Project Management is an important factor to accomplish the decision to implement large-scale Software systems (LSS) in a successful manner. The effective Project Management comes into play to plan, coordinate and control such a complex Project. Project Management factor has been argued as one of the important Critical Success Factor (CSF), which need to be measured and monitored carefully during the implementation of Enterprise Resource Planning (ERP) systems. The goal of this article is to develop â AœCSF-Live! â AI which is a method for measuring, monitoring, and controlling critical success factors of large-scale Software systems. To achieve such goal, we apply CSF-Live for the Project Management CSF. The CSF-Live uses the Goal/Question/Metric paradigm (GQM) to yield a flexible framework containing several metrics that we used it to develop a formulation to enable the measurement of the Project Management CSF. The formulation that we developed for the Project Management CSF implies that the significance of having proper Project Management when conducting an ERP system implementation, since it is positively associated with the success of the ERP.

Project managers analyze the factors that affected Projects' success, signifying performing a Project within the scopes (Time, Quality, and cost) defined in the initial step. The implication of each factor on Project success is essential since several of them have been specified in this area. Employing all of them is not feasible, and it may impose outrageous expenses on the organizations. Therefore, this article aims to identify the factors that impact Project accomplishment and pinpoint the most contributing factors to facilitate the Project's implementation. The main contribution of this paper is representing a framework by combining Machine Learning algorithms and simulation models to detect the effectiveness of leading organizational factors on Project accomplishment, beneficially leading to extracting the accurate analysis.   A logistic regression algorithm was employed to build a predictive model. The predictive model was created based on independent variables to predict whether the Software Project would be successful or fail. Also, Gamification was determined as the most influential factor on the objective by the Logistic regression feature importance method. Then, Gamified and non-Gamified models were compared by the Simulation method and showed Gamification made a 36.26% improvement in the rojects cycle time and a 15% enhancement in the quality of employers' performance by decreasing the Projects' bugs. For validating the simulation results, some Projects were implemented in the real case study, and the model results clarified the Gamification potential in improving employee engagement leading to better work progress tracking and higher performance quality.