A Discovery of the Most Efficient Decision Aid to Support Project Managers

Project management professionals are challenged with the disadvantage in predicting project performance to make a decision accordingly. The significance of the problem is predicting disadvantages doesn't provide a window of opportunity for project managers to recognize adverse effects, explore options, and decision making in a timely manner. The author investigates the hypothesis that machine learning integration is the most effective decision-making approach in aviation/aerospace project management. The research method is a qualitative approach by examining scholarly articles regarding knowledge management practices, lessons learned challenges, software tools usage, and machine learning employment in the project profession.  The research validates the machine learning technique as the best technological option for project performance indication support to project managers. However, the conclusion also identifies a hybrid machine learning technique to allow human integration providing decision control to project managers. The implication of the findings is the research validates why machine learning should be incorporated in the Project Management Body of Knowledge (PMBOK) as a knowledge area, a Knowledge Management (KM) practice, and a method for organizational learning for project team performance.