Combination of Project Cost Forecasts in Earned Value Management

Abstract

Reliable cost estimates are essential for effective project control and the management of cash flows within the project and at the company level. Conventional approaches to project cost forecasting, which rely on detailed information developed for a specific project (the bottom-up estimate or inside view), often result in cost overruns. It is argued here that the inside-view project cost estimates should be adjusted by combining them with the outside (or top-down) view of the project, which is based on statistical models of historical project data. This paper presents a probabilistic cost forecasting method and a framework for an adaptive combination of the inside view and the outside view forecasts of project cost using Bayesian inference and the Bayesian model averaging technique. During the project execution phase, the Bayesian adaptive forecasting method incorporates into the predictions the actual performance data from earned value management and revises preproject cost estimates, making full use of the available information. Qualitative examples are presented to demonstrate the validity of the proposed method as a tool for effective project cost prediction and control.