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HKH DFC USM Paper JCEM 2013

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HKH DFC USM Paper JCEM 2013
Uncertainty in Task Duration and Cost Estimates: Fusion of Probabilistic Forecasts and Deterministic Scheduling
Downloaded from ascelibrary.org by GEORGE WASHINGTON UNIVERSITY on 04/08/14. Copyright ASCE. For personal use only; all rights reserved.

Homayoun Khamooshi, Ph.D. 1; and Denis F. Cioffi, Ph.D. 2
Abstract: A model for project budgeting and scheduling with uncertainty is developed. The traditional critical-path method (CPM) misleads because there are few, if any, real-life deterministic situations for which CPM is a great match; program evaluation and review technique
(PERT) has been seen to have its problems, too (e.g., merge bias, unavailability of data, difficulty of implementation by practitioners). A dual focus on the distributions of the possible errors in the time and cost estimates and on the reliability of the estimates used as planned values suggests an approach for developing reliable schedules and budgets with buffers for time and cost. This method for budgeting and scheduling is executed through either simulation or a simple analytical approximation. The dynamic buffers provide much-needed flexibility, accounting for the errors in cost and duration estimates associated with planning any real project, thus providing a realistic, practical, and dynamic approach to planning and scheduling. DOI: 10.1061/(ASCE)CO.1943-7862.0000616. © 2013 American Society of Civil Engineers.
CE Database subject headings: Estimation; Scheduling; Construction costs; Budgets; Probability; Project management; Simulation;
Uncertainty principles; Forecasting.
Author keywords: Estimate; Reliability; Scheduling; Cost; Budget; Probability; Contingency; Project management; Simulation.

Introduction and Background
Inaccurate estimation has long been identified as one of the major causes of project failure (Flyvbjerg et al. 2009; Chan and
Kumaraswamy 1997; Pinto and Mantel 1990), and Standish Group reports (1998, 2009) show more projects failing and fewer successful projects.



References: Back, W. E., Boles, W. W., and Fry, G. T. (2000). “Defining triangular probability distributions from historical cost data.” J. Constr. Eng. Manage., 126(1), 29–37. Baker, W. R. (1986). “Handling uncertainty.” IJPM, 4, 205–210. Ballard, G. (1999). “Improving work flow reliability.” Proc., 7th Annual Conf Ballard, G. (2000). “Phase scheduling.” LCI White Paper No. 7, Lean Construction Institute, San Diego. Britney, B. R. (1976). “Bayesian point estimation and the PERT scheduling of stochastic activities.” Manage Buehler, R., Griffin, D., and Ross, M. (1994). “Exploring the ‘planning fallacy’: Why people underestimate their task completion times.” Burt, C. D. B., and Kemp, S. (1994). “Construction of activity duration and time management potential.” Appl Chan, D. W. M., and Kumaraswamy, M. M. (1997). “A comparative study of causes of time overruns in Hong Kong construction projects.” Int Cioffi, D. F. (2006). “Subject expertise, management effectiveness, and the newness of a project: The creation of the Oxford English Dictionary.” Cioffi, D. F., and Khamooshi, H. (2008). “A practical method to determine project risk contingency budget.” J Clark, C. E. (1961). “The greatest of a finite set of random variables.” Oper. De Meyer, L. C. H., and Pitch, M. T. (2002). “Managing project uncertainty: From variation to chaos.” MIT Sloan Management Review, Winter, 60–67. Eden, C., Williams, T., and Ackerman, F. (2005). “Analysing project cost overruns: Comparing the ‘measured mile’ analysis and system Emhejellen, M., Emhejellen, K., and Osmundsen, P. (2003). “Cost estimation overruns in the North Sea.” Proj. Manage. J, 34, 23–29. Fente, J., Schexnayder, C., and Knutson, K. (2000). “Defining a probability distribution function for construction simulation.” J Flyvbjerg, B. (2008). “Curbing optimism bias and strategic misrepresentation in planning: Reference class forecasting in practice.” Eur. Plann. Flyvbjerg, B., Garbuio, M., and Lovallo, D. (2009). “Delusion and deception in large infrastructure projects, two models for explaining and preventing executive disaster.” Calif Flyvbjerg, B., Holm, M. S., and Buhl, S. (2002). “Understanding costs in public works projects, error or lie?” J (2010). “Improving planning reliability and project performance using the reliable commitment model.” J Gutierrez, G. J., and Kouvelis, P. (1991). “Parkinson’s law and its implications for project management.” Manage. Sci., 37(8), 990–1001. Herroelen, W., De Reyck, B., and Demeulemeester, E. (1998). “Resource constrained project scheduling, a survey of recent development.” Comput Herroelen, W., and Leus, R. (2004). “The construction of stable project baseline schedules.” Eur Herroelen, W., and Leus, R. (2005). “Project scheduling under uncertainty—Survey and research potential.” Eur Howick, S. (2003). “Using system dynamics to analyze disruption and delay in complex projects for litigation: Can the modeling purposes Hughes, M. W. (1986). “Why projects fail: The effects of ignoring the obvious.” Ind

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