Intelligent statistical analysis method and system for sales data of retail commodities

Abstract

The invention discloses an intelligent statistical analysis method and system for sales data of retail commodities, and relates to the technical field of statistical analysis. During operation of the system, data from different sales channels are collected and synchronized in real time, the collected data are preprocessed and then calculated to obtain a supply chain index SCI, and the SCI is used as a supply chain index; by analyzing historical sales data and a current inventory state, commodity sales dynamics are monitored in real time, inventory pressure is evaluated, potential stockout or excessive commodities are identified, the required replenishment amount is automatically calculated according to the predicted sales trend and inventory state, and the commodity replenishment amount is automatically calculated based on the historical sales data and external factors. A machine learning algorithm is used to predict future commodity demands, production, inventory and distribution plans are dynamically adjusted, a prediction model is automatically adjusted, key nodes in a supply chain are monitored in real time, early warning is carried out on abnormal conditions, an automatic feedback mechanism is triggered, and related departments are notified to take measures in time and find potential supply chain risks in time.

Description

Technical Field

[0001] The present invention relates to the technical field of statistical analysis, and specifically to an intelligent statistical analysis method and system for retail commodity sales data. Background Art

[0002] With the continuous development of the retail industry, the intelligent statistical analysis of commodity sales data has become a key tool for merchants to improve operational efficiency. Traditional statistical methods for retail commodity sales data often rely on manual operations or regular batch data analysis, which not only leads to lagging data processing but also makes it difficult to meet the rapidly changing market demands.

[0003] Most traditional retail commodity sales data statistical systems are limited to the static analysis of historical data and cannot reflect the fluctuations of sales trends and changes in market demands in real time. As a result, the prediction of commodity inventory and the adjustment of the supply chain are not flexible enough, which in turn affects the efficiency of the entire supply chain. The problems of inventory backlog and out-of-stock are common pain points in the retail industry. Improper inventory management may lead to overstocked goods, tying up capital costs, or out-of-stock situations, unable to meet customer demands in a timely manner, affecting sales opportunities and customer satisfaction. In addition, existing supply chain management systems often lack dynamic collaboration and intelligent optimization capabilities. Most systems fail to fully utilize sales data and market changes for the prediction and adjustment of the supply chain, resulting in lagging replenishment, production, and distribution plans, slow supply chain response speed, and even situations of overstock or shortage. This inefficient supply chain management not only increases costs but also may damage the brand reputation, ultimately affecting the profitability of retailers.

[0004] The defects in inventory and supply chain management in traditional retail systems usually stem from the lagging nature of data processing and information silos. Many retailers still rely on manual operations and human intervention to update sales data and adjust inventory levels. This method is not only time-consuming but also vulnerable to human errors, resulting in information asymmetry. Especially during peak sales periods or holidays when market demands fluctuate greatly, the reaction speed of traditional systems far lags behind the changes and cannot adjust inventory and production plans in a timely manner, ultimately leading to problems such as out-of-stock and inventory backlog.

[0005] In addition, existing systems inadequately consider the impacts of external environmental factors (such as weather, holiday promotions, competitor activities, etc.) on commodity sales. Without accurate demand forecasting, it is difficult for retailers to predict in advance the amplitude and direction of market demand fluctuations, which easily leads to improper inventory management and further results in low supply chain efficiency. The inconsistent response times of suppliers and logistics systems also exacerbate these problems, ultimately leading to supply chain disruptions and sales losses. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent statistical analysis method and system for retail commodity sales data, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent statistical analysis system for retail commodity sales data includes a data collection module, a data processing module, a sales inventory analysis module, a supply chain collaboration module, a demand forecasting module, and an intelligent warning module; The data collection module is used to collect and synchronize data from different sales channels in real time, including commodity sales data, inventory data, supply chain logistics data, and external market factor data; The data processing module is used to clean, filter, and standardize the collected data, remove redundant information, fill in missing data, and calculate the supply chain index SCI after unifying the format; The sales inventory analysis module is used to evaluate the health status of commodity inventory based on data models by analyzing historical sales data and current inventory status, monitor the sales dynamics of commodities in real time, evaluate inventory pressure, and identify potential out-of-stock or overstocked commodities; The supply chain collaboration module is used to deeply integrate sales data with the supply chain system, automatically optimize replenishment, production, and distribution plans, automatically calculate the required replenishment quantity according to the predicted sales trend and inventory status, and collaborate with suppliers, production systems, and logistics departments; The demand forecasting module is used to predict future commodity demand based on historical sales data and external factors using machine learning algorithms, and dynamically adjust production, inventory, and distribution plans, and automatically adjust the forecasting model according to real-time market changes; The intelligent warning module is used to monitor key nodes in the supply chain in real time, give warnings for abnormal situations, trigger an automated feedback mechanism, notify relevant departments to take measures in a timely manner, and discover potential supply chain risks in a timely manner.

[0008] Preferably, the data collection module includes a supply chain response efficiency collection unit, a sales demand data collection unit, and an inventory data collection unit; The supply chain response efficiency collection unit is used to collect the response speed and efficiency of the supply chain from sales data collection to inventory replenishment from each sales channel in real time, including the time required for sales data to be transmitted from the terminal system to the central database, the rate of inventory consumption per unit time, the time required from inventory shortage to replenishment completion, the time required for the supplier to deliver goods after receiving the order, and the distribution time from the warehouse to the store or consumer, and obtain a supply chain data set; The supply chain data set includes: sales data response time SDRT, inventory turnover rate ITR, replenishment cycle RPL, supplier delivery timeliness STD, and logistics distribution timeliness LDT; The sales demand data collection unit is used to collect historical sales data, obtain the sales data, the influence degree of different seasons and holiday factors on commodity demand, the response ability of the system to the changes in promotion and weather in the market changes, the influence degree of external factors on commodity sales, and the change speed of consumer preferences, and obtain the sales demand data set; The sales demand data set includes: historical sales data accuracy (HSDA), seasonal demand volatility value (SDVI), market trend sensitivity (MTS), external factor influence rate (EFI), and consumer behavior change rate (CBVR); The inventory data collection unit is used to monitor the inventory status in real time, record the inventory in and out data of commodities, obtain the proportion of slow-moving commodities in the inventory, the average time required for a commodity to be sold from the time of receipt, and the frequency of the commodity inventory falling below the minimum inventory level, and obtain the inventory data set; The inventory data set includes: inventory backlog rate (IRR), inventory turnover time (ITT), and stockout rate (SOR).

[0009] Preferably, the data processing module includes a data preprocessing unit and a data fusion unit; The data preprocessing unit is used to clean the collected raw data, clean duplicate records, remove noise data, fill in missing data points using methods such as mean, median, or regression analysis, eliminate redundant information, and standardize the data format; The data fusion unit is used to fuse data from different sources, including sales data, inventory data, and external environment data, and calculate to obtain: supply chain response efficiency coefficient (SCRE), demand prediction accuracy coefficient (DPAC), inventory management optimization coefficient (IMOC), and supply chain index (SCI), construct a complete database, partition and store the data according to time and commodity category conditions, and back up the data regularly.

[0010] Preferably, the supply chain response efficiency coefficient (SCRE) is calculated and obtained through the following formula: ; In the formula, SDRT represents the sales data response time, ITR represents the inventory turnover rate, RPL represents the replenishment cycle, STD represents the supplier delivery timeliness, LDT represents the logistics distribution timeliness, and w1, w2, w3, w4, and w5 respectively represent the proportional coefficients of the sales data response time (SDRT), inventory turnover rate (ITR), replenishment cycle (RPL), supplier delivery timeliness (STD), and logistics distribution timeliness (LDT); The demand prediction accuracy coefficient (DPAC) is calculated and obtained through the following formula: ; Wherein, HSDA represents the historical sales data accuracy, SDVI represents the seasonal demand volatility value, MTS represents the market trend sensitivity, EFI represents the external factor influence rate, and CBVR represents the consumer behavior change rate. 1. 2. 3. 4 and 5 respectively represent the proportionality coefficients of the historical sales data accuracy HSDA, the seasonal demand volatility value SDVI, the market trend sensitivity MTS, the external factor influence rate EFI, and the consumer behavior change rate CBVR.

[0011] Preferably, the inventory management optimization coefficient IMOC is obtained by calculating through the following formula: ; Wherein, f1(IRR), f2(ITT), f3(SOR) represent the non-linear functions of each parameter, reflecting the influence of the inventory backlog rate, the inventory turnover time, and the stock-out rate on inventory optimization. 1. 2 and 3 respectively represent the proportionality coefficients of the inventory backlog rate, the inventory turnover time, and the stock-out rate; The supply chain index SCI is obtained by calculating through the following formula: ; Wherein, SCI(t) represents that the supply chain index is a continuous function varying with time, measuring the overall efficiency of the supply chain at a given time point, t0 represents the starting point of time, t represents the current moment, and x1, x2, and x3 respectively represent the proportionality coefficients of the supply chain response efficiency coefficient SCRE, the demand prediction accuracy coefficient DPAC, and the inventory management optimization coefficient IMOC.

[0012] Preferably, the sales inventory analysis module includes a sales trend analysis unit and an inventory health assessment unit; The sales trend analysis unit is used to identify the long-term trend and periodic fluctuations of commodity sales by analyzing historical sales data, perform trend analysis on the sales data using time series analysis methods, provide a visual display of sales growth and decline trends, and identify external factors such as seasonal fluctuations and holiday effects that affect sales. The inventory health assessment unit is used to monitor the commodity inventory status and inventory turnover in real time, evaluate the inventory health status and the inventory pressure of commodities, and identify inventory risks in a timely manner.

[0013] Preferably, the supply chain collaboration module includes an inventory data integration unit, a replenishment plan automatic generation unit, and a supply chain collaboration scheduling unit; The inventory data integration unit is used to integrate data from the sales data collection and inventory management systems, enabling data interoperability. By analyzing sales and inventory data, it automatically generates replenishment suggestions and optimizes the replenishment plan to ensure a close alignment between the inventory status and sales demand. The replenishment plan automatic generation unit is used to automatically calculate the replenishment quantity based on the inventory assessment results and sales trends, automatically send the replenishment plan to the supplier or production department, and ensure timely execution. It adjusts the replenishment plan to cope with sudden sales fluctuations or demand changes. The supply chain collaborative scheduling unit is used to automatically schedule the production and logistics links based on the requirements of the replenishment plan and production progress, coordinate the resources of different supply chain participants, optimize the efficiency of the supply chain, monitor the progress of each link in real time, and timely adjust the scheduling plan to avoid supply chain disruptions.

[0014] Preferably, the demand forecasting module includes a historical data analysis unit, an external factor impact analysis unit, and a machine learning prediction model unit. The historical data analysis unit is used to extract the sales patterns and trends in historical data through regression analysis and time series analysis methods, calculate the future demand of goods based on historical sales data, which serves as the input for the prediction model, and assist in determining the impact of seasonal factors and promotional activities on demand. The external factor impact analysis unit is used to collect external data sources, including meteorological data and holiday information, adjust the demand forecasting model in combination with sales data, and quantify the impact of external factors on demand fluctuations. The machine learning prediction model unit is used to train and predict historical sales data and external factors using machine learning models such as random forest and XGBoost, automatically adjust the demand forecasting model, optimize the prediction accuracy according to real-time market data, and generate a dynamic adjustment strategy based on the prediction results to ensure the optimization of the supply chain according to demand.

[0015] Preferably, the intelligent warning module includes an anomaly detection unit and a warning trigger mechanism unit. The anomaly detection unit is used to monitor abnormal data in real time using machine learning algorithms, including inventory backlogs and supply chain disruptions. By comparing the supply chain index SCI with a preset threshold, it automatically identifies abnormal situations such as sales fluctuations and transportation delays, obtains a risk assessment, issues a warning in a timely manner, and generates an anomaly report. By comparing the supply chain index SCI with a preset first threshold A and a second preset threshold S, a risk level assessment scheme is obtained: When the supply chain index SCI is greater than or equal to the first threshold value A, the supply chain operates in a normal state, all core links are qualified, the inventory turnover rate ITR is kept within 1.5 weeks, the inventory is consumed quickly and replenished in time, and the automated replenishment system is used to dynamically adjust the inventory level based on real-time sales data and demand forecasts to ensure that the inventory of low-turnover goods does not exceed 2 weeks of sales, and reduce the proportion of overstocked goods to maintain inventory health, ensure that the sales data response time SDRT is controlled within 2 hours, the replenishment cycle RPL does not exceed 24 hours, the supplier delivery time STD is kept within 48 hours, and the logistics delivery time LDT is ensured to be completed within 72 hours; When the supply chain index SCI is between the first threshold value A and the second threshold value S, the supply chain operation efficiency has declined and there is a potential risk. The inventory backlog rate IRR should be controlled below 5%, and the inventory turnover time ITT should be ensured not to exceed 2 weeks. Price promotions or clearances should be carried out for unsalable goods. The ABC analysis method should be used to identify high inventory and unsalable goods and clearance measures should be taken to shorten the replenishment cycle RPL to within 12 hours. When the supply chain index SCI is lower than the second threshold S, the supply chain faces serious risks and is extremely inefficient. The inventory backlog rate IRR is controlled below 3%, the inventory turnover time ITT is reduced to less than 5 days, a comprehensive inventory audit is conducted, unsalable and expired goods are cleared, the inventory level is optimized using intelligent replenishment algorithms, dynamic inventory optimization tools are introduced, inventory is allocated based on actual demand and sales expectations, the risk of inventory backlog and excessive inventory is reduced, the sales data response time SDRT is shortened to less than 30 minutes, and the replenishment cycle RPL is shortened to within 4 hours; The early warning trigger mechanism unit is used to immediately trigger an early warning when the system detects an abnormality and notify relevant departments via SMS and email, automatically generate a feedback mechanism, and notify management to take measures.

[0016] A method for intelligent statistical analysis of retail commodity sales data, comprising the following steps: Step 1: Collect and synchronize data from different sales channels in real time, including product sales data, inventory data, supply chain logistics data, and market external factors data; Step 2: Clean, filter and standardize the collected data, remove redundant information, fill in missing data, unify the format and calculate the supply chain index (SCI); Step 3: By analyzing historical sales data and current inventory status, evaluate the health of commodity inventory based on data models, monitor commodity sales dynamics in real time, evaluate inventory pressure and identify potential out-of-stock or excess commodities; Step 4: Deeply integrate the sales data with the supply chain system, automatically optimize the replenishment, production, and distribution plans, automatically calculate the required replenishment quantity based on the predicted sales trends and inventory status, and collaborate with suppliers, production systems, and logistics departments; Step 5: Based on historical sales data and external factors, use machine learning algorithms to predict future product demands, and dynamically adjust production, inventory, and distribution plans. Automatically adjust the prediction model according to real-time market changes; Step 6: Real-time monitor key nodes in the supply chain, issue early warnings for abnormal situations, and trigger an automated feedback mechanism to notify relevant departments to take timely measures and promptly detect potential supply chain risks.

[0017] The present invention provides an intelligent statistical analysis method and system for retail product sales data, having the following beneficial effects: (1) During system operation, it real-time collects and synchronizes data from different sales channels, preprocesses the collected data to calculate the supply chain index SCI, monitors the product sales dynamics in real time by analyzing historical sales data and current inventory status, evaluates the inventory pressure, identifies potential out-of-stock or overstocked products, automatically calculates the required replenishment quantity based on the predicted sales trends and inventory status, uses machine learning algorithms to predict future product demands based on historical sales data and external factors, dynamically adjusts production, inventory, and distribution plans, automatically adjusts the prediction model, real-time monitors key nodes in the supply chain, issues early warnings for abnormal situations, and triggers an automated feedback mechanism to notify relevant departments to take timely measures and promptly detect potential supply chain risks.

[0018] (2) By integrating the six modules of the intelligent statistical analysis system for retail product sales data, the system comprehensively improves the operation efficiency of retailers and completes key tasks such as sales data collection, inventory analysis, supply chain collaboration, demand prediction, and intelligent early warning. The data collection module real-time obtains sales, inventory, and supply chain logistics data to ensure timely data update; the data processing module cleans and standardizes the data to ensure the accuracy of analysis; the sales inventory analysis module real-time monitors the inventory status and optimizes inventory management; the supply chain collaboration module automatically generates replenishment and production plans to effectively optimize inventory turnover; the demand prediction module combines historical data and external factors to predict future sales trends; the intelligent early warning module real-time monitors abnormal situations and early warns of potential risks. Finally, by integrating these modules, the system greatly improves the inventory turnover rate, sales response speed, and supply chain collaboration efficiency.

[0019] (3) Compared with existing technical means, traditional retail commodity sales data analysis methods usually rely on static data, lacking real-time and intelligence. Traditional systems often lag in response when facing a dynamically changing market environment and are unable to quickly adjust the supply chain according to real-time sales data and market changes. By introducing machine learning, artificial intelligence algorithms, and big data analysis, the system can conduct in-depth demand forecasting and market trend analysis, greatly improving the accuracy of forecasting. Through real-time inventory monitoring and automatic replenishment mechanisms, the problems of inventory backlog and out-of-stock are effectively solved. Compared with traditional systems that rely on manual operations, the system significantly improves the automation and real-time nature of data collection, making inventory management more accurate and replenishment and production planning more flexible and efficient.

[0020] (4) With these innovative improvements, the system not only improves the response speed and accuracy of the supply chain but also optimizes inventory management, reducing inventory backlog and out-of-stock phenomena. Through accurate demand forecasting, retailers can timely adjust replenishment and production plans according to changes in market demand, thereby reducing the risks of excessive inventory and out-of-stock, reducing capital occupancy, and enhancing customer satisfaction. In addition, the intelligent early warning mechanism and automatic feedback system ensure that potential problems in the supply chain can be identified and processed at an early stage, thus reducing the uncertainties in operations. In short, through the collection, analysis, forecasting, and feedback of real-time data, the system not only optimizes the operational management of retailers but also significantly improves the overall efficiency and market adaptability of the supply chain, helping retailers gain greater advantages in the highly competitive market environment. Brief Description of the Drawings

[0021] Figure 1 It is a block diagram of an intelligent statistical analysis system for retail commodity sales data of the present invention; Figure 2 It is a schematic diagram of the steps of an intelligent statistical analysis method for retail commodity sales data of the present invention; Figure 3 It is a line chart of the supply chain performance of an intelligent statistical analysis system for retail commodity sales data of the present invention under different SCI thresholds. Detailed Embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment 1 The present invention provides an intelligent statistical analysis system for retail commodity sales data. Please refer to Figure 1, including a data collection module, a data processing module, a sales and inventory analysis module, a supply chain collaboration module, a demand forecasting module, and an intelligent warning module; The data collection module is used to collect and synchronize data from different sales channels in real time, including product sales data, inventory data, supply chain logistics data, and external market factor data; The data processing module is used to clean, filter, and standardize the collected data, remove redundant information, fill in missing data, and calculate the supply chain index SCI after unifying the format; The sales and inventory analysis module is used to evaluate the health status of product inventory based on a data model by analyzing historical sales data and current inventory status, monitor product sales dynamics in real time, evaluate inventory pressure, and identify potential out-of-stock or overstocked products; The supply chain collaboration module is used to deeply integrate sales data with the supply chain system, automatically optimize replenishment, production, and distribution plans, automatically calculate the required replenishment quantity according to the predicted sales trend and inventory status, and collaborate with suppliers, production systems, and logistics departments; The demand forecasting module is used to predict future product demand based on historical sales data and external factors using machine learning algorithms, and dynamically adjust production, inventory, and distribution plans, and automatically adjust the forecasting model according to real-time market changes; The intelligent warning module is used to monitor key nodes in the supply chain in real time, warn of abnormal situations, and trigger an automated feedback mechanism to notify relevant departments to take timely measures and discover potential supply chain risks in a timely manner.

[0024] In this embodiment, data from different sales channels is collected and synchronized in real time, including product sales data, inventory data, supply chain logistics data, and external market factor data. The collected data is cleaned, filtered, and standardized, redundant information is removed, missing data is filled in, and the supply chain index SCI is calculated after unifying the format. The health status of product inventory is evaluated based on a data model by analyzing historical sales data and current inventory status, product sales dynamics are monitored in real time, inventory pressure is evaluated, and potential out-of-stock or overstocked products are identified. Sales data is deeply integrated with the supply chain system, replenishment, production, and distribution plans are automatically optimized, the required replenishment quantity is automatically calculated according to the predicted sales trend and inventory status, and collaboration is carried out with suppliers, production systems, and logistics departments. Future product demand is predicted based on historical sales data and external factors using machine learning algorithms, production, inventory, and distribution plans are dynamically adjusted, and the forecasting model is automatically adjusted according to real-time market changes. Key nodes in the supply chain are monitored in real time, abnormal situations are warned, and an automated feedback mechanism is triggered to notify relevant departments to take timely measures and discover potential supply chain risks in a timely manner.

[0025] Embodiment 2 This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically: The data acquisition module includes a supply chain response efficiency acquisition unit, a sales demand data acquisition unit, and an inventory data acquisition unit; The supply chain response efficiency acquisition unit is used to collect the response speed and efficiency of the supply chain from sales data collection to inventory replenishment in real time from various sales channels, including the time required for sales data to be transmitted from the terminal system to the central database, the rate of inventory consumption per unit time, the time required from inventory shortage to replenishment completion, the time required for the supplier to deliver goods after receiving the order, and the distribution time from the warehouse to the store or consumer, and obtain a supply chain data set; The supply chain data set includes: sales data response time SDRT, inventory turnover rate ITR, replenishment cycle RPL, supplier delivery timeliness STD, and logistics distribution timeliness LDT; The sales demand data acquisition unit is used to collect historical sales data, obtain the sales data, the degree of influence of different seasons and holiday factors on commodity demand, the system's response ability to promotions and weather in the market change, the degree of influence of external factors on commodity sales, and the change speed of consumer preferences, and obtain a sales demand data set; The sales demand data set includes: historical sales data accuracy HSDA, seasonal demand volatility value SDVI, market trend sensitivity MTS, external factor influence rate EFI, and consumer behavior change rate CBVR; The inventory data acquisition unit is used to monitor the inventory status in real time, record the inventory in and out data of commodities, obtain the proportion of slow-moving commodities in the inventory, the average time required for commodities to be sold from warehousing, and the frequency of commodity inventory falling below the minimum inventory level, and obtain an inventory data set; The inventory data set includes: inventory backlog rate IRR, inventory turnover time ITT, and stock-out rate SOR.

[0026] The data processing module includes a data preprocessing unit and a data fusion unit; The data preprocessing unit is used to clean the collected raw data, clean duplicate records, remove noise data, fill in missing data points using mean, median, or regression analysis methods, eliminate redundant information, and standardize the data format; The data fusion unit is used to fuse data from different sources, including sales data, inventory data, and external environment data, calculate to obtain: supply chain response efficiency coefficient SCRE, demand prediction accuracy coefficient DPAC, inventory management optimization coefficient IMOC, and supply chain index SCI, construct a complete database, partition and store the data according to time and commodity category conditions, and back up the data regularly.

[0027] In this embodiment, by introducing the above data collection and data processing modules, the intelligent statistical analysis system for retail commodity sales data realizes highly automated and real-time data collection, processing, and analysis, greatly improving the efficiency and accuracy of supply chain management. First, the data collection module can comprehensively and real-time obtain data from sales channels, inventory, and all links of the supply chain through the supply chain response efficiency collection unit, sales demand data collection unit, and inventory data collection unit. These data not only include the sales data response time, inventory consumption rate, replenishment cycle, supplier delivery timeliness, and logistics distribution timeliness, but also cover sales demand data such as historical sales data, market trend changes, and consumer preference changes, as well as inventory data such as inventory backlog, turnover time, and out-of-stock rate. This real-time data collection and integration of comprehensive information enable retailers to accurately grasp sales dynamics, inventory status, and supply chain efficiency, thus making decisions quickly and avoiding bottlenecks and lags in the supply chain.

[0028] Secondly, the data preprocessing unit of the data processing module cleans and standardizes the collected data, eliminating redundant information and noisy data, ensuring the accuracy and consistency of the data. By using methods such as mean, median, or regression analysis to fill in missing data, the data quality is further improved. This lays a foundation for subsequent data fusion and intelligent analysis, ensuring the reliability and accuracy of the calculation results.

[0029] Embodiment 3 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The supply chain response efficiency coefficient SCRE is obtained by calculating through the following formula: ; In the formula, SDRT represents the sales data response time, ITR represents the inventory turnover rate, RPL represents the replenishment cycle, STD represents the supplier delivery timeliness, LDT represents the logistics distribution timeliness, and w1, w2, w3, w4, and w5 respectively represent the proportionality coefficients of the sales data response time SDRT, inventory turnover rate ITR, replenishment cycle RPL, supplier delivery timeliness STD, and logistics distribution timeliness LDT; The demand prediction accuracy coefficient DPAC is obtained by calculating through the following formula: ; In the formula, HSDA represents the historical sales data accuracy, SDVI represents the seasonal demand fluctuation value, MTS represents the market trend sensitivity, EFI represents the external factor influence rate, and CBVR represents the consumer behavior change rate. 1. 2. 3. 4 and 5 represent the proportionality coefficients of the historical sales data accuracy (HSDA), seasonal demand volatility index (SDVI), market trend sensitivity (MTS), external factor impact rate (EFI), and consumer behavior change rate (CBVR), respectively.

[0030] The inventory management optimization coefficient (IMOC) is calculated through the following formula: ; In the formula, f1(IRR), f2(ITT), and f3(SOR) represent the non - linear functions of each parameter, reflecting the impacts of inventory backlog rate, inventory turnover time, and stock - out rate on inventory optimization. 1, 2, and 3 represent the proportionality coefficients of the inventory backlog rate, inventory turnover time, and stock - out rate, respectively. The supply chain index (SCI) is calculated through the following formula: ; In the formula, SCI(t) represents that the supply chain index is a continuous function varying with time, measuring the overall efficiency of the supply chain at a given time point. t0 represents the starting point of time, t represents the current moment, and x1, x2, and x3 represent the proportionality coefficients of the supply chain response efficiency coefficient (SCRE), demand prediction accuracy coefficient (DPAC), and inventory management optimization coefficient (IMOC), respectively.

[0031] The sales inventory analysis module includes a sales trend analysis unit and an inventory health assessment unit; The sales trend analysis unit is used to identify the long - term trends and periodic fluctuations of commodity sales by analyzing historical sales data, perform trend analysis on sales data using time - series analysis methods, provide visual displays of sales growth and decline trends, and identify external factors such as seasonal fluctuations and holiday effects that impact sales. The inventory health assessment unit is used to monitor the commodity inventory status and inventory turnover in real - time, evaluate the inventory health status and the inventory pressure of commodities, and identify inventory risks in a timely manner.

[0032] In this embodiment, through the comprehensive analysis combining the supply chain response efficiency coefficient (SCRE), demand prediction accuracy coefficient (DPAC), inventory management optimization coefficient (IMOC), and supply chain index (SCI), the intelligent statistical analysis system for retail commodity sales data significantly improves the efficiency and accuracy of supply chain management. Specifically, the calculations of SCRE, DPAC, and IMOC are based on precise formulas, comprehensively evaluating the supply chain based on multiple key indicators, ensuring that the performance of each link can be quantified, monitored, and optimized.

[0033] First, SCRE comprehensively evaluated the supply chain response efficiency from sales data collection to inventory replenishment by analyzing the Sales Data Response Time (SDRT), Inventory Turnover Rate (ITR), Replenishment Period (RPL), Supplier Delivery Timeliness (STD), and Logistics Delivery Timeliness (LDT). By optimizing the time efficiency of these key links, the system can effectively reduce delays in the supply chain, improve the overall response speed, and thus achieve more efficient inventory management and commodity flow.

[0034] Second, DPAC improved the accuracy of demand forecasting through an accurate demand forecasting model, combined with data such as Historical Sales Data Accuracy (HSDA), Seasonal Demand Variation (SDVI), Market Trend Sensitivity (MTS), External Factor Influence (EFI), and Consumer Behavior Variation Rate (CBVR). Real-time market data analysis and intelligent forecasting enable retailers to respond quickly to upcoming demand fluctuations and market changes, reducing the risks of inventory backlogs and out-of-stock situations.

[0035] Finally, IMOC helps retailers optimize inventory management, avoid problems of excessive or insufficient inventory, and thus improve the overall efficiency of inventory management by evaluating the Inventory Backlog Rate (IRR), Inventory Turnover Time (ITT), and Stock-Out Rate (SOR), as well as a non-linear adjustment function for these parameters. Combining the sales trend analysis unit and the inventory health assessment unit, the system can monitor the sales dynamics of goods and the inventory health status in real time, promptly identify and address potential inventory risks, such as slow-moving goods and inventory pressure, and ensure the balance between inventory levels and sales demand.

[0036] Example 4 This example is an explanatory description carried out in Example 1. Please refer to Figure 1 , specifically: The supply chain collaboration module includes an inventory data integration unit, a replenishment plan automatic generation unit, and a supply chain collaboration scheduling unit; The inventory data integration unit is used to integrate data from the sales data collection and inventory management systems, achieve data interconnection, automatically generate replenishment suggestions by analyzing sales and inventory data, and optimize the replenishment plan to ensure a close connection between the inventory status and sales demand; The replenishment plan automatic generation unit is used to automatically calculate the replenishment quantity based on the inventory assessment results and sales trends, automatically send the replenishment plan to the supplier or production department, ensure timely execution, and adjust the replenishment plan to cope with sudden sales fluctuations or demand changes; The supply chain collaboration scheduling unit is used to automatically schedule the production and logistics links based on the requirements of the replenishment plan and production progress, coordinate the resources of different supply chain participants, optimize the efficiency of the supply chain, monitor the progress of each link in real time, and promptly adjust the scheduling plan to avoid supply chain disruptions.

[0037] The demand forecasting module includes a historical data analysis unit, an external factor impact analysis unit, and a machine learning prediction model unit; The historical data analysis unit is used to extract the sales patterns and trends in historical data through regression analysis and time series analysis methods, calculate the future demand of commodities based on historical sales data, serve as the input of the prediction model, and assist in determining the impact of seasonal factors and promotional activities on demand; The external factor impact analysis unit is used to collect external data sources, including meteorological data and holiday information, adjust the demand forecasting model in combination with sales data, and quantify the impact of external factors on demand fluctuations; The machine learning prediction model unit is used to train and predict historical sales data and external factors using machine learning models such as random forest and XGBoost, automatically adjust the demand forecasting model, optimize the prediction accuracy according to real-time market data, generate a dynamic adjustment strategy based on the prediction results, and ensure the optimization of the supply chain according to demand.

[0038] The intelligent warning module includes an anomaly detection unit and a warning trigger mechanism unit; The anomaly detection unit is used to monitor abnormal data in real time using machine learning algorithms, including inventory backlogs and supply chain disruptions. By comparing the supply chain index SCI with a preset threshold, it automatically identifies abnormal situations such as sales fluctuations and transportation delays, obtains a risk assessment, issues a warning in a timely manner, and generates an anomaly report; By comparing the supply chain index SCI with a preset first threshold A and a second preset threshold S, a risk level assessment scheme is obtained: When the supply chain index SCI is greater than or equal to the first threshold A, the supply chain operates in a normal state, all core links are qualified, the inventory turnover rate ITR is maintained within 1.5 weeks, ensuring rapid inventory consumption and timely replenishment. Use an automated replenishment system to dynamically adjust the inventory level based on real-time sales data and demand forecasts, ensure that the inventory of slow-moving goods does not exceed two weeks' worth of sales, reduce the proportion of backlogged goods, maintain healthy inventory, ensure that the sales data response time SDRT is controlled within 2 hours, the replenishment cycle RPL does not exceed 24 hours, the supplier delivery timeliness STD is maintained within 48 hours, and the logistics distribution timeliness LDT is ensured to be completed within 72 hours; When the supply chain index SCI is between the first threshold A and the second threshold S, the operating efficiency of the supply chain decreases, there are potential risks. Control the inventory backlog rate IRR below 5% and ensure that the inventory turnover time ITT does not exceed two weeks. Conduct price promotions or clearance sales on slow-moving goods. Use the ABC analysis method to identify high-inventory and high-slow-moving goods and take clearance measures. Shorten the replenishment cycle RPL to within 12 hours; When the Supply Chain Index SCI is lower than the second threshold S, the supply chain faces serious risks and extremely low efficiency. Control the Inventory Backlog Rate IRR below 3%, reduce the Inventory Turnover Time ITT to within 5 days, conduct a comprehensive audit of the inventory, clear slow-moving and expired goods, use intelligent replenishment algorithms to optimize the inventory level, introduce dynamic inventory optimization tools, allocate inventory based on actual demand and sales forecasts, reduce the risks of inventory backlog and excessive inventory, shorten the Sales Data Response Time SDRT to within 30 minutes, and shorten the Replenishment Period Length RPL to within 4 hours; The warning trigger mechanism unit is used to immediately trigger a warning and notify relevant departments via text message and email when the system detects an anomaly, and automatically generate a feedback mechanism to notify the management to take measures.

[0039] In this embodiment, by integrating the supply chain collaboration module, demand forecasting module, and intelligent warning module, the intelligent statistical analysis system for retail commodity sales data demonstrates significant advantages in optimizing supply chain efficiency, improving demand forecasting accuracy, and reducing risk management. First, the supply chain collaboration module ensures a close connection between inventory levels and sales demand by integrating sales data and inventory management data in real time. The automatically generated replenishment plan is not only based on real-time sales and inventory data but can also be dynamically adjusted to cope with sudden market fluctuations, greatly improving inventory management efficiency and supply chain response speed, and reducing losses caused by insufficient or excessive inventory. Second, the demand forecasting module can optimize the forecasting accuracy according to real-time market data by comprehensively using historical data analysis, external factor analysis, and machine learning algorithms. This enables retailers to more accurately predict future sales demand, thereby adjusting production, inventory, and distribution plans, avoiding the lag and inaccuracy in traditional manual forecasting methods, and enhancing the flexibility and market adaptability of the supply chain. Finally, the intelligent warning module monitors key nodes in the supply chain in real time, promptly identifies abnormal data (such as inventory backlog, supply chain interruption, transportation delay, etc.), and conducts a risk assessment by comparing with preset thresholds. The system automatically issues a warning and notifies relevant departments to ensure that the management can take corrective measures before problems occur, avoiding major impacts on the overall operation caused by supply chain risks. These intelligent and automated measures enable retailers to more precisely control inventory and supply chain conditions, improve the overall efficiency of the supply chain and customer service level, and ultimately optimize operating costs and enhance market competitiveness.

[0040] Embodiment 5 An intelligent statistical analysis method for retail commodity sales data, please refer to Figure 2 , specifically: including the following steps: Step 1: Collect and synchronize data from different sales channels in real time, including commodity sales data, inventory data, supply chain logistics data, and market external factor data; Step 2: Clean, filter, and standardize the collected data, remove redundant information, fill in missing data, and calculate the Supply Chain Index (SCI) after unifying the formats. Step 3: Evaluate the health status of product inventory by analyzing historical sales data and current inventory status, monitor the product sales dynamics in real time, assess the inventory pressure, and identify potential out-of-stock or overstocked products. Step 4: Deeply integrate the sales data with the supply chain system, automatically optimize the replenishment, production, and distribution plans, automatically calculate the required replenishment quantity based on the predicted sales trends and inventory status, and collaborate with suppliers, production systems, and logistics departments. Step 5: Based on historical sales data and external factors, use machine learning algorithms to predict future product demands, and dynamically adjust the production, inventory, and distribution plans, and automatically adjust the prediction model according to real-time market changes. Step 6: Monitor the key nodes in the supply chain in real time, give early warnings for abnormal situations, and trigger an automated feedback mechanism to notify relevant departments to take timely measures and detect potential supply chain risks in a timely manner.

[0041] In this embodiment, by implementing the above six steps, the intelligent statistical analysis system for retail product sales data effectively improves the overall efficiency, response ability, and risk management level of the supply chain. First, the real-time data collection module in Step 1 ensures the data synchronization and timeliness of each sales channel. The comprehensive collection of data from sales data to inventory data, supply chain logistics data, and then to market external factor data enables retailers to obtain all-round and real-time updated supply chain information. This provides a solid data foundation for subsequent analysis and decision-making. In Step 2, data cleaning and standardization ensure the accuracy and consistency of the data. By removing redundant information, filling in missing data, and unifying the data formats, the system eliminates data quality problems, ensures the reliability of calculating the Supply Chain Index (SCI) subsequently, and avoids operational decision-making mistakes caused by inaccurate data. Step 3 monitors the product sales dynamics and inventory health status in real time by combining historical sales data and current inventory status. By automatically identifying potential out-of-stock or overstocked problems, the system can give early warnings and help retailers optimize their inventory, improving the inventory turnover rate. Steps 4 and 5 automatically optimize the replenishment, production, and distribution plans through deep integration with the supply chain system. Finally, in Step 6, the intelligent early warning system automatically identifies potential abnormal situations, such as inventory backlogs, supply chain disruptions, transportation delays, etc., by monitoring the key nodes of the supply chain in real time, and issues early warnings in a timely manner. Through the automated feedback mechanism, management can take timely measures to ensure the smooth operation of the supply chain. Generally speaking, these steps help retailers improve operational efficiency, reduce risks, optimize inventory management, and make the supply chain more flexible and responsive, thus enhancing market competitiveness and customer satisfaction.

[0042] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent statistical analysis system for retail commodity sales data, characterized in that: It includes a data collection module, a data processing module, a sales and inventory analysis module, a supply chain collaboration module, a demand forecasting module, and an intelligent warning module; The data collection module is used to collect and synchronize data from different sales channels in real time, including sales data, inventory data, supply chain logistics data, and external market factor data of goods; The data processing module is used to clean, filter, and standardize the collected data, remove redundant information, fill in missing data, and calculate the supply chain index SCI after unifying the format; The sales and inventory analysis module is used to evaluate the health status of commodity inventory based on historical sales data and current inventory status through data models, monitor the sales dynamics of goods in real time, evaluate inventory pressure, and identify potential out-of-stock or overstocked goods; The supply chain collaboration module is used to deeply integrate sales data with the supply chain system, automatically optimize replenishment, production, and distribution plans, automatically calculate the required replenishment quantity according to the predicted sales trend and inventory status, and collaborate with suppliers, production systems, and logistics departments; The demand forecasting module is used to predict future commodity demand based on historical sales data and external factors using machine learning algorithms, dynamically adjust production, inventory, and distribution plans, and automatically adjust the forecasting model according to real-time market changes; The intelligent warning module is used to monitor key nodes in the supply chain in real time, warn of abnormal situations, trigger an automated feedback mechanism, notify relevant departments to take measures in a timely manner, and detect potential supply chain risks in a timely manner.

2. The intelligent statistical analysis system for retail commodity sales data according to claim 1, wherein: The data collection module includes a supply chain response efficiency collection unit, a sales demand data collection unit, and an inventory data collection unit; The supply chain response efficiency collection unit is used to collect the response speed and efficiency of the supply chain from sales data collection to inventory replenishment from each sales channel in real time, including the time required for sales data to be transmitted from the terminal system to the central database, the rate of inventory consumption per unit time, the time required from inventory emergency to replenishment completion, the time required for the supplier to deliver goods after receiving the order, and the distribution time from the warehouse to the store or consumer, and obtain a supply chain data set; The supply chain data set includes: sales data response time SDRT, inventory turnover rate ITR, replenishment cycle RPL, supplier delivery timeliness STD, and logistics distribution timeliness LDT; The sales demand data collection unit is used to collect historical sales data, obtain the degree of influence of sales data, different seasons and holiday factors on commodity demand, the system's response ability to promotions and weather in market changes, the degree of influence of external factors on commodity sales, and the change rate of consumer preferences, and obtain a sales demand data set; The sales demand data set includes: historical sales data accuracy HSDA, seasonal demand fluctuation value SDVI, market trend sensitivity MTS, external factor influence rate EFI, and consumer behavior change rate CBVR; The inventory data collection unit is used to monitor the inventory status in real time, record the inventory in-and-out data of goods, obtain the proportion of slow-moving goods in the inventory, the average time required for goods to be sold from receipt to warehouse, and the frequency of goods inventory falling below the minimum inventory level, and obtain an inventory data set; The inventory data group includes: inventory backlog rate IRR, inventory turnover time ITT, and stockout rate SOR.

3. The intelligent statistical analysis system for retail commodity sales data according to claim 1, wherein: The data processing module includes a data preprocessing unit and a data fusion unit; The data preprocessing unit is used to clean the collected raw data, clean duplicate records, remove noise data, fill in missing data points using mean, median or regression analysis methods, eliminate redundant information, and standardize the data format; The data fusion unit is used to fuse data from different sources, including sales data, inventory data, and external environment data, calculate and obtain: supply chain response efficiency coefficient SCRE, demand prediction accuracy coefficient DPAC, inventory management optimization coefficient IMOC, and supply chain index SCI, construct a complete database, partition and store the data according to time and commodity category conditions, and back up the data regularly.

4. The intelligent statistical analysis system for retail commodity sales data according to claim 3, wherein: The supply chain response efficiency coefficient SCRE is calculated and obtained through the following formula: ； In the formula, SDRT represents the sales data response time, ITR represents the inventory turnover rate, RPL represents the replenishment cycle, STD represents the supplier delivery timeliness, LDT represents the logistics distribution timeliness, and w1, w2, w3, w4, and w5 respectively represent the proportionality coefficients of the sales data response time SDRT, inventory turnover rate ITR, replenishment cycle RPL, supplier delivery timeliness STD, and logistics distribution timeliness LDT; The demand prediction accuracy coefficient DPAC is calculated and obtained through the following formula: ； Wherein, HSDA represents the historical sales data accuracy, SDVI represents the seasonal demand volatility value, MTS represents the market trend sensitivity, EFI represents the external factor impact rate, and CBVR represents the consumer behavior change rate.

1.

2.

3. 4 and 5 respectively represent the proportionality coefficients of the historical sales data accuracy HSDA, the seasonal demand volatility value SDVI, the market trend sensitivity MTS, the external factor impact rate EFI, and the consumer behavior change rate CBVR.

5. An intelligent statistical analysis system for retail commodity sales data according to claim 1, characterized in that: The inventory management optimization coefficient IMOC is calculated and obtained through the following formula: ； Wherein, f1(IRR), f2(ITT), and f3(SOR) represent the non - linear functions of each parameter, reflecting the impact of inventory backlog rate, inventory turnover time, and stock - out rate on inventory optimization, 1, 2, and 3 respectively represent the proportionality coefficients of the inventory backlog rate, inventory turnover time, and stock - out rate; The supply chain index SCI is calculated and obtained through the following formula: ； In the formula, SCI(t) represents that the supply chain index is a continuous function that changes over time, measuring the overall efficiency of the supply chain at a given time point, t0 represents the starting point of time, t represents the current moment, and x1, x2, and x3 respectively represent the proportionality coefficients of the supply chain response efficiency coefficient SCRE, demand prediction accuracy coefficient DPAC, and inventory management optimization coefficient IMOC.

6. The intelligent statistical analysis system for retail commodity sales data according to claim 1, characterized in that: The sales and inventory analysis module includes a sales trend analysis unit and an inventory health assessment unit; The sales trend analysis unit is used to identify the long-term trends and periodic fluctuations of commodity sales by analyzing historical sales data, perform trend analysis on sales data using time series analysis methods, provide visual displays of sales growth and decline trends, and identify external factors such as seasonal fluctuations and holiday effects that affect sales; The inventory health assessment unit is used to monitor the commodity inventory status and inventory turnover in real time, evaluate the inventory health status and the inventory pressure of commodities, and identify inventory risks in a timely manner.

7. An intelligent statistical analysis system for retail commodity sales data according to claim 1, characterized in that: The supply chain collaboration module includes an inventory data integration unit, a replenishment plan automatic generation unit, and a supply chain collaboration scheduling unit; The inventory data integration unit is used to integrate data from the sales data collection and inventory management systems, achieve data interconnection, automatically generate replenishment suggestions by analyzing sales and inventory data, and optimize the replenishment plan to ensure a close connection between the inventory status and sales demand; The replenishment plan automatic generation unit is used to automatically calculate the replenishment quantity according to the inventory assessment results and sales trends, automatically send the replenishment plan to the supplier or production department, and ensure timely execution, and adjust the replenishment plan to cope with sudden sales fluctuations or demand changes; The supply chain collaborative scheduling unit is used to automatically schedule the production and logistics links based on the requirements of the replenishment plan and production progress, coordinate the resources of different supply chain participants, optimize the efficiency of the supply chain, monitor the progress of each link in real time, and adjust the scheduling plan in a timely manner to avoid supply chain interruption.

8. The intelligent statistical analysis system for retail commodity sales data according to claim 1, characterized in that: The demand forecasting module includes a historical data analysis unit, an external factor impact analysis unit, and a machine learning forecasting model unit; The historical data analysis unit is used to extract the sales patterns and trends in historical data through regression analysis and time series analysis methods, calculate the future demand of commodities based on historical sales data, and use it as the input of the forecasting model to assist in determining the impact of seasonal factors and promotional activities on demand; The external factor impact analysis unit is used to collect external data sources, including meteorological data and holiday information, adjust the demand forecasting model in combination with sales data, and quantify the impact of external factors on demand fluctuations; The machine learning forecasting model unit is used to train and predict historical sales data and external factors using machine learning models such as random forest and XGBoost, automatically adjust the demand forecasting model, optimize the forecasting accuracy according to real-time market data, and generate dynamic adjustment strategies based on the forecasting results to ensure the optimization of the supply chain according to demand.

9. The intelligent statistical analysis system for retail commodity sales data according to claim 1, wherein: The intelligent early warning module includes an anomaly detection unit and an early warning trigger mechanism unit; The anomaly detection unit is used to monitor abnormal data in real time using machine learning algorithms, including inventory backlogs and supply chain interruptions. By comparing the supply chain index SCI with a preset threshold, it automatically identifies abnormal situations such as sales fluctuations and transportation delays, obtains a risk assessment, issues an early warning in a timely manner, and generates an anomaly report; By comparing the supply chain index SCI with a preset first threshold A and a second preset threshold S, a risk level assessment plan is obtained: When the supply chain index SCI is greater than or equal to the first threshold A, the supply chain operates in a normal state, all core links are qualified, the inventory turnover rate ITR is maintained within 1.5 weeks, ensuring rapid inventory consumption and timely replenishment. Use an automated replenishment system to dynamically adjust the inventory level based on real-time sales data and demand forecasting, ensure that the inventory of slow-moving goods does not exceed two weeks of sales volume, reduce the proportion of backlogged goods, maintain healthy inventory, ensure that the sales data response time SDRT is controlled within 2 hours, the replenishment cycle RPL does not exceed 24 hours, the supplier delivery timeliness STD is maintained within 48 hours, and the logistics distribution timeliness LDT is ensured to be completed within 72 hours; When the supply chain index SCI is between the first threshold A and the second threshold S, the operating efficiency of the supply chain decreases, there are potential risks. Control the inventory backlog rate IRR below 5% and ensure that the inventory turnover time ITT does not exceed 2 weeks. Conduct price promotions or clearance sales for slow-moving goods. Use the ABC analysis method to identify high-inventory and high-slow-moving goods and take clearance measures. Shorten the replenishment cycle RPL to within 12 hours; When the Supply Chain Index SCI is lower than the second threshold S, the supply chain faces serious risks and extremely low efficiency. Control the inventory backlog rate IRR below 3%, reduce the inventory turnover time ITT to within 5 days, conduct a comprehensive audit of the inventory, clean up slow-moving and expired goods, use intelligent replenishment algorithms to optimize the inventory level, introduce dynamic inventory optimization tools, allocate inventory based on actual demand and sales forecasts, reduce the risks of inventory backlog and excessive inventory, shorten the sales data response time SDRT to within 30 minutes, and shorten the replenishment cycle RPL to within 4 hours; The early warning trigger mechanism unit is used to immediately trigger an early warning and notify relevant departments by text message and email when the system detects an anomaly, and automatically generate a feedback mechanism to notify the management to take measures.

10. A method for intelligent statistical analysis of retail commodity sales data, applied to an intelligent statistical analysis system for retail commodity sales data according to any one of claims 1 to 9, characterized in that: It includes the following steps: Step 1: Real-time collect and synchronize data from different sales channels, including product sales data, inventory data, supply chain logistics data, and external market factor data; Step 2: Clean, filter, and standardize the collected data, remove redundant information, fill in missing data, and calculate the Supply Chain Index SCI after unifying the format; Step 3: Evaluate the health status of product inventory by analyzing historical sales data and current inventory status, monitor the product sales dynamics in real time, evaluate the inventory pressure, and identify potential out-of-stock or overstocked products; Step 4: Deeply integrate the sales data with the supply chain system, automatically optimize the replenishment, production, and distribution plans, automatically calculate the required replenishment quantity according to the predicted sales trend and inventory status, and collaborate with suppliers, production systems, and logistics departments; Step 5: Based on historical sales data and external factors, use machine learning algorithms to predict future product demand, and dynamically adjust production, inventory, and distribution plans, and automatically adjust the prediction model according to real-time market changes; Step 6: Real-time monitor the key nodes in the supply chain, issue early warnings for anomalies, and trigger an automated feedback mechanism to notify relevant departments to take measures in a timely manner and discover potential supply chain risks.

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