Myrtveit, I and Stensrud, E (2005) Identifying high performance construction projects. In: Khosrowshahi, F (ed.) Proceedings of 21st Annual ARCOM Conference, 7-9 September 2005, London, UK.
Abstract
Learning from high performance projects is crucial for construction improvement. Therefore, we need to identify outstanding projects or role models. A minimum prerequisite for identifying such projects is the ability to measure the performance. Unfortunately, two issues complicate the measuring task: i) diseconomies or economies of scale and ii) multidimensional inputs and outputs. We propose to use Data Envelopment Analysis (DEA) to measure the productivity of building projects. DEA fulfils the two requirements stated above, and to our knowledge, it is the only method complying with these two vital requirements. The presentation emphasises the strengths as well as the limitations of DEA, comparing it with regression analysis. The results from this empirical study of 58 projects extracted from a database in a large Norwegian construction company suggest that there is a 50% potential for productivity improvement by learning from and copying the role models that were identified. Also, we discuss economies and diseconomies of scale in construction projects. We recommend DEA as an appropriate method for identifying role models and for benchmarking projects. Used together with methods for hypothesis testing, DEA is a useful technique for assessing the effect of alleged process improvements.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | data envelopment analysis; productivity analysis; returns to scale; benchmarking |
| Index terms: | database, construction company, productivity, role model, process improvement, benchmarking, regression analysis, data envelopment analysis, testing, empirical study, construction project |
| Subjects: | organization, data analysis and analytics, professional practice, research methods, performance measurement, management, production management, sociology, data management, statistical analysis |
| Topics: | Quality Management, Digital Applications, Research Practice, Project Management, Engineering Principles, Business Strategy, Roles and Professions |
| Descriptive scope: | 3 PCA |
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