Integrating Risk Assessment and Actual Performance for Probabilistic Project Cost Forecasting: A Second Moment Bayesian Model

Abstract

Forecasting the actual cost to complete a project is a critical challenge of project management, particularly for data-driven decision making in contingency control, cash flow analysis, and timely project financing. This paper presents a Bayesian project cost forecasting model that adaptively integrates preproject cost risk assessment and actual performance data into a range of possible project costs at a chosen confidence level. The second moment Bayesian (SMB) model brings more realism into project cost forecasting by explicitly accounting for inherent variability of cost performance, correlation between aggregated past and future performance, and the fraction of project completed at the time of forecasting. Functionally, the SMB model fully encompasses, as restrictive cases, two most commonly used index-based cost forecasting techniques in earned value management. The SMB model provides computationally efficient algebraic formulas to conduct robust probabilistic forecasting without additional burden of data collection or sophisticated statistical analysis. Numerical examples and simulation experiments are presented to demonstrate the predictive efficacy and practical applicability of the SMB in real project environments.