A method for maintaining spare parts inventory decisions

Abstract

The application relates to a maintenance accessory safety stock decision method, and belongs to the technical field of computer statistical analysis.The application expands monthly accessory fault and scrapping data to daily data corresponding to days, and enhances data availability; according to the business characteristics of the maintainable accessory, an inventory turnover model is established, so that the established inventory turnover model contains an accessory supplement process, a warehouse delivery process and a fault accessory repair process; the expanded data is introduced into the inventory turnover model, so that a function relationship is formed between the safety stock value and corresponding generated storage costs and average delivery time, the storage costs and the average delivery time are taken as optimization targets, a genetic algorithm is used for multi-target optimization, and the optimal safety stock can be obtained.The application combines the business process of the maintainable accessory, greatly improves the reliability of the obtained result, reduces the enterprise inventory management cost, and improves the warehouse risk resistance.

Description

Technical Field

[0001] This invention relates to a method for making safety stock decisions on maintenance parts, belonging to the field of computer statistical analysis technology. Background Technology

[0002] In high-end manufacturing, making intuitive, accurate, and effective safety stock decisions has always been a challenge for enterprises. Safety stock, as an indicator of inventory status, triggers replenishment when warehouse levels fall below safety stock levels. Its fundamental purpose is to address uncertainties in the enterprise's supply chain, ensuring a healthy warehouse environment, reducing the amount of spare parts stored while meeting customer needs, decreasing capital expenditure, improving warehousing efficiency, and ultimately leading to higher economic benefits for the enterprise.

[0003] Traditional safety stock calculations are derived from statistical methods. The idea is to quantify the uncertainty of demand by simulating a normal distribution curve for demand over several months and calculating the mean and variance of the demand distribution to obtain the volatility characteristics of demand.

[0004]

[0005] Where Fn i Let Fn represent the spare parts consumption of the target warehouse in month i, and let Fn represent the average monthly consumption of the warehouse over a total of I months. The standard deviation σ of the warehouse consumption quantity can be calculated using the formula. s .

[0006] The procurement cycle and transportation cycle of the statistical warehouse are considered as the procurement lead time, taking into account the demand risk within the procurement cycle:

[0007] L = L purchase +L trans

[0008] Where L is the procurement lead time, which is determined by the procurement cycle L purchase With transportation cycle L trans constitute.

[0009] The customer demand satisfaction rate is statistically analyzed and used as a standard for enterprise service level. However, a higher service level is not necessarily better. Enterprises need to achieve a balance between service level and inventory costs. By quantifying the service level through a standard normal distribution, the service level coefficient (Z value) corresponding to a high service level can be obtained, thereby constraining the calculation of safety stock.

[0010] Service level quantitative distribution such as Figure 1 As shown, if a company wants to achieve a service level of 95%, that is, 95% of customer needs can be met in a timely manner, the corresponding service level coefficient K = 2.

[0011] In summary, the formula for calculating safety stock is:

[0012]

[0013] Traditional safety stock calculation methods are simple and reliable, but they also have limitations. Traditional methods can calculate safety stock values, but this only indicates when to replenish stock in the warehouse; on-site personnel still need to judge the replenishment quantity based on experience, which may lead to overstocking. Repairable spare parts are characterized by high value, repairability, long procurement cycles, and extremely low tolerance for stockouts. The main purpose of storing repairable spare parts in the warehouse is to promptly replace parts that fail unexpectedly on the construction site; therefore, stockouts of such parts can cause significant economic losses. Furthermore, the high value and long procurement cycles of these parts mean that there will be significant delays after the company places a procurement plan, and excessive stockpiling will tie up a large amount of capital, resulting in losses. Repairable spare parts can be replenished to the warehouse as spares after repair, which poses a challenge to the quantity of replenishment orders.

[0014] When applying classic safety stock calculation methods to such spare parts, on the one hand, the number and duration of spare part failures at construction sites (i.e., the required quantity and duration) are random and may not conform to a normal distribution; blindly applying the calculation method can lead to errors. On the other hand, if... Figure 2 As shown, due to the existence of the parts return process, the sources of repairable parts in stock are more diversified, and the warehouse operation process is close to a closed loop. The number of warehoused parts does not simply increase or decrease. Therefore, this calculation method is difficult to meet the inventory needs of related parts. Summary of the Invention

[0015] The purpose of this invention is to provide a method for determining the safety stock of maintenance parts, in order to solve the problems of poor accuracy and weak risk resistance of the safety stock values ​​calculated by formulas.

[0016] To address the aforementioned technical problems, this invention provides a method for determining the safety stock of maintenance parts, comprising the following steps:

[0017] 1) Obtain fault and scrap data for repairable parts and expand upon it;

[0018] 2) Establish an inventory turnover model based on the business process of repairable parts. The inventory turnover model includes constraints on the parts replenishment process, warehouse delivery process, and faulty parts return process.

[0019] 3) Based on the established inventory turnover model, with the goal of minimizing the unit part delivery time and the total inventory management cost within a set number of days, establish a corresponding objective function;

[0020] 4) Solve the objective function to obtain the safety stock value and the upper limit of inventory.

[0021] This invention first expands monthly spare parts failure and scrap data by randomly distributing the monthly data to daily data for corresponding days, enhancing data usability. Then, it establishes an inventory turnover model based on the business process of repairable spare parts. This model includes constraints on spare parts replenishment, warehouse delivery, and faulty spare parts return processes. An objective function is established based on minimizing the unit spare parts delivery time and total inventory management cost within a set number of days, and solving the objective function yields the required safety stock value SS and the inventory limit stocklimit. This invention, by incorporating the business process of repairable spare parts, significantly improves the reliability of the results, reduces enterprise inventory management costs, and enhances the warehouse's resilience.

[0022] Furthermore, the limitations of the parts replenishment process in step 2) are as follows:

[0023] Stock j =Stock j-1 +Apply j +Back j

[0024] Stock j <SS,Apply j+transtime =Stocklimit-Stock j -SumApply

[0025] Apply j For the number of parts delivered to the warehouse on day j, Back j Stock represents the number of parts returned to the warehouse on day j after a successful repair. j Here, SS represents the real-time inventory in the warehouse on day j, SS represents the safety stock, and Stocklimit represents the upper limit of the inventory model. Apply j+transtime This represents the number of parts that will be delivered to the warehouse on day j+transtime, and SumApply is the total number of newly requested parts that have not yet arrived at the warehouse.

[0026] Furthermore, the limitations of the warehouse delivery process in step 2) are as follows:

[0027]

[0028] Stock j Fn represents the real-time inventory in the warehouse on day j. jThis represents the number of faulty parts on day j, time. j denoted as the total delivery time of the warehouse on day j, where usualtime is the delivery time for a single component when inventory is sufficient, and emergencytime is the delivery time when unmet delivery needs are met by other channels when inventory is insufficient.

[0029] Furthermore, the limitations of the fault repair process in step 2) are as follows:

[0030] Back j+repairtime =Fn j -Dn j

[0031] Fn j Dn represents the number of parts that malfunctioned on day j. j This indicates the number of parts that are not worth repairing on day j. j+repairtime This indicates the number of parts returned to the warehouse on day j+repairtime after repair.

[0032] Furthermore, the fault and scrap data of repairable parts obtained in step 1) are time-series monthly data. The expansion refers to the randomization of the time-series monthly data to specific dates, transforming it into time-series daily data.

[0033] Furthermore, the specific formula used for expansion is as follows:

[0034]

[0035] Daydata=Monthdata*random(month i )

[0036]

[0037] data i ≥broken i

[0038] Where Monthdata represents the time-series monthly data of repairable parts, data i This represents the number of parts failures in the warehouse's region in month i, based on actual data. i Let random(month) be the total number of days in month i. i ) represents [0, month i The range of random numbers; Daydata represents the converted time-series daily data of repairable parts; broken i This represents the number of scrapped parts among the faulty parts in month i. Daybroken is derived from the previous broken parts in Daydata.i Consists of pieces of data.

[0039] Further, the objective function established in said step 3) is:

[0040] Min(COST,TIME)

[0041]

[0042]

[0043] cost j =Stock j *price

[0044] wherein, TIME is the calculated unit distribution duration generated within the set number of days, J is the set number of days, Fn j represents the number of faulty accessories generated on the j-th day, time j is the total distribution duration of the warehouse on the j-th day, COST is the total inventory management cost generated within the set number of days, cost j is the inventory management cost generated on the j-th day, price is the daily inventory cost of a single accessory, Stock j is the real-time inventory in the warehouse on the j-th day.

[0045] Further, said step 4) uses a genetic algorithm to solve the objective function.

[0046] Further, the limiting conditions of said fault repair process further include:

[0047]

[0048] wherein, status<criterion represents an accessory without maintenance value, and status>criterion represents an accessory with maintenance value. Description of Drawings

[0049] Figure 1 is a schematic diagram of quantitative distribution of service level of existing enterprises;

[0050] Figure 2 is a business processing flow chart of conventional accessories and repairable accessories;

[0051] Figure 3 is a flow chart of the safety inventory decision method for maintenance accessories of the present invention;

[0052] Figure 4 is monthly accessory fault data in an embodiment of the present invention;

[0053] Figure 5This is the daily component failure data obtained after expansion in the embodiments of the present invention;

[0054] Figure 6 This is a business process diagram of repairable parts in an embodiment of the present invention;

[0055] Figure 7 This is a graph showing the change in inventory quantity in the model of this invention embodiment;

[0056] Figure 8 This refers to the monthly component data used in the example of this invention;

[0057] Figure 9 This refers to the partial daily parts data after the monthly parts data has been expanded in the example used in this invention. Detailed Implementation

[0058] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0059] This invention, based on small sample data and considering the randomness of on-site parts failures, expands the small sample data to reflect real-world failure scenarios. It then mathematically models the warehouse process within a collaborative context of parts manufacturing and remanufacturing operations. After importing failure data, it simulates dynamic inventory changes in the warehouse under uneven parts demand, statistically analyzing and using the resulting inventory costs and average delivery time as optimization objectives. Finally, a genetic algorithm is used for multi-objective optimization to obtain the optimal safety stock and inventory ceiling values ​​suitable for the warehouse. The implementation process of this method is as follows: Figure 3 As shown, the specific implementation process is as follows.

[0060] 1. Obtain and expand upon data on the failure and scrapping of repairable parts.

[0061] The data on component failures and scrapping is usually obtained monthly, meaning it's statistical data compiled by month. Figure 4 The data shown only reflects the warehouse's specific needs in a particular month. Due to the nature of repairable parts, it's necessary to consider the return and repair of faulty parts and to set a realistic return success rate based on the data. However, monthly data distribution lacks deeper information value and is insufficient to meet these needs. Therefore, the acquired monthly data distribution of faulty and scrapped parts needs to be expanded, transforming the time-series monthly data into time-series daily data.

[0062] This invention randomly distributes the total monthly demand across specific dates, creating entirely new daily demand data. The specific data expansion method is as follows:

[0063]

[0064] Daydata=Monthdata*random(monthi (2)

[0065]

[0066] data i ≥broken i (4)

[0067] Among them, data i This represents the number of parts failures in the warehouse's region in month i, based on the actual data. i The size of the month determines the dimension of the data matrix in Monthdata; month i Let random(month) be the total number of days in month i. i ) represents [0, month i The random number within the interval, i.e., a random day in the i-th month, is used to calculate the Daydata matrix, which represents the converted daily demand. Each dimension's value represents the date the demand was generated. (broken) i This represents the number of scrapped parts among the faulty parts in month i. Daybroken is derived from the previous broken parts in Daydata. i The data consists of several components; the number of faulty parts each month is always greater than or equal to the number of parts scrapped. The expanded data has many dimensions; a partial result is shown below. Figure 5 As shown.

[0068] 2. Establish an inventory turnover model based on the business process of repairable parts.

[0069] The business process for repairable parts is as follows Figure 6 As shown, the inventory turnover model includes the parts replenishment process, the warehouse delivery process, and the faulty parts return process. Therefore, the established inventory turnover model includes a parts replenishment sub-model, a warehouse delivery sub-model, and a faulty parts return sub-model.

[0070] The parts replenishment process is as follows:

[0071] At the start of each day, the warehouse will receive replenished and returned parts and update the inventory:

[0072] Stock j =Stock j-1 +Apply j +Back j (5)

[0073] At the end of each day, the warehouse conducts an inventory count to replenish stock, following the following rules:

[0074] Stock j <SS,Applyj+transtime =Stocklimit-Stock j -SumApply (6)

[0075] At the start of each day, previously requested or returned parts are replenished to the inventory, including Apply. j For the number of parts delivered to the warehouse on day j, Back j This refers to the quantity of parts returned to the warehouse on day j after a successful repair. During the parts replenishment process, when the inventory quantity falls below the safety stock SS, the warehouse will submit a replenishment order based on the inventory limit, real-time inventory, and missing parts. Here, j represents the date, and the time measure is days. Apply j+transtime This represents the number of parts that will be shipped to the warehouse on day j+transtime. (Stock) j This represents the real-time inventory in the inventory model. SS is the safety stock, Stocklimit is the upper limit of the inventory model, and SS and Stocklimit are also the solution objectives of the model. SumApply is the sum of newly requested parts that have not yet arrived in the warehouse.

[0076] The process for dispatching parts from the warehouse is as follows:

[0077] When a faulty part is found in the area served by the warehouse, the warehouse is required to dispatch the part to replace it and then return the faulty part to the repair center. The rules are as follows:

[0078]

[0079] Among them, Fn j This represents the number of faulty parts on day j. The total delivery time for the day is calculated based on the number of faulty parts and the inventory. When the inventory is sufficient, the delivery time for a single part is usually time. When the inventory is insufficient, the unmet delivery needs are met by other channels, and the delivery time is emergency time, where emergency time > usual time.

[0080] The procedure for returning faulty parts is as follows:

[0081] Faulty parts from the warehouse area will be collected and sent to the repair center for screening and repair. Faulty parts that are no longer repairable will be scrapped. Repaired parts will be returned to their respective warehouses, following the rules as follows:

[0082]

[0083] Back j+repairtime =Fn j -Dn j (9)

[0084] Formula (8) represents the process of screening and inspecting each part, repairing parts that still have repair value, and returning them to their respective warehouses; Formula (9) represents the time and quantity of repaired parts with repair value, Fn j Dn represents the number of parts that malfunctioned on day j. j This indicates the number of parts that are not worth repairing on day j. j+repairtime This indicates the number of parts returned to the warehouse on day j+repairtime after repair.

[0085] Based on the above process, the inventory turnover model established by this invention is formula (5)-formula (9).

[0086] 3. Based on the established inventory turnover model, establish a corresponding objective function with the goal of minimizing the unit part delivery time and the total inventory management cost within a set number of days.

[0087] Based on the established inventory turnover model, the unit delivery time and total inventory management cost within a set number of days can be calculated. The specific calculation formula is as follows:

[0088]

[0089]

[0090] cost j =Stock j *price

[0091] Based on this, the objective function is established as follows:

[0092] Min(COST,TIME) (12)

[0093] 4. Solve the above objective function to determine the safety stock value SS and the upper limit of stock inventory Stocklimit.

[0094] This invention employs a genetic algorithm to perform multi-objective optimization on the established objective function. In this embodiment, the population size is set to 60, the maximum number of generations is 100, the mutation probability is 0.2, the crossover probability is 0.9, and the number of variables is 2, namely SS and stocklimit.

[0095] By optimizing the above objective function, a set of optimized solutions can be obtained. Since the timeliness of parts supply is more important in the problem of calculating the safety stock of repairable parts, the solution that prioritizes timeliness is selected as the optimal solution. Through the above process, the optimized safety stock value is 11, and the upper limit of the stock is 17. Based on the above results, the change in stock quantity in this embodiment is as follows: Figure 7 As shown.

[0096] To further verify the effectiveness of the invention, the original data was expanded 50 times to obtain 50 different sets of daily data as input. The changes in inventory status of the inventory model in the 50 data cycles were statistically analyzed to examine the actual effect of the optimized solution. The experiment showed that there were only 5 stockouts in the 50 inventory cycles, indicating that the safety stock value and inventory limit obtained by the invention are highly reliable and improve the warehouse's risk resistance.

[0097] To further illustrate the implementation process of this invention, a specific example will be used for explanation.

[0098] Assuming the monthly parts data obtained in this example is as follows: Figure 8 As shown, the first row represents the number of faults in the month, and the second row represents the number of scrapped parts in the month. It can be seen that the number of faulty parts in the month is 10, and the number of scrapped parts is 1. This means that of the faulty parts in this month, 9 parts were successfully repaired after the fault, while 1 part failed to be repaired. Following the above expansion method (i.e., according to formulas (1)-(4)), the partial daily part data is as follows: Figure 9 As shown, each row represents one day, the first column represents the number of failures on that day, and the second row represents the number of scrapped items on that day.

[0099] As can be seen, there were 0 faults on the first day; 2 faulty parts on the second day, and both of them were successfully repaired; 0 faults on the third day; ...; 1 faulty part on the tenth day, but this part was not worth repairing and was scrapped after repair failure; ...; 1 faulty part on the seventeenth day, and this part was successfully repaired.

[0100] Assuming the inventory turnover model has a safety stock value (SS) of 13 and a storage limit (Stocklimit) of 22; the initial inventory of spare parts equals the storage limit, which is 22; the standard delivery time (usualtime) is 1 day; the emergency delivery time (emergencytime) is 3 days; the daily inventory management cost per spare part is calculated at 1 unit (price); the repair time for spare parts is 15 days; and the procurement cycle (transtime) is 50 days.

[0101] Taking the first day as an example, the warehouse will first receive the previously ordered or successfully repaired parts. Since it is the first day, there are no repaired or ordered parts, so Apply and back are 0. According to formula (5), the inventory quantity Stock remains unchanged.

[0102] If it is day N, and the stock quantity is 6, and 7 parts were purchased before the 50-day procurement cycle, and 3 parts were successfully repaired before the 15-day repair cycle, then all of them will be put back into the warehouse today after the cycle ends. The total stock quantity will then be updated to Stock + Apply + Back = 6 + 7 + 3 = 16.

[0103] Check if there is demand for the day. If there are no faulty parts that day, as on the first day, then nothing will happen. If, as on the second day, there are faulty parts that occur that day, then Fn... j >0. The warehouse needs to supply spare parts to areas experiencing malfunctions, and faulty parts will be sent to the repair center for repair. First, we need to determine if the current inventory level can meet the demand. For example, if the current inventory level (Stock) is 22, and Fn... j If there are two parts, then the warehouse can meet the demand. The two parts will be shipped from the warehouse using standard transportation methods. The resulting transportation time will be calculated. j =usualtime*Fn j =1*2=2, update the inventory quantity of parts in the warehouse, Stock=Stock-Fn j =20;

[0104] When the warehouse's inventory cannot meet the demand for spare parts, for example, if the inventory is 2 units and the faulty spare part Fn is unavailable... j If there are four requests, the warehouse will fulfill two requests using the standard method, with a delivery time of "usual time." The additional two requests will be delivered via other channels using an emergency method, with a delivery time of "emergency time." The resulting transportation time will be calculated as follows:

[0105] time j =emergencytime*(Fn j -Stock j )+usualtime*Stock j =3*(4-2)+1*2=8, update the number of parts in the warehouse. Since the warehouse is empty, Stock=0.

[0106] Faulty parts are sent to the repair center for repair assessment. Based on the fault-to-scrap comparison data obtained from formulas (1)-(4), the faulty parts are assessed for scrap and the corresponding scrap quantity is calculated (refer to formulas (8) and (9)). Taking the data from the second day as an example, the number of faults Fn j The value is 2, corresponding to the scrap quantity Dn. jA value of 0 indicates that both faulty parts are repairable and will be returned to the warehouse after repair. Based on the parameters above, the repair time is 15 days. Therefore, these two parts will be successfully repaired and returned to the warehouse after 15 days. j+repairtime =Fn j -Dn j

[0107] The warehouse incurs daily storage costs for spare parts, calculated based on the current inventory level. For example, if it's the second day, after delivering two spare parts, the current inventory (Stock) is 20. The daily inventory cost per spare part is 1 unit. Therefore, the cost... j =Stock j You can calculate the daily inventory cost by using *price.

[0108] To determine if the current inventory level is less than the safety stock (SS), take the second day as an example. At this point, the inventory level is 20, while the safety stock (SS) is 13, so no replenishment is needed. If, after several days, the inventory level drops to 10, which is less than the safety stock, then replenishment is required. Since no replenishment has been performed before, the available parts (SumApply) after the request will be 0. The replenishment quantity will be stocklimit - stock - SumApply, i.e., 22 - 10 - 0 = 12. The warehouse will purchase 12 parts, which will arrive after a 50-day procurement cycle. If the inventory level remains 1 the next day... If the inventory level drops to 0, since 12 new parts have already been purchased and not yet delivered to the warehouse, then SumApply = 12, and the replenishment quantity is stocklimit - stock - SumApply, which is 22 - 10 - 12 = 0. The warehouse will not place a replenishment order. If the inventory level drops to 9 the next day, since 12 new parts have already been purchased and not yet delivered to the warehouse, then SumApply = 12, and the replenishment quantity is stocklimit - stock - SumApply, which is 22 - 9 - 12 = 1. The warehouse will place a new purchase order with a purchase quantity of 1, and the arrival time will be 50 days later.

[0109] The model calculates the data for each day using formula (5-10) and updates the warehouse parts quantity (stock) in real time. During this process, the delivery time and warehousing costs for each day can be calculated. Summing these values ​​using formula (11-12) yields the total delivery time and total warehouse costs corresponding to the safety stock value (SS) and the storage limit (Stocklimit) for that period.

[0110] This invention combines data augmentation with warehouse modeling, and then uses intelligent optimization algorithms to solve the problem. It can calculate not only the safety stock value (SS) but also the upper limit of inventory storage (stocklimit), helping companies to formulate procurement plans. Simultaneously, the results are repeatedly imported into the inventory model with continuously randomized demand data to verify the performance of the safety stock value (SS) and the upper limit of inventory (stocklimit) under different demand densities. This significantly improves the reliability of the results, reduces enterprise inventory management costs, and enhances the warehouse's resilience.

Claims

1. A method for determining safety stock of maintenance parts, characterized in that, The method includes the following steps: 1) Obtain time-series monthly data on the failure and scrapping of repairable parts, and expand it to randomly distribute the time-series monthly data across specific dates, transforming it into time-series daily data, including time-series daily data on the failure and scrapping of repairable parts; obtain the daily number of failure and scrapping parts based on the time-series daily data. 2) Establish an inventory turnover model based on the business process of repairable parts. The inventory turnover model includes: Limitations of the parts replenishment process: For the first The number of parts delivered to the warehouse each day For the first time after successful repair The number of parts returned to the warehouse each day For the first Real-time inventory in the warehouse For safety stock, This represents the upper limit of inventory in the inventory model. Represents the first The number of parts delivered to the warehouse each day; SumApply is the total number of newly requested parts that have not yet arrived at the warehouse. Limitations of the warehouse delivery process: Indicates the first Number of faulty parts per day For the first Total delivery time for the warehouse. For delivery time of a single component when inventory is sufficient, Delivery time for unmet delivery needs to be met through other channels when inventory is insufficient; Limitations of the faulty parts return and repair process: Indicates the first The number of parts that are beyond repair and must be scrapped. Indicates that after repair, on the [date] The number of parts returned to the warehouse each day; 3) Establish the objective function based on the established inventory turnover model: TIME is the unit delivery time generated within the calculated set number of days, and COST is the total inventory management cost generated within the set number of days; 4) Solve the objective function to obtain the safety stock value and the upper limit of inventory.

2. The maintenance parts safety stock decision-making method according to claim 1, characterized in that, The methods for solving the objective function to obtain the safety stock value and the inventory ceiling include: The objective function is optimized to obtain a set of optimized solutions; the optimized solutions that are more time-sensitive are selected as the optimal solutions, so that the calculation of the safety stock of repairable parts is more focused on the timeliness of parts supply.

3. The maintenance parts safety stock decision-making method according to claim 1 or 2, characterized in that, The specific formula used for expansion is as follows: in This represents the time-series monthly data for repairable parts. This represents the region where the warehouse belongs in the actual data. Monthly number of parts failures. For the first Total number of days in a month It represents Random numbers in an interval This represents the time-series daily data of the converted repairable parts. Represents the first The number of scrapped parts among the monthly faulty parts. Depend on Center front It consists of several data points.

4. The maintenance parts safety stock decision-making method according to claim 1 or 2, characterized in that, The calculation methods for unit delivery time and total inventory management costs generated within a set number of days include: Where TIME is the unit delivery time generated within the calculated set number of days, and J is the set number of days. Indicates the first The number of parts that malfunction daily. For the first The total delivery time for the warehouse within a set number of days; COST is the total inventory management cost incurred within that set number of days. For the first Inventory management costs incurred daily The daily inventory cost for a single component. For the first Real-time inventory in the warehouse.

5. The maintenance parts safety stock decision-making method according to claim 1, characterized in that, Step 4) uses a genetic algorithm to solve the objective function.

6. The maintenance parts safety stock decision-making method according to claim 1, characterized in that, The limitations of the faulty parts return and repair process also include: in, This refers to parts that are not worth repairing. This refers to parts that are worth repairing.

Images