A Bayesian Approach to Improve Estimate at Completion in Earned Value Management

Abstract

Forecasting represents a core project management process. Estimates at completion in terms of cost and schedule provide essential data and advice to the project team in order to lead and control the project and implement suitable corrective measures. In order to improve the forecasting process, a Bayesian model has been developed within the earned value management framework aiming to calculate a confidence interval for the estimates of both cost and schedule at the completion of the project. The model is based on the integration of data records and qualitative knowledge provided by experts. The model has been tested in an oil and gas project.