Applying Artificial Intelligence techniques in Project Management

The main purpose of this thesis is to create a model like Earned Value Management (EVM) model in the Project Management for better forecasting. Some machine learning concepts will be used to better predict the project duration and minimize the cost. Traditional EVM model formulas to track Projects progress relies on too many assumptions and simplifications. This oversimplification can affect the accuracy of the forecasting which can cause delay and in turn increase project costs. There is also bias related to human perceptions in the EVM model which must be eliminated. The goal is to develop a new dynamic monitoring and optimization tool to track progress of the project. Furthermore, to increase forecasting, rescheduling and reallocation processes efficiency with new DSS (decision support system). Machine learning an ever-growing field and implementation of it in project management can change how projects are tracked. Machine learning models are trained with data already available so that the computer can make a more informed decision by itself. It should identify all the combinations and provide best scheduling option which minimizes cost and financial exposures. The ultimate goal is to minimize the project duration and project costs. The another aspect which will be focused and that is the financial exposure. The aim is to minimize the financial exposure which traditional methods do not account for. This is more practical than theoretical as banks are reluctant to fund the project if the cash flow is not smooth. The financial need should be minimized.