Enhancing Supply Chain Resilience Through a Fuzzy AHP and TOPSIS to Mitigate Transportation Disruption

Abstract

Supply chain resilience is a growing concern as risk becomes increasingly challenging to interpret and anticipate due to sudden global events that disrupt the core of global supply chains. This paper discusses the use of advanced technologies to enhance supply chain resilience, proposing a two-step hybrid fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) approach that evaluates a set of different supply chain KPIs or criteria that trigger possible supply chain risks, with a focus on transportation disruptions. Using FAHP, the highest potential risks from disasters are identified, and TOPSIS is used to rank alternative solutions that enhance supply chain resilience. The approach is tested on real-world applications across multiple supply chain systems involving various companies and experts to demonstrate its validity, feasibility, and applicability. Based on five criteria and six alternatives per case study, the findings showed that for manufacturing supply chains, the highest risk was attributed to travel time (46%), and the most effective solution to mitigate it was found to be strengthening highway networks (0.72). For transportation, delivery time (56%) was the primary risk, addressed by green logistics and sustainability (0.89).