Risk Propagation in Healthcare Supply Chain using Fuzzy-ANP and Bayesian Inference

  • 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


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.

How to Cite
Afzal, F., Usman Yousaf, S., Usman, B., Afzal, F., & Ikram, A. (2021). Risk Propagation in Healthcare Supply Chain using Fuzzy-ANP and Bayesian Inference. AJSS, 5(1), 131-155. Retrieved from http://ojs.lgu.edu.pk/index.php/ajss/article/view/1457