A retail convenience store intelligent warehouse management system and control method
Patent Information
- Application Number
- CN202610986140.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-03
- Publication Date
- 2026-09-25
AI Technical Summary
[0005]进一步地，所述安全库存算法模块采用多维测算模型，核心用于解决现有技术中安全库存测算不准、易导致缺货或积压的问题，具体测算逻辑为：安全库存=日均销量配送周期(1+销量波动率)，其中销量波动率=销量标准差日均销量；销量波动率统计周期设定为近30天，同时剔除促销期间异常放大数据、单日销量超均值2倍的异常值、停业/断货期间数据，确保测算结果的稳定性和可靠性；所述预警管理模块与安全库存算法模块协同工作，为安全库存配置三级补货预警机制，逐级提升预警强度，实现从预警到补货的智能化衔接，有效降低断货风险
1.安全库存测算更科学：考虑销量波动率及异常数据剔除，结合三级补货预警，将断货率降低15%以上，有效平衡库存与缺货风险；
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Figure CN122820085A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of digital management technology for the retail supply chain, and in particular to an intelligent warehouse management system and control method for retail convenience stores. Background Technology
[0002] Existing convenience store warehouse management systems generally suffer from numerous technical deficiencies: safety stock calculation models are simplistic, failing to account for sales fluctuations and abnormal data, easily leading to stockouts or inventory backlogs; sorting paths lack optimized design, resulting in low operational efficiency and an inability to adapt to the characteristics of chain convenience stores—a large number of SKUs (Stock Keeping Units, unique product identifiers) and rapid turnover; near-expiration products are not identified in a timely manner, batch traceability is weak, and product spoilage is easily caused; warehouse location allocation relies on manual experience, resulting in low space utilization and unreasonable matching of high-turnover products with warehouse locations. Especially in regional centralized distribution warehouse scenarios, with a large number of SKUs, complex batches, and high turnover frequency, there is an urgent need to establish a warehouse management system with intelligent decision-making capabilities to achieve scientific, intelligent, and efficient warehousing operations. Summary of the Invention
[0003] The purpose of this invention is to address the aforementioned shortcomings in the existing technology by providing a smart warehouse management system and control method for retail convenience stores. Through multi-dimensional algorithm control, it achieves scientific and safe inventory calculation, sorting path optimization, risk control of near-expiry goods, and intelligent allocation of warehouse resources, thereby improving warehousing efficiency and inventory turnover, reducing loss risks, and adapting to the operational needs of regional distribution warehouses for chain convenience stores.
[0004] The technical solution adopted by this invention to solve its technical problem is: an intelligent warehouse management system for retail convenience stores, including an inventory data management module, a safety stock algorithm module, a sorting path / mode control module, a near-expiry goods early warning / outbound control module, an inbound storage location allocation algorithm module, and an early warning management module; each module achieves data interoperability through software logic interaction, and the inventory data management module achieves bidirectional data connection and real-time synchronization with the ERP system, OMS system, and store ordering system (ERP system, i.e., Enterprise Resource Planning System, used to coordinate the overall resources of the enterprise; OMS system, i.e., Order Management System, used to coordinate the entire order process; store ordering system, used by stores to initiate order requests and receive allocation results). The functions of each module are coordinated and data is interconnected, jointly realizing the intelligent management of the entire process of retail convenience store warehousing from inbound, sorting, outbound to inventory control.
[0005] Furthermore, the safety stock algorithm module employs a multi-dimensional calculation model, primarily designed to address the issues of inaccurate safety stock calculations and potential stockouts or overstocking in existing technologies. The specific calculation logic is as follows: Safety Stock = Average Daily Sales Delivery Cycle (1 + Sales Volatility), where Sales Volatility = Standard Deviation of Average Daily Sales. The sales volatility statistical period is set to the past 30 days, while excluding abnormally amplified data during promotional periods, outliers with daily sales exceeding twice the average, and data from periods of business closure / stockouts, ensuring the stability and reliability of the calculation results. The early warning management module works in conjunction with the safety stock algorithm module to configure a three-level replenishment early warning mechanism for safety stock, progressively increasing the early warning intensity and achieving intelligent connection from early warning to replenishment, effectively reducing the risk of stockouts.
[0006] Furthermore, the sorting path / mode control module is primarily designed to address the issues of low sorting efficiency, unreasonable paths, and inability to adapt to multi-SKU turnover requirements in existing systems. Specifically, its logic involves automatically switching between full-case and partial-case sorting modes based on the threshold relationship between order quantity and full-case quantity, while also supporting mixed sorting to accommodate different order requirements. A two-factor warehouse location recommendation and sorting model is employed, with sorting weights equal to 40% distance weight and 60% turnover rate weight. The sorting priority is: distance from the sorting station from nearest to farthest > product turnover rate from highest to lowest > priority for picking the same batch, thus optimizing the sorting path. Simultaneously, it supports real-time linkage with intelligent sorting equipment, PDAs, and electronic tag systems, pushing standardized partial-case data core fields to the equipment, including product number, product barcode, sorting quantity, warehouse location code, priority level, and batch number, thereby improving the level of sorting automation.
[0007] Furthermore, the near-expiry goods early warning / outbound control module is primarily designed to address the issues of untimely identification of near-expiry goods, weak batch traceability, and high loss rates. Its specific implementation logic is as follows: It employs batch number traceability management, generating a unique batch number for each batch of goods entering the warehouse. The coding structure is production date + supplier code + warehouse location code + serial number, enabling full-process batch traceability from warehousing to outbound. Outbound operations strictly adhere to the first-in, first-out (FIFO) rule, sorting by production date from earliest to latest, and within the same batch, sorting by warehousing time, with near-expiry goods given priority for outbound. It sets tiered control thresholds for near-expiry and expired goods, with the near-expiry threshold set at 30% remaining shelf life and the expired threshold at 0 remaining days. Expired goods are automatically locked in inventory, a return application is generated, and reported to the quality management module, achieving full lifecycle control of near-expiry goods.
[0008] Furthermore, the inbound storage location allocation algorithm module is primarily designed to address the issues of manual allocation, low space utilization, and unreasonable matching of high-turnover goods. Specifically, it allocates storage locations based on product category, product turnover rate, storage location capacity matching, and the principle of grouping similar products together. High-turnover goods are prioritized for allocation to areas near the picking zone, improving warehousing efficiency. A storage location saturation calculation model is constructed, where storage location saturation equals the total used volume. Dual thresholds of 80% and 90% are set to trigger high saturation warnings and prohibit inbound operations, respectively. Simultaneously, alternative storage locations are automatically recommended, achieving optimized allocation of storage space resources.
[0009] Furthermore, the inventory data management module, as the data foundation of the entire system, is used to collect and synchronize inventory and order data from ERP, OMS, and store ordering systems, providing accurate and timely data sources for the calculations of each algorithm module. At the same time, it receives real-time data after the completion of warehousing operations, updates the inventory status, and ensures data consistency.
[0010] Based on the aforementioned intelligent warehouse management system for retail convenience stores, this invention also discloses a corresponding intelligent warehouse management and control method for retail convenience stores, comprising the following operational steps: Step a: The inventory data management module collects and synchronizes inventory and order data from ERP, OMS, and store ordering systems to provide a data foundation for each algorithm module and ensure that the data obtained by each module is accurate and consistent; Step b: The safety stock algorithm module calculates the safety stock and sales volatility based on the sales data of the past 30 days. The early warning management module triggers the corresponding level of replenishment warning based on the inventory status. The level 3 warning automatically generates replenishment suggestions and synchronizes them to the ERP system to start the replenishment process. Step c: After the system receives the store delivery order, the sorting path / mode control module automatically determines the sorting mode based on the order quantity, recommends storage locations according to the two-factor sorting model, pushes the split data to the intelligent sorting equipment and guides the sorting operation to ensure efficient and accurate sorting; Step d: The near-expiry goods early warning / outbound control module generates a unique batch number for incoming goods to achieve full-process traceability. When outbound, it executes the FIFO rule to issue early warnings for near-expiry goods and lock and return expired goods to reduce product loss. Step e: After the system receives the purchase order, the warehouse location allocation algorithm module automatically allocates warehouse locations according to rules such as product category and turnover rate. It triggers warnings for warehouse locations with high saturation and recommends alternative warehouse locations to guide warehousing operations and optimize the utilization of warehouse location resources. Step f: After the warehouse completes sorting, outbound, inbound, and storage location adjustment, the data is transmitted back to the inventory data management module in real time to update the inventory status and synchronize with the ERP, OMS, and store ordering system in real time to ensure data consistency and form a complete warehouse management closed loop.
[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. More scientific safety stock calculation: Taking into account sales volatility and the removal of abnormal data, combined with a three-level replenishment warning, the stockout rate is reduced by more than 15%, effectively balancing inventory and stockout risks; 2. Higher sorting efficiency: Through automatic sorting mode determination and two-factor warehouse location sorting, picking efficiency is improved by more than 20%, while adapting to automated sorting equipment and reducing labor costs; 3. Lower losses on near-expiry goods: Through batch number traceability, FIFO outbound rules, and graded management of near-expiry / over-expiry goods, the loss rate of near-expiry goods can be reduced by more than 30%, thereby reducing the company's commodity loss costs; 4. Higher warehouse space utilization: Intelligent warehouse space allocation and warehouse space saturation control based on product category and turnover rate can increase warehouse space utilization by more than 10%, achieving optimized allocation of warehouse space resources; 5. Enhanced adaptability to warehouse automation: Standardized data fields enable real-time linkage with intelligent sorting equipment and PDAs, improving the automation and digitalization level of warehouse operations; 6. More refined warehouse management: It realizes full-dimensional algorithm control from safety stock, sorting path, near-expiry products to warehouse location allocation, eliminating the drawbacks of manual experience management and adapting to the warehousing needs of chain convenience stores with many SKUs, complex batches and fast turnover. Attached Figure Description
[0012] Figure 1 This is a block diagram of the overall architecture of the intelligent warehouse management system for retail convenience stores of the present invention, used to show the core module composition and overall logical architecture of the system; Figure 2 This is a schematic flowchart of the intelligent control method for retail convenience store warehousing of the present invention, used to illustrate the overall control process and the relationship between the steps. Detailed Implementation
[0013] The following detailed embodiments, based on actual warehousing application scenarios of chain convenience stores, provide specific examples to illustrate the technical solution of the present invention. The examples are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.
[0014] Example 1 (Safety Stock Calculation and Three-Level Early Warning Implementation): A chain convenience store's ambient temperature product A has an average daily sales volume of 20 units over the past 30 days, with a standard deviation of 4 units. After removing the outlier of 45 units sold in a single day during a promotional period (more than twice the average), the sales volatility is 420 = 0.2. The delivery cycle for this product is 2 days. Based on the safety stock calculation model: Safety stock = 202(1+0.2) = 48 units.
[0015] The system monitors the inventory status of the product in real time through the inventory data management module, and the early warning management module triggers corresponding early warnings according to the three-level early warning mechanism: - When the quantity in stock + the quantity used for outbound shipments < 4880% = 38.4 units, a Level 1 replenishment alert is triggered, and the system prompts warehouse management personnel to pay attention to inventory dynamics; - When the quantity in stock is less than 4860% = 28.8 pieces, a level 2 warning is triggered, and the system sends an inventory reminder to the purchasing department; - When the quantity in stock is less than 4840% = 19.2 pieces, a level 3 warning is triggered. The system automatically generates a replenishment suggestion, specifying the replenishment quantity, supplier and delivery cycle, and synchronizes it to the ERP system to start the replenishment process.
[0016] Example 2 (Implementation of Sorting Mode and Path Optimization): A chain convenience store's regional distribution warehouse receives orders from stores, with 15 units of product B ordered. The standard size of a full carton of this product is 12 units per carton. Since 15 > 12, the sorting path / mode control module automatically determines it to be a mixed sorting mode of full carton + individual units (1 full carton + 3 individual units).
[0017] The sorting path optimization adopts a two-factor storage location recommendation and ranking model. The ranking weight = distance weight 40% + turnover rate weight 60%. The system ranks all storage locations of product B and prioritizes recommending the storage location (storage location code A1-02-05) that is closest to the sorting station (highest distance weight) and has the highest turnover rate. At the same time, it also takes into account the centralized picking of products in the same batch to reduce the picking route.
[0018] The system pushes split data to the sorting personnel's PDA devices. The core fields include: product number (B001), product barcode (6901234567890), sorting quantity (3 pieces), storage location code (A1-02-05), priority level (level 1), and batch number (20260301-S001-A1-0001). This guides the picking operation. Practical application has verified that this optimization shortens the picking path of product B by more than 20%, significantly improving picking efficiency.
[0019] Example 3 (Implementation of Control Measures for Near-Expiry Goods): A chain convenience store receives product C (snacks) with a shelf life of 10 months (300 days) into its warehouse. Upon receipt, the near-expiry product early warning / outbound control module generates a unique batch number for this batch of products: 20260101-S002-A2-0003 (where 20260101 is the production date, S002 is the supplier code, A2 is the warehouse location code, and 0003 is the serial number), enabling full-process traceability.
[0020] The system automatically calculates the remaining shelf life of goods: when the remaining shelf life of product C is 30030%=90 days, it is determined to be a near-expiry product. The system issues a red warning on the inventory management interface and adds the product to the priority outbound queue. When the remaining shelf life of product C is 0 days, it is determined to be an expired product. The system automatically locks the inventory of this batch (batch number 20260101-S002-A2-0003), prohibits outbound operations, and automatically generates a return application, which is simultaneously reported to the quality management module. Relevant personnel then contact the supplier to handle the return procedures, effectively reducing near-expiry losses.
[0021] Example 4 (Implementation of Intelligent Allocation of Inbound Storage Locations): A chain convenience store's regional distribution warehouse receives a batch of high-turnover snack products (turnover rate level 1). The warehouse location allocation algorithm module initiates an intelligent allocation process: First, based on the product category, the products are categorized into the dedicated snack storage area; then, combined with the turnover rate level, they are preferentially allocated to warehouse locations near the sorting station (warehouse locations B1-01-01 to B1-01-10 near the picking area); at the same time, the warehouse location capacity matching is checked to ensure that the warehouse saturation does not exceed 80% after the products are put into storage.
[0022] The system calculates the saturation of each storage location in real time. Storage location B1-01-08 currently has a used volume of 4.0 cubic meters and a total volume of 5.0 cubic meters, resulting in a saturation of 80% (4.0 / 5.0). The system triggers a high saturation warning, prompting warehouse personnel to monitor the subsequent inbound shipments to this location. Storage location B1-01-09 has a used volume of 4.5 cubic meters and a total volume of 5.0 cubic meters, resulting in a saturation of 90%. The system prohibits further inbound shipments to this location and automatically recommends the available storage location B1-01-11 in the same area, ensuring the rational utilization of storage space resources. In actual application, this storage location allocation method has increased storage location utilization by more than 10%.
[0023] The above embodiments are only used to explain the present invention and are not intended to limit the protection of the present invention. Any non-substantial modifications made based on the essential solution of the present invention should fall within the protection scope of the present invention.
Claims
1. A smart warehouse management system for retail convenience stores, characterized in that: It includes an inventory data management module, a safety stock algorithm module, a sorting path / pattern control module, a near-expiry goods early warning / outbound control module, an inbound storage location allocation algorithm module, and an early warning management module; Each module achieves data communication through software logic interaction. At the same time, the inventory data management module achieves bidirectional data connection and real-time synchronization with the ERP system, OMS system and store ordering system.
2. The intelligent warehouse management system for retail convenience stores according to claim 1, characterized in that: The safety stock algorithm module adopts a multi-dimensional calculation model. Safety stock = daily average sales and delivery cycle (1 + sales volatility), where sales volatility = daily average sales standard deviation. The sales volatility statistics period is the past 30 days, excluding abnormally amplified data during promotional periods, outliers with daily sales exceeding twice the average, and data during periods of business closure / stockout.
3. The intelligent warehouse management system for retail convenience stores according to claim 2, characterized in that: The early warning management module is configured with a three-level replenishment early warning mechanism for safety stock: Level 1 warning is when the quantity in stock plus the quantity used for outbound shipments is less than 80% of safety stock; Level 2 warning is when the quantity in stock is less than 60% of safety stock; and Level 3 warning is when the inventory is less than 40% of safety stock and automatically triggers a replenishment suggestion.
4. The intelligent warehouse management system for retail convenience stores according to claim 1, characterized in that: The sorting path / mode control module automatically determines the sorting mode based on the order quantity. When the order quantity is a full carton, the full carton sorting mode is executed; when the order quantity is less than the full carton quantity, the split-item sorting mode is executed. It also supports mixed sorting modes.
5. The intelligent warehouse management system for retail convenience stores according to claim 4, characterized in that: The sorting path / mode control module adopts a two-factor storage location recommendation and sorting model. The sorting weight is 40% for distance and 60% for turnover rate. The sorting priority is from near to far from the sorting station > from high to low turnover rate > priority for picking the same batch. The core fields of the split data pushed to the intelligent sorting equipment include product number, product barcode, sorting quantity, storage location code, priority level, and batch number.
6. The intelligent warehouse management system for retail convenience stores according to claim 1, characterized in that: The near-expiry goods early warning / outbound control module adopts batch number traceability management. The batch number coding structure is production date + supplier code + warehouse location code + serial number. Outbound follows the first-in, first-out (FIFO) rule, sorted by production date from earliest to latest, and within the same batch, sorted by warehousing time, with near-expiry goods given priority for outbound.
7. The intelligent warehouse management system for retail convenience stores according to claim 6, characterized in that: The near-expiry goods early warning / outbound control module determines near-expiry status when there are 30% of the shelf life remaining, and expired goods are determined when there are 0 days remaining. Expired goods are automatically locked in inventory, a return application is generated, and the application is reported to the quality management module.
8. The intelligent warehouse management system for retail convenience stores according to claim 1, characterized in that: The warehouse location allocation algorithm module allocates warehouse locations based on product category, product turnover rate level, warehouse location capacity matching degree, and the principle of grouping products of the same category, giving priority to allocating high-turnover products to the area near the picking area. Storage saturation = total used volume. When the storage saturation reaches 80%, a high saturation warning is triggered. When it reaches 90%, further allocation of storage is prohibited and alternative storage locations are automatically recommended.
9. A control method for a smart warehouse management system for retail convenience stores based on any one of claims 1 to 8, characterized in that, Includes the following steps: Step a: The inventory data management module collects and synchronizes inventory and order data from ERP, OMS, and store ordering systems to provide a data foundation for each algorithm module; Step b: The safety stock algorithm module calculates the safety stock and sales volatility based on the sales data of the past 30 days. The early warning management module triggers the corresponding level of replenishment warning based on the inventory status. The level 3 warning automatically generates replenishment suggestions. Step c: After the system receives the store delivery order, the sorting path / mode control module automatically determines the sorting mode based on the order quantity, recommends storage locations according to the two-factor sorting model, pushes the split data to the intelligent sorting equipment and guides the sorting operation; Step d: The near-expiry goods early warning / outbound control module generates a unique batch number for incoming goods, executes the FIFO rule when outbound, issues early warnings for near-expiry goods, and locks and returns expired goods. Step e: After the system receives the purchase order, the warehouse location allocation algorithm module automatically allocates warehouse locations according to rules such as product category and turnover rate. It triggers warnings for warehouse locations with high saturation and recommends alternative warehouse locations to guide warehousing and storage operations. Step f: After the warehouse completes sorting, outbound, inbound, and storage location adjustment, the data is transmitted back to the inventory data management module in real time to update the inventory status and synchronize with the ERP, OMS, and store ordering system in real time to ensure data consistency and form a closed loop of warehouse management.