Enhancing Building Maintenance Cost Performance with Proper Management of Spare Parts

• Main Authors: Au-Yong, C.P., Ali, A.S., Ahmad, F.
• Format: Article
• Language: English
• Published: Emerald 2016
• Subjects: NA Architecture; TH Building construction
• Online Access: http://eprints.um.edu.my/17663/1/Post-Print.pdf; http://eprints.um.edu.my/17663/; http://dx.doi.org/10.1108/JQME-01-2015-0001
• Institution: Universiti Malaya

Purpose - Scheduled building maintenance requires appropriate and adequate spare parts to replace existing parts on a regular basis. Management of spare parts is seen as an important criterion to ensure the efficiency of scheduled maintenance. The purpose of this paper is to identify the contributors of spare parts management and investigate the relationship between these contributors and maintenance cost performance. Design/methodology/approach - This research adopted a quantitative approach, which utilised questionnaire surveys to study the relationship between the contributors of spare parts management and maintenance cost performance. The data were analysed through descriptive analysis, correlation, and regression analysis. Additionally, a case study was examined to validate the results obtained from the survey. Findings - The results of this research demonstrated that the quality of spare parts (QSP), budget allocation for acquisition of spare parts, and level of stocks were significantly correlated to the maintenance cost variance (MCV). Moreover, the results of the regression analysis indicated that the QSP was the significant predictor of MCV. Originality/value - This research highlights the importance of spare parts management in building maintenance. It recommends that maintenance management set up a spare parts management department for updating stocks frequently. Meanwhile, the management should avoid ad-hoc acquisition of spare parts, as this is always more expensive. The management should also select spare parts based on quality instead of lowest cost. In addition, building managers should also apply the developed regression model in practice to predict and improve maintenance performance.