Document Type : ORIGINAL RESEARCH ARTICLE
Authors
Planning, Human capital development and Council Affairs department, Tehran municipality, Tehran, Iran
Abstract
BACKGROUND AND OBJECTIVES: The roots of portfolio thinking can be traced back to ancient civilizations, where large-scale construction projects were coordinated at the empire level. During World War II, project control techniques were extensively applied to improve time management. While project control focuses mainly on schedule adherence, project management encompasses broader considerations, including cost, quality, and scope. With the increasing number of projects undertaken by organizations and the growing need for strategic alignment, resource prioritization, and cross-project integration, Project Portfolio Management has emerged as a critical discipline for achieving organizational coherence. Despite its importance, a comprehensive and practical conceptual model that guides managers in implementing Project Portfolio Management remains underdeveloped. This study therefore seeks to present an integrated conceptual framework for Project Portfolio Management.
METHODS: The proposed model was evaluated within the framework of the Balanced Scorecard to categorize projects across strategic dimensions. The Analytic Hierarchy Process was applied to determine the relative importance of evaluation criteria. The Technique for Order Preference by Similarity to Ideal Solution was then employed to assign performance scores to the projects, while Data Envelopment Analysis was used to assess their efficiency. Finally, Linear Integer Programming was implemented to incorporate budgetary constraints and identify the optimal project portfolio. To illustrate the applicability of the model, a case study of a hypothetical municipality managing 114 projects was conducted.
FINDINGS: The Linear Integer Programming analysis revealed that Technique for Order Preference by Similarity to Ideal Solution yielded superior results in the learning and growth and internal processes aspects, with objective function values of 0.22 and 0.164, respectively. By contrast, Data Envelopment Analysis was more effective in the customer and financial aspects, achieving values of 0.190 and 0.141. This divergence is largely attributable to budget allocation and methodological orientation: Data Envelopment Analysis emphasizes input–output efficiency, favoring projects with higher relative productivity, whereas Technique for Order Preference by Similarity to Ideal Solution prioritizes projects with stronger weighted performance scores. Under budget constraints, however, portfolios composed of smaller, lower-scoring projects may collectively generate greater benefits than portfolios of fewer, high-scoring but resource-intensive projects. This highlights the inherent trade-off between single-project efficiency evaluation and portfolio-level optimization.
CONCLUSION: This study introduces an eight-step conceptual framework for Project Portfolio Management, integrating Balanced Scorecard, Analytic Hierarchy Process, Technique for Order Preference by Similarity to Ideal Solution, Data Envelopment Analysis, and Linear Integer Programming to support portfolio creation and project selection. The findings suggest that while both Technique for Order Preference by Similarity to Ideal Solution and Data Envelopment Analysis offer valuable insights, their relative effectiveness depends on organizational constraints. The proposed framework provides a structured, systematic approach to portfolio decision-making, thereby enhancing the alignment of projects with strategic objectives and improving overall organizational performance.
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