This research addresses the critical need for a comprehensive framework to assess transportation disruptions and their cascading effects on supply chain performance. This study aims to overcome the limitations of traditional risk management techniques that handle disruptions in isolation, ignoring their systemic, interdependent and uncertain nature.
This research uses a two-phase integrated decision-support approach combining Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II) and fuzzy cognitive maps (FCMs). In Phase 1, transportation disruption factors are identified through systematic literature review and expert consultation, then prioritized using PROMETHEE-II based on transportation cost, time and service reliability impacts. Phase 2 develops an FCM where nodes represent risks and performance indicators, with weighted edges capturing causal influence strength and direction. MATLAB-based FCM simulations enable dynamic scenario analysis and sensitivity testing until system convergence.
The analysis identifies nine critical transportation disruption factors, with political tensions, labour strikes, logistics provider failures and natural disasters emerging as the most significant sources of disruption. FCM simulations demonstrate how these factors propagate through the system and critically impact supply chain performance metrics (cost, lead time and inventory stability). Freight damage and political tension show the highest impact on cost (20.81% and 17.09%, respectively), while labour strikes and freight damage most significantly affect lead time (18.71% and 17.14%). Natural disasters and political tension have the greatest influence on inventory performance (20.16% and 18.88%).
This hybrid approach presents the first scalable, interpretable and technically sound solution that integrates multi-criteria decision-making, semantic modelling and fuzzy systems analysis for understanding transportation disruption dynamics. The model’s convergence after nine iterations demonstrates its stability and predictive power, providing supply chain practitioners with an effective means for proactive risk reduction and resilience maximization in dynamic logistics environments. The framework bridges the gap between qualitative expert knowledge and quantitative analysis, enabling real-time, context-aware decision-making for transportation risk management.
