Preview

Transport Technician: Education and Practice

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Artificial intelligence as a key to improving the efficiency of logistics operations

https://doi.org/10.46684/2687-1033.2025.2.188-195

EDN: PXXGYM

Abstract

The article examines the application of artificial intelligence (AI) in warehouse process management and its impact on the economic efficiency of logistics companies. The main areas of AI utilization, including demand forecasting, inventory management, robotics, and computer vision technologies, are analyzed. Special attention is paid to the experience of logistics companies such as DHL, Walmart, and X5 Group, which have successfully integrated AI into their operations. The article also explores examples of AI use in seaports, such as the Port of Los Angeles, where technologies have enhanced cargo flow management.

The article presents the results of a survey conducted among logistics industry professionals, which identified the level of AI adoption, key areas of application, and expected benefits. It discusses both the advantages, such as increased accuracy and reduced processing time, and the challenges, including implementation costs and the shortage of qualified specialists. The role of AI in reducing operating costs and accelerating data processing in large-scale logistics chains is emphasized. As a result, the application of AI in logistics, while requiring significant investment, is transforming traditional management practices and leading to more efficient and sustainable operations.

About the Authors

O. Korostin

Belgium

Oleksandr Korostin - independent researcher



A. Blazhkovskii
Tver State University (TSU)
Russian Federation

Anatolii Blazhkovskii - specialist,

33 Zhelyabova st., Tver, 170100



I. Tretiakov
Kellogg School of Management at Northwestern University
United States

Ilia Tretiakov - master;

2211 Campus Drive, Evanston, 60208



M. Stepanov
Chuvash State University named after I.N. Ulyanov (ChuvSU named after I.N. Ulyanov)
Russian Federation

Maksim Stepanov - master,

15 Moskovsky pr., Cheboksary, 428015



References

1. Kidassova M. Enhancing business operational efficiency through supply chain optimization. Norwegian Journal of Development of the International Science. 2024;144:37-39. DOI: 10.5281/zenodo.14169113

2. Magerramov A. Cost minimization in supply chains: approaches to expense management and risk reduction in volatile markets. Annali D’italia. 2024;62:30-32. DOI: 10.5281/zenodo.14552207

3. Peregorodova O.O. Application of artificial intelligence in logistics. Matrix of Scientific Knowledge. 2020;6:97-101. EDN UCSDLU. (In Russ.).

4. Ivut R.B., Popov P.V., Lapkovskaya P.I., Prokopov S.V. Theoretical and methodological substantiation of the assessment and development of logistics infrastructure. Science and Technique. 2023;22(1):69-78. DOI: 10.21122/2227-1031-2023-23-1-69-78. EDN HCZRIE. (In Russ.)

5. Vanoy R.J.A. Logistics 4.0: Exploring artificial intelligence trends in efficient supply chain management. Data and Metadata. 2023;2:145. DOI: 10.56294/dm2023145

6. Khoroshilova T. The role of artificial intelligence in logistics: efficiency, challenges and solutions. Universum: technical sciences. 2024;11-5(128):41-45. DOI: 10.32743/UniTech.2024.128.11.18548. EDN FMYONH. (In Russ.)

7. Ogarkov A. Application of big data analytics to improve business customer service. Innovation Science. 2024;7-1:61-65. EDN EKYPBQ.

8. Petrova A.V. Artificial intelligence in the management of logistics activities of the organization. Natural-Humanitarian Research. 2024;1(51):411-413. EDN YMEDYK. (In Russ.).

9. Yusufova O.M., Shiboldenkov V.A., Andreeva A.A. Analysis of digital logistics technologies for automation and service integration of the organization’s warehouse processes. Russian Journal of Innovation Economics. 2020;10(3):1759-1772. DOI: 10.18334/vinec.10.3.110285. EDN YTVMPW. (In Russ.)

10. Malikov A. Digital transformation and its impact on the structure and efficiency of modern business. Annali D’italia. 2024;(62):112-115. DOI: 10.5281/zenodo.14558548

11. Tretiakov I. Intelligent models for demand forecasting using AI. Scientific discussion. 2024;95:28-30. DOI: 10.5281/zenodo.14498995

12. Berdnikova A.A., Kabirov I.R., Krivonogov S.V. Using automated information systems to improve the delivery management process: prospects and challenges. International Journal of Open Information Technologies. 2024;12(10):120-128. EDN DQKLFB. (In Russ.).

13. Kolokutskii A. Artificial intelligence in transport logistics: route optimization and cost reduction. Eurasian Scientific Journal. 2024;(2):4-8 (In Russ.).


Supplementary files

Review

For citations:


Korostin O., Blazhkovskii A., Tretiakov I., Stepanov M. Artificial intelligence as a key to improving the efficiency of logistics operations. Transport Technician: Education and Practice. 2025;6(2):188-195. (In Russ.) https://doi.org/10.46684/2687-1033.2025.2.188-195. EDN: PXXGYM

Views: 81


ISSN 2687-1025 (Print)
ISSN 2687-1033 (Online)