A risk management approach for prediction of contingency sum for public-sector construction projects
Date
2017
Authors
Lam, T.
Siwingw, N.
Editors
Wu, Y.
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Conference paper
Citation
Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate, 2017 / Wu, Y. (ed./s), pp.367-378
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20th International Symposium on Advancement of Construction Management and Real Estate (23 Oct 2015 - 25 Oct 2015 : Hangzhou, China)
Abstract
A review of the literature supports the inclusion of sufficient contingency to cover risks in construction projects. From the client's point of view, too much contingency may result in a project being aborted or uneconomical and too little may lead to cost escalation if the risk occurs. Risk factors at the construction phase causing cost overruns will be identified and an accurate method for estimation of contingency sum will be determined. Qualitative interviews were conducted with five expert practitioners working in a public works department in Zambia to determine how the contingency sum is estimated and what risk factors are considered. Multiple regression analysis was conducted using cost and risks data collected from 30 building and refurbishment projects recently completed in the department. The qualitative study found that project budget overruns constitute a major issue. The regression analysis results proved that the contingency sum was positively correlated to the estimated contract sum. The qualitative interview results and Pearson correlation coefficient showed there was also a positive correlation between contingency sum and project complexity, although such factor needs to be further reaffirmed by regression analysis using a larger sample size. To enhance project success in terms of cost, it is necessary that project managers should actively implement risk management in projects when calculating the contingency sum. Related project-specific risks should be identified and multiple regression method can be used to predict the contingency fund accordingly.
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Copyright 2017 Springer Science+Business Media Singapore