Supply Chain Replenishment Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to a supply chain replenishment method, apparatus, computer device, and storage medium. The method includes: obtaining the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area; determining an initial function value for calculating replenishment reference data, a functional relationship for iterative calculation, and an iteration stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area; determining an initial replenishment lot size of the product according to the initial function value, product cost data, product geometric feature data, and the accommodation space data of the picking area; determining a final replenishment lot size of the product and a final reorder point of the product according to the initial replenishment lot size of the product, the functional relationship for iterative calculation, and the iteration stop condition; and determining product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data of the product, the final replenishment lot size of the product, and the final reorder point of the product.

Description

Technical Field

[0001] The present application relates to the technical field of logistics, and particularly to a supply chain replenishment method, device, computer device, and storage medium. Background Art

[0002] In the application scenario of the warehousing supply chain, the warehouse is divided into a storage area and a picking area. The storage area is the area where products are stored, and the picking area is the area where pickers pack and ship according to customer orders. When the remaining quantity of products in the picking area is insufficient, replenishment is required from the storage area to the picking area.

[0003] For the existing replenishment solutions, generally, warehouse personnel manually evaluate the replenishment situation of the item based on the historical sales volume of the item, such as whether replenishment is required and the replenishment quantity. However, with the change of sales methods and the increasing variety of item types, predicting the replenishment quantity based on the historical sales volume of the item by warehouse personnel has the problem of low accuracy in predicting the replenishment quantity. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a supply chain replenishment method, device, computer device, and storage medium that can improve the accuracy of predicting the replenishment quantity.

[0005] A supply chain replenishment method, the method comprising:

[0006] Obtain the actual inventory data, product cost data, product geometric feature data of the products in the picking area in the supply chain, and the accommodation space data of the picking area;

[0007] Based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes product replenishment batch and product reorder point;

[0008] Determine the initial product replenishment batch according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area;

[0009] Determine the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition;

[0010] Determine the product replenishment data for replenishing the products from the storage area to the picking area in the supply chain according to the actual inventory data of the products, the final product replenishment batch, and the final product reorder point.

[0011] In one embodiment, determining the final replenishment quantity and the final reorder point of the product according to the initial replenishment quantity of the product, the function relationship calculated in a loop, and the loop stop condition includes:

[0012] Obtain the first function relationship between the product cost data, the product geometric feature data and the correction coefficient, and the second function relationship between the correction coefficient for loop calculation and the product geometric feature data;

[0013] According to the initial replenishment quantity of the product and the first function relationship, obtain the replenishment reference data for the current loop;

[0014] Determine the correction coefficient for the next loop according to the second function relationship;

[0015] Based on the correction coefficient for the next loop and the first function relationship, obtain the replenishment reference data for the next loop;

[0016] When the difference between the replenishment reference data for the next loop and the replenishment reference data for the current loop is less than a preset threshold, use the product replenishment quantity for the next loop as the final replenishment quantity of the product, and use the product reorder point for the next loop as the final reorder point of the product.

[0017] In one embodiment, obtaining the replenishment reference data for the current loop according to the initial replenishment quantity of the product and the first function relationship includes:

[0018] Perform a derivative operation on the first function relationship to obtain a third function relationship between the product demand empirical distribution data and the product replenishment quantity;

[0019] According to the initial replenishment quantity of the product and the third function relationship, obtain the product demand empirical distribution value for the current loop;

[0020] Based on the product demand empirical distribution value for the current loop, determine the product reorder point for the current loop.

[0021] In one embodiment, after determining the product reorder point for the current loop based on the product demand empirical distribution value for the current loop, it further includes:

[0022] Perform a derivative operation on the first function relationship to obtain a fourth function relationship between the product replenishment quantity, the product cost data, the product geometric feature data, and the correction coefficient;

[0023] Determine the product out-of-stock data according to the product reorder point for the current loop;

[0024] According to the product out-of-stock data and the fourth function relationship, obtain the product replenishment quantity for the current loop.

[0025] In one embodiment, determining the product replenishment data for replenishing from the storage area to the picking area in the supply chain according to the actual product inventory data, the final replenishment lot size of the product, and the final reorder point of the product includes:

[0026] When the actual product inventory data is less than or equal to the final reorder point of the product, using the final replenishment lot size data as the product replenishment data for replenishing from the storage area to the picking area in the supply chain;

[0027] When the actual product inventory data is greater than the reorder point of the product, determining that the product replenishment data for replenishing from the storage area to the picking area in the supply chain is 0.

[0028] In one embodiment, the product cost data includes product holding cost, product shortage cost, and product reorder cost.

[0029] In one embodiment, the product geometric feature data includes product volume.

[0030] A supply chain replenishment device, the device includes:

[0031] A data acquisition module, configured to acquire the actual product inventory data, product cost data, product geometric feature data in the picking area of the supply chain, and the accommodation space data of the picking area;

[0032] A loop processing module, configured to determine an initial function value for calculating replenishment reference data, a function relationship for loop calculation, and a loop stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area, where the replenishment reference data includes product replenishment lot size and product reorder point;

[0033] A first processing module, configured to determine an initial product replenishment lot size according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area;

[0034] A second processing module, configured to determine the final product replenishment lot size and the final reorder point of the product according to the initial product replenishment lot size, the function relationship for loop calculation, and the loop stop condition;

[0035] A replenishment data determination module, configured to determine the product replenishment data for replenishing from the storage area to the picking area in the supply chain according to the actual product inventory data, the final product replenishment lot size, and the final reorder point of the product.

[0036] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Obtain the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area;

[0038] Based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes product replenishment batch and product reorder point;

[0039] Determine the initial product replenishment batch according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area;

[0040] Determine the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition;

[0041] Determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data of the product, the final product replenishment batch, and the final product reorder point.

[0042] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area;

[0044] Based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes product replenishment batch and product reorder point;

[0045] Determine the initial product replenishment batch according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area;

[0046] Determine the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition;

[0047] Determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data of the product, the final product replenishment batch, and the final product reorder point.

[0048] The above-mentioned supply chain replenishment method, device, computer equipment and storage medium obtain the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area; based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes the product replenishment batch and the product reorder point; determine the initial product replenishment batch according to the initial function value, product cost data, product geometric feature data, and the accommodation space data of the picking area; determine the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition; determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data, the final product replenishment batch, and the final product reorder point. The above replenishment plan is based on the principle of minimizing replenishment cost and the space constraint of the picking area, and through iterative calculation according to the product cost data, product geometric feature data, and the accommodation space data of the picking area, determines the final product replenishment batch and the final product reorder point. The replenishment reference data obtained thereby is more accurate, thus improving the accuracy of replenishment quantity prediction. Brief Description of the Drawings

[0049] Figure 1 FIG. is an application environment diagram of the supply chain replenishment method in an embodiment;

[0050] Figure 2 FIG. is a schematic flowchart of the supply chain replenishment method in an embodiment;

[0051] Figure 3 FIG. is a schematic flowchart of the step for determining replenishment reference data in an embodiment;

[0052] Figure 4 FIG. is a structural block diagram of the supply chain replenishment device in an embodiment;

[0053] Figure 5 FIG. is an internal structure diagram of the computer equipment in an embodiment. Detailed Description of the Embodiments

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] The supply chain replenishment method provided by the present application can be applied as Figure 1In the application environment shown, the user imports the actual inventory data, product cost data, product geometric feature data, and the accommodation space data of the picking area in the supply chain into the mobile terminal through the mobile terminal. The mobile terminal obtains the actual inventory data, product cost data, product geometric feature data, and the accommodation space data of the picking area in the supply chain; based on the principle of minimizing replenishment cost and the space constraint of the picking area, determines the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes the product replenishment batch and the product reorder point; determines the initial product replenishment batch according to the initial function value, product cost data, product geometric feature data, and the accommodation space data of the picking area; determines the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition; determines the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data, the final product replenishment batch, and the final product reorder point. Among them, the mobile terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc. Specifically, the mobile terminal executes the supply chain replenishment method in the embodiments of the present application through a processor.

[0056] In one embodiment, as Figure 2 shown, a supply chain replenishment method is provided. Taking the mobile terminal in Figure 1 as an example, the method includes the following steps:

[0057] Step 202, obtain the actual inventory data, product cost data, product geometric feature data, and the accommodation space data of the picking area in the supply chain.

[0058] The actual inventory data of the product refers to the remaining data of the product in the warehouse picking area. The product cost data refers to the data that affects the total cost during the replenishment cycle. The product cost data specifically includes the product holding cost, the product shortage cost, and the product reorder cost. The product geometric feature data may specifically include the product volume, and the accommodation space data of the picking area is used to identify the space accommodation capacity of the picking area.

[0059] Step 204, based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes the product replenishment batch and the product reorder point.

[0060] The principle of minimizing replenishment cost and the space constraint of the picking area can be specifically expressed as:

[0061]

[0062] Among them, Denote the total expected holding cost of the $i$-th product during the replenishment cycle. is the average order quantity of the $i$-th product, $R$ i $-D$ i $l$ i is the safety stock of the $i$-th product during the replenishment cycle. Denote the expected reordering cost of the $i$-th product during the replenishment cycle. Denote the shortage cost of the $i$-th product during the replenishment cycle, $Q$ i $+R$ i Denote the replenishment lot size of the $i$-th product + the reorder point of the $i$-th product, i.e., the maximum storage quantity in the picking area; $v$ i Denote the volume of the $i$-th product, $V$ represents the allowable storage volume in the picking area.

[0063] Step 206: Determine the initial replenishment lot size of the product according to the initial function value, product cost data, product geometric feature data, and the accommodation space data of the picking area.

[0064] Specifically, assume Take the derivative of the replenishment lot size $Q$ i to obtain: Let $\lambda = 0$, $p$ i $= 0$, and obtain

[0065] Step 208: Determine the final replenishment lot size of the product and the final reorder point of the product according to the initial replenishment lot size of the product, the function relationship of the cyclic calculation, and the cyclic stop condition.

[0066] According to the initial replenishment lot size of the product, the function relationship of the cyclic calculation, and the cyclic stop condition, perform cyclic calculation and solution on the replenishment reference data until the cyclic stop condition is met. At this time, obtain the final replenishment lot size of the product and the final reorder point of the product.

[0067] Step 210: Determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data of the product, the final replenishment lot size of the product, and the final reorder point of the product.

[0068] After determining the replenishment batch and reorder point of a certain product, compare the inventory of the product in the picking area with the reorder point of the product. According to the comparison result of the inventory of the product in the picking area and the reorder point of the product, determine the replenishment quantity for the product replenishment from the storage area to the picking area. Specifically, according to the actual inventory data of the product, the final replenishment batch of the product, and the final reorder point of the product, determine the product replenishment data for the product replenishment from the storage area to the picking area in the supply chain, including: when the actual inventory data of the product is less than or equal to the reorder point of the product, use the final replenishment batch of the product as the product replenishment data for the product replenishment from the storage area to the picking area in the supply chain; when the actual inventory data of the product is greater than the final reorder point of the product, determine that the product replenishment data for the product replenishment from the storage area to the picking area in the supply chain is 0.

[0069] The above supply chain replenishment method obtains the actual inventory data of the product in the picking area, the product cost data, the product geometric feature data, and the accommodation space data of the picking area in the supply chain; based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition. The replenishment reference data includes the product replenishment batch and the product reorder point; according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area, determine the initial product replenishment batch; according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition, determine the final product replenishment batch and the final reorder point of the product; according to the actual inventory data of the product, the final product replenishment batch, and the final reorder point of the product, determine the product replenishment data for the product replenishment from the storage area to the picking area in the supply chain. The above replenishment plan, based on the principle of minimizing replenishment cost and the space constraint of the picking area, performs iterative calculation according to the product cost data, the product geometric feature data, and the accommodation space data of the picking area to determine the final product replenishment batch and the final reorder point of the product. The replenishment reference data obtained thereby is more accurate, and thus the accuracy of replenishment quantity prediction can be improved.

[0070] In one embodiment, such as Figure 3As shown, according to the initial replenishment batch of the product, the functional relationship calculated cyclically, and the cycle stop condition, determining the final replenishment batch of the product and the final reorder point of the product includes: Step 302, obtaining the first functional relationship between the product cost data, the product geometric feature data, and the correction coefficient, and the second functional relationship between the correction coefficient for cyclic calculation and the product geometric feature data; Step 304, obtaining the replenishment reference data for the current cycle according to the initial replenishment batch of the product and the first functional relationship; Step 306, determining the correction coefficient for the next cycle according to the second functional relationship; Step 308, obtaining the replenishment reference data for the next cycle based on the correction coefficient for the next cycle and the first functional relationship; Step 310, when the difference between the replenishment reference data for the next cycle and the replenishment reference data for the current cycle is less than the preset threshold, taking the product replenishment batch for the next cycle as the final replenishment batch of the product, and taking the product reorder point for the next cycle as the final reorder point of the product. Specifically, the second functional relationship can be expressed as: where λ represents the correction coefficient, α represents the learning rate, which is a constant defined by the user, and v i represents the volume of the i-th product. The first functional relationship can be expressed as where C(Q,R) represents the total cost within the replenishment cycle, v i represents the volume of the i-th product, Q i represents the replenishment batch of the i-th product, R i is the reorder point of the i-th product, V represents the total allowable storage volume of the warehouse. The product cost data D i represents the expected daily demand of the i-th product, h i represents the holding cost of the i-th product, p i represents the shortage cost of the i-th product, l i represents the replenishment lead time of the i-th product, K i represents the reorder cost of the i-th product, n(R i ) represents the expected value of the shortage quantity of the i-th product.

[0071] In one embodiment, replenishment reference data for the current cycle is obtained according to the initial replenishment batch of the product and the first functional relationship, including: performing a derivative operation on the first functional relationship to obtain a third functional relationship between the empirical distribution data of product demand and the product replenishment batch; obtaining the empirical distribution value of product demand for the current cycle according to the initial replenishment batch of the product and the third functional relationship; determining the reorder point of the product for the current cycle based on the empirical distribution value of product demand for the current cycle. Specifically, after determining the reorder point of the product for the current cycle based on the empirical distribution value of product demand for the current cycle, it further includes: performing a derivative operation on the first functional relationship to obtain a fourth functional relationship between the product replenishment batch and product cost data, product geometric feature data, and correction factors; determining product shortage data according to the reorder point of the product for the current cycle; obtaining the product replenishment batch for the current cycle according to the product shortage data and the fourth functional relationship. Performing a derivative on the first functional relationship means respectively taking the derivatives of the replenishment batch Q i and the reorder point R i to obtain Let λ = 0, p i = 0, and obtain Substitute into to obtain the empirical distribution function value F(R i ) corresponding to product demand. Based on the empirical distribution function value F(R i ), calculate the Z value of the standard normal distribution through ; after obtaining the Z value of the product, calculate the reorder point R value of the product according to R = σZ + μ. Based on the reorder point R value of the product, calculate the expected value of the product shortage quantity through ; based on the expected value of the product shortage quantity and calculate the replenishment batch Q i .

[0072] In one embodiment, the product replenishment batch and the product reorder point can be determined through a replenishment model. Input the product cost data, product geometric feature data, and the accommodation space data of the picking area into the replenishment model, and obtain the product replenishment batch and the product reorder point based on the output data of the replenishment model. The goal of the replenishment model is to minimize the cost within the replenishment cycle and meet the warehousing space limit. The cost includes holding cost, shortage cost, and purchase cost.

[0073] Assume that the demand distribution of the product follows a normal distribution, and the parameter definitions are as follows: D i represents the daily demand expected value of the i-th SKU (Stock Keeping Unit), h i represents the holding cost of the i-th SKU, unit: yuan / item; p iDenote the out-of-stock cost of the \(i\)th SKU, unit: yuan per piece; \(l\) i Denote the replenishment lead time of the \(i\)th SKU, unit: days; \(K\) i Denote the reorder cost of the \(i\)th SKU, unit: yuan per piece; \(\beta\) i Denote the target order fill rate of the \(i\)th SKU, \(0\leqslant\beta\) i \(\leqslant1\); \(n(R\) i ) Denote the expected value of the shortage quantity of the \(i\)th SKU, where \(R\) i is the reorder point of the \(i\)th SKU, \(\mu\) is the average demand during the replenishment lead time, \(\sigma\) is the standard deviation of the demand during the replenishment lead time. \(L\) is the standard loss function; \(Q\) i Denote the replenishment lot size of the \(i\)th SKU, \(f(x)\) denotes the demand distribution function during the replenishment lead time, \(N\) denotes the total number of SKUs; \(v\) i Denote the volume of the \(i\)th SKU, unit: \(m\) 3 ; \(V\) denotes the total allowable storage volume of the warehouse, unit: \(m\) 3 ; \(C(Q, R)\) denotes the total cost during the replenishment cycle.

[0074] The objective of the replenishment model is to minimize the total cost during the replenishment cycle and satisfy the warehouse space constraint. Thus, the expression of the replenishment model is as follows:

[0075]

[0076] where, Denote the expected total holding cost of the \(i\)th SKU during the replenishment cycle, is the average order quantity of the \(i\)th SKU, \(R\) i -D i \(l\) i is the safety stock of the \(i\)th SKU during the replenishment cycle. Denote the expected reorder cost of the \(i\)th SKU during the replenishment cycle, Denote the out-of-stock cost of the \(i\)th SKU during the replenishment cycle, \(Q\) i +R i Denote the replenishment lot size of the \(i\)th SKU + the reorder point of the \(i\)th SKU, that is, the maximum storage quantity.

[0077] Introduce the Lagrange multiplier to solve the above expression of the replenishment model. Assume the Lagrange multiplier is \(\lambda\), \(\lambda\geqslant0\), and we get:

[0078]

[0079] Take the partial derivatives of the above formula with respect to the replenishment lot size \(Q\) i and the reorder point \(R\) i respectively, and we get:

[0080]

[0081]

[0082]

[0083]

[0084] Among them, F(R i ) represents the empirical distribution function corresponding to the product demand.

[0085] The data processing flow of the replenishment model is obtained as follows:

[0086] 1. Initialize L(Q, R), let λ = 0, p i = 0, and obtain

[0087] 2. Since Substitute the result obtained from the initialization process into it to obtain the value of the empirical distribution function F(R ) corresponding to the product demand. i )

[0088] 3. Based on the value of the empirical distribution function F(R i ) corresponding to the product demand, calculate the Z value of the standard normal distribution through z Ri = 1 - F(R i ); after obtaining the Z value of the product, calculate the reorder point R value of the product according to R = σz R + μ.

[0089] 4. Based on the reorder point R value of the product, calculate the expected value of the product shortage quantity through ; based on the expected value of the product shortage quantity and calculate the replenishment batch Q of the product i .

[0090] 5. Update λ according to , where α represents the learning rate, which is a constant defined by the user. Repeat the multi-round calculation of the product reorder point and the product replenishment batch until the difference between the product reorder point and the product replenishment batch obtained in a certain round of calculation and the product reorder point and the product replenishment batch obtained in the previous round of calculation is less than the preset value, then stop the iterative calculation of updating λ, that is, the product reorder point R and the product replenishment batch Q obtained in this round of calculation converge, and the space limit meets the requirements. If the iterative calculation cannot stop, it may be that the space limit is too small to meet the requirements of the target order fulfillment rate.

[0091] It should be understood that although Figure 2-3The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-3 At least some of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least some of the steps or stages in other steps or other steps.

[0092] In one embodiment, as Figure 4 shown, a supply chain replenishment device is provided, including: a data acquisition module 402, a loop processing module 404, a first processing module 406, a second processing module 408, and a replenishment data determination module 410. Among them, the data acquisition module 402 is used to acquire the actual inventory data of products in the picking area of the supply chain, product cost data, product geometric feature data, and the accommodation space data of the picking area; the loop processing module 404 is used to determine the initial function value for calculating replenishment reference data, the function relationship for loop calculation, and the loop stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area. The replenishment reference data includes product replenishment batch and product reorder point; the first processing module 406 is used to determine the initial product replenishment batch according to the initial function value, product cost data, product geometric feature data, and the accommodation space data of the picking area; the second processing module 408 is used to determine the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the function relationship for loop calculation, and the loop stop condition; the replenishment data determination module 410 is used to determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual product inventory data, the final product replenishment batch, and the final product reorder point.

[0093] In one embodiment, the second processing module is further used to obtain the first function relationship between product cost data, product geometric feature data, and a correction coefficient, and the second function relationship between the correction coefficient for loop calculation and product geometric feature data; obtain the replenishment reference data for the current loop according to the initial product replenishment batch and the first function relationship; determine the correction coefficient for the next loop according to the second function relationship; obtain the replenishment reference data for the next loop based on the correction coefficient for the next loop and the first function relationship; when the difference between the replenishment reference data for the next loop and the replenishment reference data for the current loop is less than a preset threshold, use the product replenishment batch for the next loop as the final product replenishment batch, and use the product reorder point for the next loop as the final product reorder point.

[0094] In one embodiment, the second processing module is further configured to perform a derivative processing on the first function relationship to obtain a third function relationship between the product demand empirical distribution data and the product replenishment lot size; according to the initial product replenishment lot size and the third function relationship, obtain the product demand empirical distribution value of the current cycle; and based on the product demand empirical distribution value of the current cycle, determine the product reorder point of the current cycle.

[0095] In one embodiment, the second processing module is further configured to perform a derivative processing on the first function relationship to obtain a fourth function relationship between the product replenishment lot size, the product cost data, the product geometric feature data, and the correction coefficient; determine the product out-of-stock data according to the product reorder point of the current cycle; and according to the product out-of-stock data and the fourth function relationship, obtain the product replenishment lot size of the current cycle.

[0096] In one embodiment, the replenishment data determination module is further configured to use the product final replenishment lot size data as the product replenishment data for replenishing from the storage area to the picking area in the supply chain when the actual product inventory data is less than or equal to the product final reorder point; and when the actual product inventory data is greater than the product reorder point, determine that the product replenishment data for replenishing from the storage area to the picking area in the supply chain is 0.

[0097] For the specific limitations of the supply chain replenishment device, reference can be made to the limitations on the supply chain replenishment method in the foregoing text, which will not be elaborated here. Each module in the above supply chain replenishment device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0098] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5As shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a supply chain replenishment method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0099] Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0100] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented: obtaining the actual inventory data, product cost data, product geometric feature data, and storage space data of the picking area in the supply chain; determining the initial function value for calculating replenishment reference data, the function relationship for iterative calculation, and the iteration stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area. The replenishment reference data includes the product replenishment batch and the product reorder point; determining the initial product replenishment batch according to the initial function value, product cost data, product geometric feature data, and storage space data of the picking area; determining the final product replenishment batch and the final product reorder point according to the initial product replenishment batch, the function relationship for iterative calculation, and the iteration stop condition; determining the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual inventory data, the final product replenishment batch, and the final product reorder point.

[0101] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining a first functional relationship between product cost data, product geometric feature data, and a correction coefficient, and a second functional relationship between the correction coefficient for iterative calculation and product geometric feature data; obtaining replenishment reference data for the current cycle according to the initial product replenishment lot size and the first functional relationship; determining the correction coefficient for the next cycle according to the second functional relationship; obtaining replenishment reference data for the next cycle based on the correction coefficient for the next cycle and the first functional relationship; when the difference between the replenishment reference data for the next cycle and the replenishment reference data for the current cycle is less than a preset threshold, taking the product replenishment lot size for the next cycle as the final product replenishment lot size, and taking the product reorder point for the next cycle as the final product reorder point.

[0102] In one embodiment, when the processor executes the computer program, the following steps are further implemented: performing a derivative operation on the first functional relationship to obtain a third functional relationship between the empirical product demand distribution data and the product replenishment lot size; obtaining the empirical product demand distribution value for the current cycle according to the initial product replenishment lot size and the third functional relationship; determining the product reorder point for the current cycle based on the empirical product demand distribution value for the current cycle.

[0103] In one embodiment, when the processor executes the computer program, the following steps are further implemented: performing a derivative operation on the first functional relationship to obtain a fourth functional relationship between the product replenishment lot size, product cost data, product geometric feature data, and the correction coefficient; determining product out-of-stock data according to the product reorder point for the current cycle; obtaining the product replenishment lot size for the current cycle according to the product out-of-stock data and the fourth functional relationship.

[0104] In one embodiment, when the processor executes the computer program, the following steps are further implemented: when the actual product inventory data is less than or equal to the final product reorder point, taking the final product replenishment lot size data as the product replenishment data for replenishing from the storage area to the picking area in the supply chain; when the actual product inventory data is greater than the product reorder point, determining that the product replenishment data for replenishing from the storage area to the picking area in the supply chain is 0.

[0105] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtaining actual inventory data of products, product cost data, product geometric feature data, and accommodation space data of the picking area in the supply chain; determining an initial function value for calculating replenishment reference data, a functional relationship for iterative calculation, and an iteration stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area, where the replenishment reference data includes product replenishment batch and product reorder point; determining an initial product replenishment batch according to the initial function value, product cost data, product geometric feature data, and accommodation space data of the picking area; determining a final product replenishment batch and a final product reorder point according to the initial product replenishment batch, the functional relationship for iterative calculation, and the iteration stop condition; and determining product replenishment data for replenishing products from the storage area to the picking area in the supply chain according to the actual inventory data of products, the final product replenishment batch, and the final product reorder point.

[0106] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining a first functional relationship between product cost data, product geometric feature data, and a correction coefficient, and a second functional relationship between the correction coefficient for iterative calculation and product geometric feature data; obtaining replenishment reference data for the current iteration according to the initial product replenishment batch and the first functional relationship; determining the correction coefficient for the next iteration according to the second functional relationship; obtaining replenishment reference data for the next iteration based on the correction coefficient for the next iteration and the first functional relationship; when the difference between the replenishment reference data for the next iteration and the replenishment reference data for the current iteration is less than a preset threshold, taking the product replenishment batch for the next iteration as the final product replenishment batch and taking the product reorder point for the next iteration as the final product reorder point.

[0107] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: performing a derivative operation on the first functional relationship to obtain a third functional relationship between the empirical distribution data of product demand and the product replenishment batch; obtaining the empirical distribution value of product demand for the current iteration according to the initial product replenishment batch and the third functional relationship; and determining the product reorder point for the current iteration based on the empirical distribution value of product demand for the current iteration.

[0108] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: performing a derivative operation on the first functional relationship to obtain a fourth functional relationship between the product replenishment batch and product cost data, product geometric feature data, and the correction coefficient; determining product out-of-stock data according to the product reorder point for the current iteration; and obtaining the product replenishment batch for the current iteration according to the product out-of-stock data and the fourth functional relationship.

[0109] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: when the actual inventory data of a product is less than or equal to the final reorder point of the product, the final replenishment batch data of the product is used as the product replenishment data for replenishing the picking area from the storage area in the supply chain; when the actual inventory data of the product is greater than the reorder point of the product, it is determined that the product replenishment data for replenishing the picking area from the storage area in the supply chain is 0.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-described method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0111] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0112] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A supply chain replenishment method, characterized in that, the method includes: Obtain the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area, wherein the product cost data includes product holding cost, product shortage cost, and product reorder cost; Based on the principle of minimizing replenishment cost and the space constraint of the picking area, determine the initial function value for calculating replenishment reference data, the functional relationship for iterative calculation, and the iteration stop condition, where the replenishment reference data includes product replenishment quantity and product reorder point; Determine the initial product replenishment quantity according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area; Obtain the first functional relationship between the product cost data, the product geometric feature data and the correction coefficient, and the second functional relationship between the correction coefficient for loop calculation and the product geometric feature data; obtain the replenishment reference data for the current loop according to the initial replenishment batch of the product and the first functional relationship; determine the correction coefficient for the next loop according to the second functional relationship; obtain the replenishment reference data for the next loop based on the correction coefficient for the next loop and the first functional relationship; when the difference between the replenishment reference data for the next loop and the replenishment reference data for the current loop is less than the preset threshold, use the product replenishment batch for the next loop as the final product replenishment batch, and use the product reorder point for the next loop as the final product reorder point, where the first functional relationship is expressed by the formula: , represents the total cost within the replenishment cycle, represents the correction coefficient, represents the volume of the th product, represents the replenishment batch of the th product, represents the reorder point of the th product; the second functional relationship is expressed by the formula: , is the learning rate, is the correction coefficient for the next loop; Determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual product inventory data, the final product replenishment quantity, and the final product reorder point.

2. The method according to claim 1, characterized in that, the obtaining the replenishment reference data of the current iteration according to the initial product replenishment quantity and the first functional relationship includes: Derive the first functional relationship to obtain a third functional relationship between the empirical distribution data of product demand and the product replenishment quantity; Obtain the empirical distribution value of product demand for the current iteration according to the initial product replenishment quantity and the third functional relationship; Determine the product reorder point for the current iteration based on the empirical distribution value of product demand for the current iteration.

3. The method according to claim 2, characterized in that, after determining the product reorder point for the current iteration based on the empirical distribution value of product demand for the current iteration, it further includes: Derive the first functional relationship to obtain a fourth functional relationship between the product replenishment quantity, the product cost data, the product geometric feature data, and the correction factor; Determine the product shortage data according to the product reorder point for the current iteration; Obtain the product replenishment quantity for the current iteration according to the product shortage data and the fourth functional relationship.

4. The method according to claim 1, characterized in that, the determining the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual product inventory data, the final product replenishment quantity, and the final product reorder point includes: When the actual product inventory data is less than or equal to the final product reorder point, use the final product replenishment quantity data as the product replenishment data for replenishing the picking area from the storage area in the supply chain; When the actual product inventory data is greater than the reorder point, determine that the product replenishment data for replenishing the picking area from the storage area in the supply chain is 0.

5. The method according to any one of claims 1 to 4, characterized in that, the product geometric feature data includes product volume.

6. A supply chain replenishment device, characterized in that, the device includes: A data acquisition module, configured to acquire the actual inventory data, product cost data, product geometric feature data of the picking area in the supply chain, and the accommodation space data of the picking area, wherein the product cost data includes product holding cost, product shortage cost, and product reorder cost; A loop processing module, configured to determine an initial function value for calculating replenishment reference data, a functional relationship for loop calculation, and a loop stop condition based on the principle of minimizing replenishment cost and the space constraint of the picking area, where the replenishment reference data includes product replenishment batch and product reorder point; A first processing module, configured to determine an initial product replenishment batch according to the initial function value, the product cost data, the product geometric feature data, and the accommodation space data of the picking area; A second processing module, configured to obtain a first functional relationship between the product cost data, the product geometric feature data, and a correction coefficient, and a second functional relationship between the correction coefficient for iterative calculation and the product geometric feature data; obtain replenishment reference data for the current iteration according to the initial replenishment lot size of the product and the first functional relationship; determine the correction coefficient for the next iteration according to the second functional relationship; obtain replenishment reference data for the next iteration based on the correction coefficient for the next iteration and the first functional relationship; when the difference between the replenishment reference data for the next iteration and the replenishment reference data for the current iteration is less than a preset threshold, use the product replenishment lot size for the next iteration as the final product replenishment lot size, and use the product reorder point for the next iteration as the final product reorder point, where the first functional relationship is expressed by the formula: , represents the total cost within the replenishment cycle, represents the correction coefficient, represents the volume of the th product, represents the replenishment lot size of the th product, represents the reorder point of the th product; the second functional relationship is expressed by the formula: , is the learning rate, is the correction coefficient for the next iteration; A replenishment data determination module, configured to determine the product replenishment data for replenishing the picking area from the storage area in the supply chain according to the actual product inventory data, the final product replenishment batch, and the final product reorder point.

7. The device according to claim 6, wherein, the second processing module is further configured to perform a derivative processing on the first functional relationship to obtain a third functional relationship between the product demand empirical distribution data and the product replenishment batch; obtain the product demand empirical distribution value of the current loop according to the initial product replenishment batch and the third functional relationship; and determine the product reorder point of the current loop based on the product demand empirical distribution value of the current loop.

8. The device according to claim 7, wherein, the second processing module is further configured to perform a derivative processing on the first functional relationship to obtain a fourth functional relationship between the product replenishment batch, the product cost data, the product geometric feature data, and a correction coefficient; determine product shortage data according to the product reorder point of the current loop; and obtain the product replenishment batch of the current loop according to the product shortage data and the fourth functional relationship.

9. A computer device, comprising a memory and a processor, where the memory stores a computer program, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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