Effective decision-making in supply chains requires collaboration among multiple stakeholders, each with distinct preferences and objectives. This complexity is further amplified by the uncertainty inherent in stakeholder judgments and the need to evaluate diverse, often conflicting criteria. In response, this paper introduces an innovative multi-criteria group decision-making (MCGDM) approach that combines the discrete monotonic ordered weighted averaging (DMOWA) operator with intuitionistic fuzzy sets to address uncertainties and improve decision accuracy. Moreover, the proposed approach incorporates the complexity based ranking named comprehensive distance-based ranking (COBRA) method to rank and prioritize the potential alternatives, facilitating a more balanced and comprehensive evaluation process. A simple illustrative example on logistics software selection is presented to demonstrate the practical applicability and advantages of the proposed approach. The results show that the DMOWA-COBRA approach enhances the decision-making process by ensuring consensus-based, data-driven outcomes in dynamic supply chain environments. This study contributes to the advancement of MCGDM methodologies and provides valuable insights for supply chain managers aiming to make strategic, well-informed decisions.