The study aims to develop a robust, fuzzy, data-driven ERM model that incorporates the decision-makers’ varied levels of expertise and the relative importance of risk factors.
The study presents a robust multi-criteria fuzzy model that integrates inputs from multiple decision-makers to enhance risk prioritization in supply chain operations. It employs triangular fuzzy numbers to normalize decision-maker weights and uses the fuzzy AHP to determine risk criteria weights. Risks are evaluated using fuzzy linguistic terms, such as fuzzy FMEA, followed by weighted fuzzy aggregation. Finally, defuzzification generates priority numbers for ranking risks.
This approach enhances user-friendliness and promotes greater acceptance, making the model particularly suitable for implementation in typical steel plant settings, which may be extendable to the general industry with suitable modifications of model parameters on a “case-to-case” basis.
Due to its advanced calculations and multi-step processes, the framework’s complexity may deter adoption, especially in organizations unfamiliar with fuzzy logic. Implementation demands specialized training or software support, posing challenges for smaller enterprises. Customization to specific industrial contexts requires substantial resources, making adoption difficult for resource-constrained organizations.
The proposed fuzzy framework delivers a more nuanced approach to risk management by integrating imprecise information and leveraging diverse expertise. This contribution broadens supply chain knowledge, particularly within the context of complex, multi-tiered risks, advancing beyond traditional linear perspectives in risk management literature.
The proposed model is novel in terms of successful validation under a steel plant environment using fuzzy AHP combined with fuzzy FMEA.
