E-commerce inventory dynamic allocation system based on real-time sales data

Abstract

This invention discloses an e-commerce inventory dynamic allocation system based on real-time sales data, belonging to the field of e-commerce supply chain management technology. It includes a real-time data access module, a data fusion and anomaly identification module, a SKU hierarchical management module, a three-dimensional dynamic weight decision-making module, a multi-warehouse collaborative allocation execution module, a logistics collaborative scheduling module, an anomaly emergency response module, an inventory safety early warning module, a data visualization monitoring module, a hierarchical access control module, a cross-platform data synchronization module, and a feedback optimization module. The three-dimensional dynamic weight decision-making module connects to the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module. Both the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module are bidirectionally interconnected with the anomaly emergency response module. The hierarchical access control module manages all modules. This invention possesses the technical advantages of strong real-time performance, accurate allocation, efficient multi-module collaboration, timely anomaly handling, and the ability to autonomously iterate and optimize.

Description

Technical Field

[0001] This invention belongs to the field of e-commerce supply chain management technology, and in particular relates to an e-commerce inventory dynamic allocation system based on real-time sales data. Background Technology

[0002] The rapid development of the e-commerce industry and the rise of omnichannel models have placed higher demands on the real-time, accurate, collaborative, and end-to-end control capabilities of inventory management. Dynamic inventory allocation, as a core link in the supply chain, directly impacts the fulfillment efficiency, customer satisfaction, and operating costs of e-commerce companies. While existing e-commerce inventory allocation technologies achieve basic inventory management and allocation functions, they have some shortcomings and are difficult to adapt to the current business needs of e-commerce characterized by high volatility, rapid fulfillment, and refined control.

[0003] Existing technologies generally suffer from serious data silos, static decision-making models, insufficient coordination between multiple warehouses and logistics, and weak emergency response capabilities. This leads to lag in inventory allocation and poor targeting of allocation strategies. When encountering major promotions and sales fluctuations, situations such as stockouts, overselling, and allocation interruptions are likely to occur. At the same time, inventory backlog and stockout problems coexist, resulting in low utilization of warehousing resources. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an e-commerce inventory dynamic allocation system based on real-time sales data. It has the advantages of strong real-time performance, accurate allocation, efficient multi-module collaboration, timely handling of anomalies, and the ability to autonomously iterate and optimize. It solves the problems of serious data silos, static decision-making, insufficient multi-warehouse and logistics collaboration, and weak emergency response capabilities in the prior art, which lead to delayed allocation, coexistence of stockouts and backlogs, and low warehouse utilization.

[0005] This invention is implemented as follows: an e-commerce inventory dynamic allocation system based on real-time sales data includes a real-time data access module, a data fusion and anomaly identification module, an SKU hierarchical management module, a three-dimensional dynamic weight decision module, a multi-warehouse collaborative allocation execution module, a logistics collaborative scheduling module, an anomaly emergency response module, an inventory safety early warning module, a data visualization monitoring module, a hierarchical access control module, a cross-platform data synchronization module, and a feedback optimization module. The real-time data access module connects to the data fusion and anomaly identification module and the cross-platform data synchronization module, transmitting the collected raw data. The data fusion and anomaly identification module, the SKU hierarchical management module, and the inventory safety early warning module are all connected to the three-dimensional dynamic weight decision module, providing data support and control parameters. The three-dimensional dynamic weight decision module connects to the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module, issuing allocation and logistics matching instructions. The multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module are bidirectionally interconnected with the anomaly emergency response module, promptly reporting and handling execution anomalies. The hierarchical access control module manages all modules, achieving full-process access control.

[0006] As a preferred embodiment of the present invention, the feedback optimization module collects the operating data of the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module, and reversely optimizes the parameters of the data fusion and anomaly identification module, the three-dimensional dynamic weight decision module, and the SKU hierarchical management module to form a closed loop. The cross-platform data synchronization module links the real-time data access module and the multi-warehouse collaborative allocation execution module to ensure data consistency throughout the system.

[0007] This setting enables closed-loop iteration of the entire system process, ensuring continuous optimization of module parameters to adapt to dynamic business changes. At the same time, it completely breaks down data silos, ensuring real-time data synchronization across systems and stages, avoiding issues such as allocation errors and overselling caused by data inconsistencies, and improving the stability and accuracy of system operation.

[0008] As a preferred embodiment of the present invention, the real-time data access module is used to realize the real-time collection and transmission of multi-source business data; the data fusion and anomaly identification module performs standardized processing on the collected data and identifies hierarchical sales anomalies; the SKU hierarchical management module is used to hierarchically manage products, dynamically update hierarchical results and management parameters; and the three-dimensional dynamic weight decision-making module adopts a general comprehensive evaluation method, combined with multi-dimensional data and dynamic weight adaptation rules, to generate the optimal inventory allocation plan.

[0009] This setting provides the system with accurate and unified basic data support, accurately captures sales anomalies and enables refined management of goods, ensuring that allocation decisions are aligned with product characteristics and actual sales, avoiding blind allocation strategies, and improving the rationality and applicability of allocation plans.

[0010] In a preferred embodiment of the present invention, the multi-warehouse collaborative allocation execution module implements allocation instructions to prevent overselling and adapt to different order types; the logistics collaborative scheduling module matches optimal material resources, tracks logistics status, and handles anomalies; the inventory safety early warning module sets differentiated thresholds based on product classification and pushes inventory risk warnings in real time; the anomaly emergency handling module has built-in emergency plans, automatically handles common anomalies, and supports manual intervention; the data visualization monitoring module realizes full-process data monitoring and traceability; the hierarchical permission control module realizes hierarchical permission management and operation traceability; the cross-platform data synchronization module realizes real-time synchronization and conflict handling of data from multiple systems; and the feedback optimization module realizes autonomous iterative optimization of system parameters.

[0011] This setup enables end-to-end control of inventory allocation, from data collection, decision generation, and instruction execution to anomaly handling, monitoring and tracing, and iterative optimization. Each module performs its specific function and works in tandem, ensuring allocation efficiency and accuracy while improving the system's operability, security, and sustainable optimization capabilities, thus adapting to the operational needs of e-commerce across multiple scenarios.

[0012] As a preferred embodiment of the present invention, the three-dimensional dynamic weight decision-making module adaptively adjusts the weight ratio of sales volume, fulfillment timeliness, and allocation cost according to three different scenarios: daily operation, platform promotion, and product order surge, to adapt to the allocation needs of different scenarios. The data fusion and anomaly identification module adopts a time-series analysis method, combining historical data and multi-dimensional verification information to classify sales anomaly levels, accurately identify malicious order behavior, and issue warnings.

[0013] This setting enables allocation decisions to be flexibly adapted to different operational scenarios, taking into account efficiency, cost, and sales volume requirements. It also accurately distinguishes between normal sales fluctuations and abnormal situations, avoiding allocation errors caused by malicious orders or sales anomalies, and improving the pertinence and reliability of decisions.

[0014] As a preferred embodiment of the present invention, the SKU hierarchical management module divides products into four levels based on real-time sales, profit margins, and inventory turnover speed. The hierarchical results are updated monthly based on operating data, and the allocation and inventory control rules of each level are adjusted synchronously. The multi-warehouse collaborative allocation execution module sets up three allocation channels: emergency, regular, and clearance, which are adapted to different order types such as spot goods and pre-sales, to achieve precise allocation by warehouse and ensure allocation efficiency.

[0015] This setting enables refined management of goods, ensuring priority supply of core products and timely clearance of slow-moving goods, thus optimizing the allocation of warehousing resources. At the same time, it adapts to different order types and allocation needs, enabling rapid response in emergency scenarios, cost consideration in daily scenarios, and improved turnover in clearance scenarios, thereby further enhancing allocation efficiency and resource utilization.

[0016] As a preferred embodiment of the present invention, the abnormal emergency response module has built-in 12 common abnormal scenarios with preset emergency plans, supports both automatic triggering and manual intervention adjustment modes, and ensures the stability of the allocation process.

[0017] This setting enables rapid response and efficient handling of 12 common anomalies, including allocation delays, inventory shortages, logistics disruptions, system failures, malicious order placement, order anomalies, warehouse overload, weather impacts, policy controls, supplier supply disruptions, surges in returns, and data anomalies. It avoids process stagnation caused by anomalies such as allocation delays, logistics disruptions, and inventory shortages. The dual handling mode ensures both efficiency and the ability to cope with complex anomaly scenarios, ensuring the continuous and stable inventory allocation process.

[0018] As a preferred embodiment of the present invention, the inventory safety early warning module sets differentiated upper and lower limits of inventory and replenishment trigger thresholds according to different product levels, and pushes inventory anomaly reminders in a graded manner according to the urgency of the warning.

[0019] This setting enables advance prediction and tiered management of inventory risks, allowing for precise control of inventory levels for different product tiers. It avoids stockouts of core products and stockpiling of regular products. At the same time, the tiered early warning system facilitates managers to prioritize handling emergencies, thereby improving the precision of inventory management.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves real-time, precise, and end-to-end controllable inventory allocation through multi-module collaborative linkage; real-time data collection and synchronization improves data consistency and decision response speed, generating allocation plans within minutes and effectively solving the problem of allocation lag; SKU hierarchical management and three-dimensional dynamic weight decision-making ensure that allocation strategies align with product characteristics and scenario needs, reducing stockout rates and inventory backlog rates, and improving warehousing resource utilization; abnormal emergency handling and closed-loop optimization ensure the stability of the allocation process, enabling system autonomous iteration and adapting to the highly volatile and refined operational needs of e-commerce; simultaneously, access control and visual monitoring enhance system security and operability, comprehensively improving the fulfillment efficiency and customer satisfaction of e-commerce enterprises, reducing operating costs, and possessing strong practicality and promotional value. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the system flow provided in an embodiment of the present invention. Detailed Implementation

[0022] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given in conjunction with the accompanying drawings.

[0023] The structure of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] refer to Figure 1As shown in the figure, the e-commerce inventory dynamic allocation system based on real-time sales data provided in this embodiment of the invention includes a real-time data access module, a data fusion and anomaly identification module, an SKU hierarchical management module, a three-dimensional dynamic weight decision module, a multi-warehouse collaborative allocation execution module, a logistics collaborative scheduling module, an anomaly emergency response module, an inventory safety early warning module, a data visualization monitoring module, a hierarchical access control module, a cross-platform data synchronization module, and a feedback optimization module. The real-time data access module is connected to the data fusion and anomaly identification module and the cross-platform data synchronization module to transmit the collected raw data. The data fusion and anomaly identification module, the SKU hierarchical management module, and the inventory safety early warning module are all connected to the three-dimensional dynamic weight decision module to provide data support and control parameters. The three-dimensional dynamic weight decision module is connected to the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module to issue allocation and logistics matching instructions. The multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module are bidirectionally interconnected with the anomaly emergency response module to promptly report and handle execution anomalies. The hierarchical access control module manages all modules to achieve full-process access control.

[0025] Specifically, the feedback optimization module collects operational data from the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module, and reversely optimizes the parameters of the data fusion and anomaly identification module, the three-dimensional dynamic weight decision module, and the SKU hierarchical management module to form a closed loop. The cross-platform data synchronization module links the real-time data access module and the multi-warehouse collaborative allocation execution module to ensure data consistency across the entire system.

[0026] By adopting the above solution, a closed-loop iteration of the entire system process can be achieved, ensuring that the parameters of each module are continuously optimized and adapted to dynamic changes in business. At the same time, data silos are completely broken down, ensuring real-time data synchronization of each system and each link, avoiding problems such as allocation errors and overselling caused by data inconsistency, and improving the stability and accuracy of system operation.

[0027] Specifically, the real-time data access module is used to realize the real-time collection and transmission of multi-source business data; the data fusion and anomaly identification module performs standardized processing on the collected data and identifies hierarchical sales anomalies; the SKU hierarchical management module is used to hierarchically control products, dynamically update hierarchical results and control parameters; and the three-dimensional dynamic weight decision module adopts a general comprehensive evaluation method, combined with multi-dimensional data and dynamic weight adaptation rules, to generate the optimal inventory allocation plan.

[0028] The above solution provides the system with accurate and unified basic data support, accurately captures sales anomalies and achieves refined management of goods, ensures that allocation decisions are in line with the characteristics of goods and the actual sales situation, avoids blind allocation strategies, and improves the rationality and applicability of allocation plans.

[0029] Specifically, the multi-warehouse collaborative allocation execution module implements allocation instructions, prevents overselling, and adapts to different order types; the logistics collaborative scheduling module matches optimal material resources, tracks logistics status, and handles anomalies; the inventory safety early warning module sets differentiated thresholds based on product grading and pushes inventory risk warnings in real time; the anomaly emergency handling module has built-in emergency plans, automatically handles common anomalies, and supports manual intervention; the data visualization monitoring module realizes full-process data monitoring and traceability; the hierarchical permission control module realizes hierarchical permission management and operation traceability; the cross-platform data synchronization module realizes real-time data synchronization and conflict handling across multiple systems; and the feedback optimization module realizes autonomous iterative optimization of system parameters.

[0030] By adopting the above solution, the entire chain of inventory transfer can be controlled, from data collection, decision generation, instruction execution to anomaly handling, monitoring and tracing, and iterative optimization. Each module performs its own function and works together to ensure the efficiency and accuracy of the transfer, while also improving the operability, security and sustainable optimization capabilities of the system, and adapting to the multi-scenario operation needs of e-commerce.

[0031] Specifically, the three-dimensional dynamic weight decision-making module adaptively adjusts the weight ratio of sales volume, fulfillment timeliness, and allocation cost according to three different scenarios: daily operation, platform promotion, and product order surge, to adapt to the allocation needs of different scenarios. The data fusion and anomaly identification module adopts a time-series analysis method, combining historical data and multi-dimensional verification information to classify sales anomaly levels, accurately identify malicious order behavior, and issue warnings.

[0032] By adopting the above solution, allocation decisions can be flexibly adapted to different operational scenarios, taking into account efficiency, cost and sales demand. At the same time, it can accurately distinguish between normal sales fluctuations and abnormal situations, avoid allocation errors caused by malicious orders or sales anomalies, and improve the pertinence and reliability of decisions.

[0033] Specifically, the SKU hierarchical management module divides products into four levels based on real-time sales, profit margins, and inventory turnover speed. The hierarchical results are updated monthly based on operating data, and the allocation and inventory control rules for each level are adjusted synchronously. The multi-warehouse collaborative allocation execution module sets up three allocation channels: emergency, regular, and clearance, which are adapted to different order types such as spot goods and pre-sales, to achieve precise allocation across warehouses and ensure allocation efficiency.

[0034] By adopting the above solution, we can achieve refined management and control of goods, ensure priority supply of core goods, clear out slow-moving goods in a timely manner, and optimize the allocation of warehousing resources. At the same time, we can adapt to different order types and allocation needs, respond quickly in emergency scenarios, take cost into account in daily scenarios, and improve turnover in clearance scenarios, thereby further improving allocation efficiency and resource utilization.

[0035] Specifically, the abnormal emergency response module has built-in 12 common abnormal scenarios with preset emergency plans, supporting both automatic triggering and manual intervention modes to ensure the stability of the allocation process.

[0036] The above solution addresses 12 common anomalies, including allocation delays, inventory shortages, logistics disruptions, system failures, malicious order placement, order anomalies, warehouse overload, weather impacts, policy controls, supplier supply disruptions, surges in returns, and data anomalies. This enables rapid response and efficient handling of anomaly scenarios, preventing process stagnation due to anomalies such as allocation delays, logistics disruptions, and inventory shortages. The dual handling mode ensures both efficiency and the ability to cope with complex anomaly scenarios, ensuring the continuous and stable inventory allocation process.

[0037] Specifically, the inventory safety early warning module sets differentiated upper and lower limits for inventory and replenishment trigger thresholds according to different product levels, and pushes inventory anomaly alerts in a tiered manner according to the urgency of the alert.

[0038] By adopting the above solution, inventory risks can be predicted in advance and managed in a tiered manner. Inventory levels can be accurately controlled for different levels of goods to avoid shortages of core products and overstocking of regular products. At the same time, the tiered early warning system allows managers to prioritize handling of emergencies and improves the precision of inventory management.

[0039] Working principle of the invention: In operation, the system first collects various business data from the entire network, including sales, warehousing, and logistics, through a real-time data access module. This data is synchronized to various business platforms to ensure consistency and simultaneously transmitted to a data fusion and anomaly identification module for data standardization and identification of sales fluctuations and abnormal orders. Next, combining the product levels defined by the SKU hierarchical management module and the inventory balance monitored by the inventory safety early warning module, the data is aggregated and transmitted to a three-dimensional dynamic weighted decision-making module. This module flexibly adjusts the weighting of sales, timeliness, and cost considerations based on different operational scenarios such as daily operations, major promotions, and order surges, automatically generating a reasonable cross-warehouse transfer plan. Subsequently, the transfer instruction is issued to the multi-warehouse collaborative transfer execution module. The system completes inventory allocation and simultaneously coordinates with the logistics coordination and scheduling module to match optimal transportation resources. Throughout the process, the emergency response module monitors for faults in real time and automatically activates corresponding contingency plans to handle emergencies. Finally, the feedback optimization module collects operational data on allocation execution, logistics distribution, and inventory changes throughout the process, and uses this data to correct various judgment criteria, weight parameters, and control rules within the system. This forms a complete closed loop of data collection, analysis and judgment, decision generation, implementation, anomaly control, and iterative optimization. Combined with the data visualization monitoring module to view the operational status in real time and the permission hierarchical control module to standardize operational permissions, the system ultimately achieves intelligent, refined, and adaptive dynamic allocation of e-commerce inventory throughout the entire process.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An e-commerce inventory dynamic allocation system based on real-time sales data, characterized in that: It includes a real-time data access module, a data fusion and anomaly identification module, a SKU hierarchical management module, a three-dimensional dynamic weight decision-making module, a multi-warehouse collaborative allocation execution module, a logistics collaborative scheduling module, an anomaly emergency handling module, an inventory safety early warning module, a data visualization monitoring module, a hierarchical access control module, a cross-platform data synchronization module, and a feedback optimization module; The real-time data access module connects to the data fusion and anomaly identification module and the cross-platform data synchronization module to transmit the collected raw data. The data fusion and anomaly identification module, SKU hierarchical management module, and inventory safety early warning module are all connected to the three-dimensional dynamic weight decision module, providing them with data support and control parameters. The three-dimensional dynamic weight decision module connects to the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module to issue allocation and logistics matching instructions. The multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module are bidirectionally interconnected with the abnormal emergency response module to promptly report and handle execution anomalies. The hierarchical permission control module manages all modules to achieve full-process permission control.

2. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The feedback optimization module collects operational data from the multi-warehouse collaborative allocation execution module and the logistics collaborative scheduling module, and reversely optimizes the parameters of the data fusion and anomaly identification module, the three-dimensional dynamic weight decision module, and the SKU hierarchical management module to form a closed loop. The cross-platform data synchronization module links the real-time data access module and the multi-warehouse collaborative allocation execution module to ensure data consistency across the entire system.

3. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The real-time data access module is used to realize the real-time collection and transmission of multi-source business data. The data fusion and anomaly identification module performs standardized processing on the collected data and identifies hierarchical sales anomalies. The SKU hierarchical management module is used to hierarchically control products, dynamically update hierarchical results and control parameters. The three-dimensional dynamic weight decision module adopts a general comprehensive evaluation method, combined with multi-dimensional data and dynamic weight adaptation rules, to generate the optimal inventory allocation plan.

4. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The multi-warehouse collaborative allocation execution module implements allocation instructions, prevents overselling, and adapts to different order types. The logistics collaborative scheduling module matches optimal material resources, tracks logistics status, and handles anomalies. The inventory safety early warning module sets differentiated thresholds based on product grading and pushes inventory risk warnings in real time. The anomaly emergency handling module has built-in emergency plans, automatically handles common anomalies, and supports manual intervention. The data visualization monitoring module realizes full-process data monitoring and traceability. The hierarchical permission control module realizes hierarchical permission management and operation traceability. The cross-platform data synchronization module realizes real-time data synchronization and conflict handling across multiple systems. The feedback optimization module realizes autonomous iterative optimization of system parameters.

5. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The three-dimensional dynamic weight decision-making module adaptively adjusts the weight ratio of sales volume, fulfillment timeliness, and allocation cost according to three different scenarios: daily operation, platform promotion, and product order surge, to adapt to the allocation needs of different scenarios. The data fusion and anomaly identification module adopts a time-series analysis method, combining historical data and multi-dimensional verification information to classify sales anomaly levels, accurately identify malicious order behavior, and issue warnings.

6. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The SKU hierarchical management module divides products into four tiers based on real-time sales, profit margins, and inventory turnover. The tiering results are updated monthly based on operating data, and the allocation and inventory control rules for each tier are adjusted accordingly. The multi-warehouse collaborative allocation execution module sets up three allocation channels: emergency, regular, and clearance, to accommodate different order types, including spot and pre-sale orders, enabling precise allocation across warehouses and ensuring allocation efficiency.

7. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The abnormal emergency response module has built-in 12 common abnormal scenarios with preset emergency plans, supporting both automatic triggering and manual intervention modes to ensure the stability of the allocation process.

8. The e-commerce inventory dynamic allocation system based on real-time sales data as described in claim 1, characterized in that: The inventory safety early warning module sets differentiated upper and lower limits for inventory and replenishment trigger thresholds according to different product levels, and pushes inventory anomaly alerts in a tiered manner according to the urgency of the alert.

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