Risk Propagation in  Healthcare Supply  Chain: The  implications of  Fuzzy-ANP and  Bayesian Inference

Fahim Afzal; Sheikh  Usman Yousaf; Bushra  Usman; Farman Afzal; Amir  Ikram

doi:10.54692/ajss.2021.05011457

Fahim Afzal Business School of Hohai University, Nanjing, China
Sheikh Usman Yousaf Hailey College of Commerce, University of the Punjab, Lahore, Pakistan
Bushra Usman Forman Christian College, Lahore, Pakistan
Farman Afzal Institute of Business and Management, University of Engineering and Technology, Lahore, Pakistan
Amir Ikram Institute of Business & Management, University of Engineering & Technology, Lahore, Pakistan

DOI: https://doi.org/10.54692/ajss.2021.05011457

Keywords: Supply Chain Management, Risk Assessment, Fuzzy-ANP, Bayesian Inference

Abstract

Since the beginning of 2020, healthcare industry has been under constant pressure to maintain and provide best health related services to the public. Therefore, this study attempts to evaluate and measure the critical supply chain risks in the healthcare industry that troubled the flow of supply chain. For that purpose, a comprehensive list of critical risk factors has been developed that impact on the healthcare supply chain. Fuzzy analytical network processing gives a comprehensive list of risks probability based on experts’ judgment. Hereafter, Bayesian inference helps out to analyze the multi-echelon network with different risk bearing attitudes (i.e., pessimistic, most likely, optimistic) of healthcare professionals’ simultaneous propagation. The findings of risk prorogation help the professionals to evaluate the critical supply chain risks persists during covid-19 pandemic. Further, a proposed risk modeling gives an opportunity to achieve supply chain goals in terms of cost reduction, quality, and availability of equipment and drugs.