Multi-Criteria Decision-Making for Selecting Supplier of Supply Chain in Uncertain Environment

Abstract

Supplier selection in supply chain management represents a critical strategic decision that directly influences organizational performance, cost efficiency, and competitive advantage. The complexity of this decision is significantly amplified in uncertain environments where decision-makers must contend with incomplete information, vague assessments, and unpredictable market conditions. This study addresses the multifaceted challenge of selecting suppliers under uncertainty by developing a comprehensive multi-criteria decision-making (MCDM) framework. The research integrates fuzzy set theory with established MCDM methodologies to handle the inherent vagueness and imprecision in decision-maker judgments. A novel hybrid approach combining subjective and objective weighting techniques is proposed, incorporating the Analytic Hierarchy Process (AHP), Fuzzy AHP, Entropy method, and their hybrid combinations. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for supplier ranking based on a comprehensive set of criteria encompassing quality, cost, delivery performance, service, and sustainability considerations. The methodology is validated through a numerical case study in the manufacturing sector, demonstrating its practical applicability. Sensitivity analysis across multiple scenarios confirms the robustness and stability of the proposed framework, with hybrid methods demonstrating superior stability compared to purely subjective or objective approaches. The findings reveal that integrated frameworks combining expert judgment with data-driven learning provide the most reliable decision support for supplier selection in uncertain environments, offering significant implications for both academic research and industrial practice.