Development of a Project Portfolio Framework based on Multi-Criteria Decision Analysis

Project portfolio management helps companies to optimize resource allocation to projects to maximize value and minimize risk while reaching strategic goals. The conventional project selection methods that have been used in complicated industrial settings are usually based on subjective evaluation or monetary criteria and thus result in inconsistency and a lack of transparency in decisions. The classification of projects with systematic multi-criteria decisions in both research and practice is present in academic literature and practice. To be able to make transparent and informed decisions, this thesis proposes a systematized project portfolio optimization strategy, which utilizes project categorization and multi-criteria decision-making for project selection. The recommended framework will make projects easily comparable and reduce the time and subjectivity for the decision-making process by standardizing projects. To establish the evaluation criteria and categorize the projects, the Delphi methodology engages expert opinion. The Analytical Hierarchy Process (AHP) is used to rank assessment criteria in accordance with business objectives used in selecting and prioritizing a project after project evaluation and create an optimum portfolio. Finally, the Technique of Order of Preference by Similarity to Ideal Solution (TOPSIS) model is adopted to rank projects in order of execution by measuring their relative closeness to the ideal solution. There is a practical industrial case study that confirms the utility and effectiveness of the framework. As one could observe from the findings, the integrated methodology supports open project selection, uniform decision-making, and balanced portfolio creation, which are all congruent with corporate objectives. Moreover, the framework allows decision-makers to evaluate projects in terms of sustainability, risk, strategic alignment, and financial concerns. The study introduces a model of an effective, flexible, and replicable method of decision-supporting project portfolio optimization, which can be applied both theoretically and practically. The suggested model provides the foundation of superior portfolio optimization and dynamic decision-making studies, and possible implementations in various fields.