Commenced in January 2007
Paper Count: 30174
Probabilistic Model Development for Project Performance Forecasting
Abstract:In this paper, based on the past project cost and time performance, a model for forecasting project cost performance is developed. This study presents a probabilistic project control concept to assure an acceptable forecast of project cost performance. In this concept project activities are classified into sub-groups entitled control accounts. Then obtain the Stochastic S-Curve (SS-Curve), for each sub-group and the project SS-Curve is obtained by summing sub-groups- SS-Curves. In this model, project cost uncertainties are considered through Beta distribution functions of the project activities costs required to complete the project at every selected time sections through project accomplishment, which are extracted from a variety of sources. Based on this model, after a percentage of the project progress, the project performance is measured via Earned Value Management to adjust the primary cost probability distribution functions. Then, accordingly the future project cost performance is predicted by using the Monte-Carlo simulation method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082459Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2255
 K. C. Crandall, and J. C. Woolery, ÔÇÿÔÇÿSchedule development under stochastic scheduling.-- J. Constr. Div., Am. Soc. Civ. Eng., Vol. 108, no. 2, pp. 321-329, Feb. 1982.
 P. Gardoni, K. F. Reinschmidt, and R. Kumar, "A probabilistic framework for Bayesian adaptive forecasting of project progress." Comput. Aided Civ. Infrastruct. Eng., Vol. 22, no. 3, pp. 182-196, Mar. 2007.
 M. Pultar, "Progress-based construction scheduling." J. Constr. Eng. Manage., vol. 116, no. 4, pp. 670-688, Apr. 1990.
 S. A. Ward, and T. Lithfield, Cost control in design and construction. New York: McGraw-Hill, 1980, ch. 5.
 PMI. Practice Standard for Earned Value Management. Pennsylvania: Project Management Institute, Inc., 2005, ch. 4.
 G. A. Barraza, E. Back, and F. Mata, "Probabilistic Forecasting of Project Performance Using Stochastic S Curves." J. Constr. Eng. Manage., Vol. 130, no. 1, pp. 25-32, Jan. 2004.
 B. C. kim, and K. F. Reinschmidt, "Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution." J. Constr. Eng. Manage., Vol. 135, no. 3, pp. 178-186, Mar. 2009.
 K. R. Molenaar, "Programmatic Cost Risk Analysis for Highway Megaprojects." J. Constr. Eng. Manage., vol. 131, no. 3, pp. 343-353, Mar. 2005.
 G. A. Barraza, E. Back, and F. Mata, "Probabilistic Monitoring of Project Performance Using SS-Curves." J. Constr. Eng. Manage., Vol. 126, no. 2, pp. 142-148, Feb. 2000.
 S. M. AbouRizk, D. W. Halpin, and J. R. Wilson, "Visual Interactive Fitting of Beta Distributions," J. Constr. Eng. Manage., Vol. 117, no. 4, pp. 589-605, Apr. 1991.
 A. Touran, M. Atgun, and I. Bhurisith, "Analysis of the United States Dept. of Transportation prompt pay provisions." J. Constr. Eng. Manage., Vol. 130, no. 5, pp. 719-725, May. 2004.
 S. M. AbouRizk, D. W. Halpin, "Statistical Properties of Construction Duration Data." J. Constr. Eng. Manage., Vol. 118, no. 3, pp. 525-544, Mar. 1992.
 PMI. A guide to the project management body of knowledge. 4th ed. Pennsylvania: Project Management Institute Inc., 2008, ch. 7,
 H. Kerzner, Project Management: A Systems Approach to Planning, Scheduling, and Controlling. 10th ed. New york: John Wiley & Sons, 2009, ch. 15.
 CSC, CSI, MasterFormat. Virginia: The Construction Specifications Institute and Construction Specifications Canada, 2004.
 C. Chapman, and W. Ward, Project Risk Management Processes, Techniques and Insights. 2nd ed. New york: John Wiley & Sons, 2003, ch. 1.