A Bayesian approach to improve estimate at completion in earned value management
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 to lead and control the project and implement suitable corrective measures. This paper introduces an estimate at completion model that utilizes expert opinion elicitation. To improve the forecasting process, a Bayesian model has been developed within the earned value management framework (EVM) aiming to calculate a confidence interval for the estimates of both cost and schedule at the completion of the project. The paper introduces the typical features of a Bayesian approach and then it describes the development of the Bayesian model in the EVM framework. The model is based on the integration of data records and qualitative knowledge provided by experts, and the paper explains the elicitation process to obtain the experts' judgments and the prior estimates of the CPIf and SPI(t)f indices for work remaining (WR).