Plastic tray management method, system and equipment and storage medium

Abstract

The invention provides a plastic tray management method, system and device and a storage medium, and relates to the technical field of supply management, and the method comprises the steps: predicting the first demand of a plastic tray in a second preset time according to the use data of the plastic tray in a first preset time of a target enterprise; obtaining a time interval corresponding to the second preset duration, and determining a season parameter corresponding to the time interval; determining an industry fluctuation coefficient of the target enterprise in a second preset duration according to the season parameter, and adjusting the first demand quantity according to the industry fluctuation coefficient to obtain a second demand quantity; according to the historical order data and the customer cooperation data of the target enterprise in the first preset duration, generating an order prediction quantity of the target enterprise in a second preset duration; and adjusting the second demand quantity according to the order pre-quantity to obtain a target demand quantity, and generating a supplement plan of the plastic tray according to the target demand quantity. The method has the technical effect that the demand quantity of the plastic trays is accurately predicted.

Description

Technical Field

[0001] The present application relates to the field of supply management technology, and in particular to a plastic pallet management method, system, device and storage medium. Background Art

[0002] Plastic pallets are an indispensable tool for modern logistics and production. The accurate forecast of demand has a significant impact on the production operations and resource allocation of enterprises. If the pallet supply is insufficient, it may lead to bottlenecks in the production and transportation links, affecting the normal operation of the enterprise; while excessive pallet reserves will cause a waste of storage space and a backlog of funds.

[0003] At present, some companies use simple statistical methods to predict the demand for plastic pallets, such as calculating the average value based on historical data or using linear regression models, etc. Although these methods can predict the demand for plastic pallets to a certain extent, they fail to fully consider the various internal and external factors that affect the demand for plastic pallets. In actual applications, they are often difficult to adapt to the complex and changing market environment, resulting in low accuracy in predicting the demand for plastic pallets. Summary of the invention

[0004] The present application provides a plastic pallet management method, system, device and storage medium for accurately predicting the demand for plastic pallets.

[0005] In a first aspect, the present application provides a plastic pallet management method, the method comprising: obtaining usage data of plastic pallets of a target enterprise within a first preset time period, and predicting a first demand for the plastic pallets within a second preset time period based on the usage data, wherein the first preset time period is before the second preset time period; obtaining a time interval corresponding to the second preset time period, and determining a season parameter corresponding to the time interval based on a pre-established season division model; determining an industry fluctuation coefficient of the target enterprise within the second preset time period based on the season parameter, and adjusting the first demand based on the industry fluctuation coefficient to obtain a second demand; obtaining historical order data and customer cooperation data of the target enterprise within the first preset time period, and generating an order forecast for the target enterprise within the second preset time period based on the historical order data and the customer cooperation data; adjusting the second demand based on the order forecast to obtain a target demand, and generating a replenishment plan for the plastic pallets based on the target demand.

[0006] By adopting the above technical solution, through phased data processing and multi-dimensional adjustment mechanism, the demand forecast of pallets is made more accurate. First, the preliminary first demand is obtained based on the usage data within the first preset time period, and then the first adjustment is made in combination with the seasonal division model and the industry volatility coefficient to obtain the second demand that is more in line with the market environment. Finally, the second adjustment is made through historical order data and customer cooperation data to finally obtain the accurate target demand. This step-by-step optimization forecasting method not only ensures the reliability of the basic data, but also fully considers the impact of market fluctuations and the actual operating conditions of the enterprise, thereby accurately predicting the demand for plastic pallets.

[0007] Optionally, the usage data includes: average daily usage of pallets, average daily damage to pallets and turnover time of pallets, and predicting the first demand for the plastic pallets within a second preset time period based on the usage data includes: determining a basic demand for the plastic pallets based on the average daily usage of pallets; adjusting the basic demand based on the average daily damage to pallets to determine a safety reserve of the plastic pallets; determining a replenishment cycle of the plastic pallets based on the turnover time of the pallets; and predicting the first demand for the plastic pallets within the second preset time period based on the safety reserve and the replenishment cycle.

[0008] By adopting the above technical solution, the basic demand is determined by the average daily usage of pallets, and the safety reserve is calculated based on the average daily damage of pallets. Finally, the replenishment cycle is determined based on the turnover time of pallets. This step-by-step calculation method not only takes into account daily usage needs, but also incorporates pallet loss and turnover efficiency into the prediction system, so that the prediction result of the first demand is more in line with the actual operation of the enterprise, providing a reliable data basis for subsequent demand adjustments.

[0009] Optionally, the method of combining the safety reserve and the replenishment cycle to predict the first demand for the plastic pallet within a second preset time period includes: determining a replenishment coefficient for the plastic pallet based on the replenishment cycle; and arithmetically multiplying the safety reserve by the replenishment coefficient to predict the first demand for the plastic pallet within the second preset time period.

[0010] By adopting the above technical solution, by establishing the corresponding relationship between the replenishment cycle and the replenishment coefficient, and performing arithmetic product operation with the safety reserve, the quantitative calculation of the first demand is realized. This calculation method converts the time factor of pallet replenishment into a quantifiable replenishment coefficient, so that the safety reserve can be adjusted accordingly according to different replenishment cycles, so that the forecast result of the first demand not only takes into account the enterprise's safety inventory needs, but also reflects the actual impact of the replenishment cycle, improving the scientificity and accuracy of the forecast.

[0011] Optionally, determining the industry fluctuation coefficient of the target enterprise within the second preset time period according to the seasonal parameters, and adjusting the first demand according to the industry fluctuation coefficient to obtain the second demand, includes: determining the industry operating rate and capacity utilization rate of the target enterprise within the second preset time period according to the seasonal parameters, and determining the weight coefficient of the industry operating rate as the first weight coefficient, and the weight coefficient of the capacity utilization rate as the second weight coefficient; arithmetically multiplying the industry operating rate by the first weight coefficient to generate a first fluctuation coefficient; arithmetically multiplying the capacity utilization rate by the second weight coefficient to generate a second fluctuation coefficient; arithmetically adding the first fluctuation coefficient and the second fluctuation coefficient to generate the industry fluctuation coefficient of the target enterprise within the second preset time period; arithmetically multiplying the first demand by the industry fluctuation coefficient to obtain the second demand.

[0012] By adopting the above technical solution, the solution realizes the precise adjustment of the first demand by concretizing the industry fluctuation factors into two quantitative indicators, namely the industry operating rate and capacity utilization rate, and using the calculation method with preset weights to obtain the industry fluctuation coefficient. This method based on double-indicator weighted calculation not only takes into account the overall operation status of the industry, but also takes into account the actual production situation of the enterprise. Through arithmetic product operations, the impact of industry fluctuations is directly reflected in the adjustment of demand, so that the second demand can better adapt to the development and changes of the industry, and improve the adaptability of the forecast results to the market environment.

[0013] Optionally, generating the forecast order volume of the target enterprise within the second preset time period based on the historical order data and the customer cooperation data includes: determining the order completion volume and order growth rate of the target enterprise within the first preset time period based on the historical order data; determining the customer stability coefficient of the target enterprise based on the customer cooperation data; taking the product of the order completion volume and the order growth rate as a basic forecast value; and arithmetically multiplying the basic forecast value by the customer stability coefficient to obtain the forecast order volume.

[0014] By adopting the above technical solution, the product of the order completion volume and the order growth rate is used as the basic forecast value, reflecting the historical performance and development trend of the enterprise's orders; then the customer stability coefficient is introduced for secondary adjustment, the stability factor of the customer relationship is included in the forecast system, and the final order forecast is obtained through arithmetic product operation. This forecasting method takes into account the order growth trend of the enterprise itself and combines the stability of customer cooperation, making the order forecast closer to the actual operating conditions of the enterprise and improving the reliability of the forecast.

[0015] Optionally, the customer cooperation data include customer cooperation years and customer historical order volume, and determining the customer stability coefficient of the target enterprise based on the customer cooperation data includes: grading each customer according to the customer cooperation years, and assigning a corresponding weight coefficient according to the grade of each customer; determining the order contribution coefficient of each customer according to the proportion of the customer's historical order volume to the total order volume of the enterprise; arithmetically multiplying the weight coefficient corresponding to each customer by the order contribution coefficient to obtain the stability index of each customer; and arithmetically averaging the stability indexes of all customers to determine the customer stability coefficient of the target enterprise.

[0016] By adopting the above technical solution, accurate quantification of customer stability is achieved through multi-level data processing. Specifically, the customer cooperation years are converted into grade weights, and the stability index of each customer is calculated in combination with the order contribution coefficient, and finally the overall customer stability coefficient is obtained through arithmetic average. This calculation method not only takes into account the time dimension of the customer cooperation relationship, but also includes the customer's business contribution in the evaluation scope. Through weight allocation and multi-step calculation, the customer stability coefficient can fully reflect the stability of the enterprise customer group, providing a more accurate basis for the calculation of order forecasts.

[0017] Optionally, after generating the replenishment plan for the plastic pallets according to the target demand, the method further includes: obtaining the number of available pallets and the number of pallets to be replenished of the target enterprise; calculating the difference between the number of available pallets and the target demand; adjusting the replenishment quantity of the plastic pallets according to the difference, and generating a pallet procurement plan based on the replenishment quantity; executing the pallet procurement plan in batches according to the storage resources and financial status of the target enterprise, and monitoring the changes in pallet inventory in real time.

[0018] By adopting the above technical solutions and establishing a dynamic pallet replenishment mechanism, closed-loop management from demand forecasting to actual procurement is achieved. Specifically, the actual replenishment demand is determined by calculating the difference between the number of available pallets and the target demand, and the procurement plan is executed in batches in combination with the company's warehousing resources and financial status. At the same time, a real-time monitoring mechanism is established to track inventory changes. This complete execution mechanism not only ensures the feasibility of the pallet replenishment plan, but also effectively balances the company's resource input and inventory management needs through batch procurement and real-time monitoring, making the pallet resource replenishment process more flexible and controllable.

[0019] In a second aspect, the present application provides a plastic pallet management system, the system comprising: a first acquisition module, a second acquisition module, a first adjustment module, a generation module and a second adjustment module; wherein the first acquisition module is used to acquire the usage data of the plastic pallets of the target enterprise within a first preset time period, and predict the first demand of the plastic pallets within a second preset time period based on the usage data, wherein the first preset time period is before the second preset time period; the second acquisition module is used to acquire the time interval corresponding to the second preset time period, and determine the season parameter corresponding to the time interval according to a pre-established season division model; the first adjustment module is used to determine the industry fluctuation coefficient of the target enterprise within the second preset time period according to the seasonal parameter, and adjust the first demand according to the industry fluctuation coefficient to obtain the second demand; the generation module is used to acquire the historical order data and customer cooperation data of the target enterprise within the first preset time period, and generate the order forecast data of the target enterprise within the second preset time period based on the historical order data and the customer cooperation data; the second adjustment module is used to adjust the second demand according to the order forecast data to obtain the target demand, and generate the replenishment plan of the plastic pallet according to the target demand.

[0020] In a third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned plastic pallet management methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned plastic pallet management methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. Through phased data processing and multi-dimensional adjustment mechanisms, the pallet demand forecast is made more accurate. First, the preliminary first demand is obtained based on the usage data within the first preset time period, and then the first adjustment is made in combination with the seasonal division model and the industry volatility coefficient to obtain the second demand that is more in line with the market environment. Finally, the second adjustment is made through historical order data and customer cooperation data to finally obtain the accurate target demand. This step-by-step optimization forecasting method not only ensures the reliability of the basic data, but also fully considers the impact of market fluctuations and the actual operating conditions of the enterprise, thereby significantly improving the accuracy of pallet demand forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a plastic pallet management method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a plastic pallet management system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] With the rapid development of industrial production, plastic pallets have been widely used as important logistics carriers in chemical fiber and other industries. At present, enterprises mainly manage the purchase, inventory and turnover of plastic pallets through enterprise resource planning (ERP) systems and warehouse management systems (WMS). However, the existing management methods have the following shortcomings: First, the demand forecasting accuracy is low. The existing system mainly relies on historical data for simple demand forecasting, and fails to fully consider the impact of seasonal fluctuations, industry prosperity and other factors on pallet demand, resulting in a large deviation between the forecast results and actual demand. This deviation often causes companies to have insufficient reserves that affect production, or inventory backlogs that occupy funds.

[0028] Second, the supply chain is not responsive in a timely manner. Traditional pallet management methods lack the ability to monitor and analyze usage data in real time, and are unable to perceive demand changes in a timely manner. When market demand fluctuates, it is difficult for companies to quickly adjust pallet replenishment plans, affecting the agility of the supply chain.

[0029] Third, inventory management is extensive. Under the current management model, companies often use empirical estimates to determine safety inventory levels and fail to establish a scientific inventory optimization mechanism. At the same time, due to the lack of in-depth analysis of customer stability and order forecasts, it is difficult for companies to achieve reasonable allocation of pallet resources.

[0030] Fourth, data analysis capabilities are insufficient. Although the existing system can record basic usage data, it lacks systematic analysis of the data of the entire life cycle of the pallet. For example, it is impossible to accurately track the loss of pallets, nor can it make differentiated resource allocation based on customer partnerships.

[0031] In view of the above problems, it is urgent to develop a plastic pallet management method that can achieve accurate demand forecasting and intelligent inventory management to improve the company's operational efficiency and resource utilization.

[0032] Figure 1 Schematic diagram of a plastic pallet management method provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining usage data of plastic pallets of a target enterprise within a first preset time period, and predicting a first demand for plastic pallets within a second preset time period based on the usage data, wherein the first preset time period is before the second preset time period.

[0033] In the specific implementation process, in order to accurately predict the demand for plastic pallets, the system first needs to obtain the target company's plastic pallet usage data within the first preset period. The first preset period can be the past 3 months, and the second preset period can be the next 1 month. By setting the first preset period before the second preset period, it is ensured that the forecast is based on the latest historical data, thereby improving the timeliness and accuracy of the forecast.

[0034] Specifically, the usage data includes the average daily usage of pallets, the average daily damage of pallets, and the turnover time of pallets. Among them, the average daily usage of pallets reflects the basic demand level of the enterprise, the average daily damage of pallets reflects the loss of pallets, and the turnover time of pallets indicates the circulation speed of pallets in each link. The system obtains this data through real-time data acquisition equipment and stores it in the database for analysis and processing.

[0035] When predicting the first demand, the system first determines the basic demand for plastic pallets based on the average daily usage of pallets. Then, considering that pallets are inevitably damaged during use, the system adjusts the basic demand based on the average daily damage of pallets to determine the safety reserve of plastic pallets. In addition, the system also determines the replenishment cycle of plastic pallets based on the turnover time of pallets, and calculates the replenishment coefficient accordingly. Finally, the safety reserve is multiplied by the replenishment coefficient to obtain the first demand for plastic pallets within the second preset time.

[0036] Based on the above embodiment, as an optional implementation, S101, the usage data includes: the average daily usage of pallets, the average daily damage of pallets and the turnover time of pallets. According to the usage data, predicting the first demand for plastic pallets within the second preset time specifically includes S11-S14: S11. Determine the basic demand for plastic pallets based on the average daily usage of pallets.

[0037] S12, according to the average daily damage of pallets, adjust the basic demand and determine the safe reserve of plastic pallets.

[0038] S13, determining the replenishment cycle of the plastic pallet according to the pallet turnover time.

[0039] When determining the basic demand, the system first counts the average daily usage of pallets within the first preset time period. Specifically, the system records the number of pallets put into use each day through real-time data acquisition equipment and calculates their arithmetic mean. For example, if the first preset time period is the past three months, the system will add up the number of pallets used each day during this period and divide it by the actual number of days to get the average daily usage of pallets. This value reflects the basic demand level of the company for pallets under normal production conditions.

[0040] Subsequently, considering that pallets may be damaged or lost during use, the system needs to adjust the basic demand according to the average daily damage of pallets. The system calculates the average daily damage of pallets by recording the number of pallets scrapped or in need of repair every day within the first preset time period. The average daily damage of pallets is added to the basic demand, and a certain fluctuation coefficient (such as 1.2) is considered to obtain the safety reserve. This adjustment ensures that the company can maintain normal production operations even when the pallet loss rate is high.

[0041] When determining the replenishment cycle, the system focuses on analyzing the pallet turnover time data. The pallet turnover time includes the time the pallet stays in each use link, such as the production line usage time, storage cycle, transportation time, etc. The system calculates the average turnover time by tracking and recording the complete cycle of the pallet from outbound to return. Based on this time, the system sets a reasonable replenishment cycle to ensure that the newly replenished pallets can be in place in time before the original pallets complete the turnover. For example, if the average turnover time is 7 days, the system may set the replenishment cycle to 5 days to ensure the continuity of pallet supply.

[0042] S14, combining the safety reserve and replenishment cycle, predicting the first demand for plastic pallets within a second preset time period.

[0043] Based on the above embodiment, as an optional implementation, in S14, combining the safety reserve and the replenishment cycle, predicting the first demand for plastic pallets within the second preset time period specifically includes S141-S142: S141, determining a replenishment coefficient of the plastic pallet according to the replenishment cycle.

[0044] In actual operation, the system first sets the baseline replenishment coefficient based on the determined replenishment cycle and the production characteristics of the enterprise. For example, when the replenishment cycle is 5 days, considering the possible delays in the transportation and acceptance of pallets, the system will set the baseline replenishment coefficient to 1.2, which means that the number of pallets replenished each time is 20% higher than the theoretical demand. Subsequently, the system will dynamically adjust the baseline replenishment coefficient according to the length of the replenishment cycle.

[0045] Specifically, when the replenishment cycle is longer, the system will appropriately increase the replenishment coefficient to cope with possible demand fluctuations within the cycle; when the replenishment cycle is short, a relatively low replenishment coefficient can be used, because short-cycle replenishment can respond more flexibly to changes in demand.

[0046] When determining the final replenishment coefficient, the system also considers the ratio of the replenishment cycle to the pallet turnover time. When the replenishment cycle is close to the turnover time, the system will further increase the replenishment coefficient to prevent a gap in the pallet supply; when the replenishment cycle is much shorter than the turnover time, a more conservative replenishment coefficient can be used. Through this dynamic adjustment mechanism, the system can ensure supply security while avoiding waste of resources caused by excessive reserves.

[0047] S142, arithmetically multiplying the safety reserve by the replenishment coefficient to predict the first demand for plastic pallets within a second preset time period.

[0048] In the specific calculation process, the system first retrieves the safety reserve data determined in the early stage. This value already includes the comprehensive consideration of the average daily usage and average daily damage of pallets. Subsequently, the system performs arithmetic multiplication of the safety reserve and the replenishment coefficient. For example, when the safety reserve is 1,000 and the replenishment coefficient is 1.2, the first demand calculated by the system is 1,200. This calculation method ensures that the forecast results take into account the company's basic pallet demand and loss replenishment demand, and also take into account the additional demand caused by the replenishment cycle.

[0049] When calculating the first demand, the system pays special attention to the time span of the second preset duration. If the second preset duration spans multiple replenishment cycles, the system will adjust the calculation result accordingly based on the number of replenishment cycles. Specifically, the system multiplies the demand for a single replenishment cycle by the number of replenishment cycles to obtain the total demand for the entire preset duration. This calculation method can more accurately reflect the changes in pallet demand over a longer period of time.

[0050] S102, obtaining a time interval corresponding to a second preset duration, and determining a season parameter corresponding to the time interval according to a pre-established season division model.

[0051] Specifically, in actual production and operation, the demand for pallets of enterprises often fluctuates with the seasons. In order to improve the accuracy of demand forecasting, the system needs to obtain the time interval corresponding to the second preset time length, and determine the seasonal parameters corresponding to the time interval according to the pre-established seasonal division model. For example, when the second preset time length is one month in the future, the system will obtain the specific time interval of this month and input it into the seasonal division model for analysis.

[0052] The seasonal division model is constructed based on historical data and industry characteristics. The model not only takes into account the division of natural seasons, but more importantly, combines the production characteristics and market laws of the chemical fiber industry. Specifically, the model divides a year into traditional peak seasons (such as "golden September and silver October"), traditional off-seasons (such as around the Spring Festival), and shoulder seasons. For cross-seasonal time intervals, the model will calculate the comprehensive seasonal parameters through weighted calculations based on the time proportion of different seasons. For example, if the forecast time interval spans late September and early October, the system will combine the higher industry operating rate and market demand during this period to assign a larger seasonal parameter value.

[0053] In the process of determining seasonal parameters, the system also considers the cyclical characteristics of the industry. The chemical fiber industry usually has obvious seasonal fluctuations. For example, weaving companies will prepare stocks in advance when preparing for the next season's orders, which will drive up the demand for pallets. Through the seasonal division model, the system can accurately identify these cyclical changes and quantify them into specific seasonal parameter values, providing an important reference for subsequent demand forecasts.

[0054] S103, determining the industry fluctuation coefficient of the target enterprise within a second preset period of time according to the seasonal parameters, and adjusting the first demand according to the industry fluctuation coefficient to obtain a second demand.

[0055] In the process of forecasting demand for plastic pallets, only considering seasonal parameters is not enough to accurately reflect the actual operating conditions of the industry. In order to make the forecast results closer to the actual situation, the system needs to further determine the industry fluctuation coefficient based on seasonal parameters and adjust the first demand accordingly. This adjustment mechanism can take into account the impact of the overall operating conditions of the industry on the demand for enterprise pallets, thereby improving the accuracy of the forecast.

[0056] In specific implementation, the system first determines the industry operating rate and capacity utilization rate of the target enterprise within the second preset time period based on seasonal parameters. Among them, the industry operating rate reflects the overall production activity of the industry, which usually increases significantly during the peak season; the capacity utilization rate reflects the actual use of the enterprise's production equipment, which is directly related to the intensity of pallet use. These two indicators reflect the operating status of the industry from the macro and micro levels respectively, and play an important role in indicating the demand for pallets.

[0057] When calculating the industry volatility coefficient, the system uses a weight calculation method to set the weight of the industry operating rate to the first preset value and the weight of the capacity utilization rate to the second preset value. For example, the weight of the industry operating rate can be set to 0.6 and the weight of the capacity utilization rate can be set to 0.4. This weight configuration fully considers the impact of the two indicators on the demand for pallets, and obtains a more accurate industry volatility coefficient through weighted calculation. Subsequently, the system arithmetically multiplies the first demand by the industry volatility coefficient to obtain the second demand adjusted by the industry conditions.

[0058] Based on the above embodiment, as an optional implementation, in S103, according to the seasonal parameter, the industry fluctuation coefficient of the target enterprise within the second preset time period is determined, and the first demand is adjusted according to the industry fluctuation coefficient. The second demand is obtained, which specifically includes S301-S303: S301, determining the industry operating rate and capacity utilization rate of the target enterprise within a second preset period of time according to the seasonal parameters, and determining the weight coefficient of the industry operating rate as the first weight coefficient, and the weight coefficient of the capacity utilization rate as the second weight coefficient.

[0059] In the specific implementation process, the system first analyzes the seasonal laws of the industry based on the seasonal characteristics of the second preset duration. For example, for manufacturing companies, there are usually peak production seasons in the middle and end of the year, while they may face a decline in operating rates during the Spring Festival. The system will establish a seasonal parameter model based on historical data, divide the whole year into different seasonal cycles, and assign corresponding seasonal coefficients to each cycle. When the second preset duration spans multiple seasonal cycles, the system will weight the seasonal coefficients of each period according to the proportion of time.

[0060] When determining the industry operating rate, the system multiplies the baseline operating rate by the seasonal coefficient. For example, if a company's baseline operating rate is 85%, it is currently in the peak production season, and the seasonal coefficient is 1.2, the actual industry operating rate calculated is 102%. At the same time, the system will also make corrections based on the overall operating data of the industry to ensure that the forecast results are in line with the actual situation of the industry. For the calculation of capacity utilization, the system uses a similar method, but pays more attention to the production characteristics of the company itself. The system first determines the company's standard capacity utilization, and then adjusts it according to the seasonal parameters. For example, when the standard capacity utilization is 75% and the seasonal coefficient is 1.1, the adjusted capacity utilization is 82.5%.

[0061] S302, arithmetically multiply the industry operating rate by the first weight coefficient to generate a first fluctuation coefficient; arithmetically multiply the capacity utilization rate by the second weight coefficient to generate a second fluctuation coefficient; arithmetically add the first fluctuation coefficient and the second fluctuation coefficient to generate the industry fluctuation coefficient of the target enterprise within the second preset time period.

[0062] In actual operation, the system first sets the weight values ​​of industry operating rate and capacity utilization rate. Generally speaking, since the industry operating rate can better reflect the overall market environment, its weight value (first preset value) is usually set to 0.6, while the capacity utilization rate mainly reflects the production status of the enterprise itself, and its weight value (second preset value) is set to 0.4. This weight distribution ensures that the macro situation is fully considered without ignoring the influence of individual characteristics of enterprises. When calculating the industry volatility coefficient, the system adopts the weighted average method, that is, multiplying the industry operating rate by the first preset value, multiplying the capacity utilization rate by the second preset value, and then adding the two products to obtain the final industry volatility coefficient. For example, when the industry operating rate is 102% and the capacity utilization rate is 82.5%, the industry volatility coefficient calculated according to the above weights is: 102%×0.6+82.5%×0.4=94.2%.

[0063] During the calculation process, the system will pay special attention to the changing trends of the industry's operating rate and capacity utilization. When the changing trends of the two indicators are consistent, it means that the overall operation of the industry is relatively stable, and the calculated industry volatility coefficient has a high degree of credibility. When the two indicators diverge, the system will further analyze the reasons for the deviation and adjust the weight value if necessary to better reflect the actual situation. For example, if the enterprise is in the period of technological transformation, the fluctuation of capacity utilization may be more valuable for reference. At this time, the system will appropriately increase the proportion of the second preset value.

[0064] S303, arithmetically multiplying the first demand by the industry fluctuation coefficient to obtain a second demand.

[0065] S104, obtaining historical order data and customer cooperation data of the target enterprise within a first preset time period, and generating a predicted order volume of the target enterprise within a second preset time period based on the historical order data and customer cooperation data.

[0066] In the specific implementation process, the system first determines the target enterprise's order completion volume and order growth rate within the first preset period of time based on historical order data. The order completion volume reflects the scale of the enterprise's basic business, while the order growth rate reflects the dynamic trend of business development. At the same time, the system will also analyze customer cooperation data, including customer cooperation years and customer historical order volume, to determine the customer stability coefficient. Among them, the longer the customer cooperation years and the more stable the cooperative relationship, the higher the weight coefficient will be assigned by the system; the proportion of the customer's historical order volume to the company's total order volume is used to determine the order contribution coefficient of each customer.

[0067] When generating order forecasts, the system first uses the product of order completion and order growth rate as the basic forecast value, which reflects the natural growth trend of the company's business. Subsequently, the system calculates the customer stability coefficient based on customer cooperation data. Specifically, the system first classifies customers according to the years of cooperation and assigns corresponding weight coefficients, and then calculates the stability index of each customer based on the order contribution coefficient of each customer. The overall customer stability coefficient of the target company can be obtained by taking the arithmetic average of the stability indexes of all customers. Finally, the system multiplies the basic forecast value by the customer stability coefficient to obtain the final order forecast.

[0068] Based on the above embodiment, as an optional implementation, in S104, generating the predicted order quantity of the target enterprise within the second preset time period according to the historical order data and the customer cooperation data specifically includes S401-S404: S401, determining the order completion volume and order growth rate of the target enterprise within a first preset period of time based on historical order data.

[0069] When determining the number of completed orders, the system will count the cumulative number of all completed orders of the company within the first preset time period. In specific operations, the system first extracts the order data within this time period from the company's order management system, including key information such as order quantity and delivery time. For orders that span multiple periods, the system will reasonably allocate them to the corresponding periods based on the actual completion time. For example, if the first preset time period is three months, the system will summarize the number of all completed orders during this period to obtain the number of completed orders. At the same time, the system will also record the usage of pallets corresponding to each order and establish a corresponding relationship between the order quantity and the pallet demand.

[0070] When calculating the order growth rate, the system uses a month-on-month analysis method to compare the current order completion volume of the first preset time with the data of the previous same time. The specific calculation formula is: (current order completion volume - previous order completion volume) / previous order completion volume × 100%. For example, if the order completion volume in the current three months is 1,000 orders, and the previous three months is 800 orders, the order growth rate is 25%. The system will eliminate the impact of abnormal orders during calculation to ensure the reliability of the growth rate data. In addition, the system will also analyze the time distribution characteristics of the orders and identify the cyclical changes in the order volume, which is of great reference value for predicting future order trends.

[0071] S402, determining the customer stability coefficient of the target enterprise based on the customer cooperation data.

[0072] When determining the customer stability coefficient, the system first conducts a comprehensive analysis of the company's customer cooperation data, including multiple dimensions such as cooperation duration, order frequency, and transaction amount. For long-term cooperative customers, the system will focus on their cooperation history and use the continuous years of cooperation as the basic evaluation indicator. For example, customers who have cooperated for more than 3 years are given a higher stability weight, while customers who have newly established cooperative relationships are given a relatively lower weight. At the same time, the system will also analyze the customer's order patterns, including characteristics such as the periodicity of orders and scale stability. For customers who place orders regularly and have relatively stable order volumes, the system will give a higher stability score.

[0073] The system uses a comprehensive scoring method in the calculation process to divide customers into different levels according to the cooperative relationship. For example, customers are divided into three categories: A, B, and C. Category A represents the most stable core customers, accounting for 30% and a stability coefficient of 1.2; Category B is relatively stable important customers, accounting for 50% and a stability coefficient of 1.0; Category C is general customers, accounting for 20% and a stability coefficient of 0.8. The overall customer stability coefficient of the enterprise is obtained through weighted calculation: 1.2×30%+1.0×50%+0.8×20%=1.04. This coefficient reflects the overall stability level of the enterprise's customer base.

[0074] Based on the above embodiment, as an optional implementation, in S402, the customer cooperation data includes the customer cooperation years and the customer historical order volume. According to the customer cooperation data, determining the customer stability coefficient of the target enterprise specifically includes S4021-S4024: S4021, classify each customer according to the length of cooperation with the customer, and assign a corresponding weight coefficient according to the level of each customer.

[0075] S4022: Determine the order contribution coefficient of each customer based on the proportion of the customer's historical order volume to the company's total order volume.

[0076] S4023, arithmetically multiplying the weight coefficient corresponding to each customer by the order contribution coefficient to obtain the stability index of each customer.

[0077] S4024, taking the arithmetic average of the stability indexes of all customers to determine the customer stability coefficient of the target enterprise.

[0078] In the specific implementation process, the system first conducts a graded assessment based on the number of years of cooperation with customers. For example, customers who have cooperated for more than 5 years are classified as Class A, with a weight coefficient of 1.5; customers who have cooperated for 3-5 years are classified as Class B, with a weight coefficient of 1.2; customers who have cooperated for 1-3 years are classified as Class C, with a weight coefficient of 1.0; customers who have cooperated for less than 1 year are classified as Class D, with a weight coefficient of 0.8. This grading method reflects the important value of long-term cooperative customers and helps to identify core customer groups. At the same time, the system will count the order volume of each customer in the past year, calculate its proportion of the company's total order volume, and derive the order contribution coefficient. For example, if the annual order volume of a Class A customer accounts for 15% of the company's total order volume, its order contribution coefficient is 0.15.

[0079] When determining the stability index of each customer, the system multiplies the weight coefficient by the order contribution coefficient. Taking the above-mentioned Class A customer as an example, its stability index is: 1.5×0.15=0.225. This calculation method takes into account both the depth of the customer partnership and the importance of the customer's business volume. For all customers, the system uses the same calculation method to obtain their respective stability indexes. Finally, the system takes the arithmetic average of the stability indexes of all customers to obtain the overall customer stability coefficient of the enterprise. For example, if an enterprise has 100 customers and the total stability index is 80, the customer stability coefficient is 0.8.

[0080] S403: The product of the order completion quantity and the order growth rate is used as a basic prediction value.

[0081] S404, arithmetically multiplying the basic forecast value by the customer stability coefficient to obtain the order forecast quantity.

[0082] S105, adjusting the second demand quantity according to the predicted order quantity to obtain the target demand quantity, and generating a replenishment plan for the plastic pallets according to the target demand quantity.

[0083] In actual production, in order to make the replenishment plan of plastic pallets more reasonable, it is necessary to integrate the order forecast results with the previous demand forecast. By adjusting the order forecast quantity for the second demand quantity, the system can obtain a more accurate target demand quantity, thereby formulating a scientific pallet replenishment plan. This method not only takes into account the industry fluctuation factors, but also combines the actual order situation of the enterprise, which can effectively avoid the waste or shortage of pallet resources.

[0084] During the adjustment process, the system first calculates the ratio of the order forecast to the second demand and uses it as the order adjustment coefficient. When the order adjustment coefficient is greater than 1, it indicates that the forecasted order volume exceeds the demand forecast based on seasonality and industry fluctuations, and the system will increase the pallet demand forecast accordingly; when the order adjustment coefficient is less than 1, it indicates that the actual order may be lower than industry expectations, and the system will appropriately reduce the pallet demand forecast. During specific adjustments, the system multiplies the second demand by the order adjustment coefficient to obtain the final target demand. This dynamic adjustment mechanism enables the forecast results to better adapt to the actual operating conditions of the enterprise.

[0085] When generating a pallet replenishment plan, the system takes into account factors such as target demand, existing inventory levels, and procurement cycles. First, the system calculates the difference between the target demand and the existing inventory to determine the number of pallets that need to be replenished. Then, considering that the procurement and transportation links take a certain amount of time, the system sets an advance order point based on the supplier's delivery cycle. When the inventory level drops to the advance order point, the system automatically triggers the replenishment process. At the same time, in order to smooth out procurement costs and warehousing pressure, the system will reasonably distribute the total replenishment amount to multiple batches, and the specific quantity of each batch is determined according to the time distribution of the target demand.

[0086] After generating a replenishment plan for plastic pallets based on target demand, it also includes: Obtain the target enterprise's available pallet quantity and pallet replenishment quantity; calculate the difference between the available pallet quantity and the target demand; adjust the replenishment quantity of plastic pallets based on the difference, and generate a pallet procurement plan based on the replenishment quantity; execute the pallet procurement plan in batches based on the target enterprise's storage resources and financial status, and monitor pallet inventory changes in real time.

[0087] In actual operation, the system first obtains the current number of available pallets through the enterprise asset management system, including the sum of pallets in stock and pallets in transit. At the same time, the system also counts the number of replenishments to be executed in the established pallet replenishment plan. For example, the current number of available pallets is 8,000, the planned replenishment quantity is 2,000, and the target demand based on the previous forecast is 12,000. The system will calculate the actual difference as 2,000 (12,000-8,000-2,000=2,000). This difference calculation method ensures that the replenishment plan can respond to changes in demand in a timely manner.

[0088] Based on the calculated difference, the system will adjust the original pallet replenishment quantity. During the adjustment process, the system will consider multiple factors: first, the positive or negative difference, a positive value indicates that the replenishment quantity needs to be increased, and a negative value indicates that the replenishment quantity can be reduced; second, the urgency of the adjustment, which is determined by the proportion of the difference to the target demand; and finally, the feasibility of the adjustment, which needs to consider the supplier's supply capacity. For example, for a positive difference of 2,000, if the proportion of the target demand exceeds 15%, the system will mark it as an item that needs urgent adjustment.

[0089] When generating a pallet purchase plan, the system will plan in batches based on the actual situation of the company. First, the system will evaluate the company's storage capacity to ensure that the purchase volume of each batch will not cause storage pressure. For example, if the company's warehouse can receive a maximum of 1,000 pallets at a time, then the purchase plan for 2,000 pallets needs to be divided into at least two batches. Secondly, the system will analyze the company's financial situation and arrange the payment rhythm reasonably to avoid a tight capital chain. For example, the purchase plan can be divided into four batches, each with 500 pallets, and executed every 15 days.

[0090] During the execution of the procurement plan, the system will monitor the changes in pallet inventory in real time. By setting the inventory warning threshold, when the inventory is lower than the safety inventory level, the system will automatically issue a warning message. At the same time, the system will also record the use and loss of pallets and update the number of available pallets in a timely manner. This dynamic monitoring mechanism enables enterprises to promptly discover and solve problems in pallet supply and ensure smooth production operations.

[0091] Based on the above method, the present application also discloses a plastic pallet management system, such as Figure 2 As shown, Figure 2 : is a structural diagram of a plastic pallet management system provided by an embodiment of the present application, the system includes: a first acquisition module, a second acquisition module, a first adjustment module, a generation module and a second adjustment module; wherein, The first acquisition module is used to obtain the usage data of plastic pallets of the target enterprise within a first preset time period, and predict the first demand for plastic pallets within a second preset time period based on the usage data, wherein the first preset time period is before the second preset time period; the second acquisition module is used to obtain the time interval corresponding to the second preset time period, and determine the season parameter corresponding to the time interval according to the pre-established season division model; the first adjustment module is used to determine the industry volatility coefficient of the target enterprise within the second preset time period according to the seasonal parameters, and adjust the first demand according to the industry volatility coefficient to obtain the second demand; the generation module is used to obtain the historical order data and customer cooperation data of the target enterprise within the first preset time period, and generate the order forecast data of the target enterprise within the second preset time period based on the historical order data and customer cooperation data; the second adjustment module is used to adjust the second demand according to the order forecast data to obtain the target demand, and generate a replenishment plan for plastic pallets according to the target demand.

[0092] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0093] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0094] The communication bus 1002 is used to realize the connection and communication between these components.

[0095] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0096] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0097] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0098] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a plastic pallet management method.

[0099] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program storing a plastic pallet management method in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0100] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0101] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0102] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0107] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A plastic pallet management method, characterized in that: The method comprises: Obtaining usage data of plastic pallets of a target enterprise within a first preset time period, and predicting a first demand quantity of the plastic pallets within a second preset time period based on the usage data, wherein the first preset time period is before the second preset time period; Acquire a time interval corresponding to the second preset duration, and determine a season parameter corresponding to the time interval according to a pre-established season division model; Determine the industry fluctuation coefficient of the target enterprise within the second preset period according to the seasonal parameter, and adjust the first demand according to the industry fluctuation coefficient to obtain a second demand; Acquire historical order data and customer cooperation data of the target enterprise within the first preset time period, and generate a predicted order volume of the target enterprise within the second preset time period based on the historical order data and the customer cooperation data; According to the predicted order quantity, the second demand quantity is adjusted to obtain a target demand quantity, and a replenishment plan for the plastic pallets is generated according to the target demand quantity.

2. The plastic pallet management method according to claim 1, characterized in that: The usage data includes: the average daily usage of pallets, the average daily damage of pallets and the turnover time of pallets. The first demand for the plastic pallets within the second preset time period is predicted based on the usage data, including: Determine the basic demand for the plastic pallets based on the average daily usage of the pallets; According to the average daily damage amount of the pallets, the basic demand is adjusted to determine the safety reserve amount of the plastic pallets; Determining a replenishment cycle of the plastic pallet according to the turnover time of the pallet; The first demand quantity of the plastic pallets within a second preset time period is predicted by combining the safety reserve quantity and the replenishment cycle.

3. The plastic pallet management method according to claim 2, characterized in that: The step of combining the safety reserve amount and the replenishment cycle to predict a first demand amount of the plastic pallet within a second preset time period includes: Determining a replenishment coefficient of the plastic pallet according to the replenishment cycle; The safety reserve quantity is arithmetically multiplied by the replenishment coefficient to predict the first demand quantity of the plastic pallets within a second preset time period.

4. The plastic pallet management method according to claim 1, characterized in that: The step of determining the industry fluctuation coefficient of the target enterprise within the second preset time period according to the seasonal parameter, and adjusting the first demand according to the industry fluctuation coefficient to obtain the second demand includes: Determine the industry operating rate and capacity utilization rate of the target enterprise within the second preset time period according to the seasonal parameter, and determine the weight coefficient of the industry operating rate as the first weight coefficient, and the weight coefficient of the capacity utilization rate as the second weight coefficient; The industry operating rate is arithmetically multiplied by the first weight coefficient to generate a first fluctuation coefficient; the capacity utilization rate is arithmetically multiplied by the second weight coefficient to generate a second fluctuation coefficient; arithmetically adding the first volatility coefficient and the second volatility coefficient to generate the industry volatility coefficient of the target enterprise within the second preset time period; The first demand is arithmetically multiplied by the industry volatility coefficient to obtain a second demand.

5. The plastic pallet management method according to claim 1, characterized in that: The step of generating a forecast order quantity of the target enterprise within the second preset period of time according to the historical order data and the customer cooperation data includes: Determine the target enterprise's order completion volume and order growth rate within the first preset period of time based on the historical order data; Determine the customer stability coefficient of the target enterprise based on the customer cooperation data; The product of the order completion volume and the order growth rate is used as a basic forecast value; The basic forecast value is arithmetically multiplied by the customer stability coefficient to obtain the order forecast quantity.

6. The plastic pallet management method according to claim 5, characterized in that: The customer cooperation data includes the customer cooperation years and the customer historical order volume. The customer stability coefficient of the target enterprise is determined based on the customer cooperation data, including: Classify each customer according to the years of cooperation with the customer, and assign a corresponding weight coefficient according to the level of each customer; Determine the order contribution coefficient of each customer according to the proportion of the customer's historical order volume to the company's total order volume; arithmetically multiplying the weight coefficient corresponding to each of the customers by the order contribution coefficient to obtain the stability index of each of the customers; Take the arithmetic average of the stability indexes of all customers to determine the customer stability coefficient of the target enterprise.

7. The plastic pallet management method according to claim 1, characterized in that: After generating the replenishment plan of the plastic pallet according to the target demand, the method further includes: Obtain the available pallet quantity and pallet replenishment quantity of the target enterprise; Calculate the difference between the available pallet quantity and the target demand quantity; According to the difference, adjusting the replenishment quantity of the plastic pallet, and generating a pallet procurement plan according to the replenishment quantity; According to the storage resources and financial status of the target enterprise, the pallet procurement plan is executed in batches, and the changes in pallet inventory are monitored in real time.

8. A plastic pallet management system, characterized in that: The system comprises: a first acquisition module, a second acquisition module, a first adjustment module, a generation module and a second adjustment module; wherein, The first acquisition module is used to acquire the usage data of the plastic pallets of the target enterprise within a first preset time period, and predict the first demand quantity of the plastic pallets within a second preset time period according to the usage data, wherein the first preset time period is before the second preset time period; The second acquisition module is used to acquire the time interval corresponding to the second preset duration, and determine the season parameter corresponding to the time interval according to a pre-established season division model; The first adjustment module is used to determine the industry fluctuation coefficient of the target enterprise within the second preset time period according to the seasonal parameter, and adjust the first demand according to the industry fluctuation coefficient to obtain a second demand; The generating module is used to obtain the historical order data and customer cooperation data of the target enterprise within the first preset time period, and generate the order forecast data of the target enterprise within the second preset time period according to the historical order data and the customer cooperation data; The second adjustment module is used to adjust the second demand quantity according to the order forecast data to obtain a target demand quantity, and generate a replenishment plan for the plastic pallets according to the target demand quantity.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

Images