• International Journal of Reliability and Applications
• /
• v.18 no.1
• /
• pp.9-20
• /
• 2017

In this paper, we consider a renewable repair-replacement warranty strategy with age-dependent minimal repair service and propose an optimal maintenance model during post-warranty period. Such model implements the repair time limit under warranty and follows with a certain form of system maintenance strategy when the warranty expires. The expected cost rate is investigated per unit time during the life period of the system as for the standard for optimality. Based on the cost design defined for each failure of the system, the expected cost rate is derived during the life period of the system, considering that a renewable minimal repair-replacement warranty strategy with the repair time limit is provided to the customer under warranty. When the warranty is finished, the maintenance of the system is the customer's responsibility. The life period of the system is defined and the expected cost rate is developed from the viewpoint of the customer's perspective. We obtain the optimal maintenance strategy during the maintenance period by minimizing such a cost rate after a warranty expires. Numerical examples using field data are shown to exemplify the application of the methodologies proposed in this paper.

• International Journal of Reliability and Applications
• /
• v.18 no.1
• /
• pp.1-8
• /
• 2017

In this paper, we study an extended warranty model under minimal repair-replacement warranty (MRRW) which is suggested by Park, Jung and Park (2013). Under MRRW policy, the manufacturer is responsible for providing the minimal repair-replacement services upon the system failures during the warranty period. And if the failure occurs during the extended warranty period, only the minimal repair is conducted. Following the expiration of extended warranty, the user is solely responsible for maintaining the system for a fixed length of time period and replaces the system at the end of such a maintenance period. During the maintenance period, only the minimally repair is given for each system failure. The main purpose of this article is to suggest the extended warranty and replacement model with MRRW. Given the cost structures incurred during the life cycle of the system, we formulate the expected cost and the expected length of life cycle to obtain the expected cost rate.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.11a
• /
• pp.48-49
• /
• 2019

The number of disputes over defects after completion of construction work in apartment buildings is increasing every year. In this situation, the prediction of reasonable defect repair costs is very important. In this paper, we are going to collect basic data for predicting defect repair costs through the correlation analysis of the construction period and defect repair cost of apartment houses. For this purpose, first of all, the construction period and defect repair cost of apartment houses were analyzed to analyze the construction period for each type of work, the construction period for each project type, and the construction period for each standard calculation. Next, the correlation between defect repair cost and the independent variables of the candidate was conducted. According to the analysis, the ratio of framing air, the ratio of finishing air, and the number of delay days showed strong correlation.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2010.05a
• /
• pp.235-236
• /
• 2010

Since many structures in the sea environment are damaged by chloride, appropriate repair strategy is required. Therefore in the paper, optimum period for the RC structure's repair is calculated with consideration of economic efficiency. Moreover, when the concrete members are repaired with the other material such as polymer mortar forr section restoration, their expected service life also calculated to predict more accurate repair period during the life span.

• Communications for Statistical Applications and Methods
• /
• v.18 no.3
• /
• pp.277-286
• /
• 2011

Global companies can sell their products with dierent warranty periods based on location and times. Customers can select the length of warranty on their own if they pay an additional fee. In this paper, we consider the warranty period and the repair time limit as random variables. A two-dimensional warranty policy is considered with repair times and failure times. The repair times are considered within the repair time limit and the failure times are considered within the warranty period. Under the non-renewable warranty policy, we obtain the expected number of warranty services and their variances in the censored area by warranty period and repair time limit to conduct a warranty cost analysis. Numerical examples are discussed to demonstrate the applicability of the methodologies and results using field data based on the proposed approach in the paper.

• Journal of the Korea Safety Management & Science
• /
• v.16 no.1
• /
• pp.139-146
• /
• 2014

As Crosby notes, the most companies spend 15 to 20% of their sales dollars on quality costs. Generally the most effective way to manage quality costs is to avoid having defects in the first place. In this paper we have studied about the repair(service) problem of domestic gas boiler within warranty period. We develop a system, which man could find the cause of the problem at an early stage and could devise a countermove to the problem under supposing that the service(repair) rate follows exponential distribution and the product is manufactured lot-for-lot continually. Using the developed early sensing system. it is expected to improve the reliability of the product, to save expenses of company and to improve customer's satisfaction. And the system will be expended to incorporate information technology, which can detect the repair rate automatically.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2014.05a
• /
• pp.162-163
• /
• 2014

Operation and maintenance stage consists the largest portion of project life cycle cost. Appropriate management and analysis of such stages have massive effect on the total project cost. The effective prediction of optimized repair period is one of main factors in ㅌ management. However, it has been analyzed that the prediction of appropriate repair period revealed limitations in reliability. Therefore, this study suggests a methodology of repair period prediction by dividing finished projects into similar groups with same properties to be compared with the target project using quantitative variables.

• Journal of the Korean Home Economics Association
• /
• v.33 no.5
• /
• pp.143-151
• /
• 1995

The purpose of this study is to suggest a defect-repair management and judgement of apartment housing for the private housing construction company. The objectives are : 1) to investigate how the defect-repair were managed and 2) to suggest the range, period of defect-repair and the proper managial size of housing company. Data were collected by the interviews from the heads of management departments of housing construction companies. Twenty-one companies selected were divided into three groups according to the amount of housing supply. Major result were as follows : 1) departments of defect-repair in housing construction company are divided into sub-system according to the size of housing supply. 2) the judgement of the range and period of defect-repair are extracted. They are, according to the order of their importance, constructional defect, functional defect, aesthetical defect and the defect in use.

• Journal of Applied Reliability
• /
• v.11 no.1
• /
• pp.43-57
• /
• 2011

In the paper, we consider two-dimensional warranty policy with failure times and repair times. The failure times are considered within the warranty period and the repair times are considered within the repair time limit. Under the renewable warranty policy and non-renewable warranty policy, we consider the number of warranty services in the censored area by warranty period and repair time limit to conduct warranty cost analysis. We investigate the field data to check their dependency and implement our proposed approaches to conduct warranty cost analysis using the parametric methods. Numerical examples are discussed to demonstrate the applicability of the methodologies and results based on the proposed approach in the paper.

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