ENHANCING CONTRACTOR SELECTION THROUGH A MULTI-CRITERIA EVALUATION FRAMEWORK: THE CONTRACTOR INTEGRATION METHOD (CIM-MCE)
DOI:
https://doi.org/10.31732/2663-2209-2025-78-286-296Keywords:
CIM-MCE, contractor selection, multi-criteria decision-making, project management, performance metrics, risk management, MastergazAbstract
This study develops and empirically validates the Contractor Integration Method through Multi-Criteria Evaluation (CIM-MCE), a novel framework for enhancing contractor selection within Mastergaz, an engineering and IT company specializing in complex residential construction. The scientific innovation introduces the Integral Contractor Selection Index (ICSI), standardizing qualitative and quantitative criteria into a unified metric for comprehensive assessment. The research employs mixed methods, combining statistical analysis with expert evaluations to holistically assess contractor capabilities across multiple dimensions. The study analyzes 50 contractors using a multi-criteria model accounting for cost factors, execution time, work quality, and client interaction ratings, with data systematically collected through the BOS CIS ERP system to ensure consistency and reliability. Following CIM-MCE implementation, project completion rates increased to 95% and client satisfaction ratings reached 4.7/5, significantly outperforming traditional selection approaches that often prioritize cost alone. The comparative analysis with classical decision-making methods (AHP, TOPSIS) reveals CIM-MCE's superior adaptability, sensitivity to weight adjustments, and capacity to integrate emerging parameters including sustainability and social responsibility factors. The research emphasizes the critical balance between subjective and objective dimensions in effective contractor management processes. Its practical significance lies in enhancing transparency and rationality of contractor selection in complex project environments with multiple stakeholders and competing priorities. The study recommends scaling CIM-MCE to larger multi-sector projects and integrating machine learning technologies to further automate evaluation processes. This research advances adaptive, data-driven methodologies in modern project management, focusing on sustainability, transparency, and operational efficiency in contractor selection procedures, especially in contexts requiring dynamic assessment of diverse performance indicators
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