Scenario Planning for Health Supply Chain Integration with Intuitive Fuzzy Logic Approach

Document Type : Original Article

Authors

1 Master of Business Administration, Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran.

2 Associate Professor, Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran.

Abstract

Introduction and Objectives: Supply chain integration in the health sector is one of the key challenges that can have a significant impact on improving the quality of healthcare services and increasing the resilience of health systems. Given the complex and dynamic nature of this key supply chain, the use of traditional methods for its management faces limitations. Scenario planning has been proposed as an efficient and effective method for analyzing uncertainties and developing optimal solutions in different situations. In this study, a combination of scenario planning and intuitive fuzzy logic has been used to promote the integration of the health supply chain. The aim of the present study is to provide a comprehensive analytical model for developing appropriate strategies in conditions of complex uncertainties in the decision-making space.
Methods: The main question in the present study is how to provide a set of efficient scenarios for integrating the health supply chain using the scenario-based method? For this purpose, this study uses a combined approach based on scenario analysis and intuitive fuzzy logic-based decision-making. First, through environmental analysis and expert opinions, key uncertainties in the health supply chain were identified, including economic sanctions and inflation. Then, based on these uncertainties, four possible scenarios were developed. In the next step, intuitive fuzzy logic was used to evaluate these scenarios, which allows for the consideration of the degree of uncertainty and instability in decision-making. Finally, the effects of these scenarios on health supply chain performance indicators were analyzed using an optimization model.
Findings: The results showed that economic sanctions and inflation are two key factors affecting health supply chain performance that can disrupt the supply of medical equipment, increase operating costs, and reduce the efficiency of the healthcare system. Based on these uncertainties, four different scenarios were analyzed, which are: removal of economic sanctions and low inflation, presence of economic sanctions and low inflation, presence of economic sanctions and high inflation, and removal of economic sanctions and high inflation. The results of the analysis show that the strategy of extensive cooperation between stakeholders and the development of digitalization can help reduce the negative effects of sanctions and inflation. In addition, the proposed model showed that the use of modern information technologies and improved communication between suppliers and health centers has a positive effect on increasing the resilience of the supply chain. Also, the findings showed that the proposed model, by reducing the effects of uncertainty, reduces operating costs, increases the speed of response in critical situations, and improves the quality of health services. Comparing the results of the intuitive fuzzy logic method with other decision-making methods also showed that this model has higher accuracy in analyzing uncertainty scenarios.
Conclusion: This research showed that the combination of scenario planning and intuitive fuzzy logic can be used as an effective tool for managing uncertainty in the health supply chain. In addition to increasing the accuracy of decision-making, the use of this approach improves the flexibility and resilience of the supply chain in the face of economic crises and environmental changes. It is suggested that future research should consider the effects of other uncertainty factors, such as policy changes and technology-based developments, in modeling health supply chain integration.

Keywords

Main Subjects


  1. Ahmad, W., Asghar, I., & Rajper, S. Z. (2024). Identification and analysis of human errors in the logistics of healthcare supply chain using Fuzzy Delphi and DEMATEL approaches. International Journal of Disaster Risk Reduction, 104, 104213. [https://doi.org/10.1016/j.ijdrr.2024.104213]
  2. Alidoost, S., Saidi-Mehrabad, M., & Heidari, R. (2025). Perishable health care supply chain simulation models: A systematic review. Simulation Modelling Practice and Theory, 139, 102991. https://doi.org/10.1016/j.simpat.2025.102991.
  3. Alemsan, A., Morabito Neto, V., & Silva, M. F. (2025). Integration of lean and resilient supply chain paradigms in healthcare supply chains. Operations Management Research. https://doi.org/10.1007/s12063-025-00389-9.
  4. Alqahtani, A. Y., Ajmal, M. M., Helo, P., Alsadi, A., & Khan, S. (2024). A fuzzy Delphi and fuzzy AHP approach for identifying and prioritizing human error factors in manufacturing. Journal of Manufacturing Technology Management, 35(9), 107–126. https://doi.org/10.1108/JMTM-09-2023-0410.
  5. Altabsh, M. (2023). Integrating Healthcare Processes Through Supply Chain Principles.
  6. Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557.
  7. Biswas, S. (2020). Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods. Decision Making: Applications in Management and Engineering, 3(2), 162-189. https://doi.org/10.31181/dmame2003162b
  8. Chawla, S., Henshaw, R., Seeger, L., Choy, E., Blay, J. Y., Ferrari, S.,... & Jacobs, I. (2013). Safety and efficacy of denosumab for adults and skeletally mature adolescents with giant cell tumour of bone: interim analysis of an open-label, parallel-group, phase 2 study. The Lancet Oncology, 14(9), 901-908. https://doi.org/10.1016/S1470-2045(13)70277-8
  9. Chung, T. H., Rostami, V., Bastani, H., & Bastani, O. (2022). Decision-aware learning for optimizing health supply chains. arXiv preprint .https://doi.org/arXiv:2211.08507. 48550.
  10. Damasio, M. M., Salari, M., & Naseraldin, H. (2025). Designing and planning a resilient healthcare supply chain distribution network under uncertainty. Computers & Industrial Engineering, 195, 110007. https://doi.org/10.1016/j.cie.2024.110007.
  11. Debnath, B., Bari, A. M., Haq, M. M., de Jesus Pacheco, D. A., & Khan, M. A. (2023). An integrated stepwise weight assessment ratio analysis and weighted aggregated sum product assessment framework for sustainable supplier selection in the healthcare supply chains. Supply Chain Analytics, 1, 100001. https://doi.org/10.1016/j.sca.2022.100001.
  12. Fariman, M. J., Rezaei, N., & Aghajani, M. (2024). A robust multi-objective optimization approach for designing resilient blood supply chains under uncertainty. Applied Mathematical Modelling, 122, 401–421. https://doi.org/10.1016/j.apm.2024.01.018.
  13. Hamzehlou, M., Pishvaee, M. S., & Jolai, F. (2024). A system dynamics approach to improve agility and resilience in pharmaceutical supply chains. International Journal of Production Research, 62(10), 3082–3100. https://doi.org/10.1080/00207543.2023.2254125.
  14. Haszlinna Mustaffa, N., & Potter, A. (2009). Healthcare supply chain management in Malaysia: a case study. Supply Chain Management: An International Journal, 14(3), 234-243. https://doi.org/10.1108/13598540910954575.
  15. Hendijani, R. and Norouzi, M. (2023). The Effect of Supply Chain Integration on Firm Performance with the Mediating Role of Supply Chain Resilience during COVID-19 Pandemic (Firms in the Food Industry in Tehran Province). Journal of Industrial Management Perspective, 13(3), 285-318. https://doi.org/ 10.48308/jimp.13.3.285
  16. Kamali, S., Hosseini-Motlagh, S. M., & Nematollahi, M. R. (2024). A bi-level programming model for integrated healthcare supply chain network design considering coordination contracts. Computers & Operations Research, 164, 106372. https://doi.org/10.1016/j.cor.2024.106372.
  17. 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 Review, https://doi.org/138, 101967. 1016/j.tre.2020.101967
  18. Nejad, A. K. J., Kahnali, R. A., & Heyrani, A. (2021). Developing hospital resilient supply chain scenario through cross-impact analysis method. Depiction of Health, 12(4), 310-319. https://doi.org/10.34172/doh.2021.30.
  19. Kitsiou, S., Matopoulos, A., Vlachopoulou, M., & Manthou, V. (2009). Integration issues in the healthcare supply chain. In Handbook of research on information technology management and clinical data administration in healthcare (pp. 582-597). IGI Global. https://doi.org/4018/978-1-60566-356-2.ch036.
  20. Kunc, M. (2024). Combining system dynamics and scenario planning: A methodological framework. Technological Forecasting and Social Change, 199, 122984. https://doi.org/10.1016/j.techfore.2023.122984.
  21. Lee, S. M., Lee, D., & Schniederjans, M. J. (2011). Supply chain innovation and organizational performance in the healthcare industry. International Journal of Operations & Production Management, 31(11), 1193-1214. https://doi.org/10.1108/01443571111178493
  22. Lamé, G., Jouini, O., & Stal-Le Cardinal, J. (2019). Methods and contexts: Challenges of planning with scenarios in a hospital’s division. Futures, 105, 78-90. https://doi.org/1016/j.futures.2018.09.005.
  23. Long, T., Wang, X., & Xu, M. (2023). Deep reinforcement learning for smart mode selection in healthcare supply chains under uncertainty. Omega, 117, 102770. https://doi.org/10.1016/j.omega.2023.102770.
  24. Nasiri, A. , Mansory, A. and Mohammadi, N. (2022). Developing an Integrated Model for Evaluating the Performance of Green and Resilient Suppliers by Combining Path Analysis, Sawara and TOPSIS Decision-Making Techniques. Journal of Industrial Management Perspective, 12(2), 227-251. doi: 10.52547/jimp.12.2.227 (In persian).
  25. Othman, A. A., Sundram, V. K., Sayuti, N. M., & Bahrin, A. S. (2016). The relationship between supply chain integration, just-in-time and logistics performance: A supplier’s perspective on the automotive industry in Malaysia. International Journal of Supply Chain Management, 5(1), 44-51.
  26. Pamucar, D., Torkayesh, A. E., & Biswas, S. (2023). Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach. Annals of Operations Research, 328(1), 977-1019. https://doi.org/10.1007/s10479-022-04529-2
  27. Shahbahrami, S., Pishvaee, M. S., & Akbari, M. (2024). A sustainable dynamic model for inpatient pharmacy supply chain management. Journal of Cleaner Production, 427, 138913. https://doi.org/10.1016/j.jclepro.2023.138913.
  28. Smith, B. K., Nachtmann, H., & Pohl, E. A. (2012). Improving healthcare supply chain processes via data standardization. Engineering Management Journal, 24(1), 3-10. . https://doi.org/10.1080/10429247.2012.11431924
  29. Stevens, G. C., & Johnson, M. (2016). Integrating the supply chain… 25 years on. International Journal of Physical Distribution & Logistics Management, 46(1), 19-42. https://doi.org/1108/IJPDLM-07-2015-0175
  30. Vargas-Muñoz, J. A., Pereira, C. R., & Silva, A. L. (2025). Prioritizing circular economy loops in healthcare supply chains: A multi-criteria decision-making approach. Resources, Conservation and Recycling, 211, 106108. https://doi.org/10.1016/j.resconrec.2025.106108.
  31. Vollmar, H. C., Ostermann, T., & Redaèlli, M. (2015). Using the scenario method in the context of health and health care–a scoping review. BMC medical research methodology, 15(1), 89. https://doi.org/10.1186/s12874-015-0083-1.
  32. Wang, T., Yu, V., & colleagues. (2021). The role of big data analytics capability in the development of integrated hospital supply chains and operational resilience: A study in China and the UK. Journal of Healthcare Operations and Logistics, 10(4), 276-292. https://doi.org/10.1016/j.techfore.2020.120417
  33. Williams, K. A., Kolar, M. M., Reger, B. E., & Pearson, J. C. (2001). Evaluation of a wellness-based mindfulness stress reduction intervention: A controlled trial. American Journal of Health Promotion, 15(6), 422-432. https://doi.org/10.4278/0890-1171-15.6.422.