Intuitionistic Fuzzy Credibility Implicative Ideals in BCK-algebra for Efficiency Evaluation, Supply Chain and Risk Analysis

Abstract

 This study proposes a robust multi-criteria decision-making (MCDM)frameworkforefficiencyevaluation, supplychain, andrisk anal ysis based on intuitionistic fuzzy credibility sets (IFCSs) and intuitionis tic fuzzy credibility implicative ideals (IFCIIs) in BCK-algebras. An in tegrated LOPCOW–SWARA–ERUNS framework is developed to handle decision-making under uncertainty. The proposed approach is applied to evaluate leading companies, including Amazon, Walmart, and Alibaba, by considering key factors such as cybersecurity, geopolitical instability, en vironmental impacts, economic conditions, technological aspects, social issues, and logistics-related risks. Conventional MCDM methods often struggle to address uncertainty and subjective bias, resulting in unreliable outcomes for complex decision problems. To overcome these challenges, SWARAisemployed to determine subjective weights, while LOPCOW is used to compute objective weights. The ERUNS method is then applied to rank the alternatives, ensuring a comprehensive and reliable evaluation. Sensitivity and comparative analysis are conducted to assess the robust ness of the proposed framework. The results confirm that the proposed method significantly enhances MCDM accuracy by effectively managing uncertainty.