The Application of Intelligent Logistics Systems in Supply Chain Management and Its Challenges: Case Studies of Automated Warehousing and Unmanned Delivery
DOI:
https://doi.org/10.54097/rrxgp164Keywords:
Intelligent logistics, Automated warehousing, Unmanned delivery, Supply chain management, AI integrationAbstract
This work explores the application of intelligent logistics systems in supply chain management, focusing on automated warehousing and unmanned delivery technologies. These systems integrate advanced technologies like AI, IoT, and big data analytics to optimize efficiency, reduce costs, and enhance decision-making. The discussion covers the importance of automated warehousing in improving storage, retrieval, and inventory management, as well as the relevance of unmanned delivery in overcoming last-mile logistics challenges. Key operational challenges such as high initial costs, infrastructure requirements, and workforce adaptation are examined in detail. The work also identifies the critical components that make up intelligent logistics systems, emphasizing the synergy between IoT, automation, AI, and cloud computing. The insights provided aim to shed light on how businesses can leverage these technologies to build resilient, adaptive supply chains in an increasingly competitive environment.
Downloads
References
[1] Nozari, H., Szmelter-Jarosz, A., & Ghahremani-Nahr, J. (2022). Analysis of the challenges of artificial intelligence of things (AIoT) for the smart supply chain (case study: FMCG industries). Sensors, 22(8), 2931.
[2] Odimarha, A. C., Ayodeji, S. A., & Abaku, E. A. (2024). The role of technology in supply chain risk management: Innovations and challenges in logistics. Magna Scientia Advanced Research and Reviews, 10(2), 138-145.
[3] Xie, Y., Yin, Y., Xue, W., Shi, H., & Chong, D. (2020). Intelligent supply chain performance measurement in Industry 4.0. Systems Research and Behavioral Science, 37(4), 711-718.
[4] Jagtap, S., Bader, F., Garcia-Garcia, G., Trollman, H., Fadiji, T., & Salonitis, K. (2020). Food logistics 4.0: Opportunities and challenges. Logistics, 5(1), 2.
[5] Tran-Dang, H., Krommenacker, N., Charpentier, P., & Kim, D. S. (2022). The Internet of Things for logistics: Perspectives, application review, and challenges. IETE Technical Review, 39(1), 93-121.
[6] Pan, S., Trentesaux, D., McFarlane, D., Montreuil, B., Ballot, E., & Huang, G. Q. (2021). Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards Physical Internet. Computers in industry, 128, 103435.
[7] Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549.
[8] Akbari, M., & Do, T. N. A. (2021). A systematic review of machine learning in logistics and supply chain management: current trends and future directions. Benchmarking: An International Journal, 28(10), 2977-3005.
[9] Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research, 60(24), 7527-7550.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
