A spare part configuration method, system, electronic device and computer storage medium

Abstract

The present invention relates to a spare part configuration method, system, electronic device and computer storage medium. The method includes obtaining historical data of spare parts; the historical data includes the average demand of spare parts per unit time for each year and the equipment usage time for each year; constructing a spare part demand prediction model according to the historical data; determining the initial configuration quantity of spare parts according to the spare part configuration quantity weight coefficient; determining the annual safety inventory quantity according to the spare part safety inventory weight coefficient and the spare part demand prediction model; determining the secondary configuration time and the secondary configuration quantity of spare parts according to the annual safety inventory quantity, the initial configuration quantity of spare parts, the remaining quantity of spare parts at the end of each unit time after the equipment is put into use in the current year and the secondary configuration quantity weight coefficient; and summing the initial configuration quantity of spare parts and the secondary configuration quantity of spare parts to obtain the total annual spare part configuration quantity. The present invention can ensure that there is no out-of-stock phenomenon of spare parts and reduce the backlog of spare parts.

Description

Technical Field

[0001] The present invention relates to the field of spare part configuration, and particularly to a spare part configuration method, system, electronic device and computer storage medium. Background Art

[0002] The problem of spare part configuration refers to a series of issues regarding spare part ordering and storage management to ensure the normal operation of equipment during its use and maintenance. Generally, a spare part configuration plan is made annually. Since spare part configuration needs to meet the demand for spare part consumption while avoiding excessive backlog of spare parts, this leads to a dilemma in spare part configuration work and it is difficult to grasp. On the other hand, usually a small number of spare parts are configured after a new device is put into use, but as time goes by, the probability of equipment failure often increases. The probability of equipment failure is affected by the equipment's own failure rate, as well as environmental factors, use and maintenance factors, etc. This state makes it difficult to accurately describe the spare part consumption process with a certain probability distribution.

[0003] In order to comprehensively do a good job in the spare part support for equipment and effectively ensure the normal use of equipment, there is an urgent need for a simple and effective spare part configuration method. Summary of the Invention

[0004] The purpose of the present invention is to provide a spare part configuration method, system, electronic device and computer storage medium, which can not only ensure that there is no shortage of spare parts, but also reduce the backlog of spare parts.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] A spare part configuration method includes:

[0007] Obtaining historical data of spare parts; the historical data includes the average demand of spare parts per unit time for each year and the equipment usage time for each year;

[0008] Constructing a spare part demand prediction model according to the historical data;

[0009] Determining the initial configuration quantity of spare parts according to the spare part configuration quantity weight coefficient;

[0010] Determining the annual safety inventory according to the spare part safety inventory weight coefficient and the spare part demand prediction model;

[0011] Determining the secondary configuration time and secondary configuration quantity of spare parts according to the annual safety inventory, the initial configuration quantity of spare parts, the remaining quantity of spare parts at the end of each unit time after the annual equipment is put into use, and the secondary configuration quantity weight coefficient;

[0012] Summing the initial configuration quantity of spare parts and the secondary configuration quantity of spare parts to obtain the total annual spare part configuration quantity.

[0013] Optionally, the expression of the spare part demand prediction model is:

[0014]

[0015] where y n+1 is the demand for spare parts in the predicted year, T n+1 is the equipment usage time in the (n + 1)-th predicted year, s j is the average demand per unit time for spare parts of the set model in each year, j is the given annual data number, j = 1, 2,..., n, and n corresponds to the current year.

[0016] Optionally, the expression of the initial spare part allocation quantity is:

[0017]

[0018] where F n+1 is the initial spare part allocation quantity, and α1 is the weight coefficient of the spare part allocation quantity.

[0019] Optionally, the expression of the annual safety stock quantity is:

[0020]

[0021] where A n+1 is the safety stock quantity in the (n + 1)-th year, and α2 is the weight coefficient of the spare part safety stock.

[0022] Optionally, determining the secondary allocation time and the secondary allocation quantity of spare parts according to the annual safety stock quantity, the initial spare part allocation quantity and the secondary allocation quantity weight coefficient specifically includes:

[0023] Determining the secondary allocation time of spare parts and the sample standard deviation of the spare part consumption per unit time according to the annual safety stock quantity, the spare part surplus and the initial spare part allocation quantity;

[0024] Determining the secondary allocation quantity of spare parts according to the annual safety stock quantity, the spare part surplus, the secondary allocation time of spare parts, the sample standard deviation, the initial spare part allocation quantity and the secondary allocation quantity weight coefficient.

[0025] Optionally, after the equipment has been used for i * unit time in a year, the secondary allocation of spare parts is carried out, and the secondary allocation time of spare parts is S t , where the condition satisfied by the equipment using i * unit time is:

[0026]

[0027] where is the end moment of the i * th time unit of the spare part surplus; is the end moment of the (i * -1)th time unit of the spare part surplus;

[0028] The expression of the sample standard deviation is as follows:

[0029]

[0030] where s is the sample standard deviation of the spare part consumption per unit time in the first i * time units of equipment use, r i is the spare part surplus at the end moment t i of the i-th time unit, and i = 1, 2,..., i * .

[0031] Optionally, the expression of the secondary configuration quantity of the spare part is as follows:

[0032]

[0033] where S n+1 is the secondary configuration quantity of the spare part, and α3 is the weight coefficient of the secondary configuration quantity.

[0034] The present invention also provides a spare part configuration system, including:

[0035] An acquisition module, configured to acquire historical data of spare parts; the historical data includes the average demand of spare parts per unit time for each year and the equipment usage time for each year;

[0036] A construction module, configured to construct a spare part demand prediction model according to the historical data;

[0037] A primary configuration quantity determination module of spare parts, configured to determine the primary configuration quantity of spare parts according to the weight coefficient of the spare part configuration quantity;

[0038] An annual safety inventory determination module, configured to determine the annual safety inventory according to the weight coefficient of the spare part safety inventory and the spare part demand prediction model;

[0039] A secondary configuration quantity determination module of spare parts, configured to determine the secondary configuration time and the secondary configuration quantity of the spare parts according to the annual safety inventory, the primary configuration quantity of the spare parts, the spare part surplus at the end moment of each unit time after the annual equipment is put into use, and the weight coefficient of the secondary configuration quantity;

[0040] An annual total spare part configuration quantity determination module, configured to sum the primary configuration quantity of the spare parts and the secondary configuration quantity of the spare parts to obtain the annual total spare part configuration quantity.

[0041] The present invention also provides an electronic device, including:

[0042] One or more processors;

[0043] A storage device storing one or more programs thereon;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method described in any one of the above.

[0045] The present invention also provides a computer storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the method described in any one of the above.

[0046] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0047] The present invention obtains historical data of spare parts; the historical data includes the average demand of spare parts per unit time for each year and the equipment usage time for each year; constructs a prediction model for spare parts demand according to the historical data; determines the initial configuration quantity of spare parts according to the spare parts configuration quantity weight coefficient; determines the annual safety inventory according to the spare parts safety inventory weight coefficient and the spare parts demand prediction model; determines the secondary configuration time and the secondary configuration quantity of spare parts according to the annual safety inventory, the initial configuration quantity of spare parts, the remaining quantity of spare parts at the end of each unit time after the annual equipment is put into use, and the secondary configuration quantity weight coefficient; sums up the initial configuration quantity of spare parts and the secondary configuration quantity of spare parts to obtain the total annual spare parts configuration quantity. According to the historical data of spare parts consumption, roughly estimate the annual consumption of spare parts, set an initial configuration quantity of spare parts, and make a secondary configuration according to the actual situation after the configured spare parts are consumed to a certain extent, so as to meet the demand of faulty equipment for spare parts while reducing the backlog of spare parts, thereby ensuring that there is no shortage of spare parts and reducing the backlog of spare parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is a flowchart of the spare parts configuration method provided by the present invention;

[0050] Figure 2 It is a schematic diagram of the spare parts configuration method provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] The purpose of the present invention is to provide a spare part configuration method, system, electronic device and computer storage medium, which can not only ensure that there is no out-of-stock phenomenon of spare parts, but also reduce the backlog of spare parts.

[0053] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] As Figure 1 and Figure 2 shown, a spare part configuration method provided by the present invention includes:

[0055] Step 101: Obtain the historical data of spare parts; the historical data includes the average demand of spare parts per unit time in each year and the equipment usage time in each year.

[0056] Step 102: Build a prediction model for the demand of spare parts according to the historical data.

[0057] A prediction model for the demand of spare parts based on historical data is established. Input the average demand s j (j = 1, 2,..., n) pieces per unit time of a given model of spare parts in each year, where n is the number of years with data, and the equipment usage time in the (n + 1)-th year is T n+1 unit time. The prediction model for the demand of spare parts based on historical data is

[0058]

[0059] where y n+1 is the demand for spare parts in the predicted year, T n+1 is the equipment usage time in the (n + 1)-th year (predicted year) (in units of months or days, etc.), s j is the average demand of spare parts of a set model per unit time in each year, j is the data number of a given year, and the smaller the number, the longer the time from the predicted year. j = 1, 2,..., n, and n corresponds to the current year.

[0060] Step 103: Determine the initial configuration quantity of spare parts according to the spare part configuration quantity weight coefficient.

[0061] Input the spare part configuration quantity weight coefficient α1 (0 < α1 < 1), and the initial spare part configuration quantity is

[0062]

[0063] where, F n+1 is the initial spare part configuration quantity, and α1 is the spare part configuration quantity weight coefficient.

[0064] Step 104: Determine the annual safety inventory based on the spare part safety inventory weight coefficient and the spare part demand prediction model.

[0065] Input the spare part safety inventory weight coefficient α2 (0 < α2 < α1), then the safety inventory of the (n + 1)-th year is

[0066]

[0067] where, A n+1 is the safety inventory of the (n + 1)-th year, and α2 is the spare part safety inventory weight coefficient.

[0068] Step 105: Determine the spare part secondary configuration time and the spare part secondary configuration quantity based on the annual safety inventory, the initial spare part configuration quantity, the spare part remaining quantity at the end of each unit time after the annual equipment is put into use, and the secondary configuration quantity weight coefficient.

[0069] Step 105 specifically includes: determining the time of spare part secondary configuration and the sample standard deviation of the spare part consumption per unit time based on the annual safety inventory, the spare part remaining quantity, and the initial spare part configuration quantity; determining the spare part secondary configuration quantity based on the annual safety inventory, the spare part remaining quantity, the time of spare part secondary configuration, the sample standard deviation, the initial spare part configuration quantity, and the secondary configuration quantity weight coefficient.

[0070] After the annual equipment has been used for i * unit times, the spare part secondary configuration is carried out, and the spare part secondary configuration time is S t , where the condition satisfied by the equipment used for i * unit times is:

[0071]

[0072] where is the spare part remaining quantity at the end of the * i-th time unit; is the spare part remaining quantity at the end of the * (i - 1)-th time unit.

[0073] Input the weight coefficient α3, and the secondary configuration quantity of this type of spare part is:

[0074]

[0075] S n+1 is the secondary configuration quantity of spare parts, and α3 is the weight coefficient of the secondary configuration quantity.

[0076] Where s represents the sample standard deviation of the unit-time spare part consumption in the previous i * time units, that is

[0077]

[0078] Where r i is the spare part surplus at the end of the i-th (i = 1, 2,..., i * ) time unit at time t [[ID=********]] i of the spare parts.

[0079] Step 106: Sum the initial configuration quantity of the spare parts and the secondary configuration quantity of the spare parts to obtain the total annual spare part configuration quantity.

[0080] The total configuration quantity F of this type of spare parts in the (n + 1)-th year n+1 is

[0081] G n+1 = F n+1 + S n+1 (7)

[0082] Output the result and exit the calculation.

[0083] The present invention first establishes a prediction model for spare part demand based on historical data; secondly, adopts a heuristic strategy for spare part configuration; and finally, uses a spare part safety inventory strategy to determine the conditions for triggering a new round of spare part configuration. The spare part configuration method based on historical data realizes the effective configuration of spare parts required in equipment use and maintenance, ensures that the spare parts meet the equipment maintenance needs while effectively reducing the backlog of spare parts; the spare part configuration method based on historical data is easy to calculate, does not need to consider the consumption law of spare parts, involves less data in the calculation process, and the error is controllable; the spare part configuration method based on historical data has a wide range of applications and does not need to consider the statistical law of spare part consumption and many factors affecting spare part consumption.

[0084] The present invention also takes a certain fleet as an example. There are 50 trucks of the same model in a certain fleet, and each truck has 10 tires. The monthly average tire replacement information in the past ten years is shown in Table 1. The fleet plans to work for 9 months this year. Calculate the number of tires to be configured this year.

[0085] Table 1 Average monthly tire replacement quantity in the past 10 years

[0086] Year Average number of tires replaced per month Year Average number of tires replaced per month 2012 96 2017 115 2013 129 2018 100 2014 118 2019 72 2015 92 2020 95 2016 126 2021 104

[0087] According to the spare part configuration method based on historical data, the steps to calculate the number of tires configured this year are as follows:

[0088] Step 1: A tire demand prediction model based on historical data is established. Input the monthly average demand s of tires for each year j (j = 1, 2, …, n) pieces / month, n = 10, and the usage time of tires in 2022 is T n+1 = 9 months. The spare part demand prediction model based on historical data is

[0089]

[0090] Step 2: Input the spare part configuration quantity weight coefficient α1 = 0.9, and the initial configuration quantity of the spare part is

[0091]

[0092] Step 3: Input the spare part safety stock weight coefficient α2 = 0.15, then the safety stock quantity in 2022 is

[0093]

[0094] Step 4: During the operation of the fleet in 2022, after continuous operation for 5 months, the remaining spare tire quantity is r5 = 275, and after continuous operation for 6 months, the tire remaining quantity is r6 = 156. Then the conditions for secondary configuration of spare parts are met:

[0095] r6 ≤ A n+1 <r5 (11)

[0096] Step 5: Input the secondary configuration quantity weight coefficient α3 = 1, and the secondary configuration quantity of this type of spare part is

[0097]

[0098] where s represents the sample standard deviation of the unit time spare part consumption in the previous i * time units, that is

[0099]

[0100] The total configuration quantity of this type of spare part in the (n + 1)th year is

[0101] G n+1 = F n+1 + S n+1 = 1015 (14)

[0102] Output the result and exit the calculation.

[0103] The present invention sets three weight coefficients. If the value of α1 is too small, the time for secondary spare part allocation will be relatively early, which easily leads to a situation where there are too many remaining spare parts or a shortage of spare parts when the task ends after secondary allocation. The spare part safety stock weight coefficient α2 is related to the safety threshold. To avoid out-of-stock situations, α2 should satisfy that the consumption of spare parts per unit time is less than the safety stock quantity. The larger the secondary allocation quantity weight coefficient α3, the greater the probability of spare part redundancy; the smaller the secondary allocation quantity weight coefficient α3, the greater the probability of out-of-stock.

[0104] From the example solution process, it is not difficult to see that 664 tires were consumed in the first 6 months. If the monthly tire consumption in the remaining three months is independently and identically distributed with the monthly tire consumption in the first six months and both follow a normal distribution, then when α3 = 1, the sum of the secondary allocation quantity and the remaining tire quantity can meet the demand with a probability of 84%, and when α3 = 2, the sum of the secondary allocation quantity and the remaining tire quantity can meet the demand with a probability of 97.7%. Considering the constraint of spare part safety stock, the value of α3 should not be too large. Relatively speaking, in the case of not knowing the distribution information of tire consumption, the method of the present invention effectively guarantees the tire supply while reducing the backlog problem caused by excessive tire procurement.

[0105] The present invention also provides a spare part allocation system, including:

[0106] An acquisition module for acquiring historical data of spare parts; the historical data includes the average demand of spare parts per unit time for each year and the equipment usage time for each year.

[0107] A construction module for constructing a spare part demand prediction model based on the historical data.

[0108] A spare part initial allocation quantity determination module for determining the initial allocation quantity of spare parts according to the spare part allocation quantity weight coefficient.

[0109] An annual safety stock quantity determination module for determining the annual safety stock quantity according to the spare part safety stock weight coefficient and the spare part demand prediction model.

[0110] A spare part secondary allocation quantity determination module for determining the spare part secondary allocation time and the spare part secondary allocation quantity according to the annual safety stock quantity, the spare part initial allocation quantity, the remaining quantity of spare parts at the end of each unit time after the annual equipment is put into use, and the secondary allocation quantity weight coefficient.

[0111] An annual spare part allocation total quantity determination module for summing the spare part initial allocation quantity and the spare part secondary allocation quantity to obtain the annual spare part allocation total quantity.

[0112] The present invention also provides an electronic device, including:

[0113] One or more processors.

[0114] A storage device on which one or more programs are stored.

[0115] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method described above.

[0116] The present invention also provides a computer storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method described above.

[0117] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0118] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A spare part configuration method, characterized in that, including: Obtain historical data of spare parts; the historical data includes the average demand per unit time of spare parts in each year and the equipment usage time in each year; Construct a spare part demand prediction model based on the historical data; Determine the initial configuration quantity of spare parts according to the spare part configuration quantity weight coefficient; Determine the annual safety inventory quantity according to the spare part safety inventory weight coefficient and the spare part demand prediction model; Determine the spare part secondary configuration time and the spare part secondary configuration quantity according to the annual safety inventory, the initial spare part configuration quantity, the spare part surplus at the end of each unit time after the annual equipment is put into use, and the secondary configuration quantity weight coefficient. Specifically, it includes: determining the time of spare part secondary configuration and the sample standard deviation of the spare part consumption per unit time according to the annual safety inventory, the spare part surplus, and the initial spare part configuration quantity; determining the spare part secondary configuration quantity according to the annual safety inventory, the spare part surplus, the time of spare part secondary configuration, the sample standard deviation, the initial spare part configuration quantity, and the secondary configuration quantity weight coefficient; after the annual equipment has been used for i * unit time, conduct the secondary configuration of spare parts, and the spare part secondary configuration time is S t , S t =t i *, where the condition satisfied by the equipment using i * unit time is: r i *≤A n+1 ＜r i * -1 where r i * is the i * The end time of the time unit t i * Spare parts allowance; r i * -1 For the i * -1 time unit end time t i * -1 Spare parts allowance, A n+1 is the safety stock in the (n+1) year; The expression of the sample standard deviation is: Among them, s is the sample standard deviation of the unit-time spare part consumption in the first i * time units of the equipment use, r i is the spare part surplus at the end of the i-th time unit t i , i = 1, 2, …, i * , F n+1 is the initial spare part allocation quantity; The expression of the secondary configuration quantity of spare parts is: Among them, S n+1 is the secondary allocation quantity of spare parts, T n+1 is the equipment usage time in the (n + 1)-th predicted year, and α3 is the weight coefficient of secondary allocation quantity; Sum up the initial configuration quantity of spare parts and the secondary configuration quantity of spare parts to obtain the total annual spare part configuration quantity.

2. The spare part configuration method according to claim 1, characterized in that, The expression of the spare part demand prediction model is: Among them, y n+1 is the demand for spare parts in the predicted year, T n+1 is the equipment usage time in the (n + 1)-th predicted year, s j is the average demand per unit time for spare parts of the set model in each year. j is the given annual data number, j = 1, 2, …, n, and n corresponds to the current year.

3. The spare part configuration method according to claim 2, wherein The expression of the initial configuration quantity of spare parts is: Among them, F n+1 is the initial configuration quantity of spare parts, and α1 is the weight coefficient of spare parts configuration quantity.

4. The spare part configuration method according to claim 3, characterized in that The expression of the annual safety inventory quantity is: Among them, A n+1 is the safety inventory for the (n + 1)th year, and α2 is the weight coefficient of the spare part safety inventory.

5. A spare part configuration system, characterized in that, including: An acquisition module for obtaining historical data of spare parts; the historical data includes the average demand per unit time of spare parts in each year and the equipment usage time in each year; A construction module for constructing a spare part demand prediction model based on the historical data; A spare part initial configuration quantity determination module for determining the initial configuration quantity of spare parts according to the spare part configuration quantity weight coefficient; An annual safety inventory quantity determination module for determining the annual safety inventory quantity according to the spare part safety inventory weight coefficient and the spare part demand prediction model; Spare part secondary configuration quantity determination module, which is used to determine the spare part secondary configuration time and the spare part secondary configuration quantity according to the annual safety inventory, the initial configuration quantity of the spare parts, the remaining quantity of the spare parts at the end of each unit time after the annual equipment is put into use, and the secondary configuration quantity weight coefficient. Specifically, it includes: determining the time of spare part secondary configuration and the sample standard deviation of the spare part consumption per unit time according to the annual safety inventory, the remaining quantity of the spare parts, and the initial configuration quantity of the spare parts; determining the spare part secondary configuration quantity according to the annual safety inventory, the remaining quantity of the spare parts, the time of spare part secondary configuration, the sample standard deviation, the initial configuration quantity of the spare parts, and the secondary configuration quantity weight coefficient; after the annual equipment has been used for i * unit time, the spare parts are reconfigured, and the spare part secondary configuration time is S t , S t =t i *, where the conditions satisfied by the equipment using i * unit time are as follows: r i *≤A n+1 ＜r i * -1 where r i * is the spare part surplus at the end of the * i-th time unit, t i *; r i * -1 is the spare part surplus at the end of the * (i - 1)-th time unit, t i * -1 ; A n+1 is the safety inventory in the (n + 1)-th year; The expression of the sample standard deviation is: Among them, s is the sample standard deviation of the unit-time spare part consumption in the first i * time units of the equipment use, r i is the spare part surplus at the end of the i-th time unit t i , i = 1, 2, …, i * , F n+1 is the initial spare part configuration quantity; The expression of the secondary configuration quantity of spare parts is: Among them, S n+1 is the secondary configuration quantity of spare parts, T n+1 is the equipment usage time in the (n + 1)-th predicted year, and α3 is the weight coefficient of the secondary configuration quantity; An annual spare part configuration total quantity determination module for summing up the initial configuration quantity of spare parts and the secondary configuration quantity of spare parts to obtain the total annual spare part configuration quantity.

6. An electronic device, characterized in that, including: One or more processors; A storage device storing one or more programs thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer storage medium, characterized in that, A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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