Application of Artificial Intelligence in Logistics Processes: a Case Study of Michelin Poland Ltd.
Dariusz Racz
Faculty of Economic Sciences, University of Warmia and Mazury in OlsztynKlaudia Kucińska
Faculty of Economic Sciences, University of Warmia and Mazury in OlsztynAbstract
This article focuses on analyzing the use of artificial intelligence (AI) in logistics processes using the example of Michelin Poland Ltd. The purpose of the paper is to assess the degree of application of AI technology in logistics and its impact on operational efficiency. The research includes a literature analysis, a survey of Michelin employees and an evaluation of the effectiveness of the implemented solutions. The results indicate that the implementation of AI has contributed to improving process efficiency, increasing the quality of work and reducing operating costs. Challenges to AI adaptation were also identified, such as high implementation costs and potential employee layoffs. The analysis confirmed that AI is a key component of logistics management strategies in the context of Industry 4.0, contributing to the company’s competitiveness. The article underscores the importance of sustainably implementing AI technologies to maximize benefits and minimize risks, offering valuable lessons for logistics managers and researchers.
Keywords:
logistics management, digital transformation, automation, operational efficiencyReferences
Aslam, J., Lai, K., Kim, Y.B., & Treiblmaier, H. (2024). The Implications of Blockchain for Logistics Operations and Sustainability. Journal of Innovation & Knowledge, 9(4), 100611. https://doi.org/10.1016/j.jik.2024.100611. Google Scholar
Bhowmik, O., Chowdhury, S., Ashik, J.H., Mahmud, G.I., Khan, M. M., & Hossain, N.U.I. (2024). Application of Artificial Intelligence in Reverse Logistics: A Bibliometric and Network Analysis. Supply Chain Analytics, 7, 100076. https://doi.org/10.1016/j.sca.2024.100076. Google Scholar
Demartini, M., Evans, S., & Tonelli, F. (2019). Digitalization Technologies for Industrial Sustainability. Procedia Manufacturing, 33, 264-271. https://doi.org/10.1016/j.promfg.2019.04.032. Google Scholar
Dmuchowski, R. (2019). Pomiar rentowności w zarządzaniu łańcuchem dostaw. Kwartalnik Naukowy Uczelni Vistula, 4(62), 103-104. https://doi.org/10.34765/kn.0419.a08. Google Scholar
Geary, R., & Cosgrove, J. (2023). Manufacturing Reliability and Cost Improvements through Data Analytics: An Industry Case Study. Procedia Computer Science, 217, 395-402. https://doi.org/10.1016/j.procs.2022.12.235. Google Scholar
Idrissi, Z.K., Lachgar, M., & Hrimech, H. (2024). Blockchain, IoT and AI in Logistics and Transportation: A Systematic Review. Transport Economics and Management. https://doi.org/10.1016/j.team.2024.09.002. Google Scholar
Javaid, M., Haleem, A., Singh, R.P., & Suman, R. (2021). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management, 7(1), 83-111. https://doi.org/10.1142/s2424862221300040. Google Scholar
Karim, M.R., Dulal, M., Sakila, F., Aditi, P., Smrity, S.J., & Asha, N.N. (2024). Analyzing the Factors Influencing Sustainable Supply Chain Management in the Textile Sector. Cleaner Logistics and Supply Chain, 13, 100183. https://doi.org/10.1016/j.clscn.2024.100183. Google Scholar
Kaup, M., Wiktorowska-Jasik, A., Smacki, A., & Baszak, K. (2024). Information Systems and Technologies Supporting Decision-Making Processes in Logistics Companies. Procedia Computer Science, 246, 5506-5515. https://doi.org/10.1016/j.procs.2024.09.699. Google Scholar
Qin, L., & Wan, K. (2024). Real-Time Tracking System for Distribution Information of Logistics Enterprises Based on IOT Technology. Procedia Computer Science, 243, 84-91. https://doi.org/10.1016/j.procs.2024.09.012. Google Scholar
Setthachotsombut, N., Sommanawat, K., & Sua-Iam, G. (2024). Logistics Business Management of Provider in Thailand with Smart Logistics. Journal of Open Innovation Technology Market and Complexity, 10(4), 100422. https://doi.org/10.1016/j.joitmc.2024.100422. Google Scholar
Sumarlin, T., & Kusumajaya, R.A. (2024). AI Challenges and Strategies for Business Process Optimization in Industry 4.0: Systematic Literature Review. Journal of Management and Informatics, 3(2), 195-211. https://doi.org/10.51903/jmi.v3i2.25. Google Scholar
Tjaden, S., & Flämig, H. (2024). What May Automation Cost? Activity-Based Costing for Autonomous Transport in Urban Logistics. Transportation Research Procedia, 79, 84-91. https://doi.org/10.1016/j.trpro.2024.03.013. Google Scholar
Vogel, M., Strina, G., Said, C., & Schmallenbach, T. (2023). The Evolution of Artificial Intelligence Adoption in Industry. AHFE International. In: T. Ahram, J. Kalra & W. Karwowski (Eds.). Artificial Intelligence and Social Computing. AHFE (2023) International Conference. AHFE Open Access, 72. AHFE International, USA. https://doi.org/10.54941/ahfe1003282. Google Scholar
Wang, Z., Gao, L., & Wang, W. (2024). The Impact of Supply Chain Digitization and Logistics Efficiency on the Competitiveness of Industrial Enterprises: Empirical Data from Chinese Listed Companies. International Review of Economics & Finance, 97, 103759. https://doi.org/10.1016/j.iref.2024.103759. Google Scholar
Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn
Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
An Author declares that his paper has not been published before (under the same or another title, or is a part of another publication) and does not infringe copyrights of other persons**. At the same time, the Author transfers to the Publisher the exclusive right to publish and to circulate this work in print in the form of a non-serial journal publication and in a form of an electronic publication.
The journal is available on Creative Common license CC-BY-NC-ND
