Identification of Key Performance Indicators for Measuring the Performance of Value Management Studies in Construction
by Gongbo Lin, (Project Fellow, Dept. of Building and Real Estate, The Hong Kong Polytechnic Univ.), Geoffrey Qiping Shen, (corresponding author), M.ASCE, (Chair Professor, Head of Dept., Dept. of Building and Real Estate, The Hong Kong Polytechnic Univ. E-mail: bsqpshen@polyu.edu.hk), Ming Sun, (Professor, School of the Built and Natural Environment, Univ. of the West England.), and John Kelly, (Professor, Axoss Ltd, 8 Pilgrims Hill, Linlithgow, Scotland, U.K.)
Journal of Construction Engineering and Management, Vol. 137, No. 9, September 2011, pp. 698-706, (doi: http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000348)
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| Document type: |
Journal Paper |
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Section Heading: Project Planning and Design |
| Abstract: |
Value management (VM) is widely regarded as a useful tool for management to meet the challenges, such as limited resources and tight schedules arising in the construction industry. A rigorous measurement on the performance of VM studies is likely to improve the implementation of the VM methodology and enhance the confidence of clients about their investment in VM. The identification of key performance indicators (KPIs) is an essential first step in developing a proper performance measurement framework. This paper aims to identify the KPIs for measuring the performance of VM studies in construction. Delegates of international VM conferences hosted by SAVE International and Hong Kong Institute of Value Management during the period 2005 to 2007 were used as the target group for a questionnaire survey. The survey results identified 18 KPIs out of 47 potential performance indicators. They are divided into three groups: predicting indicators, process-related indicators, and outcome-related indicators, according to their characteristics. Three principal components were identified by using factor analysis of the KPIs, which reveals the interrelationship among the KPIs. Details on how to implement these KPIs, such as data providers, weightings, and scoring methods, are also presented. |
| Author Keywords: | 
| Performance characteristics |
| Value engineering |
| Measurement |
| Questionnaires |
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