A probabilistic project control tool for projects with high risks and uncertainty

Project monitoring and control are essential for project success. One of the most commonly used project control methods is Earned Value Management (EVM). EVM ensures that the projects are controlled in terms of cost, time and scope of the work and can make estimates about the completion time and cost according to the progress of the project. However, the common feature of the projects is that they contain risk and uncertainty and since EVM does not take into account uncertainty and risk factors, it is not effective in projects with high risk and uncertainty. This study aims to develop a project control tool that is capable of effective project control under uncertainty and risk. The tool can control the project in multiple dimensions in terms of cost, time and scope, and it can calculate the uncertainty and causal risk factors related to these parameters. The tool uses Bayesian Networks (BNs) to model uncertainty and risk factors in the project parameters and to make statistical calculations related to them. BNs offer a powerful modeling technique for modeling and calculating probabilistic relationships, allowing expert knowledge and data to be combined. In order to examine the applicability of the developed tool to different project areas, case studies will be examined in three different areas. Two of these case studies were based on real project data from different sectors. The positive and negative sides of the developed tool will be evaluated.