Date of Publication :5th December 2024

Abstract: Project portfolio management (PPM) plays a pivotal role in maximizing the strategic alignment of projects, ensuring resource efficiency, and mitigating risks across an organization. However, managing a portfolio effectively remains a complex task due to the dynamic nature of projects and the myriad factors influencing their outcomes. This study introduces a data-driven methodology designed to tackle three major challenges in PPM: project selection and prioritization, resource allocation, and risk assessment. The framework integrates advanced tools such as ROI (Return on Investment) and success probability for project evaluation, time-series analysis for forecasting resource demand and supply, and probabilistic risk modeling to proactively manage project risks. By applying machine learning algorithms, Monte Carlo simulations, and predictive analytics, the methodology enables organizations to make more informed, data-driven decisions that improve project success rates and optimize resource utilization. Through rigorous analysis and case studies, this research demonstrates that incorporating data analytics into PPM can result in significant improvements in proj ect performance, cost-effectiveness, and overall project success. The findings provide a robust framework for organizations to optimize their project portfolios and align them more closely with strategic objectives.

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