Intelligent stock-up method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of intelligent stock-up, in particular to an intelligent stock-up method and device, computer equipment and a storage medium. According to the method, the sales volume prediction data in the preset time period is firstly obtained, then various inventory data are comprehensively collected, the real stockout condition is calculated through comparative analysis, and finally the scientific suggested stock volume is generated, so that repeated purchase is avoided, the potential inventory shortage risk can be found in time, the stock accuracy and flexibility are improved, and the purchase efficiency is improved. And the inventory management level and the operation efficiency of an enterprise are obviously improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent stocking, and in particular to an intelligent stocking method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of the e-commerce industry and the diversification of consumer demand, companies are facing increasing pressure on inventory management. Accurately grasping changes in market demand, rationally arranging purchases and stocking, and avoiding inventory backlogs or shortages have become key factors in improving corporate competitiveness.

[0003] At present, many companies use inventory management systems to manage stocking, automatically triggering the replenishment process according to fixed inventory warning thresholds, or generating stocking recommendations based on inventory data in the ERP system.

[0004] However, the existing technology only focuses on the current inventory status in stock, fails to fully consider the dynamic changes of inventory in procurement and inventory in transit, and cannot accurately reflect the real inventory gap, which easily leads to problems such as repeated purchases or inventory shortages, and the stocking is inaccurate and inflexible. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem of inaccurate and inflexible stocking in existing inventory management systems, the present application provides an intelligent stocking method, device, computer equipment and storage medium, which adopts the following technical solutions: In a first aspect, the present application provides an intelligent stocking method, comprising the following steps: Get sales forecast data for each product within a preset time period; Get current inventory data based on inventory in stock, inventory in purchase, and inventory in transit; Calculating the out-of-stock situation within the preset time period based on the sales forecast data and the current inventory data; Generate a recommended stocking quantity based on the out-of-stock situation.

[0006] By adopting the above technical solution, since the existing stocking system often only focuses on static inventory data in stock, but ignores the dynamically changing inventory status such as purchasing and in transit, resulting in deviations in the calculation of actual inventory, this application first obtains sales forecast data within a preset time period, and then comprehensively collects various types of inventory data, calculates the actual out-of-stock situation through comparative analysis, and finally generates a scientific recommended stocking quantity, which not only avoids repeated purchases, but also can promptly discover potential inventory shortage risks, improve stocking accuracy and flexibility, and significantly improve the company's inventory management level and operational efficiency.

[0007] Optionally, obtaining sales forecast data for each product within a preset time period includes the following steps: Get the sales data of each commodity within a preset historical time period; Calculate average daily sales based on the sales data; Obtain manual intervention speed data, compare the average daily sales volume with the manual intervention speed data, and generate final sales volume forecast data.

[0008] By adopting the above technical solution, since the sales volume of goods is affected by many factors and presents a complex pattern of changes, relying solely on historical sales data for simple average calculations often cannot accurately reflect the actual sales situation. This application first establishes a sales data benchmark within a preset historical time period, calculates the average daily sales volume as a basic reference value, and then obtains the manual intervention speed data, compares the average daily sales volume with the manual intervention speed data, and generates the final sales forecast data, which is more in line with the actual sales forecast data. It not only improves the forecast accuracy, but also can effectively respond to various special sales situations.

[0009] Optionally, calculating the out-of-stock situation within the preset time period based on the sales forecast data and the current inventory data specifically includes the following steps: Obtaining the pre-equipment delivery cycle, which includes procurement lead time, warehouse processing time, transportation time and buffer time; Divide the preset time period according to the preset equipment delivery cycle; In each of the pre-equipment stocking cycles, calculating the difference between the current inventory data and the sales forecast data; The time period in which the difference is less than a preset threshold is determined as a stock-out time period.

[0010] By adopting the above technical solution, since traditional inventory management often adopts a fixed time point inspection method, it ignores the time consumption in the process of stocking and replenishment, resulting in the inability to timely discover and prevent out-of-stock risks. This application first determines the pre-equipment stocking cycle including each link, and then divides the forecast time period according to the cycle. The difference between inventory and predicted sales is dynamically calculated in each cycle. When the difference is lower than the preset threshold, the potential out-of-stock risk can be identified. It not only realizes early warning of out-of-stock risks, but also can accurately locate the out-of-stock time period, providing enterprises with sufficient stocking decision-making time, and effectively avoiding losses caused by sudden out-of-stock situations.

[0011] Optionally, generating a recommended stocking quantity according to the out-of-stock situation specifically includes the following steps: Calculate the average daily out-of-stock quantity during the out-of-stock time period; Calculate the recommended stocking base quantity based on the average daily stock-out quantity and the estimated stocking cycle; Get the minimum packaging quantity of the product; The recommended stocking base quantity is adjusted upward to the nearest integer multiple of the minimum packaging quantity to obtain the recommended stocking quantity.

[0012] By adopting the above technical solution, since the stocking suggestions generated by traditional systems often only consider theoretical calculated values ​​and ignore the limitations of the actual packaging specifications of the goods, resulting in the inability to directly execute purchases with the recommended stocking quantities, this application first calculates the average daily stock-out quantity based on the stock-out time period, and derives the theoretical stocking benchmark quantity in combination with the pre-equipment stocking cycle, and then obtains the minimum packaging quantity information of the goods, and intelligently adjusts the stocking benchmark quantity to the closest integer multiple of the packaging quantity, thereby obtaining a recommended stocking quantity that can both meet demand and be easy to execute. This not only improves the practicability of stocking suggestions, but also reduces the workload of manual adjustments, significantly improving procurement efficiency.

[0013] Optionally, the method further comprises the following steps: Obtain the preset purchase limit, used purchase amount, and purchased but unapproved amount; Calculate the remaining available purchase amount based on the preset purchase limit, the used purchase amount and the purchased but unapproved amount; The purchase amount corresponding to the recommended stocking quantity is compared with the remaining available purchase amount, and when the purchase amount exceeds the remaining available purchase amount, an early warning prompt is issued.

[0014] By adopting the above technical solution, in order to prevent purchasing personnel from exceeding the purchase quota limit set by the enterprise when placing purchase orders, resulting in excessive financial pressure or out-of-control purchases, this application first obtains three basic data: the preset purchase upper limit, the used purchase amount, and the purchased but unapproved amount. Then, based on these data, the remaining available purchase amount is calculated in real time. Finally, the newly recommended stock purchase amount is intelligently compared with the remaining amount, and an early warning is issued in time when there is a possibility of over-limit. This can not only effectively prevent the problem of over-limit purchases, but also significantly improve the accuracy and timeliness of purchase quota management by transforming static quota management into dynamic real-time monitoring, helping enterprises to achieve refined procurement management.

[0015] Optionally, the method further comprises the following steps: Obtain supplier rating data based on supplier’s on-time delivery rate, quality pass rate and price fluctuation rate; Intelligently allocating the recommended stocking quantity according to the supplier rating data to obtain the purchase share of each supplier; When any indicator in the supplier rating data is lower than the preset standard, the corresponding procurement share will be allocated to the alternative supplier; A differentiated purchasing price range is generated based on the purchasing share.

[0016] By adopting the above technical solution, since traditional procurement often relies too much on a single supplier or makes simple allocations based on price, ignoring the supplier's comprehensive performance in terms of delivery timeliness, product quality, etc., which easily leads to concentrated procurement risks or unstable service quality, this application first conducts a comprehensive assessment of the supplier to obtain scoring data, and then scientifically allocates procurement shares based on the scoring results. At the same time, an early warning mechanism is set up to promptly detect supplier anomalies, and the supply chain is ensured to be stable through the alternative supplier mechanism. Finally, differentiated price ranges are set based on the shares of different suppliers, which not only realizes the scientific quantification of supplier management, but also establishes a risk diversification mechanism, effectively improving the professional level of procurement management.

[0017] Optionally, the method further comprises the following steps: Obtain inventory temperature and humidity data and storage time data for each batch of goods; Calculating a quality warning time point for the product based on the temperature and humidity data and the storage time data; Matching and analyzing the quality warning time point with the sales forecast data; When it is predicted that the product cannot be sold before the quality warning time point, the corresponding recommended stock quantity is adjusted.

[0018] By adopting the above technical solution, due to the differences in storage conditions and shelf life of different batches of goods, the traditional stocking method often only focuses on quantity matching and ignores the impact of quality changes, which may cause quality problems before the goods are sold. This application continuously collects inventory environment data and storage time of each batch of goods, comprehensively analyzes the quality warning time point, and intelligently matches this time point with the sales forecast. When it is discovered that the goods may not be sold before the quality deteriorates, the stocking strategy is adjusted in time, which effectively reduces inventory losses caused by quality problems and significantly improves the level of refined inventory management.

[0019] In a second aspect, the present application provides an intelligent stocking device, comprising: The sales forecast data acquisition module is used to obtain the sales forecast data of each commodity within a preset time period; The current inventory data acquisition module is used to obtain the current inventory data based on the inventory in stock, inventory in purchase and inventory in transit; A stock-out situation calculation module, used to calculate the stock-out situation within the preset time period based on the sales forecast data and the current inventory data; The suggested stock quantity generating module is used to generate the suggested stock quantity according to the out-of-stock situation.

[0020] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent stocking method when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned intelligent stocking method when executed by a processor.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: This application first obtains sales forecast data within a preset time period, then comprehensively collects various inventory data, calculates the actual out-of-stock situation through comparative analysis, and finally generates scientific recommended stocking quantities, which not only avoids repeated purchases, but also promptly discovers potential inventory shortage risks, improves stocking accuracy and flexibility, and significantly improves the company's inventory management level and operational efficiency; Since the sales volume of goods is affected by many factors and presents a complex law of change, simple average calculation based on historical sales data often cannot accurately reflect the actual sales situation. This application first establishes a sales data benchmark within a preset historical time period, calculates the average daily sales volume as a basic reference value, and then obtains the manual intervention speed data, compares the average daily sales volume with the manual intervention speed data, and generates the final sales forecast data, which is more in line with the actual sales forecast data, not only improving the forecast accuracy, but also effectively responding to various special sales situations; Since traditional inventory management often adopts a fixed-time point inspection method, it ignores the time consumption in the stocking and replenishment process, resulting in the inability to timely detect and prevent out-of-stock risks. This application first determines the pre-equipment stocking cycle including each link, and then divides the forecast time period according to the cycle. The difference between inventory and predicted sales is dynamically calculated in each cycle. When the difference is lower than the preset threshold, the potential out-of-stock risk can be identified. It not only realizes early warning of out-of-stock risks, but also can accurately locate the out-of-stock time period, providing enterprises with sufficient stocking decision-making time, and effectively avoiding losses caused by sudden out-of-stock. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of an intelligent stocking method according to an embodiment of the present application; Figure 2 It is a flowchart of step S110 in an intelligent stocking method according to an embodiment of the present application; Figure 3 It is a flowchart of step S130 in an intelligent stocking method according to an embodiment of the present application; Figure 4 It is a flowchart of step S140 in an intelligent stocking method according to an embodiment of the present application; Figure 5 It is a flow chart of a purchase amount warning in an intelligent stocking method according to an embodiment of the present application; Figure 6 It is a flowchart of differentiated purchasing in an intelligent stocking method according to an embodiment of the present application; Figure 7 It is a schematic diagram of a process for adjusting the stocking quantity in an intelligent stocking method according to an embodiment of the present application; Figure 8 This is a module schematic diagram of an intelligent stocking device according to an embodiment of the present application; Fig. 9 It is an internal structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0026] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0027] In the first aspect, the present application provides an intelligent stocking method, referring to Figure 1 , including the following steps: S110: Obtain sales forecast data for each commodity within a preset time period.

[0028] The preset time period may be a week, a month, or a quarter in the future, and the system needs to predict sales for various commodities during this period. The sales forecast data in this embodiment is obtained by analyzing historical sales data, and external data sources such as market research data and competitive product analysis can also be introduced for prediction, and corrections can be made in combination with expert experience to improve the prediction accuracy.

[0029] Specifically, this embodiment uses the moving average method to analyze the average daily sales of the past three months and makes corrections based on periodic patterns. For example, the sales of a certain snack food during holidays are usually 2-3 times that of weekdays, and the system will incorporate this pattern into the prediction model. At the same time, if it is found that the recent sales show a clear upward or downward trend, the prediction value will be adjusted accordingly.

[0030] S120. Obtain current inventory data based on the inventory in stock, inventory in purchase, and inventory in transit.

[0031] Among them, the current inventory data refers to the actual inventory sum of the goods in each link. In this embodiment, the inventory in stock refers to the number of goods that can be directly used for sale in the physical warehouse, the inventory in procurement refers to the number of goods that have been ordered but not yet shipped by the supplier, and the inventory in transit refers to the number of goods that have been shipped but not yet in the warehouse.

[0032] Specifically, the system obtains in-stock inventory data through real-time connection to the warehouse management system (WMS), obtains in-purchase inventory data through the procurement system, and obtains in-transit inventory data through the logistics system. For example, if the in-stock inventory of a certain product is 100 pieces, the in-purchase inventory is 50 pieces, and the in-transit inventory is 30 pieces, then the current inventory data is 180 pieces. The system will automatically update this data regularly to ensure the accuracy of the inventory data.

[0033] S130: Calculate the out-of-stock situation within a preset time period based on the sales forecast data and the current inventory data.

[0034] Among them, the out-of-stock situation refers to the time point and quantity when the current inventory cannot meet the predicted sales demand within a preset time period. In this embodiment, by comparing the predicted sales and current inventory, the time period when the risk of out-of-stock may occur is identified, providing a basis for subsequent stocking decisions.

[0035] Specifically, the system will accumulate the predicted sales in chronological order and gradually deduct it from the current inventory. When the remaining inventory is lower than the safety stock level, it will be marked as a stock-out risk time point.

[0036] S140. Generate a recommended stocking quantity based on the out-of-stock situation.

[0037] The recommended stocking quantity refers to the quantity of goods recommended for purchase by the system based on the out-of-stock situation analysis. In this embodiment, the calculation of the recommended stocking quantity needs to consider factors such as the out-of-stock time point, out-of-stock quantity, and replenishment cycle to ensure timely replenishment while avoiding inventory backlogs.

[0038] Specifically, the system will trigger stocking suggestions in advance based on the out-of-stock time point, and the stocking quantity usually covers at least one complete replenishment cycle from the out-of-stock time point. Users can fill in the purchasing warehouse, MSKU, FNSKU, channel and specific quantity based on these data, and finally click the purchase button to generate a purchase order, which greatly improves stocking efficiency.

[0039] In one embodiment, referring to Figure 2 In step S110, the sales forecast data of each commodity within a preset time period is obtained, which specifically includes the following steps: S111. Obtain sales data of each commodity within a preset historical time period.

[0040] Specifically, the system automatically extracts sales records for the required time period from the sales database, and performs preliminary data cleaning and outlier processing. For example, in the sales data of a snack in the past 6 months, abnormal records caused by system failures and data fluctuations caused by temporary promotional activities are removed, and data that can reflect normal sales levels are retained. After data cleaning, sales records are summarized by date dimension to form a standardized sales data set.

[0041] S112. Calculate average daily sales based on the sales data.

[0042] The average daily sales volume refers to the average daily sales level of a commodity under a standard sales environment. In this embodiment, the weighted moving average method is used to calculate the average daily sales volume, and the weight of recent sales data will be increased accordingly to better reflect the latest sales trend.

[0043] Specifically, the system assigns a weight of 0.5 to the sales data of the most recent three months, a weight of 0.3 to the sales data of the previous three months, and a weight of 0.2 to earlier sales data.

[0044] S113. Obtain manual intervention speed data, compare the average daily sales volume with the manual intervention speed data, and generate final sales volume forecast data.

[0045] In this embodiment, sales personnel can upload data through the system to intervene in sales speed. First, they filter out the products that need intervention through the screening conditions and click the intervention button; then fill in the estimated speed based on last year's year-on-year speed and the big data estimated speed, click the intervention calculation button, and the system will automatically check whether the data of the previous 6 months and the reason for intervention have been filled in; finally, wait for the intervention calculation results, and close the intervention interface after confirming that they are correct to complete the operation.

[0046] Furthermore, the system supports batch upload function to achieve large-scale sales speed intervention, which can significantly improve work efficiency. For new sales personnel, small batch upload method is adopted to facilitate timely discovery and location of problems. Specific operations include: downloading standard templates, filling in estimated data, uploading files, and waiting for the system to process the uploaded results. After becoming familiar with the operation process, gradually transition to large-scale operations to avoid repeated modifications due to data errors and waste of time.

[0047] In one embodiment, referring to Figure 3 In step S130, based on the sales forecast data and the current inventory data, the out-of-stock situation within the preset time period is calculated, which specifically includes the following steps: S131. Obtain the pre-equipment delivery cycle, which includes procurement lead time, warehouse processing time, transportation time and buffer time.

[0048] The lead time is the time from when the purchase order is issued to when the goods are available for sale. In this embodiment, the purchase lead time refers to the preparation time from when the supplier receives the order to when the goods are ready for sale. The warehouse processing time includes the time for receiving goods, quality inspection and putting on shelves. The transportation time refers to the time when the goods are in transit. The buffer time is used to deal with possible delays in each link.

[0049] Specifically, the system will set corresponding stocking cycle parameters according to different suppliers and different types of goods. For example, a food supplier's procurement lead time is 2 days, warehouse processing time is 1 day, transportation time is 3 days, plus 1 day of buffer time, then the supplier's stocking cycle is 7 days. For imported goods, longer transportation time and buffer time may be set to ensure the stability of the supply chain.

[0050] S132. Divide the preset time period according to the expected equipment delivery cycle.

[0051] Among them, the preset time period refers to the time range for the system to conduct inventory analysis and stocking planning, which can be flexibly set according to different commodity types and business characteristics. For seasonal commodities (such as clothing), it is usually set to 90 days to cover the entire seasonal sales cycle; for fast-moving consumer goods, it may be set to 30 days to quickly respond to market changes; for holiday commodities, it is determined according to the specific holiday schedule, and needs to cover the complete cycle from pre-holiday stocking to post-holiday clearance. When selecting the preset time period, the system will comprehensively consider multiple factors such as commodity characteristics, supply chain characteristics, and business models to achieve a balance between forecast accuracy and inventory management efficiency. The division of the preset time period is to more accurately evaluate the inventory status within each stocking cycle. In this embodiment, the system divides the preset time period into equal parts according to the length of the stocking cycle, which is convenient for segmented evaluation and management of inventory risks.

[0052] Specifically, if the preset time period is 30 days and the stocking cycle is 7 days, the system will divide the 30 days into 5 complete cycles (7 days each for the first 4 cycles) and 1 remaining cycle (2 days). In actual applications, the system will dynamically adjust the division method to ensure that the start and end time of each cycle matches the actual business rhythm.

[0053] S133. In each pre-equipment stocking cycle, the difference between the current inventory data and the sales forecast data is calculated.

[0054] The difference calculation is the result obtained by subtracting the predicted sales from the current inventory data, which reflects the inventory surplus or shortage in each cycle. In this embodiment, the system will take into account the dynamic changes of inventory, that is, when calculating the difference of subsequent cycles, the inventory surplus or shortage of the previous cycle will be included in the calculation.

[0055] S134: Determine a time period in which the difference is less than a preset threshold as a time period in which the product is out of stock.

[0056] Among them, the preset threshold refers to the minimum safety stock level set by the system, which is used to timely identify potential out-of-stock risks.

[0057] Specifically, the system may set the preset threshold to 50% of the average weekly sales. For example, if the average weekly sales of a product is 100 pieces, the preset threshold is 50 pieces. When the inventory difference in a period is less than 50 pieces, the period will be marked as out-of-stock period.

[0058] In one embodiment, referring to Figure 4 In step S140, a recommended stocking quantity is generated according to the out-of-stock situation, which specifically includes the following steps: S141. Calculate the average daily out-of-stock quantity during the out-of-stock time period.

[0059] The daily average out-of-stock quantity reflects the average number of commodities that need to be replenished every day during the out-of-stock period. In this embodiment, the system calculates the daily average out-of-stock level by analyzing the inventory gap during the out-of-stock period and combining the duration of the period.

[0060] Specifically, the system will take into account the changing trend of the degree of out-of-stock conditions during calculations. For example, if the out-of-stock quantity tends to increase day by day, the average daily out-of-stock quantity may be revised upward to better cope with possible future demand growth.

[0061] S142. Calculate the recommended stocking base quantity based on the average daily stock-out quantity and the expected equipment stocking cycle.

[0062] The recommended stocking base quantity refers to the quantity of goods that need to be prepared in advance in consideration of the replenishment cycle. In this embodiment, the calculation of the base quantity needs to ensure that a sufficient inventory level can be maintained throughout the entire pre-equipment stocking cycle.

[0063] Furthermore, taking into account possible fluctuations in the supply chain, the system usually adds 10%-20% of safety stock to the baseline quantity, that is, the recommended baseline stocking quantity is set to 770-840 pieces. The specific increase ratio can be adjusted according to the importance of the goods and the stability of supply.

[0064] S143. Obtain the minimum packaging quantity of the product.

[0065] The minimum packaging quantity refers to the minimum unit quantity when the supplier ships. In this embodiment, different commodities have different minimum packaging quantity standards due to their packaging characteristics and transportation requirements, and this value is usually recorded in the commodity master data.

[0066] Specifically, the system will extract the minimum packaging quantity information from the product master database. For example, if the minimum packaging quantity of a beverage product is 24 bottles / box, the order quantity must be an integer multiple of 24. The system will automatically refer to this restriction when generating order suggestions to avoid order quantities that do not meet the supplier's delivery standards.

[0067] S144. The recommended stocking base quantity is adjusted upward to the nearest integer multiple of the minimum packaging quantity to obtain the recommended stocking quantity.

[0068] The recommended stock quantity needs to meet both the actual demand and the packaging specification requirements. In this embodiment, the system will adjust the recommended stock base quantity upward to an integer multiple of the minimum packaging quantity to ensure that the order quantity meets the supplier's delivery standards.

[0069] In one embodiment, referring to Figure 5 , the method further comprises the following steps: S510. Obtain the preset purchase limit, the used purchase amount, and the purchased but unapproved amount.

[0070] Among them, the preset purchase limit is the purchase quota limit determined by the enterprise based on factors such as capital status, business scale and seasonal fluctuations, which can be set on a monthly, quarterly or annual basis; the used purchase amount refers to the total amount of purchase orders that have been approved and executed; the purchased but unapproved amount refers to the total amount of purchase orders that have been submitted but not yet reviewed. These three data together constitute the basic data for purchase quota management.

[0071] Specifically, enterprises can set differentiated preset purchase limits according to different categories, regions or business lines. For example, for seasonal categories, a higher purchase limit can be set in the peak season and lowered accordingly in the off-season; for regular categories, a relatively stable purchase limit can be maintained. The system obtains these three types of data in real time through the data interface to ensure the timeliness and accuracy of quota management.

[0072] S520. Calculate the remaining available purchase amount based on the preset purchase limit, the used purchase amount, and the purchased but unapproved amount.

[0073] The remaining available purchase amount needs to take into account both the executed and pending purchase amounts, and is calculated by deducting the used purchase amount and the purchased but unapproved amount from the preset purchase upper limit. This calculation method ensures the comprehensiveness of purchase amount management and avoids the problem of over-amount caused by the failure to timely count pending orders.

[0074] Specifically, the system uses real-time calculation to calculate the remaining available purchase amount. For example, if a company's monthly purchase limit is 1 million yuan, 500,000 yuan has been used, and the amount of orders to be approved is 200,000 yuan, then the remaining available purchase amount is 300,000 yuan. This dynamic calculation method can accurately reflect the current quota usage and provide a reliable basis for purchasing decisions.

[0075] S530. Compare the purchase amount corresponding to the recommended stocking quantity with the remaining available purchase amount. When the purchase amount exceeds the remaining available purchase amount, issue an early warning.

[0076] The system converts the intelligently generated recommended stocking quantity into the corresponding purchase amount and compares it with the remaining available purchase amount in real time. When the purchase amount exceeds the available amount, the system triggers an early warning mechanism to promptly remind purchasing personnel and relevant management personnel to pay attention to the use of the quota.

[0077] Specifically, early warnings can be provided in a variety of ways, such as system pop-ups, email notifications, or SMS reminders. For example, when a purchaser attempts to submit a purchase application for 400,000 yuan, and the current remaining available purchase amount is only 300,000 yuan, the system will immediately display an early warning message, indicating that the available amount exceeds 100,000 yuan, and recommends that the purchaser adjust the purchase plan or apply for an increase in the purchase limit. At the same time, it can provide or display the recommended stock quantity and the new shortage cycle under the condition of not exceeding the limit, so that the purchaser can quickly evaluate. This early warning mechanism effectively prevents the occurrence of over-purchase.

[0078] In one embodiment, referring to Figure 6 , the method further comprises the following steps: S610. Obtain supplier rating data based on the supplier's on-time delivery rate, quality pass rate, and price fluctuation rate.

[0079] The supplier rating adopts a multi-dimensional evaluation system to comprehensively measure the overall performance of the supplier. In this embodiment, the delivery timeliness rate reflects the ability to deliver orders on time, the quality qualification rate reflects the product quality control level, and the price volatility rate indicates the stability of the supply price.

[0080] Supplier rating data can ignore the correlation between products, and the system can calculate the scores of various indicators based on all product data in a specific period of time in the past (such as 6 months). For example, a supplier's on-time delivery rate is 95% (number of orders delivered on time / total number of orders), the quality pass rate is 98% (number of qualified products / total supply quantity), and the price volatility rate is 5% (the difference between the highest price and the lowest price). The system uses a weighted calculation method, with a weight of 0.4 for the on-time delivery rate, a weight of 0.4 for the quality pass rate, and a weight of 0.2 for the price volatility rate, and finally the supplier's comprehensive score is 96 points.

[0081] Supplier rating data can also consider the correlation between products. For example, a supplier supplies four types of goods, A, B, C, and D. The current product that needs to be stocked is A. Among B, C, and D, B and A are correlated, while C and D are not correlated with A. The correlation is, for example, similar structure or function (for example, adjustable weight) or similar category (for example, dipped dumbbells and adjustable dumbbells). If there is a correlation between the products, the scores of various indicators are calculated based on the data of A and B. By considering the correlation and calculating the data with a correlation, the supplier situation can be evaluated more comprehensively.

[0082] S620. Intelligently allocate the recommended stocking quantity based on the supplier rating data to obtain the purchase share of each supplier.

[0083] Among them, a positively correlated intelligent allocation method can be adopted, that is, the procurement share is positively correlated with the score. Specifically, the system sets a basic allocation ratio and then dynamically adjusts it according to the score difference to ensure that resources are tilted towards high-quality suppliers.

[0084] S630. When any indicator in the supplier rating data is lower than the preset standard, the corresponding procurement share will be allocated to the alternative suppliers.

[0085] The preset standard is the minimum requirement for maintaining a cooperative relationship with a supplier. In this embodiment, the system sets an early warning threshold for each indicator. Once an indicator falls below the threshold, the share adjustment mechanism will be activated.

[0086] Specifically, if a supplier's quality qualification rate is lower than the preset standard of 95%, the system will automatically transfer 50% of its purchase share to the alternative supplier. For example, of the 300 pieces originally allocated to supplier C, 150 pieces will be transferred to the alternative supplier D with the second highest score.

[0087] S640. Generate differentiated purchase price ranges based on purchase shares.

[0088] The differentiated price range is a stepped price range set according to the size of the purchase share. In this embodiment, the system analyzes historical transaction data and combines market conditions to set reasonable price ranges for different purchase scales.

[0089] Specifically, the system sets a stepped price range according to the size of the purchase share, reflecting the price advantage of large-scale purchases. For example, the purchase share of a certain commodity is divided into three levels: 100-500 pieces for small-volume purchases, 501-2000 pieces for medium-volume purchases, and more than 2000 pieces for large-volume purchases. The corresponding price ranges are set as follows: the price range for small-volume purchases is 90-100 yuan / piece, the price range for medium-volume purchases is 85-95 yuan / piece, and the price range for large-volume purchases is 80-90 yuan / piece. When setting the price range, the system considers the following factors: the fluctuation range of historical purchase prices, the price level of similar products in the market, the price trend of raw materials, and the production scale and cost structure of suppliers. In addition, the price range should be dynamically adjusted according to seasonal factors, promotional activities, long-term cooperative relationships, etc. For example, the upper limit of the price range is appropriately raised in the peak season to ensure stable supply, and the lower limit of the price range is appropriately lowered in the off-season to improve procurement efficiency.

[0090] In one embodiment, referring to Figure 7 , the method further comprises the following steps: S710. Obtain inventory temperature and humidity data and storage time data for each batch of goods.

[0091] The inventory temperature and humidity data and storage time data may be actually collected, unprocessed data.

[0092] For example, inventory temperature and humidity data are collected in real time through the storage environment monitoring system, and storage time data is automatically calculated based on the time when the goods enter the warehouse. In this embodiment, the system establishes independent environmental parameter monitoring files for different warehouse areas and different batches of goods.

[0093] Specifically, the system records inventory environment data every 30 minutes, including temperature (such as 18-22°C in normal temperature area and 0-4°C in cold storage area) and relative humidity (such as 50%-65%). At the same time, the system tracks the storage time of each batch of goods, such as Batch A has been stored for 15 days, Batch B has just been stored for 2 days, etc.

[0094] The inventory temperature and humidity data and storage time data may also be data obtained after processing the actual collected data.

[0095] Specifically, after the production of goods is completed, the main transportation routes from the production site to the sales site (for example, from China to the United States or Europe) are sea transportation, air transportation, and land transportation. Generally speaking, a commodity needs at least two modes of transportation to reach the sales site, such as land transportation-sea transportation-land transportation, or land transportation-air transportation-land transportation. In addition, different modes of transportation have different characteristics. For example, the transportation time of sea transportation is longer, and the goods will be affected by various environmental conditions during transportation, such as changes in temperature and humidity, and bumps during navigation. For example, the transportation time of air transportation is short, relatively stable, and the carrying capacity is small. For example, the transportation time of land transportation is longer, and the goods will be affected by the environment during transportation, such as bumps during driving. Therefore, even if stored for the same time under the same temperature and humidity environment, the degree of influence of different transportation methods on the goods is different. Therefore, the actual collected data can be processed, and the processed data can be used as the final inventory temperature and humidity data and storage time data. The processing method can be as follows: According to the pre-stored weight correspondence list, the weight group corresponding to the transportation tool is matched. The weight correspondence list can be generated based on the usage of the transportation tool, maintenance frequency, road conditions and other parameters. The weight group can have one to three weight values. When there is only one weight value, for example, it is only used to adjust the inventory temperature or humidity or storage time. When there are two weight values, only two of the inventory temperature, humidity and storage time are adjusted. When there are three weight values, all three of the inventory temperature, humidity and storage time are adjusted.

[0096] For example, for the land transportation stage, the vehicle usage and maintenance frequency are divided into three levels: high, medium, and low, and the road conditions are divided into three levels: good, general, and poor. Each level corresponds to a numerical value, and the numerical values ​​corresponding to each level can be the same or different. By adding the three numerical values ​​corresponding to the usage, maintenance frequency, and road conditions, the adjustment value of the land transportation stage can be obtained, and based on this, the final inventory temperature and humidity data and storage time data of the transportation stage can be obtained. The settings of the sea transportation stage and the air transportation stage are similar to those of the land transportation stage and will not be repeated here. Through data adjustment, the calculation results can be made closer to the actual situation of the goods and the accuracy of stocking can be improved. In addition, the level data of usage, maintenance frequency, and road conditions can come from the background.

[0097] S720: Calculate the quality warning time point of the product based on the temperature and humidity data and the storage time data.

[0098] The quality warning time point refers to the critical time when the quality of the product is expected to change under the current storage conditions. In this embodiment, the system dynamically calculates and updates the quality warning time point based on the product characteristics and storage environment parameters.

[0099] Specifically, there are several ways to calculate the quality warning time point: If the inventory temperature and humidity data and storage time data are processed from the actual collected data, the shelf life of the product can be predicted based on a linear relationship because it can better reflect the actual environmental conditions of the product. Assuming that the shelf life of a dairy product stored at 4°C is 21 days, the system will adjust according to the fluctuations in the actual storage temperature. If the average storage temperature is monitored to be 6°C, the system will advance the warning time point to the 18th day; if the temperature briefly rises to 8°C, it will be further advanced to the 15th day.

[0100] If the inventory temperature and humidity data and storage time data are actually collected and unprocessed data, the quality warning time point can be calculated using the formula D=D1*P-D2, where D is the quality warning time point, D1 is the ideal shelf life, P is the adjustment value, and D2 is the storage time. Due to the different characteristics of different commodities and different environmental characteristics of different destinations, the adjustment value P can be determined based on the commodity type and destination to make the calculated quality warning time point more accurate.

[0101] S730. Match and analyze the quality warning time point with the sales forecast data.

[0102] Among them, the matching analysis aims to evaluate whether the product can achieve the expected sales within the quality safety period. In this embodiment, the system uses the quality warning time point as a hard constraint on the sales period and conducts sales feasibility analysis in combination with sales forecast.

[0103] S740. When it is predicted that the product cannot be sold before the quality warning time point, adjust the corresponding recommended stock quantity.

[0104] Among them, it is recommended that the adjustment of the stocking quantity needs to balance the quality risk and the risk of out-of-stock. In this embodiment, the system will dynamically optimize the original stocking strategy according to the quality warning time point to ensure the turnover efficiency of the goods.

[0105] Specifically, the system will adjust the recommended stocking quantity to 400 boxes (40 boxes / day * 10 days) to ensure that the product can be sold out before the quality warning time point. At the same time, the system will also recommend a more frequent small-batch replenishment strategy, such as replenishing once every 5 days, replenishing 200 boxes each time, so as to ensure the freshness of the product and maintain normal sales.

[0106] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] In a second aspect, the present application provides an intelligent stocking device, and the intelligent stocking device of the present application is described below in combination with the above-mentioned intelligent stocking method.

[0108] Reference Figure 8 , an intelligent stocking device, comprising: The sales forecast data acquisition module is used to obtain the sales forecast data of each commodity within a preset time period; The current inventory data acquisition module is used to obtain the current inventory data based on the inventory in stock, inventory in purchase and inventory in transit; A stock-out situation calculation module is used to calculate the stock-out situation within a preset time period based on sales forecast data and current inventory data; The recommended stock quantity generation module is used to generate the recommended stock quantity according to the out-of-stock situation.

[0109] In one embodiment, the sales forecast data acquisition module includes: A historical sales data acquisition unit, used to acquire sales data of each commodity within a preset historical time period; An average daily sales calculation unit, used to calculate the average daily sales based on the sales data; The sales forecast data generating unit is used to obtain the manual intervention speed data, compare the average daily sales volume with the manual intervention speed data, and generate the final sales forecast data.

[0110] In one embodiment, the out-of-stock situation calculation module includes: The stocking cycle acquisition unit is used to obtain the pre-equipment stocking cycle, which includes the procurement lead time, warehouse processing time, transportation time and buffer time; A time period division unit is used to divide the preset time period according to the expected equipment and cargo cycle; The difference calculation unit is used to calculate the difference between the current inventory data and the sales forecast data in each pre-equipment stocking cycle; The out-of-stock time period determining unit is used to determine a time period in which the difference is less than a preset threshold as an out-of-stock time period.

[0111] In one embodiment, the suggested stock quantity generation module includes: The daily average out-of-stock quantity calculation unit is used to calculate the daily average out-of-stock quantity within the out-of-stock time period; The base quantity calculation unit is used to calculate the recommended stocking base quantity based on the average daily stock-out quantity and the expected stocking cycle; A packaging quantity acquisition unit is used to obtain the minimum packaging quantity of a product; The stocking quantity adjustment unit is used to adjust the recommended stocking base quantity to the nearest integer multiple of the minimum packaging quantity to obtain the recommended stocking quantity.

[0112] In one embodiment, the apparatus further comprises: The quota acquisition module is used to obtain the preset purchase limit, the used purchase amount and the purchased but unapproved quota; The module for obtaining the remaining available purchase amount is used to calculate the remaining available purchase amount based on the preset purchase limit, the used purchase amount and the purchased but unapproved amount; The early warning prompt module is used to compare the purchase amount corresponding to the recommended stocking quantity with the remaining available purchase amount, and issue an early warning prompt when the purchase amount exceeds the remaining available purchase amount.

[0113] In one embodiment, the apparatus further comprises: The supplier rating module is used to obtain supplier rating data based on the supplier's on-time delivery rate, quality qualification rate and price fluctuation rate; The procurement share allocation module is used to intelligently allocate the recommended stocking quantity according to the supplier rating data to obtain the procurement share of each supplier; The share adjustment module is used to allocate the corresponding purchase share to the alternative suppliers when any indicator in the supplier rating data is lower than the preset standard; The price range generation module is used to generate differentiated purchase price ranges based on purchase shares.

[0114] In one embodiment, the apparatus further comprises: Inventory data acquisition module, used to obtain inventory temperature and humidity data and storage time data of each batch of goods; The warning time point calculation module is used to calculate the quality warning time point of the product based on the temperature and humidity data and the storage time data; Matching analysis module, used to match and analyze quality warning time points with sales forecast data; The inventory adjustment module is used to adjust the corresponding recommended inventory when it is predicted that the goods cannot be sold before the quality warning time point.

[0115] In one embodiment, the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent stocking method is implemented.

[0116] Those skilled in the art will understand that Fig. 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0117] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0119] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent stocking method, characterized in that: The steps include: Get sales forecast data for each product within a preset time period; Get current inventory data based on inventory in stock, inventory in purchase, and inventory in transit; Calculating the out-of-stock situation within the preset time period based on the sales forecast data and the current inventory data; Generate a recommended stocking quantity based on the out-of-stock situation.

2. The intelligent stocking method according to claim 1, characterized in that: Obtaining sales forecast data for each product within a preset time period includes the following steps: Get the sales data of each commodity within a preset historical time period; Calculate average daily sales based on the sales data; Obtain manual intervention speed data, compare the average daily sales volume with the manual intervention speed data, and generate final sales volume forecast data.

3. The intelligent stocking method according to claim 1, characterized in that: Calculating the out-of-stock situation within the preset time period based on the sales forecast data and the current inventory data specifically includes the following steps: Obtaining the pre-equipment delivery cycle, which includes procurement lead time, warehouse processing time, transportation time and buffer time; Divide the preset time period according to the preset equipment delivery cycle; In each of the pre-equipment stocking cycles, calculating the difference between the current inventory data and the sales forecast data; The time period in which the difference is less than a preset threshold is determined as a stock-out time period.

4. The intelligent stocking method according to claim 3, characterized in that: Generating a recommended stocking quantity according to the out-of-stock situation specifically includes the following steps: Calculate the average daily out-of-stock quantity during the out-of-stock time period; Calculate the recommended stocking base quantity based on the average daily stock-out quantity and the estimated stocking cycle; Get the minimum packaging quantity of the product; The recommended stocking base quantity is adjusted upward to the nearest integer multiple of the minimum packaging quantity to obtain the recommended stocking quantity.

5. The intelligent stocking method according to claim 1, characterized in that: The method further comprises the steps of: Obtain the preset purchase limit, used purchase amount, and purchased but unapproved amount; Calculate the remaining available purchase amount based on the preset purchase limit, the used purchase amount and the purchased but unapproved amount; The purchase amount corresponding to the recommended stocking quantity is compared with the remaining available purchase amount, and when the purchase amount exceeds the remaining available purchase amount, an early warning prompt is issued.

6. The intelligent stocking method according to claim 1, characterized in that: The method further comprises the steps of: Obtain supplier rating data based on supplier’s on-time delivery rate, quality pass rate and price fluctuation rate; Intelligently allocating the recommended stocking quantity according to the supplier rating data to obtain the purchase share of each supplier; When any indicator in the supplier rating data is lower than the preset standard, the corresponding procurement share will be allocated to the alternative supplier; A differentiated purchasing price range is generated based on the purchasing share.

7. The intelligent stocking method according to claim 1, characterized in that: The method further comprises the steps of: Obtain inventory temperature and humidity data and storage time data for each batch of goods; Calculating a quality warning time point for the product based on the temperature and humidity data and the storage time data; Matching and analyzing the quality warning time point with the sales forecast data; When it is predicted that the product cannot be sold before the quality warning time point, the corresponding recommended stock quantity is adjusted.

8. An intelligent stocking device, characterized in that: include: The sales forecast data acquisition module is used to obtain the sales forecast data of each commodity within a preset time period; The current inventory data acquisition module is used to obtain the current inventory data based on the inventory in stock, inventory in purchase and inventory in transit; A stock-out situation calculation module, used to calculate the stock-out situation within the preset time period based on the sales forecast data and the current inventory data; The suggested stock quantity generating module is used to generate the suggested stock quantity according to the out-of-stock situation.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent stocking method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent stocking method described in any one of claims 1 to 7 are implemented.

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