Constrained Bayesian Inference for Project Performance Models

Project performance models play an important role in the management of project success. When used
for monitoring projects, they can offer predictive ability such as indications of possible delivery
problems. Approaches for monitoring project performance relies on available project information
including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in environments where information about project constraints is available and can be exploited for improved project performance. We apply the methodology to probabilistic modelling of project S-Curves, a graphical representation of a project’s cumulative progress. We show how the methodology could be used to improve confidence bounds on project performance predictions. We present results of a simulated process improvement project in agile setting to demonstrate our approach.