M.N. Belozyorov1, E.A. Kalashnikov2, A.N. Smirnov3
1, 2 National Research Technological University «MISIS» (Moscow, Russia)
3 Sberbank of Russia PJSC (Moscow, Russia)

1 mnbelozyorov@gmail.com, 2 e.a.kalashnikov@mail.ru, 3 asmirnov889@yandex.ru

The accuracy of forecasting demand for metallurgical products is critically important to ensure the sustainability of production processes. Demand forecasting plays a key role in long-term strategic planning, minimizing the risks associated with expanding production, entering new markets, and launching new products. The purpose of the study is to assess the impact of model risk on the cost of production when planning the production of secondary aluminum alloys.

Calculation of the burdening for the production of the AK5M2 alloy has been performed. The impact of the model risk caused by errors in forecasting demand for the pre-production process of this aluminum alloy has been estimated. The performed calculations demonstrate that model risk management should be a priority for industrial companies using certain models and algorithms.

Belozyorov M.N., Kalashnikov E.A., Smirnov A.N. Assessment of the impact of model risk on the cost of production of secondary aluminum alloys. Neurocomputers. 2025. V. 27. № 5. P. 35–39. DOI: https://doi.org/10.18127/j19998554-202505-04 (in Russian)

1. Borisova V.V., Demkina O.V., Savin A.V. Riski tsifrovizatsii promyshlennykh kompanij. Innovatsii i investitsii. 2019. № 12. S. 294–297. (in Russian)
2. Kurnosov A.V. SupTech- i RegTech-initsiativy: analiz bazovykh kharakteristik i model'nykh riskov. Russian Journal of Economics and law. 2021. № 4. S. 702–712. (in Russian)
3. Kirilyuk I.L. Model'nye riski v finansovoj sfere v usloviyakh ispol'zovaniya iskusstvennogo intellekta i mashinnogo obucheniya. Russian Journal of Economics and Law. 2022. T. 16. № 1. S. 40–50. (in Russian)
4. Nikitin N.A. Veroyatnostnye metody ucheta model'nykh riskov pri otsenke investitsij v tekhnologii iskusstvennogo intellekta. Innovatsionnoe razvitie ekonomiki. 2023. T. 2. S. 123–134. (in Russian)
5. Minasyan V.B., Ivko D.G. Analiz model'nogo riska ispol'zovaniya tekhnologii mul'tiplikatorov pri otsenke aktsij rossijskikh kompanij. Finansy: teoriya i praktika. 2019. T. 23. № 6. S. 91–116. (in Russian)
6. Nesterenok G. Upravlenie model'nym riskomю Bankovskij vestnik. 2021. № 4. S. 31–38. (in Russian)
7. Danielsson J. et al. Model risk of risk models. Journal of Financial Stability. 2016. V. 23. P. 79–91.
8. Glasserman P., Xu X. Robust risk measurement and model risk. Quantitative Finance. 2014. V. 14. № 1. P. 29–58.
9. Rodionov D. et al. Risk modeling in the oil and gas industry. International Journal of Technology. 2023. V. 14. № 8. P. 1663–1674.
10. Van den Eynde S. et al. Forecasting global aluminium flows to demonstrate the need for improved sorting and recycling methods. Waste Management. 2022. V. 137. P. 231–240.
11. Sharma A. et al. Force evaluation and machining parameter optimization in milling of aluminium burr composite based on response surface method. Advances in Materials and Processing Technologies. 2022. V. 8. № 4. P. 4073–4094.
12. Naveen Srinivas M. et al. Parametric optimization and multiple regression modelling for fabrication of aluminium alloy thin plate using wire arc additive manufacturing. International Journal on Interactive Design and Manufacturing (IJIDeM). 2022. P. 1–11.