AI IN LOGISTICS: TRANSFORMING THE FUTURE OF LOGISTICS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Artificial Intelligence (AI) is increasingly influencing the way logistics activities are planned, managed, and executed. This study examines the role of AI in transforming logistics operations, with particular focus on its applications, benefits, challenges, and future potential. The study is based on secondary data collected from academic literature, industry reports, technology publications, and other relevant sources. The findings highlight that AI can support demand forecasting, route optimization, warehouse automation, inventory management, delivery planning, predictive maintenance, and customer service. These applications can contribute to improved operational efficiency, reduced logistics costs and delays, better resource utilization, and enhanced customer satisfaction. However, factors such as high implementation costs, data security concerns, shortage of skilled professionals, system integration difficulties, and employee adaptation can create barriers to effective AI adoption. The study suggests that logistics organizations should focus on reliable data management, employee training, gradual technology implementation, cybersecurity, and appropriate human supervision. Overall, AI has strong potential to create more efficient, responsive, and intelligent logistics operations and is expected to play an important role in the future development of logistics and supply chain management.

References

1. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202.
2. Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39.
3. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517.
4. Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.
5. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
6. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.
7. Ivanov, D., & Dolgui, A. (2021). 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.
8. Christopher, M. (2016). Logistics & Supply Chain Management (5th ed.). Pearson.
9. Chopra, S. (2019). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson.
10. World Economic Forum. (2020). The Future of the Last-Mile Ecosystem. World Economic Forum.

Keywords

Artificial Intelligence, Logistics Management, Supply Chain, Route Optimization, Warehouse Automation, Demand Forecasting, Inventory Management, Predictive Maintenance, Customer Service, AI Adoption.