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Modelling construction project productivity using systems dynamics approach

Author

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  • Michael J. Mawdesley
  • Saad Al‐Jibouri

Abstract

Purpose - Improvement in productivity will not be achieved without bearing in mind that there is an enormous number of factors affecting productivity and that there is a necessity to locate the most influential ones among them. Doing so will enable researchers as well as practitioners to pinpoint the areas where efforts are to be directed in order to reach the optimum productivity of the studied project. The work described in this paper is based on data collected from the construction industry in the UK. In collecting the information, there are three initial aims: to determine what factors affected productivity at site level, to determine how these factors interacted and to determine the significance of the factors. Design/methodology/approach - The research method undertaken is to model productivity in construction using system dynamics. In particular, it concentrates on the use of system dynamics and project level productivity. The literature identifies 34 factors affecting productivity but based on a survey of professionals, five of these are recognised as important. They form the basis of a systems model whose development is described. Findings - The results of testing the developed model have suggested that investments in planning and control have most benefits on project productivity and that investments in safety, motivation and reduction of disruptions are beneficial. Originality/value - The use of system dynamics to model productivity represents untraditional and novel approach in research in construction. The developed model is valuable in that it can be used to evaluate management strategies and their effects on project productivity.

Suggested Citation

  • Michael J. Mawdesley & Saad Al‐Jibouri, 2010. "Modelling construction project productivity using systems dynamics approach," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 59(1), pages 18-36, January.
  • Handle: RePEc:eme:ijppmp:v:59:y:2010:i:1:p:18-36
    DOI: 10.1108/17410401011006095
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    Citations

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    Cited by:

    1. Okan Sirin & Murat Gunduz & Hazem M. Al Nawaiseh, 2024. "Developing an Adaptive Neuro-Fuzzy Inference System for Performance Evaluation of Pavement Construction Projects," Sustainability, MDPI, vol. 16(9), pages 1-24, April.

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