Unveiling the effects of big data analytic capability on improving healthcare supply chain resilience: An integrated MCDM with spherical fuzzy information approach

The objective of this study is to investigate the critical role of big data analytics capability (BDAC) in improving the resilience of healthcare supply chains. Specifically, this study integrates a hybrid multi-criteria decision-making (MCDM) framework with spherical fuzzy logic to assess the impact of BDAC on various dimensions of supply chain resilience. By employing this innovative MCDM approach, the study effectively addresses the complexities, uncertainties, and imprecision characterize in decision-making within healthcare supply chains. Thirteen BDAC criteria and seven dimensions of healthcare supply chain resilience are identified through an extensive literature review and validated by healthcare experts. The findings highlight that data generation capability, predictive analytics, and data visualization are the three most important BDAC criteria for improving healthcare supply chain resilience. Furthermore, BDAC significantly bolsters the resilience of healthcare supply chains, particularly in the areas of agility, adaptability, and visibility. This study contributes to bridging existing knowledge gaps in several ways. First, it pioneers the exploration of the impact of organizational BDAC on resilience within healthcare supply chains. Second, the application of a hybrid MCDM framework combined with spherical fuzzy logic offers a novel methodological approach that overcomes the limitations of traditional decision-making models, especially in complex and uncertain environments. Lastly the framework developed in this study is highly adaptable, with the potential for broad application across various industries, thereby offering significant opportunities to enhance supply chain resilience through BDAC in diverse sectors.