An Algorithmic Decision-Making Framework for Supply Chain Management Using Distance Measure-Based Approach

Supply chain management is a key area of study that focuses on the effective coordination of material, information, and financial flows across production and distribution networks. SCM plays a vital role in ensuring sustainability, resilience, and competitiveness by optimizing processes. However, SCM inherently involves complex decision-making scenarios under uncertain and conflicting environments. To address these challenges, this research proposes a multi-attribute decision-making approaches combined with an advanced hybrid mathematical framework known as Complex Spherical Fuzzy Soft Sets (CSpFSS). First, the notion of CSpFSS is introduced and illustrated through an example. Subsequently, two distinct distance measures for any two CSpFSS are presented, forming the basis of the proposed Multi-Attribute Decision-Making (MADM) algorithmic approach. The developed algorithm is then applied to a case study concerning the selection of the best supplier from among multiple alternatives. To validate and check the effectiveness of the presented approach, a statistical analysis employing various statistical tools is carried out. Furthermore, to establish the superiority of the proposed model, a detailed comparative analysis with existing distance-based MADM models is provided. Finally, the research concludes with a synthesis of the key findings and outlines potential future research directions.