Integrating Risk into Project Control Using Bayesian Networks

Abstract

Projects are, by definition, risky and uncertain ventures. Therefore, the performance and risk of major projects should be carefully controlled in order to increase their probability of success. Quantitative project control techniques assist project managers in detecting problems, thus responding to them early on, by comparing the baseline plan with the project progress. However, project risk and uncertainty are rarely considered by these techniques. This paper proposes a project control framework that integrates the project uncertainty and associated risk factors into project control. Our framework is based on earned value management (EVM), which is an effective and widely used quantitative project control technique. The framework uses hybrid Bayesian Networks (BNs) to enhance EVM with the ability to compute the uncertainty associated with its parameters and risk factors. The framework can be applied to projects from different domains, and we illustrate its use with a simple example and a case study of a construction project.