A wholesale-retail cooperative intelligent warehouse inventory allocation method and system

Abstract

The present application belongs to the field of supply chain management and data processing, and particularly relates to a kind of batch retail collaborative intelligent warehouse inventory allocation method and system, wherein the system includes retail end ERP, for executing local inventory pre-occupation, generating wholesale procurement request, and running pre-occupation order allocation engine;Wholesale OMS, for receiving wholesale procurement request, assembling collaborative decision-making parameters, and generating outbound priority through collaborative matching degree model;Wholesale WMS, for executing physical outbound operation of wholesale end;Intermediate warehouse data collaborative platform, for synchronizing data of both batch and retail ends;Retail WMS, for executing physical outbound operation of retail end.

Description

Technical Field

[0001] This invention belongs to the field of supply chain management and data processing technology, and more specifically, it relates to a smart warehouse inventory allocation method and system for wholesale and retail collaboration. Background Technology

[0002] In the new retail model, fulfilling online B2C orders typically relies on local warehouse inventory at the retail level. To ensure a good user experience, retailers need to stock up on large quantities of goods in advance, which not only ties up huge amounts of capital but also brings the risk of inventory backlog and expiration. When there are popular products or promotional events, retailers often experience stockouts due to insufficient inventory forecasting, leading to order cancellations or customer complaints.

[0003] In existing technologies, such as the invention patent with publication number CN116882905B, a supply chain intelligent inventory management system and method based on big data is disclosed. While the disclosed solution considers scenarios where inventory is transferred from a sub-warehouse to the parent warehouse or other sub-warehouses when a sub-warehouse is out of stock, its decisions are primarily based on logistical factors such as transportation costs and historical on-time delivery rates, lacking a coordinated consideration of the dynamic consumption of inventory at both the wholesale and retail ends and the urgency of order fulfillment. In other words, existing technologies solve the problem of where to replenish inventory, but do not adequately address the issues of how much to replenish, when to replenish, and how to accurately match replenished inventory with orders awaiting fulfillment—a series of collaborative decisions regarding wholesale and retail inventory. This results in information silos between wholesale inventory information and retail fulfillment needs, leaving significant room for improvement in the overall efficiency of the supply chain.

[0004] Therefore, in view of this, we will study and improve the existing structure and its shortcomings, and provide a smart warehouse inventory allocation method and system that combines batch and retail operations, in order to achieve a more practical value. Summary of the Invention

[0005] In view of the problems mentioned in the background art above, the present invention provides a batch-retail collaborative intelligent warehouse inventory allocation method and system.

[0006] On one hand, the technical solution adopted by the present invention is as follows: a smart warehouse inventory allocation method for wholesale and retail collaboration, comprising the following steps: S1: In response to online orders issued by the retail middle platform, the retail ERP system performs a pre-allocation operation on local inventory; S2: If pre-allocation fails, a wholesale purchase request is generated based on out-of-stock product information, and the wholesale purchase request and the order's fulfillment timeliness tag are sent to the wholesale OMS system; S3: The wholesale OMS system receives wholesale purchase requests from at least one retail end, and assembles collaborative decision parameters for each pending purchase request; the collaborative decision parameters include at least the order's fulfillment urgency coefficient and the retail end's historical collaborative trust value; S4: The wholesale OMS system allocates multiple orders based on a preset time window. Wholesale purchase requests are aggregated to generate wholesale outbound task orders; and the matching score between the wholesale outbound task orders and each retail order is calculated using a pre-set collaborative matching degree model to generate outbound priority; S5: After the wholesale WMS completes the outbound operation, it synchronizes the accompanying data to the retail ERP through the intermediate warehouse; S6: The retail ERP updates its local inventory based on the accompanying data and triggers the pre-allocation engine; the allocation engine automatically matches the newly received inventory to orders in the "combining orders" state based on the collaborative matching degree of each pending order, completing the second pre-allocation of inventory; S7: For orders that have completed inventory allocation, the retail ERP sends them to the retail WMS to execute the outbound operation to complete the fulfillment.

[0007] Furthermore, the fulfillment urgency coefficient in S3 is dynamically generated based on the difference between the expected latest delivery time of the order and the current time; the historical collaboration trust value is dynamically updated based on the fulfillment rate and accuracy data of historical purchase requests from the retail end.

[0008] Furthermore, the collaborative matching degree model in S4 is expressed as follows: Where M is the degree of collaboration matching, α is the urgency coefficient of fulfillment, β is the historical collaboration trust value, Q is the replenishment quantity, T is the expected delivery time, w1, w2, w3 are the preset weight coefficients, f1 is the urgency function, f2 is the trust function, and f3 is the inventory balance function.

[0009] Furthermore, the execution logic of the pre-allocation order engine in S6 includes: S61: obtaining a list of all orders in the "combining orders" state; S62: sorting the order list in descending order according to the collaborative matching degree; S63: allocating inventory to the orders in the sorted order until the inventory is exhausted or all orders are fulfilled.

[0010] Furthermore, the S2 wholesale purchase request includes at least the retail ID, a list of out-of-stock SKUs, the quantity out of stock, and a fulfillment urgency coefficient calculated based on the order fulfillment timeliness.

[0011] Secondly, the technical solution adopted by this invention is as follows: a smart warehouse inventory allocation system for wholesale and retail collaboration, including a retail ERP for executing local inventory pre-allocation, generating wholesale purchase requests, and running a pre-allocation order allocation engine; a wholesale OMS for receiving wholesale purchase requests, assembling collaborative decision parameters, and generating outbound priorities through a collaborative matching degree model; a wholesale WMS for executing physical outbound operations at the wholesale end; an intermediate warehouse data collaboration platform for synchronizing data at both the wholesale and retail ends; and a retail WMS for executing physical outbound operations at the retail end.

[0012] Furthermore, the collaborative decision parameters include at least the fulfillment urgency coefficient calculated and reported by the retail ERP based on order timeliness, and the retail historical collaborative trust value dynamically maintained by the wholesale OMS based on historical data.

[0013] Thirdly, the technical solution adopted by the present invention is as follows: a smart warehouse inventory management device for wholesale and retail collaboration, applied to a retail ERP system, including a pre-positioning module for performing order pre-positioning on local inventory and triggering a purchase request generation module when pre-positioning fails; a purchase request generation module for generating and reporting wholesale purchase requests containing fulfillment urgency coefficients based on stockout information; and an allocation engine module for intelligently allocating newly received inventory to pending orders based on pre-stored collaboration matching degree after receiving accompanying data from the wholesale end, thus completing a secondary pre-positioning.

[0014] Fourthly, the technical solution adopted by the present invention is as follows: a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of a batch-to-retail collaborative intelligent warehouse storage allocation method.

[0015] The beneficial effects of this invention are:

[0016] 1. By introducing a collaborative matching quantification model, the wholesale-retail collaborative decision-making is upgraded from a single judgment of inventory availability to a multi-factor intelligent assessment of urgency, trust, and inventory balance, enabling wholesale outbound shipments to more accurately match the urgent fulfillment needs of retail.

[0017] 2. By using the pre-order allocation engine to link replenishment and pending orders in real time, the lag of manual intervention is avoided, which significantly improves the inventory turnover efficiency of retail warehouses and reduces the stockout rate.

[0018] 3. By acquiring data on the coordination and matching between the wholesale and retail ends, wholesalers can more accurately plan upstream procurement, avoid inventory backlog, and optimize overall supply chain costs. Attached Figure Description

[0019] The present invention can be further illustrated by the non-limiting embodiments given in the accompanying drawings;

[0020] Figure 1 This is a flowchart illustrating the batch-retail collaborative intelligent warehouse inventory allocation method of the present invention;

[0021] Figure 2 This is a schematic diagram of the architecture of the batch-retail collaborative intelligent warehouse inventory allocation system of the present invention;

[0022] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present invention. Detailed Implementation

[0023] Example 1:

[0024] like Figure 1 As shown, a smart warehouse inventory allocation method with batch-retail collaboration includes the following steps:

[0025] S1: In response to online orders issued by the retail middle platform, the retail ERP system performs a pre-positioning operation on local inventory;

[0026] The retail middle platform distributes B2C orders from e-commerce platforms, such as Order A and Order B, to the retail ERP system. Upon receiving the order, the retail ERP system first checks the physical inventory of the corresponding SKU in its local warehouse. To ensure order fulfillment, the system logically pre-holds this inventory, effectively locking it.

[0027] In this embodiment, assuming order A requires 10 items of product X, but the local warehouse at the retail end only has 8 items, the pre-order will fail, resulting in a shortage of 2 items. For the 8 items that were successfully pre-ordered, the system will mark the status of order A as "partial pre-order / combining orders".

[0028] S2: If the pre-order fails, a wholesale purchase request is generated based on the out-of-stock product information, and the wholesale purchase request and the order fulfillment time tag are sent to the wholesale OMS system;

[0029] For the out-of-stock portion of the order due to a failed pre-order, such as product X with 2 units out of stock, the retail ERP system does not simply abandon the order. Instead, it automatically triggers a collaborative procurement mechanism. The retail ERP system encapsulates the out-of-stock information with the order's fulfillment timeliness tag, such as "same-day delivery" or "next-day delivery," to generate a structured wholesale purchase request.

[0030] Specifically, the data format of this wholesale purchase request includes: retailer ID, request timestamp, list of out-of-stock SKUs, quantity out of stock, and a calculated fulfillment urgency coefficient α. α is calculated based on the order's estimated latest delivery time, such as the difference between the user's promised delivery time before 24:00 when placing the order and the current system time. The smaller the difference, the larger the α value. For example, an order requiring delivery within 2 hours has an α value of 0.9; an order requiring delivery within 24 hours has an α value of 0.3. This wholesale purchase request is reported to the wholesale OMS system in real time via an API interface.

[0031] S3: The wholesale OMS system receives wholesale purchase requests from at least one retailer and assembles collaborative decision parameters for each pending purchase request; the collaborative decision parameters include at least the order fulfillment urgency coefficient and the retailer's historical collaborative trust value;

[0032] The wholesale OMS system, acting as a central coordination hub, continuously receives wholesale purchase requests from multiple retail outlets, such as Retailer A and Retailer B. To avoid frequent small-order outbound shipments, the wholesale OMS system employs a dynamic time window, aggregating requests every 30 minutes. Within this window, the system aggregates all received requests, generating a single wholesale outbound task order containing multiple SKUs and quantities. Simultaneously, the wholesale OMS system assembles collaborative decision parameters for each pending purchase request—the out-of-stock portion of the original order. In addition to the aforementioned fulfillment urgency coefficient α, the system also retrieves the retailer's historical collaborative trust value β from the database. This β value is a dynamically updated indicator, calculated based on the retailer's historical purchase request fulfillment rate and accuracy rate, ranging from 0 to 1. Retailers with high fulfillment and accuracy rates also have high β values. The fulfillment rate is calculated as actual received quantity / requested quantity; the accuracy rate is calculated as received discrepancies / total orders.

[0033] S4: The wholesale OMS system aggregates multiple wholesale purchase requests based on a preset time window to generate a wholesale outbound task order; and calculates the matching score between the wholesale outbound task order and each retail order through a preset collaborative matching degree model to generate outbound priority.

[0034] The wholesale OMS system uses a pre-built collaborative matching model to match and score the aggregated wholesale outbound orders with the orders from the various retail outlets it serves. The purpose of this model is to determine which retail outlet's out-of-stock orders should be prioritized when a batch of goods needs to be shipped from the wholesale end.

[0035] The formula for calculating the collaborative matching degree model is as follows:

[0036] in, : Collaboration matching degree. The higher the value, the higher the matching priority between the wholesale outbound task and the specific retail order.

[0037] The normalized fulfillment urgency coefficient is normalized so that its value range is between [0,1] because the original α may have different dimensions in different orders.

[0038] β: Historical collaboration trust value at the retail end, read directly from the database; γ: Inventory balance coefficient, generated by the function f3(Q,T). Q is the quantity of goods replenished for this retail end, and T is the estimated delivery time. When the replenishment quantity Q is large and the estimated delivery time T is short, the γ value is high, indicating that this is a large and urgent order, and the wholesale end should prioritize processing it to quickly release inventory pressure. w1, w2, w3: Weighting coefficients, dynamically adjusted by the system administrator according to business strategies. For example, during major promotions, w1 can be increased to focus more on fulfillment timeliness. Through this model, the wholesale OMS system calculates the M value of each retail order and automatically generates a picking priority sequence for wholesale outbound task orders based on the M value. For example, the goods corresponding to the order with the highest M value will be marked as "urgent priority picking".

[0039] S5: After the wholesale WMS completes the outbound operation, it synchronizes the accompanying data to the retail ERP through the intermediate warehouse;

[0040] The wholesale ERP system generates sales invoices based on the generated outbound strategy and notifies the wholesale WMS system to perform physical outbound operations, including picking, verification, and packing. After outbound operations are completed, the wholesale WMS system sends the actual results back to the wholesale ERP system for accounting. At the same time, key data—the accompanying order, including information such as the product, batch, quantity, corresponding original retail order number, and the M-value of the collaboration matching degree—is pushed to the intermediate warehouse.

[0041] S6: The retail ERP updates the local inventory based on the accompanying data and triggers the pre-allocation order allocation engine; the allocation engine automatically matches the newly received inventory to the orders in the "combining orders" state based on the coordination matching degree of each order to be fulfilled, thus completing the second pre-allocation of inventory;

[0042] The retail ERP system monitors the intermediate warehouse in real time. Upon receiving accompanying order data, it automatically triggers either the headquarters' procurement and distribution process or the direct warehouse receiving and shelving process. After the retail WMS completes the physical shelving, the inventory data is updated to the retail ERP. At this point, the retail ERP's pre-allocation order engine is activated. The engine's logic is as follows: First, it scans all orders in the "placing orders" state, i.e., those orders with insufficient pre-allocation of inventory. Second, based on the coordination matching degree M calculated when each order in the placing order was previously reported to the wholesale OMS (this value has been retrieved from the synchronized accompanying order), the order list is sorted in descending order. The order with the highest M value is ranked first. Finally, the newly received inventory is pre-allocated a second time according to the sorting results, i.e., the inventory is officially allocated to these orders. For example, 10 items X are newly received. The system finds order A with 2 missing items (M=0.95), order B with 5 missing items (M=0.85), and order C with 8 missing items (M=0.6). The allocation engine will first allocate 2 items to order A, and the status of order A will change to "Pre-reservation completed / Pending shipment"; then allocate 5 items to order B, and the status of order B will change to "Pre-reservation completed"; the remaining 3 items will be allocated to order C, and the status of order C will change to "Partial pre-reservation".

[0043] S7: For orders where inventory allocation has been completed, the retail ERP sends them to the retail WMS to execute outbound operations to fulfill the orders.

[0044] For orders whose status has changed to "pre-order completed," the retail ERP immediately forwards them to the retail WMS. The retail WMS performs wave preparation, picking, and outbound verification, and finally hands them over to logistics for delivery. After outbound delivery is completed, the retail ERP records the sales and synchronizes the shipment status to the retail middle platform, completing the closed-loop fulfillment of the entire order. Through the above steps, this invention achieves end-to-end collaboration from "retail stockout triggering" to "wholesale intelligent outbound delivery" and then to "retail precise allocation of fulfillment," with the entire process being highly automated and decision-making intelligent.

[0045] Example 2:

[0046] like Figure 2 As shown, a batch-retail collaborative intelligent warehouse inventory allocation system includes...

[0047] Retail ERP: Used to receive online orders, execute local inventory pre-positioning; generate wholesale purchase requests when pre-positioning fails; and run the pre-positioning order allocation engine to intelligently match newly received inventory to pending orders.

[0048] Wholesale OMS: Communicates with the retail ERP system to receive wholesale purchase requests; assembles collaborative decision parameters (fulfillment urgency coefficient α, historical collaborative trust value β); runs the collaborative matching degree model to generate picking priorities for wholesale outbound task orders.

[0049] Wholesale WMS: Receives outbound instructions from the wholesale ERP system, executes physical picking, verification, and outbound operations, and provides feedback on performance.

[0050] Intermediate warehouse data collaboration platform: As a bridge for data interaction between wholesale and retail, it is responsible for synchronizing the accompanying orders from the wholesale end to the retail end, ensuring data consistency and real-time performance.

[0051] Retail WMS: Receives sales orders from the retail ERP system and performs warehouse operations such as wave picking, verification, and outbound delivery.

[0052] Through the collaborative work of its various modules, the system has built an intelligent and efficient integrated wholesale and retail inventory distribution network.

[0053] Appendix Figure 3 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 3 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0054] As attached Figure 3 As shown, the computer system includes a central processing unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as executing the methods described in the above embodiments. The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0055] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard drives; and communication sections including network interface cards such as LAN (local area network) cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0056] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs various functions defined in the system of this application.

[0057] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the rule engine configuration method for early warning as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0058] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, system, or device. Computer programs contained on computer-readable media can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0059] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0060] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0061] The present invention has been described in detail above. The specific embodiments are provided only to help understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A smart warehouse inventory allocation method with batch-retail collaboration, characterized in that: Includes the following steps S1: In response to online orders issued by the retail middle platform, the retail ERP system performs a pre-positioning operation on local inventory; S2: If the pre-order fails, a wholesale purchase request is generated based on the out-of-stock product information, and the wholesale purchase request and the order fulfillment time tag are sent to the wholesale OMS system; S3: The wholesale OMS system receives wholesale purchase requests from at least one retail end and assembles collaborative decision parameters for each pending purchase request; The collaborative decision-making parameters include at least the order fulfillment urgency coefficient and the historical collaborative trust value at the retail end; S4: The wholesale OMS system aggregates multiple wholesale purchase requests based on a preset time window to generate a wholesale outbound task order; and calculates the matching score between the wholesale outbound task order and each retail order through a preset collaborative matching degree model to generate outbound priority. S5: After the wholesale WMS completes the outbound operation, it synchronizes the accompanying data to the retail ERP through the intermediate warehouse; S6: The retail ERP updates the local inventory based on the accompanying data and triggers the pre-allocation engine for orders. The allocation engine automatically matches newly received inventory to orders in the "combining orders" state based on the coordination and matching degree of each order to be fulfilled, thus completing the secondary pre-occupancy of inventory; S7: For orders where inventory allocation has been completed, the retail ERP sends them to the retail WMS to execute outbound operations to fulfill the orders.

2. The intelligent warehouse inventory allocation method for batch-retail collaboration according to claim 1, characterized in that: The fulfillment urgency coefficient in S3 is dynamically generated based on the difference between the expected latest delivery time of the order and the current time; the historical collaboration trust value is dynamically updated based on the fulfillment rate and accuracy data of historical purchase requests from the retail end.

3. The intelligent warehouse inventory allocation method for batch-retail collaboration according to claim 2, characterized in that: The collaborative matching degree model in S4 is expressed as follows: Where M is the degree of collaboration matching, α is the urgency coefficient of fulfillment, β is the historical collaboration trust value, Q is the replenishment quantity, T is the expected delivery time, w1, w2, w3 are preset weight coefficients, f1 is the urgency function, f2 is the trust function, and f3 is the inventory balance function.

4. The intelligent warehouse inventory allocation method for batch-retail collaboration according to claim 3, characterized in that: The execution logic of the pre-allocation order engine in S6 includes: S61: Retrieve a list of all orders currently in the "Add to Orders" state; S62: Sort the order list in descending order according to the collaborative matching degree; S63: Allocate inventory to orders in order of priority until inventory is depleted or all orders are fulfilled.

5. The intelligent warehouse inventory allocation method for batch-retail collaboration according to claim 4, characterized in that: The S2 wholesale purchase request includes at least the retail ID, a list of out-of-stock SKUs, the quantity out of stock, and a fulfillment urgency coefficient calculated based on the order fulfillment timeliness.

6. A batch-retail collaborative intelligent warehouse inventory allocation system, characterized in that: include Retail ERP is used to execute local inventory pre-positioning, generate wholesale purchase requests, and run the pre-positioned order allocation engine; Wholesale OMS is used to receive wholesale purchase requests, assemble collaborative decision parameters, and generate outbound priorities through a collaborative matching degree model. Wholesale WMS is used to execute physical outbound operations at the wholesale end; An intermediate database data collaboration platform is used to synchronize data between the batch and retail ends; Retail WMS is used to perform physical outbound operations at the retail level; The system is used to perform the method according to any one of claims 1 to 5.

7. The intelligent warehouse inventory allocation system with batch-retail collaboration according to claim 6, characterized in that: The collaborative decision-making parameters include at least the fulfillment urgency coefficient calculated and reported by the retail ERP based on order timeliness, and the retail historical collaborative trust value dynamically maintained by the wholesale OMS based on historical data.

8. A smart warehouse inventory management device for batch and retail collaboration, characterized in that: Applied to retail ERP systems, including The pre-positioning module is used to pre-position orders for local inventory and trigger the purchase request generation module when pre-positioning fails. The purchase request generation module is used to generate and report wholesale purchase requests containing a fulfillment urgency coefficient based on stockout information. The allocation engine module is used to intelligently allocate newly received inventory to pending orders based on the pre-stored collaboration matching degree after receiving the accompanying data from the wholesale end, thus completing the secondary pre-allocation.

9. A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 5.

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