Models for Assessing the Resistance of Sales and Marginal Income of a Metal Trading Company
Rayevnyeva O. V., Shabelnyk M. M.

Rayevnyeva, Olena V., and Shabelnyk, Mykola M. (2026) “Models for Assessing the Resistance of Sales and Marginal Income of a Metal Trading Company.” The Problems of Economy 2:271–279.
https://doi.org/10.32983/2222-0712-2026-2-271-279

Section: Economics and Enterprise Management

UDC 330.46:658.14:669

Abstract:
The contemporary development of economic processes is characterized by significant instability, which necessitates the formation, development, and strengthening of the resistant behavior of a metal trading company through the improvement of methods and approaches to the analysis and management of steel product sales planning processes in order to enhance comprehensive resilience to changes in the external environment. The aim of the study is defined as the development of a set of models for assessing the resistance of sales and marginal income of a metal trading company aimed at optimizing its sales structure and profitability. The research is based on the approach of synchronizing periods of demand growth and decline with steel product sales periods, methods of coefficient and integral assessment of resistance, as well as tools of strategic analysis and matrix-based assortment positioning. The study develops a model for assessing the resistance of steel product sales, which is based on the synchronization of periods of demand increase and decrease with sales periods and enables preliminary assessment of sales resistance using the sales resistance coefficient for each type of steel product, as well as a more comprehensive assessment based on the integral resistance coefficient. Possible behavioral strategies for a metal trading company aimed at enhancing resistance properties are proposed. A model for assessing the resistance of marginal income for each type of steel product based on resistance coefficients and integral resistance coefficients is proposed in order to synthesize possible development trajectories and strengthen the resistant behavior of a metal trading company. A matrix model for the strategic positioning of steel product assortments according to the levels of sales resistance and marginal income resistance has been developed. The model ensures the integration of quantitative resistance assessment with strategic analysis tools and forms the basis for the systematic management of steel product assortments. The implementation of the proposed models made it possible to conduct a comprehensive assessment of the stability of steel product assortments under market fluctuations, identify strategically prioritized and risk-sensitive segments, and substantiate managerial decisions regarding the optimization of the sales structure and profitability of a metal trading company.

Keywords: metal trading company; sales resistance; marginal income resistance; resistant behavior; steel product assortment; models.

Fig.: 1. Tabl.: 1. Formulae: 10. Bibl.: 20.

Rayevnyeva Olena V. – Doctor of Sciences (Economics), Professor, Head of the Department, Department of Statistics and Economic Forecasting, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkіv, 61165, Ukraine)
Email: olena.raev@gmail.com
Shabelnyk Mykola M. – Postgraduate Student, Department of Statistics and Economic Forecasting, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkіv, 61165, Ukraine)
Email: shabelnik003@gmail.com

List of references in article

Alyokhin A., Brutman A., Grabovoy A. & Shabelnyk T. (2024). Short-term forecasting of the main indicators of the COVID-19 epidemic in Ukraine based on the seasonal cycle model. System Research & Information Technologies, 4, 32–42. https://doi.org/10.20535/SRIT.2308-8893.2024.4.02
Bednarski L., Roscoe S., Blome C. & Schleper M. C. (2025). Geopolitical disruptions in global supply chains: a state-of-the-art literature review. Production Planning & Control, 4(36), 536–562. https://hdl.handle.net/10779/uos.28789337
Huang J., Ou-Yang Q. & Feng Ch. (2020). Green trade assessment for sustainable development of Chinese ferrous metal industry. Journal of Cleaner Production, 249. https://doi.org/10.1016/j.jclepro.2019.119382
Jin B. & Xu X. (2024). Predictions of steel price indices through machine learning for the regional northeast Chinese market. Neural Computing and Applications, 33(36), 20863–20882. https://doi.org/10.1007/s00521-024-10270-7
Klinshov V., Nekorkin V. & Kurths J. (2016). Stability threshold approach for complex dynamical systems. New Journal of Physics, 18. https://doi.org/10.1088/1367-2630/18/1/013004
Mateus B. C., Mendes M., Farinha J. T., Cardoso A. J. M., Assis R. & da Costa L. M. (2023). Forecasting steel production in the world – assessments based on shallow and deep neural networks. Applied Sciences, 1(13), 178. https://doi.org/10.3390/app13010178
Mayar K., Carmichael D. & Shen X. (2022). Stability and resilience – A systematic approach. Buildings, 8(12), Article 1242. https://doi.org/10.3390/buildings12081242
Mints O. Yu., Khadzhynova O. V. & Shchepka O. V. (2025). Metody prohnozuvannia realizatsii metaloproduktsii na lokalnykh rynkakh yak komponent ekonomichnoi bezpeky pidpryiemstv [Methods for forecasting the sale of metal products in local markets as a component of economic security of enterprises]. Naukovi zapysky Natsionalnoho universytetu «Ostrozka akademiia», 38 (66), 79–85. http://doi.org/10.25264/2311-5149-2025-38(66)-79-85
Piskunov Y., Shabelnyk T., Kozyr A., Bashynskyi K., Kovalev L. & Piskunov M. (2021). Method and Mathematical Algorithm for Finding the Quasi-Optimal Purpose Plan. International Journal of Computer Science and Network Security, 2(21), 88–92. https://doi.org/10.22937/IJCSNS.2021.21.2.10
Raievnieva O. V. & Touzani T. (2021). Upravlinnia povedinkoiu pidpryiemstva v umovakh nestatsionarnosti rynkovoho seredovyshcha: kontseptualni zasady ta modelnyi instrumentarii [Management of enterprise behavior in conditions of non-stationarity of the market environment: conceptual foundations and model tools]. Kharkiv: FOP Liburkina L. M.
Ramin D., Spinelli S. & Brusaferri A. (2018). Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process. Applied Energy, 225, 622–636. https://doi.org/10.1016/j.apenergy.2018.03.084
Rayevnyeva O., Filip S., Aksonova I., Brovko O. & Rui S. (2022). The Impact of Sensitivity of Economic Activities on the Economic Behaviour of Enterprise. Ekonomika rozvytku, 3(21), 27–39. https://doi.org/10.57111/econ.21(3).2022.27-39
Rayevnyeva O. V., Brovko O. I. & Zhui Su (2023). A study of the influence of environmental factors on the economic behavior of the enterprise. Biznes Inform, 8, 193–198. https://doi.org/10.32983/2222-4459-2023-8-193-198
Shabelnyk M. M. (2025). Model klasyfikatsii asortymentu metaloproduktsii za indykatoramy biznes-protsesiv rezystentnoi povedinky metalotreiderskoi kompanii [Model of classification of metal product range by business process indicators of resistant behavior of a metal trading company]. Visnyk Kharkivskoho natsionalnoho universytetu imeni V. N. Karazina. Seriia «Ekonomichna», 109, 6–16. https://doi.org/10.26565/2311-2379-2025-109-01
Sharoian F. (2021). The role of trade barriers in international trading metallurgical industry of Ukraine. Three Seas Economic Journal, 3(2), 118–123. https://doi.org/10.30525/2661-5150/2021-3-15
Strasser S. & Tripathi S. (2024). Forecasting Steel Demand: Comparative Analysis of Predictability across diverse Countries and Regions. Procedia Computer Science, 232, 2740–2750. https://doi.org/10.1016/j.procs.2024.02.091
Sukolkit N., Arunyanart S. & Apichottanakul A. (2024). An open innovative inventory management based demand forecasting approach for the steel industry. Journal of Open Innovation: Technology, Market, and Complexity, 4(10). https://doi.org/10.1016/j.joitmc.2024.100407
VShEM – KhNEU im. S. Kuznetsia (2023). Aktualni problemy systemnoho analizu ta modeliuvannia protsesiv upravlinnia [Actual problems of system analysis and modeling of management processes]. Bratyslava – Kharkiv: VShEM – KhNEU im. S. Kuznetsia.
Xu X. & Zhang Y. (2023). Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products. Mineral Economics, 4(36), 563–582. https://doi.org/10.1007/s13563-022-00357-9
Xu X. & Zhang Y. (2023). Price forecasts of ten steel products using Gaussian process regressions. Engineering Applications of Artificial Intelligence, A(126), Article 106870. https://doi.org/10.1016/j.engappai.2023.106870