A method and system for generating replenishment suggestions for logistics providers

Abstract

The present application discloses a method and system for generating replenishment suggestions for logistics providers. The method includes: calculating the daily predicted sales volume, and determining one or more available logistics provider options corresponding to each day based on the purchase time, shipping time, and arrival time; determining the daily out-of-stock situation based on the single-day inventory, in-transit replenishment quantity, and the daily predicted sales volume; determining a target logistics provider from the available logistics provider options based on the daily out-of-stock situation and a preset priority parameter, where the preset priority parameter characterizes whether using the available logistics provider to perform the replenishment operation can meet the daily out-of-stock situation and the implementation cost corresponding to using the available logistics provider to perform the replenishment operation; generating a replenishment suggestion corresponding to each day based on the target logistics provider. The present application can determine a replenishment method that reduces costs and improves operational efficiency, and can significantly reduce the system's computational workload and improve the system's stability when generating replenishment suggestions.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a method for processing batch product cost data, a method for generating replenishment suggestions for logistics providers, and corresponding systems. Background Art

[0002] An e-commerce ERP system can be deeply connected to an e-commerce platform to help domestic small and medium-sized e-commerce users uniformly manage their overseas stores, enabling an operation staff member to manage hundreds of e-commerce stores simultaneously, significantly improving the efficiency of store operations. Each functional module of the existing commercialized e-commerce ERP systems is still in the stage of gradual function update and improvement. The functional algorithms and rules formulated by each software company when developing its own e-commerce ERP system are basically different. Each functional module will continuously develop new versions as user requirements change to be compatible with more usage scenarios.

[0003] In daily e-commerce activities, inventory products are usually transferred between warehouses according to business operation requirements, including transferring inventory products from a local warehouse to another local warehouse, or shipping inventory products from a domestic warehouse to an overseas warehouse, and then generating corresponding product cost data in each warehouse database. Existing e-commerce ERP systems usually calculate the shipping cost of inventory products in the way of weighted average of total inventory, which cannot accurately calculate the actual cost of each part of the product shipped out of the warehouse, thus affecting the accuracy of inventory cost data and making it difficult to optimize the operation mode based on the inventory cost data. Moreover, when modifying the cost data of some inventory products, a large amount of time is required to determine the cost data associated with this part of the inventory products in each warehouse database for modification, resulting in low data processing efficiency.

[0004] In addition, for the warehouse replenishment link, the e-commerce ERP system has a replenishment suggestion function, which is mainly used to suggest whether the user needs to replenish the warehouse within a certain period in the future (such as half a year). Currently, the replenishment suggestion function usually compares optional replenishment methods with multiple restrictive conditions in sequence, and generates replenishment suggestions provided to the user based on the replenishment methods that can meet all restrictive conditions. However, the generation logic of the above replenishment suggestions has the following problems: 1. After finding a replenishment method that meets the conditions, the search for other replenishment methods will be terminated, thus missing the opportunity to provide the user with a replenishment method with lower cost and higher efficiency; 2. The system needs to undertake extremely heavy computing tasks, which easily leads to situations such as the system failing to find a suitable replenishment method, the system falling into an infinite computing loop, and the system crashing.

[0005] Other technical problems related to this application will be further elaborated later. The above content is only used to assist in understanding the technical solution of this application, and does not mean that all the above content is prior art. Summary of the Invention

[0006] The main objective of the present invention is to provide a method and system for processing batch product cost data, which can calculate the inventory cost for each batch of products respectively, and can quickly and accurately determine the cost data with associated relationships when modifying the cost data of batch products, thus greatly improving the data processing efficiency. In addition, this application also provides a method and system for generating replenishment suggestions for logistics providers, which can determine replenishment methods to reduce costs and improve operational efficiency, and can greatly reduce the system operation volume when generating replenishment suggestions, thereby improving the system stability.

[0007] To achieve the above objective, this application proposes a method and system for processing batch product cost data, which is used in the warehouse module of an e-commerce ERP system. The method includes:

[0008] Step S1: Set a source number for the batch of products when initially performing the warehousing operation, and generate an initial product cost associated with the source number.

[0009] Step S2: Determine a target product among the batch of products. In response to the transfer operation performed on the target product, generate a batch number corresponding to the transfer operation according to the source number, and generate a batch product cost according to the initial product cost and the additional costs corresponding to the transfer operation. The transfer operation includes the outbound operation and the inbound operation performed when transferring the target product between different regions.

[0010] Step S3: Generate first cost data based on the batch number, the batch product cost, and the source number. The first cost data is the cost data of multiple inventory products stored in the warehouse module, and the first cost data includes the batch product cost of the target product.

[0011] Step S4: In response to a cost modification instruction, modify the batch product cost of the target product.

[0012] Step S5: Based on the source number, determine first product data associated with the target product in the first cost data, and determine second product data in the first product data based on preset factors. The preset parameters include batch number time and batch number generation rules.

[0013] Step S6: Update the second product data based on the modified batch product cost to obtain second cost data.

[0014] Other features and technical effects of this application are described and explained in the later part of the specification. The technical problem-solving ideas and related product design solutions of this application are:

[0015] In daily e-commerce activities, inventory products are usually transferred between warehouses according to the needs of business operations, and corresponding product cost data is generated in the databases of each warehouse. Existing e-commerce ERP systems usually calculate the shipping cost of inventory products in the way of weighted average of total inventory: that is, dividing the total cost of inventory products by the quantity of inventory products to obtain the unit cost of inventory products.

[0016] However, the existing method for calculating the shipping cost of inventory products has the following problems: 1. The unit product costs of various parts of products stored in the same warehouse may be different due to various reasons. It is difficult for the existing cost calculation method of inventory products to reflect the cost differences of various parts of products, making it difficult for users to optimize the operation mode according to the cost data of inventory products (for example, the unit product costs of various parts of products in different shipping batches and different shipping periods are different when purchasing, or there are differences in additional costs for various parts of products using different logistics methods and passing through different logistics routes, resulting in different unit product costs); 2. When users modify the cost data of some products, since these products may be transferred, processed or split multiple times according to business needs, it is difficult for users to determine other cost data related to these products for modification, resulting in low data processing efficiency.

[0017] The applicant found that for the batch of products for which the procurement operation is executed, when the warehousing operation is initially executed, a source number is set for this batch of products, and the initial product cost of this batch of products is generated based on the procurement warehousing operation; when this batch of products subsequently executes transfer operations, processing operations or splitting operations, batch numbers associated with the source number are generated for this batch of products, and the batch product cost is generated according to the initial product cost and the additional costs generated during the transfer operation, processing operation or splitting operation, so that the corresponding product costs can be generated for each batch of products respectively, improving the accuracy of the cost data of inventory products.

[0018] Furthermore, the applicant found that when it is necessary to modify the cost data of batch products, the first data screening is performed on the inventory cost data according to the source number of this batch of products to determine other batch product data (the first product data) that has an associated relationship with this batch of products, and then the second data screening is performed on the first product data according to the preset factors (batch number time and batch number generation rule) to determine other batch product cost data (the second product data) that needs to be modified accordingly after the cost data of the current batch of products changes; among them, the second product data is the cost data of the downstream batch products generated by performing transfer operations, processing operations or splitting operations on the current batch of products; finally, the e-commerce ERP system modifies the second product data according to the modified cost data of the batch products, so as to achieve a fast and accurate modification operation for the inventory cost data and obtain the second cost data.

[0019] Thus, the batch product cost data processing method provided by the present application can generate corresponding cost data for each batch of products by setting a source number for each batch of products and generating corresponding batch numbers and batch product costs each time a transfer operation is performed on the batch of products, improving the accuracy of product cost data and quickly and accurately determining the cost data of related batch products when modifying the product cost data, thereby greatly improving the data processing efficiency.

[0020] Furthermore, the present application also provides a replenishment suggestion generation method and system for a logistics provider, which is used for the replenishment suggestion module of an e-commerce ERP system or an e-commerce management system. The method includes:

[0021] Step M1: Calculate the daily predicted sales volume, and determine one or more available logistics provider options corresponding to each day based on the procurement time, delivery time, and arrival time;

[0022] Step M2: Determine the daily out-of-stock situation based on the single-day inventory, in-transit replenishment quantity, and the daily predicted sales volume;

[0023] Step M3: Determine the target logistics provider from the available logistics provider options based on the daily out-of-stock situation and a preset priority parameter, where the preset priority parameter represents whether using the available logistics provider to perform the replenishment operation can meet the daily out-of-stock situation and the implementation cost corresponding to using the available logistics provider to perform the replenishment operation;

[0024] Step M4: Generate a replenishment suggestion corresponding to each day based on the target logistics provider.

[0025] Other features and technical effects of the present application are described in the following part of the specification. The technical problem-solving ideas and related product design solutions of the present application are:

[0026] In the warehouse replenishment link, the e-commerce ERP system has a replenishment suggestion function, which is mainly used to determine whether the warehouse needs to be replenished in a future period (such as half a year) for the user. Currently, the mainstream implementation idea of the replenishment suggestion function is achieved by forcibly coding each variable (directly translating the business), that is, comparing the optional replenishment methods with multiple constraints in turn, finding the replenishment method that can meet all the constraints as the final result, and providing the final result to the user.

[0027] It should be noted that the function of generating replenishment suggestions essentially belongs to the scope of dynamic programming algorithms. The reason is that the replenishment suggestion function needs to provide suggestions on whether to replenish the warehouse every day in a future period (such as the next six months). At the same time, whether to suggest replenishment on the current day is affected by whether the goods were replenished the previous day (if the goods were replenished the previous day, there is no need to replenish on the current day). That is to say, the replenishment suggestions for each day are not isolated, but are affected by previous replenishment operations. Therefore, generating replenishment suggestions is a dynamic programming problem.

[0028] However, different from classical dynamic programming algorithms that only involve a few variables, the variables involved in the replenishment suggestion function are very numerous, far exceeding the number of variables that dynamic programming algorithms can handle. Moreover, these variables may interact with each other. Therefore, no dynamic programming algorithm can be directly used for the replenishment suggestion function.

[0029] For example, the variables involved in replenishment suggestions mainly include: the current inventory status in the warehouse, the in-transit inventory situation (inventory that will definitely arrive at the warehouse in the future), the daily sales volume changes in the future (inventory consumption speed), whether it is mandatory to specify a certain day for replenishment (such as having an offline agreement with a logistics provider), whether it is mandatory to specify a certain day for no replenishment (such as holidays), which logistics provider to choose for replenishment (different delivery speeds and prices), the procurement duration (how long it takes to receive the goods), which warehouse to ship from (which local warehouse or which overseas warehouse), how much goods to replenish at one time (affected by logistics transportation costs, no overstocking, no out-of-stock, etc.), the minimum shipping quantity (too little and the logistics provider will not ship), the minimum purchase quantity (too little and the supplier will not accept the order), etc.

[0030] Obviously, no dynamic programming algorithm can handle the numerous variables mentioned above. Therefore, there is an unwritten consensus in the e-commerce industry regarding the implementation logic of replenishment suggestions, that is, dynamic programming algorithms cannot be applied to the replenishment suggestion function. This is actually a technical bias in the e-commerce industry in the field of replenishment suggestions. Given this technical bias as a premise, the mainstream implementation idea of the current replenishment suggestion function is to compare the optional replenishment methods with the restrictive conditions corresponding to the above variables one by one. If the current replenishment method cannot meet a certain restrictive condition, the calculation process is restarted, and the next replenishment method is switched to continue the comparison operation with the above restrictive conditions respectively.

[0031] Therefore, the current processing logic of the replenishment suggestion function has the following defects:

[0032] 1. The calculated replenishment suggestions may not be the most reasonable: Currently, the replenishment suggestion function essentially calculates a local optimal solution. Unless the calculation result of the last step is significantly in conflict with a certain constraint condition, the last local optimal solution is considered the global optimal solution. However, this solution can only be said to satisfy all constraint conditions, but it may not necessarily be the global optimal solution (for example, there are other replenishment suggestion options that can also satisfy all variables and are more cost-effective).

[0033] 2. The system may not be able to calculate the replenishment suggestions as the final result: There are numerous above-mentioned constraint conditions. If there are a large number of cross-influences among mutually conflicting constraint conditions, it will cause the system to frequently overturn previous conclusions and even be unable to deduce the final result (such as may cause the system to fall into an infinite loop or the system to crash, etc.).

[0034] The applicant found that calculating the most reasonable replenishment suggestions is essentially a problem of calculating the global optimal solution, that is, this replenishment suggestion is first of all a solution (meeting the basic demands of users: no out-of-stock, no overstock), and at the same time this replenishment suggestion is also the global optimal solution (such as the lowest implementation cost).

[0035] Calculating the global optimal solution is the forte of the dynamic programming algorithm. However, as mentioned above, the traditional dynamic programming algorithm cannot meet the problem of too many variables involved in replenishment suggestions, and it is necessary to carry out targeted upgrades and business adaptation processing on the existing dynamic programming algorithm according to the replenishment scenario.

[0036] The applicant proposed to use the knapsack algorithm (a type of dynamic programming algorithm) in the algorithm framework to solve the above-mentioned replenishment suggestion problem.

[0037] The knapsack algorithm has three variables: the capacity of the knapsack, the volume of the item, and the value of the item. The core of the knapsack algorithm is to put items of different volumes into the knapsack under the premise of a fixed knapsack capacity to maximize the value of the items in the knapsack. To meet the number of variables of the knapsack algorithm, the technical solution of this application abstracts the problem for the replenishment scenario to adapt to the knapsack algorithm: maps the knapsack capacity to the daily out-of-stock quantity, maps the item to multiple replenishment logistics available for selection, maps the item volume to the arrival quantity of the logistics on the same day, and maps the item value to the logistics cost on the same day. Referring to the knapsack algorithm, the core problem that the replenishment suggestion function needs to solve becomes: if the warehouse is out of stock on the same day (there is still space in the knapsack), what logistics should be selected for replenishment (which item to choose); the selected logistics must meet: arrive before the out-of-stock (no out-of-stock), the replenishment quantity can meet the out-of-stock situation (no out-of-stock), and the cost of the replenishment logistics is the lowest (the value of the selected item is the largest).

[0038] On this basis, other variables involved in the replenishment suggestion are mapped to the two variables of the out-of-stock quantity on the day and logistics. The specific solution is as follows: The current inventory status in the warehouse, the in-transit inventory situation, and the daily sales volume changes in the future are calculated and summarized into the out-of-stock quantity for each day; whether it is mandatory to specify which day to replenish is mapped to which day the logistics must deliver, whether it is mandatory to specify which day not to replenish is mapped to which day the logistics does not deliver, the procurement duration is mapped to the logistics transportation duration, which warehouse to ship from is mapped to different logistics types, how much to replenish at one time is mapped to the logistics transportation capacity, and the minimum shipment quantity and the minimum procurement quantity are mapped to the logistics starting threshold. The above mapping solution solves the problem of too many variables involved in the replenishment suggestion by uniformly mapping multiple variables to the logistics level; that is, these variables basically become the various dimensional attributes of logistics, and these dimensional attributes affect the choice of logistics. The technical problem faced by the replenishment suggestion is simplified to: If there is an out-of-stock on a certain day, which logistics should be selected for replenishment?

[0039] However, the above processing method still has some mismatches with the traditional knapsack algorithm. For example: The constraint attributes of items are not only volume, but also many attributes such as logistics delivery time, transportation duration, logistics type, transportation capacity, starting threshold, etc.; The knapsack algorithm may need to select multiple items to fill the knapsack, but the replenishment suggestion can only select one logistics per day (considering the cost of the starting price of logistics); The knapsack algorithm can select multiple items and thus has a filling table logic, but the replenishment suggestion has no filling table logic (because only one logistics can be selected); The knapsack algorithm does not need to consider how to coordinate multiple knapsacks, but the replenishment suggestion needs to consider in the following scenario: If the same logistics is selected every day (one logistics selection is an application of the knapsack algorithm), in reality, the same logistics will not be sent for replenishment every day, but multiple batches of logistics will be sent at the same time to save logistics costs. The above problems all indicate that in order to solve the problems faced by the replenishment suggestion, it is necessary to upgrade the traditional knapsack algorithm to form a new knapsack algorithm designed specifically for the replenishment suggestion function.

[0040] The upgrade solution is: Upgrade the filling table problem to the problem of selecting the optimal logistics, and the basis for selecting the optimal logistics is the sorting rule of logistics attributes. In the replenishment suggestion scenario, the primary requirement is not to be out of stock, which corresponds to preferentially selecting the logistics whose arrival time is not later than the out-of-stock time (to avoid affecting normal sales); the secondary requirement is not to overstock goods, which corresponds to the later the logistics is sent as long as it can arrive (to avoid increasing warehousing costs); On the premise of meeting the above two points, the priority order of other requirements corresponding to attributes is: replenish through overseas warehouses first and then through local warehouses, which corresponds to the logistics type priority attribute, and the lower the logistics cost, the better, which corresponds to the logistics cost attribute, and so on. After sorting according to the above logistics attributes, the logistics ranked first is the optimal solution for the replenishment logistics on that day.

[0041] It can be understood that the above-mentioned logistics attributes for sorting are expressed as "preset priority parameters" in the claims of this application. That is, the new knapsack algorithm specifically designed for the replenishment recommendation function proposed in this application is: sort the available logistics provider options for each day according to the various logistics attributes characterized by the preset priority parameters, select the logistics provider ranked first as the target logistics provider, and generate a replenishment recommendation corresponding to the target logistics provider for the user.

[0042] In this way, the method for generating replenishment recommendations for logistics providers provided by this application can upgrade the traditional knapsack algorithm according to the e-commerce replenishment scenario, form a new knapsack algorithm specifically designed for the replenishment recommendation function, map multiple variables involved in the replenishment recommendation, and obtain the optimal replenishment recommendation through the new knapsack algorithm. The replenishment method corresponding to the optimal replenishment recommendation has the lowest cost and the highest benefit, and greatly reduces the system operation volume when generating the replenishment recommendation, improves the system stability, and greatly reduces the number of codes required to implement the replenishment recommendation function, thereby reducing the labor cost.

[0043] This application also provides a system, which is an e-commerce ERP system or an e-commerce management system, and the system can execute the operation instructions of each method step of this application.

[0044] This application also provides a server, which includes a memory and a processor. The system in this application is stored in the memory, and the processor can run the operation instructions of each method step of this application.

[0045] This application also provides a computer device, which includes a memory and a processor. The system in this application is stored in the memory, and the processor can run the operation instructions of each method step of this application.

[0046] Reference Figure 1 , the e-commerce ERP system of this application includes one or more of the function modules such as a commodity module, a sales module, a procurement module, a logistics module, a warehouse module, a finance module, an advertising module, a customer service module, a tool module, a promotion management module, a permission management module, and a data analysis module. The function modules can be integrated with each other, can exist independently, or one function module can be a sub-module of another function module. The users of the ERP system of this application can also be called store managers, sellers, operators, operation personnel, etc. Except for special declarations, their identities are not strictly limited. Description of the Drawings

[0047] The drawings are used to provide a further understanding of this application and do not constitute a limitation to this application; the content shown in the drawings can be the actual data of the embodiments and belongs to the protection scope of this application.

[0048] Figure 1Schematic diagram of the functional modules of the e-commerce ERP system in an embodiment of the present application.

[0049] Figure 2 Schematic diagram of the principle of the e-commerce product cost data processing process in an embodiment of the present application.

[0050] Figure 3 Schematic flowchart of the method for processing batch product cost data in an embodiment of the present application.

[0051] Figure 4 Schematic diagram of the principle of the replenishment suggestion generation process in an embodiment of the present application.

[0052] Figure 5 Schematic flowchart of the method for generating replenishment suggestions for logistics providers in an embodiment of the present application. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further elaborates on the embodiments of the present application in detail through specific implementation manners in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] Figure 2 Shows a schematic diagram of the principle of the e-commerce product cost data processing process in an embodiment of the present application. As Figure 2 shown, after the user purchases products from the supplier and warehouses them in Warehouse 1, the e-commerce ERP system sets the source number for the batch of products for which the warehousing operation is initially performed, and generates the initial product cost; then, the e-commerce ERP system selects some of the products in the batch of products in Warehouse 1 as target products based on business operation requirements, and generates the batch product cost 1 corresponding to the target products; then the e-commerce ERP system transfers the target products from Warehouse 1 to Warehouse 2, obtaining the batch of products 2 in Warehouse 2, and generates the corresponding batch product cost 2 and batch number; then, the e-commerce ERP system transfers the batch of products 2 in Warehouse 2 to Warehouse 3 again based on business operation requirements, obtaining the batch of products 3 in Warehouse 3, and generates the corresponding batch product cost 3 and batch number. The above batch product cost 1, batch product cost 2, and batch product cost 3 are all components of the inventory product cost data (the first cost data). When the user triggers a cost modification instruction and modifies the batch cost data of a certain batch of products, the e-commerce ERP system determines the associated first product data based on the source number of the batch of products, and then determines the second product data in the first product data according to the batch number time or the batch number generation rule. The second product data is the batch cost data of the downstream batch of products of the above batch of products. The e-commerce ERP system modifies the second product data to obtain the new inventory product cost data (the second cost data).

[0055] Figure 3 Shows the batch product cost data processing method in an embodiment of the present application. This method mainly includes the following steps S1 to S6.

[0056] Step S1: Set a source number for the batch product that initially performs the warehousing operation, and generate an initial product cost associated with the source number.

[0057] Step S2: Determine a target product among the batch products. In response to the transfer operation performed on the target product, generate a batch number corresponding to the transfer operation according to the source number, and generate a batch product cost according to the initial product cost and the additional cost corresponding to the transfer operation. The transfer operation includes the outbound operation and the inbound operation performed when transferring the target product between different regions.

[0058] Step S3: Generate first cost data based on the batch number, the batch product cost, and the source number. The first cost data is the cost data of multiple inventory products stored in the warehouse module, and the first cost data includes the batch product cost of the target product.

[0059] Step S4: In response to a cost modification instruction, modify the batch product cost of the target product.

[0060] Step S5: Based on the source number, determine the first product data associated with the target product in the first cost data, and determine the second product data in the first product data based on preset factors. The preset parameters include the batch number time and the batch number generation rule.

[0061] Step S6: Update the second product data based on the modified batch product cost to obtain second cost data.

[0062] In this way, the batch product cost data processing method provided by the present application can generate corresponding cost data for each batch product by setting a source number for each batch product and generating the corresponding batch number and batch product cost each time a transfer operation is performed on the batch product, improving the accuracy of product cost data, and quickly determining the cost data of batch products with an associated relationship when modifying the product cost data, thereby greatly improving the data processing efficiency.

[0063] The following will separately make a detailed description of each method step in the batch product cost data processing method.

[0064] Step S1: Set a source number for the batch product that initially performs the warehousing operation, and generate an initial product cost associated with the source number.

[0065] Specifically, for the batch of products for which the user performs a procurement operation, when the batch of products first undergoes a warehousing operation, the e-commerce ERP system sets a source number for the batch of products and generates relevant data such as the supplier, initial product cost, type, quantity, and associated business documents related to the batch of products.

[0066] Step S2: Determine the target product among the batch of products. In response to the transfer operation performed on the target product, generate a batch number corresponding to the transfer operation according to the source number, and generate the batch product cost according to the initial product cost and the additional cost corresponding to the transfer operation. The transfer operation includes the outbound operation and the inbound operation performed when transferring the target product between different regions.

[0067] Specifically, according to the instruction triggered by the user during business operation, the e-commerce ERP system selects all or part of the products in the above batch of products as the target products, and sequentially performs an outbound operation and an inbound operation on the target products, thereby transferring the target products to other warehouses.

[0068] Among them, when performing an outbound operation on the target product in the current warehouse, generate an outbound batch number corresponding to the outbound operation (that is, the downstream batch product corresponding to the batch of products for which the procurement warehousing operation is performed, and the source batch number with the source number being this outbound batch number). This outbound batch number is generated according to the source number, and generate relevant data such as the supplier, product batch cost, type, quantity, and associated business documents, and record the source number.

[0069] When performing an inbound operation on the target product in another warehouse, generate an inbound batch number corresponding to the inbound operation (that is, the downstream batch product corresponding to the target product for which the outbound operation is performed, and the outbound batch number is the source batch number of the inbound batch number). This inbound batch number is generated according to the outbound batch number, and generate relevant data such as the supplier, product batch cost, type, quantity, and associated business documents, and record the source number.

[0070] Among them, the product batch cost corresponding to the inbound batch number is determined by the initial product cost and the additional cost generated during the transfer of the target product.

[0071] Step S3: Generate first cost data corresponding to the target product based on the batch number, the batch product cost, and the source number. The first cost data is multiple inventory product cost data stored in the warehouse module, and the first cost data includes the batch product cost of the target product.

[0072] Specifically, when the e-commerce ERP system performs transfer operations, processing operations, or splitting operations on all or part of the products in a batch of products, corresponding batch numbers are generated for each batch of products obtained, and the batch product costs are generated based on the initial product costs and the additional costs incurred during the transfer operation, processing operation, or splitting operation. The batch numbers, source numbers, and batch product costs of the above-mentioned batches of products are stored in the inventory cost data (i.e., the first cost data).

[0073] Step S4: In response to a cost modification instruction, modify the batch product cost of the target product.

[0074] Specifically, in response to a cost modification instruction triggered by the user, the e-commerce ERP system modifies the batch product cost of a certain batch of products in the product cost data.

[0075] Step S5: Based on the source number, determine the first product data associated with the target product in the cost data, and determine the second product data in the first product data based on preset factors. The preset parameters include the batch number time and the batch number generation rule.

[0076] Specifically, after the e-commerce ERP system modifies the batch product cost of the above-mentioned batch of products, it performs the first data screening in the inventory cost data according to the source number of the batch of products to determine other batch product data (the first product data) associated with the batch of products. Then, according to the preset factors (the batch number time and the batch number generation rule), it performs the second data screening on the first product data to determine the other batch product cost data (the second product data) that needs to be correspondingly modified after the cost data of the current batch of products changes; among them, the second product data is the cost data of the downstream batch of products generated by performing transfer operations, processing operations, or splitting operations on the current batch of products.

[0077] Step S6: Modify the second product data based on the modified batch product cost to generate the second cost data corresponding to the target product.

[0078] Specifically, after the e-commerce ERP system determines the second product data related to the above-mentioned batch of products, it modifies the second product data according to the modified product batch cost data of the batch of products, so as to achieve a fast and accurate modification operation for the inventory cost data and obtain the new inventory cost data (i.e., the second cost data).

[0079] Further, in an embodiment, when the preset factor is the batch number time, determining the second product data in the first product data in step S5 includes the following steps S5.1 and S5.2.

[0080] Step S5.1: Determine that the generation time of the batch number of the target product is the preset time standard;

[0081] Step S5.2: In the first product data, determine the target batch products whose batch number generation time is later than the preset time standard, and determine the batch product cost of the target batch products as the second product data.

[0082] Specifically, for example, after determining the target product for modifying the batch product cost according to the user instruction, since there is an association relationship between the batch numbers corresponding to the upstream batch products and the downstream batch products each time the upstream batch products perform a transfer operation to generate the downstream batch products, and the batch number of the upstream batch product is the source batch number associated with the batch number of the downstream batch product, therefore, according to the generation time of the batch number of the target product and the source batch numbers of each batch product, the downstream batch products having an association relationship with the target product can be determined as the target batch products, and the batch product cost of the target batch products is determined as the second product data for modification.

[0083] Further, in an embodiment, the batch number generation rule includes that the batch number of the downstream batch product is greater than that of the upstream batch product. When the preset factor is the batch number generation rule, step S5 of determining the second product data in the first product data based on the preset factor includes the following step S5.3.

[0084] Step S5.3: In the first product data, compare the batch numbers of each batch product with the batch number of the target product, determine the downstream batch products of the target product, and determine the batch product cost of the downstream batch products of the target product as the second product data.

[0085] Specifically, for example, for batch A products, the batch number of the upstream batch product is 001, and the batch number of the downstream batch product is 002; for batch B products, the batch number of the upstream batch product is 101, and the batch number of the downstream batch product is 102. Then, according to the batch numbers, the association relationship and the upstream and downstream batch relationships of each batch product can be judged, so as to determine the downstream batch products related to the target product as the second product data according to the source number and the batch number, and make modifications.

[0086] Further, in an embodiment, the method further includes the following steps S2.1 to S2.3.

[0087] Step S2.1: In response to the product merging instruction, determine the batch product cost of the third batch product based on the batch product costs corresponding to the first batch product and the second batch product respectively, where the third batch product is obtained by processing and assembling the first batch product and the second batch product;

[0088] Step S2.2: Generate the batch number of the third batch of products, and set an association relationship between the batch number of the third batch of products and the batch numbers corresponding to the first batch of products and the second batch of products respectively;

[0089] Step S2.3: Set the source number corresponding to the third batch of products based on the source numbers corresponding to the first batch of products and the second batch of products respectively.

[0090] Specifically, for the scenario where the third batch of products is assembled from the first batch of products and the second batch of products (such as the first batch of products being mobile phones, the second batch of products being mobile phone cases, and the third batch of products being mobile phone sets), the batch product cost of the third batch of products is obtained by adding the batch product costs corresponding to the first batch of products and the second batch of products respectively, and the batch numbers of the first batch of products and the second batch of products are both the source batch numbers of the third batch of products; and, set the source numbers corresponding to the first batch of products and the second batch of products respectively as the source numbers of the third batch of products.

[0091] Further, in an embodiment, the method further includes the following steps S2.4 to S2.6.

[0092] Step S2.4: In response to a product splitting instruction, determine the batch product costs corresponding to the fifth batch of products and the sixth batch of products respectively based on a preset ratio, where the fifth batch of products and the sixth batch of products are obtained by splitting the fourth batch of products;

[0093] Step S2.5: Generate the batch numbers corresponding to the fifth batch of products and the sixth batch of products respectively, and set an association relationship between the batch numbers corresponding to the fifth batch of products and the sixth batch of products respectively and the batch number of the fourth batch of products; and,

[0094] Step S2.6: Set the source numbers corresponding to the fifth batch of products and the sixth batch of products respectively based on the source number corresponding to the fourth batch of products.

[0095] Specifically, for the scenario where the fourth batch of products is split into the fifth batch of products and the sixth batch of products (e.g., the fourth batch of products is a mobile phone set, the fifth batch of products is mobile phones, and the sixth batch of products is mobile phone cases; since the sales effect of mobile phone sets is not good, the mobile phone sets are split into mobile phones and mobile phone cases and stored and sold separately), the batch product costs corresponding to the fifth batch of products and the sixth batch of products are obtained by splitting the batch product costs of the fourth batch of products according to a preset ratio, and the source batch numbers of the fifth batch of products and the sixth batch of products are both the batch numbers of the fourth batch of products; and, the source number of the fourth batch of products is set to the source number corresponding to the fifth batch of products and the sixth batch of products.

[0096] It is understood that the above preset ratio can be set by the user according to actual needs. For example, if product A and product B are processed and combined at a ratio of 1:1 to obtain product C, the cost of product C is obtained by adding the costs of 1 product A and 1 product B; and when product C is split into product A and product B again, the costs of the split products A and B can be allocated according to the preset ratio of 1:1.5; that is, the cost data of the split products A and B are not necessarily the same as those of the products A and B before the processing and combination.

[0097] For example, when product A and product B are processed and combined to obtain product C, processing fees will be incurred. Therefore, the cost of product C is obtained by adding the respective costs of product A and product B, and the processing fees. When product C is re-split into product A and product B, product A and product B share the above processing fees according to the preset ratio.

[0098] It can be understood that the preset ratio can be set according to factors such as the storage method, sales method, product characteristics, etc. of each product after splitting, and no specific limitation is made here.

[0099] Furthermore, in one embodiment, the method further includes the following steps S2.7 and S2.8.

[0100] Step S2.7: Determine the order of outbound delivery of each batch of products based on the generation time of the preset incoming documents;

[0101] Step S2.8: Generate batch numbers and batch product costs based on the delivery order of each batch of products.

[0102] Specifically, in this embodiment, the e-commerce ERP system implements the first-in-first-out commodity entry and exit strategy, that is, the exit order of the batch products is determined according to the generation time of the preset entry documents (purchase orders, entry orders, etc.) corresponding to the batch products when they are entered into the warehouse. Among them, the earlier the batch products are entered into the warehouse, the earlier the exit order is, which is in line with the periodic characteristics of financial accounting.

[0103] Further, on the basis of the above embodiments, step S2.8 includes the following steps S2.81 and S2.82.

[0104] Step S2.81: In response to a preset outbound instruction, determine target outbound products corresponding to the preset outbound instruction among the batches of products.

[0105] Step S2.82: Adjust the outbound order of the target outbound products to give priority to outbound, and generate batch numbers and batch product costs corresponding to the target outbound products.

[0106] Specifically, as a feasible implementation, the user can specify that a specific batch of products is given priority for outbound, so as to calculate the batch numbers and batch product costs of the specific batch of products first. For example, if the user no longer renews the cooperation with Supplier A, the user specifies that the batch of products related to Supplier A in the warehouse is given priority for outbound, so as to clear the product inventory related to Supplier A as soon as possible and complete the relevant financial accounting operations.

[0107] Further, in one embodiment, after step S6, the method further includes the following steps S7.1 to S7.3.

[0108] Step S7.1: In the second cost data, detect the batch cost data of batches of products belonging to the same type.

[0109] Step S7.2: Determine the first batch of products according to the detection results. The unit product cost of the first batch of products is higher than the average unit cost, and the ratio between the unit product cost of the first batch of products and the average unit cost reaches a first preset ratio.

[0110] Step S7.3: Analyze the purchase quantity, purchase time, and additional expenses of the first batch of products, and generate a first optimization suggestion according to the analysis results.

[0111] The applicant found that after recording the corresponding batch product costs for batches of products, the Partial batches of products Product batch product costs in the warehouse are significantly higher than those of other batches of the same type. The reason is that the unit cost data of the products stored in the warehouse may be modified, or the batch products may be transferred between warehouses multiple times, and the additional expenses generated by the transfer operations are included in the batch product costs, resulting in higher batch product costs for some batches of products. That is to say, the higher costs of some batches of products are caused by the business operation mode.

[0112] On this basis, the applicant proposes that when the unit product cost of some batches of products (i.e., the cost of a single product) is significantly higher than the average unit cost of other batches of the same type of products, and the margin reaches the first preset ratio, it is determined that there are obvious differences in the business operation mode of this part of the batches of products (i.e., the first batch of products): for example, the purchase quantity is too small, resulting in a higher initial product cost; for example, purchasing at a specific time results in a higher purchase price; for example, the turnover times are too many, resulting in higher logistics costs; for example, using a different logistics transportation method results in higher logistics costs, etc.

[0113] Furthermore, the e-commerce ERP system analyzes the parameters (purchase quantity, purchase time, logistics cost, processing cost, etc.) included in the business operation mode of the first batch of products, and generates corresponding first optimization suggestions (such as adjusting the purchase method, adjusting the purchase time, adjusting the logistics method, adjusting the processing method, etc.) according to the analysis results, so as to reduce the product cost and improve the operation efficiency.

[0114] For example, the e-commerce ERP system detects the purchase prices corresponding to different purchase times and purchase quantities of other batches of the same type of products, and generates suggestions on the purchase time and purchase quantity to enable users to obtain better purchase prices when purchasing. Another example is that the e-commerce ERP system detects the logistics providers, transportation methods, and transportation routes used by other batches of the same type of products, and generates suggestions on the logistics method to enable users to choose a logistics method that can reduce the logistics cost.

[0115] It should be noted that the above First preset ratio can be determined according to actual needs. For example, the first preset ratio can be set to 110%, 125%, 150%, etc. according to the product type, and no specific limitation is made here.

[0116] Furthermore, in an embodiment, after step S6, the method further includes the following steps S7.4 to S7.6.

[0117] Step S7.4: Detect the turnover times of each batch of products in the second cost data;

[0118] Step S7.5: Determine the second batch of products according to the detection result. The turnover times of the second batch of products are higher than the average turnover times, and the difference between the turnover times of the second batch of costs and the average turnover times reaches the second preset ratio;

[0119] Step S7.6: Judge whether the second batch of products is a seasonal product or a hot-selling product, and generate a second optimization suggestion according to the judgment result.

[0120] Specifically, in the inventory cost data of the e-commerce ERP system, the turnover times of each batch of products are detected. If the turnover times of some batches of products are significantly higher than those of other products, it indicates that this batch of products (the second batch of products) may need to be replenished frequently, or the warehouses in multiple regions are in a shortage state.

[0121] On this basis, the e-commerce ERP system uses a preset judgment method, such as retrieving and comparing the product type of the second batch of products with big data, or connecting to an external AI model for judgment, or comparing with local product sales data, etc., to judge whether the second batch of products is a seasonal product or a hot-selling product.

[0122] If the e-commerce ERP system determines that the second batch of products is a seasonal product or a hot-selling product, it generates corresponding second optimization suggestions. The second optimization suggestions include adjusting the selling price of the second batch of products, adjusting the replenishment method (shortening the replenishment cycle or increasing the replenishment quantity), adjusting the safety inventory of the second batch of products in the warehouse, pushing other products associated with the second batch of products, etc. In this way, users can adjust the business operation mode for the second batch of products according to the above second optimization suggestions, thereby improving the operation efficiency.

[0123] In a feasible embodiment, the present application also proposes a method for generating replenishment suggestions for logistics providers, which is used to generate corresponding replenishment suggestions from the dimension of selecting logistics providers during the daily operation of e-commerce business. These replenishment suggestions can reduce the replenishment cost and improve the e-commerce operation efficiency.

[0124] Figure 4 Shows a schematic flow chart of the e-commerce ERP system generating replenishment suggestions for logistics providers in an embodiment of the present application. As Figure 4 shown, within the time interval set by the user (such as within the next two months), it is determined that all available logistics provider options for daily shipments (such as shipments on Date 1, Date 2, and Date 3) in the initial state are basic logistics provider options. Then, based on the procurement time, shipping time, arrival time, and the daily predicted sales volume and out-of-stock situation in each region, a first screening operation is performed on the basic logistics provider options. In the first screening operation, the dates with procurement time not within the time interval, arrival time not within the time interval, and dates without shipments are removed, obtaining Date 2 that needs to perform the shipping operation, and the available logistics providers corresponding to the shipment on Date 2. Then, based on the priority parameters preset by the user, including arrival time, logistics timeliness, logistics provider type, logistics price, etc., a second screening operation is performed on the available logistics providers corresponding to the shipment on Date 2, obtaining the target logistics provider finally applied to the shipment on Date 2, and generating corresponding replenishment suggestions.

[0125] Figure 5Shows a method for generating replenishment suggestions for logistics providers in an embodiment of the present application. This method mainly includes the following steps M1 to M4.

[0126] Step M1: Calculate the daily predicted sales volume, and determine one or more available logistics provider options corresponding to each day based on the procurement time, shipping time, and arrival time.

[0127] Step M2: Determine the daily out-of-stock situation based on the single-day inventory, the in-transit replenishment quantity, and the daily predicted sales volume.

[0128] Step M3: Determine the target logistics provider from the available logistics provider options based on the daily out-of-stock situation and a preset priority parameter. The preset priority parameter characterizes whether using the available logistics provider to perform the replenishment operation can meet the daily out-of-stock situation, and the implementation cost corresponding to using the available logistics provider to perform the replenishment operation.

[0129] Step M4: Generate a replenishment suggestion corresponding to each day based on the target logistics provider.

[0130] In this way, the method for generating replenishment suggestions for logistics providers provided by the present application can upgrade the traditional knapsack algorithm according to the e-commerce replenishment scenario, form a new knapsack algorithm designed specifically for the replenishment suggestion function. After mapping multiple variables involved in the replenishment suggestion, the optimal replenishment suggestion is obtained through the new knapsack algorithm. The replenishment method corresponding to the optimal replenishment suggestion has the lowest cost and the highest benefit, and significantly reduces the system operation volume when generating the replenishment suggestion, improves the system stability, and significantly reduces the amount of code required to implement the replenishment suggestion function, thereby reducing the labor cost.

[0131] The following will explain each method step in the method for generating replenishment suggestions for logistics providers in detail.

[0132] Step M1: Calculate the daily predicted sales volume, and determine one or more available logistics provider options corresponding to each day based on the procurement time, shipping time, and arrival time.

[0133] Specifically, within the time interval when the replenishment suggestion is applied (such as within the next two months), the e-commerce ERP system calculates the daily predicted sales volume, and determines one or more available logistics provider options corresponding to each day's shipment within the time interval based on relevant constraint conditions such as the procurement time not being within the time interval, no shipment on a specific date, and the arrival time not being within the time interval.

[0134] Step M2: Determine the daily out-of-stock situation based on the single-day inventory, the in-transit replenishment quantity, and the daily predicted sales volume.

[0135] Specifically, the e-commerce ERP system determines the daily out-of-stock situation of each regional warehouse based on the daily inventory corresponding to the warehouse within the time interval, the in-transit replenishment quantity, and the daily predicted sales volume, so as to determine the date for shipment within the time interval according to the daily out-of-stock situation.

[0136] Step M3: Determine a target logistics provider from the available logistics provider options based on the daily out-of-stock situation and a preset priority parameter, where the preset priority parameter characterizes whether using the available logistics provider to perform the replenishment operation can meet the daily out-of-stock situation and the implementation cost corresponding to using the available logistics provider to perform the replenishment operation.

[0137] Specifically, the e-commerce ERP system determines the shipment date for performing the replenishment operation according to the daily out-of-stock situation, and sorts the available logistics providers corresponding to the shipment date according to the priority parameters pre-set by the user, including arrival time, logistics timeliness, logistics provider type, logistics price, etc., and determines the target logistics provider actually applied when shipping on the shipment date according to the sorting result.

[0138] For example, the e-commerce ERP system determines logistics provider A with the latest arrival time (ensuring no overstocking), the longest logistics timeliness (ensuring the lowest logistics cost), and the logistics provider type of overseas warehouse shipment (prioritizing using overseas warehouses for replenishment), and uses logistics provider A for shipment on the shipment date to achieve the purpose of replenishment.

[0139] Step M4: Generate a replenishment suggestion corresponding to each day based on the target logistics provider.

[0140] Specifically, after the e-commerce ERP system determines the shipment date and the target logistics provider, it generates corresponding replenishment suggestions and provides them to the user.

[0141] Further, in one embodiment, when it is detected that there are at least two consecutive days as the shipment date and the same target logistics provider is used for shipment, since the logistics provider has a starting price, the e-commerce ERP system automatically generates a replenishment suggestion to combine the shipment dates using the same target logistics provider for combined shipment, that is, combining the shipment operations for multiple days into one day for shipment, thereby reducing the logistics cost.

[0142] Further, in one embodiment, the replenishment suggestion module includes an engineering module and an algorithm module. The engineering module is used to implement the functional logics corresponding to the above-mentioned step M1 and the above-mentioned step M2 respectively, and the algorithm module is used to implement the functional logic corresponding to the above-mentioned step M3. The editing permission of the algorithm module is higher than that of the engineering module.

[0143] It should be noted that in the current code module design for implementing the replenishment suggestion function, all parameters and all calculation processes used to generate replenishment suggestion results are used as algorithm scopes, which will be frequently modified in actual applications, thus seriously affecting the optimization iteration efficiency of the replenishment suggestion module and increasing the difficulty of manual optimization.

[0144] This application proposes to divide the replenishment suggestion function module into the engineering side and the algorithm side, and set independent editing permissions for the engineering side and the algorithm side, optimize the editing method and editing process of the replenishment suggestion function module, thereby improving the optimization iteration efficiency of the replenishment suggestion module and reducing the difficulty of manual optimization.

[0145] Specifically, the boundary between engineering and algorithms is: the algorithm side processes normal data input according to ingeniously designed logic; the engineering side processes abnormal input data to ensure that normal data is given to the algorithm in order to reduce the burden or complexity of the algorithm (the cost of understanding the algorithm is much higher than engineering).

[0146] In the e-commerce replenishment scenario, the following situations are all abnormal scenarios and should be handled on the engineering side (before entering the algorithm). These scenarios should not be compatible with the algorithm (this is also one of the main reasons why the algorithm for implementing the replenishment suggestion function is difficult to maintain):

[0147] Problems with wrong logistics selection due to non-delivery dates, non-purchasing dates, etc., which leads to the need to switch logistics (for example, determining a replenishment method and finding that it cannot be used, which results in the need to switch delivery logistics); frequent adjustments to delivery plans due to decimal daily sales (dynamic sales scenarios, such as daily sales x 10%, which results in decimal daily sales, and the replenishment quantity cannot be a decimal); various scenarios that require continuous extension of the timeline (such as estimating the time point of out-of-stock), etc.

[0148] In this application, the functions related to abnormal scenario processing and preliminary data processing are completed by the engineering side function module, and the functions related to data calculation, determination of delivery date and target logistics providers are completed by the algorithm side function module. Among them, the engineering side function module has lower editing authority and can be edited and modified by a larger number of operating accounts or managers, so that the engineering side function module can be quickly optimized through editing and modification to adapt to changes in e-commerce operations; the algorithm side function module has higher editing authority and can only be edited and modified by a smaller number of specific operating accounts or specific managers, so that the algorithm side function module remains as stable as possible, and when the engineering side function module is adaptively adjusted, the algorithm side function module maintains independence, thereby ensuring that the algorithm side function module can operate normally.

[0149] Furthermore, in one embodiment, the calculation of daily predicted sales in step M1 includes the following steps M1.1 and M1.2.

[0150] Step M1.1: Obtain historical sales data corresponding to a preset time period, and set corresponding calculation weights for the historical sales data in each sub-time period based on a preset time node. The preset time period is divided into multiple sub-time periods according to the preset time node;

[0151] Step M1.2: Calculate the daily predicted sales volume based on the historical sales data and the calculation weights.

[0152] Specifically, the e-commerce ERP system selects historical sales data within a specific time period, and performs weighted calculations on the historical sales data corresponding to each sub-time period included in the specific time period. According to the calculation results, the daily predicted sales volume that can reflect the sales volume change trend is obtained.

[0153] For example, the daily predicted sales volume = average daily sales volume in the past 3 days * 50% + average daily sales volume in the past 7 days * 30% + average daily sales volume in the past 14 days * 20%.

[0154] Furthermore, in an embodiment, the preset priority parameter includes a sorting parameter and a filtering parameter, and the method further includes the following step M6.1.

[0155] Step M6.1: Select the sorting parameter and the filtering parameter on a preset management page. The sorting parameter is used to perform priority sorting on the available logistics providers, and the filtering parameter is used to veto the available logistics providers.

[0156] Among them, the filtering parameter includes the maximum delivery volume of the available logistics providers, and the sorting parameter includes the arrival time order of the available logistics providers, the logistics timeliness of the available logistics providers, and the type of logistics provider.

[0157] Specifically, the user triggers an instruction on the preset management page of the e-commerce ERP system, and the e-commerce ERP system selects the preset priority parameter in response to the user instruction. Among them, the preset priority parameter includes a sorting parameter, such as the arrival time order of the available logistics providers (ensuring no overstocking with a later arrival time), the logistics timeliness of the available logistics providers (the longer the logistics timeliness, the cheaper the logistics price), the type of logistics provider (preferably using the logistics of overseas warehouses for replenishment), etc. The sorting parameter is used to sort the available logistics providers; and, the preset priority parameter includes a filtering parameter, such as the maximum delivery volume of the available logistics providers (if the replenishment volume exceeds the maximum delivery volume of the available logistics provider, then the available logistics provider is unavailable for this replenishment operation), the prohibited delivery date (the user selects certain dates not to deliver, or certain logistics providers do not deliver on specified dates), etc. The filtering parameter is used to directly veto the available logistics providers. Finally, the e-commerce ERP system determines the target logistics provider as the available logistics provider with a higher ranking and not vetoed based on the filtering parameter.

[0158] Further, based on the above embodiments, the method further includes the following steps M6.2 and M6.3.

[0159] Step M6.2: Determine the business transaction volume of the user account, and determine the first priority corresponding to each of the sorting parameters according to the business transaction volume.

[0160] Step M6.3: Adjust the first priority to a second priority based on the marketing metrics, and determine the daily predicted sales volume corresponding to the second priority based on the marketing metrics, where the marketing metrics represent the degree of market demand for the product and the degree of product promotion.

[0161] Specifically, in the above embodiments, the user can select multiple preset priority parameters and set priorities for the preset priority parameters, so as to sort the available logistics providers. However, the applicant has found that in actual e-commerce operation activities, due to different operation characteristics of various users, the priority requirements for the preset priority parameters are also different.

[0162] For example, for user A with a relatively large scale and a large business transaction volume, since a large number of products are involved in daily operations, if there is overstocking, it will seriously affect the capital flow of user A and cause high storage costs. Therefore, user A attaches more importance to the priority of the logistics arrival date, while the priority of the logistics price is relatively lower for user A. Similarly, for user B with a relatively small scale and a small business transaction volume, since a small number of products are involved in daily operations, user B pays more attention to the logistics costs directly incurred during the replenishment process, such as the logistics price, while the priority of overstocking is relatively low (since the number of products is small, the storage cost is low).

[0163] The applicant proposes to adjust the priority of the preset priority parameters according to the business transaction volume of the user account, so that the replenishment recommendation algorithm can have a higher matching degree with the actual situation of the user. For example, when the business transaction volume reaches a preset threshold, the user account is determined as a first-tier user, and the preset priority parameters related to the product transaction volume, such as the arrival date, are adjusted to a higher priority, and the preset priority parameters directly related to the logistics cost, such as the logistics price, are adjusted to a lower priority.

[0164] On this basis, the applicant further found that when the marketing indicators change significantly, the number of products involved in user operation changes greatly, and the replenishment suggestions generated by the e-commerce ERP system based on the initial daily predicted sales volume have a low degree of matching with the current situation and cannot meet the user's needs. Among them, the marketing indicators include the historical sales volume of products, changes in product prices, market penetration rate, and channel coverage rate, etc. When there are significant changes in product prices in the market (such as a large price increase, reflecting an increase in product demand; or a large price decrease, reflecting a decrease in product demand), or the market penetration rate has reached a relatively high level (the market potential is low, and it is difficult to significantly increase the product sales volume), or there are significant changes in the channel coverage rate (adopting a large-scale promotion and marketing channels; or reducing the promotion and marketing channels).

[0165] In this application, when the marketing indicators change significantly (such as the change range of the marketing indicators reaches 20%, 25% or 30%, etc.), according to the change situation of the marketing indicators, the priority of the preset priority parameter is adjusted. For example, when the product price drops significantly and the channel coverage rate drops significantly, the e-commerce ERP system adjusts the daily predicted sales volume accordingly. If the daily predicted sales volume is low at this time, that is, the number of products involved in user replenishment is small, then the preset priority parameter related to the product transaction volume, such as the arrival date, is adjusted to a lower priority, and the preset priority parameter directly related to the logistics cost, such as the logistics price, is adjusted to a higher priority; thus making the replenishment suggestion algorithm more adaptable to the current situation, reducing the replenishment cost according to the current situation, and improving the operation efficiency, rather than being bound to the initial setting state of the user.

[0166] As a feasible implementation method, the e-commerce ERP system can determine the historical sales data corresponding to the marketing indicators in each time period by calling the AI model or performing big data analysis, and then analyze to obtain the daily predicted sales volume.

[0167] Furthermore, in one embodiment, before step M3, the method further includes the following step M3.1.

[0168] Step M3.1: If there are multiple regions in a shortage state for the same product and the replenishment quantity cannot meet the shortage states of the multiple regions at the same time, perform replenishment priority sorting on the multiple regions according to the regional sales volume, so as to generate replenishment suggestions for the multiple regions based on the replenishment priority.

[0169] Specifically, when the warehouses in multiple regions all need to replenish the same product and the current inventory products cannot meet the replenishment requirements of the warehouses in the above multiple regions at the same time, the e-commerce ERP system preferentially selects the warehouse in the region with a higher sales volume for replenishment.

[0170] For example, the e-commerce ERP system sorts the replenishment priorities of each warehouse according to the regional sales volume. Warehouses in regions with better sales volumes will be ranked relatively higher in the replenishment sorting, and replenishments will be made to the regional warehouses in turn according to the sorting results.

[0171] For another example, the e-commerce ERP system sorts the replenishment ratios of each warehouse according to the regional sales volume. Warehouses in regions with better sales volumes have higher replenishment ratios, and the e-commerce ERP system makes replenishments to the regional warehouses respectively according to the replenishment ratios.

[0172] Further, in one embodiment, before step M1, the method further includes the following step M7.

[0173] Step M7: In response to a preset instruction, determine the time interval for applying the replenishment recommendation function to generate available logistics provider options corresponding to each day within the time interval.

[0174] Specifically, when the user triggers the preset instruction, the e-commerce ERP system determines the time interval for applying the replenishment recommendation function according to the preset instruction. For example, the e-commerce ERP system determines to generate corresponding replenishment recommendations for the next month or the next two months. Within this time interval, the e-commerce ERP system initializes and generates available logistics provider options for daily shipments, and removes the available logistics provider options whose purchase dates are not within the time interval and whose arrival dates are not within the time region.

[0175] As a feasible implementation manner, the e-commerce ERP system continuously generates replenishment recommendations corresponding to the time interval at specific time intervals. For example, the e-commerce ERP system generates replenishment recommendations corresponding to the next two months (time interval) every day at nine o'clock according to the current in-transit replenishment quantity, the single-day inventory of the warehouse, and the daily predicted sales volume, so as to ensure that the replenishment recommendations have high timeliness.

[0176] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. All equivalent transformations made under the inventive concept of the present application by using the content of the specification and drawings of the present application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for generating replenishment suggestions for logistics providers, characterized in that, A replenishment recommendation module for an e-commerce ERP system or an e-commerce management system, the method comprising: Step M1: Calculate the daily predicted sales volume; Step M2: Determine the daily out-of-stock situation based on the single-day inventory, the replenishment quantity in transit, and the daily predicted sales volume; Step M3: Determine the target logistics provider based on the daily out-of-stock situation and a preset priority parameter, where the preset priority parameter characterizes the logistics attributes for sorting; The method generates a replenishment recommendation based on the knapsack algorithm, which includes three variables: the capacity of the knapsack, the volume of the item, and the value of the item. The method comprises: Step N1: Map the knapsack capacity to the daily out-of-stock quantity, map the item to multiple available logistics providers, map the item volume to the arrival quantity on the day of the logistics, and map the item value to the logistics cost on the day; Step N2: Map the relevant variables of the replenishment recommendation to logistics attributes, including: combining and calculating the single-day inventory, the replenishment quantity in transit, and the daily predicted sales volume to obtain the daily out-of-stock quantity, mapping whether it is mandatory to specify which day to replenish to which day the logistics must deliver, mapping whether it is mandatory to specify which day not to replenish to which day the logistics does not deliver, mapping the procurement duration to the logistics transportation duration, mapping from which warehouse to ship to different logistics types, mapping how much to replenish at one time to the logistics transportation capacity, and mapping the minimum shipping quantity and the minimum purchase quantity to the logistics starting threshold; Step N3: Upgrade the filling logic of the knapsack algorithm to select the optimal logistics provider, and select the optimal logistics according to the sorting rules of the logistics attributes. The sorting rules include: ① preferentially select the logistics whose arrival time is not later than the out-of-stock time; ② secondly, select the later logistics on the premise that the logistics can arrive; ③ on the premise of satisfying items ① and ②, sort the daily available logistics provider options according to the various logistics attributes characterized by the preset priority parameter, and select the logistics provider ranked first; Step N4: Calculate the global optimal solution based on the knapsack algorithm, and generate a replenishment recommendation based on the global optimal solution.

2. The method according to claim 1, characterized in that The method further comprises: Step M5.1: When it is detected that the corresponding replenishment recommendations for at least two consecutive days or more adopt the same target logistics provider, generate a corresponding combined replenishment recommendation based on the same target logistics provider.

3. The method according to claim 1, characterized in that, The replenishment recommendation module includes an engineering module and an algorithm module. The engineering module is used to implement the functional logics corresponding to Step M1 and Step M2 respectively, and the algorithm module is used to implement the functional logic corresponding to Step M3. The editing permission of the algorithm module is higher than that of the engineering module.

4. The method according to claim 1, wherein Calculating the daily predicted sales volume in Step M1 includes: Step M1.1: Obtain historical sales data corresponding to a preset time period, and set corresponding calculation weights for the historical sales data in each sub-time period based on a preset time node. The preset time period is divided into multiple sub-time periods according to the preset time node; Step M1.2: Calculate the daily predicted sales volume based on the historical sales data and the calculation weights.

5. The method according to claim 1, characterized in that, The preset priority parameter includes a sorting parameter and a screening parameter. The method further comprises: Step M6.1: Select sorting parameters and filtering parameters on a preset management page. The sorting parameters are used to perform priority sorting on the available logistics providers, and the filtering parameters are used to reject the available logistics providers.

6. The method according to claim 5, characterized in that The filtering parameters include the maximum shipment volume of the available logistics providers, and the sorting parameters include the arrival time sequence of the available logistics providers, the logistics timeliness of the available logistics providers, and the logistics provider type.

7. The method according to claim 5, characterized in that The method further includes: Step M6.2: Determine the business transaction volume of the user account, and determine the first priority corresponding to each of the sorting parameters according to the business transaction volume. Step M6.3: Adjust the first priority to a second priority based on marketing indicators, and determine the daily predicted sales volume corresponding to the second priority based on marketing indicators. The marketing indicators represent the degree of market demand for the product and the degree of promotion of the product.

8. The method according to claim 1, wherein Before step M3, the method further includes: Step M3.1: If multiple regions are out of stock of the same product and the replenishment quantity cannot satisfy the out-of-stock status of the multiple regions at the same time, perform replenishment priority sorting on the multiple regions according to the regional sales volume, so as to generate replenishment suggestions for the multiple regions based on the replenishment priority.

9. The method according to claim 1, wherein Before step M1, the method further includes: Step M7: In response to a preset instruction, determine the time interval for applying the replenishment suggestion function, so as to generate available logistics provider options corresponding to each day within the time interval.

10. A replenishment suggestion generation system for logistics providers, characterized in that, The system is an e-commerce ERP system or an e-commerce management system, and the system is used to execute the operation instructions included in the replenishment suggestion generation method for logistics providers according to any one of claims 1-9.

Images