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Abdel-Wahab, M and Vogl, B (2011) Trends of productivity growth in the construction industry across Europe, US and Japan. Construction Management and Economics, 29(06), 635–44.
Camilleri, M, Jaques, R and Isaacs, N (2001) Impacts of climate change on building performance in New Zealand. Building Research & Information, 29(06), 430–50.
Chi, C S F and Nicole Javernick‐Will, A (2011) Institutional effects on project arrangement: high‐speed rail projects in China and Taiwan. Construction Management and Economics, 29(06), 595–611.
Edwards, D J (2001) Predicting construction plant maintenance expenditure. Building Research & Information, 29(06), 417–27.
- Type: Journal Article
- Keywords: construction plant; financial management; linear interpolation; maintenance; management; prediction; stochastic random numbers; UK
- ISBN/ISSN: 0961-3218
- URL: http://taylorandfrancis.metapress.com/link.asp?id=vkf7jnu7y4f6e4r9
- Abstract:
Utilization of construction plant and equipment forms an essential part of construction contractors' efforts to augment productivity and reduce costs. However, efficient and effective management of plant and equipment is requisite to maximizing its potential benefits. A problem in this respect is not knowing when a maintenance event might occur, or its extent in terms of cost and time. This paper presents a stochastic mathematical modelling methodology (using random numbers) to predict the probable cost of 'the next' maintenance event for tracked hydraulic excavators; where a maintenance event includes both breakdown and scheduled maintenance. Information on a random sample of 33 360° tracked hydraulic excavators were obtained from contractors operating within the opencast mining industry. From these, 9473 maintenance event 'cost' observations were recorded and modelled. Validation of the model is achieved using a random 'hold out' sample of 50 maintenance cost observations taken from nine machines. Analyses reveal that overall model predictive performance is robust, having a mean percentage error (MPE) of 4.46 and a mean absolute percentage error (MAPE) of 23.63. Pearsons correlation coefficient (r) and a paired t-test are conducted to determine the accuracy and consistency of model predictions respectively. With an r of 0.76 and no significant difference being found between the mean of predicted/actual values, the model shows both accurate and consistent cost predictions. The random number technique shows potential for improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which includes: (1) the application of this research to plant items working in other operational environments (e.g. civil engineering and construction); and (2) prediction of the next breakdown event.
Gambatese, J A and Hallowell, M (2011) Enabling and measuring innovation in the construction industry. Construction Management and Economics, 29(06), 553–67.
Gundes, S (2011) Input structure of the construction industry: a cross‐country analysis, 1968–90. Construction Management and Economics, 29(06), 613–21.
Hartono, B and Yap, C M (2011) Understanding risky bidding: a prospect‐contingent perspective. Construction Management and Economics, 29(06), 579–93.
Murray, B and Smyth, H (2011) Franchising in the US remodelling market: growth opportunities and barriers faced by general contractors. Construction Management and Economics, 29(06), 623–34.
Scheublin, F J M (2001) Project alliance contract in The Netherlands. Building Research & Information, 29(06), 451–5.
Westberg, K, Noren, J and Kus, H (2001) On using available environmental data in service life estimates. Building Research & Information, 29(06), 428–39.
Zhang, H, Xing, F and Liu, J (2011) Rehabilitation decision-making for buildings in the Wenchuan area. Construction Management and Economics, 29(06), 569–78.