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Abstract
The automotive industry has become a robust manufacturing sector in the global manufacturing field, boosting and developing at high speed based on technology updates, environmental protection rules and regulation changes, and customers' new needs and demands. Such dynamics give rise to complex problems like optimizing cost strategy and innovation, operating under high sustainability standards, and navigating the supply chain. These difficulties have led to the need for more structured, logical and effective approaches to decision-making that can handle cross purposes and the world of slow changes and unpredictability. To mitigate these problems, in this context, this study presents the Circular Intuitionistic Fuzzy EDAS (CIF-EDAS) approach to handle decision-making challenges in the automotive industry. Integrating the circular intuitionistic fuzzy sets (CIFSs) with the EDAS approach provides a better assessment of uncertainty and hesitation, enhancing the reliability and strength of multi-criteria decision-making (MCDM) methods. The applicability of the proposed approach is illustrated by a case study in the automotive industry, in which the assessment of different scenarios based on conflicting criteria is effective. Moreover, a comparison analysis has been conducted, demonstrating the proposed approach's superiority and highlighting its efficacy in facilitating robust decision-making. The CIF-EDAS approach is clarified to exemplify its applicability in optimizing decision-making of complicated real-life industrial problems and demonstrating its capability to evolve for the automotive industry's newly emergent restrictive and dynamic demand.
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