Edwards, D J (1999) A methodology for predicting the total average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. PhD thesis, University of Wolverhampton, UK.
Abstract
Research into the financial management of construction plant and equipment maintenance is scant, despite the increased utilisation of mechanisation to augment productivity in recent years. This thesis addresses the shortage of meaningful research by developing a methodology for predicting the total average hourly maintenance costs of tracked hydraulic excavators operating in opencast mining. Initial pilot and field studies conducted revealed that maintenance management (in the form of record keeping and attitude to used oil analysis) within the plant hire and general construction industry was generally poor. Hence, the decision was made to focus the research upon plant operated by opencast mining contractors. Here, plant managers were found to utilise an optimum blend of predictive and fixed-time-to maintenance and also maintain a depth of machine history file data. Modelling total maintenance costs using multiple regression (MR) analysis at the five percent level of significance identified four key predictor variables. These were: machine weight; attitude to used oil analysis (regular use or not); type of industry (opencast coal or slate); and type of machine (backacter or front shovel). However, in order to determine the model’s robustness an alternative modelling technique, namely artificial neural networks (ANN) was applied using the same variables identified as significant predictor variables in the regression analysis. Performance analysis conducted on the predictive power of both MR and ANN models revealed that overall the ANN model exhibited greater predictive performance. The thesis concludes with direction for future research and moreover, identifies the need for a more fastidious approach to maintenance management.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | artificial neural network; financial management; maintenance; plant and equipment |
| Index terms: | field study, construction industry, hydraulic, performance analysis, multiple-regression, maintenance management, maintenance cost, methodology, construction plant, history, plant hire, regression analysis, plant and equipment, artificial neural network, financial management, manager, modelling, productivity, mining |
| Subjects: | analytical methods, research methods, economic analysis, fluid mechanics, industry analysis, manufacturing engineering, construction equipment, financial management, modelling and simulation, maintenance engineering, architectural and construction history, practitioner, statistical analysis, geotechnical engineering, management, data analysis and analytics |
| Topics: | Business Strategy, Engineering Principles, Roles and Professions, Research Practice, Plant and Equipment |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here