Artificial Intelligence in The Logistics Sector: Efficiency, Automation and Current Trends

Artificial Intelligence in The Logistics Sector: Efficiency, Automation and Current Trends

Authors

DOI:

https://doi.org/10.62844/jerf.23

Keywords:

Artificial intelligence, Logistics, Automation, Efficiency, Big data

Abstract

This study examines the multifaceted impact of artificial intelligence (AI) in the logistics sector. It highlights how AI-driven solutions such as route optimization, automated warehouse management, demand forecasting, and resource planning contribute to enhanced operational efficiency, cost savings, and sustainability. The paper also analyzes real-world applications developed by Huawei in road, rail, air, and urban transportation systems across various countries. The findings demonstrate that AI supports digitalization in logistics and offers strategic benefits for decision-making processes. However, challenges such as data security, insufficient infrastructure, and lack of skilled professionals may hinder effective implementation. Ultimately, realizing the full potential of AI in logistics requires a comprehensive and integrated strategic approach.

References

Alkinani, M. H., Almazroi, A. A., Adhikari, M., & Menon, V. G. (2022). Artificial intelligence-empowered logistic traffic management system using empirical intelligent XGBoost technique in vehicular edge networks. IEEE Transactions on Intelligent Transportation Systems, 24(4), 4499-4508.

Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476

Blecken, A. (2010), "Supply chain process modelling for humanitarian organizations", International Journal of Physical Distribution & Logistics Management, Vol. 40 No. 8/9, pp. 675-692. https://doi.org/10.1108/09600031011079328

Chottani, A., Hastings, G., Murnane, J., & Neuhaus, F. (2018). McKinsey. https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/distraction-or-disruption-autonomous-trucks-gain-ground-in-us-logistics#/

Dey, A., LaGuardia, P. and Srinivasan, M. (2011), "Building sustainability in logistics operations: a research agenda", Management Research Review, Vol. 34 No. 11, pp. 1237-1259. https://doi.org/10.1108/01409171111178774

DHL. (2023). Freight Connections. https://dhl-freight-connections.com/en/trends/logistics-trends-2023-2024/

Dong, K., Romanov, I., Mclellan, C., & Esen, A. F. (2022). Recent text-based research and applications in railways: A critical review and future trends. Engineering Applications of Artificial Intelligence, 116, 105435. https://doi.org/10.1016/j.engappai.2022.105435

Hangl, J., Behrens, V. J., & Krause, S. (2022). Barriers, drivers, and social considerations for AI adoption in supply chain management: a tertiary study. Logistics, 6(3), 63.

Hompel, M., & Schmidt, T. (2006). Warehouse management: automation and organisation of warehouse and order picking systems. Springer Science & Business Media.

HUAWEI. (2024). Amplifying Industrial Digitalization & Intelligence Practice White Paper. Huawei Technologies Co. Ltd.

Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

Jebbor I, Benmamoun Z, Hachmi H. (2024). Revolutionizing cleaner production: The role of artificial intelligence in enhancing sustainability across industries. Journal of Infrastructure, Policy and Development. 8(10): 7455. https://doi.org/10.24294/jipd.v8i10.7455

Jindal, A., Sharma, S. K., Sangwan, K. S., & Gupta, G. (2021). Modelling supply chain agility antecedents using fuzzy DEMATEL. Procedia CIRP, 98, 436-441. https://doi.org/10.1016/j.procir.2021.01.130

Kumar, V., Mishra, N., Chan, F. T. S., & Verma, A. (2011). Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach. International Journal of Production Research, 49(21), 6407–6426. https://doi.org/10.1080/00207543.2010.528057

Li, F. (2022). [Retracted] Logistics Distribution Path Optimization Algorithm Based on Intelligent Management System. Computational Intelligence and Neuroscience, 2022(1), https://doi.org/10.1155/2022/3699990.

Li, X., Ng, X. Y. C., Zhou, Y., & Yuen, K. F. (2021). A ranking of critical competencies for shore-based maritime logistics executives in the digital era. Technology Analysis & Strategic Management, 35(7), 919–934. https://doi.org/10.1080/09537325.2021.1988920

Minea, M., Dumitrescu, C. M., & Dima, M. (2021). Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion. Sensors (Basel, Switzerland), 21(20), 6876. https://doi.org/10.3390/s21206876

Mohsen, B. (2023) Impact of Artificial Intelligence on Supply Chain Management Performance. Journal of Service Science and Management, 16, 44-58. https://doi.org/10.4236/jssm.2023.161004

Morales, V. E., Sanchez, J., Escalera, J., Sharma, V., & Wheeler, B. (2024). Artificial intelligence & aviation: Content analysis of research publications from 2013-2023. https://doi.org/10.62704/10057/28469

Pandey (2023). Digital Transformation In Supply Chain Management: Role of IoT. International Journal of Advanced Research in Management and Social Sciences. 12(12), 55-62.

Pandian, A. P. (2019). Artificial intelligence application in smart warehousing environment for automated logistics. Journal of Artificial Intelligence, 1(02), 63-72. https://doi.org/10.36548/jaicn.2019.2.002

Phusakulkajorn, W., Núñez, A., Wang, H., Jamshidi, A., Zoeteman, A., Ripke, B., ... & Li, Z. (2023). Artificial intelligence in railway infrastructure: Current research, challenges, and future opportunities. Intelligent Transportation Infrastructure, 2, liad016. https://doi.org/10.1093/iti/liad016

Pilon, R. V. (2023). Artificial Intelligence in Commercial Aviation: Use cases and emerging strategies. Routledge.

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. https://doi.org/10.1111/jbl.12364

Rosendorff, A., Hodes, A., & Fabian, B. (2021). Artificial Intelligence for last-mile logistics-Procedures and architecture. The Online Journal of Applied Knowledge Management (OJAKM), 9(1), 46-61.

Sadou, A. M., & Njoya, E. T. (2023). Applications of artificial intelligence in the air transport industry: a bibliometric and systematic literature review. Journal of Aerospace Technology and Management, 15. https://doi.org/10.1590/jatm.v15.1312

Tang, R., De Donato, L., Besinović, N., Flammini, F., Goverde, R. M., Lin, Z., ... & Wang, Z. (2022). A literature review of Artificial Intelligence applications in railway systems. Transportation Research Part C: Emerging Technologies, 140. https://doi.org/10.1016/j.trc.2022.103679

Tsidulko, J. (2024). Oracle. https://www.oracle.com/scm/ai-supply-chain/#challenges

Tsolakis, N., Niedenzu, D., Simonetto, M., Dora, M., & Kumar, M. (2021). Supply network design to address United Nations Sustainable Development Goals: A case study of blockchain implementation in Thai fish industry. Journal of Business Research, 131, 495-519. https://doi.org/10.1016/j.jbusres.2020.08.003

Wang, Y., Yao, Y., & Li, Y. (2022). The Impact of Artificial Intelligence Development on the Logistics Industry Talent Demand and Countermeasures. In DMI (pp. 349-359). https://doi:10.3233/FAIA230035

Zhang, H., Hu, R., Chen, A., Lei, Y., & Qu, H. (2024, June). Enhancing Concrete Supply Chain Efficiency in Civil Engineering through Digital Transformation: A Comprehensive Review. In 2024 8th International Conference on Civil Architecture and Structural Engineering (ICCASE 2024) (pp. 741-753). Atlantis Press. https://doi.org/10.2991/978-94-6463-449-5_73

Zhang, J., & Zhang, J. (2023). Artificial intelligence applied on traffic planning and management for rail transport: a review and perspective. Discrete Dynamics in Nature and Society, 2023(1). https://doi.org/10.1155/2023/1832501

Zhu, X., Liu, N., & Shi, Y. (2022). Artificial intelligence technology in modern logistics system. International Journal of Technology, Policy and Management, 22(1-2), 66-81. https://doi.org/10.1504/IJTPM.2022.122537

Published

2025-09-30

How to Cite

Nalbantoğlu, C. B. (2025). Artificial Intelligence in The Logistics Sector: Efficiency, Automation and Current Trends: Artificial Intelligence in The Logistics Sector: Efficiency, Automation and Current Trends. Journal of Economic Research Foundation, 2(2), 160–175. https://doi.org/10.62844/jerf.23