A supply chain multi-party sharing management system based on a digital trading platform

Abstract

The present invention discloses a supply chain multi-party sharing management system based on a digital transaction platform. The present invention relates to the technical field of supply chain management systems, including a supply chain management platform, wherein the supply chain management platform is communicatively connected to a data acquisition and integration module, a forecasting and analysis module, a resource scheduling module, a collaborative operation module, and a monitoring and optimization module, wherein electrical signals are connected between the modules. The supply chain multi-party sharing management system based on the digital transaction platform dynamically adjusts resource allocation by utilizing resource scheduling demand forecast results in combination with current resource conditions to ensure that resources are effectively utilized at the right time and place. The ability to dynamically adjust and optimize configuration significantly improves resource utilization efficiency, reduces resource waste, and can quickly adapt to market changes and respond to emergency needs. The resource scheduling evaluation index is used to provide a quantitative evaluation tool for resource scheduling, so as to intelligently allocate and optimize various resources in the supply chain.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management systems, and in particular to a supply chain multi-party sharing management system based on a digital transaction platform. Background Art

[0002] With the deepening of global economicization and the rapid development of information technology, competition among enterprises has shifted from single product competition to competition in overall supply chain efficiency and service level. The advancement of information technology, especially the widespread application of cloud computing, big data, the Internet of Things, artificial intelligence and other technologies, has provided strong technical support for the digitalization and intelligence of supply chain management. Against the background of increasingly refined global division of labor, supply chain management has become increasingly complex. The supply chain involves many participants, including suppliers, manufacturers, distributors, retailers, etc. Information sharing and collaborative cooperation among all participants are crucial to improving the overall efficiency of the supply chain. As a new type of supply chain management tool, the digital trading platform has the advantages of information integration and sharing and process optimization and collaboration, which helps to improve the overall efficiency of the supply chain.

[0003] In supply chain management, resource scheduling and allocation often involve multiple goals, and there are certain conflicts between different goals, which makes the resource scheduling and allocation problem more complicated. Therefore, how to evaluate the resource scheduling and allocation of the supply chain management process and realize dynamic adjustment and allocation of resource scheduling based on the resource scheduling effect is the problem we need to solve. To this end, a supply chain multi-party sharing management system based on a digital transaction platform is proposed. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a supply chain multi-party shared management system based on a digital transaction platform, comprising a supply chain management platform, wherein the supply chain management platform is communicatively connected to a data acquisition and integration module, a forecasting and analysis module, a resource scheduling module, a collaborative operation module, and a monitoring and optimization module, wherein the modules are electrically connected;

[0005] The data collection and integration module is used to collect multi-source supply chain data from various supply chain participants, including inventory levels, production progress, and sales data, and to pre-process and integrate the collected multi-source supply chain data, wherein the supply chain participants include suppliers, manufacturers, and distributors;

[0006] The forecasting and analysis module is used to perform demand analysis on supply chain data, extract forecasting features of the supply chain's production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock level, traverse relevant data of the forecasting features to obtain a supply chain forecasting feature table, and build a supply chain scheduling forecasting model based on machine learning technology to forecast and analyze the supply chain's resource demand, sales trends, and inventory changes, and evaluate the resource scheduling effect;

[0007] The resource scheduling module is used to coordinate the flow of resources in each link of the supply chain based on the forecast results and the current resource status, ensuring that resources are reasonably utilized at the right time and place, realizing dynamic adjustment and optimal allocation of resources, and improving resource utilization efficiency;

[0008] The collaborative operation module is used to promote collaborative operations and information sharing among all parties involved in the supply chain, provide online collaboration tools, support real-time communication and collaboration among multiple parties, coordinate and resolve problems and conflicts encountered in collaborative operations, and improve the response speed and flexibility of the entire supply chain;

[0009] The monitoring and optimization module is used to monitor the overall process of supply chain management and continuously optimize and upgrade the system based on actual operation conditions and user feedback.

[0010] Preferably, the data acquisition and integration module includes a data collection unit, a pre-processing unit and a data integration unit;

[0011] The data collection unit is used to identify the types of supply chain data to be collected and to establish a data interface to collect supply chain data;

[0012] The preprocessing unit is used to preprocess the collected data, and the preprocessing process includes cleaning, sorting and standardization to ensure data quality and consistency;

[0013] The data integration unit is used to integrate the pre-processed multi-source supply chain data, and prioritize the resources to be scheduled according to the importance of the supply chain resource scheduling process, obtain a resource scheduling sequence table, and form a global view of the supply chain, providing a basis for subsequent analysis and prediction.

[0014] Preferably, in the data collection and integration module, the process of preprocessing and integrating the collected multi-source supply chain data includes:

[0015] Clearly define the types of supply chain data that need to be collected from each supply chain participant, namely inventory, production progress, and sales data. Establish a unified data interface and transmission protocol to identify each supply chain participant, namely suppliers, manufacturers, and distributors, and ensure that data can be transmitted smoothly and securely. Inventory includes raw material inventory, work-in-progress inventory, and finished product inventory. Production progress includes production plans, actual production volume, and production delays. Sales data includes order volume, sales volume, sales revenue, and returns.

[0016] Use ETL tools to extract the required data from the data sources of each supply chain participant, collect multi-source supply chain data from each participant, and transfer the extracted data to the data warehouse. When extracting data, it is necessary to ensure the completeness and accuracy of the data to avoid omissions or errors. The data sources of each supply chain participant include ERP systems, CRM systems, and databases.

[0017] Preprocessing the collected data by cleaning to remove duplicate data, process missing values, and correct erroneous data helps improve the quality and accuracy of the data, providing a reliable basis for subsequent analysis and prediction. It also converts data from different sources and formats into a unified format and standard so that it can be easily processed in the subsequent data integration and analysis process.

[0018] Classify the pre-processed supply chain data according to the supply chain data type, associate data from different sources, and fuse the associated data to integrate inventory data, production progress data, sales data, etc. to fully understand the operation of the supply chain;

[0019] Analyze resource demands in the supply chain based on the integrated supply chain data, prioritize resources to be scheduled based on the importance and urgency of the resource scheduling process, and determine the resource scheduling sequence;

[0020] Based on the results of the priority division, a resource scheduling sequence table is generated to clarify the scheduling order and priority of each resource. Based on the integrated data and resource scheduling sequence table, a global view of the supply chain is constructed to display each link, participant, resource flow, etc. of the supply chain, so as to fully understand the operating status and potential problems of the supply chain.

[0021] Preferably, in the prediction analysis module, the process of evaluating the resource scheduling effect includes:

[0022] Traverse the pre-processed inventory, production progress, and sales data collected from various supply chain participants to conduct demand analysis and extract relevant prediction features for supply chain forecasting, namely the production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock. The production-demand ratio reflects the ratio of the number of products produced by an enterprise to the demand for the product by its users. It is an important indicator for measuring the degree of matching between production and sales. The production-sales ratio is similar to the production-demand ratio, but focuses more on the sales link, reflecting the market acceptance and sales efficiency of the enterprise's products. The sales growth rate represents the growth of sales volume and is an important indicator for evaluating market potential and enterprise growth capabilities. The inventory turnover rate reflects the speed of inventory turnover and is a key indicator for measuring inventory management efficiency. The safety stock is the inventory level set to ensure the stability of the supply chain and is used to cope with sudden demand or supply disruptions.

[0023] Combine each forecast feature with pre-processed inventory, production progress, and sales data to integrate forecast feature-related data and obtain a supply chain forecast feature table.

[0024] The historical data of each prediction feature in the supply chain prediction feature table is divided into training sets and test sets. The training set data is combined with machine learning technology to build a supply chain scheduling prediction model, and the test set data is used to test the supply chain scheduling prediction model. The supply chain scheduling prediction model is built based on the linear regression model.

[0025] Combining the resource scheduling sequence table, forecast feature data and supply chain scheduling forecast model, we get the resource scheduling evaluation index. We set different scheduling levels based on the resource scheduling evaluation index, namely excellent scheduling level, medium scheduling level and poor scheduling level, and set corresponding evaluation thresholds for each scheduling level.

[0026] Based on the resource scheduling evaluation index and supply chain scheduling forecasting model, the supply chain resource demand, sales trends and inventory changes are forecasted and analyzed to determine the scheduling level and evaluate the quality of the current resource scheduling effect.

[0027] Preferably, the process of obtaining the resource scheduling evaluation index includes:

[0028] Calculate the resource demand satisfaction index based on the resource scheduling sequence table and the predicted resource demand, and preset the standard value Std of the resource demand satisfaction index D , calculate the resource demand satisfaction index adjustment value, analyze the change trend of resource demand, and the expression of the resource demand satisfaction index is: D r is the resource demand satisfaction index, D is the actual resource demand, is the predicted resource demand output by the model, D a is the actual amount of allocated resources, D rThe larger the value of , the higher the resource demand satisfaction;

[0029] Analyze the consistency between actual sales and predicted sales trends, calculate the sales trend matching index, and preset the standard value Std of the sales trend matching index S , calculate the sales trend matching index adjustment value, analyze the degree of sales trend matching index relative to the reference value, and the expression of the sales trend matching index is: S t is the sales trend matching index, S is the actual sales volume, is the predicted sales volume output by the model, S t The larger the value of , the higher the sales trend matching degree;

[0030] Evaluate the management effect of inventory turnover rate and safety stock, calculate the inventory change control index, and preset the standard value Std of the inventory change control index I , calculate the inventory change control index adjustment value and analyze the inventory change control effect. The expression of the inventory change control index is: I c is the inventory change control index, ITO is the actual inventory turnover rate, ITO t is the target inventory turnover rate, SI is the safety stock, SI op is the optimal safety stock, α and β are weight factors used to adjust the impact of inventory turnover rate and safety stock on the overall evaluation;

[0031] Combining the resource demand satisfaction index, sales trend matching index, inventory change control index and their corresponding adjustment values, a weighted calculation is performed to obtain the resource scheduling evaluation index, and the scheduling effect is analyzed.

[0032] Preferably, the resource demand satisfaction index adjustment value is expressed as:

[0033]

[0034] Among them, Ad D is the resource demand satisfaction index adjustment value, D r is the resource demand satisfaction index, Std D is the standard value of resource demand satisfaction index, Ad D The value of is between 0 and 1, 0 means completely unsatisfied and 1 means completely satisfied;

[0035] The expression of the sales trend matching index adjustment value is:

[0036]

[0037] Among them, Ad S S is the sales trend matching index adjustment value,t is the sales trend matching index, Std S is the standard value of the sales trend matching index, Ad S The value of is between 0 and 1, where 0 means no match at all and 1 means a perfect match;

[0038] The expression of the inventory change control index adjustment value is:

[0039]

[0040] Among them, Ad I is the inventory change control index adjustment value, I c is the inventory change control index, Std I is the standard value of the inventory change control index, Ad I The value of is between 0 and 1, where 0 indicates extremely poor inventory control and 1 indicates perfect inventory control.

[0041] Preferably, the resource scheduling evaluation index is expressed as:

[0042]

[0043] Among them, RI is the resource scheduling evaluation index, Ad D is the resource demand satisfaction index adjustment value, Std D is the standard value of the sales trend matching index, w D is the weight coefficient of resource demand satisfaction, Ad S is the inventory change control index adjustment value, Std S is the standard value of the sales trend matching index, w S is the weight coefficient of inventory change control, Ad I is the inventory change control index adjustment value, Std I is the standard value of the inventory change control index, w I is the weight coefficient for inventory change control.

[0044] Preferably, the plurality of scheduling levels correspond to a plurality of evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;

[0045] The plurality of scheduling levels and the plurality of evaluation thresholds satisfy the following relationship:

[0046] Excellent scheduling level 0<RI <RI M A low RI value indicates excellent resource scheduling, with resources being used effectively at the right time and in the right place. This indicates high resource efficiency. The current scheduling strategy should be continued to maintain supply chain stability and efficiency, and optimization should be continued.

[0047] Medium scheduling level RIM ≤RI <RI L The RI value is at a medium level, indicating that the resource scheduling effect is average and there is room for improvement. We need to conduct in-depth analysis of each link in the scheduling process to identify the key factors and bottlenecks that affect the scheduling effect. We should formulate specific improvement measures based on the analyzed issues. After implementing the improvement measures, we should closely monitor the changes in the RI value and make necessary adjustments and optimizations based on the actual situation.

[0048] Poor scheduling level RI ≥ RI L A high RI value indicates poor resource scheduling and low resource utilization efficiency, leaving much room for improvement. Immediately activate the emergency response mechanism and take emergency measures to alleviate the current resource shortage or surplus situation to ensure the continuity and stability of the supply chain. Conduct a comprehensive review of the scheduling strategy and resource allocation mechanism to identify the root cause of the problem and strengthen communication and collaboration among all links in the supply chain.

[0049] Among them, RI is the resource scheduling evaluation index, RI M The lower threshold corresponding to the medium scheduling level and the upper threshold corresponding to the excellent scheduling level, RI L It is the lower threshold corresponding to the poor scheduling level and the upper threshold corresponding to the medium scheduling level.

[0050] Preferably, in the resource scheduling module, the process of implementing dynamic resource adjustment and optimal configuration includes:

[0051] Collect and integrate current resource status, run the supply chain scheduling forecast model, analyze resource demand, sales trends, and inventory changes, and calculate the current resource scheduling evaluation index to evaluate the current resource scheduling effect. If the resource scheduling evaluation index is high, it means that the current scheduling effect is not ideal and needs to be adjusted. If the resource scheduling evaluation index is low, it means that the scheduling effect is good, but there is also room for further optimization.

[0052] Based on the forecast results and analysis of resource scheduling evaluation index, a specific resource scheduling plan is formulated, including determining the resource allocation quantity, allocation time, allocation location, and transportation method, and the formulated resource scheduling plan is communicated to all participants in the supply chain;

[0053] Monitor and track the actual situation of resource flow, including the arrival time, quantity, quality, etc. of resources, and adjust resource scheduling plans based on monitoring results and new forecast data to cope with uncertainties and changes in the supply chain.

[0054] Preferably, in the monitoring and optimization module, the process of monitoring the overall supply chain management process includes:

[0055] Collect operational data from all links of the supply chain and track every key link in the supply chain in real time to identify and address potential problems and bottlenecks;

[0056] Collect user feedback, combine it with the supply chain scheduling prediction model and the resource scheduling evaluation index to analyze the resource scheduling effect, identify problems and bottlenecks in supply chain management, and conduct in-depth analysis of the identified problems to find the root causes and influencing factors;

[0057] Based on the results of problem diagnosis, formulate corresponding response measures, such as adjusting production plans, optimizing inventory strategies, improving logistics processes, etc., to adjust and optimize the overall supply chain management strategy.

[0058] The present invention provides a supply chain multi-party sharing management system based on a digital trading platform. It has the following beneficial effects:

[0059] 1. This supply chain multi-party shared management system based on a digital trading platform uses resource scheduling demand forecast results and combines them with current resource conditions to dynamically adjust resource allocation to ensure that resources are effectively utilized at the right time and place. The ability to dynamically adjust and optimize configuration significantly improves resource utilization efficiency, reduces resource waste, and can quickly adapt to market changes and respond to urgent needs. The resource scheduling evaluation index provides a quantitative evaluation tool for resource scheduling, so as to intelligently allocate and optimize various resources in the supply chain.

[0060] 2. This supply chain multi-party shared management system based on a digital trading platform, through the application of supply chain scheduling prediction model and resource scheduling evaluation index, enables users to adjust resource scheduling measurements according to the evaluation results of the current resource scheduling effect, so as to achieve accurate resource allocation, reduce resource waste, improve resource utilization efficiency, and thus enhance the resilience and stability of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a module structure diagram of a supply chain multi-party sharing management system based on a digital transaction platform according to the present invention;

[0062] Figure 2 A flow chart for evaluating resource scheduling effectiveness according to the present invention;

[0063] Figure 3 This is a flow chart for obtaining the resource scheduling evaluation index of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0065] The first embodiment, as Figures 1 to 3 As shown, the present invention provides a technical solution: a supply chain multi-party shared management system based on a digital transaction platform, including a supply chain management platform, the supply chain management platform is communicatively connected to a data acquisition and integration module, a forecasting and analysis module, a resource scheduling module, a collaborative operation module, and a monitoring and optimization module, wherein the modules are electrically connected;

[0066] The data collection and integration module is used to collect multi-source supply chain data from various supply chain participants, including inventory, production progress and sales data, and pre-process and integrate the collected multi-source supply chain data. The supply chain participants include suppliers, manufacturers and distributors. The data collection and integration module includes a data collection unit, a pre-processing unit and a data integration unit. The data collection unit is used to clarify the type of supply chain data to be collected and establish a data interface to collect supply chain data. The pre-processing unit is used to pre-process the collected data. The pre-processing process includes cleaning, sorting and standardization to ensure data quality and consistency. The data integration unit is used to integrate the pre-processed multi-source supply chain data and prioritize the resources to be scheduled according to the importance of the supply chain resource scheduling process. This obtains a resource scheduling sequence list and forms a global view of the supply chain, providing a basis for subsequent analysis and prediction.

[0067] Furthermore, the process of preprocessing and integrating the collected multi-source supply chain data includes:

[0068] Clearly define the types of supply chain data that need to be collected from each supply chain participant, namely inventory, production progress and sales data, and establish a unified data interface and transmission protocol to identify each supply chain participant, namely suppliers, manufacturers, and distributors, to ensure that data can be transmitted smoothly and securely. Inventory includes raw material inventory, work-in-progress inventory, finished product inventory, etc. Production progress includes production plan, actual production volume, production delays, etc. Sales data includes order volume, sales volume, sales revenue, return volume, etc. Use ETL tools to extract the required data from the data sources of each supply chain participant, collect multi-source supply chain data of each participant, and transfer the extracted data to the data warehouse. When extracting data, it is necessary to ensure the integrity and accuracy of the data to avoid omissions or errors. Among them, the data sources of each supply chain participant are ERP systems, CRM systems, and databases. Preprocessing the collected data by cleaning to remove duplicate data, process missing values, correct erroneous data, etc., helps to improve the quality and accuracy of the data. , providing a reliable foundation for subsequent analysis and prediction, and converting data from different sources and formats into a unified format and standard so that it can be easily processed in the subsequent data integration and analysis process, classifying the pre-processed supply chain data according to the supply chain data type, correlating data from different sources, and fusing the correlated data, fusing inventory data, production progress data, sales data, etc. together to fully understand the operation of the supply chain, analyzing the resource demand in the supply chain based on the integrated supply chain data, and prioritizing the resources to be scheduled according to the importance and urgency of the resource scheduling process, determining the resource scheduling sequence, generating a resource scheduling sequence table based on the priority classification results, clarifying the scheduling order and priority of each resource, and building a global view of the supply chain based on the integrated data and resource scheduling sequence table, showing each link, participant, resource flow, etc. of the supply chain, so as to fully understand the operation status and potential problems of the supply chain;

[0069] The forecasting analysis module is used to perform demand analysis on supply chain data, extract the forecasting features of the supply chain's production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock, traverse the relevant data of the forecasting features, obtain the supply chain forecasting feature table, and build a supply chain scheduling forecasting model based on machine learning technology. It forecasts and analyzes the resource demand, sales trend, and inventory changes of the supply chain, evaluates the resource scheduling effect, traverses the pre-processed inventory, production progress, and sales data collected from all participants in the supply chain, conducts demand analysis, and extracts the associated forecasting features of the supply chain forecast, which are the production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock. The production-demand ratio reflects the ratio of the number of products produced by the enterprise to the demand of its users for the product. It is an important indicator for measuring the degree of matching between production and sales. The production-sales ratio is similar to the production-demand ratio, but focuses more on the sales link, reflecting the market acceptance and sales efficiency of the enterprise's products. The sales growth rate represents the growth of sales volume. It is an important indicator for evaluating market potential and enterprise growth capabilities. The inventory turnover rate reflects the speed of inventory turnover and is an indicator for measuring inventory management efficiency. The safety stock is the inventory level set to ensure the stability of the supply chain and is used to cope with sudden demand or supply interruption. The forecast feature data are integrated with the pre-processed inventory, production schedule and sales data to obtain the supply chain forecast feature table. The historical data of each forecast feature in the supply chain forecast feature table are divided to obtain the training set and the test set. The training set data is combined with machine learning technology to build a supply chain scheduling forecast model, and the test set data is used to test the supply chain scheduling forecast model. The supply chain scheduling forecast model is built based on the linear regression model. The resource scheduling sequence table, forecast feature data and supply chain scheduling forecast model are combined to obtain the resource scheduling evaluation index. Different scheduling levels are set according to the resource scheduling evaluation index, namely excellent scheduling level, medium scheduling level and poor scheduling level, and corresponding evaluation thresholds are set for each scheduling level. Based on the resource scheduling evaluation index and the supply chain scheduling forecast model, the resource demand, sales trend and inventory changes of the supply chain are forecasted and analyzed to determine the scheduling level and evaluate the quality of the current resource scheduling effect.

[0070] Furthermore, the process of obtaining the resource scheduling evaluation index includes:

[0071] Calculate the resource demand satisfaction index based on the resource scheduling sequence table and the predicted resource demand, and preset the standard value Std of the resource demand satisfaction index D , calculate the resource demand satisfaction index adjustment value, analyze the changing trend of resource demand, and the expression of resource demand satisfaction index is: D r is the resource demand satisfaction index, D is the actual resource demand, is the predicted resource demand output by the model, Da is the actual amount of allocated resources, D r The larger the value of , the higher the resource demand satisfaction;

[0072] Analyze the consistency between actual sales and predicted sales trends, calculate the sales trend matching index, and preset the standard value Std of the sales trend matching index S , calculate the sales trend matching index adjustment value, analyze the degree of sales trend matching index relative to the reference value, the expression of sales trend matching index is: S t is the sales trend matching index, S is the actual sales volume, is the predicted sales volume output by the model, S t The larger the value of , the higher the sales trend matching degree;

[0073] Evaluate the management effect of inventory turnover rate and safety stock, calculate the inventory change control index, and preset the standard value Std of the inventory change control index I , calculate the inventory change control index adjustment value, analyze the inventory change control effect, the expression of the inventory change control index is: I c is the inventory change control index, ITO is the actual inventory turnover rate, ITO t is the target inventory turnover rate, SI is the safety stock, SI op is the optimal safety stock, α and β are weight factors used to adjust the impact of inventory turnover rate and safety stock on the overall evaluation;

[0074] Combining the resource demand satisfaction index, sales trend matching index, inventory change control index, and their corresponding adjustment values, a weighted calculation is performed to obtain the resource scheduling evaluation index, and the scheduling effect is analyzed.

[0075] The resource scheduling module is used to coordinate the flow of resources in each link of the supply chain based on the forecast results and current resource status, ensuring that resources are reasonably utilized at the right time and place, realizing dynamic adjustment and optimal allocation of resources, and improving resource utilization efficiency;

[0076] The collaborative operation module is used to promote collaborative operations and information sharing among all parties involved in the supply chain. It provides online collaborative tools, supports real-time communication and collaboration among multiple parties, coordinates and resolves problems and conflicts encountered in collaborative operations, and improves the response speed and flexibility of the entire supply chain.

[0077] The monitoring and optimization module is used to monitor the overall process of supply chain management and continuously optimize and upgrade the system based on actual operation conditions and user feedback.

[0078] The second embodiment, based on the first embodiment, see Figures 1 to 3As shown in Figure 2, the expression for the resource demand satisfaction index adjustment value is:

[0079]

[0080] Among them, Ad D is the resource demand satisfaction index adjustment value, D r is the resource demand satisfaction index, Std D is the standard value of resource demand satisfaction index, Ad D The value of is between 0 and 1, where 0 means completely unsatisfied and 1 means completely satisfied. When the resource scheduling strategy can accurately and timely meet all or most resource requirements, the value approaches 1. On the contrary, if the resource shortage or surplus leads to unsatisfied demand, the value will decrease. D The value indicates that the resource scheduling strategy performs well in meeting demand, which helps improve customer satisfaction and business operation efficiency;

[0081] The expression for the sales trend matching index adjustment value is:

[0082]

[0083] Among them, Ad S is the sales trend matching index adjustment value, S t is the sales trend matching index, Std S is the standard value of the sales trend matching index, Ad S The value of is between 0 and 1, where 0 indicates a complete mismatch and 1 indicates a complete match. When the resource scheduling strategy can accurately predict and adapt to changes in sales trends, the value approaches 1. If there is a deviation between resource scheduling and actual sales trends, the value will decrease. 高 Ad The S value indicates that the resource scheduling strategy is highly flexible and forward-looking, which helps reduce inventory backlogs and improve sales efficiency;

[0084] The expression of inventory change control index adjustment value is:

[0085]

[0086] Among them, Ad I is the inventory change control index adjustment value, I c is the inventory change control index, Std I is the standard value of the inventory change control index, Ad I The value of Ad is between 0 and 1, where 0 indicates extremely poor inventory control and 1 indicates perfect inventory control. When inventory levels are kept within a reasonable range, neither too high nor too low, the value approaches 1. Too high inventory will increase storage costs, while too low inventory may lead to stockouts. Both of these situations will cause the value to decrease.I The value indicates that the resource scheduling strategy performs well in controlling inventory, which helps reduce operating costs and improve capital turnover;

[0087] Furthermore, the expression of resource scheduling evaluation index is:

[0088]

[0089] Among them, RI is the resource scheduling evaluation index, Ad D is the resource demand satisfaction index adjustment value, Std D is the standard value of the sales trend matching index, w D is the weight coefficient of resource demand satisfaction, Ad S is the inventory change control index adjustment value, Std S is the standard value of the sales trend matching index, w S is the weight coefficient of inventory change control, Ad I is the inventory change control index adjustment value, Std I is the standard value of the inventory change control index, w I is the weight coefficient of inventory change control. It should be noted that when all Ad D 、Ad S 、Ad I Close to its corresponding Std D 、Std S 、Std I When Ad D 、Ad S 、Ad I Deviation from its corresponding Std D 、Std S 、Std I When , RI will increase. The greater the deviation, the higher the RI value, indicating that the scheduling effect deviates more from the standard value;

[0090] In addition, multiple scheduling levels correspond to multiple evaluation thresholds, where the evaluation thresholds include an upper threshold and a lower threshold;

[0091] Multiple scheduling levels and multiple evaluation thresholds satisfy the following relationship:

[0092] Excellent scheduling level 0<RI <RI M A low RI value indicates excellent resource scheduling, with resources being used effectively at the right time and in the right place. This indicates high resource efficiency. The current scheduling strategy should be continued to maintain supply chain stability and efficiency, and optimization should be continued.

[0093] Medium scheduling level RIM ≤RI <RI L The RI value is at a medium level, indicating that the resource scheduling effect is average and there is room for improvement. We need to conduct in-depth analysis of each link in the scheduling process to identify the key factors and bottlenecks that affect the scheduling effect. We should formulate specific improvement measures based on the analyzed issues. After implementing the improvement measures, we should closely monitor the changes in the RI value and make necessary adjustments and optimizations based on the actual situation.

[0094] Poor scheduling level RI ≥ RI L A high RI value indicates poor resource scheduling and low resource utilization efficiency, leaving much room for improvement. Immediately activate the emergency response mechanism and take emergency measures to alleviate the current resource shortage or surplus situation to ensure the continuity and stability of the supply chain. Conduct a comprehensive review of the scheduling strategy and resource allocation mechanism to identify the root cause of the problem and strengthen communication and collaboration among all links in the supply chain.

[0095] Among them, RI is the resource scheduling evaluation index, RI M The lower threshold corresponding to the medium scheduling level and the upper threshold corresponding to the excellent scheduling level, RI L The lower threshold corresponding to the poor scheduling level and the upper threshold corresponding to the medium scheduling level;

[0096] In the resource scheduling module, the process of implementing dynamic resource adjustment and optimal configuration includes:

[0097] Collect and integrate current resource status, run the supply chain scheduling forecast model, analyze resource demand, sales trends and inventory changes, and calculate the current resource scheduling evaluation index to evaluate the current resource scheduling effect. If the value of the resource scheduling evaluation index is high, it means that the current scheduling effect is not ideal and needs to be adjusted; if the value of the resource scheduling evaluation index is low, it means that the scheduling effect is good, but there is also room for further optimization. Based on the forecast results and the analysis of the resource scheduling evaluation index, formulate a specific resource scheduling plan, including determining the resource allocation amount, allocation time, allocation location and transportation method, and communicate the formulated resource scheduling plan to all participants in the supply chain, monitor and track the actual situation of resource flow, including the arrival time, quantity, quality, etc. of resources, and adjust the resource scheduling plan based on the monitoring results and new forecast data to cope with uncertainties and changes in the supply chain;

[0098] In the monitoring and optimization module, the process of monitoring the overall supply chain management process includes:

[0099] Collect operational data from all links of the supply chain and conduct real-time tracking of each key link in the supply chain, discover and respond to potential problems and bottlenecks, collect user feedback, combine the supply chain scheduling prediction model and the evaluation results of the resource scheduling evaluation index, analyze the resource scheduling effect, identify problems and bottlenecks in supply chain management, and conduct in-depth analysis of the identified problems to find out the root causes and influencing factors of the problems. Based on the results of the problem diagnosis, formulate corresponding response measures, such as adjusting production plans, optimizing inventory strategies, improving logistics processes, etc., to adjust and optimize the overall supply chain management strategy.

[0100] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative efforts should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention shall be implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A supply chain multi-party shared management system based on a digital transaction platform, including a supply chain management platform, characterized in that: The supply chain management platform is communicatively connected to a data collection and integration module, a forecasting and analysis module, a resource scheduling module, a collaborative operation module, and a monitoring and optimization module, wherein electrical signal connections are made between the modules; The data collection and integration module is used to collect multi-source supply chain data from various supply chain participants, including inventory levels, production progress, and sales data, and to pre-process and integrate the collected multi-source supply chain data, wherein the supply chain participants include suppliers, manufacturers, and distributors; The forecasting and analysis module is used to perform demand analysis on supply chain data, extract forecasting features of the supply chain's production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock, traverse relevant data of the forecasting features, obtain a supply chain forecasting feature table, and build a supply chain scheduling forecasting model based on machine learning technology. It forecasts and analyzes the supply chain's resource demand, sales trends, and inventory changes, and evaluates the resource scheduling effect. The process of evaluating the resource scheduling effect includes: Traverse the pre-processed inventory, production progress, and sales data collected from various supply chain participants to conduct demand analysis and extract relevant prediction features for supply chain forecasting, namely, production-demand ratio, production-sales ratio, sales growth rate, inventory turnover rate, and safety stock; Combine each forecast feature with pre-processed inventory, production progress, and sales data to integrate forecast feature-related data and obtain a supply chain forecast feature table. The historical data of each prediction feature in the supply chain prediction feature table is divided into training sets and test sets. The training set data is combined with machine learning technology to build a supply chain scheduling prediction model, and the test set data is used to test the supply chain scheduling prediction model. The supply chain scheduling prediction model is built based on the linear regression model. Combining the resource scheduling sequence table, forecast feature data and supply chain scheduling forecast model, we get the resource scheduling evaluation index. We set different scheduling levels based on the resource scheduling evaluation index, namely excellent scheduling level, medium scheduling level and poor scheduling level, and set corresponding evaluation thresholds for each scheduling level. Based on the resource scheduling evaluation index and supply chain scheduling forecast model, we conduct forecast analysis on the supply chain's resource demand, sales trends, and inventory changes, determine the scheduling level, and evaluate the effectiveness of the current resource scheduling. The resource scheduling module is used to coordinate the flow of resources in each link of the supply chain based on the forecast results and current resource status, and realize dynamic adjustment and optimal allocation of resources; The collaborative operation module is used to promote collaborative operation and information sharing among various supply chain participants; The monitoring and optimization module is used to monitor the overall process of supply chain management and continuously optimize and upgrade the system based on actual operation conditions and user feedback.

2. A supply chain multi-party shared management system based on a digital trading platform according to claim 1, characterized in that: The data acquisition and integration module includes a data collection unit, a pre-processing unit and a data integration unit; The data collection unit is used to identify the types of supply chain data to be collected and to establish a data interface to collect supply chain data; The preprocessing unit is used to preprocess the collected data; The data integration unit is used to integrate the pre-processed multi-source supply chain data, and prioritize the resources to be scheduled according to the importance of the supply chain resource scheduling process to obtain a resource scheduling sequence table.

3. The supply chain multi-party shared management system based on a digital transaction platform according to claim 2, characterized in that: In the data collection and integration module, the process of preprocessing and integrating the collected multi-source supply chain data includes: Clarify the types of supply chain data that need to be collected from each supply chain participant, namely inventory, production progress, and sales data, and establish a unified data interface and transmission protocol to identify each supply chain participant, namely suppliers, manufacturers, and distributors; Use ETL tools to extract the required data from the data sources of each supply chain participant, collect multi-source supply chain data from each participant, and transfer the extracted data to the data warehouse. The data sources of each supply chain participant include ERP systems, CRM systems, and databases. Pre-process the collected data and convert data from different sources and formats into a unified format and standard; Classify the pre-processed supply chain data according to the supply chain data type, associate data from different sources, and fuse the associated data; Analyze resource demands in the supply chain based on the integrated supply chain data, prioritize resources to be scheduled based on the importance and urgency of the resource scheduling process, and determine the resource scheduling sequence; Based on the results of priority division, a resource scheduling sequence table is generated to clarify the scheduling order and priority of each resource, and a global view of the supply chain is constructed based on the integrated data and resource scheduling sequence table.

4. The supply chain multi-party shared management system based on a digital transaction platform according to claim 1, characterized in that: The process of obtaining the resource scheduling evaluation index includes: Calculate the resource demand satisfaction index based on the resource scheduling sequence table and the predicted resource demand, and preset the standard value Std of the resource demand satisfaction index D , calculate the resource demand satisfaction index adjustment value, analyze the change trend of resource demand, and the expression of the resource demand satisfaction index is: is the resource demand satisfaction index, D is the actual resource demand, is the predicted resource demand output by the model, D a is the actual amount of allocated resources, D r The larger the value of , the higher the resource demand satisfaction; Analyze the consistency between actual sales and predicted sales trends, calculate the sales trend matching index, and preset the standard value Std of the sales trend matching index S , calculate the sales trend matching index adjustment value, analyze the degree of sales trend matching index relative to the reference value, and the expression of the sales trend matching index is: S t is the sales trend matching index, S is the actual sales volume, is the predicted sales volume output by the model, S t The larger the value of , the higher the sales trend matching degree; Evaluate the management effect of inventory turnover rate and safety stock, calculate the inventory change control index, and preset the standard value Std of the inventory change control index I , calculate the inventory change control index adjustment value and analyze the inventory change control effect. The expression of the inventory change control index is: I c is the inventory change control index, ITO is the actual inventory turnover rate, ITO t is the target inventory turnover rate, SI is the safety stock, SI op is the optimal safety stock, α and β are weight factors used to adjust the impact of inventory turnover rate and safety stock on the overall evaluation; Combining the resource demand satisfaction index, sales trend matching index, inventory change control index and their corresponding adjustment values, a weighted calculation is performed to obtain the resource scheduling evaluation index, and the scheduling effect is analyzed.

5. The supply chain multi-party shared management system based on a digital transaction platform according to claim 4, characterized in that: The expression of the resource demand satisfaction index adjustment value is: Among them, Ad D is the resource demand satisfaction index adjustment value, D r is the resource demand satisfaction index, Std D is the standard value of resource demand satisfaction index, Ad D The value of is between 0 and 1, 0 means completely unsatisfied and 1 means completely satisfied; The expression of the sales trend matching index adjustment value is: Among them, Ad S S is the sales trend matching index adjustment value, t is the sales trend matching index, Std S is the standard value of the sales trend matching index, Ad S The value of is between 0 and 1, where 0 means no match at all and 1 means a perfect match; The expression of the inventory change control index adjustment value is: Among them, Ad I is the inventory change control index adjustment value, I c is the inventory change control index, Std I is the standard value of the inventory change control index, Ad I The value of is between 0 and 1, where 0 indicates extremely poor inventory control and 1 indicates perfect inventory control.

6. The supply chain multi-party shared management system based on a digital transaction platform according to claim 5, characterized in that: The expression of the resource scheduling evaluation index is: Among them, RI is the resource scheduling evaluation index, Ad D is the resource demand satisfaction index adjustment value, Std D is the standard value of the sales trend matching index, w D is the weight coefficient of resource demand satisfaction, Ad S is the inventory change control index adjustment value, Std S is the standard value of the sales trend matching index, w S is the weight coefficient of inventory change control, Ad I is the inventory change control index adjustment value, Std I is the standard value of the inventory change control index, w I is the weight coefficient for inventory change control. It should be noted that the higher the RI value, the greater the deviation between the scheduling effect and the standard value.

7. The supply chain multi-party shared management system based on a digital transaction platform according to claim 6, characterized in that: The plurality of scheduling levels correspond to a plurality of evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold; The plurality of scheduling levels and the plurality of evaluation thresholds satisfy the following relationship: Excellent scheduling level 0 <RI<RI M ; Medium scheduling level RI M ≤RI <RI L ; Poor scheduling level RI ≥ RI L ; Among them, RI is the resource scheduling evaluation index, RI M The lower threshold corresponding to the medium scheduling level and the upper threshold corresponding to the excellent scheduling level, RI L It is the lower threshold corresponding to the poor scheduling level and the upper threshold corresponding to the medium scheduling level.

8. The supply chain multi-party shared management system based on a digital transaction platform according to claim 7, characterized in that: In the resource scheduling module, the process of implementing dynamic resource adjustment and optimal configuration includes: Collect and integrate current resource status, run supply chain scheduling forecasting models, analyze resource demand, sales trends, and inventory changes, and calculate the current resource scheduling evaluation index to evaluate the current resource scheduling effect; Based on the analysis of forecast results and resource scheduling evaluation index, formulate a specific resource scheduling plan and communicate the formulated resource scheduling plan to all participants in the supply chain; Monitor and track the actual situation of resource flow, and adjust resource scheduling plans based on monitoring results and new forecast data to cope with uncertainties and changes in the supply chain.

9. The supply chain multi-party shared management system based on a digital transaction platform according to claim 8, characterized in that: In the monitoring and optimization module, the process of monitoring the overall supply chain management process includes: Collect operational data from all links of the supply chain and track every key link in the supply chain in real time to identify and address potential problems and bottlenecks; Collect user feedback, combine it with the supply chain scheduling prediction model and the resource scheduling evaluation index to analyze the resource scheduling effect, identify problems and bottlenecks in supply chain management, and conduct in-depth analysis of the identified problems to find the root causes and influencing factors; Based on the results of problem diagnosis, formulate corresponding response measures and adjust and optimize the overall supply chain management strategy.

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