<p>In today’s increasingly complex and interconnected supply chains, effective decision-making often requires collaboration among multiple stakeholders to address critical challenges, including supplier selection, facility location planning, technology adoption, and scheduling. These challenges are further compounded by the presence of uncertainty in stakeholders’ preferences and the diversity of evaluation criteria. This paper introduces a novel multi-criteria group decision-making (MCGDM) approach that combines a probabilistic aggregation algorithm based on spherical fuzzy numbers (SFNs) with the OWA operator to address uncertainty and variability in decision-makers’ inputs (percentages, intervals, and combinations of crisp and fuzzy values). The proposed approach uniquely separates high and low preference probabilities among decision-makers (DMs), allowing for a more balanced and thorough evaluation of alternatives while improving risk management. The OWA operator under probabilities over the inputs is employed to capture the expected behavior based on both DMs alternatives preferences probabilities and order. Moreover, the used combined compromise solution (CoCoSo) ranking method accommodates DM preferences more effectively through its adaptable weighting and criteria aggregation mechanisms, making it particularly suitable for uncertain and MCDM environments. The approach’s applicability and effectiveness are demonstrated through an illustrative numerical example on risk management strategies implementation, showcasing its potential to address the intricacies of collaborative decision-making in dynamic supply chain environments. This study not only provides a methodological advancement for MCGDM but also highlights its practical relevance in facilitating strategic, consensus-driven decisions in supply chain management.</p>