Establishing a Link between Contractor Selection Strategy and Project Outcomes: Simulation Study
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 10
Abstract
Perceived benefits of the best-value strategy and the problems the lowest-price strategy have caused in the construction industry have led to an increase in the use of the best-value strategy for selecting contractors. Although some research has tried to establish a direct relationship between a contractor selection strategy and project outcomes, there is hardly any empirical research that tries to establish this relationship. This paper presents a quantifiable method of assessing the risk of selecting different contractor selection strategies using educational facilities projects in the United Kingdom. A Monte Carlo–simulation study was conducted to assess how the lowest-priced contractor would have fared against the best-value contractor had the former been awarded the contract instead. It was concluded that selecting the best-value contractor in educational facilities projects is not necessary in terms of cost. Furthermore, although the results are limited to educational facilities project, the method can be adapted to other sectors.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request.
References
Abdelrahman, M., T. Zayed, and A. Elyamany. 2008. “Best-value model based on project specific characteristics.” J. Constr. Eng. Manage. 134 (3): 179–188. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:3(179).
AGC and NASFA (Associated General Contractors of America and National Association of State Facilities Administrators). 2008. “Best practices for use of best value selections.” Accessed January 5, 2019. https://www.agc.org/sites/default/files/Project%20Delivery%20-%20Best%20Value%20Selection.pdf.
Angus, G. Y., P. D. Flett, and J. A. Bowers. 2005. “Developing a value-centred proposal for assessing project success.” Int. J. Project Manage. 23 (6): 428–436. https://doi.org/10.1016/j.ijproman.2005.01.008.
Assaf, S. A., and S. Al-Hejji. 2006. “Causes of delay in large construction projects.” Int. J. Project Manage. 24 (4): 349–357. https://doi.org/10.1016/j.ijproman.2005.11.010.
Bergman, A. M., and S. Lundberg. 2013. “Tender evaluation and supplier selection methods in public procurement.” J. Purchasing Supply Manage. 19 (2): 73–83. https://doi.org/10.1016/j.pursup.2013.02.003.
Cheng, E. W., and H. Li. 2004. “Contractor selection using the analytic network process.” Constr. Manage. Econ. 22 (10): 1021–1032. https://doi.org/10.1080/0144619042000202852.
Crosby, P. 1979. Quality is free. New York: McGraw-Hill.
Eke, G., and J. Elgy. 2017. “Testing the value of best value: Evidence from educational facilities projects.” In Proc., 25th Annual Conf. of the Int. Group for Lean Construction, 19–26. Hersonissos, Greece: International Group for Lean Construction. https://doi.org/10.24928/2017/0090.
Eke, G., G. Wedawatta, and J. Elgy. 2017. “A quantifiable method of assessing the risk of selecting the lowest bidder in construction projects: A literature review.” In Proc., 13th Int. Postgraduate Research Conf. 637–646. Salford, UK: Univ. of Salford.
El-Abbasy, S. M., T. Zayed, M. Ahmed, H. Alzraiee, and M. Abouhamad. 2013. “Contractor selection model for highway projects using integrated simulation and analytic network process.” J. Constr. Eng. Manage. 139 (7): 755–767. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000647.
El Asmar, M., A. Hanna, and C. Chang. 2009. “Monte Carlo simulation approach to support alliance team selection.” J. Constr. Eng. Manage. 135 (10): 1087–1095. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000074.
Flyvbjerg, B. 2008. “Curbing optimism bias and strategic misrepresentation in planning: Reference class forecasting in practice.” Eur. Plann. Stud. 16 (1): 3–21. https://doi.org/10.1080/09654310701747936.
Fong, P. S., and S. K. Choi. 2000. “Final contractor selection using the analytical hierarchy process.” Constr. Manage. Econ. 18 (5): 547–557. https://doi.org/10.1080/014461900407356.
Girmscheid, G., and T. A. Busch. 2007. “Risikomanagement-Prozess-Modell für Baunternehmen–Risikobelastungsdimension.” Bauingenieur 82 (2): 53–61.
Glenigan. 2016. The UK industry performance report 2016. London: Glenigan.
Holt, G. D., P. O. Olomolaiye, and F. C. Harris. 1994. “Evaluating prequalification criteria in contractor selection.” Build. Environ. 29 (4): 437–448. https://doi.org/10.1016/0360-1323(94)90003-5.
Juran, J. 1951. Quality control handbook. 1st ed. New York: McGraw-Hill.
Lo, W., and M. Yan. 2009. “Evaluating qualification-based selection system: A simulation approach.” J. Constr. Eng. Manage. 135 (6): 458–465. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000013.
Love, P. E. D., C.-P. Sing, X. Wang, Z. Irani, and D. W. Thwala. 2012. “Overruns in transportation infrastructure projects.” Struct. Infrastruct. Eng. 10 (2): 141–159. https://doi.org/10.1080/15732479.2012.715173.
Matalas, N. C. 1967. “Mathematical assessment of synthetic hydrology.” Water Resour. Res. 3 (4): 937–945.
Mateus, R., J. A. Ferreira, and J. Carreira. 2010. “Full disclosure of tender evaluation models: Background and application in Portuguese public procurement.” J. Purchasing Supply Manage. 16 (3): 206–215. https://doi.org/10.1016/j.pursup.2010.04.001.
Mulla, S. S., and A. P. Waghmare. 2015. “Influencing factors caused for time & cost overruns in construction projects in Pune-India & their remedies.” Int. J. Innovative Sci. Eng. Technol. 2 (10): 622–633.
Nureize, A., and J. Watada. 2011. “Multi-attribute decision making in contractor selection under hybrid uncertainty.” J. Adv. Comput. Intell. Intell. Inf. 15 (4): 465–472. https://doi.org/10.20965/jaciii.2011.p0465.
Olaniran, O. J. 2015. “The effects of cost-based contractor selection on construction project performance.” J. Financial Manage. Property Constr. 20 (3): 235–251. https://doi.org/10.1108/JFMPC-06-2014-0008.
Panthi, K. M. S., S. M. Ahmed, and S. O. Ogunlana. 2009. “Contingency estimation for construction projects through risk analysis.” Int. J. Constr. Educ. Res. 5 (2): 79–94. https://doi.org/10.1080/15578770902952181.
Peleskei, A. C., V. Dorca, A. R. Munteanu, and R. Munteanu. 2015. “Risk consideration and cost estimation in construction projects using Monte Carlo simulation.” Management 10 (2): 163–176.
Quenouille, M. H. 1957. The analysis of multiple time series. London: Griffin.
Siemiatycki, M. 2010. “Managing optimism biases in the delivery of large-infrastructure projects: A corporate performance benchmarking approach.” Eur. J. Transp. Infrastruct. Res. 10 (1): 30–41.
Wall, M. D. 1997. “Distributions and correlations in Monte Carlo simulation.” Constr. Manage. Econ. 15: 241–258.
Wang, N., Y.-C. Chang, and A. A. El-Sheikh. 2012. “Monte Carlo simulation approach to life cycle cost management.” Struct. Infrastruct. Eng. 8 (8): 739–746. https://doi.org/10.1080/15732479.2010.481304.
Yu, W.-D., and K.-W. Wang. 2012. “Best value or lowest bid? A quantitative perspective.” J. Constr. Eng. Manage. 138 (1): 128–134. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000414.
Information & Authors
Information
Published In
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 17, 2018
Accepted: Mar 7, 2019
Published online: Aug 15, 2019
Published in print: Oct 1, 2019
Discussion open until: Jan 15, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.