A systematic and objective approach for evaluating performance and selecting subcontractors in construction projects

Abstract

The management of the contractor-subcontractor relationship is a pivotal component of supply chain management in the construction industry, significantly impacting project success. This thesis explores approaches to assess performance of subcontractor, select the best subcontractor and the critical role of subcontractors in construction projects. Traditionally, process of subcontractor assessment performance and selection have been influenced by subjective assessments, often leading to biased decision-making processes. To address this, the research develops a comprehensive framework that links performance assessment upon project completion with the selection process for new projects, using objective criteria and systematic evaluation methods. The study begins with an extensive literature review and expert consultations to identify key indices and criteria for subcontractor evaluation, including time, cost, quality, safety, leadership, pricing, and experience. A hybrid approach is employed, combining Monte Carlo simulation and the Analytic Hierarchy Process (AHP) to weigh these criteria, ensuring statistical significance and reducing judgment uncertainty. This innovative method generates pairwise comparison matrices and utilizes probability distributions to establish objective weights for each criterion. The developed evaluation model incorporates a Linear Additive Utility Model (LAUM) to calculate a Performance Index (PI) that quantifies subcontractor performance across various levels, from outstanding to poor. By integrating these assessments into a Decision Support System (DSS), the research provides a tool for general contractors to systematically evaluate subcontractors and make informed decisions. The DSS employs the Complex Proportional Assessment (COPRAS) method, a multi-criteria decision-making (MCDM) approach that ranks subcontractors based on comprehensive performance data, aligning selection decisions with empirical evidence and industry best practices. This thesis demonstrates the potential for enhanced transparency, accuracy, and efficiency in subcontractor selection, contributing to improved project outcomes by optimizing subcontractor capabilities, resource allocation, and overall project delivery. By bridging the gap between performance assessment and selection processes, the research establishes a foundation for more informed and effective subcontractor management in the construction industry.