Document Type : Original Article


1 MA Student, Kharazmi University.

2 Assistant Professor, Kharazmi University.


The COVID-19 pandemic has posed a significant challenge to the COVID-19 vaccine supply chain that need to be resolved for a successful exit from the pandemic. It is essential to identify and evaluate the risks associated with the vaccine supply chain. We propose a new measure for quantifying the resilience of the vaccine supply chain in Iran. To evaluate the resilience of the vaccine supply chain, we use a Bayesian network to consider the vulnerability and recoverability indices as well as the associated disruption propagation. This approach enables managers to measure the resilience of the supply chain and identify the reasons for performance decline due to the ripple effect. Our results show that outbreak risks, lack of access to vaccine suppliers, low efficacy of the vaccine against new variants, inaccurate prediction of vaccine demand, and failure to choose the right suppliers are the risks likely to have a high impact on the disruption of the vaccine supply chain. Our approach provides a useful tool for evaluating the resilience of the vaccine supply chain and identifying the critical risks. It can be used by decision-makers to mitigate the impact of disruptions and improve overall resilience of the vaccine supply chain.


Main Subjects

  1. Abbasi, B., Fadaki, M., Kokshagina, O., Saeed, N., & Chhetri, P. (2020). Modeling vaccine allocations in the covid-19 pandemic: A case study in australia. Available at SSRN 37445
  2. Afzal, F., Yousaf, S. U., Usman, B., Afzal, F., & Ikram, A. (2021). Risk Propagation in Healthcare Supply Chain: The implications of Fuzzy-ANP and Bayesian Inference. Academic Journal of Social Sciences (AJSS)5(1), 162-192.
  3. Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics239, 108193.
  4. Aljadir, A., & Alnemsh, M. (2020). Exploration of the COVID-19 pandemic in relation to the healthcare industry Supply Chain.
  5. Antal, C., Cioara, T., Antal, M., & Anghel, I. (2021). Blockchain platform for COVID-19 vaccine supply management. IEEE Open Journal of the Computer Society2, 164-178.
  6. Bozorgi, A., & Fahimnia, B. (2021). Transforming the vaccine supply chain in Australia: Opportunities and challenges. Vaccine, 39(41), 6157–6165.
  7. Breen, L. (2008). A preliminary examination of risk in the pharmaceutical supply chain (PSC) in the national health service (NHS).
  8. Chai, J., & Ngai, E. W. (2015). Multi-perspective strategic supplier selection in uncertain environments. International Journal of Production Economics166, 215-225.
  9. Chandra, D., & Kumar, D. (2021). Evaluating the effect of key performance indicators of vaccine supply chain on sustainable development of mission indradhanush: A structural equation modeling approach. Omega101, 102258.
  10. Christopher, M., & Peck, H. (2004). Building the resilient supply chain.
  11. Clauson, K. A., Breeden, E. A., Davidson, C., & Mackey, T. K. (2018). Leveraging Blockchain Technology to Enhance Supply Chain Management in Healthcare: An exploration of challenges and opportunities in the health supply chain. Blockchain in healthcare today, 1(1), 1–12.
  12. Cockburn, G., & Tesfamariam, S. (2012). Earthquake disaster risk index for Canadian cities using Bayesian belief networks. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards6(2), 128-140.
  13. Chowdhury, M. M. H., & Quaddus, M. (2017). Supply chain resilience: Conceptualization and scale development using dynamic capability theory. International Journal of Production Economics188, 185-204.
  14. Dizbay, İ. E., & Öztürkoğlu, Ö. (2021). Determining significant factors affecting vaccine demand and factor relationships using fuzzy DEMATEL method. In Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23, 2020(pp. 682-689). Springer International Publishing.
  15. Dungu, B. (2020). The role of vaccine banks in resilience, response and recovery in respect of animal diseases. Revue Scientifique et Technique (International Office of Epizootics)39(2), 543-550.
  16. Duijzer, L. E., Van Jaarsveld, W., & Dekker, R. (2018). Literature review: The vaccine supply chain. European Journal of Operational Research268(1), 174-192.
  17. Enyinda, C. I., Gebremikael, F., & Ogbuehi, A. O. (2014). An analytical model for healthcare supply chain risk management. African Journal of Business and Economic Research9(1), 13-27.
  18. Fenton, N., & Neil, M. (2018). Risk assessment and decision analysis with Bayesian networks. Crc Press.
  19. Guttieres, D., Sinskey, A. J., & Springs, S. L. (2021). Modeling framework to evaluate vaccine strategies against the COVID-19 pandemic. Systems9(1),
  20. Golan, M. S., Trump, B. D., Cegan, J. C., & Linkov, I. (2021). Supply chain resilience for vaccines: review of modeling approaches in the context of the COVID-19 pandemic. Industrial Management & Data Systems,121(7), 1723-1748.‏
  21. Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review138, 101967.
  22. Hänninen, M., Banda, O. A. V., & Kujala, P. (2014). Bayesian network model of maritime safety management. Expert Systems with Applications41(17), 7837-7846.
  23. Hosseini, S., Al Khaled, A., & Sarder, M. D. (2016). A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer. Journal of Manufacturing Systems41, 211-227.
  24. Häger, D., & Andersen, L. B. (2010). A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants. European Journal of Operational Research207(3), 1635-1644.
  25. Henry, D., & Ramirez-Marquez, J. E. (2012). Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety99, 114-122.
  26. Hodgson, S. H., Mansatta, K., Mallett, G., Harris, V., Emary, K. R., & Pollard, A. J. (2021). What defines an efficacious COVID-19 vaccine? A review of the challenges assessing the clinical efficacy of vaccines against SARS-CoV-2. The lancet infectious diseases21(2), e26-e35.‏
  27. Hajian Heidary, M., & Mirzaaliyan, M. (2022). Supply Chain Resilience Analysis Considering Disruption in the Natural Stone Industry Using a Discrete-Event Simulation Approach. The Journal of Industrial Management Perspective12(4), 97-129. (In Persian).
  28. Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs(Vol. 2). New York: Springer.
  29. Jahani, M., Moghbel Baarz, A., & Azar, A. (2017). Designing a Model for the Measurement of Supply Chain Resilience through SEM Approach. The Journal of Industrial Management Perspective7(1), 91-114. (In Persian).
  30. Jovanović, A., Klimek, P., Renn, O., Schneider, R., Øien, K., Brown, J., ... & Chhantyal, P. (2020). Assessing resilience of healthcare infrastructure exposed to COVID-19: emerging risks, resilience indicators, interdependencies and international standards. Environment Systems and Decisions40, 252-286.
  31. Käki, A., Salo, A., & Talluri, S. (2015). Disruptions in supply networks: A probabilistic risk assessment approach. Journal of Business Logistics36(3), 273-287.
  32. Khakzad, N. (2015). Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliability Engineering & System Safety138, 263-272.
  33. Khubchandani, J., Sharma, S., Price, J. H., Wiblishauser, M. J., Sharma, M., & Webb, F. J. (2021). COVID-19 vaccination hesitancy in the United States: a rapid national assessment. Journal of community health46, 270-277.
  34. Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research56(17), 5795-5819.
  35. Ocampo, L., & Yamagishi, K. (2020). Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis. Socio-Economic Planning Sciences72, 100911.
  36. Qazi, A., Dickson, A., Quigley, J., & Gaudenzi, B. (2018). Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks. International Journal of Production Economics196, 24-42.
  37. Rahimi Sheikh, H., Sharifi, M., & Shahriari, M. R. (2017). Designing a Resiliense Supply Chain Model (Case Study: the Welfare Organization of Iran).The Journal of Industrial Management Perspective7(3), 127-150. (In Persian).
  38. Rele, S. (2021). COVID-19 vaccine development during pandemic: gap analysis, opportunities, and impact on future emerging infectious disease development strategies. Human Vaccines & Immunotherapeutics17(4), 1122-1127.
  39. Richards, A. D. (2020). Ethical guidelines for deliberately infecting volunteers with COVID-19. Journal of medical ethics46(8), 502-504.
  40. Routt, D. (2008). The Economic Impoact of the Black Death.
  41. Selmi, R., & Bouoiyour, J. (2020). Global market's diagnosis on coronavirus: A tug of war between hope and fear.
  42. Sakib, N., Hossain, N. U. I., Nur, F., Talluri, S., Jaradat, R., & Lawrence, J. M. (2021). An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network. International Journal of Production Economics235, 108107.
  43. Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research54(1), 152-169.
  44. Song, B., Lee, C., & Park, Y. (2013). Assessing the risks of service failures based on ripple effects: A Bayesian network approach. International Journal of Production Economics141(2), 493-504.
  45. Song, W., Ming, X., & Liu, H. C. (2017). Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method. Journal of Cleaner Production143, 100-115.
  46. Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling203(3-4), 312-318.
  47. Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics126(1), 121-129.
  48. Weintraub, R. L., Subramanian, L., Karlage, A., Ahmad, I., & Rosenberg, J. (2021). COVID-19 Vaccine to Vaccination: Why Leaders Must Invest In Delivery Strategies Now: Analysis describe lessons learned from past pandemics and vaccine campaigns about the path to successful vaccine delivery for COVID-19. Health Affairs40(1), 33-41.