Spares demand forecasting method and apparatus

Abstract

The application provides a spare part demand prediction method and device, and relates to the technical field of equipment maintenance. The method comprises the following steps: collecting spare part data according to at least three dimensions of different equipment, maintenance levels and storage locations to obtain the current usage of the spare parts in at least three dimensions; determining the average usage of a single equipment in at least three dimensions of equipment type, maintenance level and storage location according to the number of repaired equipment and the current usage; and predicting the demand range of the spare parts in each storage location according to the storage cycle range of the spare parts in different storage locations, the maintenance plan and the average usage. The application can reasonably and accurately predict the demand of the spare parts in different storage locations, thereby avoiding the situation of insufficient production materials or excessively high storage fund occupation.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance technology, and in particular to a method and apparatus for predicting the demand for replacement parts. Background Technology

[0002] With the development of high-speed trains, the scale of maintenance is also increasing year by year. Under the influence of multiple factors such as uneven and unstable advanced maintenance plans and continuous improvement of product technology and maintenance processes, reasonably controlling the inventory of occasional replacement parts (occasional replacement parts refer to new parts that are replaced during advanced maintenance due to occasional occurrences exceeding maintenance standards or limits) has become a challenge for cost control of maintenance projects.

[0003] Low inventory levels can lead to material shortages and delayed vehicle deliveries; high inventory levels result in excessive capital tied up in inventory, increasing business costs. Current technologies generally rely on manual experience to estimate replacement parts demand. However, due to the risk of contract defaults caused by delayed vehicle deliveries, actual estimates often result in significantly higher inventory levels and persistently high capital tied up in inventory.

[0004] Therefore, existing methods for forecasting the demand for replacement parts are difficult to achieve reasonable forecasting of the demand for replacement parts in advanced maintenance projects. In other words, the forecast of the demand for replacement parts is inaccurate, and it is impossible to avoid situations such as material shortages in production or excessive capital tied up in inventory. Summary of the Invention

[0005] This invention provides a method and apparatus for predicting the demand for replacement parts, in order to solve the problem of inaccurate prediction of the demand for replacement parts in advanced maintenance projects in the prior art.

[0006] This invention provides a method for predicting demand for interchangeable parts, comprising:

[0007] Data on contactless replacement parts should be collected according to at least three dimensions: different equipment, maintenance level, and storage location, in order to obtain the current usage of contactless replacement parts under at least three dimensions;

[0008] Based on the number of repaired equipment and the current usage, determine the average usage of the occasional replacement parts per unit of equipment in at least three dimensions: equipment type, maintenance level, and storage location.

[0009] Based on the reserve period range of spare parts at different storage locations, maintenance plans, and the average usage, the demand range of spare parts at each storage location is predicted. The reserve period range includes the minimum and maximum reserve periods starting from the prediction date. The maintenance plan includes planned maintenance data with maintenance start dates between T+1 and T+M days, where T represents the prediction date and M is greater than or equal to the maximum reserve period.

[0010] According to a method for forecasting the demand for spare parts provided by the present invention, the method forecasts the demand range for spare parts at each inventory location based on the inventory cycle range, maintenance plan, and average usage of spare parts at different inventory locations, including:

[0011] Obtain the first maintenance data from the maintenance plan if the maintenance start date falls within the predicted date and the minimum reserve period;

[0012] Based on at least three dimensions—equipment type, maintenance level, and inventory location—the individual demand for replacement parts for the equipment to be maintained in the first maintenance data is set to the average usage of the maintained equipment under the same dimension values.

[0013] For each inventory location, the individual demand for replacement parts for all equipment to be repaired is accumulated in the first maintenance data to obtain the minimum demand for replacement parts for each inventory location.

[0014] Obtain second maintenance data from the maintenance plan where the maintenance start date falls within the predicted date and the maximum reserve period;

[0015] Based on at least three dimensions—equipment type, maintenance level, and inventory location—the individual demand for replacement parts for the equipment to be maintained in the second maintenance data is set to the average usage of the repaired equipment under the same dimension values.

[0016] For each inventory location, the individual demand for replacement parts for all equipment to be repaired is accumulated in the second maintenance data to obtain the maximum demand for replacement parts for each inventory location.

[0017] The demand range is determined based on the minimum demand and the maximum demand.

[0018] According to the method for forecasting the demand for spare parts provided by the present invention, under the condition that the forecasting frequency is once a month, after forecasting the demand range of spare parts for each inventory location based on the inventory cycle range, maintenance plan and average usage of spare parts at different inventory locations, the method further includes:

[0019] If the actual consumption of the spare parts in the previous month is less than or equal to 50% of the minimum demand for the spare parts in the previous month, calculate the ratio of the actual consumption of the spare parts in the previous month to the minimum demand for the spare parts in the previous month.

[0020] The predicted range of demand for replacement parts for the current month is adjusted according to the ratio using the following formula;

[0021] n min =r·w1·n′ min

[0022] nmax=r·w2·n′ max

[0023] Where, n′ min and n′ max These represent the minimum and maximum demand quantities within the predicted monthly demand range for replacement parts, respectively; r is the ratio; w1 and w2 are adjustment weights, ranging from 1 to 1.5; and n... min and n max These are the minimum and maximum demand quantities, respectively, after adjusting for the predicted range of demand for occasional replacement parts in the current month.

[0024] The adjusted minimum and maximum demand quantities are used as the predicted range for the monthly demand for occasional replacement parts.

[0025] According to the method for forecasting demand for spare parts provided by the present invention, before determining the average usage of spare parts per unit of equipment in at least three dimensions—equipment type, maintenance level, and inventory location—based on the number of repaired equipment and the current usage, the method further includes:

[0026] Data cleaning is performed on the replacement component data to exclude replacement component data from equipment that has experienced an accident.

[0027] According to the method for predicting the demand for replacement parts provided by the present invention, the dimension further includes: maintenance cycle.

[0028] The method for predicting demand for interchangeable parts provided by the present invention further includes:

[0029] Get the current inventory and daily usage of replacement parts at each inventory location;

[0030] An inventory warning is issued if the remaining available inventory, obtained by subtracting the daily usage from the current inventory, exceeds the range of the stated demand.

[0031] The present invention also provides a device for predicting the demand of interchangeable parts, comprising:

[0032] The data collection module for mobile replacement parts is used to collect mobile replacement part data in at least three dimensions, including different equipment, maintenance level, and storage location, so as to obtain the current usage of mobile replacement parts in at least three dimensions.

[0033] The average usage determination module is used to determine the average usage of the replacement parts per unit of equipment based on the number of repaired equipment and the current usage, in at least three dimensions: equipment type, maintenance level, and inventory location.

[0034] The demand forecasting module is used to forecast the demand range of spare parts for each inventory location based on the reserve period range, maintenance plan, and average usage of spare parts at different inventory locations. The reserve period range includes the minimum and maximum reserve periods with the forecast date as the starting time. The maintenance plan includes planned maintenance data with maintenance start dates between T+1 and T+M days, where T represents the forecast date and M is greater than or equal to the maximum reserve period.

[0035] According to the present invention, a demand forecasting device for a coupling component is provided, wherein the demand forecasting module includes:

[0036] The first maintenance data acquisition module is used to acquire first maintenance data in the maintenance plan where the maintenance start date is within the predicted date and the minimum reserve period.

[0037] The first unit demand setting module is used to set the unit demand of the replacement parts of the equipment to be repaired in the first maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location.

[0038] The minimum demand calculation module is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the first maintenance data for each inventory location, so as to obtain the minimum demand of replacement parts for each inventory location.

[0039] The second maintenance data acquisition module is used to acquire second maintenance data from the maintenance plan where the maintenance start date is within the predicted date and the maximum reserve period.

[0040] The second unit demand setting module is used to set the unit demand of the replacement parts of the equipment to be repaired in the second maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location.

[0041] The maximum demand calculation module is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the second maintenance data for each inventory location, so as to obtain the maximum demand of replacement parts for each inventory location.

[0042] The demand range determination module is used to determine the demand range based on the minimum demand and the maximum demand.

[0043] The device for predicting demand for replacement parts according to the present invention further includes:

[0044] The data cleaning module is used to clean the data of the replacement parts in order to exclude the replacement parts data of the equipment that has experienced an accident.

[0045] According to the present invention, a replacement part demand prediction device further includes, in this dimension, maintenance cycle.

[0046] The device for predicting demand for replacement parts according to the present invention further includes:

[0047] The current inventory acquisition module is used to obtain the current inventory and daily usage of replacement parts in each inventory location;

[0048] The inventory warning module is used to issue an inventory warning when the remaining available inventory (obtained by subtracting the daily usage from the current inventory) exceeds the range of the demand.

[0049] The method and apparatus for predicting the demand for spare parts provided by this invention collects spare parts data according to at least three dimensions: different equipment, maintenance level, and storage location, to obtain the current usage of spare parts under at least three dimensions; based on the number of repaired equipment and the current usage, the average usage of spare parts per unit of equipment is determined under at least three dimensions: equipment type, maintenance level, and storage location; based on the reserve cycle range, maintenance plan, and average usage of spare parts at different storage locations, the demand range of spare parts for each storage location is predicted. This allows for reasonable and accurate prediction of the demand for spare parts for different storage locations, thereby avoiding situations such as production shortages or excessive inventory capital occupation. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the demand forecasting method for interchangeable parts provided by the present invention;

[0052] Figure 2 This is a schematic diagram illustrating the specific prediction process for the demand range of the occasional replacement parts in the demand prediction method provided by this invention.

[0053] Figure 3 This is a schematic diagram of the demand prediction device for coupling components provided by the present invention.

[0054] Figure 4 This is a schematic diagram of the specific structure of the demand prediction module in the demand prediction device for coupling components provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0056] The method for predicting demand for interchangeable parts according to embodiments of the present invention, such as Figure 1 As shown, it includes:

[0057] Step S110: Collect replacement part data according to at least three dimensions: different equipment, maintenance level, and inventory location, to obtain the current usage of replacement parts under at least three dimensions. Different types of equipment have different replacement parts, and each maintenance level has different requirements for replacement parts. Moreover, replacement part demand forecasting is based on inventory location to avoid situations where there is a shortage of production materials or excessive inventory capital occupation. Therefore, at least the above three dimensions need to be considered when collecting replacement part data. Replacement part data can be obtained from historical maintenance records. Taking the maintenance of rail vehicles as an example, the specific content of the replacement part data for a single maintained piece of equipment is shown in Table 1 below. The vehicle type is the equipment type, the column number represents a single train car under that equipment type, which can identify a unique maintenance object, and the maintenance process represents the maintenance level. The replacement part data includes: material code, material name, unit, and the current usage of replacement parts used in each maintained train car. For example, the data in the first row of Table 1 below indicates that when the train with model E01 and train number 2005 was inspected in the second round of maintenance of the fourth-level maintenance process, 20 nylon cable ties of 203 x 4.7 were used from the bogie warehouse inventory.

[0058] Table 1 Summary of Replacement Parts Data

[0059]

[0060]

[0061] Step S120: Based on the number of repaired equipment and the current usage, determine the average usage of replacement parts per unit under at least three dimensions: equipment type, maintenance level, and inventory location. Again, taking the maintenance of rail vehicles as an example, the replacement part data in Table 1 is summarized again according to the same equipment type. For example, as shown in Table 2 below, the data for two trains, 2005 and 2007, with model E01 in Table 1 are merged. The current usage of replacement parts in the bogie inventory is the sum of the current usage of replacement parts for each of the two trains, totaling 40.

[0062] After summarizing, determine the average usage of replacement parts per unit across at least three dimensions: equipment type, maintenance level, and inventory location. In this step, the total number of trains of the same model and the number of trains repaired can be obtained from previous maintenance records. For example, in the first row of Table 2, the total number of trains of type E01 is 100, and 11 trains have been repaired. These 11 repaired trains share 40 replacement parts (i.e., nylon cable ties 203 x 4.7) from the bogie inventory. Therefore, the average quantity per train (i.e., the average usage per unit) is 40 / 11 = 3.64. This data will serve as a reference for predicting replacement parts for the same type of vehicles (equipment), maintenance level, and inventory location during subsequent demand forecasting.

[0063] Table 2. Analysis Results of Partial Replacement Data

[0064]

[0065]

[0066] Step S130: Predict the demand range for spare parts at each inventory location based on the inventory cycle range, maintenance plan, and average usage of spare parts at different inventory locations. Specifically, the inventory cycle range for spare parts is set according to the procurement cycle range of the spare parts; the longer the procurement cycle, the longer the inventory cycle. As shown in Table 3 below, the minimum inventory cycle for spare parts with a procurement cycle range of 1 to 30 days is 40 days, i.e., T+40, where T represents the prediction date, and the maximum inventory cycle is 90 days, i.e., T+90. The prediction frequency is usually once a day. When the total product volume is large, it can also be predicted once a month; in this case, the inventory cycle can be appropriately extended.

[0067] The maintenance plan includes planned maintenance data for maintenance start dates between T+1 and T+M days, where T represents the forecast date and M is greater than or equal to the maximum reserve period, so that there is enough planned maintenance data in the maintenance plan for the period from the forecast date to the maximum reserve period for forecasting.

[0068] Since the prediction method in this embodiment is based on the maintenance plan, when the time range of the established maintenance plan is too small, for example, if only a maintenance plan for the next M = 30 days (T + 30 days) is established, that is, only maintenance data within T + 30 days is available, the prediction will be inaccurate when calculating the minimum reserve requirement of the replacement part according to the minimum reserve period of 40 days (T + 40 days). This is because the maintenance plan data is insufficient (actually only the next 30 days are calculated). Therefore, the time range of the equipment maintenance plan must be sufficient to cover the reserve period. For example, if the maximum reserve period for a certain replacement part is 90 days, then the established equipment maintenance plan must be at least T + 90 days. Taking T as 2022 / 8 / 1 and the maximum reserve period of 90 days as an example, the specific content is shown in the first six columns of Table 4 below, which are the planned maintenance data from 2022 / 8 / 1 to 2022 / 8 / 1 + 90 days. As shown in the first row of the first six columns in Table 4, for train car model E01 and train number 2024, the second round of maintenance of the fourth-level overhaul is planned to be completed between August 8, 2022 and September 7, 2022.

[0069] Table 3. Reserve Period Range for Replaceable Parts

[0070]

[0071] Table 4. Data on the maintenance plan and forecast of replacement parts demand.

[0072]

[0073]

[0074] In this embodiment, the specific steps for predicting the demand range of the replacement component in step S130 are as follows: Figure 2 As shown, it includes:

[0075] Step S210: Obtain the first maintenance data whose maintenance start date falls within the predicted date and minimum reserve period from the maintenance plan. For example, if the predicted date is T = August 1, 2022, and the minimum reserve period is 40 days, then the first maintenance data whose maintenance start date falls within the predicted date and minimum reserve period is the first 14 rows of data in Table 4 above. For a specific replacement part, its reserve period range is determined based on its procurement cycle and can be found in Table 3.

[0076] Step S220: Based on at least three dimensions—equipment type, maintenance level, and inventory location—set the individual demand for replacement parts for the equipment to be repaired in the first maintenance data to the average usage of the repaired equipment under the same dimension values. Specifically, as shown in the first row of Tables 2 and 4 above, the vehicle model is E01, the maintenance level is level four, and the inventory location of the replacement parts used is the bogie warehouse (i.e., the three dimensions of equipment type, maintenance level, and inventory location are the same). Therefore, the individual demand for replacement parts required by the train with column number 2024 in the first row of Table 4 in the bogie warehouse inventory is 3.64. Similarly, the individual demand for replacement parts required by the train corresponding to each row of data in the first maintenance data in different inventory locations can be set.

[0077] Step S230: For each inventory location, accumulate the individual demand for replacement parts of all equipment to be repaired in the first maintenance data to obtain the minimum demand for replacement parts at each inventory location. As shown in Table 4 above, according to the current maintenance plan, the minimum demand for replacement parts (i.e., nylon cable ties 203X4.7) with material number 190106 in the bogie warehouse inventory is 45.74, and the minimum demand in the final assembly warehouse inventory is 86.04. Of course, these demands need to be rounded up, and the minimum demand after rounding is 46 and 87 respectively.

[0078] Step S240: Obtain the second maintenance data from the maintenance plan, where the maintenance start date falls within the predicted date and the maximum reserve period. As shown in Table 4 above, all data within the above range are considered second maintenance data.

[0079] Step S250: Based on at least three dimensions—equipment type, maintenance level, and inventory location—set the individual demand for replacement parts of the equipment to be maintained in the second maintenance data to the average usage of the repaired equipment under the same dimension values.

[0080] Step S260: For each inventory location, accumulate the individual demand for replacement parts for all equipment to be repaired in the second maintenance data to obtain the maximum demand for replacement parts for each inventory location. The specific processes of steps S250 and S260 are basically the same as those of steps S220 and S230, respectively, and will not be repeated here. As shown in Table 4 above, according to the current maintenance plan, the maximum demand for replacement parts (i.e., nylon cable ties 203X4.7) with material number 190106 in the bogie warehouse inventory is 83.74, and the maximum demand in the final assembly warehouse inventory is 173.13. After rounding, the maximum demand is 84 and 174 respectively.

[0081] Step S270: Determine the demand range based on the minimum and maximum demand. For the bogie inventory, the demand range for the replacement part with material number 190106 is [46, 84]. For the final assembly inventory, the demand range for the replacement part with material number 190106 is [87, 174]. The resulting replacement part demand range data table is shown in Table 5 below.

[0082] Table 5. Demand Range Data for Replacement Parts

[0083]

[0084] The method for predicting the demand for spare parts in this embodiment predicts the range of spare parts required to complete the entire maintenance plan for each inventory location based on the quantity of spare parts used in the repaired equipment at different inventory locations, the inventory cycle of the spare parts, and the number of equipment to be repaired in the maintenance plan. This method can reasonably and accurately predict the demand for spare parts for different inventory locations, thereby avoiding situations where there is a shortage of materials for production or excessive inventory capital occupation, and saving enterprise costs.

[0085] In this embodiment, before step S120, the method further includes: data cleaning of the spare parts data to exclude spare parts data of equipment that has experienced an accident. In reality, some equipment, such as a certain train of a certain model, may have a large number of spare parts due to accidents or other special reasons, exceeding the normal maintenance requirements. Therefore, the data on the number of spare parts used for the relevant vehicles needs to be removed. Thus, in step S110, spare parts data is not summarized by equipment type, but by different types of equipment to exclude abnormal data (if spare parts data is summarized by equipment type, the impact of abnormal data will be evenly distributed across different equipment of the same type, making it impossible to detect abnormalities). By excluding abnormal situations, the accuracy of spare parts demand prediction is further improved. Specifically, relevant personnel mark the equipment that has experienced an accident. Before step S120, the spare parts data table summarized in step S110, i.e., Table 1, is checked to see if the equipment is marked. If it is marked, the data entry is cleaned.

[0086] In this embodiment, the dimension further includes: maintenance cycle. Each maintenance level involves multiple maintenance operations. The later the maintenance cycle, the longer the train's service life and the greater the wear, potentially requiring more replacement parts. Therefore, adding the maintenance cycle dimension allows for a more accurate prediction of the replacement parts demand. Considering the maintenance cycle dimension, steps S110 to S130 are as follows:

[0087] Data on replacement parts are collected according to four dimensions: different equipment, maintenance level, maintenance cycle, and storage location, so as to obtain the current usage of replacement parts under the four dimensions.

[0088] Based on the number of repaired equipment and the current usage, determine the average usage of the replacement parts per unit across four dimensions: equipment type, maintenance level, maintenance cycle, and storage location.

[0089] The demand range for spare parts at each storage location is predicted based on the inventory cycle range, maintenance plan, and average usage of spare parts at different storage locations.

[0090] The demand forecasting method for occasional replacement parts in this embodiment also includes a step of providing inventory warning based on the predicted demand range and current inventory status. Specifically, it includes:

[0091] Get the current inventory and daily usage of replacement parts for each inventory location.

[0092] Inventory alerts are issued when the remaining available inventory (calculated by subtracting the daily usage from the current inventory) exceeds the stated demand range. Specifically, as shown in Table 6, when the remaining available inventory is less than the minimum demand, the spare parts inventory alert indicates "low," requiring an increase in inventory; when the remaining available inventory is greater than the maximum demand, the spare parts inventory alert indicates "high," prohibiting an increase in inventory; when the remaining available inventory is between the minimum and maximum demand forecasts, the spare parts inventory alert indicates "normal," indicating a reasonable inventory level where no increase in inventory is necessary or appropriate. Spare parts inventory alerts promptly remind relevant personnel to adjust spare parts inventory in a timely manner, thereby preventing situations such as material shortages or excessive inventory capital tied up.

[0093] Table 6 Inventory Warning Table for Occasional Replacement Parts

[0094]

[0095] The demand forecasting method for spare parts in this embodiment is particularly applicable to the demand forecasting of spare parts inventory reserves for advanced maintenance projects on high-speed trains. By collecting and processing data on spare parts for different levels of advanced maintenance projects across all types of high-speed trains, and based on the analyzed and evaluated types and average usage of spare parts, combined with future advanced maintenance plans for each type of high-speed train, the demand for spare parts is uniformly predicted, enabling accurate forecasting of spare parts requirements for high-speed trains. Using the demand forecasting results as a quantitative basis for spare parts inventory reserves allows for maintaining inventory levels at a reasonably low level, solving the problem of excessively high inventory capital occupation, and is of great significance for cost control in advanced maintenance of high-speed trains.

[0096] The demand forecasting frequency for occasional replacement parts is typically once a day. When the total number of equipment is large, it can also be forecasted once a month. Due to the long forecasting interval, the average usage of occasional replacement parts changes slowly. For example, if there are 100 pieces of equipment, and the average usage of 20 pieces that have been repaired is relatively high, and then all equipment undergoes some kind of technical modification (for equipment already delivered to the user, the after-sales service department will handle the modification on-site; for equipment under repair at the factory, the modification will be carried out simultaneously with the repair), the actual usage of the occasional replacement part will decrease rapidly after the equipment is technically modified. In this case, the calculated average usage of the occasional replacement part will gradually decrease as subsequent equipment is repaired, rather than decreasing rapidly. If the demand range of the occasional replacement part is then predicted based on the average usage, the forecast range will be too large, wasting inventory costs. Therefore, in this embodiment, under the condition that the forecasting frequency is once a month, the method after step S130 further includes:

[0097] If the actual consumption of spare parts in the previous month is less than or equal to 50% of the minimum demand for spare parts in the previous month, calculate the ratio of the actual consumption of spare parts in the previous month to the minimum demand for spare parts in the previous month.

[0098] The predicted range of demand for replacement parts for the current month is adjusted according to the ratio using the following formula.

[0099] n min =r·w1·n′ min

[0100] n max =r·w2·n′ max

[0101] Where, n′ min and n′ max These represent the minimum and maximum demand quantities within the predicted monthly demand range for replacement parts, respectively; r is the ratio; w1 and w2 are adjustment weights, ranging from 1 to 1.5; w1 and w2 can be equal or unequal; n min and n max These represent the minimum and maximum demand quantities, respectively, after adjusting for the predicted range of demand for occasional replacement parts in the current month.

[0102] The adjusted minimum and maximum demand quantities are used as the predicted range for the monthly demand for occasional replacement parts.

[0103] The demand forecasting device for occasional replacement parts provided by the present invention is described below. The demand forecasting device for occasional replacement parts described below can be referred to in correspondence with the demand forecasting method for occasional replacement parts described above.

[0104] The replacement component demand prediction device of the present invention, as described in the embodiments, is as follows: Figure 3 As shown, it includes:

[0105] The data collection module 310 for the replacement parts is used to collect replacement parts data in at least three dimensions, including different equipment, maintenance level and storage location, so as to obtain the current usage of replacement parts in at least three dimensions.

[0106] The average usage determination module 320 is used to determine the average usage of a single device for occasional replacement parts in at least three dimensions: device type, maintenance level, and storage location, based on the number of repaired devices and the current usage.

[0107] The demand forecasting module 330 is used to forecast the demand range of spare parts for each inventory location based on the reserve period range, maintenance plan and average usage of spare parts at different inventory locations. The reserve period range includes the minimum and maximum reserve periods with the forecast date as the starting time. The maintenance plan includes planned maintenance data with maintenance start dates between T+1 and T+M days, where T represents the forecast date and M is greater than or equal to the maximum reserve period.

[0108] Optionally, the demand forecasting module 330 has the following structure: Figure 4 As shown, it includes:

[0109] The first maintenance data acquisition module 410 is used to acquire first maintenance data from the maintenance plan where the maintenance start date is within the predicted date and the minimum reserve period.

[0110] The first unit demand setting module 420 is used to set the unit demand of the replacement parts of the equipment to be repaired in the first maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location.

[0111] The minimum demand calculation module 430 is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the first maintenance data for each inventory location, so as to obtain the minimum demand of replacement parts for each inventory location.

[0112] The second maintenance data acquisition module 440 is used to acquire second maintenance data from the maintenance plan where the maintenance start date is located within the predicted date and the maximum reserve period.

[0113] The second unit demand setting module 450 is used to set the unit demand of the replacement parts of the equipment to be repaired in the second maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location.

[0114] The maximum demand calculation module 460 is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the second maintenance data for each inventory location, so as to obtain the maximum demand of replacement parts for each inventory location.

[0115] The demand range determination module 470 is used to determine the demand range based on the minimum demand and the maximum demand.

[0116] Optionally, the replacement component demand prediction device of the present invention further includes:

[0117] The data cleaning module is used to clean the data of the replacement parts in order to exclude the replacement parts data of the equipment that has experienced an accident.

[0118] Optionally, the dimension may also include: maintenance cycle.

[0119] Optionally, the replacement component demand prediction device of the present invention further includes:

[0120] The current inventory acquisition module is used to obtain the current inventory and daily usage of replacement parts in each inventory location.

[0121] The inventory warning module is used to issue an inventory warning when the remaining available inventory (obtained by subtracting the daily usage from the current inventory) exceeds the range of the demand.

[0122] Optionally, the replacement part demand forecasting device of the present invention further includes: a forecast range adjustment module, which, under the condition that the forecast frequency is once a month, is used for:

[0123] If the actual consumption of spare parts in the previous month is less than or equal to 50% of the minimum demand for spare parts in the previous month, calculate the ratio of the actual consumption of spare parts in the previous month to the minimum demand for spare parts in the previous month.

[0124] The predicted range of demand for replacement parts for the current month is adjusted according to the ratio using the following formula.

[0125] n min =r·w1·n′ min

[0126] n max =r·w2·n′ max

[0127] Where, n′ min and n′ max These represent the minimum and maximum demand quantities within the predicted monthly demand range for replacement parts, respectively; r is the ratio; w1 and w2 are adjustment weights, ranging from 1 to 1.5; w1 and w2 can be equal or unequal; n min and n max These represent the minimum and maximum demand quantities, respectively, after adjusting for the predicted range of demand for occasional replacement parts in the current month.

[0128] The adjusted minimum and maximum demand quantities are used as the range of demand for occasional replacement parts for the current month.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for forecasting demand for occasionally replaceable parts, characterized in that, include: Data on contactless replacement parts should be collected according to at least three dimensions: different equipment, maintenance level, and storage location, in order to obtain the current usage of contactless replacement parts under at least three dimensions; Based on the number of repaired equipment and the current usage, determine the average usage of the occasional replacement parts per unit of equipment in at least three dimensions: equipment type, maintenance level, and storage location. Based on the reserve period range of spare parts at different storage locations, maintenance plans, and the average usage, the demand range of spare parts at each storage location is predicted. The reserve period range includes the minimum and maximum reserve periods starting from the prediction date. The maintenance plan includes planned maintenance data with maintenance start dates between T+1 and T+M days, where T represents the prediction date and M is greater than or equal to the maximum reserve period.

2. The method for predicting demand for interchangeable parts according to claim 1, characterized in that, Based on the inventory cycle range, maintenance schedule, and average usage of spare parts at different inventory locations, the demand range for spare parts at each inventory location is predicted, including: Obtain the first maintenance data from the maintenance plan if the maintenance start date falls within the predicted date and the minimum reserve period; Based on at least three dimensions—equipment type, maintenance level, and inventory location—the individual demand for replacement parts for the equipment to be maintained in the first maintenance data is set to the average usage of the maintained equipment under the same dimension values. For each inventory location, the individual demand for replacement parts for all equipment to be repaired is accumulated in the first maintenance data to obtain the minimum demand for replacement parts for each inventory location. Obtain second maintenance data from the maintenance plan where the maintenance start date falls within the predicted date and the maximum reserve period; Based on at least three dimensions—equipment type, maintenance level, and inventory location—the individual demand for replacement parts for the equipment to be maintained in the second maintenance data is set to the average usage of the repaired equipment under the same dimension values. For each inventory location, the individual demand for replacement parts for all equipment to be repaired is accumulated in the second maintenance data to obtain the maximum demand for replacement parts for each inventory location. The demand range is determined based on the minimum demand and the maximum demand.

3. The method for predicting demand for interchangeable parts according to claim 2, characterized in that, Assuming a forecasting frequency of once a month, after forecasting the demand range for spare parts at each inventory location based on the inventory cycle range, maintenance plan, and average usage, the method further includes: If the actual consumption of the spare parts in the previous month is less than or equal to 50% of the minimum demand for the spare parts in the previous month, calculate the ratio of the actual consumption of the spare parts in the previous month to the minimum demand for the spare parts in the previous month. The predicted range of demand for replacement parts for the current month is adjusted according to the ratio using the following formula; n min =r·w1·n′ min n max =r·w2·n′ max Where, n′ min and n′ max These represent the minimum and maximum demand quantities within the predicted monthly demand range for replacement parts, respectively; r is the ratio; w1 and w2 are adjustment weights, ranging from 1 to 1.5; and n... min and n max These are the minimum and maximum demand quantities, respectively, after adjusting for the predicted range of demand for occasional replacement parts in the current month. The adjusted minimum and maximum demand quantities are used as the predicted range for the monthly demand for occasional replacement parts.

4. The method for predicting demand for interchangeable parts according to claim 1, characterized in that, Before determining the average usage of interchangeable parts per unit across at least three dimensions—equipment type, maintenance level, and storage location—based on the number of repaired equipment and the current usage, the process also includes: Data cleaning is performed on the replacement component data to exclude replacement component data from equipment that has experienced an accident.

5. The method for predicting demand for interchangeable parts according to claim 1, characterized in that, The dimension also includes: maintenance cycle.

6. The method for predicting demand for interchangeable parts according to any one of claims 1 to 5, characterized in that, Also includes: Get the current inventory and daily usage of replacement parts at each inventory location; An inventory warning is issued if the remaining available inventory, obtained by subtracting the daily usage from the current inventory, exceeds the range of the stated demand.

7. A device for predicting demand for interchangeable parts, characterized in that, include: The data collection module for mobile replacement parts is used to collect mobile replacement part data in at least three dimensions, including different equipment, maintenance level, and storage location, so as to obtain the current usage of mobile replacement parts in at least three dimensions. The average usage determination module is used to determine the average usage of the replacement parts per unit of equipment based on the number of repaired equipment and the current usage, in at least three dimensions: equipment type, maintenance level, and inventory location. The demand forecasting module is used to forecast the demand range of spare parts for each inventory location based on the reserve period range, maintenance plan, and average usage of spare parts at different inventory locations. The reserve period range includes the minimum and maximum reserve periods with the forecast date as the starting time. The maintenance plan includes planned maintenance data with maintenance start dates between T+1 and T+M days, where T represents the forecast date and M is greater than or equal to the maximum reserve period.

8. The device for predicting demand for interchangeable parts according to claim 7, characterized in that, The demand forecasting module includes: The first maintenance data acquisition module is used to acquire first maintenance data in the maintenance plan where the maintenance start date is within the predicted date and the minimum reserve period. The first unit demand setting module is used to set the unit demand of the replacement parts of the equipment to be repaired in the first maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location. The minimum demand calculation module is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the first maintenance data for each inventory location, so as to obtain the minimum demand of replacement parts for each inventory location. The second maintenance data acquisition module is used to acquire second maintenance data from the maintenance plan where the maintenance start date is within the predicted date and the maximum reserve period. The second unit demand setting module is used to set the unit demand of the replacement parts of the equipment to be repaired in the second maintenance data to the average usage of the repaired equipment under the same dimension value, according to at least three dimensions: equipment type, maintenance level and inventory location. The maximum demand calculation module is used to accumulate the individual demand of all replacement parts for all equipment to be repaired in the second maintenance data for each inventory location, so as to obtain the maximum demand of replacement parts for each inventory location. The demand range determination module is used to determine the demand range based on the minimum demand and the maximum demand.

9. The device for predicting demand for interchangeable parts according to claim 7, characterized in that, Also includes: The data cleaning module is used to clean the data of the replacement parts in order to exclude the replacement parts data of the equipment that has experienced an accident.

10. The device for predicting demand for interchangeable parts according to any one of claims 7 to 9, characterized in that, Also includes: The current inventory acquisition module is used to obtain the current inventory and daily usage of replacement parts in each inventory location; The inventory warning module is used to issue an inventory warning when the remaining available inventory (obtained by subtracting the daily usage from the current inventory) exceeds the range of the demand.

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