Leveraging Fuzzy Logic for Resilient Agricultural Supply Chains: Risk Mitigation and Decision-Making in Jordan

This study explored the use of fuzzy logic as a decision-making framework to address uncertainties and prioritize risks in the agricultural supply chain. Fuzzy logic's ability to handle imprecise and incomplete data was leveraged to develop a Risk Severity Index, assess risks across supply chain stages, and simulate various disruption scenarios. The research utilized a descriptive-analytical design, combining fuzzy logic modeling with risk mapping, correlation analysis, and scenario simulation. Primary data were collected through stakeholder inputs, and secondary data included weather patterns, market trends, and logistical disruptions. The fuzzy inference system converted qualitative data into linguistic risk classifications (Low, Moderate, High), providing a robust method for ranking risks. Scenario simulations tested the framework's adaptability to changing conditions, such as extreme weather events. The findings revealed significant regional disparities in risk levels, with the South region identified as the most vulnerable due to high rainfall variability, pest outbreaks, and logistical challenges. The fuzzy logic framework proved effective in identifying and prioritizing risks, enhancing decision-making and resilience across the supply chain. This study validates fuzzy logic as a practical tool for risk assessment and mitigation in agricultural supply chains. By integrating fuzzy logic with risk mapping and scenario analysis, the study provides actionable insights for stakeholders, enabling localized and adaptive interventions. Policymakers are encouraged to invest in climate-resilient infrastructure, market stabilization strategies, and capacity-building initiatives to strengthen the sustainability and efficiency of Jordan’s agricultural supply chains.