Evaluation of smart logistics in the field of relief and medical care in the armies of seven countries

Document Type : Original Article

Authors

1 Master of Science in Rail Transportation Engineering, Iran University of Science and Technology, Tehran, Iran.

2 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering and Flight, Imam Ali (AS) Military University, Tehran, Iran.

Abstract

Military medical support constitutes a critical pillar of sustainment within armed forces, playing a decisive role in preserving combat readiness, reducing casualties, and enhancing operational resilience. In recent decades, major militaries worldwide have recognized the strategic importance of this domain and have invested heavily in developing logistical frameworks, modernizing medical systems, and adopting advanced technologies to achieve smarter capabilities. This study provides a comparative analysis of the structures, theoretical foundations, and historical developments of military logistics and medical support in seven leading armed forces (the United States, the United Kingdom, Russia, China, Turkey, India, and Pakistan). It aims to extract and evaluate successful models of intelligent military medical support by examining indicators such as command‐and‐control architecture, adoption of cutting‐edge technologies, battlefield medical support models, and operational experiences. An Improved Analytic Hierarchy Process (IAHP) is then applied to assess and rank the level of smart logistics integration in Class VIII (medical) supply chains across the selected nations, based on predefined criteria. Notably, the ranking results are presented from both the researcher’s own analysis and an expert‐opinion questionnaire, enabling a direct comparison. The findings reveal that countries with agile organizational structures, technology-driven smart policies, and integrated civil-military collaboration have developed more efficient and adaptive medical support systems. Furthermore, all seven militaries have begun deploying intelligent tools—such as artificial intelligence, Internet of Things platforms, unmanned aerial medical vehicles, robotics, and RFID—in their medical logistics operations. These best practices offer valuable insights for redesigning and upgrading the military medical support system of the Islamic Republic of Iran’s armed forces.

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Main Subjects

References

  1. آرمون، آرش،1385: 11، مروری بر مفاهیم و ماهیت لجستیک نظامی،دومین کنفرانس لجستیک و زنجیره تامین،تهران،https://civilica.com/doc/8944.

    Anand, M., Nikhil, K., Nisarga, C. M., Rakshitha, K. N., & S-AI-Smruthi, A. P. (2024). “Jeevan Raksha” for Military Ground Robot Using IoT and ML. International Journal of Advanced Research in Computer and Communication Engineering, 13(12).

    Borisov, D. N., Kul, S. V., Sevryukov, V. V., Troshko, I. V., & Lemeshkin, R. N. (2019). APPLICATION OF ARTIFICIAL INTELLIGENCE AT THE ORGANIZATION OF TREATMENT AND DIAGNOSTIC EVENTS. Russian Military Medical Academy Reports, 38(4), 122-125.

    Bora, E. S. (2023). Artificial Intelligence in Emergency Medicine. Journal of Experimental and Basic Medical Sciences, 4(1), 033-036.

    Babar, R. (2024). Unlocking IoMT and AI’s Potential for Pakistan’s Defence Infrastructure. Centre for Strategic and Contemporary Research. Retrieved from https://cscr.pk/explore/themes/defense security/unlocking-iomt-and-AIs-potential-for-pakistans-defence-infrastructure.

    Christopher, M. (2016). Logistics & Supply Chain Management. Pearson Education.

     

    Castro, L., Lefebvre, E., & Lefebvre, L. A. (2013). Adding intelligence to mobile asset management in hospitals: the true value of RFID. Journal of medical systems, 37, 1-17.

    Day, J. M., Melnyk, S. A., Larson, P. D., Davis, E. W., & Whybark, D. C. (2012). Humanitarian and disaster relief supply chains: a matter of life and death. Journal of Supply Chain Management, 48(2), 21-36.

    Di Vizio, D., Erdbruegger, U., & Lötvall, J. (2023). ISEV Abstract Book.

    Demir, K., & Yilmaz, E. (2022). Bayraktar Med-Drone for Combat. Defence Technology, 18(4), 112-125.

    Behera, A. A., et al. (2023). ATRACT: A Trustworthy Robotic Autonomous system to support Casualty Triage (project). Edge Hill University / University of Portsmouth. Retrieved March 21, 2023.

    Gilmore, C. K., Chaykowsky, M., & Thomas, B. (2019). Autonomous unmanned aerial vehicles for blood delivery. RAND Corporation, Santa Monica, California.

    Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7-8), 494-506.

    1. Holgu´ın-Veras, N. P´erez, S. Ukkusuri, T. Wachtendorf, andB. Brown, “Emergency logistics issues affecting the response to Katrina,” Transportation Research Record: Journal of theTransportation Research Board, vol. 2022, no. 1, pp. 76–82,2007.

    Janney, J. B., Premkumar, J., Krishnakumar, S., Shivani, S. A., Atchaya, E., & Kanmani, P. G. (2022, August). AI ambulance drone for medical surveillance. In Journal of Physics: Conference Series (Vol. 2318, No. 1, p. 012023). IOP Publishing.

    Kovács, G., & Spens, K. M. (2009). Identifying Challenges in Humanitarian Logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506-528.

    Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International journal of physical distribution & logistics management, 37(2), 99-114.

    Kotiv, B. N., Budko, I. A., Ivanov, I. A., & Trosko, I. U. (2021). Artificial intelligence using for medical diagnosis via implementation of expert systems. Bulletin of the Russian Military Medical Academy, 23(1), 215-224.

    Litke, R. M. (2005). Military Logistics: A Historical Perspective. Journal of Logistics Management, 12(3), 45-60.

    Lambert, D. M., Stock, J. R., & Ellram, L. M. (1998). Fundamentals of Logistics Management. McGraw-Hill.

    1. Consuelos Salas Consuelos Salas, M. Robles C´ardenas Robles C´ardenas, and M. Zhang, “Inventory policies for humanitarian aid during hurricanes,” Socio-Economic Planning Sci-ences, vol. 46, no. 4, pp. 272–280, 2012.

    Lu, W. (2023). Application cost of intelligent intrusion detection in medical logistics management under public cloud environment. Computers and Electrical Engineering, 112, 109014.

    Li, Y., Wang, M., Wang, L., Cao, Y., Liu, Y., Zhao, Y., & Kang, H. (2024). Advances in the application of robots in critical care: scoping review. Journal of Medical Internet Research, 26, e54095.

    Liu, X., & Wang, Q. (2022). Autonomous Rescue Robot for High-Altitude Casualty Evacuation. Nature Military Technology, 3(1), 112-125.

    Mangan, J., Lalwani, C., & Butcher, T. (2012). Global Logistics and Supply Chain Management. Wiley.

    NATO. (2019). Medical support manual for NATO operations. NATO Standardization Office.

    Nemeth, C., Amos-Binks, A., Rule, G., Laufersweiler, D., Keeney, N., Flint, I., Pinevich, Y., & Herasevich, V. (2023). TCCC Decision Support with Machine Learning Prediction of Hemorrhage Risk, Shock Probability. Military Medicine, 188(Suppl 6), 659–665.

    Overstreet, R. E., Hall, D., Hanna, J. B., & Kelly Rainer Jr, R. (2011). Research in humanitarian logistics. Journal of humanitarian logistics and supply chain management, 1(2), 114-131.

    Petrov, D., & Sokolov, V. (2022). Robotic MedEvac Systems in Russian Military Operations: Uran-9 Medical Module. Robotics in Medicine, 4(3), 112-125.

    Pauer, F., Schmidt, K., Babac, A., Damm, K., Frank, M., & von der Schulenburg, J. M. G. (2016). Comparison of different approaches applied in analytic hierarchy process–An example of information needs of patients with rare diseases. BMC medical informatics and decision making, 16(1), 117.

    Rizzardi, A., Sicari, S., & Coen-Porisini, A. (2024). IoT-driven blockchain to manage the healthcare supply chain and protect medical records. Future Generation Computer Systems, 161, 415-431.

    Richard m. Leighton (2001) “military logistics”. Britanica encyclopedia.

    Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.

    Smith, K., Park, J., & Son, C. (2024, June). Artificial Intelligence for Emergency Medical Service. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 13, No. 1, pp. 1-6). Sage CA: Los Angeles, CA: SAGE Publications.

    Stallings, J. D., Laxminarayan, S., Yu, C., Kapela, A., Frock, A., Cap, A. P., & Reifman, J. (2023). APPR-AI-SE-HRI: an artificial intelligence algorithm for triage of hemorrhage casualties. Shock, 60(2), 199-205.

    Sheridan, D. (2023). Self-driving army vehicle allows injured soldiers to operate it while lying down.

    Tan, S., & Mills, G. (2024). Designing Chinese hospital emergency departments to leverage artificial intelligence—a systematic literature review on the challenges and opportunities. Frontiers in Medical Technology, 6, 1307625.

    Umer, L., Khan, M. H., & Ayaz, Y. (2023). Transforming healthcare with artificial intelligence in pakistan: a comprehensive overview. Pak Armed Forces Med J, 73(4), 955-63.

    Van Wassenhove, L. N. (2006). Humanitarian aid logistics: supply chain management in high gear. Journal of the Operational research Society, 57(5), 475-489.

Defensive Researches and Management
Volume 24, Issue 110
April 2026
Pages 83-114

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History

  • Receive Date: 07 August 2025
  • Revise Date: 21 December 2025
  • Accept Date: 29 April 2026

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