Research Article

A Review on The Use of Artificial Intelligence and Machine Learning Technologies in The Logistics Sector

Volume: 38 Number: 4 October 15, 2024
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A Review on The Use of Artificial Intelligence and Machine Learning Technologies in The Logistics Sector

Abstract

In recent years, developments in Artificial Intelligence (AI) and MachineLearning (ML) technologies have had profound effects on all sectors. The logistics industry has also become a sector where these technologies are being used to a significant extent. The emergence of intelligent logistics systems offers several opportunities for the advancement of this sector by facilitating digital transformation in supply chain and logistics. The aim of this study is to provide a comprehensive review of recent studies examining the use of AI and ML systems in the logistics industry. In this study, which is designed as a systematic study, firstly, based on the existing literature, the basic concepts, trends, researchers and countries working on AI and ML systems in the logistics sector are examined by bibliometric analysis method. Then, information about the prominent AI and ML systems in logistics is given. It is seen that the most frequently used AI and ML technologies in logistics are Deep Learning, Optimization, Internet of Things (IoT), Data Mining and Predictive Models. The methodologies presented in the study have a practical importance in increasing efficiency, transparency and planning in the logistics.

Keywords

Logistics , Artificial Intelligence , Machine Learning , Technology , Bibliometric Analysis

References

  1. Abosuliman, S. S., & Almagrabi, A. O. (2021). Computer vision assisted human computer interaction for logistics management using Deep Learning. Computers & Electrical Engineering, 96, 107555. [CrossRef]
  2. Boujarra, M., Lechhab, A., Al Karkouri, A., Zrigui, I., Fakhri, Y., & Bourekkadi, S. (2024). Revolutionizing logistics through Deep Learning: Innovative solutions to optimize data security. Journal of Theoretical and Applied Information Technology, 102(4), 1593-1607. [CrossRef]
  3. Boukerche, A., & Wang, J. (2020). Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks, 181, 107530. [CrossRef]
  4. Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4–13. [CrossRef]
  5. Çetiner, Ö. (2024). Avrupa yeşil mutabakati konusundaki akademik çalişmalarin görsel haritalama tekniği ile bibliyometrik analizi. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(1), 275-295. [CrossRef]
  6. Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep Learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41. [CrossRef]
  7. Cheah, J. H., Kersten, W., Ringle, C. M., & Wallenburg, C. (2023). Guest editorial: Predictive modeling in logistics and supply chain management research using partial least squares structural equation modeling. International Journal of Physical Distribution & Logistics Management, 53(7/8), 709-717. [CrossRef]
  8. Chin, W., Cheah, J. H., Liu, Y., Ting, H., Lim, X. J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161-2209. [CrossRef]
  9. Chung, S. H. (2021). Applications of smart technologies in logistics and transport: A review. Transportation Research Part E: Logistics and Transportation Review, 153, 102455. [CrossRef]
  10. Congna, Q., Huifeng, Z., & Bo, L. (2009). Study on Application of Data Mining Technology to Modern Logistics Management Decision. 2009 International Forum on Information Technology and Applications, 433-436. [CrossRef]
APA
Oğuz, S., & Yalçıntaş, D. (2024). A Review on The Use of Artificial Intelligence and Machine Learning Technologies in The Logistics Sector. Trends in Business and Economics, 38(4), 218-225. https://doi.org/10.16951/trendbusecon.1494826