Keywords
road transportation
digitalization transformation
logistics
economic instability
How to Cite
Abstract
In modern times, logistics services play a key role in the global economy, ensuring the continuity of supply chains in the face of globalization, technological transformation, and crises such as the COVID-19 pandemic and the Russian-Ukrainian war. Road transportation, which accounts for 75% of freight traffic in Europe, is highly sensitive to fluctuations in fuel prices, environmental standards, digitalization, and geopolitical instability. The 33.3% increase in road transportation prices in 2018–2023 emphasizes the need for adaptive pricing models. The study’s relevance is driven by the need to develop effective strategies for logistics companies that would consider these factors and ensure competitiveness. The purpose of the study is to analyze the dynamics of road transportation prices in Europe in 2018–2023, compare them with rail and sea transportation, identify key factors of influence (fuel costs, digitalization, environmental costs, demand, instability) and create a pricing model with a forecasting accuracy of at least 90%. The study is based on multiple regression analysis, which revealed a significant impact of five factors on road transportation prices with a determination coefficient of R² = 0.92. In particular, rising fuel prices increase tariffs by 0.8% for every 1%, digitalization reduces them by 0.5%, and environmental standards add 1.2%. The COVID-19 pandemic (2020) and the full-scale invasion (2022) caused peak price hikes of 15%, while stabilization in 2023 reduced the increase to 3%. Rail transportation proved to be more stable, while sea transportation was vulnerable to delays. The cases of DHL, Nova Poshta, and Maersk confirm the effectiveness of IoT (cost reduction by 6%) and blockchain (8% savings) in logistics. Regional differences (Western vs. Eastern Europe) emphasize the importance of technology and green initiatives. It is recommended to implement IoT, AI, and green technologies to reduce costs and attract customers with premiums of up to 12%. Future research should include non-linear models and analysis of emerging markets to improve accuracy to 95%.
References
1. Eurostat. (2023). Freight Transport Statistics – modal split. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Freight_transport_statistics_-_modal_split
2. Bednarski, L., Roscoe, S., Blome, C., & Schleper, M. C. (2023). Geopolitical disruptions in global supply chains: A state-of-the-art literature review. Production Planning & Control, 36(4), 536–562. https://doi.org/10.1080/09537287.2023.2286283
3. Smokers, R., Tavasszy, L., Chen, M., & Guis, E. (2014). Options for competitive and sustainable logistics. In C. Macharis, S. Melo, J. Woxenius, & T. Van Lier (Eds.), Sustainable logistics (Transport and Sustainability, Vol. 6, pp. 1–30). Emerald Group Publishing Limited. https://doi.org/10.1108/S2044-994120140000006001
4. Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply chain management: An international journal, 16(4), 246–259. https://doi.org/10.1108/13598541111139062
5. Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, (162), 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
6. Roy, S., & Mohanty, R. P. (2024). Green logistics operations and its impact on supply chain sustainability: An empirical study. Business Strategy and the Environment, 33(2), 1447–1476. https://doi.org/10.1002/bse.3531
7. Jabbar, S., Lloyd, H., Hammoudeh, M., Adebisi, B., & Raza, U. (2021). Blockchain-enabled supply chain: analysis, challenges, and future directions. Multimedia systems, (27), 787–806. https://doi.org/10.1007/s00530-020-00687-0
8. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, (229), 107791. https://doi.org/10.1016/j.ijpe.2020.107791
9. Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
10. Karakas, S., Acar, A. Z., & Kucukaltan, B. (2021). Blockchain adoption in logistics and supply chain: a literature review and research agenda. International Journal of Production Research, 62(22), 8193–8216. https://doi.org/10.1080/00207543.2021.2012613
11. Katsaliaki, K., Galetsi, P., & Kumar, S. (2022). Supply chain disruptions and resilience: A major review and future research agenda. Annals of Operations Research, 319(3), 965–1002. https://doi.org/10.1007/s10479-020-03912-1
12. Sarkis, J. (2021). Supply chain sustainability: Learning from the COVID-19 pandemic. International Journal of Operations & Production Management, 41(1), 63–73. https://doi.org/10.1108/IJOPM-08-2020-0568
13. Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: role of complexities and strategies. International Journal of Production Research, 53(22), 6809–6819. https://doi.org/10.1080/00207543.2015.1093667
14. Mentzer, J. T., Myers, M. B., & Stank, T. P. (Eds.). (2007). Handbook of global supply chain management. SAGE Publications, Inc. https://doi.org/10.4135/9781412976169
15. Aydin, G., Cattani, K., & Druehl, C. (2014). Global supply chain management. Business horizons, 57(4), 453–457. https://doi.org/10.1016/j.bushor.2014.04.001
16. Chopra, S., & Meindl, P. (2016). Supply Chain Management (6th ed.). Pearson.
17. Rushton, A., Croucher, P., & Baker, P. (2017). The Handbook of Logistics and Distribution Management (5th ed.). Kogan Page. https://surli.cc/kgvsqk
18. Humeniuk, А., & Biloshkurska, N. (2023). Ways of improving logistics activities of enterprises. Modeling the Development of the Economic Systems, (3), 14–19. https://doi.org/10.31891/mdes/2023-9-2 (in Ukrainian)
19. Biloshkurska, N. V., Biloshkurskyi, M. V., & Kravchenko, R. O. (2017). Marketing analysis of the strategic competitiveness of regional establishments of higher education. Economies’ Horizons, 2(3), 25–30. https://doi.org/10.31499/2616-5236.2(3).2017.128097 (in Ukrainian)
20. Lysenko, N. O., & Biloshkurska, N. V. (2012). Applying the production function in the analysis Tinbergen innovative component of economic security of agricultural enterprises. Innovatsiina ekonomika, (4), 140–144. (in Ukrainian)
21. Biloshkurska, N. V. (2012). Comprehensive models for assessing the economic security of enterprises. Information Processing Systems, 4(102), 9–11. http://nbuv.gov.ua/UJRN/soi_2012_2_4_4 (in Ukrainian)
22. World Bank. (2023). Logistics Performance Index (LPI). https://lpi.worldbank.org/international/global
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Nataliia Biloshkurska