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Journals / Journal of Management Marketing and Logistics / 2026 / Volume: 13 Issue: 1

AUTOMATING MEDICAL DEVICE WAREHOUSES: STRATEGIES FOR MEDIUM TO LARGE VOLUME

Naveen Kukkala(University of the Cumberlands)0009-0000-3346-3405
Pages
—
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results showed improved operational efficiency, enhanced inventory accuracy, and reduced labour costs, highlighting tangible financial and logistical benefits for medical device warehousing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose- The study explores automation strategies in medical device warehousing, where efficiency, accuracy, and regulatory compliance are critical.
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Key Findings & Data:Automation technologies such as Automated Storage and Retrieval Systems (AS/RS), collaborative robots (cobots), and IoT-enabled inventory tracking were integrated into workflow optimisation.
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Conclusion & Impact:Results showed improved operational efficiency, enhanced inventory accuracy, and reduced labour costs, highlighting tangible financial and logistical benefits for medical device warehousing.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Medical device warehousing · warehouse automation · robotics

Abstract

Purpose- The study explores automation strategies in medical device warehousing, where efficiency, accuracy, and regulatory compliance are critical. It focuses on leveraging machine learning and advanced technologies to optimise inventory management, quality control, and storage processes in medium- to large-volume operations. Methodology- A large-scale dataset was analysed using machine learning models, including XGBoost and Long Short-Term Memory (LSTM), for demand forecasting. Feature engineering and rigorous model evaluation were applied. Automation technologies such as Automated Storage and Retrieval Systems (AS/RS), collaborative robots (cobots), and IoT-enabled inventory tracking were integrated into workflow optimisation. Findings- The models demonstrated strong performance in forecasting demand and supporting automated processes. Results showed improved operational efficiency, enhanced inventory accuracy, and reduced labour costs, highlighting tangible financial and logistical benefits for medical device warehousing. Conclusion- Machine learning and automation provide transformative solutions for medical device warehouses, enabling regulatory compliance, cost efficiency, and scalability in high-volume environments

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Cite This Article

Kukkala, N. (2026). AUTOMATING MEDICAL DEVICE WAREHOUSES: STRATEGIES FOR MEDIUM TO LARGE VOLUME. Journal of Management Marketing and Logistics, Volume: 13 Issue: 1. https://doi.org/10.17261/Pressacademia.2026.2050

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References (22)

  1. Abdulkadir, R. (2023). The Dynamics of Supply Chain Integration: Empirical Evidence from Public Health Supply Chains in Kaduna State, Nigeria Liverpool John Moores University (United Kingdom)].
  2. Abedi, S., Kwon, S., & Yoon, S. W. (2023). Healthcare and Pharmaceutical Supply Chain Automation. In Springer Handbook of Automation (pp. 1289-1308). Springer.
  3. Ahmad, A., & Ahmad, R. (2024). Evaluating enterprise systems for production, procurement, documentation, and compliance in pharmaceutical companies. AI, IoT and the Fourth Industrial Revolution Review, 14(2), 39-50.
  4. Alherimi, N., Saihi, A., & Ben-Daya, M. (2024). A Systematic Review of Optimization Approaches Employed in Digital Warehousing Transformation. IEEE Access.
  5. Ammar, M., Haleem, A., Javaid, M., Walia, R., & Bahl, S. (2021). Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. Materials Today: Proceedings, 45, 5089-5096.
  6. Camacho-Cogollo, J. E., Bonet, I., & Iadanza, E. (2020). RFID technology in health care. In Clinical Engineering Handbook (pp. 33-41). Elsevier.
  7. Duc, H. L. (2020). Optimizing pharmaceutical company's warehouse system.
  8. Gan, Z. L., Musa, S. N., & Yap, H. J. (2023). A review of the high-mix, low-volume manufacturing industry. Applied Sciences, 13(3), 1687-1699.
  9. Güngör, B. (2024). The success factors of cobotics implementation in facilities management University of Twente].
  10. Issantu, I. (2021). User acceptance of logistics 4.0 and robotic warehouse solutions (RWS). Capella University.
  11. Jayapaul, M. S. (2024). Implementation of real-time data monitoring in supply chain management. International Journal Of Logistics And Supply Chain Management Research And Development (IJLSCMRD), 2(1), 32-49.
  12. Kasana, H., Chander, H., & Mathur, A. (2024). A systematic review of regulatory requirements of biosimilar products: WHO, India, European Union and USFDA. Research Journal of Pharmacy and Technology, 17(5), 2413-2420.
  13. Licardo, J. T., Domjan, M., & Orehovački, T. (2024). Intelligent robotics—A systematic review of emerging technologies and trends. Electronics, 13(3), 542-551.
  14. Madamidola, O. A., Daramola, O., Akintola, K., & Adeboje, O. (2024). A Review of existing inventory management systems. International Journal of Research in Engineering and Science (IJRES), 12(9), 40-50.
  15. Nordeide, A., & Rørtveit, S. (2021). The Impact of Automated Storage and Retrieval Systems on Warehouse Operations Høgskolen i Molde- Vitenskapelig høgskole i logistikk].
  16. Okolo, C. A., Ijeh, S., Arowoogun, J. O., Adeniyi, A. O., & Omotayo, O. (2024). Reviewing the impact of health information technology on healthcare management efficiency. International Medical Science Research Journal, 4(4), 420-440.
  17. Pramod, D. (2022). Robotic process automation for industry: adoption status, benefits, challenges and research agenda. Benchmarking: an international journal, 29(5), 1562-1586.
  18. Sharma, N., & Cupek, R. (2023). Real-time control and optimization of internal logistics systems with collaborative robots. Procedia Computer Science, 225, 248-258.
  19. Smith, A. J. (2020). Framework for establishing asset visibility and traceability of medical devices Massachusetts Institute of Technology].
  20. Sorell, T. (2022). Cobots,“co-operation” and the replacement of human skill. Ethics and Information Technology, 24(4), 44-56.
  21. Varriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2021). Sustainable supply chains with blockchain, IoT and RFID: A simulation on order management. Sustainability, 13(11), 6372-6381.
  22. Zuo, J., Feng, J., Gameiro, M. G., Tian, Y., Liang, J., Wang, Y., Ding, J., & He, Q. (2022). RFID-based sensing in smart packaging for food applications: A review. Future Foods, 6, 100198.