FACTORS INFLUENCING THE FORMATION AND DEVELOPMENT OF THE TRANSFER POTENTIAL OF AGROHOLDING LOGISTICS CENTERS
Abstract
Identification and classification of factors are of particular relevance, as they make it possible to systematize the influence of internal and external determinants, identify the most significant ones, and outline the directions of managerial impact. In the context of market turbulence in the agricultural sector, limited logistics routes, and the need to restore export chains after crisis periods, a structured analysis of factors serves as the basis for forecasting the effectiveness of logistics decisions and improving the performance of agroholding logistics centers. The article substantiates that the efficiency of forming and developing the transfer potential of agroholding logistics centers is a function of the interaction between technological, economic, organizational-institutional, human, legal, and environmental factors, which manifested themselves through quantitative shifts in the industry’s performance indicators during 2021–2024. It is shown that the increase in investments in logistics infrastructure to USD 620 million in 2024 and the rise in the level of process digitalization to 57% of enterprises have significantly strengthened the transfer potential of agricultural logistics. It is argued that the stabilization of maritime cargo turnover at 97.2 million tons and the growth of transport via the “solidarity lanes” to 196 million tons indicate the effectiveness of integrating Ukraine’s transport system into the European market. The combined effect of positive factors - modernization of infrastructure, development of partnerships with European operators, growth of institutional support, and digital transformation - ensured the structural adaptation of logistics centers to crisis conditions and created a foundation for their long-term resilience. It has been proven that the systematic classification of 36 identified factors by content, nature, level of regulation, origin, and area of manifestation has practical importance, as it allows identifying priority directions for managerial influence and improving the controllability of processes within agricultural logistics. The study argues that the combination of investment dynamics, enterprises’ digital maturity, and spatial diversification of routes generates a synergistic effect that enhances the competitiveness of agroholdings and creates the foundation for Ukraine’s integration into the unified European transport and logistics space.
References
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Wenterodt, T., & Herwig, H. (2014). The entropic potential concept: A new way to look at energy transfer operations. Entropy, no. 16(4), pp. 2071–2084. DOI: https://doi.org/10.3390/e16042071
Jacobson, L., Stevenson, J., Ramezanghorbani, F., Ghoreishi, D., Leswing, K., Harder, E., & Abel, R. (2021). Transferable neural network potential energy surfaces for closed-shell organic molecules: Extension to ions. Journal of Chemical Theory and Computation. no. 18(4). DOI: https://doi.org/10.33774/chemrxiv-2021-tmsdg
Adamovich, I., & Rich, J. (2024). Semiclassical analytic theory of electronic energy transfer in 3D atomic collisions. The Journal of Chemical Physics, no. 160(19). DOI: https://doi.org/10.1063/5.0209058
Agbo, P. (2024). Rate-potential decoupling: A biophysical perspective of electrocatalysis. Journal of Physics D: Applied Physics. no. 57(46). DOI: https://doi.org/10.1088/1361-6463/ad6008
Azimi, S., & Gallicchio, E. (2024). Potential distribution theory of alchemical transfer. arXiv preprint. DOI: https://doi.org/10.48550/arXiv.2407.14713
Bertani, M., Charpentier, T., Faglioni, F., & Pedone, A. (2024). Accurate and transferable machine learning potential for molecular dynamics simulation of sodium silicate glasses. Journal of Chemical Theory and Computation. no. 20(3). DOI: https://doi.org/10.1021/acs.jctc.3c01115
Chen, S., Liu, Z., Zhang, Z., Xu, R., Pang, D., & Xu, Y. (2024). Systematic investigation of nucleon optical model potentials in (p, d) transfer reactions. Chinese Physics C, no. 48. DOI: https://doi.org/10.1088/1674-1137/ad4269
Ding, T., Xie, C., Liu, K., Peng, Y., Li, G., Jiang, T., Wu, S., & Gao, J. (2024). Discharge characteristics and development process of potential transfer gap in live-line work. AIP Advances. no. 14. DOI: https://doi.org/10.1063/5.0196303
Jinnouchi, R., Karsai, F., & Kresse, G. (2024). Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: Machine learning aided first principles calculations. Chemical Science. no. 5. DOI: https://doi.org/10.1039/D4SC03378G
Khan, A., Vaish, P., Pang, Y., Kowshik, N., Chen, M., Batton, C., Rotskoff, G., Mullinax, J., Clark, B., Rubenstein, B., & Tubman, N. (2024). Quantum hardware-enabled molecular dynamics via transfer learning. arXiv preprint. DOI: https://doi.org/10.48550/arXiv.2406.08554
Maxson, T., & Szilvási, T. (2024). Transferable water potentials using equivariant neural networks. The Journal of Physical Chemistry Letters, no. 15, pp. 3740–3747. DOI: https://doi.org/10.1021/acs.jpclett.4c00605
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