Batch product cost data processing method and system

Abstract

The invention discloses a batch product cost data processing method and system, and the method comprises the steps: setting a source number for a batch product which carries out the warehousing operation for the first time, and generating an initial product cost; determining a target product, generating a batch number according to the source number, and generating a batch product cost according to the initial product cost and the additional cost; generating first cost data corresponding to the target product based on the batch number, the batch product cost and the source number; modifying the batch product cost of the target product; first product data is determined in the cost data, second product data is determined in the first product data based on preset factors, and preset parameters comprise batch number time and a batch number generation rule; and modifying the second product data based on the modified batch product cost to generate second cost data. According to the method, the accuracy of the product cost data can be improved, and the related data with the association relationship can be quickly determined when the product cost data is modified, so that the data processing efficiency is greatly improved.

Description

Technical Field

[0001] The present 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 a corresponding system. Background Art

[0002] The e-commerce ERP system can be deeply connected with the e-commerce platform to help domestic small and medium-sized e-commerce users to uniformly manage their overseas stores, so that one operator can manage hundreds of e-commerce stores at the same time, greatly improving the efficiency of store operations. The functional modules of the existing commercialized e-commerce ERP systems are still in the stage of gradual functional update and improvement. The functional algorithms and rules formulated by various software companies when developing their own e-commerce ERP systems are basically different. As user needs change, each functional module will continue to develop new versions to be compatible with more usage scenarios.

[0003] In daily e-commerce activities, inventory products are usually transferred between warehouses according to business operation needs, including inventory products from warehouses in the local area to warehouses in another area, or inventory products from domestic warehouses to overseas warehouses, and then the corresponding product cost data is generated in each warehouse database. Existing e-commerce ERP systems usually calculate the shipping cost of inventory products in the form 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 model based on inventory cost data; and when modifying the cost data of some inventory products, it takes a lot of time to determine the cost data associated with the part of the inventory products in the database of each warehouse for modification, which leads to 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 of time in the future (such as half a year). At present, the replenishment suggestion function usually compares the optional replenishment methods with multiple constraints in turn, and generates replenishment suggestions for users based on the replenishment methods that can meet all the constraints. However, the generation logic of the above replenishment suggestions has the following problems: 1. After finding a replenishment method that meets the conditions, it will stop looking for other replenishment methods, thereby missing out on providing users with a lower-cost and more efficient replenishment method; 2. The system needs to undertake extremely heavy computing tasks, which can easily lead to the system failing to find a suitable replenishment method, the system falling into a computing dead loop, and the system crashing.

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

[0006] The main purpose of the present invention is to provide a batch product cost data processing method and system, which can calculate the inventory cost for each batch of products separately, and when modifying the cost data of batch products, quickly and accurately determine the cost data with correlation, thereby greatly improving the data processing efficiency. In addition, the present application also provides a method and system for generating replenishment suggestions for logistics providers, which can determine the replenishment method that reduces costs and improves operational efficiency, and when generating replenishment suggestions, it can greatly reduce the system operation amount and improve system stability.

[0007] To achieve the above purpose, the present application proposes a batch product cost data processing method and system, which is used in the warehouse module of the e-commerce ERP system. The method includes: Step S1: setting a source number for a batch of products that are first put into storage, and generating an initial product cost associated with the source number; Step S2: determining a target product from the batch products, and in response to a transfer operation performed on the target product, generating a batch number corresponding to the transfer operation according to the source number, and generating a batch product cost according to the initial product cost and an additional fee corresponding to the transfer operation, wherein the transfer operation includes a warehouse-out operation and a warehouse-in operation performed when the target product is transferred between different regions; Step S3: generating first cost data based on the batch number, the batch product cost and the source number, wherein the first cost data is a plurality of inventory product cost data stored in the warehouse module, and the first cost data includes the batch product cost of the target product; Step S4: in response to the cost modification instruction, modify the batch product cost of the target product; Step S5: Based on the source number, determining first product data associated with the target product in the first cost data, and determining second product data in the first product data based on preset factors, wherein the preset parameters include batch number time and batch number generation rules; Step S6: updating the second product data based on the modified batch product cost to obtain second cost data.

[0008] Other features and technical effects of this application are described in the following part of the specification. The technical problem solving ideas and related product design solutions of this application are: In daily e-commerce activities, inventory products are usually transferred between warehouses according to business operation needs, and corresponding product cost data is generated in each warehouse database. Existing e-commerce ERP systems usually calculate the delivery cost of inventory products in the form of weighted average of total inventory: that is, the unit cost of inventory products is obtained by dividing the total cost of inventory products by the number of inventory products.

[0009] However, the existing method of calculating the shipping cost of inventory products has the following problems: 1. Different products stored in the same warehouse may have different unit product costs due to various reasons. The existing method of calculating the cost of inventory products is difficult to reflect the cost differences of different products, making it difficult for users to optimize the operation model based on the cost data of inventory products (for example, different unit product costs of different products in different shipping batches and different shipping periods are different when purchasing, or different products using different logistics methods and passing through different logistics routes have different additional charges, which leads to 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 to modify, resulting in low data processing efficiency.

[0010] The applicant discovered that for batch products that undergo a purchasing operation, a source number is set for the batch products when the warehousing operation is first performed, and the initial product cost of the batch products is generated based on the purchasing warehousing operation; when the batch products subsequently undergo a transfer operation, a processing operation, or a splitting operation, a batch number associated with the source number is generated for the batch products, and the batch product cost is generated based on the initial product cost and the additional costs incurred when the transfer operation, the processing operation, or the splitting operation is performed, thereby generating corresponding product costs for each batch of products, thereby improving the accuracy of the cost data of inventory products.

[0011] Furthermore, the applicant found that when it is necessary to modify the cost data of a batch product, a first data screening is performed on the inventory cost data according to the source number of the batch product to determine other batch product data (first product data) that have an associated relationship with the batch product, and then a second data screening is performed on the first product data according to preset factors (batch number time and batch number generation rules) to determine other batch product cost data (second product data) that need to be modified accordingly after the cost data of the current batch product has changed; wherein the second product data is the cost data of 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 cost data of the modified batch product, thereby realizing fast and accurate modification operations on the inventory cost data to obtain the second cost data.

[0012] In this way, 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 a corresponding batch number and batch product cost each time a transfer operation is performed on the batch product. This improves the accuracy of the product cost data, and can quickly and accurately determine the cost data of batch products with an associated relationship when modifying the product cost data, thereby greatly improving data processing efficiency.

[0013] Furthermore, the present application also provides a method and system for generating replenishment suggestions for logistics providers, which are used in a replenishment suggestion module of an e-commerce ERP system or an e-commerce management system. The method includes: Step M1: Calculate the daily forecast sales volume, and determine one or more available logistics provider options corresponding to each day based on the purchase time, delivery time, and arrival time; Step M2: Determine the daily out-of-stock situation based on the daily inventory, the in-transit replenishment quantity and the daily forecast sales volume; Step M3: determining a target logistics provider from the available logistics provider options based on the daily stock-out situation and a preset priority parameter, wherein the preset priority parameter indicates whether the replenishment operation using the available logistics provider can meet the daily stock-out situation and the corresponding implementation cost of the replenishment operation using the available logistics provider; Step M4: Generate replenishment suggestions corresponding to each day based on the target logistics provider.

[0014] Other features and technical effects of this application are described in the following part of the specification. The technical problem solving ideas and related product design solutions of this application are: In the warehouse replenishment link, the e-commerce ERP system has a replenishment suggestion function, which is mainly used to determine whether the user needs to replenish the warehouse within a certain period of time in the future (such as half a year). At present, the mainstream implementation idea of ​​the replenishment suggestion function is to implement it based on the forced coding of each variable (direct translation business), that is, to compare the optional replenishment methods with multiple constraints in turn, find the replenishment method that meets all the constraints as the final result, and provide the final result to the user.

[0015] It should be noted that the function of generating replenishment suggestions is essentially within the processing scope of the dynamic programming algorithm. The reason is that the replenishment suggestion function needs to provide suggestions on whether the warehouse needs to be replenished every day in the future (such as the next six months). At the same time, whether replenishment is recommended on the current day is affected by whether the goods have been replenished on the previous day (if the goods have been replenished on the previous day, then no replenishment is needed on the current day). In other words, each day's replenishment suggestion is not isolated, but is affected by the previous replenishment operation. Therefore, generating replenishment suggestions is a dynamic programming problem.

[0016] However, unlike the classic dynamic programming algorithm which only involves a few variables, the replenishment suggestion function involves a large number of variables, far exceeding the number of variables that the dynamic programming algorithm can handle, and these variables may cross-influence each other, so no dynamic programming algorithm can be directly used for the replenishment suggestion function.

[0017] For example, the variables involved in replenishment suggestions mainly include: the current inventory status in the warehouse, the inventory in transit (inventory that will definitely arrive at the warehouse in the future), changes in sales volume each day in the future (inventory consumption rate), whether to specify a specific day that must be replenished (such as an offline agreement with the logistics company), whether to specify a specific day that must not be replenished (such as holidays), which logistics company to choose for replenishment (different delivery speeds and different asking prices), procurement time (how long it will take to arrive), which warehouse to ship from (which local warehouse or which overseas warehouse), how much to replenish at a time (affected by logistics transportation costs, no inventory pressure, no out-of-stock, etc.), the minimum shipment quantity (too little and the logistics company will not ship), the minimum purchase quantity (too little and the supplier will not accept the order), etc.

[0018] Obviously, no dynamic programming algorithm can handle the numerous variables in the above data. Therefore, there is an unwritten consensus in the e-commerce industry on 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 field of replenishment suggestions in the e-commerce industry. With the above technical bias as a premise, the current mainstream implementation idea of ​​the replenishment suggestion function is to compare the optional replenishment methods with the constraints corresponding to the above variables in turn. If the current replenishment method cannot meet one of the constraints, the calculation process will be restarted, and the next replenishment method will be switched to continue to perform the comparison operation with the above constraints.

[0019] Therefore, the current processing logic of the replenishment suggestion function has the following defects: 1. The calculated replenishment suggestions are not necessarily the most reasonable: At present, the replenishment suggestion function essentially calculates the local optimal solution. Unless the calculation result of the last step obviously conflicts with a certain constraint, the last local optimal solution is considered to be the global optimal solution. However, in fact, this solution can only be said to meet all constraints, but it is not necessarily the global optimal solution (for example, there are other replenishment suggestion options that can also meet all variables and save more money) 2. The system may not be able to calculate the replenishment recommendation as the final result: There are a large number of the above constraints. If there are a lot of cross-influences among the conflicting constraints, the system will need to frequently overturn previous conclusions, or even be unable to calculate the final result (which may cause the system to fall into an infinite loop or crash, etc.).

[0020] The applicant found that calculating the most reasonable replenishment suggestion is essentially calculating the global optimal solution problem, that is, the replenishment suggestion is first of all a solution (meeting the basic demands of users: no shortage of stock, no pressure on goods), and at the same time the replenishment suggestion is also the global optimal solution (such as the lowest implementation cost).

[0021] Calculating the global optimal solution is the strength 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. It is necessary to carry out targeted upgrades and business adaptation of the existing dynamic programming algorithm according to the replenishment scenario.

[0022] The applicant proposed to use the knapsack algorithm (a type of dynamic programming algorithm) in the algorithm framework to solve the above-mentioned replenishment recommendation problem.

[0023] The backpack algorithm has three variables: the capacity of the backpack, the volume of the items, and the value of the items. The core of the backpack algorithm is to put items of different volumes under the premise of a fixed backpack capacity to maximize the value of the items in the backpack. In order to meet the number of variables of the backpack algorithm, the technical solution of this application abstracts the problem of replenishment scenarios to adapt to the backpack algorithm: mapping the backpack capacity to the daily out-of-stock quantity, mapping the items to multiple replenishment logistics available for selection, mapping the volume of the items to the number of arrivals on the day of the logistics, and mapping the value of the items to the logistics cost of the day. Referring to the backpack algorithm, the core problem that the replenishment suggestion function needs to solve becomes: if the warehouse is out of stock on the day (there is still space in the backpack), what logistics should be selected for replenishment (which item to choose); the selected logistics must meet the following requirements: it can arrive before the out-of-stock situation (not out-of-stock), the replenishment quantity can meet the out-of-stock situation (not out-of-stock), and the cost of the replenishment logistics is the lowest (the selected item has the greatest value).

[0024] On this basis, other variables involved in the replenishment suggestion are mapped to the two variables of the day's out-of-stock quantity and logistics. The specific solution is: the inventory status in the warehouse, the inventory status in transit, and the sales volume changes in the future are calculated and summarized into the out-of-stock quantity of each day; whether to force a certain day to be replenished is mapped to the day on which logistics must be shipped, whether to force a certain day not to be replenished is mapped to the day on which logistics will not be shipped, the procurement time is mapped to the logistics transportation time, which warehouse to ship from is mapped to different logistics types, how much to replenish at a time is mapped to the logistics transportation capacity, and the minimum shipment and minimum purchase quantity are mapped to the logistics delivery threshold. The above mapping solution solves the problem of too many variables involved in the replenishment suggestion by uniformly mapping multiple variables to the processing method at 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 a shortage of stock on a certain day, which logistics should be selected to replenish the stock.

[0025] However, the above processing methods still have some incompatibility with the traditional backpack algorithm. For example, the constraint attributes of items are not only volume, but also logistics delivery time, transportation time, logistics type, transportation capacity, delivery threshold and many other attributes; the backpack algorithm may need to select multiple items to fill the backpack, but the replenishment suggestion can only select one logistics per day (based on the cost consideration of logistics with a delivery price); the backpack algorithm can select multiple items and therefore has a form filling logic, but the replenishment suggestion does not have a form filling logic (because only one logistics can be selected); the backpack algorithm does not need to consider how to coordinate multiple backpacks, but the replenishment suggestion needs it in the following scenarios: if the same logistics is selected every day (one logistics selection is one backpack algorithm application), in reality, the same logistics replenishment will not be sent separately every day, but multiple batches of logistics replenishment will be sent at the same time, thereby saving logistics costs. The above problems all show that in order to solve the problems faced by replenishment suggestions, the traditional backpack algorithm needs to be upgraded to form a new backpack algorithm designed specifically for the replenishment suggestion function.

[0026] The upgrade plan is to upgrade the form filling problem to the problem of selecting the optimal logistics. The basis for selecting the optimal logistics is the sorting rules of logistics attributes. In the replenishment suggestion scenario, the primary demand is not to be out of stock, which corresponds to the priority of logistics whose arrival time is not later than the out-of-stock time (to avoid affecting normal sales); the secondary demand is not to overstock, which corresponds to the premise that the logistics can arrive, the later the better (to avoid increasing storage costs); under the premise of meeting the above two points, the priority order of attributes corresponding to other demands is: replenishing through overseas warehouses first, and then replenishing through local warehouses corresponds to the logistics type priority attribute, the lower the logistics cost, the better 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 replenishment logistics on the same day.

[0027] It can be understood that the above-mentioned logistics attributes used for sorting are expressed as "preset priority parameters" in the claims of this application, that is, the new backpack algorithm designed for the replenishment suggestion function proposed in this application is: sorting the available logistics provider options daily according to the various logistics attributes represented by the preset priority parameters, selecting the logistics provider ranked first as the target logistics provider, and generating replenishment suggestions corresponding to the target logistics provider to the user.

[0028] In this way, the replenishment suggestion generation method for logistics providers provided in the present application can upgrade the traditional backpack algorithm according to the e-commerce replenishment scenario, forming a new backpack 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 backpack algorithm. The replenishment method corresponding to the optimal replenishment suggestion has the lowest cost and the highest benefit. In addition, when generating replenishment suggestions, the system computing power is greatly reduced, the system stability is improved, and the number of codes required to implement the replenishment suggestion function is greatly reduced, thereby reducing labor costs.

[0029] The present application also provides a system, which is an e-commerce ERP system or an e-commerce management system, and the system can execute the operating instructions of each method step of the present application.

[0030] The present application also provides a server, which includes a memory and a processor. The system in the present application is stored in the memory, and the processor can run the operating instructions of each method step of the present application.

[0031] The present application also provides a computer device, which includes a memory and a processor. The system in the present application is stored in the memory, and the processor can execute the operating instructions of each method step in the present application.

[0032] refer to Figure 1 The e-commerce ERP system of this application includes one or more of the functional modules such as commodity module, sales module, procurement module, logistics module, warehouse module, financial module, advertising module, customer service module, tool module, promotion management module, authority management module and data analysis module. The functional modules can be integrated with each other or exist independently, and one functional module can be a submodule of another functional module. The users of the ERP system of this application can also be called store managers, sellers, operators, operation personnel, etc., and their identities are not strictly limited unless otherwise stated. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide further understanding of the present application and do not constitute a limitation of the present application; the contents shown in the accompanying drawings may be real data of the embodiments and belong to the protection scope of the present application.

[0034] Figure 1 This is a schematic diagram of the functional modules of an e-commerce ERP system in one embodiment of the present application.

[0035] Figure 2 This is a schematic diagram of the principles of the e-commerce product cost data processing process in one embodiment of the present application.

[0036] Figure 3 This is a flow chart of a method for processing batch product cost data in one embodiment of the present application.

[0037] Figure 4 The figure is a schematic diagram of the principle of the replenishment suggestion generation process in one embodiment of the present application.

[0038] Figure 5 This is a flow chart of a method for generating replenishment suggestions for a logistics provider in one embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of this application more clear, the following is a further detailed description of the embodiments of this application through specific implementation methods combined with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0040] Figure 2 The schematic diagram of the principle of the e-commerce product cost data processing process in one embodiment of the present application is shown. Figure 2 As shown, when the user purchases products from suppliers and puts them into warehouse 1, the e-commerce ERP system sets the source number for the batch products that perform the initial warehousing operation, and generates the initial product cost; then, the e-commerce ERP system selects some products from the batch products in warehouse 1 as target products based on business operation requirements, and generates the batch product cost 1 corresponding to the target product; then the e-commerce ERP system transfers the target product from warehouse 1 to warehouse 2, obtains batch product 2 in warehouse 2, and generates the corresponding batch product cost 2 and batch number; then, the e-commerce ERP system transfers batch product 2 in warehouse 2 to warehouse 3 again based on business operation requirements, obtains batch product 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 (first cost data). When the user triggers the cost modification instruction and modifies the batch cost data of a 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 batch number generation rule. The second product data is the batch cost data of the downstream batch products of the above batch products. The e-commerce ERP system modifies the second product data to obtain new inventory product cost data (second cost data).

[0041] Figure 3 A batch product cost data processing method in one embodiment of the present application is shown, and the method mainly includes the following steps S1 to S6.

[0042] Step S1: setting a source number for a batch of products that are first put into storage, and generating an initial product cost associated with the source number; Step S2: determining a target product from the batch products, and in response to a transfer operation performed on the target product, generating a batch number corresponding to the transfer operation according to the source number, and generating a batch product cost according to the initial product cost and an additional fee corresponding to the transfer operation, wherein the transfer operation includes a warehouse-out operation and a warehouse-in operation performed when the target product is transferred between different regions; Step S3: generating first cost data based on the batch number, the batch product cost and the source number, wherein the first cost data is a plurality of inventory product cost data stored in the warehouse module, and the first cost data includes the batch product cost of the target product; Step S4: in response to the cost modification instruction, modify the batch product cost of the target product; Step S5: Based on the source number, determining first product data associated with the target product in the first cost data, and determining second product data in the first product data based on preset factors, wherein the preset parameters include batch number time and batch number generation rules; Step S6: updating the second product data based on the modified batch product cost to obtain second cost data.

[0043] In this way, 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 a corresponding batch number and batch product cost each time the batch product performs a transfer operation. This improves the accuracy of the product cost data and can quickly determine the cost data of batch products with an associated relationship when modifying the product cost data, thereby greatly improving data processing efficiency.

[0044] The following is a detailed explanation of each step in the batch product cost data processing method.

[0045] Step S1: Setting a source number for a batch of products that are put into storage for the first time, and generating an initial product cost associated with the source number.

[0046] Specifically, for batch products for which users perform purchasing operations, when the batch products are first put into storage, the e-commerce ERP system sets a source number for the batch products and generates relevant data related to the batch products, such as the supplier, initial product cost, type, quantity, and related business documents.

[0047] Step S2: Determine a target product from the batch products, and in response to a transfer operation performed on the target product, generate a batch number corresponding to the transfer operation based on the source number, and generate a batch product cost based on the initial product cost and the additional fee corresponding to the transfer operation, wherein the transfer operation includes an outbound operation and an inbound operation performed when the target product is transferred between different regions.

[0048] Specifically, according to the instructions triggered by the user during the business operation process, the e-commerce ERP system selects all or part of the products in the above-mentioned batches as target products, and performs outbound and inbound operations on the target products in sequence, thereby transferring the target products to other warehouses.

[0049] Among them, when the current warehouse performs an outbound operation on the target product, an outbound batch number corresponding to the outbound operation is generated (that is, the downstream batch product corresponding to the batch product that performs the purchase warehousing operation, and the source number is the source batch number of the outbound batch number). The outbound batch number is generated based on the source number, and related data such as the supplier, product batch cost, type, quantity, related business documents, etc. are generated, and the source number is recorded.

[0050] When another warehouse performs an incoming operation on the target product, an incoming batch number corresponding to the incoming operation is generated (that is, the downstream batch product corresponding to the target product for the outgoing operation, and the outgoing batch number is the source batch number of the incoming batch number). The incoming batch number is generated based on the outgoing batch number, and related data such as the supplier, product batch cost, type, quantity, related business documents, and the source number are generated.

[0051] Among them, the product batch cost corresponding to the incoming batch number is determined by the initial product cost and the additional costs incurred by the target product during the transfer.

[0052] 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 being a plurality of inventory product cost data stored in the warehouse module, and the first cost data including the batch product cost of the target product.

[0053] Specifically, when the e-commerce ERP system performs transfer operations, processing operations or splitting operations on all or part of the batch products, it generates a corresponding batch number for each batch of products obtained, and generates the batch product cost based on the initial product cost and the additional costs incurred when performing the transfer operation, processing operation or splitting operation, and stores the batch number, source number and batch product cost of each of the above batches of products in the inventory cost data (i.e., the first cost data).

[0054] Step S4: In response to the cost modification instruction, modify the batch product cost of the target product.

[0055] Specifically, the e-commerce ERP system modifies the batch product cost of a certain batch of products in the product cost data in response to a cost modification instruction triggered by a user.

[0056] 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, and the preset parameters include batch number time and batch number generation rules.

[0057] Specifically, after modifying the batch product cost of the above-mentioned batch products, the e-commerce ERP system performs a first data screening in the inventory cost data according to the source number of the batch products, and determines other batch product data (first product data) that have an associated relationship with the batch products, and then performs a second data screening on the first product data according to preset factors (batch number time and batch number generation rules) to determine other batch product cost data (second product data) that need to be modified accordingly after the cost data of the current batch products has changed; wherein the second product data is the cost data of downstream batch products generated by performing transfer operations, processing operations or splitting operations on the current batch products.

[0058] Step S6: modifying the second product data based on the modified batch product cost to generate second cost data corresponding to the target product.

[0059] Specifically, after determining the second product data related to the above-mentioned batch of products, the e-commerce ERP system modifies the second product data according to the modified product batch cost data of the above-mentioned batch of products, thereby realizing fast and accurate modification operations on the inventory cost data and obtaining new inventory cost data (i.e., the second cost data).

[0060] Further, in one embodiment, when the preset factor is the batch numbering time, step S5 of determining the second product data in the first product data based on the preset factor includes the following steps S5.1 and S5.2.

[0061] Step S5.1: Determine that the generation time of the batch number of the target product is a preset time standard; 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.

[0062] Specifically, for example, after the target product for modifying the batch product cost is determined according to the user's instructions, since the batch numbers corresponding to the upstream batch products and the downstream batch products are associated with each other each time the upstream batch products perform a transfer operation to generate downstream batch products, the batch number of the upstream batch products is the source batch number associated with the batch number of the downstream batch products. Therefore, according to the generation time of the batch number of the target product and the source batch number of each batch of products, the downstream batch products that are associated with the target product can be determined as the target batch products, and the batch product cost of the target batch products can be determined as the second product data for modification.

[0063] Further, in one 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.

[0064] Step S5.3: In the first product data, the batch number of each batch of products is compared with the batch number of the target product to determine the downstream batch products of the target product, and the batch product cost of the downstream batch products of the target product is determined as the second product data.

[0065] Specifically, for example, for batch A products, the batch number of the upstream batch products is 001, and the batch number of the downstream batch products is 002; for batch B products, the batch number of the upstream batch products is 101, and the batch number of the downstream batch products is 102. The association relationship between each batch of products and the upstream and downstream batch relationship can be determined based on the batch number, and the downstream batch products related to the target product can be determined as the second product data based on the source number and batch number, and modified.

[0066] Furthermore, in one embodiment, the method further includes the following steps S2.1 to S2.3.

[0067] Step S2.1: In response to the product merging instruction, determining the batch product cost of the third batch of products based on the batch product costs corresponding to the first batch of products and the second batch of products, respectively, wherein the third batch of products is obtained by processing and assembling the first batch of products and the second batch of products; Step S2.2: Generate a batch number for the third batch of products, and associate the batch number for the third batch of products with the batch numbers corresponding to the first batch of products and the second batch of products respectively; Step S2.3: Setting a 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.

[0068] Specifically, for the scenario where the third batch of products is assembled from the first batch of products and the second batch of products (e.g., the first batch of products are mobile phones, the second batch of products are mobile phone cases, and the third batch of products are 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, the source numbers corresponding to the first batch of products and the second batch of products, respectively, are set as the source numbers of the third batch of products.

[0069] Furthermore, in one embodiment, the method further includes the following steps S2.4 to S2.6.

[0070] Step S2.4: In response to the product splitting instruction, the batch product costs of the fourth batch of products are used to determine the batch product costs corresponding to the fifth batch of products and the sixth batch of products respectively based on a preset ratio, wherein the fifth batch of products and the sixth batch of products are obtained by splitting the fourth batch of products; Step S2.5: generating batch numbers corresponding to the fifth batch of products and the sixth batch of products respectively, and setting 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, Step S2.6: setting the source numbers corresponding to the fifth batch of products and the sixth batch of products respectively based on the source numbers corresponding to the fourth batch of products.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] Furthermore, in one embodiment, the method further includes the following steps S2.7 and S2.8.

[0076] Step S2.7: Determine the order of outbound delivery of each batch of products based on the generation time of the preset incoming documents; Step S2.8: Generate batch numbers and batch product costs based on the delivery order of each batch of products.

[0077] 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.

[0078] Further, based on the above embodiment, step S2.8 includes the following step S2.81 and step S2.82.

[0079] Step S2.81: In response to a preset delivery instruction, determining a target delivery product corresponding to the preset delivery instruction from each batch of products; Step S2.82: Adjust the delivery order of the target delivery product to priority delivery, and generate a batch number and batch product cost corresponding to the target delivery product.

[0080] Specifically, as a feasible implementation, the user can specify a specific batch of products to be shipped out first, so as to give priority to calculating the batch number and batch product cost of the specific batch of products. For example, if the user does not renew the contract with supplier A, the user can specify that the batch of products related to supplier A in the warehouse should be shipped out first, so as to clear the product inventory related to supplier A as soon as possible and complete the relevant financial accounting operations.

[0081] Furthermore, in one embodiment, after step S6, the method further includes the following steps S7.1 to S7.3.

[0082] Step S7.1: Detecting batch cost data of batches of products of the same type in the second cost data; Step S7.2: Determine the first batch of products according to the test 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; Step S7.3: Analyze the purchase quantity, purchase time and additional costs of the first batch of products, and generate a first optimization suggestion based on the analysis results.

[0083] The applicant found that after recording the corresponding batch product costs for batch products, Some batches of Taste The batch product cost is significantly higher than other batch products of the same type. The reason is that the unit cost data of products stored in the warehouse may be modified, or the batch products are transferred between warehouses many times, and the additional costs incurred by the transfer operation are included in the batch product cost, resulting in the high batch product cost of some batch products. In other words, the high cost of some batch products is caused by the business operation model.

[0084] On this basis, the applicant proposed 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 products of the same type, and the magnitude reaches the first preset ratio, it is determined that these batches of products (i.e. the first batch of products) have obvious differences in their business operation models: for example, the purchase quantity is too small, resulting in a higher initial product cost; for example, purchases are made at a specific time, resulting in a higher purchase price; for example, the turnover times are too many, resulting in higher logistics costs; for example, different logistics transportation methods are used, resulting in higher logistics costs, etc.

[0085] Furthermore, the e-commerce ERP system analyzes the parameters (purchase quantity, purchase time, logistics costs, processing costs, etc.) included in the business operation model of the first batch of products, and generates corresponding first optimization suggestions based on the analysis results (such as adjusting the purchase method, adjusting the purchase time, adjusting the logistics method, adjusting the processing method, etc.), thereby reducing product costs and improving operational efficiency.

[0086] For example, the e-commerce ERP system detects the purchase prices of other batches of the same type of products at different purchase times and purchase quantities, and generates suggestions on purchase time and purchase quantity so that users can 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 logistics methods so that users can choose logistics methods to reduce logistics costs.

[0087] It should be noted that the above First preset ratio It can be determined according to actual needs, such as setting the first preset ratio to 110%, 125%, 150% etc. according to the product type, and no specific limitation is made here.

[0088] Furthermore, in one embodiment, after step S6, the method further includes the following steps S7.4 to S7.6.

[0089] Step S7.4: Detecting the turnover times of each batch of products in the second cost data; Step S7.5: Determine a second batch of products according to the test results, the turnover number of the second batch of products is higher than the average turnover number, and the difference between the turnover number of the second batch of products and the average turnover number reaches a second preset ratio; Step S7.6: Determine whether the second batch of products are seasonal products or hot-selling products, and generate a second optimization suggestion based on the determination result.

[0090] Specifically, the e-commerce ERP system detects the turnover number of each batch of products in the inventory cost data. If the turnover number of some batches of products is significantly higher than that of other products, it means that this batch of products (the second batch of products) may need frequent replenishment, or that warehouses in multiple regions are in short supply.

[0091] On this basis, the e-commerce ERP system determines whether the second batch of products are seasonal products or hot-selling products through preset judgment methods, such as searching and comparing the product types 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.

[0092] If the e-commerce ERP system determines that the second batch of products is a seasonal product or a hot-selling product, it will generate a corresponding second optimization suggestion. The second optimization suggestion includes adjusting the sales price of the second batch of products, adjusting the replenishment method (shortening the replenishment cycle or increasing the replenishment quantity), adjusting the warehouse's safety stock for the second batch of products, and pushing other products related to the second batch of products. In this way, users can adjust the business operation model for the second batch of products based on the above second optimization suggestions, thereby improving operational efficiency.

[0093] In a feasible embodiment, the present application also proposes a method for generating replenishment suggestions for logistics providers, which is used in the daily operation of e-commerce business, and the e-commerce ERP system or e-commerce management system generates corresponding replenishment suggestions from the dimension of selecting logistics providers. The replenishment suggestions can reduce replenishment costs and improve the efficiency of e-commerce operations.

[0094] Figure 4 FIG. 1 shows a flow chart of an e-commerce ERP system generating replenishment suggestions for logistics providers in one embodiment of the present application. Figure 4As shown, within the time interval set by the user (such as within the next two months), all available logistics provider options for daily shipments (such as shipments on date 1, date 2, and date 3) are determined as basic logistics provider options in the initial state, and then the first screening operation is performed on the basic logistics provider options based on the purchase time, shipment time, arrival time, and daily predicted sales and out-of-stock conditions in each region. In the first screening operation, the purchase time that is not within the time interval, the arrival time that is not within the time interval, and the date that does not require shipment are removed to obtain the date 2 that needs to perform the shipment operation and the available logistics providers corresponding to the shipment on date 2. Then, based on the priority parameters pre-set by the user, including arrival time, logistics timeliness, logistics provider type, logistics price, etc., the second screening operation is performed on the available logistics providers corresponding to the shipment on date 2 to obtain the target logistics provider that is finally applied to the shipment on date 2, and the corresponding replenishment suggestions are generated.

[0095] Figure 5 A method for generating replenishment suggestions for a logistics provider in one embodiment of the present application is shown, and the method mainly includes the following steps M1 to M4.

[0096] Step M1: Calculate the daily forecast sales volume, and determine one or more available logistics provider options corresponding to each day based on the purchase time, delivery time, and arrival time; Step M2: Determine the daily out-of-stock situation based on the daily inventory, the in-transit replenishment quantity and the daily forecast sales volume; Step M3: determining a target logistics provider from the available logistics provider options based on the daily stock-out situation and a preset priority parameter, wherein the preset priority parameter indicates whether the replenishment operation using the available logistics provider can meet the daily stock-out situation and the corresponding implementation cost of the replenishment operation using the available logistics provider; Step M4: Generate replenishment suggestions corresponding to each day based on the target logistics provider.

[0097] In this way, the replenishment suggestion generation method for logistics providers provided in the present application can upgrade the traditional backpack algorithm according to the e-commerce replenishment scenario, forming a new backpack 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 backpack algorithm. The replenishment method corresponding to the optimal replenishment suggestion has the lowest cost and the highest benefit. In addition, when generating replenishment suggestions, the system computing power is greatly reduced, the system stability is improved, and the number of codes required to implement the replenishment suggestion function is greatly reduced, thereby reducing labor costs.

[0098] The following is a detailed description of each method step in the method for generating replenishment suggestions for logistics providers.

[0099] Step M1: Calculate the daily forecast sales volume, and determine one or more available logistics provider options corresponding to each day based on the purchase time, delivery time, and arrival time.

[0100] Specifically, within the time period when replenishment recommendations are applied (e.g., within the next two months), the e-commerce ERP system calculates the daily forecast sales, and based on relevant constraints such as the purchase time not being within the time period, no shipment on a specific date, and the arrival time not being within the time period, determines one or more available logistics provider options corresponding to daily shipments within the time period.

[0101] Step M2: Determine the daily out-of-stock situation based on the daily inventory, the in-transit replenishment quantity and the daily predicted sales volume.

[0102] Specifically, the e-commerce ERP system determines the daily stock-out situation of warehouses in each region based on the daily inventory, in-transit replenishment quantity and daily forecast sales of the warehouse within the time period, and thus determines the date required for delivery within the time period based on the daily stock-out situation.

[0103] Step M3: Determine a target logistics provider from the available logistics provider options based on the daily stock-out situation and a preset priority parameter, wherein the preset priority parameter indicates whether the replenishment operation using the available logistics provider can meet the daily stock-out situation and the corresponding implementation cost of the replenishment operation using the available logistics provider.

[0104] Specifically, the e-commerce ERP system determines the delivery date for replenishment operations based on the daily out-of-stock situation, and sorts the available logistics providers corresponding to the delivery 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 used for delivery on the delivery date based on the sorting results.

[0105] For example, the e-commerce ERP system determines the logistics provider A with the latest arrival time (to ensure no inventory pressure), the longest logistics efficiency (to ensure the lowest logistics cost), and the type of logistics provider is overseas warehouse delivery logistics (overseas warehouse replenishment is used preferentially), and uses logistics provider A to deliver the goods on the delivery date to achieve the replenishment purpose.

[0106] Step M4: Generate replenishment suggestions corresponding to each day based on the target logistics provider.

[0107] Specifically, after determining the delivery date and target logistics provider, the e-commerce ERP system generates corresponding replenishment suggestions and provides them to users.

[0108] Furthermore, in one embodiment, when it is detected that at least two consecutive days are shipping dates and the same target logistics provider is used for shipping, since the logistics provider has a minimum delivery price, the e-commerce ERP system automatically generates a replenishment suggestion for combined shipment using the same target logistics provider's shipping date, that is, combining multiple days of shipping operations into one day for shipment, thereby reducing logistics costs.

[0109] Furthermore, in one embodiment, the replenishment suggestion module includes an engineering module and an algorithm module, the engineering module is used to implement the functional logic corresponding to the above step M1 and the above step M2 respectively, the algorithm module is used to implement the functional logic corresponding to the above step M3, and the algorithm module has higher editing authority than the engineering module.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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): 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.

[0114] 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.

[0115] Furthermore, in one embodiment, the calculation of daily predicted sales in step M1 includes the following steps M1.1 and M1.2.

[0116] 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, wherein the preset time period is divided into a plurality of sub-time periods according to the preset time nodes; Step M1.2: Calculate daily predicted sales based on the historical sales data and the calculated weights.

[0117] Specifically, the e-commerce ERP system selects historical sales data within a specific time period, and performs weighted calculation on the historical sales data corresponding to each sub-time period contained in the specific time period, and obtains daily predicted sales that can reflect the sales volume change trend based on the calculation results.

[0118] For example, daily predicted sales = average daily sales of the past 3 days*50% + average daily sales of the past 7 days*30% + average daily sales of the past 14 days*20%.

[0119] Furthermore, in one embodiment, the preset priority parameters include sorting parameters and screening parameters, and the method further includes the following step M6.1.

[0120] Step M6.1: Select a sorting parameter and a screening parameter on the preset management page, wherein the sorting parameter is used to prioritize the available logistics providers, and the screening parameter is used to reject the available logistics providers.

[0121] The screening 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 time efficiency of the available logistics providers, and the type of logistics providers.

[0122] 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 parameters in response to the user's instruction. Among them, the preset priority parameters include sorting parameters, such as the order of arrival time of available logistics providers (late arrival time ensures that there is no pressure on goods), the logistics time of available logistics providers (the longer the logistics time, the cheaper the logistics price), the type of logistics providers (giving priority to the use of overseas warehouse logistics for replenishment), etc., and the sorting parameters are used to sort the available logistics providers; and the preset priority parameters include screening parameters, such as the maximum shipment volume of available logistics providers (if the replenishment volume exceeds the maximum shipment volume of the available logistics provider, the available logistics provider is not available for this replenishment operation), prohibited shipment date (the user selects certain dates not to ship, or certain logistics providers do not ship on the specified date), etc., and the screening parameters are used to directly reject available logistics providers. Finally, the e-commerce ERP system determines the available logistics providers that are ranked high and have not been rejected based on the screening parameters as the target logistics providers.

[0123] Furthermore, based on the above embodiment, the method also includes the following step M6.2 and step M6.3.

[0124] Step M6.2: Determine the business transaction volume of the user account, and determine the first priority corresponding to each of the ranking parameters according to the business transaction volume; Step M6.3: Adjust the first priority to the second priority based on marketing indicators, and determine the daily forecast sales corresponding to the second priority based on the marketing indicators, wherein the marketing indicators represent the market demand for the product and the promotion degree of the product.

[0125] Specifically, in the above embodiment, the user can select multiple preset priority parameters and set priorities for the preset priority parameters to sort the available logistics providers; however, the applicant has found that in actual e-commerce operation activities, various users have different operation characteristics, and therefore have different priority requirements for the corresponding preset priority parameters.

[0126] For example, for user A, who is large in scale and has a large number of business transactions, since a large number of goods are involved in daily operations, if there is a backlog of goods, it will seriously affect the capital circulation of user A and cause high storage costs. Therefore, user A attaches more priority to the logistics arrival date, while the logistics price has a lower priority than user A. Similarly, for user B, who is small in scale and has a small number of business transactions, since a small number of goods are involved in daily operations, user B pays more attention to the logistics costs directly caused by the replenishment process, such as logistics prices, etc., and has a lower priority for backlogs (small number of goods, so lower storage costs).

[0127] The applicant proposed to adjust the priority of the preset priority parameters according to the business transaction volume of the user account, so that the replenishment suggestion algorithm can be more closely matched with the actual situation of the user. For example, when the business transaction volume reaches the preset threshold, the user account is determined to be a first-volume user, and the preset priority parameters related to the commodity 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.

[0128] On this basis, the applicant further discovered that when the marketing indicators change significantly, the number of goods involved in the user's operation changes significantly, and the replenishment suggestions generated by the e-commerce ERP system based on the initial daily forecast sales are less matched with the current situation and cannot meet user needs. Among them, marketing indicators include historical sales of goods, changes in commodity prices, market penetration and channel coverage. When commodity prices in the market change significantly (such as a sharp increase in prices, reflecting an increase in commodity demand; or a sharp drop in prices, reflecting a decrease in commodity demand), or the market penetration rate has reached a high level (the market potential is low and it is difficult to significantly increase commodity sales), the channel coverage rate changes significantly (large-scale promotion and marketing channels are adopted; or promotion and marketing channels are reduced).

[0129] This application adjusts the priority of the preset priority parameters according to the changes in marketing indicators when there are significant changes in marketing indicators (such as when the marketing indicators change by 20%, 25% or 30%, etc.). For example, when the price of goods drops sharply and the channel coverage rate drops sharply, the e-commerce ERP system adjusts the daily forecast sales accordingly. If the daily forecast sales are low at this time, that is, the number of goods involved in the user's replenishment is small, the preset priority parameters involving the transaction volume of goods, such as the arrival date, will be adjusted to a lower priority, and the preset priority parameters directly related to the logistics cost, such as the logistics price, will be adjusted to a higher priority; thereby making the replenishment recommendation algorithm more adaptable to the current situation, reducing the replenishment cost according to the current situation, and improving operational efficiency, rather than being bound to the user's initial setting status.

[0130] 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 it to obtain the daily predicted sales volume.

[0131] Furthermore, in one embodiment, before step M3, the method further includes the following step M3.1.

[0132] Step M3.1: If there are multiple regions that are out of stock of the same product, and the replenishment quantity cannot simultaneously satisfy the out-of-stock status of the multiple regions, the multiple regions are prioritized for replenishment according to regional sales to generate replenishment suggestions for the multiple regions based on the replenishment priorities.

[0133] Specifically, when warehouses in multiple regions need to replenish the same goods, and the current inventory cannot simultaneously meet the replenishment needs of warehouses in the above multiple regions, the e-commerce ERP system gives priority to warehouses in regions with higher sales for replenishment.

[0134] For example, an e-commerce ERP system ranks the replenishment priorities of each warehouse based on regional sales. Warehouses in areas with better sales will be ranked higher in the replenishment ranking, and warehouses in each region will be replenished in turn based on the ranking results.

[0135] For example, the e-commerce ERP system ranks the replenishment ratio of each warehouse according to regional sales. Warehouses in areas with better sales have higher replenishment ratios. The e-commerce ERP system replenishes warehouses in each region based on the replenishment ratio.

[0136] Furthermore, in one embodiment, before step M1, the method further includes the following step M7.

[0137] Step M7: In response to the preset instruction, determine the time interval for applying the replenishment suggestion function to generate available logistics provider options corresponding to each day within the time interval.

[0138] Specifically, the user triggers the preset instruction, and the e-commerce ERP system determines the time interval for applying the replenishment suggestion function according to the preset instruction, such as the e-commerce ERP system determines to generate corresponding replenishment suggestions for the next month or the next two months. Within the time interval, the e-commerce ERP system initializes and generates available logistics provider options for daily shipments, and removes available logistics provider options whose purchase date is not within the time interval and whose arrival date is not within the time zone.

[0139] As a feasible implementation method, the e-commerce ERP system continuously generates replenishment suggestions corresponding to time intervals at specific time intervals. For example, at 9 o'clock every day, the e-commerce ERP system will generate replenishment suggestions corresponding to the next two months (time interval) based on the current replenishment quantity in transit, the warehouse's daily inventory, and the daily predicted sales, thereby ensuring that the replenishment suggestions are highly timely.

[0140] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. All equivalent transformations made by using the contents of the present application specification and drawings under the inventive concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for processing batch product cost data, characterized in that: A warehouse module for an e-commerce ERP system or an e-commerce management system, the method comprising: Step S1: setting a source number for a batch of products that are first put into storage, and generating an initial product cost associated with the source number; Step S2: determining a target product from the batch products, and in response to a transfer operation performed on the target product, generating a batch number corresponding to the transfer operation according to the source number, and generating a batch product cost according to the initial product cost and an additional fee corresponding to the transfer operation, wherein the transfer operation includes a warehouse-out operation and a warehouse-in operation performed when the target product is transferred between different regions; Step S3: generating first cost data based on the batch number, the batch product cost and the source number, wherein the first cost data is a plurality of inventory product cost data stored in the warehouse module, and the first cost data includes the batch product cost of the target product; Step S4: in response to the cost modification instruction, modify the batch product cost of the target product; Step S5: Based on the source number, determining first product data associated with the target product in the first cost data, and determining second product data in the first product data based on preset factors, wherein the preset parameters include batch number time and batch number generation rules; Step S6: updating the second product data based on the modified batch product cost to obtain second cost data.

2. The method according to claim 1, characterized in that When the preset factor is the batch numbering time, the step S5 of determining the second product data in the first product data based on the preset factor includes: Step S5.1: Determine that the generation time of the batch number of the target product is a preset time standard; 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.

3. The method according to claim 1, characterized in that The batch number generation rule is 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, the step S5 of determining the second product data in the first product data based on the preset factor includes: Step S5.3: In the first product data, the batch number of each batch of products is compared with the batch number of the target product to determine the downstream batch products of the target product, and the batch product cost of the downstream batch products of the target product is determined as the second product data.

4. The method according to claim 1, characterized in that The method further comprises: Step S2.1: In response to the product merging instruction, determining the batch product cost of the third batch of products based on the batch product costs corresponding to the first batch of products and the second batch of products, respectively, wherein the third batch of products is obtained by processing and assembling the first batch of products and the second batch of products; Step S2.2: Generate a batch number for the third batch of products, and associate the batch number for the third batch of products with the batch numbers corresponding to the first batch of products and the second batch of products respectively; and, Step S2.3: Setting a 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.

5. The method according to claim 1, characterized in that The method further comprises: Step S2.4: In response to the product splitting instruction, the batch product costs of the fourth batch of products are used to determine the batch product costs corresponding to the fifth batch of products and the sixth batch of products respectively based on a preset ratio, wherein the fifth batch of products and the sixth batch of products are obtained by splitting the fourth batch of products; Step S2.5: generating batch numbers corresponding to the fifth batch of products and the sixth batch of products respectively, and setting 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, Step S2.6: setting the source numbers corresponding to the fifth batch of products and the sixth batch of products respectively based on the source numbers corresponding to the fourth batch of products.

6. The method according to claim 1, characterized in that The method further comprises: Step S2.7: Determine the order of outbound delivery of each batch of products based on the generation time of the preset incoming documents; Step S2.8: Generate batch numbers and batch product costs based on the delivery order of each batch of products.

7. The method according to claim 6, characterized in that Step S2.8 includes: Step S2.81: In response to a preset delivery instruction, determining a target delivery product corresponding to the preset delivery instruction from each batch of products; Step S2.82: Adjust the delivery order of the target delivery product to priority delivery, and generate a batch number and batch product cost corresponding to the target delivery product.

8. The method according to claim 1, characterized in that After step S6, the method further comprises: Step S7.1: Detecting batch cost data of batches of products of the same type in the second cost data; Step S7.2: Determine the first batch of products according to the test 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; Step S7.3: Analyze the purchase quantity, purchase time and additional costs of the first batch of products, and generate a first optimization suggestion based on the analysis results.

9. The method according to claim 1, characterized in that After step S6, the method further comprises: Step S7.4: Detecting the turnover times of each batch of products in the second cost data; Step S7.5: Determine a second batch of products according to the test results, the turnover number of the second batch of products is higher than the average turnover number, and the difference between the turnover number of the second batch of products and the average turnover number reaches a second preset ratio; Step S7.6: Determine whether the second batch of products are seasonal products or hot-selling products, and generate a second optimization suggestion based on the determination result.

10. An e-commerce product cost data processing system, 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 batch product cost data processing method as described in any one of claims 1 to 9.

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