Abstract
The aim of the study was to quantitatively determine the impact of implementing artificial intelligence (AI) solutions on the operational efficiency of logistics warehouses. To achieve this, an extended linear regression model was developed, which, in addition to classical determinants such as the level of automation and service quality, included a variable describing the degree of AI technology integration in the analyzed units. The model was estimated using the Ordinary Least Squares (OLS) method on a synthetic dataset (n = 100), generated based on average statistics from official sources and market proportions. The results of the analysis showed that the AI variable was the only one to achieve statistical significance (p < 0.05), indicating its positive impact on warehouse performance. The coefficient of determination for the extended model was R² = 0.061, confirming the validity of its construction. The study indicates that integrating AI with warehouse processes may be a key factor in enhancing operational efficiency and organizational competitiveness. The results obtained justify the need for further research using empirical data and nonlinear models. Model estimation was carried out in the Python environment using the statsmodels library.
Paper Type
Full Paper
DOI
10.62036/ISD.2025.147
Artificial Intelligence in Warehouse Logistics as a Tool for Spatial Structure Optimization
The aim of the study was to quantitatively determine the impact of implementing artificial intelligence (AI) solutions on the operational efficiency of logistics warehouses. To achieve this, an extended linear regression model was developed, which, in addition to classical determinants such as the level of automation and service quality, included a variable describing the degree of AI technology integration in the analyzed units. The model was estimated using the Ordinary Least Squares (OLS) method on a synthetic dataset (n = 100), generated based on average statistics from official sources and market proportions. The results of the analysis showed that the AI variable was the only one to achieve statistical significance (p < 0.05), indicating its positive impact on warehouse performance. The coefficient of determination for the extended model was R² = 0.061, confirming the validity of its construction. The study indicates that integrating AI with warehouse processes may be a key factor in enhancing operational efficiency and organizational competitiveness. The results obtained justify the need for further research using empirical data and nonlinear models. Model estimation was carried out in the Python environment using the statsmodels library.
Recommended Citation
Markiewicz, R., Norek, T., Rabe, M., Łopatka, A., Bilan, Y. & Gawlik, A. (2025). Artificial Intelligence in Warehouse Logistics as a Tool for Spatial Structure OptimizationIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.147