FACILITY MANAGEMENT BENCHMARKING: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS IN HONG KONG

Philip Y. L. Wong (), Stephen C. H. Leung () and John D. Gilleard ()
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Philip Y. L. Wong: Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong
Stephen C. H. Leung: Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
John D. Gilleard: CoreNet Global, Hong Kong

Asia-Pacific Journal of Operational Research (APJOR), 2013, vol. 30, issue 05, 1-16

Abstract: This paper proposes Data Envelopment Analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in services' efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data, simultaneously. It also contributes to a future research of a tradeoff sensitivity test between FM cost, services performance and reliability.

Keywords: Facility management; benchmarking; data envelopment analysis (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0217595913500139

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