Supply chain management system, method, electronic device, and medium

Abstract

This application relates to the field of computer technology and provides a supply chain management system, method, electronic device, and medium. The system includes: a data acquisition and fusion layer for acquiring multi-source heterogeneous data; a demand forecasting engine layer for performing multi-modal data fusion on the multi-source heterogeneous data to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium-to-long-term demand forecasting model to provide B-end medium-to-long-term demand forecasting results; a dual-warehouse collaborative management layer for managing the inventory strategies and allocation mechanisms of sample warehouses and central warehouses based on the C-end spot demand forecasting results and the B-end medium-to-long-term demand forecasting results; and a logistics configuration optimization layer for dynamically optimizing the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecasting results. Thus, fully automated decision-making from demand forecasting to logistics configuration is achieved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a supply chain management system, method, electronic device, and medium. Background Technology

[0002] The development of artificial intelligence (AI) technology has enabled global trade and cross-border supply chain logistics management. The basic process of cross-border commodity trade begins with accepting overseas orders, followed by selecting suppliers for material preparation and production, and then initiating cross-border logistics from factory shipment to customs clearance in China, and finally customs clearance and warehousing in the destination country. Introducing AI technology can help improve the efficiency of cross-border commodity transactions, cross-border supply chain management, and cross-border financial services. However, cross-border logistics and supply chain management face many technical challenges, such as insufficient forecasting accuracy leading to inventory backlogs or stockouts, low utilization of warehousing resources, potential imbalances in regional logistics allocation resulting in slower delivery times and higher costs, and inefficient inventory allocation. Existing logistics management solutions mainly focus on configuring e-commerce logistics supply chains, lacking overall optimization of cross-border logistics supply chains. Examples include Chinese patent applications with publication numbers CN113139690A, CN118195528A, CN120725764A, and CN121117310A, and Chinese patent applications with authorization announcement numbers CN118333531B, CN119090223B, CN119417349B, CN119539636B, CN119648406B, and CN120471412B.

[0003] Therefore, this application provides a supply chain management system, method, electronic device, and medium to address the technical challenges in the prior art. Summary of the Invention

[0004] Firstly, this application provides a supply chain management system. The supply chain management system includes: a data acquisition and fusion layer for acquiring multi-source heterogeneous data, including C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data; a demand forecasting engine layer for performing multi-modal data fusion on the multi-source heterogeneous data to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium-to-long-term demand forecasting model to provide B-end medium-to-long-term demand forecasting results, wherein the B-end medium-to-long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand; a dual-warehouse collaborative management layer for managing the respective inventory strategies and allocation mechanisms of sample warehouses and central warehouses based on the C-end spot demand forecasting results and the B-end medium-to-long-term demand forecasting results, wherein the inventory strategy of the sample warehouse is rapid turnover and low inventory, and the inventory strategy of the central warehouse is large-scale reserves and high inventory; and a logistics configuration optimization layer for dynamically optimizing the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecasting results.

[0005] Through the first aspect of this application, forecasting accuracy is improved, market changes can be detected in advance, inventory efficiency is optimized, logistics costs are saved, service levels are improved, and fully automated decision-making is achieved from demand forecasting to logistics configuration.

[0006] In one possible implementation of the first aspect of this application, the data acquisition and fusion layer is further used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product coding mapping table.

[0007] In one possible implementation of the first aspect of this application, the data acquisition and fusion layer is further configured to perform data quality governance on the multi-source heterogeneous data according to a three-level data quality governance strategy, wherein the three-level data quality governance strategy includes a real-time verification layer, an anomaly detection layer, and a missing value processing layer.

[0008] In one possible implementation of the first aspect of this application, the C-end spot demand forecasting model includes a multi-scale time series forecasting model, which is used for time series feature extraction, real-time signal fusion, promotional effect modeling, and regional difference modeling.

[0009] In one possible implementation of the first aspect of this application, the C-end spot demand forecasting model calculates the C-end spot demand forecast value for a given region at a given time based on the C-end forecasting formula. The parameters of the C-end forecasting formula include a long-term trend term, daily seasonality, weekly seasonality, monthly seasonality, promotion effect multiplier, regional characteristic coefficient, and random disturbance term.

[0010] In one possible implementation of the first aspect of this application, the B-end medium- and long-term demand forecasting model includes a multi-dimensional demand extrapolation model, which is used for contract demand analysis, trend extrapolation, industry correlation analysis, and C-end to B-end conversion modeling.

[0011] In one possible implementation of the first aspect of this application, the B-end medium- and long-term demand forecasting model calculates the predicted value of B-end medium- and long-term demand for a given region at a given time based on the B-end forecasting formula. The parameters of the B-end forecasting formula include the deterministic demand of signed contracts, the B-end trend term, the industry prosperity index, the conversion coefficient from C-end to B-end, the demand transmission lag, and the random disturbance term.

[0012] In one possible implementation of the first aspect of this application, the C-end to B-end conversion modeling is achieved through a demand transmission model. The demand transmission model is used to establish the transmission time lag relationship between C-end spot demand and B-end medium- and long-term demand, and further to establish a quantitative transmission mechanism for the C-end spot demand to B-end medium- and long-term demand. The transmission time lag relationship between C-end spot demand and B-end medium- and long-term demand includes the time window during which C-end demand changes lead B-end procurement decisions. The quantitative transmission mechanism for C-end spot demand to B-end medium- and long-term demand includes the dynamic estimation of the C-end to B-end conversion coefficient.

[0013] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer uses C-end sales demand analysis and scheduling strategies to manage the inventory strategy and allocation mechanism of the sample warehouse, wherein the C-end sales demand analysis and scheduling strategies include best-selling product prediction and configuration, dynamic pricing and promotion, user profile-driven product selection, and real-time inventory visualization.

[0014] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer further utilizes an inventory management model combining strategy and dynamic safety stock to manage the inventory strategy and allocation mechanism of the sample warehouse. The inventory management model combining strategy and dynamic safety stock includes reorder point parameters and target inventory parameters. The reorder point parameters are calculated based on replenishment lead time, average demand within the lead time, service level coefficient, and standard deviation of lead time demand. The target inventory parameters are calculated based on economic replenishment quantity and balancing ordering costs and holding costs.

[0015] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer utilizes a medium- to long-term reserve optimization strategy to manage the inventory strategy and allocation mechanism of the central warehouse, wherein the medium- to long-term reserve optimization strategy includes contract inventory locking, seasonal reserves, and safety stock optimization.

[0016] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer further utilizes an intelligent allocation decision model based on demand forecasting and inventory status to manage the inventory strategy and allocation mechanism of the central warehouse. The intelligent allocation decision model based on demand forecasting and inventory status includes allocation triggering condition parameters and allocation quantity calculation parameters. The allocation triggering condition parameters predict the risk of stockouts in the sample warehouse within a certain number of days in the future when the inventory in the sample warehouse is lower than the reorder point, thereby determining that the central warehouse has surplus inventory or batches of goods awaiting warehousing. The allocation quantity calculation parameters are the minimum value among the current inventory, available inventory, and maximum single-transport capacity.

[0017] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer further utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse, wherein the dual-warehouse collaborative optimization algorithm includes a collaborative objective function and employs a decomposition and coordination algorithm to achieve collaborative optimization solution.

[0018] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is used to evaluate the logistics demand intensity of each of the multiple regions based on their respective consumption record data and demand forecast results. The logistics demand intensity of each of the multiple regions is reflected as a logistics demand index, which is calculated based on a weighted average of the C-end demand forecast value, the B-end demand forecast value, the demand volatility, and the demand growth rate of a given region.

[0019] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is further configured to use a regional clustering analysis algorithm to cluster the logistics demand characteristics of the multiple regions, thereby dividing the multiple regions into multiple clustering types and adopting multiple logistics configuration strategies that correspond one-to-one with the multiple clustering types.

[0020] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is further configured to determine the location of the logistics warehouse using a multi-objective location model, wherein the multi-objective location model is the optimal solution of a multi-objective optimization function, and the objective parameters of the multi-objective optimization function include total transportation cost, average delivery time, warehouse construction cost, warehouse rental cost, and supply interruption risk.

[0021] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is further used to determine the location of the logistics warehouse using a heuristic solution algorithm, wherein the heuristic solution algorithm is a hybrid algorithm of genetic algorithm and simulated annealing.

[0022] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is further used to infer the logistics warehouse capacity demand based on demand forecasting results and inventory strategies, thereby realizing a dynamic capacity adjustment mechanism. The logistics warehouse capacity demand is determined based on the average inventory level of each commodity among a variety of commodities, the volume occupied by each commodity, the volume fluctuation coefficient, the batch occupancy coefficient, the warehouse space utilization rate, and the shelf height utilization coefficient.

[0023] In one possible implementation of the first aspect of this application, the supply chain management system further generates supply chain control recommendations, which include inventory control recommendations and logistics optimization recommendations.

[0024] In one possible implementation of the first aspect of this application, the inventory control recommendations include multiple recommendation types, each of which has triggering conditions and recommendation content. The multiple recommendation types include replenishment recommendations, transfer recommendations, clearance recommendations, and reserve recommendations.

[0025] In one possible implementation of the first aspect of this application, the logistics optimization suggestions include route optimization suggestions, power configuration suggestions, and warehouse network optimization suggestions.

[0026] In one possible implementation of the first aspect of this application, the supply chain management system further includes a visual decision dashboard for providing demand forecasting dashboards, inventory health dashboards, logistics efficiency dashboards, and anomaly warning dashboards.

[0027] In one possible implementation of the first aspect of this application, the supply chain management system further includes a multi-scale prediction fusion model for improving prediction accuracy, wherein the multi-scale prediction fusion model is trained by training a Prophet model to capture seasonality, training an LSTM model to capture nonlinear trends, and training an XGBoost model to capture feature interaction effects as base learners, and then using a meta-learner and dynamic weights for weighted fusion.

[0028] In one possible implementation of the first aspect of this application, the dual-warehouse collaborative management layer further utilizes a reinforcement learning algorithm to optimize the dual-warehouse collaborative strategy, for managing the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse, wherein the reinforcement learning algorithm sets up a state space, an action space, and a reward function.

[0029] In one possible implementation of the first aspect of this application, the logistics configuration optimization layer is further configured to: utilize a regional logistics network optimization algorithm to determine the optimal location of the central warehouse through a demand-weighted algorithm; and repeatedly iterate through a greedy addition algorithm to find the candidate location with the greatest reduction in total cost, thereby achieving the goal of pre-setting the number of warehouses or preventing further cost reduction.

[0030] In one possible implementation of the first aspect of this application, the supply chain management system is applied to cross-border e-commerce supply chain optimization, fast-moving consumer goods distribution network restructuring, and industrial goods MRO supply chain management.

[0031] Secondly, this application provides a supply chain management method. The supply chain management method includes: collecting multi-source heterogeneous data through a data acquisition and fusion layer, wherein the multi-source heterogeneous data includes C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data; performing multi-modal data fusion on the multi-source heterogeneous data through a demand forecasting engine layer to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium-to-long-term demand forecasting model to provide B-end medium-to-long-term demand forecasting results, wherein the B-end medium-to-long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand; managing the inventory strategies and allocation mechanisms of sample warehouses and central warehouses based on the C-end spot demand forecasting results and the B-end medium-to-long-term demand forecasting results through a dual-warehouse collaborative management layer, wherein the inventory strategy of the sample warehouse is rapid turnover and low inventory, and the inventory strategy of the central warehouse is large-scale reserves and high inventory; and dynamically optimizing the logistics warehouse layout, capacity configuration, and distribution network based on multi-regional demand forecasting results through a logistics configuration optimization layer.

[0032] The second aspect of this application improves forecast accuracy, enables early detection of market changes, optimizes inventory efficiency, saves logistics costs, improves service levels, and achieves fully automated decision-making from demand forecasting to logistics configuration.

[0033] In one possible implementation of the second aspect of this application, the data acquisition and fusion layer is further used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product coding mapping table.

[0034] In one possible implementation of the second aspect of this application, the dual-warehouse collaborative management layer further utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse, wherein the dual-warehouse collaborative optimization algorithm includes a collaborative objective function and employs a decomposition and coordination algorithm to achieve collaborative optimization solution.

[0035] In one possible implementation of the second aspect of this application, the dual-warehouse collaborative management layer further utilizes a reinforcement learning algorithm to optimize the dual-warehouse collaborative strategy, for managing the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse, wherein the reinforcement learning algorithm sets up a state space, an action space, and a reward function.

[0036] Thirdly, embodiments of this application also provide a computer device, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method according to any of the above-mentioned implementations.

[0037] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer instructions that, when executed on a computer device, cause the computer device to perform a method according to any of the above-described implementations.

[0038] Fifthly, embodiments of this application also provide a computer program product, the computer program product including instructions stored on a computer-readable storage medium, which, when executed on a computer device, cause the computer device to perform a method according to any of the above-described aspects. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic diagram of an artificial intelligence-based supply chain management system provided for an embodiment of this application; Figure 2 A schematic diagram of a storage architecture including a central warehouse and multiple sample warehouses provided for embodiments of this application; Figure 3 A schematic diagram of the model layer architecture of multiple artificial intelligence models of a supply chain intelligent control module provided in an embodiment of this application; Figure 4A flowchart illustrating an intelligent replenishment algorithm provided in an embodiment of this application; Figure 5 A schematic diagram of an end-to-end data flow provided in an embodiment of this application; Figure 6 A system deployment architecture diagram provided for an embodiment of this application; Figure 7 A flowchart illustrating an artificial intelligence-based supply chain management method provided in this application embodiment; Figure 8 A schematic diagram of a supply chain management system based on dual-warehouse collaborative intelligent forecasting and logistics configuration provided for an embodiment of this application; Figure 9 A flowchart illustrating a supply chain management method based on dual-warehouse collaborative intelligent forecasting and logistics configuration provided in this application embodiment; Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0041] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0042] It should be understood that in the description of this application, "at least one" means one or more, and "multiple" means two or more. In addition, the words "first," "second," etc., unless otherwise stated, are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance or order.

[0043] AI-based optimization of the entire cross-border supply chain Figure 1 This is a schematic diagram of an artificial intelligence-based supply chain management system provided as an embodiment of this application. Figure 1 As shown, The supply chain management system includes multiple data acquisition modules and a supply chain intelligent control module 110. The multiple data acquisition modules provide multi-source heterogeneous data. These modules include at least a consumer module 132 for collecting individual consumer order demand and enterprise consumer order demand, a transportation module 134 for collecting logistics transportation trajectory data, a production module 136 configured to collect manufacturing execution data, and a warehouse module 138 for collecting inventory data from a central warehouse and multiple sample warehouses. The coverage area of ​​the central warehouse includes the coverage areas of each of the multiple sample warehouses. The supply chain intelligent control module 110 is communicatively connected to each of the multiple data acquisition modules. The supply chain intelligent control module 110 is used to fuse and analyze the multi-source heterogeneous data, and utilizes multiple artificial intelligence models to generate short-term demand forecasts related to individual consumer order demand and medium-to-long-term demand forecasts related to enterprise consumer order demand. Then, based on these forecasts, it generates medium-to-long-term replenishment control instructions related to the central warehouse and short-term regional transfer control instructions between the multiple sample warehouses. The intelligent supply chain control module 110 includes a data aggregation engine 112 for receiving multi-source heterogeneous data through a standardized interface, a visualization display module 114 for displaying multi-dimensional operational indicators in real time, an intelligent early warning module 116 for identifying abnormal events based on threshold rules and machine learning models, and an instruction issuance engine 118 for converting artificial intelligence decision results into executable instructions. The intelligent supply chain control module 110 also includes a message queue 120 for enabling communication between multiple modules.

[0044] See Figure 1This AI-based supply chain management system divides the supply chain into four processes: consumer, transportation, production, and warehousing. Data collection and quantitative analysis are performed on each process to achieve intelligent supply chain management based on AI. Furthermore, it considers the difference between individual consumer order demand (C-end) and enterprise consumer order demand (B-end). C-end orders are driven by immediate needs, while B-end orders are driven by medium- to long-term needs. Therefore, the system provides sample warehouses that cater to immediate needs and central warehouses that cater to medium- to long-term needs. By analyzing the consumption records of sellers in various regions, purchasing demand is predicted, and the corresponding regional logistics warehouse configuration is estimated, providing suggestions for logistics supply chain control. For cross-border supply chains such as bulk commodities, the sample warehouse is primarily used for sample display needs; therefore, replenishment is based on intelligent predictions made according to demand analysis. By dividing the warehouse into sample warehouses and central warehouses, sample warehouses are primarily used for regional needs. Replenishment in central warehouses is based on AI model predictions, mainly relying on medium- to long-term forecasts and historical trend analysis. Additionally, the management of central warehouses also needs to be combined with payment terms management, tariff policies, and logistics management. By unifying multiple data sources for real-time fusion, the problem of data silos caused by the independent operation of various systems is overcome; real-time decision-making using artificial intelligence models enables rapid response; the intelligent supply chain control module 110 serves as a control tower for overall control, enabling closed-loop operation and end-to-end collaboration across the entire supply chain; and the dual-layer intelligent linkage of the central warehouse and sample warehouse enables dynamic allocation, supporting efficient collaborative management of cross-regional supply chains.

[0045] Continue reading Figure 1 The consumer module 132 can connect to multiple API interfaces from e-commerce platforms, CRM, and POS systems, aggregating order data streams from all channels in real time to achieve dynamic capture of the entire consumer order chain. The production module 136 deeply integrates with MES and SCADA industrial systems, accessing real-time data from production line equipment to achieve second-level collection and reporting of equipment status and production conditions. The transportation module 134 integrates GPS positioning and IoT smart sensors to monitor transportation trajectories, temperature, humidity, and other environmental indicators in real time, ensuring visible and controllable transportation. The warehouse module 138, based on a WMS system and combined with RFID automatic identification technology, uses an event-driven model to achieve real-time updates of inventory changes and digital management of warehousing operations. Additionally, the visualization display module 114 can be a large digital screen, providing visualization of multi-dimensional quantitative indicators. The intelligent early warning module 116 is used for automatic anomaly identification and proactive intervention. Through the command issuance engine 118, unified scheduling and collaborative management among multiple carriers can be achieved.

[0046] Figure 2 This is a schematic diagram of a storage architecture including a central warehouse and multiple sample warehouses, provided for an embodiment of this application. Figure 2As shown, the global supplier network 200 includes factories in multiple countries, including China, Southeast Asia, Europe, and the Americas. The overseas central warehouse 210 purchases orders from the global supplier network 200, featuring large-volume inventory preparation and customs clearance capabilities. The overseas central warehouse 210 has a large capacity, exceeding 100,000 SKUs, and a high inventory depth, typically 2 to 3 months, covering regions including North America, Europe, and the Asia-Pacific region. In contrast, multiple sample warehouses include Sample Warehouse A (US West Coast), Sample Warehouse B (US East Coast), and Sample Warehouse C (Europe). These sample warehouses are located close to the markets in their respective covered regions to meet rapid fulfillment needs, have a lower inventory depth (2 to 4 weeks), and generally only cover an area within a 500-kilometer radius. Replenishment from the overseas central warehouse 210 to the various sample warehouses is delegated through intelligent scheduling algorithms.

[0047] See Figure 1 and Figure 2This approach breaks down cross-border supply chain management into four stages: consumption, transportation, production, and warehousing. Four corresponding data collection modules are used to gather multi-source heterogeneous data. A supply chain intelligent control module, similar to a central control tower, receives reported data from multiple collection modules and issues commands to perform fusion analysis on the multi-source heterogeneous data. For example, based on multimodal feature extraction and intelligent matching, it obtains supply chain data classification and structured processing results. It can uniformly analyze, extract features, classify, and structure multimodal supply chain data from different language regions, thereby providing efficient cross-regional supply chain collaboration and intelligent decision-making. Furthermore, considering the challenges frequently encountered in cross-border supply chain management, such as different regional consumption demands, cross-border warehousing costs, and information delays from the consumer to the supply side, a two-tiered warehousing architecture of central and sample warehouses is provided. Combined with the timeliness and characteristics of individual consumer order demand and corporate consumer order demand, multiple artificial intelligence models can be used to generate short-term demand forecasts related to individual consumer order demand and medium- to long-term demand forecasts related to corporate consumer order demand. Based on the predictions provided by the artificial intelligence model—specifically, the short-term demand forecasts related to individual consumer orders and the medium- to long-term demand forecasts related to enterprise consumer orders—the system leverages the intelligent linkage between the central warehouse and sample warehouses to generate medium- to long-term replenishment control instructions for the central warehouse and short-term regional transfer control instructions between the multiple sample warehouses. This optimizes the entire cross-border supply chain management process, balancing both C-end and B-end demands, and fully utilizing the inventory buffering and replenishment control functions of the central warehouse and the advantages of sample warehouses—being closer to the market and enabling rapid fulfillment. Furthermore, corresponding artificial intelligence technologies can be used to highlight and strengthen the respective warehousing roles of the central warehouse and sample warehouses, allowing for better medium- to long-term replenishment forecasts for the central warehouse and regional transfers for the sample warehouses, reducing stockout rates and improving order fulfillment rates. Furthermore, the impact of tariffs and policies in different countries and regions, as well as cash flow and payment term management, can be reflected in the intelligent management of the central warehouse through corresponding artificial intelligence models and decision-making mechanisms. This means that complex decision-making mechanisms can be confined to the intelligent management of the central warehouse, while the intelligent management of the sample warehouse can maintain relatively simple short-term regional allocation control, thus helping to improve the overall system's operational efficiency. In summary, by breaking down cross-border supply chain management into four links—consumption, transportation, production, and warehousing—and optimizing them separately using various artificial intelligence technologies, and by employing two warehousing types—central warehouses and sample warehouses—and differentiated inventory strategies, the central warehouses are used to implement large-volume stocking strategies and act as a global inventory buffer, while the sample warehouses are used to implement rapid response strategies to achieve rapid fulfillment by being close to the end market, thus optimizing the entire cross-border supply chain management chain.

[0048] See Figure 1 and Figure 2 In one possible implementation, the supply chain intelligence module is further configured to execute a bulk inventory preparation strategy for the central warehouse and a rapid response strategy for the multiple sample warehouses. The central warehouse serves as a global inventory buffer, and the multiple sample warehouses are used for rapid market fulfillment within their respective coverage areas. Thus, by employing two warehousing types—central warehouse and sample warehouses—and a differentiated inventory strategy, the central warehouse handles bulk inventory preparation and acts as a global inventory buffer, while the sample warehouses execute a rapid response strategy to achieve rapid fulfillment closer to the end market, thereby optimizing the entire cross-border supply chain management process.

[0049] See Figure 1 and Figure 2 In one possible implementation, the consumer module is also used to integrate multi-system data from the e-commerce platform, the production module is also used to directly connect data to production equipment, the transportation module is also used to monitor trajectory temperature and humidity sensor data in real time, and the warehouse module is also used to update inventory status based on inventory change events.

[0050] See Figure 1 and Figure 2 In one possible implementation, the multi-source heterogeneous data includes structured data from the system application programming interface, semi-structured data from the logistics platform, and unstructured data from sensors. Thus, through a multi-source heterogeneous data fusion engine, various types of data are fused and analyzed to provide a basis for decision-making.

[0051] Figure 3 This is a schematic diagram of the model layer architecture of multiple artificial intelligence models in a supply chain intelligent control module provided in an embodiment of this application. For example... Figure 3 As shown, the artificial intelligence model layer 310 includes multiple artificial intelligence models, including a demand forecasting model, a replenishment decision model, an anomaly detection model, and a tariff payment term model. These multiple artificial intelligence models in the artificial intelligence model layer 310 are also connected to a decision fusion engine 320. The decision fusion engine 320 is used to provide solutions such as multi-objective optimization, constraint-satisfied solutions, and risk assessment. Details regarding the various artificial intelligence models can be found in Table 1 below. Table 1

[0052] See Figure 3 As shown in Table 1, in one possible implementation, the supply chain intelligent control module is further configured to utilize the multiple artificial intelligence models to extract various input features from the multi-source heterogeneous data for various output decisions. This achieves a decision-making mechanism that links the artificial intelligence models.

[0053] In some embodiments, the plurality of artificial intelligence models include a demand forecasting model. The input features corresponding to the demand forecasting model among the plurality of input features include historical sales data, promotional data, seasonal data, and macroeconomic data. The output decisions corresponding to the demand forecasting model among the plurality of output decisions include future demand distribution. In some embodiments, the demand forecasting model further includes a multi-timescale demand forecasting model for providing short-term, medium-term, and long-term demand forecasts. The multi-timescale demand forecasting model includes an LSTM model and an attention model for providing short-term demand forecasts, a Prophe model and an external regression model for providing medium-term demand forecasts, and a Transformer time series model for providing long-term demand forecasts. Thus, for demand forecasting, multiple data types can be combined as input features, and a multi-timescale demand forecasting model can be used to make forecasts at different time scales. For example, for short-term time scales such as 1 to 4 weeks, an LSTM model and an attention model can be used; for medium-term time scales such as 1 to 3 months, a Prophe model and an external regression model can be used; and for long-term time scales such as one or more quarters, a Transformer time series model can be used. By using corresponding models at different time scales and then weighting and fusing the results of multiple models, dynamic weight fusion can be achieved, and the resulting future demand distribution has good accuracy at different time scales.

[0054] See Figure 3 As shown in Table 1, in one possible implementation, the multiple artificial intelligence models include a replenishment decision model. Among the multiple input features corresponding to the replenishment decision model, these include forecast results, inventory data, cost data, and tariff data. Among the multiple output decisions corresponding to the replenishment decision model, these include order instructions and transfer instructions. The demand forecasting model is linked to the replenishment decision model. Thus, the linkage between multiple models helps to make better replenishment decisions. Furthermore, by combining information such as costs and tariffs, it helps to provide a more economically optimized decision-making strategy.

[0055] In some embodiments, the replenishment decision model includes a replenishment decision optimizer based on reinforcement learning and mixed-integer linear programming algorithms. The constraints of the replenishment decision model include budget constraints, inventory balance constraints, safety stock constraints, and tariff window constraints. For example, the objective of the replenishment decision optimizer can be set as minimizing the total cost equal to the sum of procurement cost, inventory holding cost, stockout cost, transportation cost, and tariff cost. Additionally, the constraints of the replenishment decision optimizer may include inventory capacity, over-the-counter (OTB) budget, minimum order quantity, and tariff window period. To optimize the replenishment decision model, reinforcement learning techniques can be utilized. Specifically, the state space can be defined to include warehouse inventory, in-transit inventory, demand forecast, and cash flow; the action space can be defined to include replenishment quantity, replenishment time, supplier selection, and transportation method; and the reward function can be defined to include cost savings plus service level improvement minus risk penalties. Exemplary reinforcement learning algorithms include Proximal Policy Optimization (PPO) and mixed-integer linear programming. By setting a series of constraints, such as budget constraints, inventory balance constraints, safety stock constraints, and tariff window constraints, it is helpful to optimize the objective function and the final prediction result.

[0056] See Figure 3 According to Table 1, in one possible implementation, the plurality of artificial intelligence models includes an anomaly detection model. Among the plurality of input features, the input features corresponding to the anomaly detection model include real-time location data, sensor data, and fulfillment status data. Among the plurality of output decisions, the output decisions corresponding to the anomaly detection model include anomaly labels and anomaly scores. The replenishment decision model is linked to the anomaly detection model, and the anomaly detection model is linked to the emergency dispatch model and the insurance compliance model. Details regarding the anomaly detection model can be found in Table 2 below. Table 2

[0057] Referring to Table 2, in some embodiments, the anomaly detection model provides multi-dimensional anomaly results, including anomalies in transportation timeliness based on historical transportation timelines, trajectory deviation anomalies based on GPS coordinate flow, environmental mutation anomalies based on temperature and humidity sensor data, and fulfillment anomalies based on order status data. Thus, the anomaly detection model helps to promptly identify various anomalies, thereby improving overall efficiency and inference performance.

[0058] See Figure 3According to Table 1, in one possible implementation, the multiple artificial intelligence models include a tariff payment period model. The input features corresponding to the tariff payment period model among the multiple input features include policy data, tax rate data, exchange rate data, and payment period data. The output decisions corresponding to the tariff payment period model among the multiple output decisions include cost simulation and tariff strategy suggestions. The tariff payment period model is linked to the replenishment decision model. Thus, through the linkage between multiple artificial intelligence models, the efficiency and performance of the decision-making mechanism are improved. Through multi-objective optimization and constraint setting, it is possible to better generate short-term demand forecasts related to individual consumer order demand and medium-to-long-term demand forecasts related to enterprise consumer order demand, as well as better generate medium-to-long-term replenishment control instructions related to the central warehouse and short-term regional transfer control instructions between the multiple sample warehouses.

[0059] See Figures 1 to 3 In one possible implementation, at least before the supply chain intelligent control module integrates and analyzes the multi-source heterogeneous data, data cleaning operations are performed on the multi-source heterogeneous data to achieve at least one of the following: data deduplication, missing value handling, outlier filtering, standardization and coding, and data quality scoring. Thus, through data cleaning operations, such as providing interpolation or mean imputation, or providing mode imputation, or filtering outliers based on business rules, data quality is improved, which also helps to improve the processing efficiency and effectiveness of subsequent processes.

[0060] See Figures 1 to 3 In one possible implementation, the supply chain intelligent control module is further configured to acquire the multi-source heterogeneous data from the multiple acquisition modules based on multiple data sampling methods, wherein each of the multiple data sampling methods has different data types, sampling frequencies, sampling techniques, and storage strategies. Specifically, please refer to Table 3 below: Table 3

[0061] Referring to Table 3, by adopting multiple data sampling methods, it is possible to better adapt to data sources and corresponding core algorithms. For example, market sales data, with its strong timeliness, can be adapted to user behavior analysis, demand fluctuation calculation, and regional heat maps. Furthermore, GPS trajectory and sensor data, such as temperature and humidity data, as well as data from vehicle terminals, can be adapted to trajectory clustering algorithms, time-series anomaly detection algorithms, and arrival time prediction algorithms. Thus, by employing diverse data sampling methods, the potential of the data is fully explored, helping to provide more comprehensive and richer information and characteristics for subsequent decision-making mechanisms.

[0062] See Figures 1 to 3In one possible implementation, the supply chain intelligent control module is further used to evaluate the process compliance of the multi-source heterogeneous data acquired by the multiple acquisition modules based on various process indicator evaluation methods. This is illustrated below with reference to Tables 4 and 5: Table 4

[0063] Refer to Table 4, which lists consumer process metrics. By introducing these metrics, we can assess performance on consumer-related predictions, order response rates, and market satisfaction, thereby improving the parameter configuration and decision-making mechanisms of AI models. Table 5 is below: Table 5

[0064] Refer to Table 5, which lists transportation process indicators. By introducing these indicators, performance evaluations can be conducted on order on-time delivery, transportation efficiency, and transportation costs, thereby helping to establish a quantitative indicator system. It should be understood that the cross-border supply chain management solution breaks down the entire chain into four stages: consumption, transportation, production, and warehousing. A corresponding process indicator can be established for each stage, thus creating a quantitative indicator system for each of the four stages. Tables 4 and 5 are for illustrative purposes only.

[0065] See Figures 1 to 3 In one possible implementation, the supply chain intelligent control module is further configured to utilize the multiple artificial intelligence models to extract time-series features, calendar features, external features, regional features, and product features from the multi-source heterogeneous data, and then use dynamic weights to weight and fuse the results of the multiple models. This is illustrated below with reference to Table 6: Table 6

[0066] Referring to Table 6, feature engineering can extract multiple feature categories from the multi-source heterogeneous data, and each of these feature categories has corresponding specific features and processing methods. Thus, by fusing and analyzing multi-source heterogeneous data, performing weighted fusion of multi-model results based on dynamic weights, and utilizing feature engineering to extract multiple feature categories, it is helpful to obtain richer and more comprehensive information, tap into the potential of the data, and improve prediction results.

[0067] Figure 4 This is a flowchart illustrating an intelligent replenishment algorithm provided in an embodiment of this application. Figure 4 As shown, the intelligent replenishment algorithm includes the following steps.

[0068] Step S401: Input regional demand forecast.

[0069] Step S403: Determine whether the inventory in the sample warehouse meets the lead time requirements. If not, proceed to step S405; if yes, proceed to step S407.

[0070] Step S405: Maintain the status quo and do not replenish stock.

[0071] Step S407: Calculate the replenishment quantity by multiplying the maximum displacement demand by the replenishment coefficient and then subtracting the current inventory.

[0072] Step S409: Generate a transfer order and entrust the central warehouse to replenish the sample warehouse.

[0073] Step S410: Determine whether the inventory in the central warehouse is less than the transfer requirement plus the safety stock. If not, proceed to step S412; if yes, proceed to step S414.

[0074] Step S412: Wait for production replenishment.

[0075] Step S414: Calculate the purchase quantity and select the optimal supplier, based on tariff and payment terms optimization.

[0076] Step S416: Generate a purchase order and send it from the factory to the central warehouse.

[0077] See Figure 4 By leveraging a dual-layer linkage mechanism and warehousing architecture between the central warehouse and sample warehouse, combined with regional demand forecasting, intelligent replenishment and allocation were achieved, supporting intelligent management and control of the entire cross-border supply chain. Regarding the intelligent replenishment algorithm, the results of intelligent management and control can be optimized through key parameter settings. Key parameter settings can be found in Table 7 below: Table 7

[0078] Figure 5 This is a schematic diagram of an end-to-end data flow provided in an embodiment of this application. As shown in Figure 5, the end-to-end data flow includes the following steps.

[0079] Step S501: The business system initiates a data flow.

[0080] Step S503: The data acquisition layer acquires data.

[0081] Step S505: Data cleaning, transformation and standardization.

[0082] Step S507: Data Warehouse.

[0083] Step S509: Artificial intelligence control tower prediction model, decision engine and anomaly detection.

[0084] Step S510: Instruction issued.

[0085] Step S512: The business middle platform performs order execution and status tracking.

[0086] Step S514: Execute the system execution command.

[0087] See Figure 5 Through end-to-end data flow and a central AI control tower, a closed-loop process is achieved, from initiation by the business system to final execution by the execution system. Furthermore, the end-to-end data flow can improve data transmission and processing efficiency through a series of interface specifications. For example, standardized replenishment suggestion interfaces and transportation anomaly alarm interfaces can be provided. Database efficiency can be improved through optimization of the core table structure, such as providing sales forecast tables and inventory tables.

[0088] Figure 6 This is a system deployment architecture diagram provided for an embodiment of this application. For example... Figure 6 As shown in the system deployment architecture diagram, it includes an application programming interface (API) management layer 610, a microservice cluster 620, and a data layer 630. The API management layer 610 may include authentication and authorization, rate limiting, routing, and protocol conversion functions. The microservice cluster 620 may include services such as data acquisition, prediction, decision-making, visualization, cleaning, training, optimization, and notification. The data layer 630 may include a distributed database, message queues, a cache cluster, and object storage.

[0089] See Figure 6 This system deployment architecture can meet requirements such as disaster recovery and scalability design. It allows for server-level deployments, facilitating the utilization of resources such as data centers, high-performance servers, and compute nodes. For more information on the system deployment architecture, please refer to high availability assurance, as shown in Table 8 below: Table 8

[0090] See Figures 1 to 6 The AI-based supply chain management system divides the supply chain into four processes: consumer, transportation, production, and warehousing. Data is collected and quantitatively analyzed for each process to achieve intelligent supply chain management based on AI. Assuming a North American central warehouse stores consumer electronics, input data could include: historical sales data, such as weekly sales data over the past 52 weeks; external factors, such as second-quarter promotional plans, new product launch calendars, and competitor pricing; and constraints, such as a budget of two million US dollars and a two-month safety stock. Referring to the multi-model processing flow described above, short-term forecasts corresponding to a 4-week demand distribution, medium-term forecasts corresponding to an 8-week trend, and long-term forecasts corresponding to a quarterly pattern can be generated. Through dynamic weighting and integration, replenishment recommendations are finally generated for the central warehouse. Table 9 below illustrates the model output as an example: Table 9

[0091] Referring to Table 9, the quantifiable effects include increased inventory turnover, reduced stockout rate, reduced inventory holding costs, reduced stockout losses, and increased return on investment (ROI).

[0092] See Figures 1 to 6 An AI-based supply chain management system divides the supply chain into four processes: consumer, transportation, production, and warehousing. Data is collected and quantitatively analyzed in each process to achieve intelligent supply chain management based on AI. For example, consider a sample warehouse in Europe facing a surge in demand. Input data could include current inventory (e.g., 500 units), a two-week forecast (e.g., 800 units), central warehouse inventory (e.g., 10,000 units), neighboring sample warehouse inventory (e.g., 1,200 units), and a safety stock of 400 units. The decision-making process could involve calculating a demand gap of 300 units, calculating a central warehouse transfer of 360 units, checking the central warehouse inventory to ensure it meets the safety stock requirement, and finally generating the transfer order. This reduces stockout rates, improves order fulfillment rates, enhances customer satisfaction, and reduces regional inventory capital tied up in inventory.

[0093] See Figures 1 to 6 This invention relates to an AI-based supply chain management system, method, electronic equipment, and media, employing a dual-layer linkage mechanism of central warehouses and sample warehouses. The warehouse module distinguishes between two storage types: central warehouses and sample warehouses. The control center implements differentiated inventory strategies for each type: the central warehouse employs a large-volume stocking strategy, acting as a global inventory buffer; the sample warehouse employs a rapid response strategy, achieving short lead time fulfillment by being close to the end market. Utilizing a fully optimized cross-border supply chain management solution, an intelligent replenishment method for overseas warehouses can be implemented, for example, by sequentially executing: calculating the sample warehouse replenishment trigger point based on demand forecasts for each sales region; generating a transfer instruction from the central warehouse to the sample warehouse when the sample warehouse inventory is lower than the sum of lead time demand and safety stock; summarizing the transfer requests from each sample warehouse to calculate the central warehouse replenishment demand; and comprehensively optimizing the combination of tariff costs, transportation costs, and payment terms when generating external purchase orders. Furthermore, demand forecasting can employ a multi-timescale integrated model to generate demand forecasts at different time scales. In addition, by utilizing a cross-border supply chain management solution optimized across the entire chain, a method for detecting supply chain transportation anomalies can be implemented, including: using an LSTM model to predict the normal time of transportation routes; monitoring the actual transportation time in real time and triggering a delay warning when the actual value exceeds a predetermined threshold of the predicted value; using the DBSCAN clustering algorithm to identify GPS trajectory deviations from the conventional route clusters; calculating an anomaly risk score by combining timeliness deviation and trajectory deviation; and automatically generating a graded emergency response strategy based on the risk score.

[0094] Figure 7This is a flowchart illustrating an artificial intelligence-based supply chain management method provided in an embodiment of this application. The method is applied to a supply chain management system, which includes multiple data acquisition modules and a supply chain intelligent control module communicatively connected to each of the multiple data acquisition modules. Figure 7 As shown, the method includes the following steps.

[0095] Step S701: Obtain multi-source heterogeneous data through the multiple acquisition modules, wherein the multiple acquisition modules include at least a consumer module for collecting individual consumer order demand and enterprise consumer order demand, a transportation module for collecting logistics transportation trajectory data, a production module for being configured to collect manufacturing execution data, and a warehouse module for collecting inventory data of the central warehouse and inventory data of multiple sample warehouses, wherein the coverage area of ​​the central warehouse includes the coverage area of ​​each of the multiple sample warehouses.

[0096] Step S703: Through the supply chain intelligent control module, the multi-source heterogeneous data is integrated and analyzed, and multiple artificial intelligence models are used to generate short-term demand forecasts related to individual consumer order demand and medium- and long-term demand forecasts related to enterprise consumer order demand. Then, based on the short-term demand forecasts related to individual consumer order demand and the medium- and long-term demand forecasts related to enterprise consumer order demand, medium- and long-term replenishment control instructions related to the central warehouse and short-term regional transfer control instructions between the multiple sample warehouses are generated.

[0097] See Figure 7This approach breaks down cross-border supply chain management into four stages: consumption, transportation, production, and warehousing. Four corresponding data collection modules are used to gather multi-source heterogeneous data. A supply chain intelligent control module, similar to a central control tower, receives reported data from multiple collection modules and issues commands to perform fusion analysis on the multi-source heterogeneous data. For example, based on multimodal feature extraction and intelligent matching, it obtains supply chain data classification and structured processing results. It can uniformly analyze, extract features, classify, and structure multimodal supply chain data from different language regions, thereby providing efficient cross-regional supply chain collaboration and intelligent decision-making. Furthermore, considering the challenges frequently encountered in cross-border supply chain management, such as different regional consumption demands, cross-border warehousing costs, and information delays from the consumer to the supply side, a two-tiered warehousing architecture of central and sample warehouses is provided. Combined with the timeliness and characteristics of individual consumer order demand and corporate consumer order demand, multiple artificial intelligence models can be used to generate short-term demand forecasts related to individual consumer order demand and medium- to long-term demand forecasts related to corporate consumer order demand. Based on the predictions provided by the artificial intelligence model—specifically, the short-term demand forecasts related to individual consumer orders and the medium- to long-term demand forecasts related to enterprise consumer orders—the system leverages the intelligent linkage between the central warehouse and sample warehouses to generate medium- to long-term replenishment control instructions for the central warehouse and short-term regional transfer control instructions between the multiple sample warehouses. This optimizes the entire cross-border supply chain management process, balancing both C-end and B-end demands, and fully utilizing the inventory buffering and replenishment control functions of the central warehouse and the advantages of sample warehouses—being closer to the market and enabling rapid fulfillment. Furthermore, corresponding artificial intelligence technologies can be used to highlight and strengthen the respective warehousing roles of the central warehouse and sample warehouses, allowing for better medium- to long-term replenishment forecasts for the central warehouse and regional transfers for the sample warehouses, reducing stockout rates and improving order fulfillment rates. Furthermore, the impact of tariffs and policies in different countries and regions, as well as cash flow and payment term management, can be reflected in the intelligent management of the central warehouse through corresponding artificial intelligence models and decision-making mechanisms. This means that complex decision-making mechanisms can be confined to the intelligent management of the central warehouse, while the intelligent management of the sample warehouse can maintain relatively simple short-term regional allocation control, thus helping to improve the overall system's operational efficiency. In summary, by breaking down cross-border supply chain management into four links—consumption, transportation, production, and warehousing—and optimizing them separately using various artificial intelligence technologies, and by employing two warehousing types—central warehouses and sample warehouses—and differentiated inventory strategies, the central warehouses are used to implement large-volume stocking strategies and act as a global inventory buffer, while the sample warehouses are used to implement rapid response strategies to achieve rapid fulfillment by being close to the end market, thus optimizing the entire cross-border supply chain management chain.

[0098] See Figure 7In one possible implementation, the method further includes: Based on the regional demand forecast of the coverage area of ​​each of the multiple sample warehouses, calculate the replenishment trigger point of each of the multiple sample warehouses; When the inventory of a given sample warehouse among the plurality of sample warehouses is lower than the sum of lead time demand and safety stock, a transfer instruction is generated from the central warehouse to the given sample warehouse. Summarize the allocation instructions from the multiple sample warehouses and calculate the replenishment requirements of the central warehouse; External purchase orders are generated through the combined optimization of tariff costs, transportation costs, and payment terms.

[0099] In this way, by using two types of warehousing, namely central warehouses and sample warehouses, and a differentiated inventory strategy, the central warehouses are used to carry out a large-volume stocking strategy and serve as a global inventory buffer, while the sample warehouses are used to carry out a rapid response strategy to get close to the end market and achieve rapid fulfillment, thus optimizing the entire chain of cross-border supply chain management.

[0100] See Figure 7 In one possible implementation, the multiple artificial intelligence models include a demand forecasting model, which further includes a multi-timescale demand forecasting model for providing short-term, medium-term, and long-term demand forecasts. The multi-timescale demand forecasting model includes an LSTM model and an attention model for providing short-term demand forecasts, a Prophe model and an external regression model for providing medium-term demand forecasts, and a Transformer time-series model for providing long-term demand forecasts. Thus, for demand forecasting, multiple data types can be combined as input features, and the multi-timescale demand forecasting model can be used to make predictions at different time scales. For example, for short-term time scales such as 1 to 4 weeks, an LSTM model and an attention model can be used; for medium-term time scales such as 1 to 3 months, a Prophe model and an external regression model can be used; and for long-term time scales such as one or more quarters, a Transformer time-series model can be used. By using corresponding models at different time scales and then weighted and fused together the results of multiple models, dynamic weight fusion can be achieved, resulting in a future demand distribution with good accuracy across different time scales.

[0101] See Figure 7 In one possible implementation, the plurality of artificial intelligence models include an anomaly detection model, the anomaly detection model being used for: LSTM models are used to predict the normal travel time of transportation routes, thereby setting a predetermined time threshold. The system monitors actual transportation time in real time, and triggers a delay warning when the actual transportation time exceeds the predetermined time threshold. The DBSCAN clustering algorithm is used to identify GPS trajectory deviations from conventional routes. The risk score is used to automatically generate a tiered emergency response strategy.

[0102] Thus, anomaly detection models can help to detect various anomalies in a timely manner, thereby improving overall efficiency and inference performance.

[0103] See Figure 7 In one possible implementation, the intelligent supply chain control module includes a data aggregation engine for receiving multi-source heterogeneous data through a standardized interface, a visualization module for displaying multi-dimensional operational indicators in real time, an intelligent early warning module for identifying abnormal events based on threshold rules and machine learning models, and an instruction issuance engine for converting artificial intelligence decision results into executable instructions. Thus, the AI-based supply chain management system divides the supply chain into four processes: consumer, transportation, production, and warehousing, performing data statistical collection and quantitative analysis on each, thereby achieving AI-based intelligent supply chain management and control.

[0104] See Figure 7 In one possible implementation, the method further includes implementing a bulk inventory preparation strategy for the central warehouse and a rapid response strategy for the multiple sample warehouses. The central warehouse serves as a global inventory buffer, and the multiple sample warehouses are used for rapid market fulfillment within their respective coverage areas. Thus, by employing two warehousing types—central warehouses and sample warehouses—and a differentiated inventory strategy, the central warehouses handle bulk inventory preparation and act as a global inventory buffer, while the sample warehouses implement a rapid response strategy to achieve rapid fulfillment closer to the end market, thereby optimizing the entire cross-border supply chain management process.

[0105] Supply chain management based on dual-warehouse collaborative intelligent forecasting and logistics configuration Figure 8 This is a schematic diagram of a supply chain management system based on dual-warehouse collaborative intelligent forecasting and logistics configuration, provided as an embodiment of this application. Figure 8As shown, the supply chain management system 800 includes: a data acquisition and fusion layer 810, used to acquire multi-source heterogeneous data, including C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data; a demand forecasting engine layer 812, used to perform multi-modal data fusion on the multi-source heterogeneous data to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium-to-long-term demand forecasting model to provide B-end medium-to-long-term demand forecasting results, and the B-end medium-to-long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand; a dual-warehouse collaborative management layer 814, used to manage the inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse based on the C-end spot demand forecasting results and the B-end medium-to-long-term demand forecasting results, wherein the inventory strategy of the sample warehouse is rapid turnover and low inventory, and the inventory strategy of the central warehouse is large-scale reserves and high inventory; and a logistics configuration optimization layer 816, used to dynamically optimize the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecasting results.

[0106] See Figure 8In cross-border supply chain management and logistics configuration applications, such as cross-border e-commerce supply chain optimization, FMCG distribution network restructuring, and industrial product indirect materials and services (Maintenance, Repair & Operations, MRO) supply chain management, there are two main types of demand: C-end (consumer-facing) demand focused on individual consumers and B-end (business-facing) demand focused on enterprise organizations. These two types of demand have different characteristics and vary across different countries and regions. This leads to a series of challenges, such as insufficient forecasting accuracy resulting in inventory backlogs or stockouts, low utilization of warehousing resources, potential imbalances in regional logistics configuration leading to slower delivery times and higher costs, and inefficient inventory transfer. To address these challenges, this supply chain management system, based on C-end order data and B-end procurement data, analyzes the consumption records of sellers in various regions to predict purchasing demand. Combining C-end spot demand with B-end medium- and long-term demand, it constructs a two-tiered warehousing system: a sample warehouse that closely aligns with spot demand and a central warehouse that closely aligns with medium- and long-term demand. This enables intelligent configuration of logistics warehouses and optimized control of the supply chain. Furthermore, this supply chain management system effectively integrates real-time order data from consumers (C-end) with medium- to long-term demand from businesses (B-end) through multiple demand forecasting dimensions, achieving high forecasting accuracy and reducing the risks of inventory backlog and stockouts. It constructs a two-tiered warehousing system that coordinates sample warehouses and central warehouses, establishing dual-warehouse collaboration and intelligent linkage between the two, avoiding the lack of planning capabilities caused by decentralized warehousing models and improving the utilization rate of warehousing resources. Dynamically optimizing logistics warehouse configuration based on regional consumption characteristics helps achieve a balance between delivery timeliness and cost. It establishes a bridge for the transformation from C-end spot demand to B-end medium- to long-term demand, and combined with the collaboration and linkage between the central warehouse and sample warehouse, it achieves high inventory allocation efficiency.

[0107] Continue reading Figure 8 The overall system architecture adopts a four-layer design of "data-driven - intelligent prediction - dual-warehouse collaboration - dynamic configuration". These four layers have different core responsibilities, as shown in Table 10 below: Table 10

[0108] Referring to Table 10, this supply chain management system supports a dual-demand driven model, proposing a dual-track forecasting mechanism for both C-end spot demand and B-end medium-to-long-term demand. It also supports a dual-warehouse collaborative architecture, designing a two-tiered warehousing system consisting of a sample warehouse and a central warehouse, achieving a balance between rapid response and economies of scale. Furthermore, it enables dynamic allocation of regional logistics, achieving intelligent location selection and dynamic capacity adjustment of logistics warehouses based on regional consumption characteristics clustering. In some embodiments, the supply chain management system employs a C-end strategy migration mechanism, systematically applying C-end e-commerce demand analysis and scheduling strategies to sample warehouse management.

[0109] Additionally, the demand forecasting engine layer 812 is used to perform multimodal data fusion on the aforementioned multi-source heterogeneous data. Based on this multimodal data fusion, a C-end spot demand forecasting model and a B-end medium-to-long-term demand forecasting model are constructed. In some examples, the logic of the multimodal feature fusion scoring formula is: Product matching comprehensive score = Image similarity + Text similarity + Price matching degree + Pronunciation similarity (weights are dynamically adjusted according to information quality). Therefore, generally, when identifying products, four dimensions—image, text, price, and voice—are comprehensively judged, and the weight of the clearest information is automatically increased, making decisions flexibly like the human brain.

[0110] In addition, the multi-source heterogeneous data includes C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data. Thus, a five-dimensional data collection system is established, as shown in Table 11 below: Table 11

[0111] Referring to Table 11, by establishing a five-dimensional data collection system, comprehensive coverage of supply chain-related data can be achieved, providing rich information for subsequent processes. Figure 8As shown in Table 10, a C-end spot demand forecasting model is constructed to provide C-end spot demand forecasting results, and a B-end medium-to-long-term demand forecasting model is constructed to provide B-end medium-to-long-term demand forecasting results. These C-end spot demand forecasting results and B-end medium-to-long-term demand forecasting results are provided. Furthermore, because the B-end medium-to-long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand, a bridge is established for the transformation from C-end spot demand to B-end medium-to-long-term demand. Through the fusion of C-end and B-end dual-track forecasting, the demand forecast accuracy (MAPE) is significantly improved. Moreover, utilizing the quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand allows medium-to-long-term forecasts to anticipate market changes in advance, extending the forecast lead time and better adapting to market changes. In addition, the inventory strategy of the sample warehouse is rapid turnover and low inventory, while the inventory strategy of the central warehouse is large-scale storage and high inventory. This establishes a dual-warehouse collaborative system, which helps improve the overall inventory turnover rate, increases the spot fulfillment rate of the sample warehouse while controlling inventory days, and helps reduce procurement costs through the scale effect of the central warehouse, thus optimizing overall inventory efficiency. In addition, the logistics configuration optimization layer dynamically optimizes the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecasts. This dynamic logistics configuration based on regional demand characteristics reduces transportation costs, and intelligent allocation mechanisms reduce the frequency of emergency shipments and lower expedited logistics fees. Through a four-layer design of "data-driven – intelligent forecasting – dual-warehouse collaboration – dynamic configuration," the entire process from demand forecasting to logistics configuration is automated, improving service levels, including shortening the fulfillment time for C-end orders, increasing the on-time delivery rate of B-end orders, and reducing stockout rates.

[0112] See Figure 8 In one possible implementation, the data acquisition and fusion layer is further used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product code mapping table. Thus, the system uses a unified spatiotemporal benchmark for multi-source data alignment, including time alignment (such as establishing a unified timestamp standard and resampling and interpolating data from different frequencies); spatial alignment (based on a GIS geocoding system, uniformly mapping delivery addresses and warehouse locations to standard geographic coordinates); and product alignment (establishing a Master Data Management (MDM) system, unifying product identifiers from different sources through a product code mapping table). This achieves multi-source data alignment, improves data quality, and facilitates subsequent process processing.

[0113] See Figure 8In one possible implementation, the data acquisition and fusion layer is further used to perform data quality governance on the multi-source heterogeneous data according to a three-level data quality governance strategy. This three-level data quality governance strategy includes a real-time verification layer, an anomaly detection layer, and a missing value handling layer. Thus, the system implements a three-level data quality governance strategy: Real-time verification layer: performing real-time verification of field integrity, format compliance, and business logic consistency on incoming data; Anomaly detection layer: using the Isolation Forest algorithm to detect abnormal orders and abnormal consumption records; Missing value handling layer: intelligently filling in missing data based on KNN imputation and a spatiotemporal correlation model.

[0114] See Figure 8 In one possible implementation, the C-end spot demand forecasting model includes a multi-scale time-series forecasting model, which is used for time-series feature extraction, real-time signal fusion, promotional effect modeling, and regional difference modeling. In some embodiments, the C-end spot demand forecasting model calculates the predicted C-end spot demand value for a given region at a given time based on a C-end forecasting formula. The parameters of the C-end forecasting formula include a long-term trend term, daily seasonality, weekly seasonality, monthly seasonality, promotional effect multiplier, regional characteristic coefficient, and random disturbance term. Thus, a dual-track parallel forecasting architecture for C-end spot demand and B-end medium- and long-term demand is proposed. Specifically, a multi-scale time-series forecasting model is constructed to address the short-cycle and high-volatility characteristics of C-end spot demand, as illustrated in Table 12 below: Table 12

[0115] Referring to Table 12, different model components can be used with corresponding models and technical implementation methods to complete the corresponding functions, thereby extracting the corresponding features and information for subsequent processing. Furthermore, the C-end forecasting formula considers the influence of multiple factors and, combined with a multi-scale time series forecasting model, provides an accurate forecast of C-end spot demand for a given region at a given time. In some examples, the logic of the C-end spot demand forecasting formula is: C-end spot demand in a given region at a certain time = Basic trend × Daily cycle × Weekly cycle × Monthly cycle × Promotional impact × Regional differences × Random fluctuations. Therefore, generally, the long-term trend of demand is first determined, then the seasonal patterns of the three time dimensions (weekly, monthly, and yearly) are superimposed, then the fluctuations brought about by promotional activities are multiplied, then the consumption differences in different regions are considered, and finally unpredictable random factors are added.

[0116] See Figure 8In one possible implementation, the B2B medium-to-long-term demand forecasting model includes a multi-dimensional demand extrapolation model. This model is used for contract demand analysis, trend extrapolation, industry correlation analysis, and C2C-to-B2B conversion modeling. In some embodiments, the B2B medium-to-long-term demand forecasting model calculates the predicted value of B2B medium-to-long-term demand for a given region at a given time based on a B2B forecasting formula. The parameters of the B2B forecasting formula include deterministic demand from signed contracts, a B2B trend term, an industry prosperity index, a C2C-to-B2B conversion coefficient, demand transmission lag, and a random disturbance term. Thus, considering the planned and contractual characteristics of B2B medium-to-long-term demand, a multi-dimensional demand extrapolation model is constructed, as illustrated in Table 13. Table 13

[0117] Referring to Table 13, different model components can be used with corresponding models and technical implementation methods to complete corresponding functions, thereby extracting corresponding features and information for subsequent processing. Furthermore, the B-end forecasting formula considers the influence of multiple factors and, combined with a multi-dimensional demand extrapolation model, provides accurate forecasts of medium- and long-term B-end demand for a given region at a given time. Moreover, the multi-dimensional demand extrapolation model includes a C-to-B conversion modeling component, and the B-end forecasting formula also includes the C-to-B conversion coefficient and the demand transmission lag. Therefore, a quantitative transmission mechanism for C-end spot demand to B-end medium- and long-term demand is established, enabling medium- and long-term forecasts to anticipate market changes in advance, extending the forecast lead time, and better adapting to market changes. In some examples, the logic of the B-end medium- and long-term demand forecasting formula is: B-end demand = Contracted contract volume + (Trend × Industry prosperity) + (C-end demand converted after time lag) + Random fluctuations. Therefore, B2B demand generally consists of three parts: first, confirmed orders with signed contracts; second, incremental demand brought about by the overall development trend of the industry; and third, and most importantly, C2C transmission. That is, the hot sales of spot goods on the C2C side will be transmitted to the B2B side after a period of time (such as 2 to 4 weeks for fast-moving consumer goods), prompting the B2B side to replenish its inventory. This establishes a bridge from spot goods to medium- and long-term demand.

[0118] In some embodiments, the C-end to B-end conversion modeling is achieved through a demand transmission model. This model establishes the transmission lag relationship between C-end spot demand and B-end medium- to long-term demand, thereby establishing a quantitative transmission mechanism for this transition. The transmission lag relationship includes the time window during which changes in C-end demand precede B-end purchasing decisions, and the quantitative transmission mechanism includes the dynamic estimation of the C-end to B-end conversion coefficient. This establishes a quantitative transmission mechanism for the transformation of C-end spot demand into B-end medium- to long-term demand. The transmission lag identification can be achieved through Granger causality tests and cross-correlation analysis, identifying the time window during which changes in C-end demand precede B-end purchasing decisions. For example, the transmission lag for fast-moving consumer goods is approximately 2 to 4 weeks, for durable consumer goods it is approximately 8 to 12 weeks, and for industrial goods it is approximately 12 to 24 weeks. Furthermore, the dynamic estimation of the conversion coefficient employs rolling window regression and a state-space model to dynamically estimate the C-to-B conversion coefficient. The calculation of the conversion coefficient is influenced by factors such as inventory turnover rate, supplier response speed, market uncertainty, and contract coverage. For example, when C-end demand remains strong and inventory turnover accelerates, the conversion coefficient increases, indicating increased B-end restocking demand; conversely, the conversion coefficient decreases.

[0119] See Figure 8 In one possible implementation, the dual-warehouse collaborative management layer utilizes C-end sales demand analysis and scheduling strategies to manage the inventory strategy and allocation mechanism of the sample warehouse. The C-end sales demand analysis and scheduling strategies include best-selling product prediction and allocation, dynamic pricing and promotion, user profile-driven product selection, and real-time inventory visualization. As mentioned above, the inventory strategy of the sample warehouse is rapid turnover and low inventory, while the inventory strategy of the central warehouse is large-scale reserves and high inventory, as illustrated in Table 14: Table 14

[0120] Referring to Table 14, a dual-warehouse system of sample warehouse and central warehouse is provided. A C-end e-commerce strategy migration mechanism can also be established to systematically migrate mature C-end e-commerce sales demand analysis and scheduling strategies to sample warehouse management, thereby realizing a series of functions of the aforementioned C-end sales demand analysis and scheduling strategies. For example, the C-end sales demand analysis and scheduling strategies include: Bestseller prediction and pre-positioning: drawing on e-commerce bestseller identification mechanisms, predicting potential hot-selling products in a region based on real-time C-end data, and pre-positioning inventory in the sample warehouse; Dynamic pricing and promotion: introducing e-commerce dynamic pricing strategies, implementing intelligent promotions for near-expiry inventory in the sample warehouse to accelerate turnover; User profile-driven product selection: optimizing the sample warehouse SKU combination based on regional consumer profiles (category preferences, price sensitivity, purchase frequency); Real-time inventory visualization: drawing on e-commerce inventory transparency strategies, displaying the sample warehouse inventory status to B-end customers in real time to promote spot transactions.

[0121] In some embodiments, the dual-warehouse collaborative management layer also utilizes an inventory management model combining strategy and dynamic safety stock to manage the inventory strategy and allocation mechanism of the sample warehouse. This strategy-dynamic safety stock inventory management model includes reorder point parameters and target inventory parameters. The reorder point parameters are calculated based on replenishment lead time, average demand within the lead time, service level coefficient, and standard deviation of lead time demand. The target inventory parameters are calculated based on economic replenishment quantity and balancing ordering and holding costs. This achieves inventory optimization and determines the reorder point of the sample warehouse for subsequent process handling. In some examples, the logic of the sample warehouse reorder point formula is: Reorder Point = Expected Lead Time Consumption + Safety Buffer Inventory. Therefore, generally, when the sample warehouse inventory drops to the level of "expected sales on the way to replenishment + safety stock to prevent unexpected overselling," a replenishment order should be placed with the central warehouse.

[0122] See Figure 8In one possible implementation, the dual-warehouse collaborative management layer utilizes a medium- to long-term reserve optimization strategy to manage the central warehouse's inventory strategy and allocation mechanism. This strategy includes contract inventory locking, seasonal reserves, and safety stock optimization. This supports medium- to long-term reserve optimization, where the central warehouse implements strategic reserves and dynamic replenishment based on medium- to long-term demand forecasts from B-end customers. This includes contract inventory locking: locking corresponding inventory in the CW for B-end contracted demand to ensure delivery commitments; seasonal reserves: reserving seasonal goods in the CW 2-3 quarters in advance based on annual demand forecasts; and safety stock optimization: employing a dynamic safety stock formula based on demand and supply uncertainties. The dynamic safety stock equals the product of the service level coefficient and the overall uncertainty. The overall uncertainty is composed of the square root of the sum of the squares of two components: the demand uncertainty component determined by the product of the variance of the long-term demand forecast error and the replenishment lead time, and the supply uncertainty component determined by the product of the square of the average demand rate and the variance of the lead time fluctuation. In some embodiments, the logic of the dynamic safety stock formula for a central warehouse is: Safety Stock = Service Level Coefficient × √(Demand Forecast Error + Supplier Delivery Error). Therefore, generally, the safety stock of a central warehouse must protect against two things simultaneously: inaccurate forecasting and inaccurate supplier delivery. After calculating both risks together, the final safety stock is determined based on the desired service level (e.g., 95%).

[0123] In some embodiments, the dual-warehouse collaborative management layer also utilizes an intelligent allocation decision model based on demand forecasting and inventory status to manage the inventory strategy and allocation mechanism of the central warehouse. This intelligent allocation decision model includes allocation triggering condition parameters and allocation quantity calculation parameters. The allocation triggering condition parameters predict a stockout risk in the sample warehouse within a certain number of days if the sample warehouse's inventory is below the reorder point, thereby determining whether the central warehouse has surplus inventory or a batch of goods awaiting warehousing. The allocation quantity calculation parameters are the minimum value among the current inventory, available inventory, and maximum single-transport capacity. Thus, an intelligent allocation decision model based on demand forecasting and inventory status is established. Allocation triggering conditions can be set; when the sample warehouse's inventory is found to be below the reorder point, a prediction is made that the sample warehouse will face a stockout risk within the next L days (combined with a stockout probability threshold), and then it is determined whether the central warehouse has surplus inventory or a batch of goods about to be received. Additionally, allocation quantity calculation is performed to find the minimum value among the current inventory, available inventory, and maximum single-transport capacity. In this way, through an intelligent allocation mechanism from the central warehouse to the sample warehouse, automated intelligent allocation is achieved based on demand forecasting and inventory status, which improves overall efficiency and reduces warehousing costs.

[0124] See Figure 8In one possible implementation, the dual-warehouse collaborative management layer further utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. This algorithm includes a collaborative objective function and employs a decomposition-coordination algorithm to achieve the collaborative optimization solution. Thus, the objective of dual-warehouse collaborative optimization is to minimize total cost while meeting service level requirements. The constraints of the collaborative objective function can be set to require a service level greater than or equal to a target value, such as 95%, and can also include constraints on the sample warehouse capacity, allocation frequency, and transportation resources. Furthermore, the decomposition-coordination algorithm can be used to solve the problem, which may include a decomposition phase: decomposing the global optimization problem into SW subproblems and CW subproblems, solving each locally optimally; a coordination phase: coordinating the allocation decisions of the two subproblems using the Lagrange multiplier method to achieve the global optimum; and iterative optimization: repeating the decomposition and coordination process until convergence. In some examples, the objective function for dual-warehouse collaboration is to minimize (sample warehouse holding cost + central warehouse holding cost + allocation cost + stockout cost + transportation cost) while simultaneously ensuring a service level of ≥95%, preventing warehouse overload, and avoiding transportation overload. Therefore, generally, the goal is not simply to save money, but to find the optimal balance between sample warehouse and central warehouse inventory configurations under the constraints of "no stockouts, no warehouse overload, and sufficient transportation capacity," thereby minimizing the total cost.

[0125] See Figure 8 In one possible implementation, the logistics configuration optimization layer is used to assess the logistics demand intensity of each of the multiple regions based on their respective consumption record data and demand forecast results. The logistics demand intensity of each region is represented by a logistics demand index, which is calculated as a weighted average of the C-end demand forecast, the B-end demand forecast, demand volatility, and demand growth rate for a given region. Thus, by assessing the regional logistics demand intensity based on the consumption record data and demand forecast results of each region, and by calculating the Logistics Demand Index (LDI), dynamic optimization of logistics warehouse configuration based on regional consumption characteristics is achieved, helping to balance delivery timeliness and cost. In some examples, the logic of the Logistics Demand Index (LDI) formula is: Regional Logistics Demand Intensity = C-end Demand Contribution + B-end Demand Contribution + Volatility Risk Contribution + Growth Potential Contribution. Therefore, generally, the amount of logistics resources needed for a region depends not only on the current sales volume of C-end and B-end goods, but also on the stability of demand (large fluctuations require elastic buffers) and the speed of growth (fast growth requires expansion space).

[0126] In some embodiments, the logistics configuration optimization layer is further configured to use a regional clustering analysis algorithm to cluster the logistics demand characteristics of the multiple regions, thereby dividing the multiple regions into multiple cluster types and adopting multiple logistics configuration strategies corresponding one-to-one with the multiple cluster types. Thus, a clustering algorithm can be used to cluster the logistics demand characteristics of regions, as shown in Table 15 below: Table 15

[0127] Referring to Table 15, feature clustering can better implement logistics allocation strategies and improve overall logistics efficiency.

[0128] See Figure 8 In one possible implementation, the logistics configuration optimization layer is further used to determine the location of logistics warehouses using a multi-objective location model. The multi-objective location model is the optimal solution of a multi-objective optimization function, whose objective parameters include total transportation cost, average delivery time, warehouse construction cost, warehouse rental cost, and supply disruption risk. Thus, by comprehensively considering multiple objectives such as transportation cost, delivery time, construction cost, and expansion potential, location efficiency is improved.

[0129] See Figure 8 In one possible implementation, the logistics configuration optimization layer is further used to determine the location of logistics warehouses using a heuristic solution algorithm, wherein the heuristic solution algorithm is a hybrid algorithm of genetic algorithm and simulated annealing. Thus, the location problem is solved using a hybrid algorithm of genetic algorithm (GA) and simulated annealing (SA): Encoding scheme: Integer encoding is used, with chromosomes representing warehouse location and capacity configuration schemes; Fitness function: f = 1 / (Z + penalty), where penalty is the constraint violation penalty; Genetic operations: Selection (roulette wheel), crossover (partial mapping crossover PMX), mutation (exchange mutation); Local search: SA local optimization is performed on the excellent individuals generated by GA to avoid premature convergence.

[0130] See Figure 8In one possible implementation, the logistics configuration optimization layer is further used to infer the logistics warehouse capacity requirement based on demand forecasting results and inventory strategies, thereby realizing a dynamic capacity adjustment mechanism. The logistics warehouse capacity requirement is determined based on the average inventory level of each commodity among multiple commodities, the volume occupied per unit commodity, the volume fluctuation coefficient, the batch occupancy coefficient, the warehouse space utilization rate, and the shelf height utilization coefficient. Thus, by inferring the logistics warehouse capacity requirement based on demand forecasting results and inventory strategies, a flexible capacity adjustment mechanism is realized. A dynamic capacity adjustment mechanism based on demand forecasting errors is established: Expansion trigger: When actual demand exceeds the forecast value by more than 20% for three consecutive months, an expansion assessment is triggered; Contraction trigger: When the warehouse utilization rate is below 60% for six consecutive months, a resource integration assessment is triggered; Temporary expansion: During peak seasons, temporary leasing or shared warehouse models are used to cope with short-term demand peaks. In some examples, the logic of the logistics warehouse capacity requirement inverse formula is: Required warehouse area = Sum of all commodities (inventory quantity × unit volume × safety buffer × batch buffer) ÷ (floor utilization rate × height utilization rate). Therefore, generally, first calculate the actual volume occupied by all goods (including safety stock and batch management space), then divide by the actual usable space in the warehouse (deducting aisles and office areas, and considering shelf height restrictions) to obtain the total required warehouse area.

[0131] See Figure 8 In one possible implementation, the supply chain management system further generates supply chain control suggestions, including inventory control suggestions and logistics optimization suggestions. In some embodiments, the inventory control suggestions include multiple suggestion types, each with triggering conditions and suggestion content, including replenishment suggestions, allocation suggestions, clearance suggestions, and reserve suggestions. In some embodiments, the logistics optimization suggestions include route optimization suggestions, power configuration suggestions, and warehouse network optimization suggestions. Thus, based on the prediction and optimization results, the system automatically generates supply chain control suggestions, as detailed in Table 16 below: Table 16

[0132] Referring to Table 16, supply chain control recommendations can be categorized by recommendation type to correspond to each stage, which facilitates automated decision-making and recommendations. Additionally, logistics optimization recommendations include route optimization recommendations: based on actual transportation data, suggestions to merge delivery routes and adjust delivery frequencies; capacity allocation recommendations: based on demand forecasts, suggestions to adjust the ratio of in-house capacity to third-party logistics; and warehouse network optimization recommendations: based on changes in regional demand, suggestions to add, merge, or relocate warehouse nodes.

[0133] See Figure 8In one possible implementation, the supply chain management system further includes a visual decision dashboard to provide demand forecasting dashboards, inventory health dashboards, logistics efficiency dashboards, and anomaly warning dashboards. Thus, a Supply Chain Control Tower is constructed, providing: a demand forecasting dashboard: displaying demand forecasting trends and accuracy for C-end and B-end customers in each region; an inventory health dashboard: real-time monitoring of SW and CW inventory levels, turnover rates, and stockout risks; a logistics efficiency dashboard: displaying KPIs such as delivery timeliness, transportation costs, and warehouse utilization; and anomaly warning dashboards: automatically identifying demand anomalies, inventory anomalies, and logistics anomalies and pushing alerts.

[0134] See Figure 8 In one possible implementation, the supply chain management system further includes a multi-scale prediction fusion model to improve prediction accuracy. This model trains a Prophet model to capture seasonality, an LSTM model to capture non-linear trends, and an XGBoost model to capture feature interaction effects as base learners. Then, a meta-learner and dynamic weights are used for weighted fusion. This, combined with online updates, employs an exponentially weighted moving average (EWMA) to dynamically adjust the weights of the base learners. When the recent prediction error of a model increases, its weight is automatically reduced, thus helping to improve prediction accuracy.

[0135] See Figure 8 In one possible implementation, the dual-warehouse collaborative management layer further utilizes a reinforcement learning algorithm to optimize the dual-warehouse collaborative strategy, used to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The reinforcement learning algorithm defines a state space, an action space, and a reward function. Thus, by employing reinforcement learning-based dual-warehouse collaborative strategy optimization, the agent can be trained using the Proximal Policy Optimization (PPO) algorithm to learn the optimal inventory control strategy.

[0136] See Figure 8 In one possible implementation, the logistics configuration optimization layer is further used to determine the optimal location of the central warehouse using a regional logistics network optimization algorithm and a demand-weighted algorithm, and to iteratively add candidate locations that result in the greatest reduction in total cost using a greedy addition algorithm, thereby achieving the goal of a preset number of warehouses or a point where costs no longer decrease. Thus, the regional logistics network optimization algorithm is implemented, employing an improved greedy addition algorithm, including initializing the solution: an empty warehouse set; iterative addition: each time, selecting the candidate location that results in the greatest reduction in total cost and adding it to the solution set; local search: performing neighborhood operations such as swapping and moving on the current solution; termination condition: reaching a preset number of warehouses or a point where costs no longer significantly decrease.

[0137] See Figure 8 In one possible implementation, the supply chain management system is applied to cross-border e-commerce supply chain optimization, FMCG distribution network restructuring, and industrial MRO supply chain management. Thus, it offers beneficial effects in various application scenarios. For example, in scenario 1: cross-border e-commerce supply chain optimization, cross-border e-commerce platforms face complex supply chain environments involving multiple countries and languages. By analyzing the consumption records of sellers in various countries and regions, predicting local purchasing demand, and configuring overseas sample warehouses and central warehouses, localized rapid delivery can be achieved, improving the overseas consumer experience. Another example is in scenario 2: FMCG distribution network restructuring. FMCG companies need to balance channel inventory and service response speed. Establishing regional sample warehouses close to terminal retailers and central warehouses supporting bulk purchases by large distributors reduces channel inventory backlog and increases terminal distribution rates. Yet another example is in scenario 3: industrial MRO supply chain management. Industrial MRO demand is fragmented and highly urgent. The traditional central warehouse model has a slow response. Establishing sample warehouses in industrial parks to store high-frequency MRO materials and central warehouses to store long-tail materials increases the emergency demand fulfillment rate to over 90%.

[0138] Figure 9 This is a flowchart illustrating a supply chain management method based on dual-warehouse collaborative intelligent forecasting and logistics configuration, provided as an embodiment of this application. Figure 9 As shown, the supply chain management method includes the following steps.

[0139] Step S901: Collect multi-source heterogeneous data through the data acquisition and fusion layer, wherein the multi-source heterogeneous data includes C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data.

[0140] Step S903: Through the demand forecasting engine layer, multimodal data fusion is performed on the multi-source heterogeneous data to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium- and long-term demand forecasting model to provide B-end medium- and long-term demand forecasting results. The B-end medium- and long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium- and long-term demand.

[0141] Step S905: Through the dual-warehouse collaborative management system, based on the C-end spot demand forecast results and the B-end medium- and long-term demand forecast results, manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The inventory strategy of the sample warehouse is rapid turnover and low inventory, while the inventory strategy of the central warehouse is large-scale reserves and high inventory.

[0142] Step S907: Through the logistics configuration optimization layer, dynamically optimize the layout, capacity configuration, and distribution network of logistics warehouses based on the multi-regional demand forecast results.

[0143] See Figure 9 This supply chain management method constructs a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium-to-long-term demand forecasting model to provide B-end medium-to-long-term demand forecasting results. These C-end spot demand forecasting results and B-end medium-to-long-term demand forecasting results are provided. Furthermore, because the B-end medium-to-long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand, a bridge is established for the transformation from C-end spot demand to B-end medium-to-long-term demand. Through the fusion of C-end and B-end dual-track forecasting, the accuracy of demand forecasting (MAPE) is significantly improved. Moreover, by utilizing the quantitative transmission mechanism from C-end spot demand to B-end medium-to-long-term demand, medium-to-long-term forecasts can anticipate market changes in advance, extending the forecast lead time and enabling better adaptation to market changes. Furthermore, the sample warehouse employs a rapid turnover and low inventory strategy, while the central warehouse adopts a large-scale reserve and high inventory strategy. This establishes a dual-warehouse collaborative system, which helps improve overall inventory turnover rate, increases the on-site fulfillment rate of the sample warehouse while controlling inventory days, and helps reduce procurement costs through the scale effect of the central warehouse, thus optimizing overall inventory efficiency. Additionally, the logistics configuration optimization layer dynamically optimizes the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecasts. This dynamic logistics configuration based on regional demand characteristics reduces transportation costs, and the intelligent allocation mechanism reduces the frequency of emergency transportation and lowers expedited logistics costs. Through a four-layer design of "data-driven – intelligent forecasting – dual-warehouse collaboration – dynamic configuration," the entire process from demand forecasting to logistics configuration is automated, improving service levels, including shortening the fulfillment time of C-end orders, improving the on-time delivery rate of B-end orders, and reducing stockout rates.

[0144] See Figure 9 In one possible implementation, the data acquisition and fusion layer is further used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product code mapping table. This achieves multi-source data alignment, improves data quality, and facilitates subsequent processing.

[0145] See Figure 9In one possible implementation, the dual-warehouse collaborative management layer further utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. This algorithm includes a collaborative objective function and employs a decomposition-coordination algorithm to achieve the collaborative optimization solution. Thus, the objective of dual-warehouse collaborative optimization is to minimize total cost while meeting service level requirements. The constraints of the collaborative objective function can be set to require a service level greater than or equal to a target value, such as 95%, and can also include constraints on the sample warehouse capacity, allocation frequency, and transportation resources. Furthermore, the decomposition-coordination algorithm can be used to solve the problem, which may include a decomposition phase: decomposing the global optimization problem into SW subproblems and CW subproblems, solving each locally optimally; a coordination phase: coordinating the allocation decisions of the two subproblems using the Lagrange multiplier method to achieve the global optimum; and iterative optimization: repeating the decomposition and coordination process until convergence.

[0146] See Figure 9 In one possible implementation, the dual-warehouse collaborative management layer further utilizes a reinforcement learning algorithm to optimize the dual-warehouse collaborative strategy, used to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The reinforcement learning algorithm defines a state space, an action space, and a reward function. Thus, by employing reinforcement learning-based dual-warehouse collaborative strategy optimization, the agent can be trained using the Proximal Policy Optimization (PPO) algorithm to learn the optimal inventory control strategy.

[0147] Figure 10This is a schematic diagram of the structure of a computer device 1000 provided in an embodiment of this application. The computer device 1000 includes one or more processors 1010, a communication interface 1020, and a memory 1030. The processors 1010, the communication interface 1020, and the memory 1030 are interconnected via a bus 1040. Optionally, the computer device 1000 may further include an input / output interface 1050, which is connected to input / output devices for receiving user-set parameters, etc. The computer device 1000 can be used to implement some or all of the functions of the device embodiment or system embodiment in the above-described embodiments of this application; the processor 1010 can also be used to implement some or all of the operation steps of the method embodiment in the above-described embodiments of this application. For example, the specific implementation of various operations performed by the computer device 1000 can be referred to the specific details in the above embodiments, such as the processor 1010 being used to execute some or all of the steps or operations in the above-described method embodiments. For example, in the embodiments of this application, the computer device 1000 can be used to implement some or all of the functions of one or more components in the above-described device embodiments. In addition, the communication interface 1020 can be used for communication functions necessary to implement the functions of these devices and components, and the processor 1010 can be used for processing functions necessary to implement the functions of these devices and components.

[0148] It should be understood that, Figure 10 The computer device 1000 may include one or more processors 1010, and the multiple processors 1010 may cooperate to provide processing power in a parallel connection mode, a serial connection mode, a serial-parallel connection mode, or an arbitrary connection mode; or the multiple processors 1010 may form a processor sequence or a processor array; or the multiple processors 1010 may be divided into a main processor and an auxiliary processor; or the multiple processors 1010 may have different architectures, such as adopting a heterogeneous computing architecture. Furthermore, Figure 10 The structural and functional descriptions of the computer device 1000 shown are exemplary and non-limiting. In some exemplary embodiments, the computer device 1000 may include... Figure 10 The diagram shows more or fewer components, or combinations of some components, or splitting of some components, or different arrangements of components.

[0149] The processor 1010 can have various specific implementations. For example, it may include one or more combinations of a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), a tensor processing unit (TPU), or a data processing unit (DPU). This application does not impose specific limitations on these embodiments. The processor 1010 can also be a single-core or multi-core processor. The processor 1010 can be a combination of a CPU and hardware chips. These hardware chips can be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The PLDs can be complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), generic array logic (GALs), or any combination thereof. The processor 1010 can also be implemented using logic devices with built-in processing logic, such as FPGAs or digital signal processors (DSPs). The communication interface 1020 can be a wired interface or a wireless interface, used to communicate with other modules or devices. The wired interface can be an Ethernet interface, a local interconnect network (LIN), etc., and the wireless interface can be a cellular network interface or a wireless LAN interface, etc.

[0150] Memory 1030 may be non-volatile memory, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Memory 1030 may also be volatile memory, which may be random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM). The memory 1030 can also be used to store program code and data, so that the processor 1010 can call the program code stored in the memory 1030 to execute some or all of the operation steps in the above method embodiments, or to execute the corresponding functions in the above device embodiments. Furthermore, the computer device 1000 may include, compared to... Figure 10 The number of components displayed may be more or less, or there may be different component configurations.

[0151] The 1040 bus can be a Peripheral Component Interconnect Express (PCIe) bus, or an Extended Industry Standard Architecture (EISA) bus, a Unified Bus (Ubus or UB), a Compute Express Link (CXL) bus, a Cache Coherent Interconnect for Accelerators (CCIX) bus, etc. The 1040 bus can be divided into an address bus, a data bus, and a control bus. In addition to the data bus, the 1040 bus can also include a power bus, a control bus, and a status signal bus. However, for clarity, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0152] The methods and devices provided in this application are based on the same inventive concept. Since the principles by which the methods and devices solve problems are similar, the embodiments, implementation methods, examples, or methods of implementation of the methods and devices can be referred to each other, and repeated details will not be repeated. This application also provides a system comprising multiple computer devices, the structure of each computer device of which can refer to the structure of the computer devices described above. The functions or operations achievable by this system can refer to the specific implementation steps in the above method embodiments and / or the specific functions described in the above device embodiments, and will not be repeated here.

[0153] This application also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed on a computer device (such as one or more processors), they can implement the method steps described in the above method embodiments. The specific implementation of the above method steps by the processor of the computer-readable storage medium can refer to the specific operations described in the above method embodiments and / or the specific functions described in the above device embodiments, and will not be repeated here.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. This application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Embodiments of this application can be implemented wholly or partially by software, hardware, firmware, or any other combination. When implemented in software, the above embodiments can be implemented wholly or partially as a computer program product. This application can take the form of a computer program product embodied on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless network communication, microwave, etc.) means. Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (such as floppy disks, hard disks, and magnetic tapes), optical media, or semiconductor media. Semiconductor media can be solid-state drives, random access memory, flash memory, read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, or any other suitable form of storage medium.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. Each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. The steps in the methods of the embodiments of this application can be adjusted in order, combined, or deleted according to actual needs; the modules in the systems of the embodiments of this application can be divided, combined, or deleted according to actual needs. If these modifications and variations of the embodiments of this application fall within the scope of the claims of this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A supply chain management system, characterized by, The supply chain management system includes: The data acquisition and fusion layer is used to collect multi-source heterogeneous data, including C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data. The demand forecasting engine layer is used to perform multimodal data fusion on the multi-source heterogeneous data, thereby constructing a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium- and long-term demand forecasting model to provide B-end medium- and long-term demand forecasting results. Furthermore, the B-end medium- and long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium- and long-term demand. The dual-warehouse collaborative management layer is used to manage the inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse based on the C-end spot demand forecast results and the B-end medium- and long-term demand forecast results. The inventory strategy of the sample warehouse is rapid turnover and low inventory, while the inventory strategy of the central warehouse is large-scale reserves and high inventory. The logistics configuration optimization layer is used to dynamically optimize the layout, capacity configuration, and distribution network of logistics warehouses based on multi-regional demand forecast results.

2. The supply chain management system of claim 1, wherein, The data acquisition and fusion layer is also used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product coding mapping table.

3. The supply chain management system of claim 1, wherein, The data acquisition and fusion layer is also used to perform data quality governance on the multi-source heterogeneous data according to a three-level data quality governance strategy, wherein the three-level data quality governance strategy includes a real-time verification layer, an anomaly detection layer, and a missing value processing layer.

4. The supply chain management system of claim 1, wherein, The C-end spot demand forecasting model includes a multi-scale time series forecasting model, which is used for time series feature extraction, real-time signal fusion, promotion effect modeling, and regional difference modeling.

5. The supply chain management system of claim 4, wherein, The C-end spot demand forecasting model calculates the C-end spot demand forecast for a given region at a given time based on the C-end forecasting formula. The parameters of the C-end forecasting formula include long-term trend terms, daily seasonality, weekly seasonality, monthly seasonality, promotion effect multiplier, regional characteristic coefficient, and random disturbance terms.

6. The supply chain management system of claim 1, wherein, The B-end medium- and long-term demand forecasting model includes a multi-dimensional demand extrapolation model, which is used for contract demand analysis, trend extrapolation, industry correlation analysis, and C-end to B-end conversion modeling.

7. The supply chain management system according to claim 6, characterized in that, The B-end medium- and long-term demand forecasting model is based on the B-end forecasting formula to calculate the predicted value of B-end medium- and long-term demand for a given region at a given time. The parameters of the B-end forecasting formula include the deterministic demand of signed contracts, the B-end trend term, the industry prosperity index, the conversion coefficient from C-end to B-end, the demand transmission lag, and the random disturbance term.

8. The supply chain management system according to claim 6, characterized in that, The C-end to B-end conversion modeling is achieved through a demand transmission model. This model is used to establish the transmission time lag relationship between C-end spot demand and B-end medium- and long-term demand, and further to establish a quantitative transmission mechanism for the C-end spot demand to B-end medium- and long-term demand. The transmission time lag relationship between C-end spot demand and B-end medium- and long-term demand includes the time window during which changes in C-end demand lead B-end procurement decisions. The quantitative transmission mechanism for C-end spot demand to B-end medium- and long-term demand includes the dynamic estimation of the C-end to B-end conversion coefficient.

9. The supply chain management system according to claim 1, characterized in that, The dual-warehouse collaborative management layer uses C-end sales demand analysis and scheduling strategies to manage the inventory strategy and allocation mechanism of the sample warehouse. The C-end sales demand analysis and scheduling strategies include best-selling product prediction and configuration, dynamic pricing and promotion, user profile-driven product selection, and real-time inventory visualization.

10. The supply chain management system according to claim 9, characterized in that, The dual-warehouse collaborative management layer also utilizes an inventory management model that combines strategy and dynamic safety stock to manage the inventory strategy and allocation mechanism of the sample warehouse. The inventory management model that combines strategy and dynamic safety stock includes reorder point parameters and target inventory parameters. The reorder point parameters are calculated based on replenishment lead time, average demand within the lead time, service level coefficient, and standard deviation of lead time demand. The target inventory parameters are calculated based on economic replenishment quantity and balancing ordering costs and holding costs.

11. The supply chain management system according to claim 1, characterized in that, The dual-warehouse collaborative management layer utilizes medium- and long-term reserve optimization strategies to manage the central warehouse's inventory strategy and allocation mechanism. These medium- and long-term reserve optimization strategies include contract inventory locking, seasonal reserves, and safety stock optimization.

12. The supply chain management system according to claim 11, characterized in that, The dual-warehouse collaborative management layer also utilizes an intelligent allocation decision model based on demand forecasting and inventory status to manage the central warehouse's inventory strategy and allocation mechanism. This intelligent allocation decision model includes allocation triggering condition parameters and allocation quantity calculation parameters. The allocation triggering condition parameters predict the risk of stockouts in the sample warehouse within a certain number of days if the sample warehouse's inventory is below the reorder point, thereby determining whether the central warehouse has surplus inventory or batches of goods awaiting warehousing. The allocation quantity calculation parameters are the minimum value among current inventory, available inventory, and maximum single-transport capacity.

13. The supply chain management system according to claim 1, characterized in that, The dual-warehouse collaborative management layer also utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The dual-warehouse collaborative optimization algorithm includes a collaborative objective function and employs a decomposition and coordination algorithm to achieve collaborative optimization solutions.

14. The supply chain management system according to claim 1, characterized in that, The logistics configuration optimization layer is used to evaluate the logistics demand intensity of each of the multiple regions based on their respective consumption record data and demand forecast results. The logistics demand intensity of each of the multiple regions is reflected in the logistics demand index. The logistics demand index is calculated based on the weighted average of the C-end demand forecast value, the B-end demand forecast value, the demand volatility, and the demand growth rate of a given region.

15. The supply chain management system according to claim 14, characterized in that, The logistics configuration optimization layer is also used to cluster the logistics demand characteristics of the multiple regions using a regional clustering analysis algorithm, thereby dividing the multiple regions into multiple cluster types and adopting multiple logistics configuration strategies that correspond one-to-one with the multiple cluster types.

16. The supply chain management system according to claim 1, characterized in that, The logistics configuration optimization layer is also used to determine the location of logistics warehouses using a multi-objective location model, wherein the multi-objective location model is the optimal solution of a multi-objective optimization function, and the objective parameters of the multi-objective optimization function include total transportation cost, average delivery time, warehouse construction cost, warehouse rental cost, and supply interruption risk.

17. The supply chain management system according to claim 1, characterized in that, The logistics configuration optimization layer is also used to determine the location of logistics warehouses using a heuristic solution algorithm, wherein the heuristic solution algorithm is a hybrid algorithm of genetic algorithm and simulated annealing.

18. The supply chain management system according to claim 1, characterized in that, The logistics configuration optimization layer is also used to reverse-engineer the logistics warehouse capacity demand based on demand forecasting results and inventory strategies, thereby realizing a dynamic capacity adjustment mechanism. The logistics warehouse capacity demand is determined based on the average inventory level of each commodity, the volume occupied by the unit commodity, the volume fluctuation coefficient, the batch occupancy coefficient, the warehouse space utilization rate, and the shelf height utilization coefficient.

19. The supply chain management system according to claim 1, characterized in that, The supply chain management system also generates supply chain control recommendations, including inventory control recommendations and logistics optimization recommendations.

20. The supply chain management system according to claim 19, characterized in that, The inventory control recommendations include multiple recommendation types, each with its own triggering conditions and recommendation content. These multiple recommendation types include replenishment recommendations, transfer recommendations, clearance recommendations, and reserve recommendations.

21. The supply chain management system according to claim 19, characterized in that, The logistics optimization suggestions include route optimization suggestions, power configuration suggestions, and warehouse network optimization suggestions.

22. The supply chain management system according to claim 1, characterized in that, The supply chain management system also includes a visual decision dashboard to provide demand forecasting dashboards, inventory health dashboards, logistics efficiency dashboards, and anomaly warning dashboards.

23. The supply chain management system according to claim 1, characterized in that, The supply chain management system also includes a multi-scale prediction fusion model to improve prediction accuracy. The multi-scale prediction fusion model trains a Prophet model to capture seasonality, trains an LSTM model to capture nonlinear trends, and trains an XGBoost model to capture feature interaction effects as base learners. Then, it uses a meta-learner and dynamic weights for weighted fusion.

24. The supply chain management system according to claim 1, characterized in that, The dual-warehouse collaborative management layer also utilizes reinforcement learning algorithms to optimize the dual-warehouse collaborative strategy, which is used to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The reinforcement learning algorithm sets up a state space, an action space, and a reward function.

25. The supply chain management system according to claim 1, characterized in that, The logistics configuration optimization layer is also used to determine the optimal location of the central warehouse by using a regional logistics network optimization algorithm and a demand-weighted algorithm, and to iterate repeatedly to the candidate location with the greatest reduction in total cost by using a greedy addition algorithm, so as to achieve the goal of preset warehouse quantity or no longer reducing cost.

26. The supply chain management system according to claim 1, characterized in that, The supply chain management system is applied to cross-border e-commerce supply chain optimization, fast-moving consumer goods distribution network restructuring, and industrial product MRO supply chain management.

27. A supply chain management method, characterized in that, The supply chain management method includes: Through the data acquisition and fusion layer, multi-source heterogeneous data is collected, including C-end order data, B-end procurement data, regional consumption records, external market data, and logistics operation data. Through the demand forecasting engine layer, multimodal data fusion is performed on the multi-source heterogeneous data to construct a C-end spot demand forecasting model to provide C-end spot demand forecasting results and a B-end medium- and long-term demand forecasting model to provide B-end medium- and long-term demand forecasting results. Furthermore, the B-end medium- and long-term demand forecasting model includes a quantitative transmission mechanism from C-end spot demand to B-end medium- and long-term demand. Through the dual-warehouse collaborative management system, based on the C-end spot demand forecast results and the B-end medium- and long-term demand forecast results, the system manages the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The inventory strategy of the sample warehouse is rapid turnover and low inventory, while the inventory strategy of the central warehouse is large-scale reserves and high inventory. Through the logistics configuration optimization layer, the layout, capacity configuration, and distribution network of logistics warehouses are dynamically optimized based on the demand forecast results of multiple regions.

28. The method according to claim 27, characterized in that, The data acquisition and fusion layer is also used to perform multi-source data alignment on the multi-source heterogeneous data based on a unified spatiotemporal standard, thereby achieving time alignment under a unified timestamp standard, spatial alignment under a unified geocoding standard, and product alignment under a unified product coding mapping table.

29. The method according to claim 27, characterized in that, The dual-warehouse collaborative management layer also utilizes a dual-warehouse collaborative optimization algorithm to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The dual-warehouse collaborative optimization algorithm includes a collaborative objective function and employs a decomposition and coordination algorithm to achieve collaborative optimization solutions.

30. The method according to claim 27, characterized in that, The dual-warehouse collaborative management layer also utilizes reinforcement learning algorithms to optimize the dual-warehouse collaborative strategy, which is used to manage the respective inventory strategies and allocation mechanisms of the sample warehouse and the central warehouse. The reinforcement learning algorithm sets up a state space, an action space, and a reward function.

31. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 27 to 30.

32. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer device, cause the computer device to perform the method according to any one of claims 27 to 30.

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