Multi-sku whole vehicle joint ordering method and system based on sales synchronization

Abstract

The application discloses a kind of based on the multi SKU whole car joint ordering method and system of sales synchronization, it is related to warehousing and logistics technical field, the method includes the following steps: according to specific supplier obtains safety stock and order point;Based on safety stock and order point, real-time inventory monitoring is carried out and triggers ordering;Commonly available days are introduced, and ordering quantity is calculated based on commonly available days;For commonly available days value, the optimal vehicle type is determined by three constraint selection and constraint verification;According to the determined commonly available days and the selected optimal vehicle type, output ordering result.Solve the multi SKU ordering in prior art Lack of whole car constraint overall mechanism, poor inventory rhythm synchronicity, and high degree of artificial dependence, leading to low inventory turnover rate and capital use efficiency.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics technology, and more specifically, to a method and system for joint ordering of multi-SKU vehicles based on sales synchronization. Background Technology

[0002] Currently, with the continuous development of internet, big data, and artificial intelligence technologies, traditional supply chain service companies are constantly iterating towards higher efficiency and lower costs. In the traditional model, goods often pass through multiple levels of distributors and wholesalers from manufacturers to end retailers. Each intermediate link merely performs a transportation function without creating actual value, resulting in overall inefficiency and high costs. Based on this, various supply chain service platforms have emerged, directly connecting upstream manufacturing enterprises with downstream end customers, eliminating value-creating transportation links, significantly improving operational efficiency and reducing distribution costs.

[0003] However, for such platform-based supply chains, the materials they provide are often the production materials of downstream customers. Stockouts will directly impact customers' production and operations, causing significant service losses. Excessive safety stock, on the other hand, increases capital tied up and holding costs, reduces inventory turnover, and consequently drags down overall operational efficiency. Therefore, accurately setting safety stock levels is crucial for supply chain service companies.

[0004] Meanwhile, upstream suppliers typically include several manufacturing companies with varying order preparation and delivery cycles. Effectively issuing replenishment notices to ensure timely delivery and avoid stockouts, while maximizing inventory turnover efficiency, presents a significant challenge for every supply chain service platform. Traditionally, purchasing staff monitor real-time inventory daily and determine replenishment quantities for each SKU (stock keeping unit) based on experience. This approach is not only resource-intensive but also inconsistent in replenishment quantities, making standardization difficult. Furthermore, to ensure uninterrupted supply, staff often tend to stockpile large quantities, leading to low inventory turnover and capital efficiency, increased risk of unsold inventory and losses, and substantial resource waste.

[0005] Furthermore, when supply chain service platforms place replenishment orders with upstream manufacturers, the quantities are typically large. In such cases, specific full-truckload transport conditions must be met, such as requiring a fully loaded light van (approximately 4.2 meters long), a medium-sized three-axle van (approximately 6.8 meters long), or a heavy-duty four-axle van (approximately 9.6 meters long) before shipment. This further complicates replenishment efforts. Upstream manufacturers supply thousands of SKUs, each with varying production and delivery cycles, and downstream customer demand is constantly changing. The requirement to meet full-truckload transport restrictions makes manual, precise order placement by purchasing staff virtually impossible. A common problem is that a small order quantity for a particular SKU leads to stockouts for subsequent orders from that supplier. Since piecemeal replenishment is not possible, full-truckload replenishment is required, resulting in inventory buildup or even warehouse overload, further reducing inventory turnover and capital efficiency.

[0006] In summary, existing technologies have at least the following shortcomings in multi-SKU joint ordering: Firstly, the traditional method calculates the replenishment quantity independently for multiple SKUs under the same production supplier, without taking into account the constraints of full truckload transportation, which leads to frequent small-amount replenishment or forced over-replenishment. Secondly, the independent replenishment of each SKU leads to uneven inventory sales rhythms, with best-selling SKUs quickly running out of stock while slow-moving SKUs accumulate in the long term, resulting in an unbalanced inventory structure. Third, the existing system relies on experienced merchandisers to make manual judgments, which is difficult to standardize and scale, and cannot adapt to dynamic fluctuations in demand; Fourth, in existing technologies, the replenishment quantity of each SKU is determined independently, and the "common available days" mechanism has not been introduced to coordinate the inventory and sales rhythm of each SKU. The overall inventory efficiency is low, and the cost of transportation has not been minimized within the framework of vehicle constraints on the basis of multiple SKUs from the same supplier.

[0007] Chinese patent CN119761973A discloses a warehouse inventory management method and system, primarily providing a method for calculating safety stock to address the issue of non-normal distributions of demand and supply when calculating safety stock based on the standard deviation of demand. Specifically, this technology obtains the historical daily sales volume and daily replenishment volume of goods in the warehouse inventory and calculates the volatility of demand and supply. Clearly, this technology assumes that supply, demand, and safety stock are independent. However, in most manufacturing industries, supply is less affected by capacity or other factors. Furthermore, for inventory management, when pursuing optimal inventory turnover and capital utilization efficiency, supply is generally determined by sales volume and safety stock. Therefore, the above technology is insufficient to provide effective guidance for ordering quantities. Summary of the Invention

[0008] To address these issues, this invention provides a multi-SKU vehicle joint ordering method and system based on sales synchronization, thereby solving the technical problems in the prior art where multi-SKU ordering lacks a vehicle-constrained overall planning mechanism, has poor inventory rhythm synchronization, and is highly dependent on manual labor, resulting in low inventory turnover and capital utilization efficiency.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A method for joint ordering of multi-SKU vehicles based on sales synchronization includes the following steps: Obtain safety stock and reorder points from specific suppliers; Based on safety stock and reorder point, real-time inventory monitoring is performed and orders are triggered. Introduce a common availability number of days and calculate the order quantity based on the common availability number of days; For the common available days, the optimal vehicle model is determined by selecting through three constraints and verifying the constraints. The ordering results are output based on the determined common available days and the selected optimal vehicle model.

[0010] Based on the above technical solution, the present invention is further described as follows: As a further aspect of the present invention, The method of obtaining safety stock and reorder points based on specific suppliers specifically includes: Acquiring suppliers K Next each SKU i Daily sales volume μ i Standard deviation of daily sales σ i Unit weight m i Unit volume v i and suppliers K lead time T k ; Calculate safety stock SS i With ordering point ROP i The specific formula is as follows: in, Z The corresponding service level is the normal distribution quantile; supplier K lead time T k This refers to the time required from order placement to goods arriving at the warehouse. safety stock SS i This refers to emergency stockpiles, used to cope with a surge in orders or delivery delays to prevent stockouts. The ordering point ROP i This refers to the inventory warning line, which is used as a basis for ordering replenishment. The above calculation results are stored in the safety stock calculation module to provide a benchmark for subsequent inventory monitoring.

[0011] As a further aspect of the present invention, The system, based on safety stock and reorder points, performs real-time inventory monitoring and triggers orders, specifically includes: The inventory monitoring module polls and checks each SKU i Current inventory Inv i Connect it with the corresponding order point ROP i Real-time comparison; If there exists any SKU that satisfies Inv i < ROP i Then determine the supplier to which any SKU belongs. K When a replenishment point has been reached, the system automatically triggers a joint ordering process. Suppliers K All SKUs are included in the calculation scope of this joint order.

[0012] As a further aspect of the present invention, The introduction of a common available days and the calculation of order quantity based on the common available days specifically includes: Introducing common available days D This refers to the planned joint order quantity and the number of days that the inventory of all SKUs participating in this joint order can be sold together after the goods arrive. Construct an inventory balance equation, that is, the current total inventory level. Subtract lead time T k Internal consumption plus order quantity Q i It should equal the consumption within the common available days D, as shown in the following formula: Therefore, we can solve for each SKU i Order quantity Q i The expression is as follows: Due to the actual calculated order quantity Q i Negative numbers are possible, representing negative order quantities. In this case, returns are required instead of new orders. Therefore, regarding order quantities... Q i Maximum value constraint; Among them, if a certain SKU i Calculation results Q i If the value is 0, then this SKU will not be included in the vehicle loading calculation this time.

[0013] As a further aspect of the present invention, The process of determining the optimal vehicle model based on the common available days, through a three-constraint selection and constraint verification, specifically includes: Iterate through the candidate vehicle set starting from the smallest vehicle type, and for each vehicle type... j All have the largest volume V j and maximum load W j Only considering Q i For SKUs with a value greater than 0, solve for the volume constraints respectively. and quality constraints ; Continue to establish the number of days available for solving both the volume constraint equation and the mass constraint equation. D The details are as follows: Volume constraint equation, i.e., the order quantity of each participating SKU. Q i Its unit volume The sum of their products equals the maximum volume. V j Solving for the common available days under the volume constraint yields the solution. : The quality constraint equation, i.e., the order quantity for each participating SKU. Q i Its unit weight The sum of their products equals the maximum load. W j Solve for the common available days under quality constraints. : As a further aspect of the present invention, The process of determining the optimal vehicle model based on three constraints and constraint verification for selecting the common available days specifically includes: Solve based on the volume constraint equation and the mass constraint equation respectively and and the number of days available in common. D Values: when The volume is filled first, when The quality is at full load first.

[0014] As a further aspect of the present invention, The process of determining the optimal vehicle model based on three constraints and constraint verification for selecting the common available days specifically includes: The number of days available in common D Perform three constraint checks: Physical capacity verification volume constraints, using the common available days of the above values. D After substituting the values ​​into the calculation, does the total volume of the order quantity for each SKU meet the requirement of not exceeding the limit? V j ,Right now ; Physical capacity verification quality constraints, based on the common available days of the above values. D After substituting the values ​​into the calculation, does the total order quantity and quality of each SKU meet the requirement of not exceeding the limit? W j ,Right now ; Time coverage check: the total number of days that the above values ​​are available. D Does the substitution satisfy the condition? This is to ensure that the inventory can support the next batch of goods after the goods arrive; If all three constraint checks are satisfied, then the vehicle model is determined to be the optimal vehicle model. If any condition is not met, upgrade to the next larger model and repeat the above process; If all constraints cannot be satisfied simultaneously after iterating through all vehicle models, select the largest vehicle model and output a prompt message.

[0015] As a further aspect of the present invention, The process of outputting ordering results based on the determined common available days and the selected optimal vehicle model specifically includes: Based on the final determined number of common available days D Given the selected optimal vehicle model, calculate the final order quantity for each SKU. Based on this, a purchase order is generated, and the order data is transmitted to the procurement execution stage in real time, thereby completing the entire process of joint ordering of multiple SKU vehicles.

[0016] A multi-SKU vehicle joint ordering system for implementing the aforementioned sales synchronization-based multi-SKU vehicle joint ordering method, the system comprising: The data acquisition module is used to obtain safety stock and reorder points based on specific suppliers; The safety stock calculation module is used to monitor inventory and trigger orders in real time based on safety stock and reorder point; The inventory monitoring module is used to introduce common available days and calculate the order quantity based on the common available days; The joint ordering calculation module is used to select the optimal vehicle model based on the number of common available days, and through three constraints and constraint verification. The order generation module is used to output order results based on the determined common available days and the selected optimal vehicle model.

[0017] The present invention has the following beneficial effects: 1. Guarantee service levels: The safety stock mechanism significantly reduces the probability of stockouts; 2. Full load and economy of the whole vehicle: Automatically select the minimum feasible vehicle type, and achieve effective full load through the constraint mechanism of transport volume and transport weight (volume / mass is taken as minimum), thereby reducing transportation costs; 3. Inventory rhythm synchronization: By establishing a common number of available days, the inventory sales days of all SKUs in the same train are made consistent, avoiding structural backlogs and stockouts; 4. Reduced manual intervention: Through fully automated calculations and standardized output, it can be scaled to several SKUs; 5. Dynamically adapt to demand fluctuations: Based on real-time inventory and historical sales data, it can automatically adjust replenishment quantity and replenishment time. 6. No pre-allocated storage capacity restrictions: The purchase quantity is dynamically determined by demand, effectively reducing the low utilization rate caused by static storage capacity allocation; 7. Prevent negative orders: Ensure no return orders are generated by calculating the maximum order quantity; 8. Adaptable to different cargo densities: It can handle both "bulky cargo" (volume first) and "heavy cargo" (weight first) scenarios, making it more applicable. 9. Time coverage guarantee: through Constraints ensure that the inventory after the goods arrive can support the next batch of goods, significantly reducing the risk of stockouts from a time perspective. Attached Figure Description

[0018] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The structures, proportions, sizes, etc., drawn in this specification are only used to complement the content disclosed in the specification, so that those skilled in the art can understand and read them. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0019] Figure 1 This is one of the overall process diagrams of the multi-SKU vehicle joint ordering method based on sales synchronization provided in the embodiments of the present invention.

[0020] Figure 2 This is the second schematic diagram of the overall process of the multi-SKU vehicle joint ordering method based on sales synchronization provided in the embodiments of the present invention.

[0021] Figure 3 The flowchart illustrates the calculation of safety stock and reorder point in the multi-SKU vehicle joint ordering method based on sales synchronization provided in this embodiment of the invention.

[0022] Figure 4 The flowchart illustrates the process of determining vehicle selection and common available days in the multi-SKU vehicle joint ordering method based on sales synchronization provided in this embodiment of the invention, which involves constraints on transport volume and transport weight.

[0023] Figure 5 This is a schematic diagram illustrating the overall architecture of a multi-SKU vehicle joint ordering system based on sales synchronization, as provided in an embodiment of the present invention. Detailed Implementation

[0024] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The terms "upper," "lower," "left," "right," and "middle" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0026] like Figures 1 to 4 As shown, this invention provides a multi-SKU vehicle joint ordering method based on sales synchronization. It effectively achieves real-time tracking and anomaly warning of each SKU's inventory status through the coordinated operation of a safety stock calculation module and an inventory monitoring module. Simultaneously, it utilizes a shared available days mechanism to coordinate the replenishment rhythm of multiple SKUs to eliminate structural inventory imbalances. Furthermore, by leveraging a traversal mechanism constraining vehicle models by transport capacity and volume, it minimizes freight costs while meeting vehicle transportation conditions, significantly improving inventory turnover and capital utilization efficiency, and enhancing overall functionality. Specifically, it includes the following steps: S1: Obtain safety stock and reorder points from specific suppliers; The specific process is as follows: Acquiring suppliers K Next each SKU i Daily sales volume μ i Standard deviation of daily sales σ i Unit weight m i Unit volume v i and suppliers K lead time T k (i.e., the time required from placing an order to the goods being received into the warehouse); Calculate safety stock SS i With ordering point ROP i The specific formula is as follows: in, Z To find the quantiles of the normal distribution corresponding to the service level, take the value at 99% service level. Z =2.33; safety stock SS i This refers to emergency stockpiles, used to cope with a surge in orders or delivery delays to prevent stockouts. The ordering point ROP i This refers to the inventory warning line, which is used as a basis for ordering replenishment. The above calculation results are stored in the safety stock calculation module to provide a judgment benchmark for subsequent inventory monitoring; S2: Based on safety stock and reorder point, monitor inventory in real time and trigger orders; The specific process is as follows: The inventory monitoring module polls and checks each SKU i Current inventory Inv i Connect it with the corresponding order point ROP i Real-time comparison; if any SKU satisfies Inv i < ROP i Then determine the supplier to which any SKU belongs. K When a replenishment point is reached, the system automatically triggers a joint ordering process, involving suppliers. K All SKUs are included in the calculation scope of this joint order; S3: Introduce common available days and calculate order quantity based on common available days; The specific process is as follows: Introducing common available days D This refers to the planned joint order quantity and the number of days that the inventory of all SKUs participating in this joint order can be sold together after the goods arrive. Construct an inventory balance equation, that is, the current total inventory level. Subtract lead time T k Internal consumption plus order quantity Q i It should equal the consumption within the common available days D, as shown in the following formula: Therefore, we can solve for each SKU i Order quantity Q i The expression is as follows: Due to the actual calculated order quantity Q i Negative numbers are possible, representing negative order quantities. In this case, returns are required instead of new orders. Therefore, regarding order quantities... Q i Maximum value constraint; Among them, if a certain SKU i Calculation results Q i =0, then this SKU will not be included in the vehicle loading calculation this time; S4: For the common available days, the optimal vehicle model is determined by selecting through three constraints and verifying the constraints. The specific process is as follows: Iterate through the candidate vehicle set starting from the smallest vehicle type, and for each vehicle type... j All have the largest volume V j and maximum load W j Only considering Q i For SKUs with a value greater than 0, solve for the volume constraints respectively. and quality constraints ; Continue to establish the number of days available for solving both the volume constraint equation and the mass constraint equation. D The details are as follows: Volume constraint equation, i.e., the order quantity of each participating SKU. Q i Its unit volume The sum of their products equals the maximum volume. V j Solving for the common available days under the volume constraint yields the solution. : The quality constraint equation, i.e., the order quantity for each participating SKU. Q i Its unit weight The sum of their products equals the maximum load. W j Solve for the common available days under quality constraints. : Solve based on the volume constraint equation and the mass constraint equation respectively and and the number of days available in common. D Values: when The volume is filled first, when At the same time, the quality is fully loaded first; The number of days available in common D Perform three constraint checks: Physical capacity verification volume constraints, using the common available days of the above values. D After substituting the values ​​into the calculation, does the total volume of the order quantity for each SKU meet the requirement of not exceeding the limit? V j ,Right now ; Physical capacity verification quality constraints, based on the common available days of the above values. D After substituting the values ​​into the calculation, does the total order quantity and quality of each SKU meet the requirement of not exceeding the limit? W j ,Right now ; Time coverage check: the total number of days that the above values ​​are available. D Does the substitution satisfy the condition? This is to ensure that the inventory can support the next batch of goods after the goods arrive; If all three constraint checks are satisfied, then the vehicle model is determined to be the optimal vehicle model. If any condition is not met, upgrade to the next larger model and repeat the above process; If it is still impossible to satisfy all constraints after traversing all vehicle models, select the largest vehicle model and output a prompt message; S5: Output the ordering results based on the determined common available days and the selected optimal vehicle model; The specific process is as follows: Based on the final determined number of common available daysD Given the selected optimal vehicle model, calculate the final order quantity for each SKU. Based on this, a purchase order is generated. The order content includes, but is not limited to, key information such as supplier number, SKU number, order quantity, total order volume and total order weight. The order data is then transmitted to the procurement execution stage in real time, thereby completing the entire process of joint ordering of multiple SKU vehicles.

[0027] like Figure 5 As shown, this embodiment of the invention also provides a multi-SKU vehicle joint ordering system based on sales synchronization, including a data acquisition module, a safety stock calculation module, an inventory monitoring module, a joint ordering calculation module, and an order generation module. Each module works in concert to achieve the complete functionality of the above method. The system comprises several modules: a data acquisition module to obtain historical sales data, unit volume, unit quality parameters, and supplier lead time for each SKU, and to update this data to the system database in real time; a safety stock calculation module to calculate the safety stock and reorder point for each SKU based on the historical data provided by the data acquisition module using the normal distribution quantile method, and to establish an SKU-level inventory warning line; an inventory monitoring module to poll the current inventory of each SKU in real time and dynamically compare it with the reorder point output by the safety stock calculation module, and to send a trigger signal to the joint ordering calculation module once any SKU reaches the reorder point; a joint ordering calculation module to establish a multi-SKU inventory balance equation based on the common available days D, and to solve for the common available days under volume and quality constraints through a vehicle traversal mechanism, taking the minimum value and verifying the time coverage constraint, thereby determining the optimal vehicle model and outputting the order quantity for each SKU; and an order generation module to integrate the order data output by the joint ordering calculation module to form a standardized purchase order and trigger the procurement execution process, thereby achieving a closed-loop automated management of the entire process of multi-SKU joint ordering.

[0028] As a preferred embodiment of the present invention, the joint order calculation module further includes an exception handling unit, which is used to select the largest available vehicle model and output a prompt message to the operator when none of the three constraints can be satisfied simultaneously after traversing all vehicle models, thereby ensuring the stable operation of the system under boundary conditions.

[0029] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for joint ordering of multiple SKU vehicles based on synchronized sales, characterized in that, Includes the following steps: Obtain safety stock and reorder points from specific suppliers; Based on safety stock and reorder point, real-time inventory monitoring is performed and orders are triggered. Introduce a common availability number of days and calculate the order quantity based on the common availability number of days; For the common available days, the optimal vehicle model is determined by selecting through three constraints and verifying the constraints. The ordering results are output based on the determined common available days and the selected optimal vehicle model.

2. The multi-SKU vehicle joint ordering method based on sales synchronization as described in claim 1, characterized in that, The method of obtaining safety stock and reorder points based on specific suppliers specifically includes: Acquiring suppliers K Next each SKU i Daily sales volume μ i Standard deviation of daily sales σ i Unit weight m i Unit volume v i and suppliers K lead time T k ; Calculate safety stock SS i With ordering point ROP i The specific formula is as follows: in, Z The corresponding service level is the normal distribution quantile; supplier K lead time T k This refers to the time required from order placement to goods arriving at the warehouse. safety stock SS i This refers to emergency stockpiles, used to cope with a surge in orders or delivery delays to prevent stockouts. The ordering point ROP i This refers to the inventory warning line, which is used as a basis for ordering replenishment. The above calculation results are stored in the safety stock calculation module to provide a benchmark for subsequent inventory monitoring.

3. The multi-SKU vehicle joint ordering method based on sales synchronization as described in claim 2, characterized in that, The system, based on safety stock and reorder points, performs real-time inventory monitoring and triggers orders, specifically includes: The inventory monitoring module polls and checks each SKU i Current inventory Inv i Connect it with the corresponding order point ROP i Real-time comparison; If there exists any SKU that satisfies Inv i < ROP i Then determine the supplier to which any SKU belongs. K When a replenishment point has been reached, the system automatically triggers a joint ordering process. Suppliers K All SKUs are included in the calculation scope of this joint order.

4. The multi-SKU vehicle joint ordering method based on sales synchronization according to claim 3, characterized in that, The introduction of a common available days and the calculation of order quantity based on the common available days specifically includes: Introducing common available days D This refers to the planned joint order quantity and the number of days that the inventory of all SKUs participating in this joint order can be sold together after the goods arrive. Construct an inventory balance equation, that is, the current total inventory level. Subtract lead time T k Internal consumption plus order quantity Q i It should equal the consumption within the common available days D, as shown in the following formula: Therefore, we can solve for each SKU i Order quantity Q i The expression is as follows: Due to the actual calculated order quantity Q i Negative numbers are possible, representing negative order quantities. In this case, returns are required instead of new orders. Therefore, regarding order quantities... Q i Maximum value constraint; Among them, if a certain SKU i Calculation results Q i If the value is 0, then this SKU will not be included in the vehicle loading calculation this time.

5. The multi-SKU vehicle joint ordering method based on sales synchronization according to claim 4, characterized in that, The process of determining the optimal vehicle model based on the common available days, through a three-constraint selection and constraint verification, specifically includes: Iterate through the candidate vehicle set starting from the smallest vehicle type, and for each vehicle type... j All have the largest volume V j and maximum load W j Only considering Q i For SKUs with a value greater than 0, solve for the volume constraints respectively. and quality constraints ; Continue to establish the number of days available for solving both the volume constraint equation and the mass constraint equation. D The details are as follows: Volume constraint equation, i.e., the order quantity of each participating SKU. Q i Its unit volume The sum of their products equals the maximum volume. V j Solving for the common available days under the volume constraint yields the solution. : The quality constraint equation, i.e., the order quantity for each participating SKU. Q i Its unit weight The sum of their products equals the maximum load. W j Solve for the common available days under quality constraints. : 。 6. The multi-SKU vehicle joint ordering method based on sales synchronization as described in claim 5, characterized in that, The process of determining the optimal vehicle model based on three constraints and constraint verification for selecting the common available days specifically includes: Solve based on the volume constraint equation and the mass constraint equation respectively and and the number of days available in common. D Values: when The volume is filled first, when The quality is at full load first.

7. The multi-SKU vehicle joint ordering method based on sales synchronization as described in claim 6, characterized in that, The process of determining the optimal vehicle model based on three constraints and constraint verification for selecting the common available days specifically includes: The number of days available in common D Perform three constraint checks: Physical capacity verification volume constraints, using the common available days of the above values. D After substituting the values ​​into the calculation, does the total volume of the order quantity for each SKU meet the requirement of not exceeding the limit? V j ,Right now ; Physical capacity verification quality constraints, based on the common available days of the above values. D After substituting the values ​​into the calculation, does the total order quantity and weight of each SKU meet the requirement of not exceeding the limit? W j ,Right now ; Time coverage check: the total number of days that the above values ​​are available. D Does the substitution satisfy the condition? This is to ensure that the inventory can support the next batch of goods after the goods arrive; If all three constraint checks are satisfied, then the vehicle model is determined to be the optimal vehicle model. If any condition is not met, upgrade to the next larger model and repeat the above process; If all constraints cannot be satisfied simultaneously after iterating through all vehicle models, select the largest vehicle model and output a prompt message.

8. The multi-SKU vehicle joint ordering method based on sales synchronization according to claim 8, characterized in that, The process of outputting ordering results based on the determined common available days and the selected optimal vehicle model specifically includes: Based on the final determined number of common available days D Given the selected optimal vehicle model, calculate the final order quantity for each SKU. Based on this, a purchase order is generated, and the order data is transmitted to the procurement execution stage in real time, thereby completing the entire process of joint ordering of multiple SKU vehicles.

9. A multi-SKU vehicle joint ordering system that implements the multi-SKU vehicle joint ordering method based on sales synchronization as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition module is used to obtain safety stock and reorder points based on specific suppliers; The safety stock calculation module is used to monitor inventory and trigger orders in real time based on safety stock and reorder point; The inventory monitoring module is used to introduce common available days and calculate the order quantity based on the common available days; The joint ordering calculation module is used to select the optimal vehicle model based on the number of common available days, and through three constraints and constraint verification. The order generation module is used to output order results based on the determined common available days and the selected optimal vehicle model.

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