Multi-warehouse intelligent distribution and scheduling method for raw material inventory

Abstract

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent warehouse allocation, and in particular, relates to a multi-warehouse intelligent allocation and scheduling method for raw material inventory. Background Art

[0002] In modern manufacturing and supply chain management, warehouse raw material inventory management is an important link to ensure smooth production, reduce production downtime and optimize resource utilization. Traditional inventory management methods usually rely on manual records and regular inspections, which is not only inefficient, but also prone to inaccurate data and poor timeliness. Although existing inventory management systems have achieved informatization to a certain extent, they usually lack real-time monitoring and dynamic adjustment capabilities, and cannot quickly respond to market changes and fluctuations in production demand.

[0003] Existing inventory management systems usually lack the ability to conduct in-depth analysis of inventory data and are unable to effectively identify and predict inventory surpluses or shortages. These systems mainly rely on simple inventory quantity statistics and basic inventory reports, and lack the ability to conduct comprehensive analysis of historical data, real-time data, and market trends. Due to the lack of advanced data analysis tools and forecasting models, existing systems have difficulty identifying potential inventory problems in advance and are unable to provide managers with effective decision support. This limitation results in managers only being able to take remedial measures after the problem occurs, which is often too late, affecting production continuity and the effective use of resources.

[0004] Traditional inventory management systems lack flexibility in handling the demand for different types of raw materials, and usually adopt a one-size-fits-all management approach, which makes it difficult to optimize scheduling efficiency. Existing systems often do not have a classification management mechanism for different raw material characteristics, and use the same management strategy for all types of raw materials, ignoring the differences in demand priority, frequency of use, and storage conditions of various types of raw materials. Such a management approach not only reduces resource utilization efficiency, but may also lead to insufficient supply of important raw materials or untimely scheduling in emergency situations, thereby affecting the overall efficiency of production and product quality. The demand characteristics of different raw materials require different management and scheduling strategies to achieve the best allocation and utilization of resources.

[0005] In view of this, the present invention is proposed. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a multi-warehouse intelligent allocation and scheduling method for raw material inventory, thereby solving the problems raised in the above-mentioned background technology.

[0007] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0008] A multi-warehouse intelligent allocation and scheduling method for raw material inventory includes the following steps:

[0009] Collect raw material inventory data of each warehouse and monitor the warehouse inventory data in real time;

[0010] Based on real-time inventory data and historical inventory data, analyze the inventory status of each warehouse to determine the inventory balance status of each warehouse and identify warehouses with excess or shortage inventory;

[0011] Based on the inventory status analysis results, a dynamic adjustment algorithm is used to calculate the optimal inventory allocation plan;

[0012] Categorize and manage different types of raw materials, optimize scheduling based on the demand priority and urgency of each type of raw materials, and generate scheduling plans;

[0013] Send the generated scheduling plan to each warehouse and transportation department, allocate and transport raw materials according to the plan and monitor the execution of the scheduling plan;

[0014] In the event of an emergency, the rapid response mechanism activates the emergency dispatch algorithm and calculates the emergency dispatch plan in real time.

[0015] Optionally, sensors and monitoring equipment are installed in each warehouse to collect the quantity and status of raw materials in the warehouse in real time.

[0016] Optionally, based on the real-time inventory data and historical inventory data, the inventory status of each warehouse is analyzed to determine the inventory balance status of each warehouse, and the steps for identifying warehouses with excess or shortage inventory are as follows:

[0017] Collect the current inventory of each product in each warehouse and obtain the historical inventory changes, historical replenishment records, and historical consumption records of each warehouse.

[0018] Process and analyze real-time inventory data and historical inventory data, and use formulas to Calculate inventory forecast Predict the inventory volume in the future and generate a forecast report, including: is the current inventory of product i in warehouse k at time t, is the replenishment quantity of product i in warehouse k at time t, is the consumption of product i in warehouse k at time t;

[0019] Based on the forecast report, the formula Calculate the inventory balance status of each warehouse to determine whether there is excess or shortage of inventory, including: represents the expected demand for product i in warehouse k at time t.

[0020] Optionally, different types of raw materials can be classified and managed, and the steps for scheduling optimization based on the demand priority and urgency of each type of raw material are as follows:

[0021] Based on the acquired real-time inventory data and historical inventory data, analyze the inventory status of each warehouse and generate an inventory status report;

[0022] Based on the minimization of the objective function, the optimal inventory allocation plan is solved through linear programming or genetic algorithm, the optimal inventory allocation plan is calculated, and actual allocation is carried out according to the calculation results.

[0023] Optionally, different types of raw materials can be classified and managed, and the steps for scheduling optimization based on the demand priority and urgency of each type of raw material are as follows:

[0024] Using historical data and market analysis, the formula Predict future raw material demand, where y t is the forecast demand at time t, μ is a constant term, φ i is the autoregressive coefficient, θ j is the moving average system, ∈ t is the error term, X t，k is the kth exogenous variable at time t (such as market trends, seasonal factors, etc.), β k is the coefficient of the exogenous variable;

[0025] According to production plan, customer orders and market demand, through formula The priority of each raw material, where P i is the priority score of the i-th raw material, w j is the weight of the jth factor (determined by AHP), r ij is the score of the i-th raw material on the j-th factor;

[0026] The supply chain reliability index (SCRI) is used to evaluate the stability of the supply chain, and the inventory level and urgency are determined by combining inventory turnover and inventory holding costs. The expression of the supply chain reliability index is: Where N is the number of nodes in the supply chain, R k is the reliability score of the kth node.

[0027] Optionally, the step of generating a scheduling plan includes:

[0028] An optimization model based on inventory control theory is used to set the safety stock, reorder point and maximum inventory. According to the demand forecast and inventory model, the demand is matched with the existing inventory to find the gap. Finally, the integer programming model is used to optimize the procurement and scheduling plan. The expression of the reorder point RRR is: R = dL + z·σL, d is the average demand rate, L is the delivery lead time, z is the standard normal distribution value corresponding to the service level, σ L is the standard deviation of delivery lead time demand.

[0029] The steps to distribute the generated scheduling plan to each warehouse and transportation department, allocate and transport raw materials according to the plan, and monitor the execution of the scheduling plan are as follows:

[0030] Organize the optimized scheduling plan into the material list, transportation arrangement, and timetable of each warehouse and send the scheduling plan to each warehouse and transportation department through the communication system;

[0031] After receiving the dispatch plan, the transportation department arranges vehicles and routes according to the dispatch plan;

[0032] During the transportation process, the warehouse management system (WMS) and transportation management system (TMS) are used to monitor the execution of the scheduling plan in real time and track the inventory status and transportation progress of raw materials.

[0033] Optionally, in the event of an emergency, the rapid response mechanism starts the emergency dispatch algorithm, and the steps for calculating the emergency dispatch plan in real time are:

[0034] Identify anomalies using preset thresholds and rules, then classify them according to their type and severity and prioritize them;

[0035] Construct a mixed integer linear programming (MILP) model and establish demand constraints, inventory balance constraints, and emergency transportation constraints.

[0036] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0037] 1. Through the collection of real-time inventory data and historical inventory data, the inventory status of each warehouse is deeply analyzed to determine the inventory balance status and identify warehouses with excess or shortage inventory. Through this real-time data monitoring and analysis, the system can provide accurate inventory warnings, help managers take timely measures to avoid production interruptions and resource waste, and improve the accuracy and predictability of inventory management.

[0038] 2. Classify and manage different types of raw materials, optimize scheduling based on the demand priority and urgency of each type of raw materials, and generate scheduling plans. Through this classification management and scheduling optimization, the system can effectively improve scheduling efficiency and resource utilization, ensure that important and urgent raw materials can be guaranteed first, reduce bottlenecks and delays in the production process, and improve overall production efficiency and resource allocation effects.

[0039] 3. In the event of an emergency, the rapid response mechanism of this application starts the emergency dispatch algorithm and calculates the emergency dispatch plan in real time. The system can respond and adjust quickly to ensure the supply of key raw materials and reduce production interruptions and losses. Through this efficient emergency dispatch mechanism, the system improves its resilience and risk resistance, ensures the continuous and stable operation of the supply chain, and significantly improves the reliability and emergency response capabilities of the overall inventory management system.

[0040] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] In the figure:

[0043] Figure 1 This is a block diagram of the intelligent allocation and scheduling method.

[0044] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] The present invention will now be described in further detail with reference to the accompanying drawings.

[0046] See also Figure 1 As shown, in this embodiment, a multi-warehouse intelligent allocation and scheduling method for raw material inventory is provided, including collecting raw material inventory data of each warehouse and monitoring the warehouse inventory data in real time; real-time collection and monitoring of warehouse inventory data can significantly improve the efficiency and accuracy of enterprise inventory management. First, this real-time data collection can prevent inventory information from lagging behind and avoid inventory shortages or surpluses caused by untimely data updates.

[0047] Based on real-time inventory data and historical inventory data, the inventory status of each warehouse is analyzed to determine the inventory balance status of each warehouse and identify warehouses with excess or shortage inventory; using real-time and historical inventory data to analyze inventory status helps to fully grasp the inventory dynamics of each warehouse and optimize inventory configuration. First of all, this analysis method can accurately identify the balance status of inventory and find out the excess or shortage of inventory in each warehouse, thereby avoiding waste of resources and production interruptions caused by shortages. Through the analysis of historical data, the patterns and trends of inventory fluctuations can also be identified, providing an important reference for future inventory management.

[0048] Based on the results of inventory status analysis, a dynamic adjustment algorithm is used to calculate the optimal inventory allocation plan; using a dynamic adjustment algorithm to calculate the optimal inventory allocation plan can significantly improve the flexibility and efficiency of inventory management. First, this algorithm can dynamically adjust inventory allocation according to real-time inventory status and demand changes to ensure that the inventory of each warehouse remains at a reasonable level and avoid excess or shortage of inventory. By optimizing the inventory allocation plan, enterprises can effectively reduce inventory holding costs and transportation costs and improve capital turnover efficiency. Secondly, the dynamic adjustment algorithm can quickly respond to changes in market demand and fluctuations in the supply chain to ensure the real-time and accuracy of the inventory allocation plan.

[0049] Classify and manage different types of raw materials, optimize scheduling based on the demand priority and urgency of each type of raw materials, and generate scheduling plans; Classify and manage different types of raw materials, optimize scheduling based on demand priority and urgency, which can significantly improve the management efficiency and response speed of the supply chain. First of all, classification management can enable enterprises to understand the characteristics and needs of different raw materials more clearly, making it easier to formulate more targeted management and scheduling strategies. This refined management method can effectively reduce inventory backlogs and out-of-stock situations and improve inventory utilization.

[0050] The generated scheduling plan is sent to each warehouse and transportation department, and the raw materials are allocated and transported according to the plan and the execution of the scheduling plan is monitored; The generated scheduling plan is sent to each warehouse and transportation department, and the raw materials are allocated and transported according to the plan, which can effectively improve the execution and coordination efficiency of the supply chain. First of all, a clear scheduling plan can ensure that each warehouse and transportation department has clear guidance during the execution process, reducing uncertainty and errors in communication and execution.

[0051] In the event of an emergency, the rapid response mechanism activates the emergency dispatch algorithm and calculates the emergency dispatch plan in real time. The rapid response mechanism can identify emergencies in the shortest time and activate emergency plans to reduce the impact and influence of emergencies on the supply chain. The emergency dispatch algorithm can quickly calculate the optimal emergency dispatch plan based on real-time data to ensure the timely supply of key materials and ensure the timely satisfaction of production and customer needs. This rapid response capability can significantly improve the company's resilience and crisis handling capabilities, and reduce production stagnation and customer loss caused by emergencies.

[0052] According to the multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1, it is characterized in that sensors and monitoring equipment are installed in each warehouse to collect the quantity and status of raw materials in the warehouse in real time.

[0053] The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that, based on real-time inventory data and historical inventory data, the inventory status of each warehouse is analyzed to determine the inventory balance status of each warehouse, and the steps of identifying warehouses with excess or shortage inventory are as follows:

[0054] Collect the current inventory of each product in each warehouse and obtain the historical inventory changes, historical replenishment records, and historical consumption records of each warehouse. Collecting the current inventory of each product in each warehouse and its historical data can provide a solid data foundation for subsequent inventory management and optimization. First of all, this comprehensive data collection can help companies accurately grasp the current inventory status and ensure the accuracy and scientificity of decision-making. By understanding the real-time inventory of each warehouse, companies can promptly discover and handle inventory anomalies to avoid overstocking or shortages.

[0055] Process and analyze real-time inventory data and historical inventory data, and use formulas to Calculate inventory forecast Predict the inventory volume in the future and generate a forecast report, including: is the current inventory of product i in warehouse k at time t, is the replenishment quantity of product i in warehouse k at time t, is the consumption of product i in warehouse k at time t; processing and analyzing real-time inventory data and historical inventory data, and calculating inventory forecasts, can significantly improve the foresight and accuracy of inventory management. Through in-depth analysis of these data, companies can identify patterns and trends in inventory changes and predict inventory demand in the future. This forecasting capability can help companies make replenishment plans in advance and avoid production interruptions due to insufficient inventory or capital waste due to excess inventory.

[0056] Based on the forecast report, the formula Calculate the inventory balance status of each warehouse to determine whether there is excess or shortage of inventory, including: represents the expected demand for product i in warehouse k at time t. Inventory forecast reports can help companies accurately assess inventory health and take timely measures to adjust inventory levels. First, this calculation can identify which warehouses are at risk of excess or shortage inventory, ensuring predictive and proactive inventory management. By considering expected demand, companies can more accurately assess whether inventory is sufficient and avoid inventory imbalances caused by inaccurate forecasts.

[0057] The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that different types of raw materials are classified and managed, and the steps of scheduling optimization based on the demand priority and urgency of each type of raw material are as follows:

[0058] Based on the real-time inventory data and historical inventory data obtained, the inventory status of each warehouse is analyzed and an inventory status report is generated. Analyzing the real-time inventory data and historical inventory data and generating an inventory status report can significantly improve the company's inventory management efficiency and decision-making capabilities.

[0059] Based on the minimization of the objective function, the optimal inventory allocation plan is solved by linear programming or genetic algorithm, the optimal inventory allocation plan is calculated, and actual allocation is carried out according to the calculation results. By minimizing the objective function, the optimal inventory allocation plan is solved by linear programming or genetic algorithm, which can achieve the optimization of inventory management and the rational allocation of resources. First of all, linear programming and genetic algorithms are powerful optimization tools that can find the optimal solution under complex constraints, thereby ensuring the scientificity and effectiveness of the inventory allocation plan. The objective function usually takes into account multiple factors, such as inventory cost, transportation cost and service level. Minimizing the objective function can improve the efficiency of the supply chain while reducing costs. Secondly, the calculation of the optimal inventory allocation plan can give enterprises clear operational guidelines in the actual allocation process, ensuring that the inventory level of each warehouse reaches the optimal state. This method can improve inventory utilization, reduce unnecessary inventory backlogs and shortages, and optimize capital flow.

[0060] The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that different types of raw materials are classified and managed, and the steps of scheduling optimization based on the demand priority and urgency of each type of raw material are as follows:

[0061] Using historical data and market analysis, the formula Predict future raw material demand, where y t is the forecast demand at time t, μ is a constant term, φi is the autoregressive coefficient, θ j is the moving average system, ∈ t is the error term, X t，k is the kth exogenous variable at time t (such as market trends, seasonal factors, etc.), β k It is the coefficient of the exogenous variable; historical data provides a true record of past demand changes and can reveal the cyclical and trend changes in demand. Through autoregression and moving average coefficients, important features in the demand time series can be captured to improve the accuracy of the forecasting model. Market analysis introduces exogenous variables, such as market trends and seasonal factors, which can more comprehensively reflect the factors affecting future demand.

[0062] According to production plan, customer orders and market demand, through formula The priority of each raw material, where P i is the priority score of the i-th raw material, w j is the weight of the jth factor (determined by AHP), r ij is the score of the i-th raw material on the j-th factor; the priority calculation method can ensure the priority supply of key raw materials, avoid production stagnation and customer order delays, and improve production efficiency and customer satisfaction. Secondly, by clarifying the priority, enterprises can arrange procurement and inventory more reasonably, reduce unnecessary inventory backlogs and capital occupation, and optimize resource allocation. In addition, priority scoring can also help enterprises make the best decisions when resources are limited, and improve the flexibility and resilience of the supply chain.

[0063] The supply chain reliability index (SCRI) is used to evaluate the stability of the supply chain, and the inventory level and urgency are determined by combining inventory turnover and inventory holding costs. The expression of the supply chain reliability index is: Where N is the number of nodes in the supply chain, R k is the reliability score of the kth node. By evaluating the reliability score of each node, the weak links that affect the stability of the supply chain can be identified and targeted improvement measures can be taken. Combined with the inventory turnover rate, enterprises can measure the efficiency of inventory management, ensure that inventory can be turned over in a timely manner, and reduce backlogs and waste.

[0064] The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that the step of generating a scheduling plan includes:

[0065] An optimization model based on inventory control theory is used to set the safety stock, reorder point and maximum inventory. According to the demand forecast and inventory model, the demand is matched with the existing inventory to find the gap. Finally, the integer programming model is used to optimize the procurement and scheduling plan. The expression of the reorder point RRR is: R = dL + z·σL, d is the average demand rate, L is the delivery lead time, z is the standard normal distribution value corresponding to the service level, σ L is the standard deviation of the demand during the delivery lead time. Using an optimization model based on inventory control theory, setting safety stock, reorder point and maximum inventory, and optimizing management in combination with demand forecasting and inventory models can significantly improve the efficiency and accuracy of an enterprise's inventory management. First, by setting a reasonable safety stock level, it is possible to effectively respond to demand fluctuations and supply chain disruptions and ensure the stability of production and customer demand. The reorder point can take into account the average demand rate, delivery lead time and standard deviation of demand to ensure that the inventory level is within the expected demand fluctuation range. This scientific calculation method can reduce the risk of excess and shortage inventory and optimize inventory configuration. Using integer programming models to optimize procurement and scheduling plans can minimize inventory holding costs and transportation costs while meeting demand, thereby improving the overall efficiency of the supply chain.

[0066] The steps to distribute the generated scheduling plan to each warehouse and transportation department, allocate and transport raw materials according to the plan, and monitor the execution of the scheduling plan are as follows:

[0067] The optimized scheduling plan is organized into the bill of materials, transportation arrangements, and timetables for each warehouse, and the scheduling plan is sent to each warehouse and transportation department through the communication system; The optimized scheduling plan is organized into the bill of materials, transportation arrangements, and timetables for each warehouse, and sent to each warehouse and transportation department through the communication system, which can significantly improve the efficiency and accuracy of scheduling execution. First of all, clear and specific bills of materials and transportation arrangements can help each warehouse and transportation department accurately understand and execute the scheduling plan, reducing execution deviations and errors caused by unclear information.

[0068] After receiving the dispatch plan, the transportation department arranges vehicles and routes according to the dispatch plan;

[0069] During the transportation process, the warehouse management system (WMS) and transportation management system (TMS) are used to monitor the execution of the scheduling plan in real time and track the inventory status and transportation progress of raw materials. Using the warehouse management system (WMS) and transportation management system (TMS) to monitor the execution of the scheduling plan in real time during transportation can significantly improve the transparency and controllability of logistics operations. First of all, real-time monitoring can ensure that the scheduling plan is executed as planned, timely discover and resolve abnormal situations during the execution process, and reduce the risk of transportation delays and material losses. By tracking the inventory status of raw materials, it is possible to accurately grasp the inventory changes of each warehouse, adjust the replenishment and allocation plan in time, and avoid inventory shortages or excesses.

[0070] The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that, in the event of an emergency, the rapid response mechanism starts the emergency scheduling algorithm, and the steps of calculating the emergency scheduling plan in real time are:

[0071] Using preset thresholds and rules to identify anomalies, and then classify them according to the type and severity of the anomalies, and determine the events to be handled first; Using preset thresholds and rules to identify anomalies, and classify them according to the type and severity of the anomalies, can help improve the ability and efficiency of enterprises to respond to emergencies. First, preset thresholds and rules can automatically monitor key indicators, and once the indicators exceed the normal range, the system will immediately issue an alarm. This real-time monitoring mechanism can quickly identify problems, reduce the burden and delays of manual monitoring, and ensure that enterprises can detect anomalies in the first place. By classifying and prioritizing anomalies, enterprises can formulate targeted emergency response measures to ensure that resources and time are focused on the most urgent and important events.

[0072] Constructing a mixed integer linear programming (MILP) model and establishing demand constraints, inventory balance constraints, and emergency transportation constraints. Constructing a mixed integer linear programming (MILP) model and establishing demand constraints, inventory balance constraints, and emergency transportation constraints can help achieve efficient and accurate emergency scheduling in emergencies. First of all, the MILP model is a powerful optimization tool that can find the optimal solution under complex multi-constraint conditions. By establishing demand constraints, it can ensure that the emergency scheduling plan meets actual needs and avoids resource waste and shortages. The inventory balance constraint can ensure that the scheduling plan will not cause new inventory shortages or surpluses while meeting demand, and maintain a reasonable level of inventory.

[0073] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A multi-warehouse intelligent allocation and scheduling method for raw material inventory, characterized in that: The following steps are involved: Collect raw material inventory data of each warehouse and monitor the warehouse inventory data in real time; Based on real-time inventory data and historical inventory data, analyze the inventory status of each warehouse to determine the inventory balance status of each warehouse and identify warehouses with excess or shortage inventory; Based on the inventory status analysis results, a dynamic adjustment algorithm is used to calculate the optimal inventory allocation plan; Categorize and manage different types of raw materials, optimize scheduling based on the demand priority and urgency of each type of raw materials, and generate scheduling plans; Send the generated scheduling plan to each warehouse and transportation department, allocate and transport raw materials according to the plan and monitor the execution of the scheduling plan; In the event of an emergency, the rapid response mechanism activates the emergency dispatch algorithm and calculates the emergency dispatch plan in real time.

2. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: Sensors and monitoring equipment are installed in each warehouse to collect the quantity and status of raw materials in the warehouse in real time.

3. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: Based on real-time inventory data and historical inventory data, the inventory status of each warehouse is analyzed to determine the inventory balance status of each warehouse. The steps to identify warehouses with excess or shortage inventory are as follows: Collect the current inventory of each product in each warehouse and obtain the historical inventory changes, historical replenishment records and historical consumption records of each warehouse; Process and analyze real-time inventory data and historical inventory data, and use formulas to Calculate inventory forecast Predict the inventory volume in the future and generate a forecast report, including: is the current inventory of product i in warehouse k at time t, is the replenishment quantity of product i in warehouse k at time t, is the consumption of product i in warehouse k at time t; Based on the forecast report, the formula Calculate the inventory balance status of each warehouse to determine whether there is excess or shortage of inventory, including: represents the expected demand for product i in warehouse k at time t.

4. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: The steps for classifying and managing different types of raw materials and optimizing scheduling based on the demand priority and urgency of each type of raw materials are as follows: Based on the acquired real-time inventory data and historical inventory data, analyze the inventory status of each warehouse and generate an inventory status report; Based on the minimization of the objective function, the optimal inventory allocation plan is solved through linear programming or genetic algorithm, the optimal inventory allocation plan is calculated, and actual allocation is carried out according to the calculation results.

5. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: The steps for classifying and managing different types of raw materials and optimizing scheduling based on the demand priority and urgency of each type of raw materials are as follows: Using historical data and market analysis, the formula Predict future raw material demand, where y t is the forecast demand at time t, μ is a constant term, φ i is the autoregressive coefficient, θ j is the moving average system, ∈ t is the error term, X t，k is the kth exogenous variable at time t (such as market trends, seasonal factors, etc.), β k is the coefficient of the exogenous variable; According to production plan, customer orders and market demand, through formula The priority of each raw material, where P i is the priority score of the i-th raw material, w j is the weight of the jth factor (determined by AHP), r ij is the score of the i-th raw material on the j-th factor. The supply chain reliability index SCRI is used to evaluate the stability of the supply chain, and the inventory level and urgency are determined by combining inventory turnover rate and inventory holding cost. The expression of the supply chain reliability index is: Where N is the number of nodes in the supply chain, R k is the reliability score of the kth node.

6. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: The steps to generate a scheduling plan include: An optimization model based on inventory control theory is used to set the safety stock, reorder point and maximum inventory. According to the demand forecast and inventory model, the demand is matched with the existing inventory to find the gap. Finally, the integer programming model is used to optimize the procurement and scheduling plan. The expression of the reorder point RRR is: R = dL + z·σL, d is the average demand rate, L is the delivery lead time, z is the standard normal distribution value corresponding to the service level, σ L is the standard deviation of delivery lead time demand.

7. The generated scheduling plan is sent to each warehouse and transportation department. The steps to allocate and transport raw materials according to the plan and monitor the execution of the scheduling plan are as follows: Organize the optimized scheduling plan into the material list, transportation arrangement, and timetable of each warehouse and send the scheduling plan to each warehouse and transportation department through the communication system; After receiving the dispatch plan, the transportation department arranges vehicles and routes according to the dispatch plan; During the transportation process, the warehouse management system (WMS) and transportation management system (TMS) are used to monitor the execution of the scheduling plan in real time and track the inventory status and transportation progress of raw materials.

8. The multi-warehouse intelligent allocation and scheduling method for raw material inventory according to claim 1 is characterized in that: In case of an emergency, the rapid response mechanism starts the emergency dispatch algorithm and calculates the emergency dispatch plan in real time as follows: Identify anomalies using preset thresholds and rules, then classify them according to their type and severity and prioritize them; Construct a mixed integer linear programming (MILP) model and establish demand constraints, inventory balance constraints, and emergency transportation constraints.

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