<p>Redesigning supply chain networks is a complex decision-making process requiring the balance of multiple, often conflicting criteria such as cost, resilience, flexibility, and sustainability. In today’s dynamic supply chains—facing fluctuating demand, geopolitical risks, and transportation disruptions—traditional methods often fail to integrate diverse expert perspectives and manage heterogeneous data, including crisp, interval, and fuzzy evaluations. To address these limitations, this study proposes a novel multi-criteria group decision-making (MCGDM) approach that uses the bounded confidence model (BCM) with experts trust levels to effectively capture and structure the inputs during the decision-making process. Additionally, the Choquet integral—enhanced to consider uncertainty in the input evaluations—is applied for robust aggregation. To ensure a balanced and consensus-driven ranking of redesign alternatives, the combined compromise for ideal solution (CoCoFISo) method is employed. The evaluation criteria are structured into core functional domains such as logistics, procurement, and operations, enabling subject-matter experts to contribute domain-specific insights and thereby enhancing decision reliability. Finally, a simple illustrative numerical example validates the effectiveness of the proposed approach, demonstrating its capacity to support collaborative and data-driven decision-making in dynamic supply chain environments. This research advances the field of MCGDM methodologies and offers practical guidance for strategic supply chain network redesign under uncertainty.</p>