The relationship between preventive maintenance characteristics and the maintenance performance of high-rise office buildings in Malaysia / Au Yong Cheong Peng

Au Yong, Cheong Peng (2013) The relationship between preventive maintenance characteristics and the maintenance performance of high-rise office buildings in Malaysia / Au Yong Cheong Peng. PhD thesis, University of Malaya.

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Abstract

Sustainability of buildings is one of the main aims in the construction industry. In achieving sustainability, maintenance of building and its facilities which include planning, implementation and outcome of maintenance activities become an important criterion. However, building maintenance costs are continuously increasing as a result of poor maintenance. Consequently, there is an urgent need to develop solutions to reduce the maintenance costs while optimizing the maintenance performance. It is a long term strategy to manage the issue of maintenance performance. Thus, a study of the maintenance strategies is aimed to improve the maintenance performance. Taking into cognizance the aim of the study, this thesis seeks to identify significant characteristics of scheduled and condition-based maintenance. This study is also dedicated to examine the relationship between the characteristics and maintenance performance, as well as to develop the regression models for maintenance planning and prediction. Furthermore, the research introduces participative mechanisms as a measure to improve the performance. The study adopts triangulation approach that includes quantitative and qualitative methods. Firstly, questionnaires were distributed to relevant respondents from research population of 398 to collect factual data regarding the characteristics of scheduled and condition-based maintenance, maintenance performance, as well as the opinion on participative mechanisms. The research identified the importance of the variables through ranking analysis. Then, correlation analysis was performed to test their relationship to the performance. Moreover, the study developed regression models for prediction purpose in practical. In order to validate the questionnaire results, semi-structured interview and archived documentation were executed to obtain further details about the characteristics and participative mechanisms in enhancing the efficiency of the maintenance strategies. Fifteen (15) experienced building managers were interviewed and four (4) high-rise office buildings were selected for archived documentation. The findings highlight the variables of maintenance characteristics and participative mechanisms that are significantly correlated to the maintenance performance. They are level of labour skill and knowledge, budget allocation for spare part and material, level of spare part and material stock, quality of spare part and material, length of vi predetermined maintenance interval, as well as amount of failure and maintenance downtime for scheduled maintenance; level of manager skill and knowledge, budget allocation for equipment and technique; availability of equipment and technique, capability to adopt equipment and technique, budget allocation for acquisition of maintenance data, reliability of maintenance data, frequency of monitoring and inspection as well as for condition-based maintenance; provision of knowledge-sharing and communication platform, provision of training, clients‟ commitment, as well as users‟ satisfaction and feedback for participative mechanisms. Then, the result produces most significant logistic regression models for characteristics of scheduled and condition-based maintenance respectively as below:  Scheduled maintenance, P[over-budget]: Z = 1.237 – 0.963 LSK + 0.728 LPM  Condition-based maintenance, P[downtime extension]: Z = 3.734 – 1.061 CAT As a conclusion, the planning and application of scheduled and condition-based maintenance strategy should consider their significant characteristics and make reference to the resulting regression models. Meanwhile, the research recommends introduction of participative mechanisms in maintenance management to enhance the performance.

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