• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of the military operations research society of Korea
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• v.30 no.2
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• pp.96-107
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• 2004

This paper deals with $\ulcorner$Requirement Decision Model for Repair Parts supplied by the Government$\lrcorner$ which is to reduce Aircraft Contract Maintenance Cost. It aims to find solutions to the fundamental problems of the Aircraft Contract Maintenance System. Under the current Aircraft Contract Maintenance System, it is hard to forecast the exact demand of repair parts, so support rate of Repair Parts supplied by the Government is restricted under 50 percent. It is inevitable to purchase Repair Parts from the firm with much higher price than those of Government source. However, absence of fixed demand pattern makes it difficult to improve accuracy of demand forecast. As a solution to these problems, this model prevents a cost increase due to the unit price difference between Repair Parts supplied by the Government and Repair Parts purchased by the Firm. It also reflects demand characteristics of each repair part, and prevents continual stock increase by setting an upper limit on the amount of Repair Parts supplied by the Government. The effectiveness of this model is verified by empirical analysis using the latest raw data. By applying this model to real situation, we expect to reduce about 4 billion won every year.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.234-241
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• 2021

By recognizing the importance of demand forecasting, the military is conducting many studies to improve the prediction accuracy for repair parts. Demand forecasting for repair parts is becoming a very important factor in budgeting and equipment availability. On the other hand, the demand for intermittent repair parts that have not constant sizes and intervals with the time series model currently used in the military is difficult to predict. This paper proposes a method to improve the prediction accuracy for intermittent repair parts of the Patriot. The authors collected intermittent repair parts data by classifying the demand types of 701 repair parts from 2013 to 2019. The temperature and operating time identified as external factors that can affect the failure were selected as input variables. The prediction accuracy was measured using both time series models and data mining models. As a result, the prediction accuracy of the data mining models was higher than that of the time series models, and the multilayer perceptron model showed the best performance.

• Journal of the military operations research society of Korea
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• v.4 no.1
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• pp.57-68
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• 1978

In forecasting the demand rate of repair parts for old tank types under the limited historical data, this reportan alyzes which techniques give the smallest forecasting error, also economic repair limits and the return for additional repair costs are formulated as a mathematical model

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.1
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• pp.79-88
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• 2017

For the PBL contract, it is necessary for the contracting parties to share information regarding the reasonable inventory-level and the cost of its repair parts for the estimated demand. There are various models which can be used for this purpose. Among them, V-METRIC model is considered to be the most efficient and is most frequently applied. However, this model is usually used for optimizing the inventory level of the repair parts of the system under operation. The model uses a time series forecast model to determine the demand rate, which is a mandatory input factor for the model, based on past field data. However, since the system at the deployment stage has no operational performance record, it is necessary to find another alternative to be used as the demand rate of the model application. This research applies the V-METRIC model to find the optimal inventory level and cost estimation for repairable items to meet the target operational availability, which is a key performance indicator, at the time of the PBL contract for the deployment system. This study uses the calculated value based on the allocated MTBF to the system as the demand rate, which is used as input data for the model. Also, we would like to examine changes in inventory level and cost according to the changes in target operational availability and MTBF allocation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.110-117
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• 2015

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.1-10
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• 2021

Recently, the repair and recycling of damaged mechanical parts via metal additive manufacturing processes have been industrial points of interest. This is because the repair and recycling of damaged mechanical parts can reduce energy and resource consumption. The directed energy deposition(DED) process has various advantages such as the possibility of selective deposition, large building space, and a small heat-affected zone. Hence, it is a suitable process for repairing damaged mechanical parts. The shaft is a core component of various mechanical systems. Although there is a high demand for the repair of the shaft, it is difficult to repair with traditional welding processes because of the thermal deformation problem. The objective of this study is to propose a repair procedure for a damaged shaft using the DED process and discuss its applications. Three types of cases, including a small shaft with a damaged surface, a medium-size shaft with a worn bearing joint, and a large shaft with serious damage, were repaired using the proposed procedure. The microstructure and hardness were examined to discuss the characteristics of the repaired component. The efficiency of the repair of the damaged shaft is also discussed.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.53-62
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• 2016

The current trends of product customization and repair of high value parts with individual defects demand automation and a high degree of flexibility of the involved manufacturing process chains. To determine the corresponding requirements this paper gives an overview of manufacturing process chains by distinguishing between horizontal and vertical process chains. The established way of modeling and programming processes with CAx systems and existing approaches is shown. Furthermore, the different types of possible adaptions of a manufacturing process chain are shown and considered as a cascaded control loop. Following this it is discussed which key requirements of repair process chains are unresolved by existing approaches. To overcome the deficits this paper introduces repair features which comprise the idea of geometric features and defines analytical auxiliary geometries based on the measurement input data. This meets challenges normally caused by working directly on reconstructed geometries in the form of triangulated surfaces which are prone to artifacts. Embedded into function blocks, this allows the use of traditional approaches for manufacturing process chains to be applied to adaptive repair process chains.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.2
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• pp.160-177
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• 2019

In this study, we examined the distribution of dealers and the repair parts management system of a Japanese car manufacturer in the Chinese market in looking at the case of Toyota. We conducted our research by obtaining information from a GAC Toyota dealer about the current distribution of dealers and locations of warehouses throughout China, as well as the status of stocks and the distribution system for repair and maintenance parts. The results of our investigation showed that although GAC Toyota has 437 dealers throughout the country, there is an imbalance in distribution towards the coastal areas, after the population ratios and other measurements are factored in. Therefore, it can be said expansion towards the inland regions, where demand for automobiles has increased in recent years, has been stunted. On the other hand, there is a high correlation between gross GDP by region and the number of stores, and it can be pointed out that the company prioritizes the sale of high-priced vehicles in major coastal areas where the economy is large, rather than selling low-priced vehicles for inland consumers with a relatively small economic scale. The company also has difficulty in securing dealers that can provide sufficient after-sales service. According to the regulations of GAC Toyota, the company require dealers to have at least 1,500 repair and maintenance parts in stock. Also, when exchanging maintenance parts, GAC Toyota's emphasis is on increasing customer satisfaction by giving sufficient explanations for customers and obtaining consent from them. As a result, the company's dealers need financial resources to continue their business from a long-term perspective. However, it can be pointed out that such dealers are limited, and it is difficult to distribute profits among manufacturers and dealers.