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

Abstract

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.