A Fuzzy Failure Mode and Effects Analysis with Cumulative Prospect Theory for Reverse Cold Chain Risk Assessment

The cold supply chain (CSC) reverse logistics segment remains underexplored in the risk assessment literature. Existing frameworks apply traditional failure mode and effects analysis (FMEA) with a three-criterion structure (severity, occurrence, detectability), which inadequately captures the time-sensitive and cost-intensive risk environment of CSC reverse logistics. This study proposes an integrated framework combining hazard analysis and critical control points (HACCP)-based node partitioning, an extended five-criterion FMEA incorporating timeliness and economic cost, triangular interval-valued fuzzy number (TIVFN) aggregation via the Aczél–Alsina operator, and cumulative prospect theory (CPT) to prioritize risk modes. Applied to a food-sector reverse CSC, 16 failure modes were identified across six HACCP-based process nodes. Severity and economic cost jointly account for 47.5% of total criterion weight. Monitoring and data management (node P6) consistently emerge as the dominant risk cluster, with data completeness (FM14), sensor accuracy (FM15), and early-warning functionality (FM16) occupying the top three positions across all weight scenarios. Relative to conventional models and the extended RPN, the TIVFN-CPT model assigns FM16 a substantially higher priority, demonstrating that CPT captures asymmetric, loss-averse risk perception that conventional methods fail to encode. Sensitivity analysis across five weight scenarios confirms the structural robustness of the rankings. By extending FMEA to a five-criterion behavioral framework and introducing TIVFNs as an uncertainty-preserving linguistic scale, this study indicates that information quality constitutes a risk factor of comparable priority to physical temperature maintenance, supporting a tiered resource allocation strategy that prioritizes investment at node P6 and targeted upgrades at nodes P1 and P4.