Spare part plan determination method and device, computer equipment, medium and program product

Abstract

The invention relates to a spare part plan determination method and device, computer equipment, a medium and a program product, and the method comprises the steps: predicting the first spare part demand of a spare part in N time periods based on the expected delivery amount and order demand of the spare part in the N time periods after the current time period in the current time period; based on the first spare part demand quantity in the N time periods and the second spare part demand quantity in each time period which is not executed in the issued spare part plan, determining a target time period in which the spare part demand quantity needs to be adjusted; wherein the number of the unexecuted time periods in the issued spare part plan is M, N and M are positive integers, and N is greater than M; and adjusting and re-issuing the second spare part demand quantity of the spare part in the target time period, and issuing the first spare part demand quantity of the spare part in the N time periods and in the time period which is not overlapped with each time period which is not executed. By adopting the method, the making accuracy of the spare part plan can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a spare parts planning method, apparatus, computer equipment, media, and program product. Background Technology

[0002] A spare parts plan is a production and / or procurement plan for spare parts. Typically, it can be developed manually based on business data provided by different business teams. By executing the developed spare parts plan, the customer's spare parts needs can be met.

[0003] However, when spare parts demand changes, the accuracy of spare parts plans developed using the above method is not high. Summary of the Invention

[0004] Therefore, it is necessary to provide a spare parts planning method, apparatus, computer equipment, media, and program product that can improve the accuracy of the formulated spare parts plan in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a spare parts plan determination method. The method includes: in a current time period, predicting a first spare parts demand for the spare parts within N time periods following the current time period, based on the expected delivery volume and order demand for each spare part within N time periods; determining a target time period for adjusting the spare parts demand based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period in the issued spare parts plan; wherein the number of unexecuted time periods in the issued spare parts plan is M, N and M are positive integers, and N is greater than M; adjusting and reissuing the second spare parts demand for the spare parts within the target time period, and issuing the first spare parts demand for the spare parts within the N time periods that do not overlap with the unexecuted time periods.

[0006] In the above embodiments, compared with formulating a spare parts plan for the spare parts demand within a time period each time, the efficiency of spare parts plan formulation can be improved. Moreover, even if the actual demand for spare parts changes, the adjusted spare parts plan can match the actual demand for spare parts without affecting the execution of the issued spare parts plan, thus improving the accuracy of the formulated spare parts plan. At the same time, the issued spare parts plan can include the predicted spare parts demand for multiple adjacent time periods, ensuring the predictive continuity of the issued spare parts plan, which can further improve the accuracy of the formulated spare parts plan.

[0007] In one embodiment, predicting the demand for a first spare part in N time periods based on the expected delivery volume and order demand of the spare part in N time periods after the current time period includes: for each of the N time periods, predicting the demand forecast of the spare part in each time period based on the historical consumption data of the spare part; and predicting the demand forecast of the first spare part in each time period based on the demand forecast, the expected delivery volume, and the order demand corresponding to each time period.

[0008] In the above embodiments, by analyzing the historical consumption data of spare parts, the predicted demand forecast is matched with the historical consumption of spare parts. Therefore, when predicting the demand for the first spare part based on the demand forecast, the accuracy of the predicted demand for the first spare part can be improved, and the accuracy of the formulated spare parts plan can be further improved.

[0009] In one embodiment, predicting the first spare part demand for each spare part in each time period based on the demand forecast, the expected delivery quantity, and the order demand for each time period includes: predicting the total replenishment inventory of the spare part in each time period based on the demand forecast, the spare part delivery time, and the safety stock for each time period; and predicting the first spare part demand for each spare part in each time period based on the demand forecast, the order demand, the expected delivery quantity, and the total replenishment inventory for each time period.

[0010] In the above embodiments, by introducing total replenishment inventory, the situation that the demand for spare parts will increase during the transportation of spare parts is taken into account. Therefore, when predicting the demand for the first spare part based on the total replenishment inventory, the accuracy of the predicted demand for the first spare part can be improved, and the accuracy of the formulated spare parts plan can be further improved.

[0011] In one embodiment, the expected delivery volume includes both in-transit quantity and pending delivery quantity; predicting the first spare part demand for each spare part in each time period based on the demand forecast, order demand, expected delivery volume, and total replenishment inventory for each time period includes: for the first time period among the N time periods, predicting the first spare part demand for the spare part in the first time period based on the available inventory of the spare part in the current time period, the demand forecast, order demand, in-transit quantity, pending delivery quantity, and total replenishment inventory for the first time period; and for the remaining time periods among the N time periods, predicting the first spare part demand for the spare part in the remaining time periods based on the predicted surplus of the spare part in the previous time period, and the demand forecast, order demand, in-transit quantity, pending delivery quantity, and total replenishment inventory for the remaining time periods.

[0012] In the above embodiments, by dividing N time periods into a first time period and the remaining time periods excluding the first time period, different methods can be used to predict the corresponding first spare parts demand, which can improve the matching degree between the prediction results and the prediction time periods, and further improve the accuracy of the formulated spare parts plan.

[0013] In one embodiment, the method further includes: for the first time period of the N time periods, the first spare part demand during the first time period is the maximum value of a first target demand or 0, wherein the first target demand is the sum of the corresponding demand forecast and the total replenishment inventory, and the difference between the available inventory, the order demand, the in-transit quantity, and the quantity to be delivered; for the remaining time periods of the N time periods, the first spare part demand during the remaining time periods is the maximum value of a second target demand or 0, wherein the second target demand is the sum of the corresponding demand forecast and the total replenishment inventory, and the difference between the predicted surplus of the spare part in the previous time period of the remaining time period, the order demand, the in-transit quantity, and the quantity to be delivered.

[0014] In the above embodiment, by dividing N time periods into a first time period and the remaining time periods excluding the first time period, the first target demand can be determined based on the total replenishment inventory for the first time period, and the second target demand can be determined based on the predicted surplus for the remaining time periods. By comparing the determined results with 0, the accuracy of the formulated spare parts plan can be improved.

[0015] In one embodiment, for the first time period among the N time periods, the predicted surplus of spare parts in the first time period is the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the available inventory, and the difference from the demand forecast; for the remaining time periods among the N time periods, the predicted surplus of spare parts in the remaining time periods is the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the predicted surplus of spare parts in the previous time period of the remaining time periods, and the difference from the demand forecast.

[0016] In the above embodiments, by dividing N time periods into a first time period and the remaining time periods excluding the first time period, different methods can be used to predict the corresponding predicted surplus. The predicted surplus of each time period is related to the data of the previous time period, thereby improving the matching degree between the predicted surplus and the predicted time period, and further improving the accuracy of the predicted first spare parts demand.

[0017] In one embodiment, determining the target time period for adjusting the spare parts demand based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period in the issued spare parts plan includes: for the same time period among the N time periods and each unexecuted time period, if the demand difference between the first spare parts demand and the second spare parts demand within the same time period is greater than a preset value, then the same time period is determined as the target time period for adjusting the spare parts demand.

[0018] In the above embodiments, by comparing two predictions of spare parts demand within the same time period, the spare parts demand corresponding to the same time period in the issued spare parts plan will not deviate significantly, thereby improving the accuracy of the formulated spare parts plan.

[0019] Secondly, this application provides a spare parts plan determination device, the device comprising: a prediction module, configured to predict, in the current time period, a first spare parts demand for the spare parts within N time periods based on the expected delivery volume and order demand of the spare parts within N time periods following the current time period; a determination module, configured to determine a target time period for adjusting the spare parts demand based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period in the issued spare parts plan; wherein the number of unexecuted time periods in the issued spare parts plan is M, N and M are positive integers, and N is greater than M; and a processing module, configured to adjust and reissue the second spare parts demand for the spare parts within the target time period, and to issue the first spare parts demand for the spare parts within the N time periods that do not overlap with the unexecuted time periods.

[0020] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, performs the following steps: In the current time period, based on the expected delivery volume and order demand of the spare parts in N time periods following the current time period, predict the first spare part demand in the N time periods; based on the first spare part demand in the N time periods and the second spare part demand in each unexecuted time period of the issued spare part plan, determine a target time period for adjusting the spare part demand; wherein the number of unexecuted time periods in the issued spare part plan is M, N and M are positive integers, and N is greater than M; adjust and reissue the second spare part demand in the target time period, and issue the first spare part demand in the N time periods that do not overlap with the unexecuted time periods.

[0021] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps: In the current time period, based on the expected delivery volume and order demand of the spare parts in N time periods following the current time period, predict the first spare part demand in the N time periods; based on the first spare part demand in the N time periods and the second spare part demand in each unexecuted time period of the issued spare part plan, determine a target time period for adjusting the spare part demand; wherein the number of unexecuted time periods in the issued spare part plan is M, N and M are positive integers, and N is greater than M; adjust and reissue the second spare part demand in the target time period, and issue the first spare part demand in the N time periods that do not overlap with the unexecuted time periods.

[0022] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0023] In the current time period, based on the expected delivery volume and order demand of the spare parts in N time periods following the current time period, the first spare part demand in the N time periods is predicted; based on the first spare part demand in the N time periods and the second spare part demand in each unexecuted time period of the issued spare part plan, the target time period for adjusting the spare part demand is determined; wherein, the number of unexecuted time periods in the issued spare part plan is M, N and M are positive integers, and N is greater than M; the second spare part demand in the target time period is adjusted and reissued, and the first spare part demand in the N time periods that does not overlap with the unexecuted time periods is also reissued.

[0024] The aforementioned spare parts planning method, apparatus, computer equipment, media, and program products, by predicting the first spare parts demand over N time periods based on the expected delivery volume and order demand of each spare part in the current time period, can improve the efficiency of spare parts planning compared to planning spare parts demand for each time period individually. Furthermore, even if the actual demand for spare parts changes, since there is a certain time interval between the demand occurrence and the unexecuted time periods in the issued spare parts plan, the method can improve efficiency by predicting the first spare parts demand over N time periods based on the expected delivery volume and order demand of each spare parts in the issued spare parts plan. The second spare parts demand within a time period can determine the target time period for adjusting the spare parts demand. Adjusting and reissuing the second spare parts demand within the target time period ensures that the adjusted spare parts plan matches the actual demand for spare parts without affecting the execution of the already issued spare parts plan, thus improving the accuracy of the formulated spare parts plan. Simultaneously, by issuing the first spare parts demand within N time periods that do not overlap with the unexecuted time periods, the issued spare parts plan includes the predicted spare parts demand within multiple adjacent time periods, guaranteeing the predictive continuity of the issued spare parts plan and further improving the accuracy of the formulated spare parts plan. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a spare parts plan determination method in one embodiment;

[0027] Figure 2This is a flowchart illustrating the process of predicting the demand for a first spare part in N time periods based on the expected delivery volume and order demand of each spare part in N time periods after the current time period, as shown in one embodiment.

[0028] Figure 3 This is a flowchart illustrating the process of predicting the demand for the first spare part in each time period based on the demand forecast, expected delivery, and order demand for each time period in one embodiment.

[0029] Figure 4 This is a flowchart illustrating the process of predicting the demand for the first spare part in each time period based on the demand forecast, order demand, expected delivery, and total replenishment inventory for each time period in one embodiment.

[0030] Figure 5 This is a flowchart illustrating a method for determining the predicted surplus amount in one embodiment;

[0031] Figure 6 This is a flowchart illustrating the spare parts planning method in another embodiment;

[0032] Figure 7 This is a structural block diagram of a spare parts planning device in one embodiment;

[0033] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0036] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0039] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0040] Typically, spare parts plans can be manually developed based on business data provided by different business teams. This includes data such as spare parts forecasting from the spare parts data management team, warehouse data from the warehousing and logistics team, supply lead time data from the spare parts support department, and spare parts repair data from the domestic service department. The data needed for developing these plans can be collected and cleaned from various business systems. However, when some business data cannot be collected by the business systems, it ends up in Excel spreadsheets on personal computers. This leads to high data acquisition and communication costs, requires manual data cleaning, and consumes significant human and material resources, resulting in complex spare parts planning and cumbersome data acquisition. Therefore, with continuous business expansion, the traditional offline, manual spare parts planning model cannot meet the requirements of the market and after-sales operations, necessitating digital transformation.

[0041] Based on this, a spare parts management system can be developed. This system can obtain the business data needed to formulate spare parts plans from different business systems. At the same time, for business data that is not collected by any business system, the corresponding business team can directly upload the business data to the spare parts management system. Thus, the collected business data does not need to be stored on personal computers, enabling centralized management of business data and reducing data acquisition and communication costs.

[0042] When developing a spare parts plan based on a spare parts management system, it is possible to configure the plan to be developed every few days, weekly, or monthly. After the plan is developed, it is then distributed for spare parts comparison. Taking monthly spare parts planning as an example, the plan for the following month can be developed at a fixed time each month. Based on this plan, the execution department can determine the demand for spare parts in the following month, i.e., the quantity of spare parts that need to be procured and / or produced in the following month.

[0043] However, when the cycle for developing a spare parts plan is long, if the developed spare parts plan has been issued to the execution department, but the spare parts demand has changed and it is not possible to re-develop the spare parts plan at the moment, directly implementing the issued spare parts plan will not meet the actual demand for spare parts, resulting in an inaccurate spare parts plan.

[0044] Therefore, when specifying a spare parts plan, it is advisable to simultaneously forecast the demand for spare parts for multiple time periods to form a complete spare parts plan. Even if the actual demand for spare parts changes after a spare parts plan has been issued, the time interval between the demand occurrence and the time period specified in the issued plan allows for adjustments to the spare parts demand in the issued plan based on the actual demand. This ensures the adjusted plan matches the actual demand. Furthermore, since the plan forecasts spare parts demand for multiple time periods in a single process, compared to forecasting demand for one spare part at a time, it avoids situations where the plan cannot be adjusted due to the inability to forecast demand, thus improving the accuracy of the developed spare parts plan.

[0045] like Figure 1 As shown, this application provides a spare parts plan determination method. Taking the application of this method to a processor in a spare parts management system as an example, the method may include the following steps:

[0046] S102, In the current time period, based on the expected delivery volume and order demand of the spare parts in the N time periods after the current time period, predict the demand of the first spare part in the N time periods.

[0047] The current time period refers to the period during which the spare parts plan is currently being developed. A time period can be represented by a week, month, etc. For example, taking a week as the time period, the spare parts planning process can be initiated when the current time reaches a fixed time each week, or when the current time is any day within a week. This process can predict the initial spare parts demand for the next N weeks. The initial spare parts demand refers to the predicted quantity of spare parts for the spare parts recipient (such as a customer) within each time period.

[0048] Spare parts can refer to parts or components used to replace damaged devices, and spare parts can be identified using a spare part number (PN).

[0049] In some embodiments, spare parts for developing a spare parts plan can be determined from the Software Bill of Materials (SBOM) in a Product Lifecycle Management (PLM) system. Specifically, the SBOM includes multiple spare parts identifiers. When a spare parts identifier matches a preset identifier, the spare parts for developing the spare parts plan are determined from the spare parts to which each of the multiple spare parts identifiers belongs. The preset identifiers are used to characterize after-sales repairability, meaning that a spare parts plan can be developed for after-sales repairable spare parts.

[0050] In some embodiments, the sales volume of each spare part to which multiple spare part identifiers belong can be obtained; from multiple types of spare parts, spare parts with sales volumes greater than or equal to a first preset threshold are identified as spare parts for which a spare parts plan is formulated. Thus, by formulating a spare parts plan for spare parts with large sales volumes, the risk of low inventory or stockouts can be avoided, and the responsiveness to spare parts demand can be improved.

[0051] Order demand refers to the quantity of spare parts ordered by the spare parts recipient before the current time period, and expected delivery quantity refers to the quantity of spare parts that were not delivered to the spare parts recipient before the current time period, but that the recipient will receive in the time period after the current time period. In some cases, for each of the N time periods, the expected delivery quantity for different time periods may be the same, zero, or different.

[0052] For each of the N time periods, the required quantity of spare parts can be predicted by combining the corresponding safety stock, expected delivery volume, and order demand. By setting a safety stock, even if urgent orders occur during the predicted time period, the safety stock can be used for turnover, preventing situations where the spare parts recipient's needs cannot be met and improving the service quality of the spare parts recipient. The safety stock for each time period can be the same or different.

[0053] Specifically, for each of the N time periods, the sum of the safety stock, expected delivery quantity, and order demand for each time period is determined as the first spare parts demand for that time period. Here, safety stock refers to the quantity of spare parts that should be reserved in the spare parts warehouse to prevent unforeseen circumstances. The safety stock for different time periods can be the same or different.

[0054] S104. Based on the first spare parts demand in N time periods and the second spare parts demand in each unexecuted time period of the issued spare parts plan, determine the target time period for adjusting the spare parts demand.

[0055] The "issued spare parts plan" refers to plans that have been issued and need to be executed before the current time period. In some cases, when spare parts requiring spare parts plans are stored in different spare parts warehouses, a corresponding spare parts plan needs to be developed for each spare parts warehouse. This means determining the spare parts plan corresponding to the spare parts in that warehouse based on business data such as expected demand and order demand matching that warehouse. Therefore, configuring at least two spare parts warehouses containing the spare parts into a spare parts warehouse list and performing a Cartesian product on the spare parts and the spare parts warehouse list can yield the basic framework of the spare parts plan corresponding to the spare parts warehouse containing the spare parts.

[0056] In some cases, the issued spare parts plan can be a plan consisting of a preset number of spare parts requirements from a pre-defined spare parts plan. That is, based on the supply lead time, the required spare parts quantity can be determined and issued from the pre-defined spare parts plan, thus obtaining the issued spare parts plan. This ensures that while meeting customer spare parts needs, the production and / or procurement of all required spare parts are not completed prematurely, guaranteeing the feasibility of the spare parts plan execution. The supply lead time can include the production cycle and the transportation cycle. In some cases, before issuing the issued spare parts plan, it can be submitted to an approval terminal for review. Once approved, the issued spare parts plan is issued.

[0057] The number of unexecuted time periods in the issued spare parts plan is M, where N and M are positive integers, and N is greater than M. Among the M unexecuted time periods in the issued spare parts plan and the N predicted time periods, there is at least one identical time period. That is, within the current time period, there are two predictions of the spare parts demand within a certain identical time period. Therefore, based on the two prediction results of the spare parts demand within the same time period, the target time period for adjusting the spare parts demand can be determined. The target time period refers to the unexecuted time period in the issued spare parts plan.

[0058] In some embodiments, the system can output the first spare parts demand quantity for N time periods and the second spare parts demand quantity for each unexecuted time period in the issued spare parts plan to the audit terminal. When the audit terminal receives input from the audit personnel, the time period matching the input operation is determined as the target time period for adjusting the spare parts demand quantity. That is, after comparing the first and second spare parts demand quantities in the same time period based on human experience, the audit personnel send the target time period for adjusting the spare parts demand quantity to the spare parts management system by inputting the same time period.

[0059] In some embodiments, for the same time period among N time periods and each unexecuted time period, if the difference between the demand for the first spare part and the demand for the second spare part in the same time period is greater than a preset value, then the same time period is determined as the target time period for adjusting the demand for spare parts.

[0060] S106, Adjust and reissue the second spare parts demand quantity within the target time period, and issue the first spare parts demand quantity within the N time periods that do not overlap with the unexecuted time periods.

[0061] The second spare part requirement during the target time period can be the same as or different from the first spare part requirement during the target time period. In some embodiments, the spare part quantity input by the auditor can be determined as the adjusted second spare part requirement during the target time period. In some embodiments, the adjusted second spare part requirement during the target time period can be the maximum of the first or second spare part requirement corresponding to the target time period. In some embodiments, the adjusted second spare part requirement during the target time period can be the average of the first and second spare part requirement corresponding to the target time period.

[0062] It should be understood that the current forecast is for the first spare part demand in N time periods after the current time period. For the last time period in the issued spare part plan, which is adjacent to the first time period in the N time periods, the time period that does not overlap with the unexecuted time periods in the N time periods refers to the last time period in the N time periods. Therefore, the forecasted spare part demand is for the second spare part in the last time period in the N time periods.

[0063] Table 1 provides a spare parts plan. The issued spare parts plan includes the predicted second spare parts demand n1 in week s1, the predicted second spare parts demand n2 in week s2, the predicted second spare parts demand n3 in week s3, etc. Therefore, when the current time period is within week s1, the first spare parts demand for the spare parts in N time periods includes the predicted first spare parts demand m1 in week s2, the first spare parts demand m2 in week s3, and the first spare parts demand m3 in week s4, etc.

[0064]

[0065] In Table 1, the second spare parts demand in the unexecuted time periods of the issued spare parts plan during the current time period includes n2, n3, etc. For the unexecuted time periods in the issued spare parts plan and the same time periods among N time periods, such as week s2, week s3, etc., when week s2 is determined to be the target time period for adjusting spare parts demand based on n2 and m1, n2 can be adjusted to p1. Similarly, when week s3 is determined to be the target time period for adjusting spare parts demand based on n3 and m2, n3 can be adjusted to p2, and the predicted first spare parts demand m3 for week s4 will be issued. Thus, the updated issued spare parts plan shown in Table 2 can be obtained, and when the current time period is week s2, it can continue to be based on... Figure 1 The method shown predicts the demand for the first spare part r1-r3, etc., of each spare part in N time periods, and so on, continuously updating the issued spare parts plan.

[0066]

[0067] based on Figure 1 The method shown predicts the demand for spare parts over N time periods based on the expected delivery volume and order demand for each spare part in the N time periods following the current time period. This improves the efficiency of spare parts planning compared to planning for each time period individually. Furthermore, even if the actual demand for spare parts changes, the method can still improve efficiency because the time between the demand occurrence and the unexecuted time periods in the issued spare parts plan is considerable. This is because the method uses the demand for the first spare part over N time periods and the demand for the second spare part in the unexecuted time periods of the issued spare parts plan as a basis for further planning. The quantity can determine the target time period for adjusting the spare parts demand, and adjust and reissue the second spare parts demand within the target time period. This ensures that the adjusted spare parts plan matches the actual demand for spare parts without affecting the execution of the already issued spare parts plan, thus improving the accuracy of the formulated spare parts plan. At the same time, by issuing the first spare parts demand within N time periods that do not overlap with the unexecuted time periods, the issued spare parts plan includes the predicted spare parts demand within multiple adjacent time periods, ensuring the forecast continuity of the issued spare parts plan and further improving the accuracy of the formulated spare parts plan.

[0068] In one embodiment, the method for predicting the first spare part demand (i.e., S104) within N time periods following the current time period, based on the expected delivery volume and order demand of the spare parts, can be as follows: Figure 2 As shown, it includes the following steps:

[0069] S202, for each of the N time periods, based on the historical consumption data of spare parts, predict the demand forecast of spare parts in each time period.

[0070] Historical consumption data for spare parts is used to characterize the usage of spare parts up to the current time period. This historical consumption data includes, but is not limited to, the sales volume, production volume, and purchase volume of spare parts. The spare parts management system also provides a query function for historical consumption data; by entering a spare part identifier, the system can display the historical consumption data corresponding to that identifier.

[0071] In some embodiments, based on historical consumption data of spare parts, the first sales quantity of each spare part in multiple concurrent time periods corresponding to each time period is determined; the multiple first sales quantities are counted to obtain the total sales quantity corresponding to each time period; the total sales quantity corresponding to each time period is input into the prediction model to obtain the demand forecast of spare parts in each time period. The demand forecast of spare parts in different time periods may be the same or different. The concurrent time period corresponding to each time period refers to the time period preceding each time period but identical to each time period. For example, if the current time period is the first week of the current month, then the concurrent time period refers to the first week of the previous month, the month before last, etc.

[0072] In some embodiments, based on historical consumption data of spare parts, the first sales quantity of each spare part in multiple concurrent time periods corresponding to each time period is determined; the average quantity of the multiple first sales quantities is determined; and the sum of the average quantity and a preset quantity is determined as the demand forecast quantity of the spare parts in each time period.

[0073] In some embodiments, historical consumption data of spare parts can be input into the prediction model, and the output of the prediction model can be determined as the predicted demand forecast of spare parts in each time period, that is, the demand forecast corresponding to different time periods is the same.

[0074] The spare parts management system can provide a demand forecast query function. By entering the time period to be queried in the corresponding query interface, the system can display the predicted demand for spare parts within the queried time period.

[0075] S204, based on the demand forecast, expected delivery and order demand for each time period, predicts the first spare part demand for each time period.

[0076] In some embodiments, the sum of the demand forecast, expected delivery and order demand for each time period can be determined as the first spare parts demand for each time period.

[0077] In some embodiments, the demand for a first spare part in each time period can be predicted based on a comparison between the demand forecast and the demand threshold, the expected delivery volume, and the order demand. The demand threshold includes a first threshold and a second threshold, which are thresholds set based on human experience to verify the reasonableness of the predicted demand value.

[0078] Specifically, when the demand forecast for each time period is greater than the first threshold or less than the second threshold, the sum of the safety stock, expected delivery quantity, and order demand for spare parts is determined as the first spare part demand for each time period. In other words, based on human experience, the demand forecast that falls within the range of [second threshold, first threshold] is considered a reasonable spare part demand forecast.

[0079] based on Figure 2 The method shown analyzes historical consumption data of spare parts to match the predicted demand with the historical consumption of spare parts. This improves the accuracy of the predicted demand for the first spare part when predicting the demand based on the predicted demand, and further enhances the accuracy of the formulated spare parts plan.

[0080] based on Figure 2 As shown, the demand forecast is based on historical sales data of spare parts. To ensure the accuracy of the demand forecast, in one embodiment, upon receiving an adjustment operation for the demand forecast, the adjusted demand forecast is determined as the forecast adjustment value. Then, based on the forecast adjustment value, the step of forecasting the first spare part demand for each time period based on the corresponding demand forecast, expected delivery quantity, and order demand quantity is returned. In other words, after the demand forecast is obtained, it can be manually reviewed to ensure the accuracy of the demand forecast.

[0081] In one embodiment, the method for predicting the first spare part demand in each time period (i.e., S202) based on the demand forecast, expected delivery quantity, and order demand for each time period can be as follows: Figure 3 As shown, it includes the following steps:

[0082] S302, based on the demand forecast for each time period, the spare parts delivery time and safety stock for each time period, predicts the total replenishment inventory of spare parts in each time period.

[0083] The spare parts delivery time refers to the time from when the spare parts are ordered by the spare parts recipient to when they are delivered to the recipient, i.e., the lead time (LT) of the spare parts. The spare parts delivery time may include the spare parts production and / or procurement time as well as the transportation time. The transportation time refers to the time that the produced and / or procured spare parts undergo during transportation.

[0084] In some embodiments, for each time period, the average daily forecasted demand can be determined based on the corresponding demand forecast and the number of days contained in each time period. The product of the average daily forecasted demand and the spare parts delivery time, plus the sum of the product and safety stock, is used to determine the total replenishment inventory of spare parts for each time period, i.e., total replenishment inventory = safety stock + average daily forecasted demand * supply LT. The total replenishment inventory reflects the total inventory of spare parts that needs to be replenished due to newly added spare parts demand during transportation.

[0085] S304, based on the demand forecast, order demand, expected delivery, and total replenishment inventory for each time period, predicts the demand for the first spare part in each time period.

[0086] In one embodiment, for each time period, the sum of the corresponding demand forecast and the total replenishment inventory is determined as a first sum; the sum of the corresponding order demand and the expected delivery quantity is determined as a second sum; when the first difference between the first sum and the second sum is greater than a second preset threshold, the first preset demand is determined as the predicted first spare part demand for each time period; when the first difference between the first sum and the second sum is less than or equal to the second preset threshold, the second preset demand is determined as the predicted first spare part demand for each time period. Wherein, the first preset demand is greater than the second preset demand.

[0087] based on Figure 3 The method shown introduces total replenishment inventory, taking into account the increased demand for spare parts during transportation. Therefore, when predicting the demand for the first spare part based on the total replenishment inventory, the accuracy of the predicted demand for the first spare part can be improved, thereby further enhancing the accuracy of the formulated spare parts plan.

[0088] In one embodiment, the expected delivery quantity includes both in-transit quantity and pending delivery quantity. Pending delivery quantity refers to the number of spare parts not yet delivered to the spare parts recipient prior to the current time period. Pending delivery quantity is the difference between the current requested quantity of spare parts and the current cumulative delivery quantity, where the current requested quantity refers to the total scheduled spare parts delivery quantity prior to the current time period. Pending delivery quantity is related to the commitment date, which refers to the committed date for delivery of manufactured and / or procured spare parts. Based on the delivered manufactured and / or procured spare parts, a corresponding quantity of spare parts is provided to the spare parts recipient. That is, if a commitment is made to deliver manufactured and / or procured spare parts within a time period after the current time period, the pending delivery quantity within that committed time period will not be 0, and vice versa. In some cases, delivery can be committed within the same time period or within different time periods; this is not limited.

[0089] The quantity in transit refers to the number of spare parts shipped before the current time period that are still in transit. It is the difference between the number of spare parts shipped and the number of spare parts received. The quantity in transit is related to the expected delivery date, which is the date the spare parts recipient expects to receive the spare parts. Typically, the commitment date for spare parts promised by the spare parts production department and / or spare parts purchasing department can be predicted based on the recipient's expected delivery date. In some embodiments, the expected delivery date of spare parts is predicted based on the spare parts' logistics data. If the predicted expected delivery date falls within a certain time period, the quantity in transit for that time period is 0; otherwise, it is not 0. In other words, for the number of spare parts that the recipient has not received before the current time period, the time period the spare parts will experience in transit can be predicted based on the spare parts' logistics data. Therefore, the quantity in transit for that predicted time period is not 0; otherwise, it is 0.

[0090] Furthermore, in one embodiment, the method for predicting the first spare part demand in each time period (i.e., S304) based on the demand forecast, order demand, expected delivery quantity, and total replenishment inventory corresponding to each time period can be referred to as follows: Figure 4 The content shown includes the following steps:

[0091] S402, for the first time period out of N time periods, based on the available inventory of spare parts in the current time period, the demand forecast, order demand, in-transit quantity, pending delivery quantity, and total replenishment inventory for the first time period, predict the demand for the first spare part in the first time period.

[0092] Available inventory refers to the quantity of spare parts that are available in stock during the current time period. It can be determined based on a comparison between the spare part's identifier and a preset identifier. The preset identifier indicates whether the part is repairable after-sales.

[0093] In some embodiments, when the spare part identifier matches a preset identifier, a matching alternative spare part can be determined based on the substitution relationship between the first and second spare parts; the first inventory of the alternative spare parts in the current time period is obtained; for the faulty spare parts that match the spare part identifier counted in the current time period, the number of spare parts after repairing the faulty spare parts is counted to obtain a second inventory; based on the first inventory, the second inventory, and the third inventory added after the production and / or procurement of spare parts, the available inventory of spare parts is determined. Here, an alternative spare part refers to a component used to replace a spare part to maintain or restore the normal operation of a component or equipment when the spare part cannot be obtained immediately or cannot be repaired in a short time.

[0094] The spare parts management system can provide a function to query alternative parts. By entering a spare part identifier, it can display the alternative spare parts corresponding to the spare part to which the entered spare part identifier belongs.

[0095] In some embodiments, when the spare part identifier is inconsistent with the preset identifier, the available inventory of the spare part is determined based on the first inventory and the third inventory. That is, when the spare part is not an after-sales repairable spare part, the quantity of spare parts after fault repair is not considered, but the quantity of production and / or procurement and the available inventory of alternative spare parts are considered.

[0096] Specifically, for the first time period out of N time periods, which is adjacent to the current time period, when predicting the demand for the first spare part in the first time period, the available inventory of the spare part in the current time period is known, and the available inventory of the spare part in the current time period affects the prediction result. Therefore, the demand for the first spare part in the first time period can be predicted based on the available inventory of the spare part in the current time period.

[0097] In one embodiment, for a first time period, a first sum value corresponding to the demand forecast and the total replenishment inventory is determined; the sum of available inventory, order demand, in-transit quantity, and pending delivery quantity is determined as a third sum value; when the first sum value is greater than the third sum value, a third preset demand quantity is determined as the predicted first spare parts demand quantity for the first time period; when the first sum value is less than or equal to the third sum value, a fourth preset demand quantity is determined as the predicted first spare parts demand quantity for the first time period.

[0098] In one embodiment, a first target demand is determined, which is the sum of the corresponding demand forecast and total replenishment inventory, and the difference between it and available inventory, order demand, in-transit quantity, and quantity awaiting delivery. The maximum value between the first target demand and 0 is determined as the predicted first spare part demand in the first time period. That is, first spare part demand = Max(demand forecast + total replenishment inventory - order demand - available inventory - (in-transit quantity + quantity awaiting delivery), 0).

[0099] S404, for the remaining time period in N time periods, based on the predicted surplus of spare parts in the previous time period, and the predicted demand, order demand, in-transit quantity, pending delivery quantity and total replenishment inventory corresponding to the remaining time period, predict the demand of the first spare part in the remaining time period.

[0100] Specifically, for the remaining time periods out of N time periods (excluding the first time period), when the available inventory of spare parts in the preceding time period cannot be known in advance, the demand for the first spare part in the remaining time period can be predicted based on the predicted surplus of spare parts in the preceding time period (i.e., the predicted surplus). The predicted surplus refers to the number of spare parts remaining after the predicted delivery in the preceding time period.

[0101] In one embodiment, for the remaining time period, a first sum value corresponding to the demand forecast and the total replenishment inventory is determined; the sum of the order demand, the predicted surplus of spare parts in the previous time period, the quantity in transit, and the quantity to be delivered is determined as a fourth sum value; when the first sum value is greater than the fourth sum value, the product of the average of the first sum value and the fourth sum value and a first preset coefficient is determined as the predicted first spare parts demand in the remaining time period; when the first sum value is less than or equal to the fourth sum value, the product of the fourth sum value and a second preset coefficient is determined as the predicted first spare parts demand in the remaining time period.

[0102] In one embodiment, a second target demand is determined, which is the sum of the corresponding demand forecast and total replenishment inventory, and the difference between this sum and the forecasted surplus of spare parts in the previous time period, order demand, in-transit quantity, and pending delivery quantity. The maximum value between the second target demand and 0 is determined as the forecasted first spare parts demand in the remaining time period. That is, second spare parts demand = Max(demand forecast + total replenishment inventory - order demand - forecasted surplus of spare parts in the previous time period - in-transit quantity - pending delivery quantity, 0).

[0103] based on Figure 4 The method shown divides N time periods into a first time period and the remaining time periods excluding the first time period, allowing for different methods to predict the corresponding first spare parts demand. This improves the matching degree between the prediction results and the prediction time periods, and further enhances the accuracy of the formulated spare parts plan.

[0104] based on Figure 4 It is known that when predicting the demand for the first spare part in the remaining time period, it is necessary to consider the predicted surplus of the spare part in the previous time period. This surplus can be configured as the predicted surplus of the spare part in multiple previous time periods; the predicted surplus for different previous time periods can be the same or different. Alternatively, it can also be... Figure 5 The method shown determines the predicted surplus, as follows:

[0105] S502, for the first time period out of N time periods, the predicted surplus of spare parts in the first time period is the sum of the corresponding order demand, in-transit quantity, pending delivery quantity and actual spare parts inventory, and the difference with the demand forecast.

[0106] The predicted spare parts inventory in the first time period is calculated as: Order demand + Quantity awaiting delivery + Quantity in transit + Available inventory - Demand forecast. Therefore, for the second time period out of N time periods, based on the predicted inventory in the first time period, the demand for spare parts in the second time period can be predicted.

[0107] S504, for the remaining time period in N time periods, the predicted surplus of spare parts in the remaining time period is the sum of the corresponding order demand, in-transit quantity, pending delivery quantity and the predicted surplus of spare parts in the previous time period, and the difference with the demand forecast.

[0108] That is, the predicted surplus of spare parts in the remaining time period = order demand + quantity to be delivered + quantity in transit + predicted surplus of spare parts in the previous time period - demand forecast. Therefore, starting from the second time period out of N time periods, the demand for the first spare part for each spare part in the second to Nth time periods can be predicted.

[0109] It should be noted that in S502 and S504, when the predicted balance is less than or equal to 0, the predicted balance is set to 0, which can improve the accuracy of the predicted balance.

[0110] In one embodiment, the spare parts management system can provide a predicted surplus quantity query function. By entering the time period to be queried in the corresponding query interface, the predicted surplus quantity of spare parts within the queried time period can be displayed.

[0111] based on Figure 5 The method shown divides N time periods into a first time period and the remaining time periods excluding the first time period, allowing for different methods to predict the corresponding forecast surplus. The forecast surplus of each time period is related to the data of the previous time period, thereby improving the matching degree between the forecast surplus and the forecast time period, and further improving the accuracy of the forecast of the first spare parts demand.

[0112] Based on the above, it is clear that by developing a spare parts management system, data from various business systems can be obtained through data interaction between the spare parts management system and these systems. Then, based on this business data, the steps described in the above embodiments can be executed to formulate and issue spare parts plans. In other words, the spare parts management system integrates the scattered business data required for spare parts planning into a single system, enabling the viewing and management of required data within a single system. This reduces data acquisition and communication costs, standardizes data quality, and allows for online spare parts planning, avoiding manual data clearing and saving manpower and resources. Furthermore, multiple business terminals can simultaneously edit relevant business data within the spare parts management system, improving efficiency. Moreover, after reviewing the formulated spare parts plan, the required spare parts quantity is distributed to the execution department for execution, connecting the front and back ends, making the process traceable, and streamlining the workflow online, greatly improving work efficiency.

[0113] In addition to providing functions for creating spare parts plans, querying replacement spare parts and historical consumption of spare parts, the spare parts management system also offers the following functions: It can differentiate historical consumption data for spare parts in different regions; it can also differentiate historical consumption data for spare parts within and / or outside the warranty period; and it can query historical consumption data such as total annual consumption, average weekly consumption, and consumption percentage of spare parts.

[0114] In some cases, the spare parts management system can also provide a query function for the relationship between spare parts warehouses and spare parts types. Spare parts types are used to characterize the characteristics of spare parts, such as whether a spare part is a raw material or an electronic component. Different types of spare parts have different spare parts identifiers. Therefore, by entering the spare parts identifier in the corresponding query interface, the system can display information such as the location of the spare parts warehouse where the spare parts corresponding to the identifier are stored, and the storage area of ​​the spare parts within the spare parts warehouse.

[0115] In some cases, the spare parts management system can also provide a spare parts supply LT query function. Different spare parts have different production cycles and transportation cycles. Therefore, by entering the spare parts identifier in the corresponding query interface, the production cycle and transportation cycle of the spare parts corresponding to the spare parts identifier can be displayed.

[0116] In some cases, the spare parts management system can also provide a demand occurrence frequency query function to check the quantity of spare parts planned by the system. It can also provide query functions for parameters such as replenishment cycle, service level, safety stock, turnover inventory, and maximum inventory. Furthermore, it can provide a shipment volume query function to check the shipment status of spare parts with different identifiers in different regions. Finally, it can provide a concurrent consumption data query function, which can summarize concurrent consumption data based on historical spare parts consumption data. For example, if the current time is 2024 / 01 / 01, the concurrent consumption data will be calculated based on historical consumption data from 2023 / 01 / 01 to 2023 / 01 / 07.

[0117] In some cases, the spare parts management system can also provide a spare parts inventory query function to check the current market availability of spare parts. Specifically, battery information can be obtained from the spare parts recipient information in the Global Sales System (GSS). Based on the battery assembly, the corresponding SBOM data can be obtained. From the SBOM data, the standard usage of the spare parts and the upper-level standard usage of the spare parts are extracted. The total inventory of the spare parts is determined by multiplying the total inventory of the spare parts, the standard usage of the spare parts, and the upper-level standard usage of the spare parts. Here, the total inventory of the spare parts refers to the total number of devices with spare parts in the current market, and the upper-level standard usage refers to the standard usage of the spare parts at the next higher level. The upper-level spare parts can be determined based on the SBOM data.

[0118] In conjunction with the above, in one embodiment, such as Figure 6 As shown, a spare parts planning method is provided. Taking the application of this method to a processor in a spare parts management system as an example, the method includes the following steps:

[0119] S602, within the current time period, for each of the N time periods following the current time period, based on the historical consumption data of spare parts, predict the demand forecast of spare parts in each time period.

[0120] S604 predicts the total replenishment inventory of spare parts in each time period based on the demand forecast for each time period, the spare parts delivery time and safety stock in each time period.

[0121] S606, for the first time period out of N time periods, determine the maximum value of the first spare part demand in the first time period as either the first target demand or 0. The first target demand is the sum of the demand forecast and total replenishment inventory of spare parts in the first time period, and the difference between the available inventory, order demand, in-transit quantity and the quantity to be delivered.

[0122] S608, the predicted surplus of spare parts in the first time period is determined as the sum of the order demand, in-transit quantity, pending delivery quantity and available inventory of spare parts in the first time period, and the difference with the demand forecast.

[0123] S610, for the remaining time period in N time periods, the predicted surplus of spare parts in the remaining time period is the sum of the order demand, in-transit quantity, and pending delivery quantity of spare parts in the remaining time period, and the difference between the predicted surplus of spare parts in the previous time period and the demand forecast quantity.

[0124] S612, determine the maximum value of the first spare part demand in the remaining time period as the second target demand or 0. The second target demand is the sum of the spare part demand forecast and the total replenishment inventory in the remaining time period, and the difference between the spare part forecast balance, order demand, in-transit quantity and pending delivery quantity in the previous time period in the remaining time period.

[0125] S614, for the same time period in each of the N time periods and the unexecuted time periods in the issued spare parts plan, based on the first spare parts demand in the N time periods, determine the first spare parts demand in the same time period, and based on the second spare parts demand in each of the unexecuted time periods in the issued spare parts plan, determine the second spare parts demand in the same time period.

[0126] S616 If the difference between the demand for the first spare part and the demand for the second spare part within the same time period is greater than a preset value, then the same time period is determined as the target time period for adjusting the demand for spare parts.

[0127] S618, adjust and reissue the second spare parts demand quantity within the target time period, and issue the first spare parts demand quantity within N time periods that do not overlap with the unexecuted time periods.

[0128] The specific content of S602-S618 can be found in the aforementioned description and will not be repeated here.

[0129] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0130] Based on the same inventive concept, this application also provides a spare parts planning device for implementing the spare parts planning method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more spare parts planning device embodiments provided below can be found in the limitations of the spare parts planning method described above, and will not be repeated here.

[0131] In one embodiment, such as Figure 7 As shown, a spare parts planning device is provided, comprising: a prediction module 702, a determination module 704, and a processing module 706, wherein:

[0132] The prediction module 702 is used to predict the demand for the first spare part in the N time periods based on the expected delivery volume and order demand of the spare part in the N time periods after the current time period.

[0133] The determination module 704 is used to determine the target time period for adjusting the spare parts demand based on the first spare parts demand in the N time periods and the second spare parts demand in each unexecuted time period of the issued spare parts plan; wherein, the number of unexecuted time periods in the issued spare parts plan is M, N and M are positive integers, and N is greater than M.

[0134] The processing module 706 is used to adjust and reissue the second spare part demand quantity of the spare part within the target time period, and to issue the first spare part demand quantity of the spare part within the N time periods that do not overlap with the unexecuted time periods.

[0135] In one embodiment, the prediction module 702 is further configured to: predict the demand forecast of the spare parts in each of the N time periods based on the historical consumption data of the spare parts; and predict the first spare parts demand of the spare parts in each time period based on the demand forecast corresponding to each time period, the expected delivery quantity, and the order demand quantity.

[0136] In one embodiment, the prediction module 702 is further configured to: predict the total replenishment inventory of the spare parts in each time period based on the demand forecast corresponding to each time period, the spare parts delivery time and safety stock of the spare parts in each time period; and predict the first spare parts demand of the spare parts in each time period based on the demand forecast corresponding to each time period, the order demand, the expected delivery quantity and the total replenishment inventory.

[0137] In one embodiment, the expected delivery quantity includes the quantity in transit and the quantity to be delivered; the prediction module 702 is further configured to: for the first time period among the N time periods, based on the available inventory of the spare parts in the current time period, the demand forecast quantity, the order demand quantity, the quantity in transit, the quantity to be delivered, and the total replenishment inventory, predict the first spare parts demand quantity of the spare parts in the first time period; for the remaining time periods among the N time periods, based on the predicted surplus quantity of the spare parts in the previous time period, and the demand forecast quantity, the order demand quantity, the quantity in transit, the quantity to be delivered, and the total replenishment inventory corresponding to the remaining time periods, predict the first spare parts demand quantity of the spare parts in the remaining time periods.

[0138] In one embodiment, the prediction module 702 is further configured to: for the first time period among the N time periods, determine the first spare part demand quantity of the spare part in the first time period as the maximum value of a first target demand quantity or 0, wherein the first target demand quantity is the sum of the corresponding demand forecast quantity and the total replenishment inventory, and the difference between the available inventory, the order demand quantity, the quantity in transit, and the quantity to be delivered; for the remaining time periods among the N time periods, determine the first spare part demand quantity of the spare part in the remaining time period as the maximum value of a second target demand quantity or 0, wherein the second target demand quantity is the sum of the corresponding demand forecast quantity and the total replenishment inventory, and the difference between the predicted surplus quantity of the spare part in the previous time period of the remaining time period, the order demand quantity, the quantity in transit, and the quantity to be delivered.

[0139] In one embodiment, the forecasting module 702 is further configured to: for the first time period among the N time periods, determine the predicted surplus of the spare parts in the first time period as the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the available inventory, and the difference from the demand forecast; for the remaining time periods among the N time periods, determine the predicted surplus of the spare parts in the remaining time periods as the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the predicted surplus of the spare parts in the previous time period of the remaining time periods, and the difference from the demand forecast.

[0140] In one embodiment, the determining module 704 is further configured to: for the same time period among the N time periods and the unexecuted time periods, if the difference between the demand for the first spare part and the demand for the second spare part in the same time period is greater than a preset value, then determine the same time period as the target time period for adjusting the demand for spare parts.

[0141] The aforementioned spare parts plan determines that each module in the device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in the computer device, or stored in software within the memory of the computer device, so that the processor can invoke and execute the operations corresponding to each module.

[0142] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data such as the predicted demand for spare parts over N time periods. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a spare parts planning method.

[0143] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0145] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0146] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining a spare parts plan, characterized in that, The method includes: In the current time period, based on the expected delivery volume and order demand of each spare part in the N time periods following the current time period, predict the first spare part demand in the N time periods. Based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period in the issued spare parts plan, the target time period for adjusting the spare parts demand is determined; wherein, the number of unexecuted time periods in the issued spare parts plan is M, N and M are positive integers, and N is greater than M. Adjust and reissue the second spare part requirement quantity for the spare part within the target time period, and issue the first spare part requirement quantity for the spare part within the N time periods that do not overlap with the unexecuted time periods.

2. The method according to claim 1, characterized in that, The method of predicting the first spare part demand in the N time periods following the current time period, based on the expected delivery volume and order demand of each spare part in the N time periods, includes: For each of the N time periods, based on the historical consumption data of the spare parts, predict the demand forecast of the spare parts in each time period; Based on the demand forecast, expected delivery volume, and order demand for each time period, the demand for the first spare part in each time period is predicted.

3. The method according to claim 2, characterized in that, The method of predicting the first spare part demand in each time period based on the demand forecast, the expected delivery quantity, and the order demand for each time period includes: Based on the demand forecast for each time period, the spare parts delivery time and safety stock for each time period, the total replenishment inventory of the spare parts in each time period is predicted. Based on the demand forecast, order demand, expected delivery volume, and total replenishment inventory corresponding to each time period, the demand for the first spare part in each time period is predicted.

4. The method according to claim 3, characterized in that, The expected delivery volume includes both in-transit and pending delivery; the prediction of the first spare part demand for each time period based on the demand forecast, order demand, expected delivery volume, and total replenishment inventory for each time period includes: For the first time period among the N time periods, based on the available inventory of the spare parts in the current time period, the demand forecast quantity corresponding to the first time period, the order demand quantity, the quantity in transit, the quantity to be delivered, and the total replenishment inventory, the demand quantity of the first spare parts in the first time period is predicted. For the remaining time periods among the N time periods, based on the predicted surplus of the spare parts in the previous time period, and the predicted demand, order demand, in-transit quantity, pending delivery quantity, and total replenishment inventory corresponding to the remaining time periods, the first spare parts demand for the spare parts in the remaining time periods is predicted.

5. The method according to claim 4, characterized in that, The method further includes: For the first time period among the N time periods, the demand for the first spare part in the first time period is the maximum value between the first target demand or 0. The first target demand is the sum of the corresponding demand forecast and the total replenishment inventory, and the difference between the available inventory, the order demand, the quantity in transit, and the quantity to be delivered. For the remaining time period among the N time periods, the first spare part demand during the remaining time period is the maximum value of the second target demand or 0. The second target demand is the sum of the corresponding demand forecast and the total replenishment inventory, and the difference between the predicted surplus of the spare parts in the previous time period, the order demand, the quantity in transit, and the quantity to be delivered.

6. The method according to claim 4, characterized in that, For the first time period among the N time periods, the predicted surplus of the spare parts in the first time period is the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the available inventory, and the difference between the sum and the predicted demand. For the remaining time periods among the N time periods, the predicted surplus of spare parts in the remaining time period is the sum of the corresponding order demand, the quantity in transit, the quantity to be delivered, and the predicted surplus of spare parts in the previous time period, and the difference between these sums and the demand forecast.

7. The method according to any one of claims 1 to 6, characterized in that, The determination of the target time period for adjusting spare parts demand, based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period of the issued spare parts plan, includes: For the same time period among the N time periods and the unexecuted time periods, if the difference between the demand for the first spare part and the demand for the second spare part within the same time period is greater than a preset value, then the same time period is determined as the target time period for adjusting the demand for spare parts.

8. A spare parts planning and determination device, characterized in that, The device includes: The prediction module is used to predict the demand for the first spare part in the N time periods based on the expected delivery volume and order demand of each spare part in the N time periods after the current time period. The determination module is used to determine the target time period for adjusting the spare parts demand based on the first spare parts demand within the N time periods and the second spare parts demand within each unexecuted time period in the issued spare parts plan; wherein, the number of unexecuted time periods in the issued spare parts plan is M, N and M are positive integers, and N is greater than M. The processing module is used to adjust and reissue the second spare part demand quantity of the spare part within the target time period, and to issue the first spare part demand quantity of the spare part within the N time periods that do not overlap with the unexecuted time periods.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images