A comparative analysis of Multi-Attributive Border Approximation Area Comparison (MABAC) model for healthcare supplier selection in fuzzy environments

Access to high-quality and affordable healthcare services is necessary for people worldwide. Achieving this goal requires healthcare providers to procure the best available equipment and pharmaceutical items at the lowest possible cost. Hence, proper supplier selection is crucial for all healthcare organizations to ensure the availability of the right products in the right quantity at the right time and minimum cost. This paper employs a multi-criteria decision making (MCDM) tool, i.e., Multi-Attributive Border Approximation Area Comparison (MABAC), in seven different fuzzy environments to solve a healthcare supplier selection problem. Fuzzy MABAC, intuitionistic fuzzy MABAC, Pythagorean fuzzy MABAC, neutrosophic fuzzy MABAC, hesitant fuzzy MABAC, spherical fuzzy MABAC, and Fermatean fuzzy MABAC are considered here to search for the most appropriate healthcare supplier while simultaneously optimizing conflicting criteria. We show that all the fuzzy-integrated MABAC methods lead to the same set of the best and worst healthcare suppliers regardless of the fuzzy environment employed. Furthermore, these fuzzy MABAC methods can efficiently deal with varying decision makers' judgments and uncertainty associated with qualitative criteria, such as quality, responsiveness, reliability, etc., that form a major component of any healthcare supplier selection problem.