The Best of Both Worlds: Combining Parametric Cost Risk Analysis with Earned Value Management Using Bayesian Parameter Learning

Cost risk analysis and earned value data are typically used separately and independently to compute Estimates at Completion (EAC). However, combining the two can significantly improve the accuracy of EAC forecasting. This paper provides a rigorous method using Bayesian methods. We also provide some examples which will illustrate the strengths of this method. In earned value management (EVM), EAC is a critical metric. It is used to forecast the effort’s total work cost as it progresses. In particular it is used to see if the work is running over or under its planned budget, specified as the budget at completion (BAC). This paper will explain how to specify the initial probability density function (PDF) and learn the later PDFs from the data tracked in EVM. We describe the technique called Bayesian parameter learning (BPL). We chose this technique because it is the most robust for exploiting small sets of progress data and is most easily used by practitioners.