Integrating Behavioral Risk Perception into Data-Driven Decision Support System for Supplier Selection

Abstract

Abstract

Supplier selection is a complex decision-making problem that requires balancing multiple criteria related to cost, quality, reliability, and market performance. To address this challenge, Multi-Criteria Decision-Making (MCDM) methods are widely used to evaluate supplier attractiveness. However, these approaches typically emphasize performance evaluation while giving limited attention to the perception of risk influencing managerial decisions. In practice, decision-makers often exhibit loss-averse behavior, assigning greater importance to potential losses than comparable gains, which can affect evaluation outcomes. This study proposes a data-driven decision support framework that integrates multi-criteria attractiveness assessment with behavioral risk evaluation. The approach combines the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method for examining supplier attractiveness with a Risk-Informed Decision-Making (RIDM) method to capture perceived risk associated with potential losses. Results show that highly attractive suppliers are not always associated with lower perceived risk, highlighting the importance of incorporating behavioral risk considerations into strategic supplier selection.