A Data-Driven Approach to Enhancing Project Management Efficiency through Machine Learning and Predictive Modeling

Abstract

In the current fast-paced and complex project environments, the traditional methods of project management frequently struggle to manage the dynamic nature and growing complexity of the projects. This study examines the possibilities of the Machine Learning (ML) and Predictive Modeling technology to enhance the efficiency of project management through more intelligent decision-making, resource management and risk management in real time. A data-driven approach that takes ML techniques such as predictive analytics, regression models, and decision trees, greatly improves on the conventional approach by enabling actionable insights from huge datasets from the projects. This research reviews the existing applications of ML in project management, discovers the prevailing challenges associated with its integration, and recommends a framework used for the adoption of predictive modelling techniques. The study reveals the important benefits of using ML in better project forecasting, resource management, and risk prediction. It also looks at the barriers to adoption such as poor quality of data, system integration, and specialized skills. The results indicate that the data-driven methodology can not only streamline the project execution but provide long-term benefits in terms of project improvements, controlling the cost and following the timeline.