Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution

Reliable forecasting is instrumental in successful project management. In order to ensure the successful completion of a project, the project manager constantly monitors actual performance and updates the current predictions of project duration and cost at completion. This study introduces a new probabilistic forecasting method for schedule performance control and risk management of on-going projects. The Bayesian betaS-curve method (BBM) is based on Bayesian inference and the beta distribution. The BBM provides confidence bounds on predictions, which can be used to determine the range of potential outcomes and the probability of success. Furthermore, it can be applied from the outset of a project by integrating prior performance information (i.e., the original estimate of project duration) with observations of new actual performance. A comparative study reveals that the BBM provides, early in the project, much more accurate forecasts than the earned value method or the earned schedule method and as accurate forecasts as the critical path method without analyzing activity-level technical data.

[1]  Simaan M. AbouRizk,et al.  Visual interactive fitting of beta distributions , 1991 .

[2]  W. Edward Back,et al.  PROBABILISTIC FORECASTING OF PROJECT PERFORMANCE USING STOCHASTIC S CURVES , 2004 .

[3]  F.T. Anbari,et al.  Earned Value Project Management Method and Extensions , 2003, IEEE Engineering Management Review.

[4]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[5]  Dong-Eun Lee.  Probability of Project Completion Using Stochastic Project Scheduling Simulation , 2005 .

[6]  Denis F. Cioffi.  A tool for managing projects: an analytic parameterization of the S-curve , 2005 .

[7]  W. Edward Back,et al.  Probabilistic Monitoring of Project Performance Using SS-Curves , 2000 .

[8]  Owen D. Wilson,et al.  A construction project cash flow model—an idiographic approach , 1986 .

[9]  Z. Miskawi,et al.  An S-curve equation for project control , 1989 .

[10]  Byung-Cheol Kim,et al.  Forecasting project progress and early warning of project overruns with probabilistic methods , 2009 .

[11]  C. Perry,et al.  Estimating the Mean and Variance of Subjective Distributions in PERT and Decision Analysis , 1975 .

[12]  Mario Vanhoucke,et al.  A simulation and evaluation of earned value metrics to forecast the project duration , 2005, J. Oper. Res. Soc..

[13]  Paolo Gardoni,et al.  A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress , 2007, Comput. Aided Civ. Infrastructure Eng..

[14]  Mario Vanhoucke,et al.  A comparison of different project duration forecasting methods using earned value metrics , 2006 .

[15]  Clifford J. Schexnayder,et al.  Construction Management Fundamentals , 2003 .

[16]  D. Malcolm,et al.  Application of a Technique for Research and Development Program Evaluation , 1959 .

[17]  Quentin W. Fleming,et al.  Earned Value Project Management , 1996 .

[18]  Ali Touran,et al.  Analysis of the United States Department of Transportation Prompt Pay Provisions , 2004 .

[19]  B. Basu,et al.  Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting , 2007 .