Evaluating Sensor-Based Supply Chain Systems for Economic Resilience: A Decision-Making Approach

Modern supply chains face growing disruptions from climate events, geopolitical tensions, pandemics, and cyberattacks, exposing their vulnerability. Effective decision-making on sensor system investments is critical to enhance economic resilience. This study presents a structured framework for formulating decision-making problem of supply chain sensor selection in a multi-dimensional approach and proposes a solution methodology using Full Consistency Method (FUCOM) and A mulTi-noRmalization mUlti-distance aSsessmenT (TRUST) methods. The framework is illustrated through a case evaluating four sensor system alternatives from basic Radio Frequency Identification (RFID) tracking to AI-enhanced predictive IoT networks across ten economic, operational, and strategic criteria. Results demonstrate that Internet of Things (IoT) Environmental Sensor Nodes with RFID provide the best balance of cost-efficiency and resilience, while AI-Enhanced Predictive Analytics offers strategic long-term benefits. Divergent rankings of lower-tier alternatives highlight trade-offs between cost, coverage, and reliability. The study offers a practical methodology for guiding resilient and sustainable sensor-based supply chain investments.