E-commerce operation method for intelligent inventory management and automatic replenishment

Abstract

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal gangue slurry, specifically to an e-commerce operation method with intelligent inventory management and automatic replenishment. BACKGROUND

[0002] Intelligent inventory management and automatic replenishment are key technical means for improving efficiency and reducing costs in modern e-commerce operations. By using technologies such as big data, Internet of Things (IoT), artificial intelligence (AI), etc., e-commerce platforms can monitor inventory status in real time, predict changes in demand for goods, and automatically trigger the replenishment process. These systems can intelligently allocate inventory based on factors such as sales trends, seasonal fluctuations, and market demand forecasts, thereby avoiding situations of stockouts or overstocking, ensuring timely delivery of orders and customer satisfaction. Automatic replenishment systems not only improve inventory turnover rates but also reduce manual intervention, lower operational costs, and ensure that e-commerce platforms maintain efficient and flexible operational capabilities in a highly competitive market.

[0003] Despite the significant advantages of intelligent inventory management and automatic replenishment in e-commerce operations, there are still some deficiencies in existing technologies, mainly including the following aspects: demand prediction accuracy: intelligent inventory management systems rely on big data and algorithms to predict future demand. However, these predictions are often limited by the accuracy and comprehensiveness of historical data, especially when faced with sudden events or rapid changes in market trends, the system may not be able to adjust in time. For example, unexpected promotional activities, seasonal changes or external environmental influences may cause demand prediction deviation, leading to overstock or out-of-stock situations. Implementation threshold of technology and cost: Although intelligent inventory management and automatic replenishment technology can significantly improve efficiency in theory, for some small and medium-sized e-commerce platforms, the initial investment required to implement these technologies is high. The development of the system, the collection and processing of data, and the integration with existing systems all require certain technical accumulation and financial support, which is a not small challenge for e-commerce enterprises with limited resources. Data quality issues: the accuracy of the system is highly dependent on the quality of the data. If the data collection of the e-commerce platform is not in place, the data is incomplete or there are errors, the effect of intelligent inventory management and replenishment will be greatly reduced. For example, incorrect inventory quantities, chaotic product classification, improper handling of return data, etc. will affect the system's decision-making, leading to inventory imbalance or supply chain disruption. Flexibility and adaptability of the system: automatic replenishment systems usually rely on set algorithms and rules, and may lack sufficient flexibility in the face of complex market environments. For example, in some special situations, the system may not be able to identify and respond to sharp fluctuations in demand in time, leading to inventory management errors. Therefore, the system may not make the best decision in some non-standard or extreme situations. Inventory accumulation and overstocking: Although the automatic replenishment system can reduce human errors, in some cases, the system may overstock, leading to inventory accumulation. Especially in cases of low prediction accuracy or large data fluctuations, the system may exhibit overly conservative replenishment behavior, causing unnecessary inventory burden and increasing warehousing costs. Risk of lack of human intervention: Relying entirely on automated systems for inventory management and replenishment may overlook the value of human intervention and industry experience. Some detailed market insights and short-term sales trends may not be accurately captured by algorithms, and human decision-making can sometimes make up for the shortcomings of the system, especially when faced with complex product lines or changing market conditions.

[0004] Therefore, we propose an e-commerce operation method for intelligent inventory management and automatic replenishment. SUMMARY

[0005] To achieve the above purpose, the present application provides the following technical scheme: an e-commerce operation method for intelligent inventory management and automatic replenishment, comprising the following steps:

[0006] S1 Data Collection and Cleaning: By integrating sales data, inventory data, return data, product attributes, supply chain data, and market environment data (such as social media, weather data, economic indicators, etc.) from e-commerce platforms, data preprocessing methods are used for cleaning and denoising to ensure data quality and integrity.

[0007] S2 Multidimensional Demand Forecasting Model: Based on sales history data, market trend analysis, seasonal fluctuations, product attribute analysis, and user behavior prediction, a hybrid forecasting algorithm is used, including deep learning models (such as LSTM, GRU) and traditional statistical models (such as ARIMA, exponential smoothing, etc.), to accurately predict demand.

[0008] S3 Abnormal Event Monitoring and Identification Mechanism: By monitoring and analyzing external factors (such as weather changes, holiday effects, sudden marketing activities, etc.) in real time, combined with external data such as social media, news, and logistics status, machine learning models (such as anomaly detection algorithms, decision trees, etc.) are used to identify potential abnormal events, and inventory forecasting and replenishment strategies are adjusted in a timely manner.

[0009] S4 Dynamic Inventory Optimization and Replenishment Decision: Based on prediction results, inventory data, transportation capacity, and market demand, dynamic inventory optimization algorithms (such as weighted dynamic inventory control models, optimal inventory models, etc.) are used to optimize replenishment plans in real time.

[0010] S5 Artificial Intelligence and Manual Decision Coordination Mechanism: By setting rules and thresholds for human intervention, when the system detects abnormal situations or low prediction accuracy, it automatically notifies human review decisions, and humans adjust the final results based on market experience and model results.

[0011] S6 Replenishment Execution and Feedback Adjustment: Automatically generate replenishment orders and interface with suppliers and logistics systems. During the replenishment process, real-time monitoring of replenishment execution progress is conducted, and feedback adjustments are made according to actual conditions and market changes to ensure the rationality of inventory and the timeliness of replenishment.

[0012] Preferably, the demand forecasting model uses a hybrid model based on time series, including a combination of deep learning and traditional regression models (such as ridge regression, support vector regression, etc.), to handle long-term trends and short-term fluctuations.

[0013] Preferably, the demand forecasting model uses a hybrid model based on time series, including a combination of deep learning and traditional regression models (such as ridge regression, support vector regression, etc.), to handle long-term trends and short-term fluctuations.

[0014] Preferably, the abnormal event monitoring and identification mechanism is real-time data capture and processing through multi-source data (such as e-commerce platform order data, logistics status, external economic data, weather forecast, social media sentiment analysis), using adaptive anomaly detection algorithm (such as based on isolation forest or self-encoder anomaly identification) to detect potential abnormal events and adjust inventory strategy.

[0015] Preferably, the dynamic inventory optimization and replenishment decision uses demand fluctuation-based inventory optimization algorithm to determine the optimal replenishment time and quantity by minimizing the total cost (including holding cost, shortage cost, replenishment cost, etc.), and uses fuzzy logic or genetic algorithm to optimize the replenishment strategy.

[0016] Preferably, the replenishment execution and feedback adjustment is seamlessly integrated with the supplier and logistics system through automatic interface, real-time update of replenishment progress, and automatic re-evaluation and adjustment of replenishment plan when the system detects replenishment delay or inventory shortage.

[0017] Compared with the prior art, the present application provides an intelligent inventory management and automatic replenishment e-commerce operation method, which has the following beneficial effects:

[0018] 1. The intelligent inventory management and automatic replenishment e-commerce operation method uses multi-dimensional data fusion and deep learning algorithm, which can fully consider historical sales data, seasonal fluctuations, market dynamics, user behavior and other factors, so compared with traditional single prediction model, it can greatly improve the accuracy of demand prediction, avoid inventory accumulation or shortage risk caused by prediction error, and through the mechanism of real-time monitoring and analysis of external events, it can quickly respond to sudden situations (such as sudden promotion, market demand fluctuation, weather change, etc.), and timely adjust the replenishment strategy to avoid inventory crisis caused by the system's inability to quickly respond to external changes.

[0019] 2. The intelligent inventory management and automatic replenishment e-commerce operation method can reduce inventory accumulation and warehouse cost, and improve inventory turnover rate through dynamic inventory optimization and replenishment decision combined with accurate prediction and optimization model. The system can meet customer demand while reducing the cost of shortage and excess inventory by optimizing the replenishment strategy, combining the advantages of artificial and intelligent decision-making, and quickly starting the manual intervention mechanism when the system encounters inaccurate prediction or cannot identify abnormal situations, thereby avoiding inaccurate decisions caused by data missing or algorithm deficiency, improving the adaptability and flexibility of the system.

[0020] 3. The intelligent inventory management and automatic replenishment e-commerce operation method can flexibly adapt to different sizes and types of e-commerce platforms due to the multi-source data and modular algorithm architecture, which can not only run efficiently on large platforms, but also realize low-cost intelligent management on small and medium-sized platforms. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0022] EMBODIMENT

[0023] Embodiment of the e-commerce operation method of intelligent inventory management and automatic replenishment

[0024] The e-commerce operation method of intelligent inventory management and automatic replenishment comprises the following steps:

[0025] S1 data collection and cleaning: through the integration of sales data, inventory data, return data, product attributes, supply chain data and market environment data (such as social media, weather data, economic indicators, etc.) of e-commerce platforms, data preprocessing methods are used for cleaning and denoising to ensure the quality and integrity of the data;

[0026] S2 multi-dimensional demand forecasting model: based on sales history data, market trend analysis, seasonal fluctuations, product attribute analysis and user behavior prediction, a hybrid prediction algorithm is used, including deep learning models (such as LSTM, GRU) and traditional statistical models (such as ARIMA, exponential smoothing method, etc.), to accurately predict demand;

[0027] S3 abnormal event monitoring and identification mechanism: through real-time monitoring and analysis of external factors (such as weather changes, holiday effects, sudden marketing activities, etc.), combined with external data such as social media, news and logistics status, machine learning models (such as anomaly detection algorithms, decision trees, etc.) are used to identify potential abnormal events, and inventory prediction and replenishment strategies are adjusted in time;

[0028] S4 dynamic inventory optimization and replenishment decision: based on prediction results, inventory data, transportation capacity and market demand, etc., dynamic inventory optimization algorithms (such as weighted dynamic inventory control model, optimal inventory model, etc.) are used to optimize replenishment plans in real time;

[0029] S5 artificial intelligence and artificial decision-making collaborative mechanism: by setting rules and thresholds for human intervention, when the system detects abnormal conditions or low prediction accuracy, it automatically notifies human review decisions, and humans make final adjustments based on market experience and model results;

[0030] S6 replenishment execution and feedback adjustment: automatically generate replenishment orders and interface with suppliers and logistics systems. During the replenishment process, real-time monitoring of replenishment execution progress, and feedback adjustment according to actual situation and market changes to ensure the rationality of inventory and timeliness of replenishment.

[0031] Specifically, the demand prediction model uses a hybrid model based on time series, including the combination of deep learning and traditional regression models (such as ridge regression, support vector regression, etc.), to handle long-term trends and short-term fluctuations respectively.

[0032] Specifically, the demand prediction model uses a hybrid model based on time series, including the combination of deep learning and traditional regression models (such as ridge regression, support vector regression, etc.), to handle long-term trends and short-term fluctuations respectively.

[0033] Specifically, the abnormal event monitoring and identification mechanism uses multi-source data (such as e-commerce platform order data, logistics status, external economic data, weather forecast, social media sentiment analysis) for real-time capture and processing, and uses adaptive anomaly detection algorithms (such as anomaly identification based on isolation forest or autoencoder) to detect potential abnormal events and adjust inventory strategies.

[0034] Specifically, dynamic inventory optimization and replenishment decision-making uses inventory optimization algorithms based on demand fluctuations to determine the optimal replenishment time and quantity by minimizing total costs (including holding costs, shortage costs, replenishment costs, etc.), and uses fuzzy logic or genetic algorithms to optimize replenishment strategies.

[0035] Specifically, replenishment execution and feedback adjustment are seamlessly integrated through automatic interfaces with suppliers and logistics systems, real-time updating of replenishment progress, and automatic re-evaluation and adjustment of replenishment plans when the system detects replenishment delays or inventory shortages.

[0036] Through the above technical scheme, in the application, multi-dimensional data fusion and deep learning algorithm are adopted, which can fully consider multiple factors such as historical sales data of goods, seasonal fluctuations, market dynamics, user behavior, etc. Therefore, compared with traditional single prediction model, the accuracy of demand prediction can be greatly improved, and the risk of inventory accumulation or shortage caused by prediction error can be avoided. Through the mechanism of real-time monitoring and analysis of external events, sudden situations (such as sudden promotion, market demand fluctuation, weather change, etc.) can be quickly responded to, and the replenishment strategy can be adjusted in time to avoid inventory crisis caused by the system's inability to quickly respond to external changes. Through dynamic inventory optimization and replenishment decision, combined with accurate prediction and optimization model, inventory accumulation and warehousing cost can be reduced, and inventory turnover rate can be improved. The system can optimize the replenishment strategy to meet customer demand while reducing the cost of shortage and excess inventory, combining the advantages of artificial and intelligent decision-making. When the system appears inaccurate prediction or cannot identify abnormal situations, the artificial intervention mechanism can be quickly started to avoid inaccurate decisions caused by data loss or algorithm deficiency, thereby improving the adaptability and flexibility of the system. Since the method uses multi-source data and modular algorithm architecture, it can flexibly adapt to different sizes and types of e-commerce platforms, not only can efficiently run on large platforms, but also can realize intelligent management with low cost in small and medium-sized platforms.

[0037] Demand prediction and data processing

[0038] Data collection and cleaning:

[0039] The system connects with e-commerce platforms through API interface or data warehouse to obtain order data, inventory data, return data, logistics data, etc. in real time. Then through the data cleaning module, the data is denoised, formatted and standardized to ensure the quality and consistency of the input data.

[0040] Multi-dimensional demand prediction:

[0041] The system uses deep learning models (such as LSTM) to model time series data, and combines product attribute information, historical sales trends, holiday effects, weather and other external factors to use weighted average and regression analysis for demand prediction. Model training uses historical data to continuously optimize prediction accuracy through supervised learning.

[0042] External data collection:

[0043] The system continuously crawls relevant information in weather forecasts, news, social media (such as Weibo, Twitter), especially information related to changes in the e-commerce industry. Through API interface, news aggregation platform and weather forecasting system are accessed to obtain external changes in real time.

[0044] Anomaly detection:

[0045] The system employs adaptive anomaly detection algorithms such as Isolation Forest and Autoencoder to analyze multi-source data, identify potential abnormal events (such as sudden outbreaks, logistics disruptions, etc.), and trigger system warnings to adjust replenishment strategies.

[0046] Inventory optimization model:

[0047] Based on historical sales data and demand forecasting results, the system uses a weighted dynamic inventory control model to calculate the optimal replenishment point, replenishment quantity, and replenishment timing in real time, optimizes inventory allocation using genetic algorithms, and reduces inventory obsolescence or shortages.

[0048] Dynamic adjustment:

[0049] The system monitors the replenishment plan and actual sales in real time. If differences are found, the system will re-evaluate the replenishment strategy and automatically adjust the replenishment quantity or time to avoid inventory shortages or overstock caused by factors such as logistics delays, demand changes, etc.

[0050] Manual intervention rules:

[0051] When the system finds that the accuracy of the prediction model is lower than the set threshold or detects abnormal events that cannot be effectively solved by algorithms, the system will automatically notify human auditors. In the visualization interface, human auditors can view replenishment plans, market data, and abnormal situations to make decisions.

[0052] Feedback mechanism:

[0053] Manually modified replenishment decisions are fed back to the system through the feedback mechanism for model learning and adjustment of replenishment rules to optimize subsequent automatic replenishment strategies.

[0054] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An e-commerce operation method for intelligent inventory management and automatic replenishment, characterized by: The following steps are involved: S1 Data Collection and Cleaning: By integrating sales data, inventory data, return data, product attributes, supply chain data, and market environment data (such as social media, weather data, economic indicators, etc.) from e-commerce platforms, data preprocessing methods are used for cleaning and denoising to ensure data quality and integrity; S2 Multi-Dimensional Demand Forecasting Model: Based on historical sales data, market trend analysis, seasonal fluctuations, product attribute analysis, and user behavior prediction, it uses a hybrid forecasting algorithm, including deep learning models (such as LSTM and GRU) and traditional statistical models (such as ARIMA and exponential smoothing), to accurately predict demand. S3 abnormal event monitoring and identification mechanism: By real-time monitoring and analysis of external factors (such as weather changes, holiday effects, and unexpected marketing activities), combined with external data such as social media, news, and logistics status, and using machine learning models (such as anomaly detection algorithms and decision trees), it identifies potential abnormal events and promptly adjusts inventory forecasting and replenishment strategies. S4 Dynamic Inventory Optimization and Replenishment Decision-Making: Based on multiple factors such as forecast results, inventory data, transportation capacity, and market demand, dynamic inventory optimization algorithms (such as weighted dynamic inventory control models and optimal inventory models) are used to optimize replenishment plans in real time. S5 AI and human decision-making collaboration mechanism: By setting rules and thresholds for human intervention, when the system detects anomalies or low prediction accuracy, it automatically notifies humans to review the decision, who then make the final adjustments based on market experience and model results. S6 Replenishment Execution and Feedback Adjustment: Automatically generates replenishment orders and connects with suppliers and logistics systems. During the replenishment process, it monitors the progress of replenishment execution in real time and makes feedback adjustments based on actual conditions and market changes to ensure the rationality of inventory and the timeliness of replenishment.

2. The e-commerce operation method for intelligent inventory management and automatic replenishment according to claim 1, characterized in that: The demand forecasting model adopts a hybrid model based on time series, including a combination of deep learning and traditional regression models (such as ridge regression, support vector regression, etc.), which respectively deal with long-term trends and short-term fluctuations.

3. The e-commerce operation method for intelligent inventory management and automatic replenishment according to claim 1, characterized in that: The abnormal event monitoring and identification mechanism captures and processes multi-source data (such as e-commerce platform order data, logistics status, external economic data, weather forecasts, and social media sentiment analysis) in real time, and uses adaptive anomaly detection algorithms (such as anomaly recognition based on isolation forests or autoencoders) to detect potential abnormal events and adjust inventory strategies.

4. The e-commerce operation method for intelligent inventory management and automatic replenishment according to claim 1, characterized in that: The dynamic inventory optimization and replenishment decision-making uses an inventory optimization algorithm based on demand fluctuations to determine the optimal replenishment time and quantity by minimizing total costs (including holding costs, out-of-stock costs, replenishment costs, etc.), and uses fuzzy logic or genetic algorithms to optimize the replenishment strategy.

5. The e-commerce operation method for intelligent inventory management and automatic replenishment according to claim 1, characterized in that: The collaborative mechanism of artificial intelligence and manual decision-making includes automatically pushing the replenishment plan to the manual operator when the system detects inaccurate predictions or abnormal events. The manual operator can adjust the replenishment plan on the visual interface, and the adjustment results are transmitted back to the system through the feedback mechanism for learning and improvement.

6. The e-commerce operation method for intelligent inventory management and automatic replenishment according to claim 1, characterized in that: The replenishment execution and feedback adjustment are seamlessly connected with the automated interface of the supplier and logistics system to update the replenishment progress in real time, and automatically re-evaluate and adjust the replenishment plan when the system detects replenishment delays or insufficient inventory.