply chain capacity data at different times to the theoretical maximum capacity, depicting the capacity loss trajectory and quantifying the changes in the throughput capacity of the supply chain production line before and after the impact.

[0068] The theoretical maximum capacity mentioned above refers to the maximum number of products that the supply chain can produce per unit of time under ideal conditions where materials are unconstrained.

[0069] S103. Determine the supply structure entropy coefficient based on the supply capacity parameters of different supply paths for each material.

[0070] This step is used to model the path redundancy and diversity distribution of critical materials at the supplier level, quantifying it in the form of entropy.

[0071] The entropy coefficient of the supply structure characterizes the diversity and balance of upstream supply sources in the supply chain. The higher the entropy value, the more dispersed and balanced the supply structure is, and the smaller the impact of a single supply path interruption is.

[0072] S104. Determine the product configuration entropy coefficient based on the market share data of different product configurations.

[0073] This step is used to quantify the range of product configurations that the supply chain can still deliver after a supply chain disruption.

[0074] The entropy coefficient of this product configuration characterizes the diversity and flexibility of the downstream product portfolio. When certain configurations are undeliverable, the entropy value decreases. The range of entropy value decrease is used to indicate the extent of the decline in the supply chain's ability to provide personalized services, such as the inability to quickly switch production of products with different configurations to cope with changes in market demand or shortages of specific materials.

[0075] S105. Based on the capacity retention coefficient, supply structure entropy coefficient, and product configuration entropy coefficient, determine the resilience assessment index. The resilience assessment index is used to quantify the resilience level of the supply chain under different material disruption conditions.

[0076] The three coefficients mentioned above are combined to calculate the final resilience assessment index. This index comprehensively reflects the overall performance of the supply chain in terms of capacity maintenance, supply diversification, and product flexibility. It can be used to quantitatively compare the resilience levels of different supply chain designs or the same supply chain under different disruption scenarios. A higher resilience assessment index indicates stronger supply chain resilience.

[0077] The technical solution provided in this embodiment first constructs a comprehensive evaluation input by integrating multi-source heterogeneous data such as production capacity, supply path, and product configuration.

[0078] Secondly, by calculating the capacity retention coefficient, supply structure entropy coefficient, and product configuration entropy coefficient respectively, the resilience of the supply chain was quantitatively analyzed from three different but complementary dimensions: the capacity retention coefficient measures the output of the supply chain after the disruption occurs, the supply structure entropy coefficient measures the supply chain's ability to diversify risks, i.e. whether the supply path is diversified, and the product configuration entropy coefficient measures the supply chain's conversion ability, i.e. whether different product configurations can be delivered.

[0079] Finally, these three factors are integrated into a single resilience assessment index, enabling a comprehensive and quantitative assessment of the resilience level of complex supply chain systems. This method overcomes the limitations of traditional approaches that focus solely on inventory or a single supplier, allowing managers to accurately pinpoint resilience weaknesses (e.g., whether it's fragile production capacity, a single supply source, or a rigid product line), and thus take more targeted improvement measures.

[0080] In this embodiment, a production capacity model is constructed. This model takes material constraints as input and describes the supply chain's capacity function as a dynamically changing sequence over time through mathematical or simulation methods. Based on this model, the loss and recovery trajectory of supply chain production capacity under material disruption scenarios can be quantitatively analyzed, thereby providing dynamic capacity data for resilience assessment.

[0081] Figure 2 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, this paper provides a detailed explanation of how to determine the capacity retention coefficient based on supply chain capacity data.

[0082] The method includes the following steps: S201, obtaining the material requirements from the set of materials needed for production. Current material inventory In-transit arrival data and supply rate These data constitute the constraint inputs of the model.

[0083] S202, Based on material requirements Current material inventory In-transit arrival data and supply rate By establishing a production model, the capacity function of the supply chain can be determined. Among them, the capacity function As a sequence representing supply chain capacity data.

[0084] S203. Determine the capacity maintenance coefficient based on the capacity function and the theoretical maximum capacity.

[0085] Specifically, the capacity retention factor can be shown as follows: .

[0086] in, Theoretical maximum capacity Units are listed in "units per day" or "units per hour". For capacity maintenance coefficient, Let t be the production capacity function, and t be the time variable.

[0087] This capacity retention coefficient is used to describe the ratio of the actual production capacity of the supply chain to the theoretical maximum capacity at different times, and is used to depict the trajectory of capacity loss.

[0088] In step S202 of this embodiment, the production model is constructed based on material constraints, and the production capacity function can be determined by one or a combination of the following two methods.

[0089] Method 1: The time-varying capacity model includes a piecewise function model, which simulates the capacity "cliff" change when materials are depleted, thereby determining the capacity function of the supply chain.

[0090] It should be noted that a cliff-like change in production capacity refers to a significant drop in production capacity at a certain point in time.

[0091] For example, a piecewise function model is used to simulate the trend of declining production capacity. When the current inventory of a certain material is lower than the installation threshold, the production capacity function value corresponding to that material undergoes a discontinuous step decrease.

[0092] Method 2: The time-varying capacity model includes a dynamic simulation model based on consumption-replenishment, which simulates the capacity recovery trajectory.

[0093] For example, the simulation model starts with the current material inventory as the initial state, uses the material demand as the consumption rate, and drives the dynamic evolution of the capacity function based on the supply rate and in-transit arrival data.

[0094] Alternatively, the simulation model can use current material inventory and material demand to obtain inventory dissipation parameters, use supply rate and in-transit arrival data to obtain replenishment time windows, and combine inventory dissipation and replenishment time windows to drive the dynamic evolution of the capacity function.

[0095] In this embodiment, by introducing a production model, this method provides a structured and modeled representation of the dynamic process of supply chain capacity being constrained by materials. This elevates the assessment of capacity maintenance capability from static point estimation or simple statistics to a quantitative characterization of the dynamic process, significantly improving the accuracy and predictability of the assessment results in depicting actual interference scenarios.

[0096] Figure 3A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 3 ,like Figure 3 As shown, in this embodiment... Figure 1 Based on the examples, this paper provides a detailed explanation of how to determine the supply structure entropy coefficient according to the supply capacity parameters.

[0097] The method includes the following steps: S301, for each material, based on the supply capacity parameters of different supply paths of the material, perform normalization processing to determine the supply weight of each path.

[0098] For each critical material, let the set of supply paths be . Each path has supply capacity parameters. .

[0099] Based on the supply capacity parameters of key materials in a single path, and their proportion to the total supply capacity parameters of all paths, the supply weight of each path is calculated.

[0100] Specifically, the supply weight for each path can be as follows: .

[0101] Where m is the quantity of the critical material, j is the number of supply paths for the critical material, and supply capacity parameter... The meaning is the supply capacity parameter of the m-th material on the i-th path.

[0102] S302. Based on the supply weight, calculate the supply entropy value of the material.

[0103] Specifically, the supply entropy value for each material can be shown as follows: .

[0104] in, It refers to the supply weight of the i-th path of the critical material m at time t.

[0105] S303. Based on the supply entropy value of each material and the corresponding material importance weight, the supply structure entropy coefficient is determined by weighted calculation.

[0106] Specifically, the entropy coefficient of the supply structure can be shown as follows: .

[0107] in, Assigning importance weights to materials, which can be adjusted in real time based on path failures or capacity reductions in disruptive scenarios. Dynamically update the structure entropy.

[0108] Considering the varying importance of different materials to production, material importance weights are introduced (e.g., based on material cost, procurement lead time, or scarcity). The overall supply structure entropy coefficient is then calculated using a weighted average. A higher supply structure entropy coefficient indicates better overall supply structure diversity.

[0109] By quantifying the redundancy of supply paths using entropy theory, companies can intuitively assess the effectiveness of multi-source procurement strategies. For example, when the entropy of a material's supply path structure is low, it indicates insufficient path redundancy, necessitating optimization of supplier configuration. This process provides a quantitative basis for diversifying the supply chain layout.

[0110] Figure 4 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 4 ,like Figure 4 As shown, in this embodiment... Figure 1 Based on the examples, a detailed explanation is provided on how to use market share data for different product configurations.

[0111] The method includes the following steps: S401, based on the market share data of different product configurations and the configuration deliverability coefficient of the corresponding product configuration, the effective delivery weight of each product configuration is determined through normalization processing; wherein, the configuration deliverability coefficient represents whether the product configuration has delivery capability at time t.

[0112] Specifically, the effective delivery weight for each product configuration can be as follows: .

[0113] Where k is the product configuration set Total number of product configurations in the list. Configure each product Corresponding market share data To configure the deliverability factor, This is used to characterize whether the product configuration at time t is capable of delivery, and .

[0114] For example, each product configuration depends on several proprietary and generic materials. If a proprietary component is missing, the product configuration cannot be delivered. If the value is 0, and all materials are available, the product configuration can be delivered normally. The value is 1. When some materials are missing, the calculation can be based on the missing information. The value of .

[0115] In this embodiment, the configuration deliverability factor is set to a time-varying factor, which will be adjusted over time.

[0116] S402. Based on the effective delivery weight, determine the product configuration entropy coefficient through information entropy calculation.

[0117] Specifically, the product configuration entropy coefficient can be shown as follows: .

[0118] in, Configure an entropy coefficient for the product, which will be adjusted over time.

[0119] Figure 5 A flowchart illustrating the supply chain resilience assessment method for customized production provided in this application. Figure 5 ,like Figure 5 As shown, in this embodiment... Figure 1 Based on the examples, a detailed explanation of how to determine the toughness assessment index is provided.

[0120] The method includes the following steps: S501, based on the supply structure entropy coefficient and the product configuration entropy coefficient, the supply side weight and the configuration side weight are introduced respectively, and the diversity preservation coefficient is calculated by weighted fusion.

[0121] In step S501, the first parameter is determined based on the ratio of the current structural entropy coefficient to the initial structural entropy coefficient.

[0122] The second parameter is determined based on the ratio of the current diversity coefficient to the initial diversity coefficient.

[0123] The diversity preservation coefficient is determined based on the first parameter, the second parameter, the supply-side weight, and the allocation-side weight.

[0124] Specifically, the diversity preservation coefficient can be shown as follows: .

[0125] in, For supply-side weighting, To configure side weights, , Supply-side weights and configuration-side weights are used to adjust the contributions of the supply structure entropy coefficient and the product configuration entropy coefficient to the calculation of the diversity retention coefficient, respectively.

[0126] The ratio of supply-side weight to configuration-side weight can characterize the relative attention paid to the supply side and configuration side in the supply chain.

[0127] The technical improvement in this embodiment lies in the introduction of supply-side weights and configuration-side weights, which enhances the configurability and strategy fit of the evaluation model.

[0128] Different types of supply chains have different resilience bottlenecks: for raw material-oriented supply chains, supply diversity may be crucial; for the FMCG or fashion industry, the ability to quickly adjust product configuration may be more critical.

[0129] By adjusting supply-side and configuration-side weights, different supply chain types can be flexibly adapted, making the assessment results more aligned with the specific management priorities of enterprises, thereby enhancing the relevance and decision-making reference value of the assessment results. It allows managers to conduct hypothetical analyses by adjusting weights, exploring whether increasing supply diversity or product diversity is more effective in improving overall resilience.

[0130] S502. Based on the diversity retention coefficient and the capacity retention coefficient, determine the resilience assessment index.

[0131] In step S502, a detailed explanation is given on how to determine the resilience assessment index based on the diversity retention coefficient and the capacity retention coefficient, including the following steps: S5021, Based on the diversity retention coefficient and the capacity retention coefficient, a time-varying resilience level index is determined through coupling processing.

[0132] Calculate the time-varying toughness level index: at each time point t, obtain the instantaneous toughness level RI(t) through coupling processing. For example, a multiplicative model is used to construct the toughness level index.

[0133] Specifically, the resilience level index can be shown as follows: .

[0134] Used to indicate in The instantaneous resilience level of the time system. It can intuitively show the real-time fluctuations in the resilience of the supply chain during the occurrence, duration, and recovery of disruption events.

[0135] S5022. Based on the preset evaluation period, the time-varying resilience level index is integrated over time and averaged, and the calculation result is used as the resilience evaluation index.

[0136] In order to obtain an evaluation cycle The overall resilience score within the period is defined by a resilience assessment index for the entire cycle. Specifically, the resilience assessment index can be as follows: .

[0137] in, As a resilience assessment index, It is a periodicity.

[0138] This resilience assessment index can be used to compare system performance under different strategies (such as dual-source, alternative processes, and inventory buffers).

[0139] This step elevates the assessment from a static point evaluation to a dynamic process evaluation, enabling the capture and quantification of the temporal evolution characteristics of supply chain resilience.

[0140] The impact of supply chain disruptions is not instantaneous but rather encompasses stages such as a warning period, an impact period, and a recovery period. A resilience assessment index is obtained by calculating and integrating a resilience level index. This not only provides a comprehensive score for a given period but also allows for analysis of the most vulnerable timeframes and recovery speeds through the resilience level index curve. This helps in a more precise assessment of the effectiveness and timeliness of environmental measures.

[0141] In some implementations, the method of this application can be widely applied in multiple enterprise decision-making scenarios as a decision support tool to help enterprises cope with supply chain disruptions and related risks.

[0142] Specific applications include: simulating the impact of supply disruptions of critical materials on production capacity and delivery diversity, and assessing the risk of supply disruptions for different materials and suppliers.

[0143] Conduct product configuration screening and prioritization to ensure that market demand is met first in the event of a disruption.

[0144] Optimize the multi-supplier path structure to improve the redundancy and flexibility of the supply chain.

[0145] By comparing different safety stock strategies, we can assess the contribution of inventory levels to overall resilience.

[0146] Optimize resilience enhancement schemes (such as adopting universal design, delaying configuration, and adding alternative processes) to improve the system's ability to cope with shocks and its recovery speed.

[0147] Through the above applications, this application can provide enterprises with comprehensive and dynamic supply chain resilience assessment and strategy optimization support. The technical improvement in this embodiment lies in the introduction of supply-side weights and configuration-side weights. The resulting technical effect is to enhance the configurability and strategy fit of the assessment model. Different supply chain types have different resilience bottlenecks: for raw material-oriented supply chains, supply diversity may be crucial; for FMCG or fashion industries, the ability to quickly adjust product configurations may be more critical.

[0148] By adjusting the weights on the supply side and the allocation side, this method can flexibly reflect this strategic focus, making the assessment results more aligned with the actual management priorities of specific enterprises, thereby enhancing the relevance and decision-making reference value of the assessment results. It allows managers to conduct "hypothesis analysis" by adjusting the weights, exploring whether increasing supply diversity or product diversity is more effective in improving overall resilience.

[0149] Figure 6A schematic diagram of the supply chain resilience assessment device for customized production provided in this application is shown below. Figure 6 As shown, the supply chain resilience assessment device 600 for customized production provided in this embodiment includes: a data acquisition module 601, used to acquire supply chain capacity data, supply capacity parameters of different supply paths for each material, and market share data of different product configurations; wherein, the supply capacity parameters at least characterize the reliability or availability of each path.

[0150] The first determining module 602 is used to determine the capacity maintenance coefficient based on supply chain capacity data.

[0151] The second determining module 603 is used to determine the supply structure entropy coefficient based on the supply capacity parameters of different supply paths for each material.

[0152] The third determining module 604 is used to determine the product configuration entropy coefficient based on the market share data of different product configurations.

[0153] The fourth determination module 605 is used to determine the resilience assessment index based on the capacity maintenance coefficient, supply structure entropy coefficient and product configuration entropy coefficient. The resilience assessment index is used to quantify the resilience level of the supply chain under different material interruption conditions.

[0154] In one possible implementation, the fourth determining module 605 is specifically used to: based on the supply structure entropy coefficient and the product configuration entropy coefficient, respectively introduce supply-side weights and configuration-side weights, and calculate the diversity preservation coefficient through weighted fusion.

[0155] A resilience assessment index is determined based on the diversity retention coefficient and the capacity retention coefficient.

[0156] In one possible implementation, the fourth determining module 605 can also be used to determine a time-varying resilience level index by coupling processing based on the diversity retention coefficient and the capacity retention coefficient.

[0157] Based on a preset evaluation period, the time-varying resilience level index is integrated over time and averaged, and the result is used as the resilience evaluation index.

[0158] In one possible implementation, the first determining module 602 is specifically used to: obtain the material demand, current material inventory, in-transit arrival data, and supply rate in the set of materials required for production.

[0159] Based on material demand, current material inventory, in-transit arrival data, and supply rate, the capacity function of the supply chain is determined through material constraint analysis. The capacity function serves as a sequence representing the supply chain capacity data.

[0160] Based on the capacity function and the theoretical maximum capacity, the capacity maintenance coefficient is determined.

[0161] In one possible implementation, the first determining module 602 is further configured to: simulate the capacity decline trend based on a piecewise function, wherein when the current material inventory of a certain material is lower than the installation threshold, the capacity function value corresponding to the material undergoes a discontinuous step decline.

[0162] Alternatively, a dynamic simulation can be used to model the production capacity recovery trajectory. In this simulation, the current material inventory is taken as the initial state, the material demand is taken as the consumption rate, and the dynamic evolution of the production capacity function is driven by the supply rate and the data of goods arriving in transit.

[0163] In one possible implementation, the second determining module 603 is specifically used to: for each material, perform normalization processing based on the supply capacity parameters of different supply paths of the material to determine the supply weight of each path.

[0164] Based on the supply weight, the supply entropy value of the material is calculated.

[0165] Based on the supply entropy value of each material and the corresponding material importance weight, the supply structure entropy coefficient is determined through weighted calculation.

[0166] In one possible implementation, the third determining module 604 is specifically used to: determine the effective delivery weight of each product configuration based on the market share data of different product configurations and the configuration deliverability coefficient of the corresponding product configuration through normalization processing; the configuration deliverability coefficient characterizes whether the product configuration has delivery capability at time t.

[0167] Based on the effective delivery weight, the product configuration entropy coefficient is determined through information entropy calculation.

[0168] The supply chain resilience assessment device for customized production provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0169] Figure 7 A schematic diagram of the structure of the electronic device provided in this application. Figure 7 As shown, the electronic device 700 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 700 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0170] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0171] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0172] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0173] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0174] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0175] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0176] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0177] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0178] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0179] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0180] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0182] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0183] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0184] Finally, it should be noted that other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and alterations may be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A supply chain resilience assessment method for customized production, characterized in that, Includes the following steps: Acquire supply chain capacity data, supply capacity parameters for different supply paths of each material, and market share data for different product configurations; wherein, the supply capacity parameters for different supply paths of each material at least characterize the reliability or availability of each path; Based on the aforementioned supply chain capacity data, determine the capacity retention coefficient; Based on the supply capacity parameters of different supply paths for each material, determine the supply structure entropy coefficient; Based on the market share data of the different product configurations, the product configuration entropy coefficient is determined; Based on the capacity retention coefficient, the supply structure entropy coefficient, and the product configuration entropy coefficient, a resilience assessment index is determined. The resilience assessment index is used to quantify the resilience level of the supply chain under different material disruption conditions. The step of determining the capacity retention coefficient based on the supply chain capacity data includes: Obtain the material demand, current material inventory, in-transit arrival data, and supply rate from the set of materials required for production; based on the material demand, current material inventory, in-transit arrival data, and supply rate, establish a production model to determine the supply chain capacity function, where the capacity function serves as a sequence representing the supply chain capacity data; based on the capacity function and the theoretical maximum capacity, determine the capacity maintenance coefficient, where the theoretical maximum capacity refers to the maximum number of products the supply chain can produce per unit time under ideal conditions where materials are unconstrained; Determining the supply structure entropy coefficient based on the supply capacity parameters includes: For each material, the supply capacity parameters of different supply paths for that material are normalized to determine the supply weight of each path; based on the supply weight, the supply entropy value of that material is calculated; based on the supply entropy value of each material and the corresponding material importance weight, the supply structure entropy coefficient is determined by weighted calculation. The step of determining the product configuration entropy coefficient based on the market share data of different product configurations includes: determining the effective delivery weight of each product configuration by normalizing the market share data of different product configurations and the configuration deliverability coefficient of the corresponding product configuration; the configuration deliverability coefficient represents whether the product configuration has delivery capability at time t; and determining the product configuration entropy coefficient by calculating the information entropy based on the effective delivery weight.

2. The method according to claim 1, characterized in that, The determination of the supply chain capacity function by establishing a production model includes at least one of the following methods: The production capacity decline trend is simulated based on a piecewise function model. When the current inventory of a certain material is lower than the installation threshold, the production capacity function value corresponding to that material undergoes a discontinuous step decline. Alternatively, a dynamic simulation model can be used to simulate the production capacity recovery trajectory. The simulation takes the current material inventory as the initial state, the material demand as the consumption rate, and drives the dynamic evolution of the production capacity function based on the supply rate and the in-transit arrival data.

3. The method according to claim 1 or 2, characterized in that, The determination of the resilience assessment index includes: Based on the supply structure entropy coefficient and the product configuration entropy coefficient, supply-side weights and configuration-side weights are introduced respectively, and a diversity preservation coefficient is calculated through weighted fusion; wherein, the supply-side weights and the configuration-side weights are used to adjust the contribution of the supply structure entropy coefficient and the product configuration entropy coefficient in the calculation of the diversity preservation coefficient, respectively. The resilience assessment index is determined based on the diversity retention coefficient and the capacity retention coefficient.

4. The method according to claim 3, characterized in that, The determination of the resilience assessment index based on the diversity retention coefficient and the production capacity retention coefficient includes: Based on the diversity retention coefficient and the production capacity retention coefficient, a time-varying resilience level index is determined through coupling processing; Based on a preset evaluation period, the time-varying resilience level index is integrated over time and averaged, and the result is used as the resilience evaluation index.

5. A supply chain resilience assessment device for customized production, characterized in that, include: The data acquisition module is used to acquire supply chain capacity data, supply capacity parameters for different supply paths of each material, and market share data for different product configurations; wherein, the supply capacity parameters at least characterize the reliability or availability of each path; The first determining module is used to determine the capacity retention coefficient based on the supply chain capacity data; The second determining module is used to determine the supply structure entropy coefficient based on the supply capacity parameters of different supply paths for each material. The third determining module is used to determine the product configuration entropy coefficient based on the market share data of the different product configurations. The fourth determining module is used to determine a resilience assessment index based on the capacity maintenance coefficient, the supply structure entropy coefficient, and the product configuration entropy coefficient. The resilience assessment index is used to quantify the resilience level of the supply chain under different material disruption conditions. The first determining module is specifically used for: acquiring the material demand, current material inventory, in-transit arrival data, and supply rate from the set of materials required for production; determining the supply chain capacity function by establishing a production model based on the material demand, current material inventory, in-transit arrival data, and supply rate, wherein the capacity function serves as a sequence representing the supply chain capacity data; and determining the capacity maintenance coefficient based on the capacity function and the theoretical maximum capacity, wherein the theoretical maximum capacity refers to the maximum number of products that the supply chain can produce per unit time under ideal conditions where materials are unconstrained. The second determining module is specifically used for: for each material, performing normalization processing based on the supply capacity parameters of different supply paths of the material to determine the supply weight of each path; calculating the supply entropy value of the material based on the supply weight; and determining the supply structure entropy coefficient based on the supply entropy value of each material and the corresponding material importance weight through weighted calculation. The third determining module is specifically used to: determine the effective delivery weight of each product configuration based on market share data of different product configurations and the configuration deliverability coefficient of the corresponding product configuration through normalization processing; the configuration deliverability coefficient represents whether the product configuration has delivery capability at time t; and determine the product configuration entropy coefficient based on the effective delivery weight through information entropy calculation.

6. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.

Images