Uncertainty and risk are common elements of all projects. Unforeseen risk events that negatively affects the performance of a project inevitably happen in every project, and these events rarely gets better in time. A project manager must actively monitor and control a project, and intervene to mitigate such risk events. Therefore, the project manager requires project control tools that compares the planned and actual performance, and support decisions about how and when to intervene.

“Development of a Probabilistic Project Control Approach for Highly Risky and Uncertain Projects” is a TUBİTAK ARDEB 3001 project (216M483, November 2017 – January 2019). The developed approach will use Bayesian networks to model and compute the parameters and risk factors related to project performance. A Bayesian network is a powerful technique to model complex and uncertain relations between a large number of variables by using a combination of expert knowledge and data. Our approach will be able to control a project in multiple dimensions including cost, time and scope. It will also be able to use parameter uncertainty and causal risk factors related to these parameters in its computations.