International Journal of Economics, Finance and Management Sciences
Volume 1, Issue 1, February 2013, Pages: 54-60
Received: Feb. 1, 2013;
Published: Feb. 20, 2013
Views 3127 Downloads 173
Milad Eghtedari Naeini, Civil Engineering Department, Pardis, Iran
Milad Eghtedari Naeini, Pardis Islamic Azad University, Pardis, Iran
In this research, a model to forecast project’s cost will be presented with due attention to performance time and cost of the project, based on Earned Value Management (EVM) and with regarding real circumstances caused by uncertainties, risk factors and using simulation methods. All the uncertainties will be related to cost of work packages as well as its changes over time by probability distribution functions. Probabilistic distribution functions will be determined based on existing information obtained from previous projects and experts’ opinion. In this model, project’s activities will be classified to subgroups calling control accounts. Each of them has a controlling limit to control project’s performance. Then, using simulation methods, stochastic s-curve for each control account will be determined to clarify project stochastic s-curve from total of these s-curves. When a percentage of the project has been performed, using modern methods of Earned Value Management, the performance of the project will be measured, therefore, it will be possible to adjust probability distribution functions and forecast the future performance of the project using simulation model of Monte Carlo.
Milad Eghtedari Naeini,
Milad Eghtedari Naeini,
Cost Control Development under Stochastic Performance Control, International Journal of Economics, Finance and Management Sciences.
Vol. 1, No. 1,
2013, pp. 54-60.
K.C. Crandall, and J.C. Woolery, "Schedule development under stochastic scheduling,"Journal of Construction Division, ASCE, vol.108, pp. 321–329, 1982.
P. Gardoni, K.F. Reinschmidt, and R. Kumar, "A probabilistic framework for Bayesian adaptive forecasting of project progress," Computer Aided and Civil Infrastruct. Engineering, vol. 22, pp. 182–196, 2007.
G.A. Barraza, E. Back, and F. Mata, "Probabilistic Forecasting of Project Performance Using Stochastic S Curves," Journal of Construction Engineering and Management, ASCE, vol. 130, pp. 25-32, 2004.
B.C. kim, and K.F. Reinschmidt, "Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution," Journal of Construction Engineering and Management, ASCE, vol. 135, pp. 178-186, 2009.
B.C. Kim, and K.F. Reinschmidt, "Combination of Project Cost Forecasts in Earned Value Management," Journal of Construction Engineering and Management, ASCE, vol. 137, pp. 958–966, 2011.
D. Lee, T. Lim, and D. Arditi, "Stochastic Project Financing Analysis System for Construction," Journal of Construction Engineering and Management, ASCE, vol. 138, pp. 376–389, 2012.
G.A. Barraza, E. Back, and F. Mata, "Probabilistic Monitoring of Project Performance Using SS-Curves," Journal of Construction Engineering and Management, ASCE, vol. 126, pp. 142-148, 2000.
M. Pultar, "Progress-based construction scheduling," Journal of Construction Engineering and Management, ASCE, vol. 116, pp. 670–688, 1990.
S.A. Ward, and T. Lithfield, Cost control in design and con-struction.McGraw-Hill, New York, NY, USA, 1980.
PMI.Practice Standard for Earned Value Management. Project Management Institute, Inc., Pennsylvania, Pa, USA, 2005.
S.M. AbouRizk, D.W. Halpin, and J.R. Wilson, "Visual Interactive Fitting of Beta Distributions," Journal of Con-struction Engineering and Management, ASCE, vol. 117, pp. 589-605, 1991.
S.M. AbouRizk, and D.W. Halpin, "Statistical Properties of Construction Duration Data," Journal of Construction Engi-neering and Management, ASCE, vol. 118, pp. 525-544, 1992.
P. Love, X. Wang, C. Sing, and R. Tiong, "Determining the Probability of Project Cost Overruns," Journal of Construction Engineering and Management, ASCE, vol. 139, pp. 321–330, 2013.
A. Touran, M. Atgun, and I. Bhurisith, "Analysis of the United States Dept. of Transportation prompt pay provisions," Journal of Construction Engineering and Management, ASCE, vol. 130, pp. 719–725, 2004.
A.S. Hanna, and A.N. Blair, "Computerized approach for forecasting the rate of cost escalation," Proc., Comput. Civ. Build. Tech. Conf., pp. 401–408, 1993.
A. Touran, and R. Lopez, "Modeling Cost Escalation in Large Infrastructure Projects," Journal of Construction Engineering and Management, ASCE, vol. 132, pp. 853–860, 2006.
S. Hwang, M. Park, H. Lee, and H. Kim, 2012. "Automated Time-Series Cost Forecasting System for Construction Ma-terials," Journal of Construction Engineering and Management, ASCE, vol. 138, pp. 1259–1269, 2012.
H. Kerzner, Project Management: A Systems Approach to Planning, Scheduling, and Controlling. 10th ed., John Wiley & Sons, New york, NY, USA, 2009.
M. Eghetdari Naeini, and G. Heravi, "Probabilistic Model Development for Project Performance Forecasting," World Academy of Science, Engineering and Technology, vol. 58, pp. 396-401, 2011.
CSC., CSI, MASTERFORMAT. The Construction Specifi-cations Institute and Construction Specifications Canada, Alexandria, VA, USA, 2004.
PMI, A guide to the project management body of know-ledge.4thed., Project Management Institute Inc., Pennsylvania, Pa, USA, 2008.
C. Chapman, and W. Ward, Project Risk Management Processes, Techniques and Insights. 2nd ed. , John Wiley & Sons, New york, NY, USA, 2003.
J.M. Antill,. and R.W. Woodhead, Critical path methods in construction practice. 4th ed. John Wiley & Sons, New york, NY, USA, 1990.