Risk Assessment for Sustainable Supply Chain Management Under Uncertainty: A Novel Three‐Stage Failure Mode and Effect Analysis‐Based Decision Model

ABSTRACT Against the backdrop of increasing uncertainty and sustainability pressures, sustainable supply chain management (SSCM) has become critical for balancing economic, environmental, and social performance. Failure mode and effect analysis (FMEA) is widely used in SSCM, yet traditional FMEA is limited in handling uncertain linguistic information, deriving reasonable risk factor weights, and capturing intrinsic correlations among failure modes. To address these gaps, this study proposes a novel three‐stage FMEA‐based decision model for SSCM risk assessment. In the first stage, trapezoidal interval type‐2 fuzzy sets (TrIT2FSs) are employed to represent uncertain expert assessments. In the second stage, an integrated deck of cards with decision‐making trial and evaluation laboratory (DOC‐DEMATEL) method is developed to determine risk factor weights by considering dual interactions among risk factors and experts. In the third stage, a TrIT2FS‐based grey relational analysis (Tr‐GRA) method is constructed to rank failure modes while capturing their intrinsic relationships. Finally, an SSCM case is analyzed, followed by sensitivity and comparative analyses to validate the model. Results show that operation complexity, opportunity loss, and lack of trust are the highest‐priority failure modes. The proposed model outperforms traditional FMEA and multi‐criteria decision‐making methods in robustness and rationality. These findings provide clear managerial insights to help enterprises strengthen risk detection, improve supply chain collaboration, and optimize operational processes toward sustainable development.