تحلیل پویایی تاب‌آوری زنجیره تامین واکسن تحت اختلالات ناشی از جهش‌های ویروسی و شیوع سایر اپیدمی‌ها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای مدیریت صنعتی، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی مشهد، مشهد، ایران.

2 استاد، گروه مدیریت، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی، مشهد، ایران.

3 استاد گروه مدیریت، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی مشهد، مشهد، ایران.

4 دانشیار گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران.

چکیده

سابقه و هدف: این مطالعه با هدف بررسی چالش‌های حیاتی در زنجیره تأمین واکسن آنفلوانزا، از جمله عدم تعادل عرضه و تقاضا، افزایش تقاضا به‌دلیل شیوع بیماری کووید-۱۹ و ظهور سویه‌های جدید ویروسی انجام شده است. تحقیق حاضر به ارزیابی تاب‌آوری زنجیره تأمین در استان خراسان رضوی، ایران، با تمرکز بر اختلالات ناشی از پیک‌های فصلی به دلیل جهش‌های ویروسی و شیوع بیماری‌های جدید می‌پردازد. هدف نهایی این مطالعه، توسعه و ارزیابی استراتژی‌های مؤثر برای افزایش تاب‌آوری زنجیره تأمین و تضمین تأمین پایدار و ایمن واکسن است.
روش: در این پژوهش از رویکرد پویایی سیستم برای شبیه‌سازی اختلالات زنجیره تأمین و فرآیندهای بازیابی استفاده شده است. برای نخستین بار، استراتژی‌های تاب‌آوری شامل ایجاد بافرهای پشتیبان، عقد قرارداد با تأمین‌کنندگان پشتیبان و افزایش ظرفیت تولید داخلی، در چارچوب سیاست‌های تخصیص بودجه به‌صورت جامع تحلیل شده‌اند.
یافته‌ها: نتایج نشان می‌دهد که سیاست‌های متمرکز بر افزایش ظرفیت تولید داخلی، به‌ویژه سیاست سوم که تولید داخلی را با تأمین‌کنندگان پشتیبان و حداقل بافرهای پشتیبان ترکیب می‌کند، به طور قابل توجهی پایداری زنجیره تأمین را ارتقاء می‌بخشد. این سیاست‌ها تعادلی بهینه میان هزینه‌ها و پاسخ‌گویی به نوسانات تقاضا ایجاد می‌کنند.
نتیجه‌گیری: یافته‌ها حاکی از آن است که افزایش ظرفیت تولید داخلی همراه با بهره‌گیری از تأمین‌کنندگان پشتیبان، ضمن ایجاد تعادل میان هزینه‌ها و موجودی واکسن، می‌تواند پایداری بلندمدت زنجیره تأمین واکسن آنفلوانزا را حتی در شرایط اوج تقاضای ناشی از جهش‌های ویروسی تضمین کند.

کلیدواژه‌ها

عنوان مقاله [English]

Dynamic resilience analysis of the vaccine supply chain under disruptions caused by viral mutations and outbreaks of other epidemics

نویسندگان [English]

  • Elnaz borji-khangheshlaghi 1
  • Alireza Poya 2
  • Zahra Naji-Azimi 3
  • Farzad Dehghanian 4
1 Ph.D. students, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
2 Professor, Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University, Mashhad, Iran.
3 Professor, Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
4 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Background and Objectives: This study aims to investigate critical challenges in the influenza vaccine supply chain, including supply–demand imbalances, increased demand due to the COVID-19 outbreak, and the emergence of new viral strains. The research evaluates the resilience of the supply chain in Razavi Khorasan Province, Iran, focusing on disruptions caused by seasonal peaks resulting from viral mutations and the spread of new diseases. The ultimate goal is to develop and assess effective strategies to enhance supply chain resilience and ensure a stable and secure vaccine supply.
Materials and Methods: A System Dynamics modelling approach was employed to simulate supply chain disruptions and recovery processes. For the first time, resilience strategies— including the establishment of backup buffers, contracting with backup suppliers, and increasing domestic production capacity—were comprehensively analysed within the framework of budget allocation policies.
Results: The results indicate that strategies focused on increasing domestic production capacity, particularly Policy 3, which combines domestic production with backup suppliers and minimal backup buffers, significantly enhance supply chain resilience. These strategies create an optimal balance between costs and responsiveness to demand fluctuations.
Conclusion: The findings suggest that increasing domestic production capacity, alongside leveraging backup suppliers, can establish a balance between vaccine costs and inventory levels, thereby ensuring the long-term resilience of the influenza vaccine supply chain even under peak demand conditions caused by viral mutations.

کلیدواژه‌ها [English]

  • Influenza vaccine supply chain
  • Demand uncertainty
  • System dynamics
  • Disruption
  • Resiliency

مراجع

 Abdullah, M., Hishamuddin, H., & Bazin, N. (2019). A system dynamics approach to investigate the effects of disruption on the supply chain with a mitigation strategy. IOP Conference Series: Materials Science and Engineering, 697(1), 012024. https://doi.org/10.1088/1757-899X/697/1/012024
Alizadeh, M., Paydar, M. M., Hosseini, S. M., & Makui, A. (2021). Influenza vaccine supply chain network design during the COVID-19 pandemic considering dynamical demand. Scientia Iranica. https://doi.org/10.24200/sci.2021.58365.5694
Arifoğlu, K., & Tang, C. S. (2022). A two-sided incentive program for coordinating the influenza vaccine supply chain. Manufacturing & Service Operations Management, 24(1), 235–255. https://doi.org/10.1287/msom.2020.0938
Chen, S., Zhang, M., Ding, Y., & Nie, R. (2020). Resilience of China’s oil import system under external shocks: A system dynamics simulation analysis. Energy Policy, 146, 111795. https://doi.org/10.1016/j.enpol.2020.111795
Cho, S.-H. (2010). The optimal composition of influenza vaccines subject to random production yields. Manufacturing & Service Operations Management, 12(2), 256–277. https://doi.org/10.1287/msom.1090.0271
Chung, J. R., Rolfes, M. A., Flannery, B., Prasad, P., O’Halloran, A., Garg, S., Fry, A. M., Singleton, J. A., Patel, M., Reed, C., & others. (2020). Effects of influenza vaccination in the United States during the 2018–2019 influenza season. Clinical Infectious Diseases, 71(8), e368–e376. https://doi.org/10.1093/cid/ciz1244
Creaco, E., Di Nardo, A., Giudicianni, C., Greco, R., Santonastaso, G. F., & others. (2018). Resilience analysis in the permanent partitioning of a water distribution network. Proceedings of 13th International Conference on Hydroinformatics.
Dai, T., Cho, S.-H., & Zhang, F. (2016). Contracting for on-time delivery in the US influenza vaccine supply chain. Manufacturing & Service Operations Management, 18(3), 332–346. https://doi.org/10.1287/msom.2015.0574
Demirci, E. Z., & Erkip, N. K. (2020). Designing intervention scheme for vaccine market: A bilevel programming approach. Flexible Services and Manufacturing Journal, 32, 453–485. https://doi.org/10.1007/s10696-019-09348-5
Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125–138. https://doi.org/10.1016/j.ijpe.2018.09.018
Ding, Y., Chen, S., Zheng, Y., Chai, S., & Nie, R. (2022). Resilience assessment of China’s natural gas system under supply shortages: A system dynamics approach. Energy, 247, 123518. https://doi.org/10.1016/j.energy.2022.123518
Dolgui, A., & Ivanov, D. (2021). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International Journal of Production Economics, 229, 107886. https://doi.org/10.1016/j.ijpe.2020.107886
Duijzer, L. E., Van Jaarsveld, W., & Dekker, R. (2018). Literature review: The vaccine supply chain. European Journal of Operational Research, 268(1), 174–192. https://doi.org/10.1016/j.ejor.2018.01.015
Duijzer, L. E., Van Jaarsveld, W., & Dekker, R. )2018) The benefits of combining early a specific vaccination with later specific vaccination. European Journal of Operational Research.;271(2):606–19.
Enayati, S., & Özaltın, O. Y. (2020). Optimal influenza vaccine distribution with equity. European Journal of Operational Research, 283(2), 714–725. https://doi.org/10.1016/j.ejor.2019.11.025
Forrester, J. W., & Senge, P. M. (1996). Tests for building confidence in system dynamics models. Modelling for Management: Simulation in Support of Systems Thinking, 2(414–434).
Georgiadis, G. P., & Georgiadis, M. C. (2021). Optimal planning of the COVID-19 vaccine supply chain. Vaccine, 39(37), 5302–5312. https://doi.org/10.1016/j.vaccine.2021.07.068
Ivanov, D. (2022). Viable supply chain model: Integrating agility, resilience and sustainability perspectives—Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 1–21. https://doi.org/10.1007/s10479-020-03640-6
Lin, Q., Zhao, Q., & Lev, B. (2020). Cold chain transportation decision in the vaccine supply chain. European Journal of Operational Research, 283(1), 182–195. https://doi.org/10.1016/j.ejor.2019.11.005
Lin, Q., Zhao, Q., & Lev, B. (2022). Influenza vaccine supply chain coordination under uncertain supply and demand. European Journal of Operational Research, 297(3), 930–948. https://doi.org/10.1016/j.ejor.2021.05.025
Lister, S., & Williams, E. D. (2004). Influenza vaccine shortages and implications.
Modares, A., Pooya, A., Emroozi, V. B., & Roozkhosh, P. (2024). Presenting a new model for evaluating the factors affecting equipment reliability using system dynamics. Quality and Reliability Engineering International. https://doi.org/10.1002/qre.3553
Mohammadi, M., Dehghan, M., Pirayesh, A., & Dolgui, A. (2022). Bi-objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID-19 pandemic. Omega, 113, 102725. https://doi.org/10.1016/j.omega.2022.102725
Özaltın, O. Y., Prokopyev, O. A., & Schaefer, A. J. (2018). Optimal design of the seasonal influenza vaccine with manufacturing autonomy. INFORMS Journal on Computing, 30(2), 371–387. https://doi.org/10.1287/ijoc.2017.0786
Özaltın, O. Y., Prokopyev, O. A., Schaefer, A. J., & Roberts, M. S. (2011). Optimizing the societal benefits of the annual influenza vaccine: A stochastic programming approach. Operations Research, 59(5), 1131–1143. https://doi.org/10.1287/opre.1110.0988
Paul, S. K., Chowdhury, P., Moktadir, M. A., & Lau, K. H. (2021). Supply chain recovery challenges in the wake of COVID-19 pandemic. Journal of Business Research, 136, 316–329. https://doi.org/10.1016/j.jbusres.2021.07.056
Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2022). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 319(1), 1159–1196. https://doi.org/10.1007/s10479-020-03685-7
Rastegar, M., Tavana, M., Meraj, A., & Mina, H. (2021). An inventory-location optimization model for equitable influenza vaccine distribution in developing countries during the COVID-19 pandemic. Vaccine, 39(3), 495–504. https://doi.org/10.1016/j.vaccine.2020.12.022
Roozkhosh, P., Pooya, A., & Agarwal, R. (2023). Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach. Operations Management Research, 16(2), 705–725. https://doi.org/10.1007/s12063-022-00336-x
Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672. https://doi.org/10.1016/j.cie.2018.10.001
Sadjadi, S. J., Ziaei, Z., & Pishvaee, M. S. (2019). The design of the vaccine supply network under uncertain condition: A robust mathematical programming approach. Journal of Modelling in Management, 14(4), 841–871. https://doi.org/10.1108/JM2-07-2018-0093
Sah, P., Medlock, J., Fitzpatrick, M. C., Singer, B. H., & Galvani, A. P. (2018). Optimizing the impact of low-efficacy influenza vaccines. Proceedings of the National Academy of Sciences, 115(20), 5151–5156. https://doi.org/10.1073/pnas.1802479115
Sai, A. R., Buckley, J., Fitzgerald, B., & Le Gear, A. (2021). Taxonomy of centralization in public blockchain systems: A systematic literature review. Information Processing & Management, 58(4), 102584. https://doi.org/10.1016/j.ipm.2021.102584
Samani, M. R. G., & Hosseini-Motlagh, S.-M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283(1), 1413–1462. https://doi.org/10.1007/s10479-018-2873-4
Sansone, M., Holmstrom, P., Hallberg, S., Nordén, R., Andersson, L.-M., & Westin, J. (2022). System dynamic modelling of healthcare associated influenza—a tool for infection control. BMC Health Services Research, 22(1), 709. https://doi.org/10.1186/s12913-022-07959-7
Sazvar, Z., Tafakkori, K., Oladzad, N., & Nayeri, S. (2021). A capacity planning approach for sustainable-resilient supply chain network design under uncertainty: A case study of vaccine supply chain. Computers & Industrial Engineering, 159, 107406. https://doi.org/10.1016/j.cie.2021.107406
Shih, W. C. (2020). Is it time to rethink globalized supply chains? MIT Sloan Management Review, 61(4), 1–3. https://sloanreview.mit.edu/article/is-it-time-to-rethink-globalized-supply-chains/
Sy, C., Bernardo, E., Miguel, A., San Juan, J. L., Mayol, A. P., Ching, P. M., Culaba, A., Ubando, A., & Mutuc, J. E. (2020). Policy development for pandemic response using system dynamics: A case study on COVID-19. Process Integration and Optimization for Sustainability, 4, 497–501. https://doi.org/10.1007/s41660-020-00130-x
Zhu, Q., Krikke, H., & Caniëls, M. C. (2021). The effects of different supply chain integration strategies on disruption recovery: A system dynamics study on the cheese industry. Logistics, 5(2), 19. https://doi.org/10.3390/logistics5020019
Zimmerman, R. K., Nowalk, M. P., Chung, J., Jackson, M. L., Jackson, L. A., Petrie, J. G., Monto, A. S., McLean, H. Q., Belongia, E. A., Gaglani, M., & others. (2016). 2014–2015 influenza vaccine effectiveness in the United States by vaccine type. Clinical Infectious Diseases. https://doi.org/10.1093/cid/ciw635
 
مدیریت زنجیره ارزش راهبردی
دوره 2، شماره 2 - شماره پیاپی 5
مقالات پژوهشی
مرداد 1404
صفحه 85-115

فایل ها

سابقه مقاله

  • تاریخ دریافت: 20 مرداد 1404
  • تاریخ بازنگری: 24 شهریور 1404
  • تاریخ پذیرش: 02 مهر 1404
  • تاریخ انتشار: 01 مرداد 1404

هم رسانی

ارجاع به این مقاله

آمار