A plastic tray management method, system, device and storage medium

Abstract

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

Description

TECHNICAL FIELD

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

[0002] As an indispensable turnover tool in modern logistics and production, the accurate prediction of the demand of plastic pallets has an important influence on the production and operation and resource allocation of enterprises. If the supply of pallets is insufficient, it may cause bottlenecks in the production and transportation links and affect the normal operation of enterprises; and if the supply of pallets is excessive, it may cause waste of storage space and accumulation of funds.

[0003] At present, some enterprises use simple statistical methods to predict the demand of plastic pallets, such as calculating the average value according to historical data or using a linear regression model. Although these methods can predict the demand of plastic pallets to some extent, they do not fully consider various internal and external factors that affect the demand of plastic pallets, and are often difficult to adapt to complex and changeable market environments in actual application, resulting in low accuracy in predicting the demand of plastic pallets. SUMMARY

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

[0005] In a first aspect, the application provides a plastic pallet management method, which comprises: obtaining usage data of plastic pallets of a target enterprise within a first preset time length, and predicting a first demand quantity of the plastic pallets within a second preset time length according to the usage data, wherein the first preset time length is before the second preset time length; obtaining a time interval corresponding to the second preset time length, and determining a seasonal parameter corresponding to the time interval according to a pre-established seasonal division model; determining an industry fluctuation coefficient of the target enterprise within the second preset time length according to the seasonal parameter, and adjusting the first demand quantity according to the industry fluctuation coefficient to obtain a second demand quantity; obtaining historical order data and customer cooperation data of the target enterprise within the first preset time length, and generating an order prediction quantity of the target enterprise within the second preset time length according to the historical order data and the customer cooperation data; adjusting the second demand quantity according to the order prediction quantity to obtain a target demand quantity, and generating a replenishment plan of the plastic pallets according to the target demand quantity.

[0006] By adopting the technical scheme, the accuracy of the pallet demand prediction is realized through phased data processing and multi-dimensional adjustment mechanism. First, a preliminary first demand is obtained based on the use data within a first preset time period, then the second demand more in line with the market environment is obtained through the first adjustment combining the season division model and the industry fluctuation coefficient, and finally the accurate target demand is obtained through the second adjustment based on the historical order data and the customer cooperation data. The step-by-step optimization prediction method not only guarantees the reliability of the basic data, but also fully considers the influence of market fluctuations and the actual business situation of the enterprise, so as to accurately predict the demand of the plastic pallet.

[0007] Optionally, the use data includes: daily average use amount of pallets, daily average damage amount of pallets, and pallet turnover time length, and the first demand of the plastic pallets within a second preset time period is predicted according to the use data, including: determining the basic demand of the plastic pallets according to the daily average use amount of the pallets; adjusting the basic demand according to the daily average damage amount of the pallets to determine the safety reserve amount of the plastic pallets; determining the replenishment period of the plastic pallets according to the pallet turnover time length; and predicting the first demand of the plastic pallets within the second preset time period in combination with the safety reserve amount and the replenishment period.

[0008] By adopting the technical scheme, the basic demand is determined by the daily average use amount of the pallets, the safety reserve amount is calculated in combination with the daily average damage amount of the pallets, and finally the replenishment period is determined based on the pallet turnover time length. This layer-by-layer progressive calculation method not only considers the daily use demand, but also takes into account the 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, and provides a reliable data basis for the subsequent demand adjustment.

[0009] Optionally, the first demand of the plastic pallets within the second preset time period is predicted in combination with the safety reserve amount and the replenishment period, including: determining the replenishment coefficient of the plastic pallets according to the replenishment period; and multiplying the safety reserve amount and the replenishment coefficient to predict the first demand of the plastic pallets within the second preset time period.

[0010] By adopting the technical scheme, the first demand is quantitatively calculated by establishing the corresponding relationship between the replenishment period and the replenishment coefficient, and performing arithmetic multiplication operation with the safety reserve amount. This calculation method converts the time factor of pallet replenishment into a quantifiable replenishment coefficient, so that the safety reserve amount can be adjusted accordingly according to different replenishment periods, so that the prediction result of the first demand not only considers the safety stock demand of the enterprise, but also reflects the actual influence of the replenishment period, thereby improving the scientificity and accuracy of the prediction.

[0011] Optionally, the method further comprises: determining an industry fluctuation coefficient of the target enterprise in the second preset time period according to the seasonal parameter, and adjusting the first demand quantity according to the industry fluctuation coefficient to obtain a second demand quantity.

[0012] By adopting the above technical solution, the industry fluctuation factor is specifically quantified as two quantitative indexes of industry work rate and capacity utilization rate, and the industry fluctuation coefficient is obtained by using a preset weight calculation method, thereby realizing accurate adjustment of the first demand quantity. This double-index weighting calculation method not only considers the overall operation of the industry, but also takes the actual production of the enterprise into account, and directly reflects the influence of industry fluctuation on the adjustment of demand quantity through arithmetic multiplication operation, so that the second demand quantity can better adapt to the changes in the industry development, and the adaptability of the prediction result to the market environment is improved.

[0013] Optionally, the method further comprises: determining an order completion quantity and an order growth rate of the target enterprise in the first preset time period according to the historical order data; determining a customer stability coefficient of the target enterprise according to the customer cooperation data; taking the product of the order completion quantity and the order growth rate as a basic prediction value; and obtaining the order prediction quantity by performing arithmetic multiplication operation on the basic prediction value and the customer stability coefficient.

[0014] By adopting the above technical solution, the product of the order completion quantity and the order growth rate is taken as the basic prediction value, which reflects the historical performance and development trend of the enterprise order; then the customer stability coefficient is introduced for secondary adjustment, and the stability factor of the customer relationship is included in the prediction system, and the final order prediction quantity is obtained through arithmetic multiplication operation. This prediction method not only considers the order growth trend of the enterprise itself, but also combines the stability degree of customer cooperation, so that the order prediction quantity is closer to the actual operating condition of the enterprise, and the reliability of the prediction is improved.

[0015] Optionally, the customer cooperation data includes customer cooperation years and customer historical order quantity, and the customer stability coefficient of the target enterprise is determined according to the customer cooperation data, including: 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 historical order quantity in the total order quantity of the enterprise; multiplying the weight coefficient corresponding to each customer and 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, the customer stability is accurately quantified through multi-level data processing. Specifically, the customer cooperation years are converted into a grade weight, 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 averaging. This calculation method not only considers the time dimension of customer cooperation relationship, but also includes the business contribution of customers in the evaluation range, and through weight distribution and multi-step operation, the customer stability coefficient can fully reflect the stability degree of the enterprise customer group, providing a more accurate basis for order prediction calculation.

[0017] Optionally, after the replenishment plan of the plastic pallet is generated according to the target demand quantity, the method further includes: obtaining the available pallet quantity and the pallet replenishment quantity of the target enterprise; calculating the difference between the available pallet quantity and the target demand quantity; adjusting the replenishment quantity of the plastic pallet according to the difference, and generating a pallet procurement plan according to the replenishment quantity; and performing the pallet procurement plan in batches according to the storage resources and financial status of the target enterprise, and monitoring the pallet inventory change in real time.

[0018] By adopting the above technical solution, a dynamic pallet replenishment mechanism is established to realize closed-loop management from demand prediction to actual procurement. Specifically, the actual replenishment demand is determined by calculating the difference between the available pallet quantity and the target demand quantity, and the procurement plan is executed in batches in combination with the storage resources and financial status of the enterprise, and a real-time monitoring mechanism is established to track the inventory change. This complete execution mechanism not only ensures the implementability of the pallet replenishment plan, but also effectively balances the resource investment and inventory management needs of the enterprise through batch procurement and real-time monitoring, making the pallet resource replenishment process more flexible and controllable.

[0019] In a second aspect, the application provides a plastic pallet management system, which comprises a first acquisition module, a second acquisition module, a first adjustment module, a generation module and a second adjustment module. The first acquisition module is configured to acquire use data of plastic pallets of a target enterprise within a first preset time period, and predict a first demand amount of the plastic pallets within a second preset time period according to the use data, wherein the first preset time period is before the second preset time period. The second acquisition module is configured to acquire a time interval corresponding to the second preset time period, and determine a seasonal parameter corresponding to the time interval according to a pre-established seasonal division model. The first adjustment module is configured to determine an industry fluctuation coefficient of the target enterprise within the second preset time period according to the seasonal parameter, and adjust the first demand amount according to the industry fluctuation coefficient to obtain a second demand amount. The generation module is configured to acquire historical order data and customer cooperation data of the target enterprise within the first preset time period, and generate order prediction 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 configured to adjust the second demand amount according to the order prediction data to obtain a target demand amount, and generate a replenishment plan of the plastic pallets according to the target demand amount.

[0020] In a third aspect, the application provides an electronic device, which adopts the following technical solution: comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to execute the computer program of any of the above plastic pallet management methods.

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

[0022] In summary, the application has at least one of the following beneficial technical effects:

[0023] 1. Through phased data processing and multi-dimensional adjustment mechanism, the accuracy of pallet demand prediction is realized. First, the preliminary first demand amount is obtained based on the use data within the first preset time period, then the second demand amount more consistent with the market environment is obtained through the first adjustment combined with the seasonal division model and the industry fluctuation coefficient, and finally the accurate target demand amount is obtained through the second adjustment of the historical order data and the customer cooperation data. This step-by-step optimization prediction method not only ensures the reliability of the basic data, but also fully considers the influence of market fluctuations and actual business conditions of the enterprise, thereby significantly improving the accuracy of pallet demand prediction. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart of a plastic pallet management method provided by an embodiment of the present application;

[0025] Figure 2 is a structural diagram of a plastic pallet management system provided by an embodiment of the present application;

[0026] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0027] Legend: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0028] In order for those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be described clearly and completely below in conjunction with the drawings in the embodiments of the present specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0029] In the description of the embodiments of the present application, the words "exemplary", "for example", or "for instance" are used to mean serving as an example, instance, or illustration. Any embodiment or design solution described as "exemplary", "for example", or "for instance" in the embodiments of the present application should not be interpreted as being more preferred or having more advantages than other embodiments or design solutions. Rather, the words "exemplary", "for example", or "for instance" are used to present the relevant concept in a specific manner.

[0030] With the rapid development of industrial production, plastic pallets, as important logistics carriers, are widely used in the chemical fiber industry and other industries. At present, enterprises mainly manage the procurement, 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:

[0031] First, the demand prediction accuracy is low. The existing system mainly relies on historical data for simple demand prediction and fails to fully consider the impact of seasonal fluctuations, industry sentiment, and other factors on pallet demand, resulting in a large deviation between the predicted results and the actual demand. This deviation often causes the enterprise to either run short of supplies and affect production or accumulate inventory and tie up funds.

[0032] Second, the supply chain response is not timely. The traditional pallet management method lacks real-time monitoring and analysis capabilities for usage data, and cannot timely perceive demand changes. When market demand fluctuates, the enterprise is difficult to quickly adjust the replenishment plan of pallets, affecting the agility of the supply chain.

[0033] Third, inventory management is extensive. Under the existing management mode, enterprises often use experience estimation to determine the safety stock level, 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 prediction, it is difficult for enterprises to realize the rational allocation of pallet resources.

[0034] Fourth, the data analysis capability is insufficient. Although the existing system can record basic usage data, it lacks systematic analysis of pallet life cycle data. For example, it cannot accurately track the loss of pallets, and it cannot allocate resources differently based on customer relationships.

[0035] In view of the above problems, it is urgent to develop a plastic pallet management method that can realize accurate demand prediction and intelligent inventory management, so as to improve the operation efficiency and resource utilization of enterprises.

[0036] Figure 1 is a flowchart of a plastic pallet management method provided by an embodiment of the present application. As shown in Figure 1 the method comprises S101-S105:

[0037] S101, obtaining the usage data of the plastic pallets of the target enterprise within a first preset time length, and predicting the first demand amount of the plastic pallets within a second preset time length according to the usage data, wherein the first preset time length is before the second preset time length.

[0038] In the specific implementation process, in order to accurately predict the demand amount of plastic pallets, the system first needs to obtain the usage data of plastic pallets of the target enterprise within a first preset time length. The first preset time length can be the past 3 months, and the second preset time length can be the future 1 month. By setting the first preset time length before the second preset time length, it is ensured that the prediction is based on the latest historical data, improving the timeliness and accuracy of the prediction.

[0039] Specifically, the usage data includes daily average usage of pallets, daily average damage of pallets and pallet turnover time. Among them, the daily average usage of pallets reflects the basic demand level of the enterprise, the daily average damage of pallets reflects the loss of pallets, and the turnover time of pallets indicates the flow speed of pallets at each link. The system obtains these data through real-time data acquisition equipment and stores them in the database for analysis and processing.

[0040] In predicting the first demand amount, the system first determines the basic demand amount of the plastic pallet according to the daily average use amount of the pallet. Then, considering that damage to the pallet is inevitable during use, the system adjusts the basic demand amount according to the daily average damage amount of the pallet, thereby determining the safety reserve amount of the plastic pallet. In addition, the system also determines the replenishment period of the plastic pallet according to the pallet turnover length, and calculates the replenishment coefficient accordingly. Finally, the safety reserve amount is multiplied by the replenishment coefficient to obtain the first demand amount of the plastic pallet within the second preset time length.

[0041] On the basis of the above embodiment, as an optional implementation, S101, the use data includes: the daily average use amount of the pallet, the daily average damage amount of the pallet and the pallet turnover length, and according to the use data, the first demand amount of the plastic pallet within the second preset time length is predicted, which specifically includes S11-S14:

[0042] S11, according to the daily average use amount of the pallet, the basic demand amount of the plastic pallet is determined.

[0043] S12, according to the daily average damage amount of the pallet, the basic demand amount is adjusted to determine the safety reserve amount of the plastic pallet.

[0044] S13, according to the pallet turnover length, the replenishment period of the plastic pallet is determined.

[0045] In determining the basic demand amount, the system first counts the daily average use amount of the pallet within the first preset time length. Specifically, the system records the number of pallets put into use every day through real-time data acquisition equipment, and calculates the arithmetic mean value. For example, if the first preset time length is the past three months, the system adds up the number of pallets used every day in this period and divides it by the actual number of days to obtain the daily average use amount of the pallet. This value reflects the basic demand level of the enterprise for pallets under normal production conditions.

[0046] Then, considering that the pallets will be damaged or consumed during use, the system needs to adjust the basic demand amount according to the daily average damage amount of the pallet. The system records the number of pallets scrapped or requiring maintenance every day within the first preset time length, and calculates the daily average damage amount of the pallet. The daily average damage amount of the pallet is added to the basic demand amount, and a certain fluctuation coefficient (such as 1.2) is considered to obtain the safety reserve amount. This adjustment ensures that the enterprise can maintain normal production and operation even in the case of high pallet consumption rate.

[0047] In determining the replenishment period, the system focuses on analyzing the data of the tray turnover duration. The tray turnover duration includes the residence time of the tray in each use link, such as the production line use time, the warehouse period, the transportation time, etc. The system calculates the average turnover duration by tracking and recording the complete cycle of the tray from the warehouse to the return. Based on this duration, the system sets a reasonable replenishment period to ensure that the newly replenished tray can be in place before the original tray completes the turnover. For example, if the average turnover duration is 7 days, the system may set the replenishment period to 5 days to ensure the continuity of tray supply.

[0048] In S14, the first demand amount of the plastic trays in the second preset duration is predicted in combination with the safety reserve amount and the replenishment period.

[0049] Based on the above embodiment, as an optional implementation, in S14, predicting the first demand amount of the plastic trays in the second preset duration in combination with the safety reserve amount and the replenishment period specifically includes S141-S142:

[0050] In S141, a replenishment coefficient of the plastic trays is determined according to the replenishment period.

[0051] In actual operation, the system first sets a benchmark replenishment coefficient according to the determined replenishment period in combination with the production characteristics of the enterprise. For example, when the replenishment period is 5 days, considering the possible delay of the tray in the transportation and acceptance link, the system will set the benchmark replenishment coefficient to 1.2, which means that the number of trays replenished each time is 20% higher than the theoretical demand. Subsequently, the system will dynamically adjust the benchmark replenishment coefficient according to the length of the replenishment period.

[0052] Specifically, when the replenishment period is longer, the system will appropriately increase the replenishment coefficient to respond to possible demand fluctuations within the period; when the replenishment period is shorter, a relatively lower replenishment coefficient can be used because short-period replenishment can more flexibly respond to demand changes.

[0053] In determining the final replenishment coefficient, the system will also consider the ratio relationship between the replenishment period and the tray turnover duration. When the replenishment period is close to the turnover duration, the system will further increase the replenishment coefficient to prevent the supply of trays from being interrupted; when the replenishment period is much shorter than the turnover duration, a more conservative replenishment coefficient can be used. Through this dynamic adjustment mechanism, the system can ensure supply safety while avoiding resource waste caused by excessive reserve.

[0054] In S142, the first demand amount of the plastic trays in the second preset duration is predicted by arithmetically multiplying the safety reserve amount and the replenishment coefficient.

[0055] In the specific calculation process, the system first calls the previously determined safety stock data, which already takes into account the daily average usage and daily average damage of the pallets. Then, the system performs an arithmetic multiplication operation between the safety stock and the replenishment coefficient. For example, when the safety stock is 1000 and the replenishment coefficient is 1.2, the system calculates the first demand to be 1200. This calculation method ensures that the prediction result takes into account both the basic pallet demand of the enterprise and the replenishment demand, as well as the additional demand brought by the replenishment period.

[0056] 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 periods, the system will adjust the calculation result accordingly based on the number of replenishment periods. Specifically, the system multiplies the demand of a single replenishment period by the number of replenishment periods to obtain the total demand in the entire preset duration. This calculation method can more accurately reflect the changes in pallet demand over a longer period of time.

[0057] S102, obtain the time interval corresponding to the second preset duration, and determine the seasonal parameter corresponding to the time interval according to the pre-established seasonal division model.

[0058] Specifically, in actual production and operation, the demand for pallets of an enterprise often fluctuates with the seasons. In order to improve the accuracy of demand prediction, the system needs to obtain the time interval corresponding to the second preset duration, and determine the seasonal parameter corresponding to the time interval according to the pre-established seasonal division model. For example, when the second preset duration 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.

[0059] The seasonal division model is constructed based on historical data and industry characteristics. This model not only considers the division of natural seasons, but more importantly, it 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 before and after the Spring Festival), and flat seasons. For time intervals that span seasons, the model will calculate a comprehensive seasonal parameter based on the time proportion of different seasons. For example, if the prediction time interval spans the last ten days of September and the first ten days of October, the system will give a larger seasonal parameter value by considering the high industry utilization rate and market demand during this period.

[0060] In the process of determining the seasonal parameter, the system also considers the periodic characteristics of the industry. The chemical fiber industry usually has obvious seasonal fluctuations, such as weaving enterprises preparing for the next season's orders in advance, which drives up the demand for pallets. Through the seasonal division model, the system can accurately identify these periodic changes and quantify them as specific seasonal parameter values, providing important reference for subsequent demand prediction.

[0061] S103, determine an industry fluctuation coefficient of the target enterprise in the second preset time length according to the seasonal parameter, and adjust the first demand quantity according to the industry fluctuation coefficient to obtain a second demand quantity.

[0062] In the process of predicting the demand for plastic pallets, only considering the seasonal parameter is not enough to accurately reflect the actual operation of the industry. In order to make the prediction result closer to the actual situation, the system needs to further determine the industry fluctuation coefficient according to the seasonal parameter, and adjust the first demand quantity accordingly. This adjustment mechanism can take into account the impact of the overall operation of the industry on the demand for enterprise pallets, thereby improving the accuracy of the prediction.

[0063] In specific implementation, the system first determines the industry work rate and capacity utilization rate of the target enterprise in the second preset time length according to the seasonal parameter. The industry work rate reflects the overall production activity of the industry, which will be significantly improved during the peak season. The capacity utilization rate reflects the actual use of the production equipment of the enterprise, which is directly related to the intensity of the use of pallets. These two indicators reflect the operation of the industry from the macro and micro levels, and have an important indication on the demand for pallets.

[0064] In calculating the industry fluctuation coefficient, the system adopts a weight calculation method, and sets the weight of the industry work rate as a first preset value and the weight of the capacity utilization rate as a second preset value. For example, the weight of the industry work 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 influence of the two indicators on the demand for pallets, and obtains a more accurate industry fluctuation coefficient through weighted calculation. Then, the system performs an arithmetic multiplication between the first demand quantity and the industry fluctuation coefficient to obtain the second demand quantity after adjusting the industry condition.

[0065] On the basis of the above embodiment, as an optional implementation, in S103, according to the seasonal parameter, the industry fluctuation coefficient of the target enterprise in the second preset time length is determined, and the first demand quantity is adjusted according to the industry fluctuation coefficient to obtain the second demand quantity, which specifically includes S301-S303:

[0066] S301, according to the seasonal parameter, determine the industry work rate and capacity utilization rate of the target enterprise in the second preset time length, and determine the weight coefficient of the industry work rate as a first weight coefficient and the weight coefficient of the capacity utilization rate as a second weight coefficient.

[0067] In the implementation process, the system first analyzes the seasonal regularity of the industry according to the seasonal characteristics of the second preset time length. For example, for manufacturing enterprises, there are usually production peaks in the middle and end of the year, while during the Spring Festival period, they may face a situation of reduced operating rate. 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 time length spans multiple seasonal cycles, the system will weight the seasonal coefficients of each period according to the time proportion.

[0068] When determining the industry operating rate, the system multiplies the benchmark operating rate by the seasonal coefficient. For example, if the benchmark operating rate of an enterprise is 85%, and it is currently in the production peak season with a seasonal coefficient of 1.2, the calculated actual industry operating rate is 102%. At the same time, the system will also make corrections combined with the overall operation data of the industry to ensure that the prediction results conform to the actual situation of the industry. For the calculation of capacity utilization rate, the system uses a similar method, but pays more attention to the production characteristics of the enterprise itself. The system first determines the standard capacity utilization rate of the enterprise, and then adjusts it according to the seasonal parameters. For example, when the standard capacity utilization rate is 75% and the seasonal coefficient is 1.1, the adjusted capacity utilization rate is 82.5%.

[0069] S302, the industry operating rate is multiplied by the first weight coefficient to generate a first fluctuation coefficient; the capacity utilization rate is multiplied by the second weight coefficient to generate a second fluctuation coefficient; the first fluctuation coefficient and the second fluctuation coefficient are added to generate the industry fluctuation coefficient of the target enterprise within the second preset time length.

[0070] 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 not only ensures sufficient consideration of macro situation, but also does not ignore the influence of individual characteristics of enterprises. When calculating the industry fluctuation coefficient, the system uses the weighted average method, that is, the industry operating rate is multiplied by the first preset value, the capacity utilization rate is multiplied by the second preset value, and then the two products are added to obtain the final industry fluctuation coefficient. For example, when the industry operating rate is 102% and the capacity utilization rate is 82.5%, the industry fluctuation coefficient calculated according to the above weight is: 102% x 0.6 + 82.5% x 0.4 = 94.2%.

[0071] During the calculation, the system will pay special attention to the trends of the industry work rate and the capacity utilization rate. When the trends of the two indicators are consistent, it means that the overall operation of the industry is stable, and the industry fluctuation coefficient calculated has high reliability. When the two indicators deviate, the system will further analyze the reasons for the deviation, and adjust the weight value as necessary to better reflect the actual situation. For example, if the enterprise is in the period of technological transformation, the fluctuation of the capacity utilization rate may have more reference value, at which time the system will appropriately increase the proportion of the second preset value.

[0072] S303, arithmetically multiplying the first demand quantity and the industry fluctuation coefficient to obtain a second demand quantity.

[0073] S104, obtaining historical order data and customer cooperation data of the target enterprise within a first preset time length, and generating an order prediction quantity of the target enterprise within a second preset time length according to the historical order data and the customer cooperation data.

[0074] In the specific implementation process, the system first determines the order completion quantity and the order growth rate of the target enterprise within the first preset time length according to the historical order data. The order completion quantity reflects the basic business scale of the enterprise, and the order growth rate embodies the dynamic trend of business development. At the same time, the system also analyzes the customer cooperation data, including the customer cooperation years and the customer historical order quantity, to determine the customer stability coefficient. The longer the customer cooperation years, the more stable the cooperation relationship, and the system will give a higher weight coefficient; the proportion of the customer historical order quantity in the total order quantity of the enterprise is used to determine the order contribution coefficient of each customer.

[0075] In generating the order prediction quantity, the system first takes the product of the order completion quantity and the order growth rate as the basic prediction value, which reflects the natural growth trend of the enterprise business. Then, the system calculates the customer stability coefficient according to the customer cooperation data. Specifically, the system first classifies customers according to cooperation years and assigns corresponding weight coefficients, and then calculates the stability index of each customer in combination with the order contribution coefficient of each customer. The stability index of all customers is arithmetically averaged to obtain the overall customer stability coefficient of the target enterprise. Finally, the system multiplies the basic prediction value and the customer stability coefficient to obtain the final order prediction quantity.

[0076] On the basis of the above embodiment, as an optional implementation manner, in S104, generating the order prediction quantity of the target enterprise within the second preset time length according to the historical order data and the customer cooperation data specifically includes S401-S404:

[0077] S401, determining the order completion quantity and the order growth rate of the target enterprise within the first preset time length according to the historical order data.

[0078] In determining the order completion quantity, the system counts the cumulative number of all completed orders of the enterprise within the first preset time period. In specific operation, the system first extracts the order data within the time period from the enterprise order management system, including order quantity, delivery time and other key information. For cross-period orders, the system will reasonably distribute them to the corresponding period according to the actual completion time. For example, if the first preset time period is three months, the system will aggregate the number of all completed orders within this period to obtain the order completion quantity. At the same time, the system also records the corresponding tray usage of each order, establishing the corresponding relationship between order quantity and tray demand quantity.

[0079] In calculating the order growth rate, the system uses the ring analysis method to compare the order completion quantity of the current first preset time period with the data of the same length of the last period. The specific calculation formula is: (current period order completion quantity - last period order completion quantity) / last period order completion quantity x 100%. For example, if the order completion quantity of the current three months is 1000, and the last three months is 800, the order growth rate is 25%. The system will eliminate the influence of abnormal orders when calculating to ensure the reliability of the growth rate data. In addition, the system also analyzes the time distribution characteristics of the order, identifies the periodic variation rule of the order quantity, which has important reference value for predicting the future order trend.

[0080] S402, according to the customer cooperation data, determine the customer stability coefficient of the target enterprise.

[0081] In determining the customer stability coefficient, the system first analyzes the customer cooperation data of the enterprise comprehensively, including cooperation time, order frequency, transaction amount and other dimensions. For long-term cooperation customers, the system will focus on their cooperation history, and take continuous cooperation years as the basic evaluation index. For example, customers who have cooperated for more than 3 years are given a higher stability weight, while customers who have just established a cooperation relationship are given a relatively lower weight. At the same time, the system also analyzes the order rules of customers, including the periodicity of orders, the stability of order size and other characteristics. For customers who order regularly and have relatively stable order quantity, the system will give them a higher stability score.

[0082] The system uses a comprehensive scoring method in the calculation process, and divides customers into different grades according to the cooperation relationship. For example, customers are divided into A, B and C three categories, among which A represents the most stable core customers, accounting for 30%, and the stability coefficient is 1.2; B represents relatively stable important customers, accounting for 50%, and the stability coefficient is 1.0; C represents general customers, accounting for 20%, and the stability coefficient is 0.8. Through weighted calculation, the overall customer stability coefficient of the enterprise is obtained: 1.2 x 30% + 1.0 x 50% + 0.8 x 20% = 1.04. This coefficient reflects the overall stability level of the enterprise customer group.

[0083] On the basis of the above embodiments, as an optional implementation, in S402, the customer cooperation data includes customer cooperation years and customer historical order quantity, and the customer stability coefficient of the target enterprise is determined according to the customer cooperation data, specifically including S4021-S4024:

[0084] S4021, each customer is classified according to the customer cooperation years, and a corresponding weight coefficient is allocated according to the grade of each customer.

[0085] S4022, the order contribution coefficient of each customer is determined according to the proportion of the customer historical order quantity in the total order quantity of the enterprise.

[0086] S4023, the weight coefficient and the order contribution coefficient corresponding to each customer are multiplied to obtain the stability index of each customer.

[0087] S4024, the stability indexes of all customers are arithmetically averaged to determine the customer stability coefficient of the target enterprise.

[0088] In the specific implementation process, the system first classifies and evaluates according to the customer cooperation years. For example, customers cooperating for more than 5 years are divided into A level, and the weight coefficient is 1.5; customers cooperating for 3-5 years are B level, and the weight coefficient is 1.2; customers cooperating for 1-3 years are C level, and the weight coefficient is 1.0; customers cooperating for less than 1 year are D level, and the weight coefficient is 0.8. This classification method reflects the importance of long-term cooperation customers and helps to identify core customer groups. At the same time, the system will count the order quantity of each customer in the past year, calculate the proportion of the total order quantity of the enterprise, and obtain the order contribution coefficient. For example, the annual order quantity of an A-level customer accounts for 15% of the total order quantity of the enterprise, and the order contribution coefficient is 0.15.

[0089] When determining the stability index of each customer, the system multiplies the weight coefficient and the order contribution coefficient. Taking the above A-level customer as an example, the stability index is: 1.5x0.15=0.225. This calculation method takes into account the depth of customer cooperation relationship and reflects the importance of customer business volume. For all customers, the system uses the same calculation method to obtain their respective stability indexes. Finally, the system arithmetically averages the stability indexes of all customers to obtain the overall customer stability coefficient of the enterprise. For example, if the enterprise has 100 customers and the total stability index is 80, the customer stability coefficient is 0.8.

[0090] S403, the product of the order completion quantity and the order growth rate is taken as the basic prediction value.

[0091] S404, the basic prediction value is multiplied by the customer stability coefficient to obtain the order prediction quantity.

[0092] S105, adjust the second demand quantity according to the order prediction, obtain a target demand quantity, and generate a replenishment plan for the plastic pallets according to the target demand quantity.

[0093] In actual production, in order to make the replenishment plan of the plastic pallets more reasonable, it is necessary to integrate the order prediction result with the previous demand prediction. By adjusting the second demand quantity according to the order prediction, the system can obtain a more accurate target demand quantity, so as to make a scientific pallet replenishment plan. This method not only considers the industry fluctuation factor, but also combines the actual order situation of the enterprise, which can effectively avoid the waste or shortage of pallet resources.

[0094] In the adjustment process, the system first calculates the ratio of the order prediction to the second demand quantity, which is taken as the order adjustment coefficient. When the order adjustment coefficient is greater than 1, it indicates that the predicted order quantity exceeds the demand quantity based on the seasonal and industry fluctuation prediction, and the system will accordingly increase the pallet demand prediction; when the order adjustment coefficient is less than 1, it indicates that the actual order may be lower than the industry expectation, and the system will appropriately reduce the pallet demand prediction. In specific adjustment, the system multiplies the second demand quantity by the order adjustment coefficient to obtain the final target demand quantity. This dynamic adjustment mechanism can make the prediction result better adapt to the actual business situation of the enterprise.

[0095] In generating the pallet replenishment plan, the system will comprehensively consider the target demand quantity, the existing inventory level and the procurement cycle and other factors. First, the system calculates the difference between the target demand quantity and the existing inventory to determine the number of pallets that need to be replenished. Then, considering that the procurement and transportation links need a certain time, the system will set an advance ordering point according to the delivery cycle of the supplier. When the inventory level falls to the advance ordering point, the system automatically triggers the replenishment process. At the same time, in order to smooth the procurement cost and warehouse pressure, the system will reasonably allocate the total replenishment quantity to multiple batches, and the specific quantity of each batch is determined according to the time distribution of the target demand quantity.

[0096] After generating the replenishment plan for the plastic pallets according to the target demand quantity, it further includes:

[0097] Obtaining the available pallet quantity and the pallet replenishment quantity of the target enterprise; calculating the difference between the available pallet quantity and the target demand quantity; adjusting the replenishment quantity of the plastic pallets according to the difference, and generating a pallet procurement plan according to the replenishment quantity; according to the warehouse resources and financial situation of the target enterprise, the pallet procurement plan is executed in batches, and the change of pallet inventory is monitored in real time.

[0098] In practice, the system first obtains the current number of available pallets through the enterprise asset management system, including the total number of pallets in stock and those in transit. Simultaneously, the system also calculates the number of pallets to be replenished according to the established pallet replenishment plan. For example, if there are currently 8,000 available pallets, the planned replenishment quantity is 2,000, and the target demand based on previous forecasts is 12,000, the system will calculate the actual difference as 2,000 pallets (12,000 - 8,000 - 2,000 = 2,000). This difference calculation method ensures that the replenishment plan can respond promptly to changes in demand.

[0099] Based on the calculated difference, the system will adjust the original pallet replenishment quantity. During the adjustment process, the system considers several factors: first, the sign of the difference (positive value indicates an increase in replenishment, negative value indicates a decrease); second, the urgency of the adjustment, judged by the proportion of the difference to the target demand; and finally, the feasibility of the adjustment, considering the supplier's supply capacity. For example, for a positive difference of 2000 units, if it accounts for more than 15% of the target demand, the system will mark it as an item requiring urgent adjustment.

[0100] When generating a pallet procurement plan, the system will plan in batches based on the company's actual situation. First, the system will assess the company's storage capacity to ensure that each batch of procurement does not cause storage pressure. For example, if the company's warehouse can receive a maximum of 1,000 pallets at a time, then a procurement plan for 2,000 pallets needs to be divided into at least two batches. Second, the system will analyze the company's financial situation and reasonably arrange the payment schedule to avoid cash flow problems. For example, the procurement plan can be divided into four batches, with 500 pallets in each batch, executed every 15 days.

[0101] During the procurement plan execution, the system monitors pallet inventory changes in real time. By setting inventory warning thresholds, the system automatically issues an alert when inventory falls below the safety stock level. Simultaneously, the system records pallet usage and wear and tear, updating the available pallet quantity promptly. This dynamic monitoring mechanism enables companies to promptly identify and resolve pallet supply issues, ensuring smooth production operations.

[0102] Based on the above method, this application also discloses a plastic pallet management system, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a plastic pallet management system provided in an embodiment of this 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,

[0103] The first obtaining module is configured to obtain use data of plastic pallets of a target enterprise in a first preset time period, and predict a first demand amount of the plastic pallets in a second preset time period according to the use data, wherein the first preset time period is before the second preset time period; the second obtaining module is configured to obtain a time interval corresponding to the second preset time period, and determine a seasonal parameter corresponding to the time interval according to a pre-established seasonal division model; the first adjusting module is configured to determine an industry fluctuation coefficient of the target enterprise in the second preset time period according to the seasonal parameter, and adjust the first demand amount according to the industry fluctuation coefficient to obtain a second demand amount; the generating module is configured to obtain historical order data and customer cooperation data of the target enterprise in the first preset time period, and generate order prediction data of the target enterprise in the second preset time period according to the historical order data and the customer cooperation data; and the second adjusting module is configured to adjust the second demand amount according to the order prediction data to obtain a target demand amount, and generate a replenishment plan for the plastic pallets according to the target demand amount.

[0104] It should be noted that the system provided in the above embodiments is only used as an example to illustrate the division of the above functional modules in realizing its functions. In actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0105] Please refer to Figure 3 The embodiment of the present application provides a structural schematic diagram of an electronic device. As shown in the figure, Figure 3 The electronic device 1000 can 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.

[0106] The communication bus 1002 is used to realize the connection and communication between the components.

[0107] The user interface 1003 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 can also include a standard wired interface and a wireless interface.

[0108] The network interface 1004 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0109] The processor 1001 can include one or more processing cores. The processor 1001 connects various parts within the server through various interfaces and lines, and performs 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. Alternatively, the processor 1001 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 1001, but can be realized by a separate chip.

[0110] The memory 1005 can include a random access memory (RAM) and a read-only memory (ROM). 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 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 1005 can also be at least one storage device located away from the aforementioned processor 1001. As shown, the memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a plastic tray management method. Figure 3

[0111] In Figure 3 ​In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 and storing a plastic tray management method, which, when executed by one or more processors, causes the electronic device to perform the method described in one or more of the above embodiments.

[0112] An electronic device readable storage medium stores instructions. When executed by one or more processors, the electronic device performs the method described in one or more of the above embodiments.

[0113] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0114] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0115] In 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. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some services interfaces, devices or units, and can be electrical or other forms.

[0116] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0117] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0118] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0119] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the art that are not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A plastic pallet management method, characterized by, The method comprises: obtaining the use data of the plastic pallets of the target enterprise within a first preset time length, and predicting a first demand quantity of the plastic pallets within a second preset time length according to the use data, wherein the first preset time length is before the second preset time length; obtaining a time interval corresponding to the second preset time length, and determining a seasonal parameter corresponding to the time interval according to a pre-established seasonal division model; determining an industry fluctuation coefficient of the target enterprise within the second preset time length according to the seasonal parameter, and adjusting the first demand quantity according to the industry fluctuation coefficient to obtain a second demand quantity, comprising: determining an industry operation rate and a capacity utilization rate of the target enterprise within the second preset time length according to the seasonal parameter, and determining a weight coefficient of the industry operation rate as a first weight coefficient and a weight coefficient of the capacity utilization rate as a second weight coefficient; arithmetic multiplying the industry operation rate and the first weight coefficient to generate a first fluctuation coefficient, and arithmetic multiplying the capacity utilization rate and the second weight coefficient to generate a second fluctuation coefficient; arithmetic 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 length; arithmetic multiplying the first demand quantity and the industry fluctuation coefficient to obtain the second demand quantity; obtaining historical order data and customer cooperation data of the target enterprise within the first preset time length, and generating an order prediction quantity of the target enterprise within the second preset time length according to the historical order data and the customer cooperation data, comprising: determining an order completion quantity and an order growth rate of the target enterprise within the first preset time length according to the historical order data; and determining a customer stability coefficient of the target enterprise according to the customer cooperation data; the customer cooperation data comprises customer cooperation years and customer historical order quantity, and the determination of the customer stability coefficient of the target enterprise according to the customer cooperation data comprises: grading each customer according to the customer cooperation years, and allocating a corresponding weight coefficient according to the grade of each customer; and determining an order contribution coefficient of each customer according to the proportion of the customer historical order quantity in the total order quantity of the enterprise; arithmetic multiplying the weight coefficient and the order contribution coefficient of each customer to obtain a stability index of each customer; and determining the customer stability coefficient of the target enterprise by arithmetic averaging the stability indexes of all customers; multiplying the order completion quantity and the order growth rate to obtain a basic prediction value; and arithmetic multiplying the basic prediction value and the customer stability coefficient to obtain the order prediction quantity; adjusting the second demand quantity according to the order prediction quantity to obtain a target demand quantity, and generating a replenishment plan of the plastic pallets according to the target demand quantity.

2. The plastic pallet management method of claim 1, wherein, The use data comprises: daily average use quantity of pallets, daily average damage quantity of pallets and pallet turnover time length, and the prediction of the first demand quantity of the plastic pallets within the second preset time length according to the use data comprises: determining a basic demand quantity of the plastic pallets according to the daily average use quantity of pallets; According to the daily damage amount of the tray, the base demand amount is adjusted to determine a safety reserve amount of the plastic tray; According to the tray turnover time length, a replenishment period of the plastic tray is determined; In combination with the safety reserve amount and the replenishment period, a first demand amount of the plastic tray within a second preset time length is predicted.

3. The plastic pallet management method of claim 2, wherein, The combination of the safety reserve amount and the replenishment period to predict the first demand amount of the plastic tray within the second preset time length comprises: According to the replenishment period, a replenishment coefficient of the plastic tray is determined; The safety reserve amount is multiplied by the replenishment coefficient to predict the first demand amount of the plastic tray within the second preset time length.

4. The plastic pallet management method of claim 1, wherein, After the replenishment plan of the plastic tray is generated according to the target demand amount, the method further comprises: Obtaining the available tray quantity and the tray replenishment quantity of the target enterprise; Calculating the difference between the available tray quantity and the target demand amount; According to the difference, the replenishment quantity of the plastic tray is adjusted, and a tray procurement plan is generated according to the replenishment quantity; According to the warehouse resources and the financial situation of the target enterprise, the tray procurement plan is executed in batches, and the tray inventory change is monitored in real time.

5. A plastic pallet management system characterized by, 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 configured to obtain the use data of the plastic tray of the target enterprise within a first preset time length, and predict a first demand amount of the plastic tray within a second preset time length according to the use data, wherein the first preset time length is before the second preset time length; The second acquisition module is configured to obtain a time interval corresponding to the second preset time length, and determine a seasonal parameter corresponding to the time interval according to a pre-established seasonal division model; The first adjustment module is configured to determine an industry fluctuation coefficient of the target enterprise within the second preset time length according to the seasonal parameter, and adjust the first demand amount according to the industry fluctuation coefficient to obtain a second demand amount, comprising: determining an industry work rate and a capacity utilization rate of the target enterprise within the second preset time length according to the seasonal parameter, and determining a weight coefficient of the industry work rate as a first weight coefficient and a weight coefficient of the capacity utilization rate as a second weight coefficient; multiplying the industry work rate by the first weight coefficient to generate a first fluctuation coefficient; multiplying the capacity utilization rate by the second weight coefficient to generate a second fluctuation coefficient; 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 length; and multiplying the first demand amount by the industry fluctuation coefficient to obtain the second demand amount. The generation module is configured to acquire historical order data and customer cooperation data of the target enterprise within the first preset time length, generate an order prediction of the target enterprise within the second preset time length according to the historical order data and the customer cooperation data, and includes: determining an order completion amount and an order growth rate of the target enterprise within the first preset time length according to the historical order data; determining a customer stability coefficient of the target enterprise according to the customer cooperation data; the customer cooperation data includes customer cooperation years and customer historical order amount; the determination of the customer stability coefficient of the target enterprise according to the customer cooperation data includes: grading each customer according to the customer cooperation years, and assigning a corresponding weight coefficient according to the customer grade; determining an order contribution coefficient of each customer according to the proportion of the customer historical order amount in the total order amount of the enterprise; multiplying the weight coefficient and the order contribution coefficient of each customer to obtain a stability index of each customer; and determining the customer stability coefficient of the target enterprise by arithmetically averaging the stability indexes of all customers; multiplying the order completion amount and the order growth rate to obtain a basic prediction value; and multiplying the basic prediction value and the customer stability coefficient to obtain the order prediction; The second adjustment module is configured to adjust the second demand amount according to the order prediction data to obtain a target demand amount, and generate a replenishment plan of the plastic pallet according to the target demand amount.

6. An electronic device, comprising: The electronic device comprises a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The memory stores a computer program capable of being loaded and executed by the processor to perform the method of any one of claims 1-4.

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