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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Keywords
Repair time data