Reliability analysis of repair time data

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Date
2004-05
Authors
Mohd Aboobaider, Burhanuddin
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Abstract
Troubleshooting time may vary on the basis of different background characteristics associated with machines and repair crews. Reliability analysis of troubleshooting time is important to ensure that the operating units achieve maximum performance over time with minimum breakdowns. Identification of the risk factors provides extensive and meaningful information on the causes oflost time to evaluate maintenance activities and coordination effectiveness. The present study examines these potential risk factors and provides necessary inputs in order to improve maintenance operation performance. The study proposes an alternative solution on assessing dynamic repair crew performance based on the progress of their repair time with quantitative measures. Once repair time patterns are detected, the service provider can provide a valid feedback to the repair crews about how current results compare with respect to expected results. This will lead - the team towards breakthrough performance. The case study uses a data set of 1169 air conditioner maintenance records in 2001 at the University Science Malaysia main campus in Penang. The sampl€ consists of repair time data, background characteristics of the technicians and some information on the air conditioner type, i.e., split units and window units. Modelling of repair time with some reliability analysis is conducted for both the non-parametric and semi-parametric approaches. The estimates of the reliability function for the repair time problem are critically examined and the major findings are highlighted in this study. The Product limit Kaplan-Meier method is used to estimate the ·reliability functions with stratified ·and competing risks models. The reliability estimates enable us to classify the technicians into three major groups based on their respective troubleshooting time performance. The Cox proportional hazards model is fitted to examine the relationships between repair time and various risk factors of interest. This study shows how to measure the risk ratios of repair time delay based on the risk factors, i.e., technicians' experience, qualifications and their ages using the model. The ke.y element of the reliability estimates shows that experienced technicians . reduce the repair time. The results show that a larger proportion of moderate and complex problems can be resolved by technically qualified technicians. The results can be used as a benchmark for developing quality service, products and in enhancing competitiveness. The present study sets a tone for the direction of future work in the development of models for reliability analysis on delays in breakdown maintenance. The statistical package, Statistical Analysis System and Microsoft Excel have been used in this study.
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Repair time data
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