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Optimization of material and technical supply management of industrial enterprises

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Category: Content №3 2022

Last Updated on 13 July 2022

Published on 13 July 2022

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Authors:

V.Stolyarov, orcid.org/0000-0002-4399-7117, LLC Berdiansk University of Management and Business, Berdiansk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

J.Pásztorová, orcid.org/0000-0002-7255-0197, University of Economics in Bratislava, Bratislava, the Slovak Republic, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.Zos-Kior, orcid.org/0000-0001-8330-2909, Poltava State Agrarian University, Poltava, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Hnatenko, orcid.org/0000-0002-0254-2466, Kyiv National University of Technologies and Design, Kyiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.Petchenko, orcid.org/0000-0003-1104-5717, Kharkiv National University of Internal Affairs, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (3): 163 - 167

https://doi.org/10.33271/nvngu/2022-3/163

Abstract:

Purpose. Development of an adaptive economic and mathematical model of raw material supply management for the production activities of industrial enterprises.

Methodology. In the course of the study, the following methods of understanding economic phenomena and processes were used to solve the problems posed in the work: system analysis, economic and mathematical modeling, abstract-logical and graphical methods.

Findings. The work identifies specific characteristics of the organization of supply of raw materials, equipment, inventory and finished products to industrial enterprises. An adaptive economic and mathematical model of optimization of material and technical supply of these enterprises is formulated. The proposed model simplifies the choice of raw material inventory management system depending on the specifics of the production process of a particular enterprise and the external conditions of its operation. The use of adaptive model is necessary in the formation of supply and demand change programs in the markets of finished products, monitoring the movement of own funds in the organization of transportation of raw materials from suppliers and redistribution of available financial resources to address the priorities of business entities.

Originality. The authors propose an adaptive economic and mathematical model for optimizing material and technical supply, which takes into account the possibility of violation of the delivery time of inventory items under the influence of force majeure, and also determines the optimal amount of raw materials for supply under certain operating conditions of a particular industrial enterprise.

Practical value. The use of the economic and mathematical model formed by the authors in the economic activity of industrial enterprises will allow the latter to timely forecast and plan their logistics costs and, as a consequence, avoid irrational spending of available financial resources.

Keywords: logistics, management, adaptation model, supply, storage, transportation costs

References:

1. Semenov, A., Kuksa, I., Hnatenko, I., Sazonova, T., Babiy, L., & Rubezhanska, V. (2021). Management of Energy and Resource - Saving Innovation Projects at Agri-Food Enterprises. TEM Journal, 10(2), 751-756. https://doi.org/10.18421/TEM102-32.

2. Brockova, K., Rossokha, V., Chaban, V., Zos-Kior, M., Hnatenko,I., & Rubezhanska, V. (2021). Economic Mechanism of Optimizing the Innovation Investment Program of the Development of Agro-Industrial Production. Management Theory and Studies for Rural Business and Infrastructure Development, 43(1), 129-135. https://doi.org/10.15544/mts.2021.11.

3. Sorooshian, S., Jambulingam, M., & Dodangeh, J. (2013). Case Study on Logistics Performance. International Journal of Engineering Business Management. https://doi.org/10.5772/56264.

4. Ivashkin, Y., & Nikitina, M. (2019). Agent-oriented modeling and optimization of material flows of multi-assortment production. IFAC-PapersOnLine, 52(13), 660-664. https://doi.org/10.1016/j.ifacol.2019.11.109.

5. Ahumada, O., & Villalobos, J. (2011). A tactical model for planning the production and distribution of fresh produce. Annals of Operations Research, 190(1), 339-358. https://doi.org/10.1007/s10479-009-0614-4.

6. Yuan, Y., Viet, N., & Behdani, B. (2019). The impact of information sharing on the performance of horizontal logistics collaboration: A simulation study in an agri-food supply chain. IFAC-PapersOnLine, 52(13), 2722-2727. https://doi.org/10.1016/j.ifacol.2019.11.619.

7. Alfonso-Lizarazo, E., Montoya-Torres, J., & Gutirrez-Franco, E. (2013). Modeling reverse logistics process in the agro-industrial sector: The case of the palm oil supply chain. Applied Mathematical Modelling, 37(23), 9652-9664. https://doi.org/10.1016/j.apm.2013.05.015.

8. Zhao, X., Wang, P., & Pal, R. (2021). The effects of agro-food supply chain integration on product quality and financial performance: Evidence from Chinese agro-food processing business. International Journal of Production Economics, 231. https://doi.org/10.1016/j.ijpe.2020.107832.

9. Raut, R., Gardas, B., Narwane, V., & Narkhede, B. (2019). Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach. Operations Research Perspectives, 6. https://doi.org/10.1016/j.orp.2019.100117.

10. Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194. https://doi.org/10.1016/j.omega.2011.08.011.

11. Furmann, R., Furmannov, B., & Wicek, D. (2017). Interactive Design of Reconfigurable Logistics Systems. Procedia Engineering, 192, 207-212. https://doi.org/10.1016/j.proeng.2017.06.036.

12. Prajapati, D., Harish, A., Daultani, Y., Singh, H., & Pratap, S. (2020). A Clustering Based Routing Heuristic for Last-Mile Logistics in Fresh Food E-Commerce. Global Business Review. https://doi.org/10.1177/0972150919889797.

13. Scholz-Reiter, B., Windt, K., & Liu, H. (2011). A multiple-logistic-objective-optimized manufacturing planning and control system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(4), 599-610. https://doi.org/10.1177/2041297510394108.

14. Gonzalez, A., Patroni, S., & Vidal, J. (2015). Developing Competencies in the Process of Hazard Identification in an Enterprise Related to the Field of Logistic and Food. Procedia Manufacturing, 3, 5052-5058. https://doi.org/10.1016/j.promfg.2015.07.518.

15. Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702.

16. Bukov, M., Skokan, R., Fusko, M., & Hodo, R. (2019). Designing of logistics systems with using of computer simulation and emulation. Transportation Research Procedia, 40, 978-985. https://doi.org/10.1016/j.trpro.2019.07.137.

17. Mishkurov, P., Fridrikhson, O., Lukyanov, V., Kornilov, S., & Say, V. (2021). Simulated Transport and Logistics Model of a Mining Enterprise. Transportation Research Procedia, 54, 411-418. https://doi.org/10.1016/j.trpro.2021.02.090.

18. Kovalsk, M., & Miieta, B. (2017). Support Planning and Optimization of Intelligent Logistics Systems. Procedia Engineering, 192, 451-456. https://doi.org/10.1016/j.proeng.2017.06.078.

19. Zos-Kior, ., Shkurupii, O., Hnatenko, I., Shulzhenko, I., & Rubezhanska, V. (2021). Modeling of the investment program formation process of ecological management of the Agrarian cluster. European Journal of Sustainable Development, 10(1), 571-583. https://doi.org/10.14207/ejsd.2021.v10n1p571.

20. Nofal, M., & Yusof, Z. (2013). Integration of Business Intelligence and Enterprise Resource Planning within Organizations. Procedia Technology, 11, 658-665. https://doi.org/10.1016/j.protcy.2013.12.242.

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