Supply chain optimization processing method and system

Abstract

The present invention provides a supply chain optimization processing method and system, which belongs to the field of supply chain management technology, and specifically includes: using the shipment data of the current order and the weather data in the shipment route to determine the reference historical orders in different warehouses, determining the matching warehouses in the warehouses according to the after-sales quality data of the reference historical orders and the saleable data of the goods, obtaining the after-sales quality data of different goods in different matching warehouses, and combining the shipment data of different goods to determine the preferred warehouse in the matching warehouses, and performing shipment processing of the goods orders based on the preferred warehouse, thereby avoiding the occurrence of the aggregation of after-sales orders in a single warehouse.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain management, and in particular relates to a supply chain optimization processing method and system. Background Art

[0002] In order to realize the supply chain management of e-commerce enterprises, batch management of goods is carried out in the invention patent application CN202011645334.6 "Commodity Batch Management System". After the goods are shipped out of the warehouse, if there are after-sales problems with the corresponding SKU, it can be traced back to the corresponding supplier and the specific production order.

[0003] When processing the shipment of goods, multiple warehouses can often handle the shipment. However, due to the quality differences of the inventory goods in different warehouses, the after-sales processing pressure faced by different warehouses varies to a certain extent. Therefore, how to determine the shipping warehouse of the goods based on the after-sales processing pressure faced by different warehouses can avoid the situation where a single warehouse fails to process the goods in a timely manner or misses them due to a large number of after-sales orders.

[0004] In response to the above technical problems, this application specifically provides a supply chain optimization processing method and system. Summary of the Invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] Specifically, this application provides a supply chain optimization processing method, which specifically includes:

[0007] S1 determines the supplier of the product based on the analysis and processing results of the product supply chain data, determines the quality defect data of the product in different production batches based on the analysis results of the product supplier, and determines a reference production batch among the production batches based on the quality defect data;

[0008] S2 uses the order containing the product of the reference production batch as a reference order, determines the shipping data of different reference orders and the weather data in the shipping route, and combines the after-sales quality data of different reference orders to determine that the product quality of the product does not meet the requirements, and then proceeds to the next step;

[0009] S3 determines reference historical orders in different warehouses based on the shipping data of the current order and weather data in the shipping route, and determines a matching warehouse among the warehouses based on the after-sales quality data of the reference historical orders and the saleability data of the product;

[0010] S4 obtains after-sales quality data of different commodities in different matching warehouses, and combines the delivery data of different commodities to determine the preferred warehouse among the matching warehouses, and performs delivery processing of commodity orders based on the preferred warehouse.

[0011] The beneficial effects of the present invention are:

[0012] The shipping data of different reference orders, the weather data in the shipping routes, and the after-sales quality data of different reference orders are used to determine whether the product quality has after-sales defects. This not only takes into account the after-sales quality data of the reference orders, but also takes into account the differences in the degree of impact of the shipping process caused by the differences in the shipping data of the reference orders. This achieves the screening of products with after-sales defects in product quality, and also lays the foundation for further generating differentiated shipping processing strategies based on after-sales defects.

[0013] Based on the after-sales quality data of different commodities in different matching warehouses and the delivery data of different commodities, the preferred warehouse in the matching warehouses is determined, thereby realizing the evaluation of the probability of after-sales defect problems faced by the matching warehouses from the perspective of after-sales quality defects of the inventory commodities in the matching warehouses and the order quantity, and realizing the screening of matching warehouses with a lower probability of after-sales defect problems, avoiding the technical problem of excessive difficulty in processing after-sales data due to the large number of after-sales problems faced by a single warehouse, and reducing the probability of delays or omissions in the process of handling after-sales problems.

[0014] A further technical solution is that the quality defect data includes the quantity of goods with different types of product quality defects.

[0015] A further technical solution is that the method for determining the reference production batch in the production batch is:

[0016] Determining the percentage of the number of products with different types of product quality defects based on the number of products with different types of product quality defects in the reference production batch within the production batch;

[0017] Determine the deviation in the percentage of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier, and use the average of the deviation in the percentage of the quality of goods with different types of product quality defects to determine the production quality deviation coefficient from other production batches;

[0018] Based on the average value of the production quality deviation coefficients with other production batches, it is determined whether the production batch is a reference production batch.

[0019] A further technical solution is that when the average value of the production quality deviation coefficient between the production batch and other production batches is less than a preset deviation coefficient threshold, the production batch is determined to be a reference production batch.

[0020] A further technical solution is that the method for determining the reference production batch in the production batch is:

[0021] Determining the percentage of the number of products with different types of product quality defects based on the number of products with different types of product quality defects in the reference production batch within the production batch;

[0022] Determine the deviation in the percentage of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier, and determine the production quality deviation batch among the other production batches by using the average of the deviation in the percentage of the quality of goods with different types of product quality defects;

[0023] Based on the number of the production quality deviation batches, it is determined whether the production batch is a reference production batch.

[0024] A further technical solution is that when the number of production quality deviations does not meet the requirement, it is determined that the production batch does not belong to the reference production batch.

[0025] A further technical solution is that the method for determining the preferred warehouse in the matching warehouse is:

[0026] Using the after-sales quality data of different products in the matching warehouse, determine the percentage of historical orders of different products in the matching warehouse that have after-sales quality defects, and use this percentage as the quality defect percentage;

[0027] Determine after-sales risk products using the quality defect ratio, and determine the shipment quantity of the after-sales risk products from the matching warehouse on different dates based on the shipment data of the after-sales risk products;

[0028] The preferred warehouse among the matching warehouses is determined based on the average value of the shipment quantity of the after-sales risk commodities on different dates.

[0029] A further technical solution is that the after-sales risk products are products whose quality defects account for a proportion greater than a preset value.

[0030] A further technical solution is that the preferred warehouse among the matching warehouses is the matching warehouse with the smallest average shipment quantity of after-sales risk goods on different dates.

[0031] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned supply chain optimization processing method when running the computer program.

[0032] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0035] Figure 1 It is a flow chart of a supply chain optimization process;

[0036] Figure 2 is a flow chart of a method for determining a reference production batch among production batches;

[0037] Figure 3 It is a flow chart for determining that the product quality of the goods does not meet the requirements;

[0038] Figure 4 is a flow chart of a method for determining a matching warehouse in a warehouse;

[0039] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0040] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0041] Existing technical solutions often use distance to process the shipment of product orders. However, when there are a large number of products with after-sales quality defects in the warehouse, it will inevitably lead to a large concentration of after-sales orders in a single warehouse, making it difficult to meet the processing efficiency requirements of after-sales orders.

[0042] In this application, the products with after-sales quality defects in different warehouses are utilized, and the warehouse with fewer products with after-sales quality defects is selected as the preferred warehouse, thereby avoiding the situation where after-sales orders are concentrated in a single warehouse.

[0043] Determine the proportion of goods with different types of product quality defects in the production batch, and use the average value of the deviation in the quality proportion of goods with different types of product quality defects to determine the production quality deviation coefficient compared with other production batches. When the average value of the production quality deviation coefficient compared with other production batches is less than 0.1, determine that the production batch is a reference production batch.

[0044] The reference orders whose deviation amounts of shipment data and weather data in different dimensions meet the requirements are divided into the same reference order group. Based on the after-sales quality data of the reference orders in different reference order groups, the proportion of reference orders with after-sales quality problems in different reference order groups is determined. When the proportion of reference orders with after-sales quality problems in different reference order groups is less than 0.05, it is determined that the product quality meets the requirements.

[0045] The matching warehouse is a warehouse where the proportion of after-sales quality defects in historical orders is less than 0.1 and the salable quantity of the product is greater than 20 pieces.

[0046] Based on the after-sales quality data of different products in the matching warehouse, the proportion of after-sales quality defects in historical orders of different products in the matching warehouse is determined and used as the quality defect ratio. Products with a quality defect ratio greater than 0.1 are considered after-sales risk products. The matching warehouse with the smallest average shipment quantity of after-sales risk products on different dates is selected as the preferred warehouse.

[0047] Example 1

[0048] like Figure 1 As shown, this application provides a supply chain optimization processing method, which specifically includes:

[0049] S1 determines the supplier of the product based on the analysis and processing results of the product supply chain data, determines the quality defect data of the product in different production batches based on the analysis results of the product supplier, and determines a reference production batch among the production batches based on the quality defect data;

[0050] S2 uses the order containing the product of the reference production batch as a reference order, determines the shipping data of different reference orders and the weather data in the shipping route, and combines the after-sales quality data of different reference orders to determine that the product quality of the product does not meet the requirements, and then proceeds to the next step;

[0051] S3 determines reference historical orders in different warehouses based on the shipping data of the current order and weather data in the shipping route, and determines a matching warehouse among the warehouses based on the after-sales quality data of the reference historical orders and the saleability data of the product;

[0052] S4 obtains after-sales quality data of different commodities in different matching warehouses, and combines the delivery data of different commodities to determine the preferred warehouse among the matching warehouses, and performs delivery processing of commodity orders based on the preferred warehouse.

[0053] Furthermore, the after-sales risk products are products whose quality defects account for a proportion greater than a preset value.

[0054] It should be noted that the preferred warehouse among the matching warehouses is the matching warehouse with the smallest average shipment quantity of after-sales risk products on different dates.

[0055] Furthermore, the quality defect data includes the quantity of products with different types of product quality defects.

[0056] Specifically, such as Figure 2 As shown, the method for determining the reference production batch in the production batch is:

[0057] Determine the percentage of each type of product quality defect based on the number of products with each type of product quality defect in the production batch;

[0058] Determine the deviation in the percentage of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier, and use the average of the deviation in the percentage of the quality of goods with different types of product quality defects to determine the production quality deviation coefficient from other production batches;

[0059] Based on the average value of the production quality deviation coefficients with other production batches, it is determined whether the production batch is a reference production batch.

[0060] It should be noted that when the average value of the production quality deviation coefficients of the production batch and other production batches is less than a preset deviation coefficient threshold, the production batch is determined to be a reference production batch.

[0061] In another possible embodiment, the method for determining the reference production batch in the production batch is:

[0062] Determine the percentage of each type of product quality defect based on the number of products with each type of product quality defect in the production batch;

[0063] Determine the deviation in the percentage of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier, and determine the production quality deviation batch among the other production batches by using the average of the deviation in the percentage of the quality of goods with different types of product quality defects;

[0064] Based on the number of the production quality deviation batches, it is determined whether the production batch is a reference production batch.

[0065] Furthermore, when the number of production quality deviations does not meet the requirement, it is determined that the production batch does not belong to the reference production batch.

[0066] In another possible embodiment, the method for determining the reference production batch in the production batch is:

[0067] S11: determining, based on the number of commodities with different types of product quality defects in the production batch, a percentage of commodities with different types of product quality defects, and determining a deviation in the percentage of commodities with different types of product quality defects between the production batch and other production batches of the supplier;

[0068] It should be noted that before proceeding to the next step, it is necessary to further determine whether the deviation in the proportion of the number of goods with different types of product quality defects between the production batch and other production batches meets the requirements. In this case, the production batch can be directly determined to be a reference production batch. If the above conditions are not met, the deviation coefficient evaluation process is performed.

[0069] S12: determining a deviation coefficient from other production batches of the supplier based on an average value of deviations in the proportion of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier;

[0070] It is understandable that in the above steps, it is also necessary to determine whether the number of other production batches whose coefficient of deviation does not meet the requirement is greater than a preset batch number threshold, then determine that the production batch does not belong to the reference production batch. When the above conditions do not meet the requirements, it is necessary to continue to determine whether the average value of the coefficient of deviation of the production batch and other production batches of the supplier meets the requirements;

[0071] Specifically, when the average value of the coefficient of deviation between the production batch and other production batches of the supplier meets the requirements, the production batch can be directly determined as the reference production batch. Only when the average value of the coefficient of deviation between the production batch and other production batches of the supplier does not meet the requirements, it is necessary to proceed to the next step to determine the quality deviation coefficient.

[0072] S13 determines the quality deviation coefficient of different types of product quality defects based on the deviation in the proportion of the number of goods with different types of product quality defects compared with other production batches, determines the fluctuation coefficient of different types of product quality defects based on the deviation in the proportion of the number of different types of product quality defects between different production batches, and determines the reference coefficient of the production batch in combination with the quality deviation coefficient of different types of product quality defects, and determines whether the production batch is a reference production batch based on the reference coefficient.

[0073] It should be noted that before evaluating the fluctuation coefficient, it is also necessary to determine whether the number of product quality defects whose quality deviation coefficient does not meet the requirements is greater than the preset number of quality defects. If so, it is determined that the production batch does not belong to the reference production batch. If not, it is necessary to evaluate the waveform coefficient.

[0074] Furthermore, the quality deviation coefficient, fluctuation coefficient and reference coefficient can be determined by the mathematical model of the hierarchical analysis method, and when the reference coefficient is greater than a certain threshold, it is determined that it belongs to the reference production batch, and whether the requirements are met is determined by setting the threshold.

[0075] It should be noted that the shipping data of the reference order includes the inventory duration, shipping destination, and shipping mileage of the reference order.

[0076] It can be understood that the weather data in the shipping route includes transportation mileage under different types of weather data.

[0077] Furthermore, the weather data includes rain, cloudy, sunny, and snow.

[0078] Specifically, such as Figure 3 As shown, it is determined that the product quality of the product does not meet the requirements, specifically including:

[0079] Based on the shipping data of different reference orders and the weather data along the shipping routes, the deviations of the shipping data and weather data in different dimensions of the reference orders are determined, and the reference orders that meet the deviations of the shipping data and weather data in different dimensions are grouped into the same reference order group.

[0080] Determine, based on after-sales quality data of reference orders in different reference order groups, the percentage of reference orders in different reference order groups that have after-sales quality problems, and use the percentage of reference orders with after-sales quality problems as the after-sales quality anomaly coefficient of the reference order group;

[0081] Based on the after-sales quality anomaly coefficients of different reference order groups, it is determined whether the product quality of the product meets the requirements.

[0082] Furthermore, when the number of reference order groups whose after-sales quality anomaly coefficients are greater than a preset quality anomaly coefficient threshold is greater than the preset number of order groups, it is determined that the product quality of the product does not meet the requirements.

[0083] It is understandable that when there are no after-sales defects in the product quality of the product, the product will be shipped using the warehouse closest to the order address of the current order.

[0084] Furthermore, when the reference abnormality coefficient is greater than a preset reference abnormality coefficient threshold, it is determined that the product quality of the product does not meet the requirements.

[0085] It should be noted that the reference historical orders of the current order are historical orders whose transportation mileage deviations of the current order under different types of weather data in different dimensions are within a preset mileage deviation range.

[0086] Specifically, such as Figure 4 As shown, the method for determining the matching warehouse in the warehouse is:

[0087] Determine the proportion of the reference historical orders in the warehouse that have after-sales quality defects based on the after-sales quality data of the reference historical orders, and use this proportion as the quality defect proportion;

[0088] Determining the salable quantity of the goods in the warehouse according to the salable data of the goods in the warehouse;

[0089] Based on the ratio of the salable quantity of the warehouse to the proportion of the quality defects, the shipment matching coefficient of the warehouse is determined, and based on the shipment matching coefficient, it is determined whether the warehouse is a matching warehouse.

[0090] Furthermore, the matching warehouse is a warehouse whose delivery matching coefficient is greater than a preset matching coefficient.

[0091] It can be understood that when there is only one warehouse whose shipment matching coefficient is greater than the preset matching coefficient, or when there is no warehouse whose shipment matching coefficient is greater than the preset matching coefficient, the warehouse with the largest shipment matching coefficient will be selected as the preferred warehouse.

[0092] Specifically, the method for determining the preferred warehouse in the matching warehouse is:

[0093] Using the after-sales quality data of different products in the matching warehouse, determine the percentage of historical orders of different products in the matching warehouse that have after-sales quality defects, and use this percentage as the quality defect percentage;

[0094] Determine after-sales risk products using the quality defect ratio, and determine the shipment quantity of the after-sales risk products from the matching warehouse on different dates based on the shipment data of the after-sales risk products;

[0095] The preferred warehouse among the matching warehouses is determined based on the average value of the shipment quantity of the after-sales risk commodities on different dates.

[0096] Furthermore, the after-sales risk products are products whose quality defects account for a proportion greater than a preset value.

[0097] It should be noted that the preferred warehouse among the matching warehouses is the matching warehouse with the smallest average shipment quantity of after-sales risk goods on different dates.

[0098] Example 2

[0099] Optionally, the method for determining the preferred warehouse in the matching warehouse is:

[0100] S41 determines, based on the after-sales quality data of different products in the matching warehouse, a percentage of after-sales quality defects in historical orders of different products in the matching warehouse, and uses the percentage as the quality defect percentage;

[0101] S42 determines the order quantity of different commodities on different dates based on the delivery data of different commodities, and determines the preset busy coefficient of different commodities based on the average of the order quantity on different dates;

[0102] S43 determines the after-sales quality defect factor of the matching warehouse based on the quality defect ratio of different commodities and a preset busy coefficient, and determines the preferred warehouse among the matching warehouses based on the after-sales quality defect factor.

[0103] Furthermore, the preferred warehouse among the matching warehouses is the matching warehouse with the smallest after-sales quality defect factor.

[0104] It can be understood that the preferred warehouse is the matching warehouse with the lowest after-sales risk factor.

[0105] Example 3

[0106] Second, as Figure 5 As shown, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned supply chain optimization processing method when running the computer program.

[0107] Optionally, the above step S41 includes the following contents:

[0108] S411 uses the after-sales quality data of different products in the matching warehouse to determine the proportion of after-sales quality defects in historical orders of different products in the matching warehouse, and uses this proportion as the quality defect proportion. If there are products whose quality defect proportion does not meet the requirement, the process proceeds to step S412; if there are no products whose quality defect proportion does not meet the requirement, the process proceeds to step S413;

[0109] S412: If the proportion of goods with quality defects in the matching warehouse does not meet the requirement, it is determined that the matching warehouse does not belong to the preferred warehouse. If the proportion of goods with quality defects in the matching warehouse does not meet the requirement, the process proceeds to step S413.

[0110] S413: Obtain an average value of the quality defect ratios of different commodities in the matching warehouse. If the average value of the quality defect ratios of different commodities in the matching warehouse is within a preset defect ratio range, proceed to step S414. If the average value of the quality defect ratios of different commodities in the matching warehouse is not within the preset defect ratio range, proceed to step S42.

[0111] S414 determines the commodity quality defect coefficient in the warehouse based on the quality defect ratio of different commodities. When the commodity quality defect coefficient in the warehouse does not meet the requirements, it is determined that the matching warehouse does not belong to the preferred warehouse. When the commodity quality defect coefficient in the warehouse meets the requirements, proceed to step S42.

[0112] It should be noted that the product quality defect coefficient is evaluated through a neural network prediction model with the quality defect ratio of different products as input, and whether it meets the requirements is determined by setting a fixed threshold.

[0113] Example 4

[0114] In another possible embodiment, determining that the product quality of the product does not meet the requirements specifically includes:

[0115] Based on the shipping data of different reference orders and the weather data along the shipping routes, the deviations of the shipping data and weather data in different dimensions of the reference orders are determined, and the reference orders that meet the deviations of the shipping data and weather data in different dimensions are grouped into the same reference order group.

[0116] Determine, based on after-sales quality data of reference orders in different reference order groups, the percentage of reference orders in different reference order groups that have after-sales quality problems, and use the percentage of reference orders with after-sales quality problems as the after-sales quality anomaly coefficient of the reference order group;

[0117] The after-sales quality abnormality coefficients of different reference order groups are combined with preset weight values ​​corresponding to different reference order groups to determine the reference abnormality coefficients, and the reference abnormality coefficients are used to determine whether the product quality of the product meets the requirements.

[0118] It should be noted that the reference abnormality coefficient is determined based on the after-sales quality abnormality coefficients of different reference order groups and the preset weight values ​​corresponding to different reference order groups, and is determined by multiplying the after-sales quality abnormality coefficients of different reference order groups by the corresponding preset weight values.

[0119] It should be noted that whether the requirements are met is determined by setting a threshold.

[0120] Optionally, the above step S42 includes the following contents:

[0121] S421 determines the order quantity of different commodities on different dates based on the shipment data of different commodities, and determines the commodities with after-sales risk among the commodities based on the proportion of quality defects. If the matching warehouse has a date on which the order quantity of commodities with after-sales risk is greater than the preset number of risk orders, the process proceeds to step S422. If the matching warehouse does not have a date on which the order quantity of commodities with after-sales risk is greater than the preset number of risk orders, the process proceeds to step S423.

[0122] S422: If the order quantity of after-sales risk goods exceeds the preset risk order quantity, and the proportion of the order quantity on the date does not meet the requirement, it is determined that the matching warehouse does not belong to the preferred warehouse. If the order quantity of after-sales risk goods exceeds the preset risk order quantity, and the proportion of the order quantity on the date meets the requirement, the process proceeds to step S423.

[0123] S423 determines the preset busy coefficients of different products based on the average number of orders on different dates. Products with a preset busy coefficient greater than a preset busy coefficient threshold are considered busy shipping products. If the proportion of products with after-sales risk in the busy shipping products does not meet the requirement, the matching warehouse is determined not to be a preferred warehouse. If the proportion of products with after-sales risk in the busy shipping products meets the requirement, the process proceeds to step S424.

[0124] S424 determines the after-sales defect coefficient of different commodities by multiplying the preset busy coefficient of different commodities by the proportion of quality defects. When the number of commodities whose after-sales defect coefficient does not meet the requirements is greater than the preset commodity quantity threshold, it is determined that the matching warehouse does not belong to the preferred warehouse. When the number of commodities whose after-sales defect coefficient does not meet the requirements is not greater than the preset commodity quantity threshold, proceed to step S43.

[0125] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0126] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A supply chain optimization processing method, characterized in that: Specifically include: Determining the supplier of the product based on the analysis and processing results of the product supply chain data, determining quality defect data of the product in different production batches based on the analysis results of the product supplier, and determining a reference production batch among the production batches based on the quality defect data; An order containing the product of the reference production batch is used as a reference order, and shipping data of different reference orders and weather data in the shipping routes are determined. When it is determined that the product quality of the product does not meet the requirements in combination with the after-sales quality data of the different reference orders, the next step is entered; Determine reference historical orders in different warehouses based on the shipping data of the current order and weather data in the shipping route, and determine matching warehouses in the warehouses based on after-sales quality data of the reference historical orders and the saleability data of the goods; Obtain after-sales quality data of different products in different matching warehouses, and combine it with the shipment data of different products to determine the preferred warehouse among the matching warehouses, and process the shipment of product orders based on the preferred warehouse; The method for determining the matching warehouse in the warehouse is: Determine the proportion of the reference historical orders in the warehouse that have after-sales quality defects based on the after-sales quality data of the reference historical orders, and use this proportion as the quality defect proportion; Determining the salable quantity of the goods in the warehouse according to the salable data of the goods in the warehouse; Determining a shipment matching coefficient of the warehouse based on a ratio of the warehouse's salable quantity to the quality defect ratio, and determining whether the warehouse is a matching warehouse based on the shipment matching coefficient; The method for determining the preferred warehouse in the matching warehouse is: Using the after-sales quality data of different products in the matching warehouse, determine the percentage of historical orders of different products in the matching warehouse that have after-sales quality defects, and use this percentage as the quality defect percentage; Determine after-sales risk products using the quality defect ratio, and determine the shipment quantity of the after-sales risk products from the matching warehouse on different dates based on the shipment data of the after-sales risk products; Determine a preferred warehouse among the matching warehouses based on an average of the shipment quantities of post-sales risk commodities on different dates; The preferred warehouse among the matching warehouses is the matching warehouse with the smallest average value of the shipment quantity of the after-sales risk commodities on different dates.

2. The supply chain optimization processing method according to claim 1, characterized in that: The quality defect data includes the quantity of commodities with different types of product quality defects.

3. The supply chain optimization processing method according to claim 1, characterized in that: The method for determining the reference production batch in the production batch is: Determine the percentage of each type of product quality defect based on the number of products with each type of product quality defect in the production batch; Determine the deviation in the percentage of the number of goods with different types of product quality defects between the production batch and other production batches of the supplier, and use the average of the deviation in the percentage of the quality of goods with different types of product quality defects to determine the production quality deviation coefficient from other production batches; Based on the average value of the production quality deviation coefficients with other production batches, it is determined whether the production batch is a reference production batch.

4. The supply chain optimization processing method according to claim 3, characterized in that: When the average value of the production quality deviation coefficients of the production batch and other production batches is less than a preset deviation coefficient threshold, the production batch is determined to be a reference production batch.

5. The supply chain optimization processing method according to claim 1, characterized in that: The weather data in the delivery route includes transportation mileage under different types of weather data.

6. The supply chain optimization processing method according to claim 1, characterized in that: The shipping data of the reference order includes the inventory duration, shipping destination, and shipping mileage of the reference order.

7. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes a supply chain optimization processing method as described in any one of claims 1-6.

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