THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE FREIGHT INDUSTRY: A COMPREHENSIVE ANALYSIS

Authors

  • Mohini Thakkar Notion Labs, USA. Author

DOI:

https://doi.org/10.5281/zenodo.13709973

Keywords:

Artificial Intelligence In Freight, Predictive Maintenance Logistics, Autonomous Freight Transport, Supply Chain Optimization, AI-driven Demand Forecasting

Abstract

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) on the freight industry, examining its applications, benefits, challenges, and prospects. It delves into how AI is revolutionizing key areas such as logistics optimization, predictive maintenance, demand forecasting, and inventory management. The article discusses the emergence of autonomous vehicles and drones in freight transport, highlighting their potential to reshape last-mile delivery and long-haul operations. Additionally, it analyzes the role of AI in enhancing customer service and communication through chatbots, virtual assistants, and advanced analytics. The article also addresses the significant challenges in AI integration, including data privacy concerns, cybersecurity risks, and the need for workforce adaptation. Looking towards the future, the article explores emerging technologies like blockchain-enabled smart contracts and AI-optimized warehousing, offering insights into how these innovations may further transform the industry. Through a critical examination of current implementations and future possibilities, this article provides a comprehensive overview of AI's role in shaping the future of global freight operations, emphasizing its potential to drive efficiency, sustainability, and innovation in the sector.

References

A. Gunasekaran, N. Subramanian, and T. Papadopoulos, "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, vol. 99, pp. 14-33, 2017. [Online]. Available: https://doi.org/10.1016/j.tre.2016.12.008

M. Woschank, E. Rauch, and H. Zsifkovits, "A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics," Sustainability, vol. 12, no. 9, p. 3760, 2020. [Online]. Available: https://doi.org/10.3390/su12093760

C. K. M. Lee, Y. Lv, K. K. H. Ng, W. Ho, and K. L. Choy, "Design and application of Internet of things-based warehouse management system for smart logistics," International Journal of Production Research, vol. 56, no. 8, pp. 2753-2768, 2018. [Online]. Available: https://doi.org/10.1080/00207543.2017.1394592

J. Wan, S. Tang, D. Li, S. Wang, C. Liu, H. Abbas, and A. V. Vasilakos, "A manufacturing big data solution for active preventive maintenance," IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2039-2047, 2017. [Online]. Available: https://doi.org/10.1109/TII.2017.2670505

Zohdi, Maryam & Rafiee, Majid & Kayvanfar, Vahid & Salamiraad, Amirhossein. (2022). Demand forecasting based machine learning algorithms on customer information: an applied approach. International Journal of Information Technology. 14. 10.1007/s41870-022-00875-3. [Online]. Available: https://link.springer.com/article/10.1007/s41870-022-00875-3

S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, "Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications," International Journal of Production Economics, vol. 219, pp. 179-194, 2020. [Online]. Available: https://doi.org/10.1016/j.ijpe.2019.05.022

T. Litman, "Autonomous vehicle implementation predictions: Implications for transport planning," Victoria Transport Policy Institute, 2020. [Online]. Available: https://www.vtpi.org/avip.pdf

Wei, Dongmei. (2021). e-Commerce Online Intelligent Customer Service System Based on Fuzzy Control. Journal of Sensors. 2021. 1-11. 10.1155/2021/4867222. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1155/2021/4867222

M. Lezzi, M. Lazoi, and A. Corallo, "Cybersecurity for Industry 4.0 in the current literature: A reference framework," Computers in Industry, vol. 103, pp. 97-110, 2018. [Online]. Available: https://doi.org/10.1016/j.compind.2018.09.004

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Published

2024-09-06

How to Cite

Mohini Thakkar. (2024). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE FREIGHT INDUSTRY: A COMPREHENSIVE ANALYSIS. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY, 15(5), 56-63. https://doi.org/10.5281/zenodo.13709973