Probabilistic Model Development for Project Performance Forecasting
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.
[1] 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.
[2] 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.
[3] M. Pultar, "Progress-based construction scheduling." J. Constr. Eng.
Manage., vol. 116, no. 4, pp. 670-688, Apr. 1990.
[4] S. A. Ward, and T. Lithfield, Cost control in design and construction.
New York: McGraw-Hill, 1980, ch. 5.
[5] PMI. Practice Standard for Earned Value Management. Pennsylvania:
Project Management Institute, Inc., 2005, ch. 4.
[6] 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.
[7] 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.
[8] K. R. Molenaar, "Programmatic Cost Risk Analysis for Highway
Megaprojects." J. Constr. Eng. Manage., vol. 131, no. 3, pp. 343-353,
Mar. 2005.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] PMI. A guide to the project management body of knowledge. 4th ed.
Pennsylvania: Project Management Institute Inc., 2008, ch. 7,
[14] H. Kerzner, Project Management: A Systems Approach to Planning,
Scheduling, and Controlling. 10th ed. New york: John Wiley & Sons,
2009, ch. 15.
[15] CSC, CSI, MasterFormat. Virginia: The Construction Specifications
Institute and Construction Specifications Canada, 2004.
[16] C. Chapman, and W. Ward, Project Risk Management Processes,
Techniques and Insights. 2nd ed. New york: John Wiley & Sons, 2003,
ch. 1.
[1] 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.
[2] 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.
[3] M. Pultar, "Progress-based construction scheduling." J. Constr. Eng.
Manage., vol. 116, no. 4, pp. 670-688, Apr. 1990.
[4] S. A. Ward, and T. Lithfield, Cost control in design and construction.
New York: McGraw-Hill, 1980, ch. 5.
[5] PMI. Practice Standard for Earned Value Management. Pennsylvania:
Project Management Institute, Inc., 2005, ch. 4.
[6] 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.
[7] 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.
[8] K. R. Molenaar, "Programmatic Cost Risk Analysis for Highway
Megaprojects." J. Constr. Eng. Manage., vol. 131, no. 3, pp. 343-353,
Mar. 2005.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] PMI. A guide to the project management body of knowledge. 4th ed.
Pennsylvania: Project Management Institute Inc., 2008, ch. 7,
[14] H. Kerzner, Project Management: A Systems Approach to Planning,
Scheduling, and Controlling. 10th ed. New york: John Wiley & Sons,
2009, ch. 15.
[15] CSC, CSI, MasterFormat. Virginia: The Construction Specifications
Institute and Construction Specifications Canada, 2004.
[16] C. Chapman, and W. Ward, Project Risk Management Processes,
Techniques and Insights. 2nd ed. New york: John Wiley & Sons, 2003,
ch. 1.
@article{"International Journal of Architectural, Civil and Construction Sciences:63161", author = "Milad Eghtedari Naeini and Gholamreza Heravi", title = "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.", keywords = "Monte Carlo method, Probabilistic model, Project
forecasting, Stochastic S-curve", volume = "5", number = "10", pages = "502-6", }