Big data-based e-commerce sales prediction and replenishment decision system

Abstract

The application discloses an e-commerce sales prediction and replenishment decision system based on big data, and belongs to the technical field of intelligent management of e-commerce sales, and comprises a data acquisition and integration module, an inventory benchmark scheduling module, a hierarchical scene decision module, a supply chain full-dimension verification module and a backtracking iterative optimization module.The inventory benchmark scheduling module is connected with the data acquisition and integration module.The hierarchical scene decision module is used for automatically identifying the current e-commerce operating scene of goods, and matching the differentiated decision weight of the corresponding scene.The supply chain full-dimension verification module is used for performing multi-dimensional constraint verification and scheme correction on an initial replenishment scheme based on the upstream supply capacity, the warehouse capacity state and the logistics transportation time limit, and outputting a final replenishment decision instruction that can be implemented.The backtracking iterative optimization module is used for collecting actual business data after the replenishment is implemented.The application has the technical effects of dynamically adjustable inventory standard, scenario-based differentiated decision, full-dimension condition checking and autonomous iterative optimization.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management technology for e-commerce sales, and in particular relates to an e-commerce sales forecasting and replenishment decision-making system based on big data. Background Technology

[0002] The e-commerce industry features a wide variety of products and frequent sales fluctuations. New product launches, holiday promotions, and inventory clearance sales occur alternately. Utilizing sales data to predict market demand and develop reasonable replenishment plans are crucial for ensuring normal store operations and controlling warehousing costs. Currently, the industry widely uses big data for sales forecasting, providing basic replenishment recommendations based on historical transaction data, which generally meets inventory replenishment needs under stable daily conditions.

[0003] However, existing replenishment judgment methods mostly use fixed inventory standards for trigger judgment, which cannot flexibly adjust the warning threshold according to the sales popularity and turnover speed of the product. At the same time, the decision-making process does not distinguish between different business scenarios and lacks the constraints and verification of real conditions such as supply, warehousing and logistics. This can easily lead to excessive replenishment causing inventory backlog and delayed replenishment causing product shortages, making it difficult to steadily improve the accuracy of long-term decisions. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides an e-commerce sales forecasting and replenishment decision-making system based on big data. It has the advantages of dynamically adjustable inventory standards, scenario-based differentiated decision-making, full-dimensional condition verification, and autonomous iterative optimization. It solves the problems of rigid inventory judgment, poor scenario adaptability, insufficient implementation of solutions, and inability to upgrade decision-making capabilities in existing technologies.

[0005] This invention is implemented as follows: a big data-based e-commerce sales forecasting and replenishment decision-making system includes a data acquisition and integration module, an inventory baseline scheduling module, a hierarchical scenario decision-making module, a supply chain full-dimensional verification module, and a backtracking iterative optimization module. The inventory baseline scheduling module is connected to the data acquisition and integration module. The hierarchical scenario decision-making module is used to automatically identify the current e-commerce operating scenario of the product, match the differentiated decision weights of the corresponding scenario, and generate an initial replenishment plan based on the big data sales trend prediction results. The supply chain full-dimensional verification module is used to perform multi-dimensional constraint verification and plan correction on the initial replenishment plan based on upstream supply capacity, warehouse capacity status, and logistics transportation timeliness, and output a final replenishment decision instruction that can be implemented. The backtracking iterative optimization module is used to collect actual business data after replenishment execution, analyze decision deviations, and iteratively optimize inventory threshold parameters and scenario decision weights to form an optimization mechanism.

[0006] As a preferred embodiment of the present invention, the data acquisition and integration module includes a sales data acquisition unit, an inventory status acquisition unit, a supply chain data acquisition unit, and a data standardization unit, which are used to collect e-commerce product sales data, inventory data, traffic data, product lifecycle data, supply capacity data, warehouse capacity data, and logistics timeliness data, and to complete the cleaning and structural integration of multi-source business data to generate a standardized decision dataset.

[0007] This setup aggregates relevant data from the entire operational process, eliminating fragmented data and providing complete and reliable data support for subsequent decision-making.

[0008] As a preferred embodiment of the present invention, the inventory baseline scheduling module includes a turnover analysis unit, a popularity assessment unit, and a threshold update unit. The turnover analysis unit is used to statistically analyze the recent inventory turnover speed and consumption patterns of goods. The popularity assessment unit is used to assess real-time sales popularity by combining product traffic, conversion rate, and platform activity status. The threshold update unit dynamically updates the safety stock threshold and replenishment trigger threshold according to the product turnover characteristics and sales popularity, thereby realizing dynamic floating adjustment of the inventory early warning standard.

[0009] This setting eliminates fixed inventory judgment criteria and adjusts replenishment trigger conditions according to the real-time sales status of goods, reducing the risks of stockouts and overstocking.

[0010] In a preferred embodiment of the present invention, the hierarchical scenario decision-making module includes a scenario identification unit, a weight matching unit, and an initial value generation unit. The scenario identification unit is used to automatically determine the operating scenario of a product based on the product's new product launch time, platform activity tags, and real-time sales rhythm. The weight matching unit is used to call preset sales volume weight, inventory weight, cost weight, and timeliness weight for different operating scenarios. The initial value generation unit combines big data sales trend prediction results to generate an initial replenishment quantity plan through weighted calculation.

[0011] This setting allows for the differentiation of decision-making logic based on different business models, ensuring that replenishment plans align with current product operation needs.

[0012] As a preferred embodiment of the present invention, the business scenarios include regular sales scenarios, holiday promotion scenarios, new product launch scenarios, and clearance sales scenarios.

[0013] This setting comprehensively covers mainstream e-commerce business scenarios, ensuring a wider range of system applicability.

[0014] As a preferred embodiment of the present invention, different decision weighting systems are configured for different business scenarios. The holiday promotion scenario emphasizes timeliness and sales volume weighting, the new product launch scenario emphasizes traffic growth weighting, the clearance sale scenario emphasizes inventory reduction and cost weighting, and the regular sales scenario emphasizes turnover balance weighting.

[0015] This setting allows for a shift in decision-making focus based on the core operational objectives of the scenario, thereby enhancing the relevance of replenishment plans.

[0016] As a preferred embodiment of the present invention, the supply chain full-dimensional verification module includes a supply verification unit, a warehouse capacity verification unit, a logistics timeliness verification unit, and a scheme correction unit. The supply verification unit is used to verify whether the supplier's real-time production capacity and delivery cycle match the replenishment needs. The warehouse capacity verification unit is used to verify the remaining storage space of the target warehouse to avoid overstocking during replenishment. The logistics timeliness verification unit is used to verify whether the logistics arrival cycle matches the current sales rhythm of the goods. The scheme correction unit makes incremental or decremental corrections to the initial replenishment scheme based on the results of multiple constraints and outputs a compliant and implementable final replenishment instruction.

[0017] This setting allows for adjustments to replenishment quantities based on actual supply chain conditions, preventing plans from becoming unrealistic and unenforceable.

[0018] As a preferred embodiment of the present invention, the backtracking iterative optimization module includes an effect statistics unit, a deviation analysis unit, and a parameter iteration unit. The effect statistics unit is used to collect actual sales, inventory balance, number of stockouts, and backlog loss data after each batch of replenishment is completed. The deviation analysis unit is used to compare the theoretical replenishment quantity with the actual consumption data to generate a decision deviation index. The parameter iteration unit automatically fine-tunes the dynamic inventory threshold and the decision weight of each scenario based on the deviation index to achieve autonomous learning and iteration of the system.

[0019] This setting allows for reverse optimization of system parameters based on historical performance, continuously improving the accuracy of subsequent replenishment decisions.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: It adopts a dynamically adjustable inventory benchmark judgment mode, which can flexibly change replenishment trigger conditions according to the product turnover rate and sales popularity, effectively adapting to sales fluctuations and reducing inventory management losses; it divides multiple e-commerce operation scenarios and configures differentiated decision weights, which can generate adapted solutions based on operational priorities, solving the problem of weak adaptability of unified decision logic; it adds a multi-dimensional supply chain verification link, combining the actual conditions of supply, warehousing, and logistics to modify the replenishment plan, improving the actual implementation effect of the decision-making solution; and it establishes a closed-loop backtracking optimization mechanism, allowing the system to autonomously adjust operating parameters based on the effects of previous replenishments, gradually improving decision-making capabilities and achieving better long-term stability. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the system flow provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the inventory benchmark scheduling module structure provided in an embodiment of the present invention; Figure 3This is a schematic diagram of the supply chain full-dimensional verification module structure 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 Figures 1 to 3 As shown in the figure, the e-commerce sales forecasting and replenishment decision-making system based on big data provided in this embodiment of the invention includes a data acquisition and integration module, an inventory benchmark scheduling module, a hierarchical scenario decision-making module, a supply chain full-dimensional verification module, and a backtracking iterative optimization module. The inventory benchmark scheduling module is connected to the data acquisition and integration module. The hierarchical scenario decision-making module is used to automatically identify the current e-commerce operation scenario of the product, match the differentiated decision weights of the corresponding scenario, and generate an initial replenishment plan by combining the big data sales trend prediction results. The supply chain full-dimensional verification module is used to perform multi-dimensional constraint verification and plan correction on the initial replenishment plan based on upstream supply capacity, warehouse capacity status, and logistics transportation timeliness, and output a final replenishment decision instruction that can be implemented. The backtracking iterative optimization module is used to collect actual business data after replenishment is executed, analyze decision deviations, and iteratively optimize inventory threshold parameters and scenario decision weights in reverse to form an optimization mechanism.

[0025] Specifically, the data acquisition and integration module includes a sales data acquisition unit, an inventory status acquisition unit, a supply chain data acquisition unit, and a data standardization unit. It is used to collect e-commerce product sales data, inventory data, traffic data, product lifecycle data, supply capacity data, warehouse capacity data, and logistics timeliness data, and to clean and structurally integrate multi-source business data to generate a standardized decision dataset.

[0026] By adopting the above solution, relevant data from the entire business process can be aggregated, eliminating the problem of fragmented data and providing complete and reliable data support for subsequent decision-making.

[0027] Specifically, the inventory baseline scheduling module includes a turnover analysis unit, a popularity assessment unit, and a threshold update unit. The turnover analysis unit is used to statistically analyze the recent inventory turnover speed and consumption patterns of goods. The popularity assessment unit is used to assess real-time sales popularity by combining product traffic, conversion rate, and platform activity status. The threshold update unit dynamically updates the safety stock threshold and replenishment trigger threshold according to product turnover characteristics and sales popularity, realizing dynamic floating adjustment of inventory early warning standards.

[0028] By adopting the above approach, fixed inventory judgment criteria are abandoned, and replenishment trigger conditions are adjusted according to the real-time sales status of goods, thereby reducing the risks of stockouts and overstocking.

[0029] Specifically, the hierarchical scenario decision-making module includes a scenario identification unit, a weight matching unit, and an initial value generation unit. The scenario identification unit is used to automatically determine the operating scenario of a product based on the product's new product launch time, platform activity tags, and real-time sales rhythm. The weight matching unit is used to call preset sales weight, inventory weight, cost weight, and timeliness weight for different operating scenarios. The initial value generation unit combines big data sales trend prediction results to generate an initial replenishment quantity plan through weighted calculation.

[0030] By adopting the above approach, we can differentiate between different business models and formulate corresponding decision-making logics to ensure that the replenishment plan meets the current needs of product operation.

[0031] Specifically, the business scenarios include regular sales, holiday promotions, new product launches, and clearance sales of remaining stock.

[0032] The above solution comprehensively covers mainstream e-commerce business scenarios, ensuring a wider range of system applicability.

[0033] Specifically, different business scenarios are configured with differentiated decision weighting systems. The holiday promotion scenario emphasizes timeliness and sales volume weighting, the new product launch scenario emphasizes traffic growth weighting, the clearance sale scenario emphasizes inventory reduction and cost weighting, and the regular sales scenario emphasizes turnover balance weighting.

[0034] By adopting the above approach, the decision-making focus is shifted according to the core operational objectives of the scenario, thereby improving the relevance of the replenishment plan.

[0035] Specifically, the supply chain full-dimensional verification module includes a supply verification unit, a warehouse capacity verification unit, a logistics timeliness verification unit, and a solution correction unit. The supply verification unit is used to check whether the supplier's real-time production capacity and delivery cycle match the replenishment needs; the warehouse capacity verification unit is used to verify the remaining storage space in the target warehouse to avoid overstocking; the logistics timeliness verification unit is used to verify whether the logistics arrival cycle matches the current sales rhythm of the goods; and the solution correction unit makes incremental or decremental adjustments to the initial replenishment plan based on the results of multiple constraints, and outputs a compliant and implementable final replenishment instruction.

[0036] By adopting the above approach and adjusting the replenishment quantity according to the actual conditions of the supply chain, we can avoid making the plan unrealistic and unenforceable.

[0037] Specifically, the backtracking iterative optimization module includes an effect statistics unit, a deviation analysis unit, and a parameter iteration unit. The effect statistics unit is used to collect data on actual sales, inventory balance, number of stockouts, and backlog losses after each batch of replenishment is completed. The deviation analysis unit is used to compare the theoretical replenishment quantity with the actual consumption data to generate a decision deviation index. The parameter iteration unit automatically fine-tunes the dynamic inventory threshold and the decision weights for each scenario based on the deviation index, enabling the system to learn and iterate autonomously.

[0038] By adopting the above approach, the system parameters are optimized in reverse based on historical performance, thereby continuously improving the accuracy of subsequent replenishment decisions.

[0039] Working principle of the invention: In use, the data collection and integration module first aggregates various information related to e-commerce sales, inventory, supply chain, and logistics. After processing, it forms unified and usable decision-making data. The inventory benchmark scheduling module dynamically adjusts safety stock and replenishment trigger standards based on product turnover and real-time sales popularity. The hierarchical scenario decision-making module automatically identifies the operating scenario of the product, matches the corresponding decision weight, and calculates the initial replenishment plan based on sales trends. Then, the supply chain full-dimensional verification module verifies and corrects the plan based on actual conditions such as supply capacity, warehouse capacity, and logistics cycle, and outputs the final executable replenishment instruction. After the replenishment task is completed, the backtracking and iterative optimization module statistically analyzes the actual operating data, compares and analyzes decision deviations, and adjusts the inventory judgment standard and scenario weight parameters in reverse to continuously optimize the system's decision-making logic and complete the intelligent replenishment management of the entire process in a loop.

[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. A big data-based e-commerce sales forecasting and replenishment decision-making system, characterized by: The system includes a data acquisition and integration module, an inventory baseline scheduling module, a hierarchical scenario decision-making module, a supply chain full-dimensional verification module, and a backtracking iterative optimization module. The inventory baseline scheduling module is connected to the data acquisition and integration module. The hierarchical scenario decision-making module is used to automatically identify the current e-commerce operation scenario of the product, match the differentiated decision weights of the corresponding scenario, and generate an initial replenishment plan by combining the big data sales trend prediction results. The supply chain full-dimensional verification module is used to perform multi-dimensional constraint verification and plan correction on the initial replenishment plan based on upstream supply capacity, warehouse capacity status, and logistics transportation timeliness, and output a final replenishment decision instruction that can be implemented. The backtracking iterative optimization module is used to collect actual business data after replenishment is executed, analyze decision deviations, and iteratively optimize inventory threshold parameters and scenario decision weights to form an optimization mechanism.

2. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 1, characterized in that: The data acquisition and integration module includes a sales data acquisition unit, an inventory status acquisition unit, a supply chain data acquisition unit, and a data standardization unit. It is used to collect e-commerce product sales data, inventory data, traffic data, product lifecycle data, supply capacity data, warehouse capacity data, and logistics timeliness data, and to clean and structurally integrate multi-source business data to generate a standardized decision dataset.

3. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 1, characterized in that: The inventory baseline scheduling module includes a turnover analysis unit, a popularity assessment unit, and a threshold update unit. The turnover analysis unit is used to statistically analyze the recent inventory turnover speed and consumption patterns of goods. The popularity assessment unit is used to assess real-time sales popularity by combining product traffic, conversion rate, and platform activity status. The threshold update unit dynamically updates the safety stock threshold and replenishment trigger threshold based on product turnover characteristics and sales popularity, realizing dynamic floating adjustment of inventory early warning standards.

4. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 1, characterized in that: The hierarchical scenario decision-making module includes a scenario identification unit, a weight matching unit, and an initial value generation unit. The scenario identification unit is used to automatically determine the operating scenario of a product based on the product's new product launch time, platform activity tags, and real-time sales rhythm. The weight matching unit is used to call preset sales weight, inventory weight, cost weight, and timeliness weight for different operating scenarios. The initial value generation unit combines big data sales trend prediction results to generate an initial replenishment quantity plan through weighted calculation.

5. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 4, characterized in that: The business scenarios include regular sales, holiday promotions, new product launches, and clearance sales of remaining stock.

6. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 5, characterized in that: Different decision-making weight systems are configured for different business scenarios. The holiday promotion scenario emphasizes timeliness and sales volume weight, the new product launch scenario emphasizes traffic growth weight, the clearance sale scenario emphasizes inventory reduction and cost weight, and the regular sales scenario emphasizes turnover balance weight.

7. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 1, characterized in that: The supply chain full-dimensional verification module includes a supply verification unit, a warehouse capacity verification unit, a logistics timeliness verification unit, and a solution correction unit. The supply verification unit is used to check whether the supplier's real-time production capacity and delivery cycle match the replenishment needs. The warehouse capacity verification unit is used to verify the remaining storage space in the target warehouse to avoid overstocking. The logistics timeliness verification unit is used to verify whether the logistics arrival cycle matches the current sales rhythm of the goods. The solution correction unit makes incremental or decremental adjustments to the initial replenishment plan based on the results of multiple constraints and outputs a compliant and implementable final replenishment instruction.

8. The e-commerce sales forecasting and replenishment decision-making system based on big data as described in claim 1, characterized in that: The backtracking iterative optimization module includes an effect statistics unit, a deviation analysis unit, and a parameter iteration unit. The effect statistics unit is used to collect data on actual sales, inventory balance, number of stockouts, and backlog losses after each batch of replenishment is completed. The deviation analysis unit is used to compare the theoretical replenishment quantity with the actual consumption data to generate a decision deviation index. The parameter iteration unit automatically fine-tunes the dynamic inventory threshold and the decision weights for each scenario based on the deviation index, enabling the system to learn and iterate autonomously.

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