International Journal of Human Capital in Urban Management

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.

Abstract BACKGROUND AND OBJECTIVES: The roots of Performance Management can be traced to ancient civilizations; however, the industrial revolution marked a turning point in this field, introducing concepts such as systematic evaluation, management by objectives, and excellence models. In modern times, PM has evolved to emphasize process analysis, self-assessment, benchmarking, and workforce development as core components. Despite these advancements, there is still a notable gap in the availability of a comprehensive, visually intuitive, and step-by-step framework to guide managers in implementing a holistic PM system. This manuscript aims to address this gap by introducing a detailed, graphical, and systematic PM model that provides clear guidance for practitioners.
METHODS: This study reviews various performance and excellence models to propose a novel conceptual framework. The proposed model is evaluated using the Analytic Hierarchy Process, Data Envelopment Analysis, and the Technique for Order of Preference by Similarity to Ideal Solution.
FINDINGS: The study constructs a new PM system by integrating an organization’s mission, vision, strategies, processes, and stakeholder perspectives into Key Performance Indicators. It involves collecting relevant data, applying a scoring mechanism, calculating departmental efficiency, and ranking organizations to establish a data-driven decision-making framework. A case study is presented to illustrate the model’s application, revealing that while DEA assigned 100% efficiency to two distinct organizations, TOPSIS yielded scores of 94.67% and 46.86%, with different rankings. The reasons for these discrepancies are thoroughly examined and discussed.
CONCLUSION: This manuscript introduces a conceptual model for PM, structured around eight key steps. These steps include team formation, indicator development, Balanced Scorecard development, weight calculation, scoring system design, data collection, data analysis, and feedback and continuous improvement. The model classifies KPIs into three main categories: specialized KPIs that are derived from missions, visions, strategies, critical success factors, and core processes. Self-assessment KPIs, developed based on established excellence models, and customer survey KPIs, designed to capture external stakeholder feedback. AHP is employed to determine the weights of the KPIs and BSC aspects, ensuring a systematic and objective prioritization. DEA is utilized for efficiency calculations, while the TOPSIS method is applied to analyze the results and derive actionable managerial insights. To demonstrate the model's applicability, it is implemented in a hypothetical municipality using arbitrary data, showcasing its capability to provide a comprehensive and structured approach to performance management.

Abstract BACKGROUND AND OBJECTIVES: The six sigma is a common methodology that has applied successfully in many organizations leading to sustainable performance improvements in products and services. However, the applied methodologies have not pay attention to Multi Criteria Decision Making models, clustering algorithm and Balance scorecard model. The purpose of this model is to apply the six-sigma methodology in Tehran Municipality and show how the Balance scorecard model, clustering algorithm, Analytic Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution can be applied in the methodology.
METHODS: Define, Measure, Analysis, Improvement and Control as a common methodology of six sigma is applied in Tehran Municipality. Several unique elements that are exist in Tehran Municipality are identified and categorized based on the Balance scorecard model into indexes, goals and perspectives. Also, Analytic Hierarchy Process for weight extraction and Technique for Order of Preference by Similarity to Ideal Solution for ranking are applied. The improvement strategies are created based on the six sigma tolerances.
FINDINGS: The findings show that the methodology can be elevated by Balance scorecard, Analytic Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution. This formulation is done and applied in Tehran Municipality and can be used in other organizations for conducting improvement strategies. Based on the arbitrary data, the best district of Tehran city is district 9 by 89.75% and the worst is district 18 by 10.12% score. Also, Wards and K-mean clustering algorithms and six sigma control limits are used to cluster the districts into superior, somehow superior, moderate, somehow inferior and inferior clusters based on their performance.  
CONCLUSION: This manuscript helps to understand the way of integration the methodology, Balance scorecard, Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution and six sigma tolerances for sustainable improvement of Tehran Municipality. The proposed formulation can be used in any organization to reach to sustainable improvement.

Abstract BACKGROUND AND OBJECTIVES: Excellence models are essential for organizations to improve performance. Deming Prize, Canada Awards for Excellence, Malcolm Baldrige National Quality Award, and European Quality Award are the most well-known excellence models worldwide. These models do not present any mathematical model in accordance with a comprehensive step-by-step roadmap for implementation. Moreover, they are general models and have not been customized for a specific organization. So, this article presents a comprehensive, graphical, step-by-step roadmap to implement an excellent model for Tehran Municipality that is elevated by Technique for order of preference by similarity to ideal solution and analytical hierarchical process to make decisions by mathematical analysis.
METHODS: Different excellence and performance models have been studied, and then an excellence model for deputies of Tehran Municipality is designed. Also, an Analytical hierarchical process for weight extraction and a Technique for order of preference by similarity to an ideal solution for ranking is applied.
FINDINGS: A novel excellence model for deputies of Tehran Municipality has been developed. Strategies, goals, objectives, targets, critical success factors, and general, proprietary, and transaction indexes are defined. Analytic hierarchy process  calculates weights of indexes based on arbitrary data, and results are presented in 11 Tables. The most important index was the proprietary index, with a weight of 70% for the deputy of technical and construction. The less important index was the general index for the deputy of planning, human capital development, and council affairs, weighing 8%. Moreover, deputies of Tehran Municipality have been ranked by TOPSIS. The best deputy got 71%, and the worse got 7% scores.
CONCLUSION: This study constructed a customized five-step excellence model for Tehran Municipality to reach excellence. The model can help Tehran Municipality for better urban planning. Step one constructs the performance assessment team. Step two extracts indexes by brainstorming method with the help of the European foundation for quality management model. Step three collects, cleans and loads the data in the data warehouse. In step four, weights of the indexes and facets are calculated based on AHP, and then facets, indexes, and goals are ranked by technique for order of preference by similarity to ideal solution as an effective multi-criteria decision making tool. Finally, the model has been implemented at offices of plan monitoring, project control, and performance evaluation in planning, human capital development, and council affairs department at Tehran Municipality.