A supply chain risk analysis method and system based on artificial intelligence big data

Abstract

The present invention discloses a supply chain risk analysis method and system based on artificial intelligence big data, which relates to the technical field of supply chain risk analysis, and includes the following steps: obtaining all data related to goods and suppliers from the internal system of the enterprise and external sources to provide comprehensive data support for subsequent analysis and evaluation; based on comprehensive data, feature engineering is performed to extract key features that can reflect the dependency relationship between goods and suppliers, and core features that can reflect the dependency relationship between goods and suppliers are extracted from big data. The present invention integrates internal systems and external data, utilizes feature engineering and machine learning models, intelligently quantifies the dependency relationship between goods and suppliers, and monitors changes in real time. This enables purchasers to take effective measures before potential risks break out, reduce the risk of supply interruptions, and ensure the continuity of production lines. In the long run, the response measures are optimized and a continuous risk management mechanism is established to enhance the resilience and stability of the supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain risk analysis, and in particular to a supply chain risk analysis method and system based on artificial intelligence big data. Background Art

[0002] Supply chain risk analysis based on artificial intelligence big data refers to the process of identifying, evaluating and predicting various potential risk factors in the supply chain using advanced artificial intelligence algorithms and massive data processing technologies. By collecting and analyzing big data from different sources, such as market trends, supplier performance, logistics information, weather changes, and socio-economic indicators, artificial intelligence can identify complex risk patterns and correlations, thereby providing early warning of possible supply chain disruptions, quality issues, or cost fluctuations. This approach not only improves the accuracy and efficiency of risk identification, but also supports companies in formulating more scientific and dynamic risk response strategies, enhancing the resilience and sustainability of the supply chain, and ultimately improving the stability and competitiveness of overall operations.

[0003] The prior art has the following deficiencies:

[0004] In supply chain risk analysis, existing technologies usually have difficulty in accurately assessing the degree of dependence of goods on suppliers through intelligent means. When certain key goods are highly dependent on specific suppliers, and existing technologies cannot intelligently analyze and identify such dependence in advance, once the supply is interrupted, it may directly lead to the stagnation of the company's production line and the inability to fulfill orders. In the manufacturing industry, this situation is particularly serious and may even cause the entire production chain to be completely paralyzed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a supply chain risk analysis method and system based on artificial intelligence big data. By integrating the internal enterprise system and external data sources, comprehensive data support is ensured, providing a reliable basis for accurately evaluating the dependency relationship between goods and suppliers. Through the application of feature engineering and machine learning models, the degree of dependence of goods on suppliers can be intelligently quantified, and changes in dependencies can be monitored in real time. This allows purchasers to take effective countermeasures before potential risks break out, reduce the risk of supply interruptions, and ensure the continuity of production lines. In the long run, it can not only optimize the effectiveness of countermeasures, but also establish a continuous risk management mechanism to prevent the recurrence of similar risks, improve the resilience and stability of the supply chain, and solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a supply chain risk analysis method based on artificial intelligence big data, comprising the following steps:

[0008] Acquire all data related to products and suppliers from internal enterprise systems and external sources to provide comprehensive data support for subsequent analysis and evaluation;

[0009] Based on comprehensive data, feature engineering is performed to extract key features that can reflect the dependency relationship between products and suppliers. The core features that can reflect the dependency relationship between products and suppliers are extracted from big data to provide data dimension support for quantitative analysis. The extracted features are dynamically quantified according to the monitoring window to construct a time series of dependency relationships.

[0010] The dynamically quantified features are used as input variables and input into the trained machine learning model. The dependency relationship between products and suppliers is quantitatively evaluated based on the training results.

[0011] Based on the assessment results and the set dependency risk threshold, determine whether there is a potential risk of high dependency between the product and the supplier. Through intelligent analysis, timely identify the high dependency risk and provide early warning information to the purchaser so that he can take countermeasures to alleviate the high dependency of the product on a single supplier.

[0012] After the countermeasures are implemented, continue to obtain supply chain data of goods and suppliers, and analyze the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, and continuously monitor the real-time dependencies between goods and suppliers to ensure that dependency risks continue to decrease;

[0013] Summarize and optimize the effectiveness of response measures and establish long-term mechanisms to prevent the recurrence of high-dependency risks.

[0014] Preferably, all data related to products and suppliers is obtained from the enterprise's internal systems and external sources. The specific steps are as follows:

[0015] First, integrate internal enterprise data, including extracting key information from ERP, supplier management systems, and inventory and logistics management systems;

[0016] Next, collect external data sources, including industry reports, market monitoring platforms, logistics tracking services, and publicly available supplier credit assessment and financial health data;

[0017] Finally, through data cleaning and standardization, the integrity and consistency of internal enterprise data and external data sources are ensured, laying the foundation for subsequent feature extraction and analysis.

[0018] Preferably, key features that can reflect the dependency relationship between goods and suppliers are extracted, wherein the extracted features include the degree of concentration of the proportion of goods supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of the goods depends on a single supplier. The extracted features are dynamically quantified according to the monitoring window to generate a supply concentration reference value and a fulfillment dependence reference value, respectively. The time series of the dependency relationship is constructed through the supply concentration reference value and the fulfillment dependence reference value, and the dependency relationship between the goods and suppliers is dynamically quantified.

[0019] Preferably, the supply concentration reference value and fulfillment dependence reference value generated by dynamically quantifying the concentration degree of the proportion of goods supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of goods depends on a single supplier are used as input variables and input into the trained machine learning model to generate a dependency index, and the dependency relationship between goods and suppliers is quantitatively evaluated based on the dependency index output by the machine learning model.

[0020] Preferably, the specific steps of extracting the concentration of the proportion of goods supplied by a single supplier in the total supply, and dynamically quantifying the extracted features according to the monitoring window to generate a supply concentration reference value are as follows:

[0021] By calculating the supply weight of a single supplier in the total supply, basic data is provided for the reference value of supply concentration, and the contribution of the supplier to the supply of goods is quantified. The calculation expression of the supply weight is:

[0022]

[0023] in: For Suppliers k The supply weight, For Suppliers k The number of items offered, For Suppliers k The supply distance is used to reflect the potential impact of the supplier's geographical distribution on supply. is the distance sensitivity factor, which is used to adjust the impact of distance on the supply weight. v represents the index of the supplier, n represents the total number of suppliers;

[0024] Based on the supply weight of each supplier, the supply concentration factor is calculated to quantify the dominant position of a single supplier in the total supply. The calculation expression of the supply concentration factor is:

[0025]

[0026] in: is the supply concentration factor, is a weight-sensitive factor used to nonlinearly amplify the impact of high-weight suppliers. For Suppliers k The supply frequency indicates the proportion of the supplier's supply times within the monitoring window;

[0027] By dynamically updating the supply concentration reference value in the monitoring window, a time series is generated to capture the dynamic change trend of the dependency relationship. The time series expression of the dynamic supply concentration reference value is:

[0028]

[0029] in: For the current monitoring window The generated supply concentration reference value, The previous monitoring window The generated supply concentration reference value, For Suppliers k In the current monitoring window The supply weight, For Suppliers k In the current monitoring window The supply frequency, is the time smoothing factor, which is used to control the weight distribution of the historical supply concentration factor and the current supply concentration reference value. The range is .

[0030] Preferably, the specific steps of extracting the degree to which the historical delivery success rate of goods depends on a single supplier and dynamically quantifying the extracted features according to the monitoring window to generate a fulfillment dependency reference value are as follows:

[0031] The supplier's fulfillment contribution factor is extracted to measure the relative contribution of each supplier in the fulfillment of commodity demand and to identify the supplier's performance in meeting the demand for commodity orders. The calculation expression of the fulfillment contribution factor is:

[0032]

[0033] in: For Suppliers k In the monitoring window The contribution factor of the internal commodity to the fulfillment of the contract reflects its direct impact on the fulfillment of the contract. For Suppliers k In the monitoring window The number of orders successfully delivered within a period of time indicates its actual fulfillment capability. For products in the monitoring window The total order demand within is used to normalize the supplier's performance. For Suppliers k The performance weight factor of

[0034] Calculate the fulfillment concentration factor of a single supplier to quantify the concentration of the fulfillment demand of the product on the supplier and clarify the potential risks of high dependence. The calculation expression of the fulfillment concentration factor is:

[0035]

[0036] in: For Suppliers k The performance concentration factor reflects its dominant position in the overall performance demand. For Suppliers k The square of the performance contribution factor emphasizes the impact of large performance contributions on concentration. is the sum of the squares of all suppliers' fulfillment contribution factors, indicating the overall fulfillment distribution and used to normalize the concentration. For Suppliers k In the monitoring window The cumulative supply volume within the period is used to reflect the impact of actual supply capacity on concentration. is the performance distribution bias correction term, where Adjust the parameter for sensitivity, controlling the rate of change of the exponential function;

[0037] Combining the fulfillment concentration factor and the fulfillment contribution factor, a fulfillment dependence reference value that reflects the dynamic dependence of the commodity on a single supplier is generated, and a time series is constructed for trend analysis. The time series expression of the fulfillment dependence reference value is:

[0038]

[0039] in: For monitoring window The fulfillment dependency reference value generated within the system comprehensively reflects the dynamic dependency relationship between the product and all suppliers. is the performance intensity adjustment item, which amplifies the impact of high-intensity performance suppliers on overall dependence, where is the adjustment coefficient, controlling the adjustment range. For monitoring window The total number of successful deliveries of the products in the contract, used to normalize the fulfillment contribution.

[0040] Preferably, the dependency index generated by the trained machine learning model when the dependency relationship between the product and the supplier is quantitatively evaluated is compared with the pre-set dependency index reference threshold, and intelligent analysis is performed to timely identify high dependency risks. The specific steps are as follows:

[0041] If the dependency index is greater than the dependency index reference threshold, it indicates that the dependency relationship between the product and the supplier is high, and the system generates early warning information so that the purchaser can take countermeasures to alleviate the high dependency of the product on a single supplier and reduce the risk of supply interruption;

[0042] If the dependency index is less than or equal to the dependency index reference threshold, it will indicate that the dependency relationship between the product and the supplier is low, and no warning information will be generated through the system.

[0043] Preferably, after the countermeasures are implemented, the supply chain data of the goods and suppliers are continuously obtained, and the changes in the real-time dependency relationships are analyzed during multiple subsequent monitoring windows. The specific steps are as follows:

[0044] After the countermeasures are implemented, continue to obtain supply chain data for commodities and suppliers to analyze changes in dependencies over multiple subsequent monitoring windows;

[0045] After obtaining the dependency index of multiple monitoring windows, a comprehensive analysis of the change trend of the obtained dependency index is conducted to preliminarily evaluate whether the response measures have effectively alleviated the dependence of the product on a single supplier. By calculating the dependency index change rate of each supplier, the improvement or deterioration of the degree of dependence is quantified. The calculation expression of the dependency index change rate is:

[0046]

[0047] in: Indicates supplier k The rate of change of the dependency index during multiple monitoring windows, T is the total number of monitoring windows obtained, indicating the time span of the evaluation, Is a supplier k In the window The dependency index within It is the reference threshold of the dependency index, which is used as a benchmark for assessing supplier dependency risk. is the time weight factor, which is used to give higher weight to the data in the recent time window, reflecting the impact of the current data on the evaluation;

[0048] if , indicating that the dependency has been alleviated; if , then the dependency has not been alleviated.

[0049] Preferably, after completing the dependency index change analysis of a single supplier, further evaluate the dependency changes of all suppliers to measure the effectiveness of the overall response measures. By combining the importance weights of each supplier, calculate the overall response effect evaluation value to provide guidance for the optimization strategy. The calculation expression of the effect evaluation value is:

[0050]

[0051] in, It is the effectiveness evaluation value of the overall response measures, reflecting the comprehensive effect of changes in all supplier dependencies. Is a supplier k The importance weight of

[0052] Calculate the changes in all supplier dependencies by weighted average to obtain an evaluation of the overall response effect ;

[0053] if , indicating that the dependency has been alleviated and the response measures are effective;

[0054] if If it is positive, it means that the effect of the countermeasures is not obvious and the supply chain strategy needs to be further optimized.

[0055] A supply chain risk analysis system based on artificial intelligence big data, including data collection and integration module, feature engineering and quantification module, machine learning assessment module, risk assessment and early warning module, response measures effect verification module and long-term mechanism establishment and optimization module;

[0056] Data collection and integration module, which obtains all data related to goods and suppliers from internal enterprise systems and external sources, providing comprehensive data support for subsequent analysis and evaluation;

[0057] The feature engineering and quantification module performs feature engineering based on comprehensive data to extract key features that can reflect the dependency relationship between products and suppliers. It extracts core features that can reflect the dependency relationship between products and suppliers from big data, provides data dimension support for quantitative analysis, dynamically quantifies the extracted features according to the monitoring window, and constructs a time series of dependency relationships.

[0058] The machine learning evaluation module uses the dynamically quantified features as input variables and inputs them into the trained machine learning model, and performs quantitative evaluation of the dependency relationship between products and suppliers based on the training results;

[0059] The risk assessment and early warning module determines whether there is a potential risk of high dependence between the product and the supplier based on the assessment results and the set dependency risk threshold. It promptly identifies the high dependence risk through intelligent analysis and provides early warning information to the purchaser so that he can take countermeasures to alleviate the high dependence of the product on a single supplier.

[0060] The countermeasures effectiveness verification module continues to obtain supply chain data of goods and suppliers after the countermeasures are implemented, and analyzes the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, continuously monitor the real-time dependencies between goods and suppliers, and ensure that the dependency risks continue to decrease;

[0061] The long-term mechanism establishment and optimization module summarizes and optimizes the effects of response measures and establishes long-term mechanisms to prevent the recurrence of high-dependency risks.

[0062] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0063] The present invention ensures comprehensive data support by integrating the internal enterprise system and external data sources, providing a reliable basis for accurately evaluating the dependency relationship between goods and suppliers. Through the application of feature engineering and machine learning models, it can intelligently quantify the degree of dependence of goods on suppliers and monitor changes in dependencies in real time. This allows purchasers to take effective countermeasures before potential risks break out, reduce the risk of supply disruptions, and ensure the continuity of production lines. In the long run, it can not only optimize the effectiveness of countermeasures, but also establish a continuous risk management mechanism to prevent the recurrence of similar risks and improve the resilience and stability of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0065] Figure 1 This is a method flow chart of a supply chain risk analysis method based on artificial intelligence big data in the present invention.

[0066] Figure 2 This is a module schematic diagram of a supply chain risk analysis system based on artificial intelligence big data in the present invention. DETAILED DESCRIPTION

[0067] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0068] The present invention provides Figure 1 A supply chain risk analysis method based on artificial intelligence big data is shown, comprising the following steps:

[0069] Obtain all data related to goods and suppliers from internal enterprise systems (such as ERP, supplier management system) and external sources (such as market reports, logistics tracking systems) to provide comprehensive data support for subsequent analysis and evaluation, ensuring the completeness and accuracy of the analysis of the relationship between goods and suppliers;

[0070] Acquiring all data related to goods and suppliers from internal enterprise systems and external sources can be divided into the following steps: First, integrate internal enterprise data, including extracting key information from ERP (Enterprise Resource Planning System), Supplier Management System (SRM), and Inventory and Logistics Management System (WMS / TMS), such as procurement records, delivery history, inventory status, and supplier performance indicators. Next, collect external data sources, including industry reports, market monitoring platforms, logistics tracking services, and publicly available supplier credit assessment and financial health data. Finally, through data cleaning and standardization, ensure the integrity and consistency of internal enterprise data and external data sources, laying a solid foundation for subsequent feature extraction and analysis.

[0071] All data related to goods and suppliers include but are not limited to supplier supply ratio, delivery on-time rate, purchase history, contract terms, supplier location, financial status, logistics methods, etc. At the same time, upstream and downstream data (such as raw material sources, terminal demand changes) are integrated to fully reflect the dynamic characteristics of the supply chain. This step requires ensuring the accuracy, completeness and real-time nature of the data to lay the foundation for subsequent feature extraction and model analysis.

[0072] Based on comprehensive data, feature engineering is performed to extract key features that can reflect the dependency relationship between products and suppliers. The core features that can reflect the dependency relationship between products and suppliers are extracted from big data to provide data dimension support for quantitative analysis. The extracted features are dynamically quantified according to the monitoring window (such as week, month or quarter) to construct a time series of dependency relationships.

[0073] The key features that can reflect the dependency relationship between goods and suppliers are extracted, among which the extracted features include the concentration degree of the proportion of goods supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of goods depends on a single supplier. The extracted features are dynamically quantified according to the monitoring window to generate supply concentration reference values ​​and fulfillment dependence reference values ​​respectively. The time series of the dependency relationship is constructed through the supply concentration reference value and the fulfillment dependence reference value, and the dependency relationship between goods and suppliers is dynamically quantified.

[0074] When the supply ratio of a single supplier accounts for a high proportion of the total supply, it usually indicates that the product is highly dependent on this supplier. This dependence means that the supplier occupies a key position in the product supply chain, and its supply capacity and stability have a direct impact on the production and delivery of the product. If the supplier interrupts supply for any reason (such as insufficient production capacity, natural disasters or operational problems), the company will face the risk of supply chain disruption, which may lead to production shutdowns, order delays and even reduced market competitiveness. Therefore, the high proportion of supply from a single supplier not only reflects the fragility of the supply chain, but also prompts companies to evaluate the feasibility of alternative suppliers and plan emergency supply chains. Dynamic monitoring of this indicator can help companies identify and reduce supply chain risks in advance.

[0075] The specific steps for extracting the concentration of the proportion of goods supplied by a single supplier in the total supply and dynamically quantifying the extracted features according to the monitoring window to generate a reference value for supply concentration are as follows:

[0076] By calculating the supply weight of a single supplier in the total supply, basic data is provided for the reference value of supply concentration, and the contribution of the supplier to the supply of goods is quantified. The calculation expression of the supply weight is:

[0077]

[0078] in: For Suppliers k The supply weight, For Suppliers k The number of items offered, For Suppliers k The supply distance is used to reflect the potential impact of the supplier's geographical distribution on supply. It is a distance sensitivity factor, which is used to adjust the influence of distance on the supply weight. , v represents the index of the supplier, n represents the total number of suppliers;

[0079] Supply weight refers to the relative proportion of a supplier in the total supply, which is used to measure the importance of the supplier in supplying a specific commodity. Its setting needs to comprehensively consider factors in multiple dimensions, such as the quantity of goods provided by the supplier (directly reflecting the contribution to supply), geographical distance (indicating the supplier's supply convenience and risk level), supply frequency (assessing the stability of the supplier), etc. Supply weight is usually calculated through a weighted formula to integrate these factors in a reasonable way. For example, the supply weight can be set as the weighted ratio of the supply volume of a single supplier to the supply volume of all suppliers, and the weight is adjusted based on geographical distance and other key characteristics. This setting method not only reflects the direct contribution of the supplier, but also incorporates additional risk and efficiency considerations, making it more scientific and practical in the analysis of supply concentration;

[0080] By combining the supply volume and supply distance, this step not only reflects the direct contribution of suppliers’ supply, but also incorporates geographical distribution differences into the weight assessment. If the distance sensitivity factor increases, the supply weight of distant suppliers will decrease significantly, further highlighting the importance of nearby suppliers.

[0081] Based on the supply weight of each supplier, the supply concentration factor is calculated to quantify the dominant position of a single supplier in the total supply. The calculation expression of the supply concentration factor is:

[0082]

[0083] in: is the supply concentration factor, is a weight-sensitive factor used to nonlinearly amplify the impact of high-weight suppliers. , For Suppliers k The supply frequency indicates the proportion of the supplier's supply times within the monitoring window;

[0084] By introducing the supply frequency, we can capture the activeness of the supplier's supply behavior during the monitoring period. If a supplier's supply weight and frequency are both high, its influence in the supply concentration factor will be significantly amplified, thus more accurately reflecting the risk of high dependence.

[0085] By dynamically updating the supply concentration reference value in the monitoring window, a time series is generated to capture the dynamic change trend of the dependency relationship. The time series expression of the dynamic supply concentration reference value is:

[0086]

[0087] in: For the current monitoring window The generated supply concentration reference value, The previous monitoring window The generated supply concentration reference value, For Suppliers k In the current monitoring window The supply weight, For Suppliers k In the current monitoring window The supply frequency, It is a time smoothing factor, which is used to control the weight distribution of the historical supply concentration factor and the current supply concentration reference value. The range is ;

[0088] By dynamically combining historical and current data, the formula can reflect short-term fluctuations and capture long-term trends, providing a comprehensive time dimension perspective for supply chain risk assessment. The introduction of can adjust the sensitivity of the supply concentration reference value to adapt to the needs of different monitoring cycles.

[0089] It can be seen from the supply concentration reference value that the concentration characteristics of the proportion of the extracted goods supplied by a single supplier in the total supply are dynamically quantified according to the monitoring window. The larger the performance value of the generated supply concentration reference value, the higher the degree of dependence of the product on a certain supplier. This is because the supply concentration reference value quantifies the dominant position of the supplier in the total supply by comprehensively evaluating the supplier's supply weight (such as supply ratio, frequency, etc.). When the supply concentration reference value is high, it means that the supply weight of a single supplier accounts for a significant proportion, and the supply chain is highly dependent on it; conversely, if the supply concentration reference value is low, it indicates that the supply is more dispersed or the degree of dependence is small, and the supply chain has a stronger ability to resist risks. Therefore, the supply concentration reference value can be used as an important reference indicator to measure the strength of the dependence relationship between goods and suppliers, which helps to identify potential supply chain risks.

[0090] When the historical delivery success rate of a product is highly dependent on a single supplier, it indicates that the product is highly dependent on that supplier. This is because the delivery success rate reflects the supplier's stability and reliability in meeting product demand. If most orders for the product are successfully delivered by a single supplier, and other suppliers have low participation or cannot meet the same quality and timeliness requirements, then once the supplier has supply problems (such as insufficient production capacity, logistics delays, or natural disasters), the supply chain of the product will face a serious risk of interruption. This high degree of dependence not only reduces the resilience of the supply chain, but also amplifies the potential impact of a single point of failure of a supplier on business operations. Therefore, it is an important indicator for evaluating the dependence between products and suppliers.

[0091] The specific steps for extracting the degree to which the historical delivery success rate of goods depends on a single supplier and dynamically quantifying the extracted features according to the monitoring window to generate the fulfillment dependency reference value are as follows:

[0092] The supplier's fulfillment contribution factor is extracted to measure the relative contribution of each supplier in the fulfillment of commodity demand, identify the supplier's performance in meeting commodity order demand, and provide basic data for subsequent calculations. The calculation expression of the fulfillment contribution factor is:

[0093]

[0094] in: For Suppliers k In the monitoring window The contribution factor of the internal commodity to the fulfillment of the contract reflects its direct impact on the fulfillment of the contract. For Suppliers k In the monitoring window The number of orders successfully delivered within a period of time indicates its actual fulfillment capability. For products in the monitoring window The total order demand within is used to normalize the supplier's performance. For Suppliers k The fulfillment weight factor takes into account the comprehensive score of multi-dimensional fulfillment capabilities such as order delivery time, delivery quality, and ability to meet special needs;

[0095] This step can quantify the suppliers' performance contributions and take into account the differences in their performance capabilities, providing accurate input for subsequent concentration calculations.

[0096] Calculate the fulfillment concentration factor of a single supplier to quantify the concentration of the fulfillment demand of the product on the supplier and clarify the potential risks of high dependence. The calculation expression of the fulfillment concentration factor is:

[0097]

[0098] in: For Suppliers k The performance concentration factor reflects its dominant position in the overall performance demand. For Suppliers k The square of the performance contribution factor emphasizes the impact of large performance contributions on concentration. is the sum of the squares of all suppliers' fulfillment contribution factors, indicating the overall fulfillment distribution and used to normalize the concentration. For Suppliers k In the monitoring window The cumulative supply volume (ratio) within the period is used to reflect the impact of actual supply capacity on concentration. is the performance distribution bias correction term, which is used to capture the enhanced effect of performance dependence when the supply volume is large, where Adjust the parameter for sensitivity, controlling the rate of change of the exponential function;

[0099] This step identifies the concentration of single suppliers in fulfillment and considers the combined impact of supply volume and fulfillment performance, highlighting the risks of high dependence.

[0100] Combining the fulfillment concentration factor and the fulfillment contribution factor, a fulfillment dependence reference value that reflects the dynamic dependence of the commodity on a single supplier is generated, and a time series is constructed for trend analysis. The time series expression of the fulfillment dependence reference value is:

[0101]

[0102] in: For monitoring window The fulfillment dependency reference value generated within the system comprehensively reflects the dynamic dependency relationship between the product and all suppliers. is the performance intensity adjustment item, which amplifies the impact of high-intensity performance suppliers on overall dependence, where is the adjustment coefficient, controlling the adjustment range. For monitoring window The total number of successful deliveries of goods within a period is used to normalize the fulfillment contribution and ensure that the results between different time windows are comparable;

[0103] This step can dynamically quantify the overall dependence of goods on suppliers, and build a time series based on the fulfillment dependence reference values ​​of multiple monitoring windows to capture the trend changes in the dependence relationship between goods and suppliers, providing a basis for subsequent risk prediction and management.

[0104] It can be seen from the performance dependency reference value that the extracted historical delivery success rate of the commodity depends on a single supplier. The degree of dependence is dynamically quantified according to the monitoring window. The larger the performance value of the generated performance dependency reference value, the higher the degree of dependence between the commodity and the single supplier, and vice versa. The performance dependency reference value quantifies the dependence of the commodity on a single supplier within the dynamic monitoring window by comprehensively analyzing the multi-dimensional characteristics such as the historical delivery success rate of the commodity, the supplier's performance contribution factor, and the concentration of supply. When the performance dependency reference value is large, it means that a single supplier occupies a dominant position in the supply and fulfillment of the commodity, and the commodity is highly dependent on the stability and continuity of its supply; on the contrary, when the performance dependency reference value is low, it means that the supply chain distribution of the commodity is more dispersed, and the dependence on a single supplier is weaker, thus having higher supply chain flexibility and risk resistance. Therefore, the performance dependency reference value is an important quantitative indicator to measure the strength of the dependence relationship between commodities and suppliers.

[0105] The dynamically quantified features are used as input variables and fed into a trained machine learning model (such as a random forest, support vector machine, or deep learning model). The dependency relationship between products and suppliers is quantitatively evaluated based on the training results.

[0106] The supply concentration reference value and fulfillment dependence reference value generated by dynamically quantifying the proportion of goods supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of goods depends on a single supplier are used as input variables and input into the trained machine learning model to generate a dependency index. The dependency relationship between goods and suppliers is quantitatively evaluated based on the dependency index output by the machine learning model.

[0107] A trained machine learning model refers to an algorithmic model that has the ability to quantitatively evaluate the dependency between goods and suppliers after data input, feature processing, model training and optimization. It uses the dynamically quantified supply concentration reference value and fulfillment dependency reference value as input variables, and outputs a dependency index that reflects the degree of dependency between goods and suppliers by learning the dependency pattern in historical data. The model training process involves the analysis of a large amount of historical data, including characteristic data such as the degree of supply concentration, fulfillment stability, and supply fluctuations between different goods and suppliers, as well as the association between these characteristics and actual supply chain performance (such as supply interruption events, fulfillment anomalies, etc.). During the training process, the model parameters are adjusted through the optimization algorithm so that it can effectively capture the nonlinear and complex associations between the input variables and the dependency relationship. Ultimately, the trained model has high predictive and generalization capabilities, and can accurately evaluate the degree of dependency under the input of new supply concentration reference values ​​and fulfillment dependency reference values.

[0108] Specifically, the trained model may include a variety of machine learning methods, such as regression analysis, support vector machine (SVM), decision tree, random forest or deep learning model (such as neural network). Model selection depends on the characteristics and complexity of the data. For example, for time series data, deep learning models based on recurrent neural network (RNN) or long short-term memory network (LSTM) can be used. These models are able to handle dynamically changing input features, capture the fluctuation trend of supply concentration and fulfillment dependence over time, and generate a dependency index as output. During the training process, the model repeatedly optimizes the loss function (such as mean square error or cross entropy loss) to make the predicted dependency index closer to the actual degree of dependence. Through the trained machine learning model, an accurate quantitative assessment of the dependency relationship between goods and suppliers can be achieved. Ultimately, the output of the model can not only reflect the current dependency status, but also predict future trends, helping companies identify potential supply chain risks and formulate scientific response strategies.

[0109] The machine learning model is not specifically limited here, and can realize the monitoring window Generated reference values ​​for supply concentration and monitoring window Generated fulfillment dependency reference value Conduct comprehensive analysis to generate dependency index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the dependency index The resulting calculation formula is:

[0110]

[0111] In the formula, The reference values ​​for supply concentration are and performance-dependent reference values The preset scaling factor of Both are greater than 0.

[0112] It can be seen from the dependency index that the concentration characteristics of the proportion of the extracted goods supplied by a single supplier in the total supply are dynamically quantified according to the monitoring window. The larger the performance value of the generated supply concentration reference value, the larger the performance value of the generated fulfillment dependence reference value. That is, the larger the performance value of the dependency index generated when the dependency relationship between the product and the supplier is quantitatively evaluated by the trained machine learning model, the higher the degree of dependence of the product on the supplier, and vice versa.

[0113] Machine learning models can capture nonlinear relationships between features and combine historical trends to predict future dependency trends. Through this process, it is possible to discover hidden risk points in dependency relationships and identify which products have the potential to be highly dependent on suppliers.

[0114] Based on the assessment results and the set dependency risk threshold, determine whether there is a potential risk of high dependency between the product and the supplier. Through intelligent analysis, timely identify the high dependency risk and provide early warning information to the purchaser so that they can take countermeasures to alleviate the high dependency of the product on a single supplier and reduce the risk of supply interruption.

[0115] The dependency index generated by the trained machine learning model when quantitatively evaluating the dependency relationship between products and suppliers is compared with the pre-set dependency index reference threshold, and intelligent analysis is performed to timely identify high dependency risks. The specific steps are as follows:

[0116] If the dependency index is greater than the dependency index reference threshold, it indicates that the dependency relationship between the product and the supplier is high, and the system generates early warning information so that the purchaser can take countermeasures to alleviate the high dependency of the product on a single supplier and reduce the risk of supply interruption;

[0117] If the dependency index is less than or equal to the dependency index reference threshold, it will indicate that the dependency relationship between the product and the supplier is low, and no warning information will be generated through the system. At this time, the risk of supply interruption of the product is low.

[0118] If the reliance on a single supplier for a product exceeds the threshold, or the reliance trend forecast shows that the risk may increase in the future, the system will immediately issue an early warning. This warning will include a detailed reliance assessment report and targeted response suggestions, such as developing new suppliers, increasing inventory or adjusting procurement strategies.

[0119] After the countermeasures are implemented, continue to obtain supply chain data of goods and suppliers, and analyze the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, and continuously monitor the real-time dependencies between goods and suppliers to ensure that dependency risks continue to decrease;

[0120] After the countermeasures are implemented, continue to obtain supply chain data of goods and suppliers, and analyze the changes in real-time dependencies during multiple subsequent monitoring windows. The specific steps are as follows:

[0121] After the countermeasures are implemented, continue to obtain supply chain data for commodities and suppliers to analyze changes in dependencies over multiple subsequent monitoring windows;

[0122] After obtaining the dependency index of multiple monitoring windows, a comprehensive analysis of the change trend of the obtained dependency index is conducted to preliminarily evaluate whether the response measures have effectively alleviated the dependence of the product on a single supplier. By calculating the dependency index change rate of each supplier, the improvement or deterioration of the degree of dependence is quantified. The calculation expression of the dependency index change rate is:

[0123]

[0124] in: Indicates supplier k The rate of change of the dependency index during multiple monitoring windows, T is the total number of monitoring windows obtained, indicating the time span of the evaluation, Is a supplier k In the window The dependency index within It is the reference threshold of the dependency index, which is used as a benchmark for assessing supplier dependency risk. is the time weight factor, which is used to give higher weight to the data in the recent time window, reflecting the impact of the current data on the evaluation;

[0125] By calculating the rate of change of the dependence index , the degree of improvement in the dependency relationship between goods and suppliers can be quantitatively evaluated. , indicating that the dependency has been alleviated; if , it means that the dependency has not been effectively alleviated and more countermeasures may be needed. This process helps companies identify whether they have successfully reduced their dependence on a single supplier for goods.

[0126] After completing the dependency index change analysis of a single supplier, further evaluate the dependency changes of all suppliers to measure the effectiveness of the overall response measures. By combining the importance weights of each supplier, calculate the overall response effect evaluation value to provide guidance for the optimization strategy. The calculation expression of the effect evaluation value is:

[0127]

[0128] in, It is the effectiveness evaluation value of the overall response measures, reflecting the comprehensive effect of changes in all supplier dependencies. Is a supplier k The importance weight of the supplier is usually set based on the supplier's strategic position or criticality to the supply of the goods;

[0129] Calculate the changes in all supplier dependencies by weighted average to obtain an evaluation of the overall response effect .if , indicating that the dependency has been effectively alleviated and the response measures are effective; if If it is positive, it means that the effect of the countermeasures is not obvious and the supply chain strategy may need to be further optimized.

[0130] The goal of this phase is to ensure that the response measures not only effectively reduce dependency risks, but also provide a basis for decision-making in future supply chain management.

[0131] Summarize and optimize the effectiveness of response measures and establish long-term mechanisms to prevent the recurrence of high-dependency risks;

[0132] After analyzing and evaluating the effect of the countermeasures on improving the dependence between goods and suppliers, we will summarize the successful experiences and shortcomings, optimize and adjust the current strategy, and formulate a systematic and continuous management mechanism to ensure that the risk of high dependence can be effectively avoided in future supply chain management. Specifically, this includes timely identifying potential dependence problems through data monitoring and dynamic analysis, establishing an alternative supplier network, adjusting procurement ratios, optimizing contract terms, and other means to reduce excessive dependence on a single supplier from the source. At the same time, through standardized risk management processes and automated early warning systems, we will continuously monitor the dependencies in the supply chain and form a long-term management mechanism that can be dynamically adjusted and optimized, thereby enhancing the resilience and risk resistance of the supply chain and preventing the recurrence of similar risk problems.

[0133] Through the above-mentioned supply chain risk analysis method based on artificial intelligence big data, dynamic monitoring and intelligent evaluation of the dependency relationship between goods and suppliers can be achieved, so as to effectively identify and mitigate the high dependency risk in the supply chain. This method ensures comprehensive data support by integrating the internal system of the enterprise and external data sources, providing a reliable basis for accurately evaluating the dependency relationship between goods and suppliers. Through the application of feature engineering and machine learning models, the degree of dependence of goods on suppliers can be intelligently quantified, and changes in dependencies can be monitored in real time. This allows buyers to take effective countermeasures before potential risks break out, reduce the risk of supply disruptions, and ensure the continuity of production lines. In the long run, this method can not only optimize the effectiveness of countermeasures, but also establish a continuous risk management mechanism to prevent the recurrence of similar risks and improve the resilience and stability of the supply chain.

[0134] The present invention provides Figure 2 A supply chain risk analysis system based on artificial intelligence big data is shown, including a data collection and integration module, a feature engineering and quantification module, a machine learning evaluation module, a risk assessment and early warning module, a response measure effect verification module, and a long-term mechanism establishment and optimization module;

[0135] Data collection and integration module, which obtains all data related to goods and suppliers from internal enterprise systems and external sources, providing comprehensive data support for subsequent analysis and evaluation;

[0136] The feature engineering and quantification module performs feature engineering based on comprehensive data to extract key features that can reflect the dependency relationship between products and suppliers. It extracts core features that can reflect the dependency relationship between products and suppliers from big data, provides data dimension support for quantitative analysis, dynamically quantifies the extracted features according to the monitoring window, and constructs a time series of dependency relationships.

[0137] The machine learning evaluation module uses the dynamically quantified features as input variables and inputs them into the trained machine learning model, and performs quantitative evaluation of the dependency relationship between products and suppliers based on the training results;

[0138] The risk assessment and early warning module determines whether there is a potential risk of high dependence between the product and the supplier based on the assessment results and the set dependency risk threshold. It promptly identifies the high dependence risk through intelligent analysis and provides early warning information to the purchaser so that he can take countermeasures to alleviate the high dependence of the product on a single supplier.

[0139] The countermeasures effectiveness verification module continues to obtain supply chain data of goods and suppliers after the countermeasures are implemented, and analyzes the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, continuously monitor the real-time dependencies between goods and suppliers, and ensure that the dependency risks continue to decrease;

[0140] Long-term mechanism establishment and optimization module, summarizes and optimizes the effects of response measures, and establishes long-term mechanisms to prevent the recurrence of high-dependency risks;

[0141] The supply chain risk analysis method based on artificial intelligence big data provided in an embodiment of the present invention is implemented through the above-mentioned supply chain risk analysis system based on artificial intelligence big data. The specific methods and processes of the supply chain risk analysis system based on artificial intelligence big data are detailed in the embodiment of the above-mentioned supply chain risk analysis method based on artificial intelligence big data, which will not be repeated here.

[0142] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0144] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A supply chain risk analysis method based on artificial intelligence big data, characterized in that: The following steps are involved: Acquire all data related to products and suppliers from internal enterprise systems and external sources to provide comprehensive data support for subsequent analysis and evaluation; Based on all the acquired data related to the goods and suppliers, feature engineering is performed to extract key features that can reflect the dependency relationship between goods and suppliers. The extracted features include the degree of concentration of the proportion of goods supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of goods depends on a single supplier. The extracted features are dynamically quantified according to the monitoring window to generate a supply concentration reference value and a fulfillment dependency reference value respectively. The generated supply concentration reference value and fulfillment dependency reference value are used as input variables and input into the trained machine learning model to generate a dependency index. The dependency relationship between goods and suppliers is quantitatively evaluated based on the dependency index output by the machine learning model. Based on the assessment results and the set dependency risk threshold, determine whether there is a potential risk of high dependency between the product and the supplier. Through intelligent analysis, timely identify the high dependency risk and provide early warning information to the purchaser so that he can take countermeasures to alleviate the high dependency of the product on a single supplier. After the countermeasures are implemented, continue to obtain supply chain data of goods and suppliers, and analyze the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, and continuously monitor the real-time dependencies between goods and suppliers to ensure that dependency risks continue to decrease; Summarize and optimize the effectiveness of response measures and establish long-term mechanisms to prevent the recurrence of high-dependency risks.

2. The supply chain risk analysis method based on artificial intelligence big data according to claim 1 is characterized in that: Obtain all data related to products and suppliers from internal enterprise systems and external sources. The specific steps are as follows: First, integrate internal enterprise data, including extracting key information from ERP, supplier management systems, and inventory and logistics management systems; Next, collect external data sources, including industry reports, market monitoring platforms, logistics tracking services, and publicly available supplier credit assessment and financial health data; Finally, through data cleaning and standardization, the integrity and consistency of internal enterprise data and external data sources are ensured, laying the foundation for subsequent feature extraction and analysis.

3. The supply chain risk analysis method based on artificial intelligence big data according to claim 1 is characterized in that: The specific steps for extracting the concentration of the proportion of goods supplied by a single supplier in the total supply and dynamically quantifying the extracted features according to the monitoring window to generate a reference value for supply concentration are as follows: By calculating the supply weight of a single supplier in the total supply, basic data is provided for the supply concentration reference value, and the contribution of the supplier to the supply of goods is quantified. The calculation expression of the supply weight is: ,in: For Suppliers k The supply weight, For Suppliers k The number of items offered, For Suppliers k The supply distance is used to reflect the potential impact of the supplier's geographical distribution on supply. is the distance sensitivity factor, which is used to adjust the impact of distance on the supply weight. v represents the index of the supplier, n represents the total number of suppliers; Based on the supply weight of each supplier, the supply concentration factor is calculated to quantify the dominant position of a single supplier in the total supply. The calculation expression of the supply concentration factor is: ,in: is the supply concentration factor, is a weight-sensitive factor used to nonlinearly amplify the impact of high-weight suppliers. For Suppliers k The supply frequency indicates the proportion of the supplier's supply times within the monitoring window; By dynamically updating the supply concentration reference value in the monitoring window, a time series is generated to capture the dynamic change trend of the dependency relationship. The time series expression of the dynamic supply concentration reference value is: ,in: For the current monitoring window The generated supply concentration reference value, The previous monitoring window The generated supply concentration reference value, For Suppliers k In the current monitoring window The supply weight, For Suppliers k In the current monitoring window The supply frequency, is the time smoothing factor, which is used to control the weight distribution of the historical supply concentration factor and the current supply concentration reference value. The range is .

4. The supply chain risk analysis method based on artificial intelligence big data according to claim 3 is characterized in that: The specific steps for extracting the degree to which the historical delivery success rate of goods depends on a single supplier and dynamically quantifying the extracted features according to the monitoring window to generate the fulfillment dependency reference value are as follows: The supplier's fulfillment contribution factor is extracted to measure the relative contribution of each supplier in the fulfillment of commodity demand and to identify the supplier's performance in meeting the demand for commodity orders. The calculation expression of the fulfillment contribution factor is: ,in: For Suppliers k In the monitoring window The contribution factor of the internal commodity to the fulfillment of the contract reflects its direct impact on the fulfillment of the contract. For Suppliers k In the monitoring window The number of orders successfully delivered within a period of time indicates its actual fulfillment capability. For products in the monitoring window The total order demand within is used to normalize the supplier's performance. For Suppliers k The performance weight factor of Calculate the fulfillment concentration factor of a single supplier to quantify the concentration of the fulfillment demand of the product on the supplier and clarify the potential risks of high dependence. The calculation expression of the fulfillment concentration factor is: ,in: For Suppliers k The performance concentration factor reflects its dominant position in the overall performance demand. For Suppliers k The square of the performance contribution factor emphasizes the impact of performance contribution on concentration. is the sum of the squares of all suppliers' fulfillment contribution factors, indicating the overall fulfillment distribution and used to normalize the concentration. For Suppliers k In the monitoring window The cumulative supply volume within the period is used to reflect the impact of actual supply capacity on concentration. is the performance distribution bias correction term, where Adjust the parameter for sensitivity, controlling the rate of change of the exponential function; Combining the fulfillment concentration factor and the fulfillment contribution factor, a fulfillment dependence reference value that reflects the dynamic dependence of the commodity on a single supplier is generated, and a time series is constructed for trend analysis. The time series expression of the fulfillment dependence reference value is: ,in: For monitoring window The fulfillment dependency reference value generated within the system comprehensively reflects the dynamic dependency relationship between the product and all suppliers. is the performance intensity adjustment item, which amplifies the impact of high-intensity performance suppliers on overall dependence, where is the adjustment coefficient, controlling the adjustment range. For monitoring window The total number of successful deliveries of the products in the contract, used to normalize the fulfillment contribution.

5. The supply chain risk analysis method based on artificial intelligence big data according to claim 1 is characterized in that: The dependency index generated by the trained machine learning model when quantitatively evaluating the dependency relationship between products and suppliers is compared with the pre-set dependency index reference threshold, and intelligent analysis is performed to timely identify high dependency risks. The specific steps are as follows: If the dependency index is greater than the dependency index reference threshold, it indicates that the dependency relationship between the product and the supplier is high, and the system generates early warning information so that the purchaser can take countermeasures to alleviate the high dependency of the product on a single supplier and reduce the risk of supply interruption; If the dependency index is less than or equal to the dependency index reference threshold, it indicates that the dependency relationship between the product and the supplier is low, and no warning information is generated through the system.

6. The supply chain risk analysis method based on artificial intelligence big data according to claim 5 is characterized in that: After the countermeasures are implemented, continue to obtain supply chain data of goods and suppliers, and analyze the changes in real-time dependencies during multiple subsequent monitoring windows. The specific steps are as follows: After the countermeasures are implemented, continue to obtain supply chain data for commodities and suppliers to analyze changes in dependencies over multiple subsequent monitoring windows; After obtaining the dependency index of multiple monitoring windows, a comprehensive analysis of the change trend of the obtained dependency index is conducted to preliminarily evaluate whether the response measures have effectively alleviated the dependence of the product on a single supplier. By calculating the dependency index change rate of each supplier, the improvement or deterioration of the degree of dependence is quantified. The calculation expression of the dependency index change rate is: ,in: Indicates supplier k The rate of change of the dependency index during multiple monitoring windows, T is the total number of monitoring windows obtained, indicating the time span of the evaluation, Is a supplier k In the window The dependency index within It is the reference threshold of the dependency index, which is used as a benchmark for assessing supplier dependency risk. is the time weight factor, which is used to give higher weight to the data in the recent time window, reflecting the impact of the current data on the evaluation; if , indicating that the dependency has been alleviated; if , then the dependency has not been alleviated.

7. The supply chain risk analysis method based on artificial intelligence big data according to claim 6 is characterized in that: After completing the dependency index change analysis of a single supplier, further evaluate the dependency changes of all suppliers to measure the effectiveness of the overall response measures. By combining the importance weights of each supplier, calculate the overall response effect evaluation value to provide guidance for the optimization strategy. The calculation expression of the effect evaluation value is: ,in, It is the effectiveness evaluation value of the overall response measures, reflecting the comprehensive effect of changes in all supplier dependencies. Is a supplier k Importance weight of Calculate the changes in all supplier dependencies by weighted average to obtain an evaluation of the overall response effect ; if , indicating that the dependency has been alleviated and the response measures are effective; if If it is positive, it means that the effect of the countermeasures is not obvious and the supply chain strategy needs to be further optimized.

8. A supply chain risk analysis system based on artificial intelligence big data, used to implement the supply chain risk analysis method based on artificial intelligence big data described in any one of claims 1 to 7, characterized in that: It includes data collection and integration module, feature engineering and quantification module, machine learning assessment module, risk assessment and early warning module, response measures effectiveness verification module and long-term mechanism establishment and optimization module; Data collection and integration module, which obtains all data related to goods and suppliers from internal enterprise systems and external sources, providing comprehensive data support for subsequent analysis and evaluation; The feature engineering and quantification module performs feature engineering based on all acquired data related to products and suppliers to extract key features that can reflect the dependency relationship between products and suppliers. The extracted features include the degree of concentration of the proportion of products supplied by a single supplier in the total supply and the degree to which the historical delivery success rate of products depends on a single supplier. The extracted features are dynamically quantified according to the monitoring window to generate a supply concentration reference value and a fulfillment dependency reference value respectively. The machine learning evaluation module uses the generated supply concentration reference value and fulfillment dependency reference value as input variables and inputs them into the trained machine learning model to generate a dependency index. The dependency relationship between the product and the supplier is quantitatively evaluated based on the dependency index output by the machine learning model. The risk assessment and early warning module determines whether there is a potential risk of high dependence between the product and the supplier based on the assessment results and the set dependency risk threshold. It promptly identifies the high dependence risk through intelligent analysis and provides early warning information to the purchaser so that he can take countermeasures to alleviate the high dependence of the product on a single supplier. The countermeasures effectiveness verification module continues to obtain supply chain data of goods and suppliers after the countermeasures are implemented, and analyzes the changes in real-time dependencies during multiple subsequent monitoring windows to verify the effectiveness of the countermeasures, continuously monitor the real-time dependencies between goods and suppliers, and ensure that the dependency risks continue to decrease; The long-term mechanism establishment and optimization module summarizes and optimizes the effects of response measures and establishes long-term mechanisms to prevent the recurrence of high-dependency risks.

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