Assessment and enhancement of economic performance in the mining industry companies
I.N. Makarov1, O.Yu. Smyslova1,2, L.N. Rubtsova1, E.A. Lemm1, O.S. Kirichenko1
1 Financial University under the Government of the Russian Federation, Moscow, Russian Federation
2 Bunin Yelets State University, Yelets, Russian Federation
Russian Mining Industry №4/ 2026 p. 129-140
Abstract: This study makes a quantitative assessment of the economic performance drivers in Russian mining companies and justifies priorities for its enhancement under the sanctions pressure, structural deficits of equipment and volatile commodity markets during 2020–2024. The relevance is driven by the sector’s role in the economy as mining accounts for approximately 13% of the national GDP, mineral products represent up to 64% of the export commodity structure, and the sector remains one of the largest taxpayers. The objective is to assess dynamics of the key performance indicators of mining companies, to identify factors that explain inter-segment differences in profitability, and quantitatively justify the effect of organizational and technological measures aimed at its enhancement. The selection covers 48 companies with the consolidated revenues of RUB 12.4 trillion (2023). The methods include the DuPont decomposition analysis, multiple linear regression, analysis of variance (ANOVA), and single-factor sensitivity analysis. It is shown that the return on sales in the “Mineral Mining” type of activity declined to 26.3% in 2024 from 32.1% in 2021, while the industry’s net profit balance fell to RUB 5.86 trillion (–28% to 2023). The coal segment showed a net loss of RUB 112.6 billion in 2024 with unit the open-pit mining costs rising to RUB 2,973 per tonne (+7.5 %). The regression model of the ROS dependence on the digital maturity index yields β1 = 0.687 (p < 0.001) and R2 = 0.80, indicating a dominant impact of technological transformation on the financial outcomes. The sensitivity analysis identifies the strongest ROS gain (+4.8 pp) from a 10% Rouble weakening, followed by a 5% reduction in the unit cost (+3.6 pp) and a 10-point digital-maturity uplift (+3.1 pp). A combined effect of the measures including automation of the drilling and loading operations, predictive maintenance and lean production is estimated at +28.4% to the labour productivity and –15.8% to the unit cost, with the capital investment payback period of 3.8–4.5 years.
Keywords: mining industry, economic performance, return on sales, digital maturity, labour productivity, DuPont model, regression analysis, production cost, import substitution, mining company
For citation: Makarov I.N., Smyslova O.Yu., Rubtsova L.N., Lemm E.A., Kirichenko O.S. Assessment and enhancement of economic performance in the mining industry companies. Russian Mining Industry. 2026;(4):129–140. https://doi.org/10.30686/1609-9192-2026-4-129-140
Article info
Received: 18.03.2026
Revised: 28.05.2026
Accepted: 11.06.2026
Information about the authors
Ivan N. Makarov – Dr. Sci. (Econ.), Assoc. Prof., Leading Researcher at the Institute of Regional Economics and Interbudgetary Relations, Professor at the Department of Management and General Humanities, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; Professor at the Department of Economics and Finance, Lipetsk Branch of the Financial University under the Government of the Russian Federation, Lipetsk, Russian Federation; https://orcid.org/0000-0002-7698-1875; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Olga Yu. Smyslova – Dr. Sci. (Econ.), Assoc. Prof., Leading Researcher at the Institute of Regional Economics and Interbudgetary Relations, Professor at the Department of Management and General Humanities, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; Professor at the Department of Economics and Management named after N.G. Nechaev, Bunin Yelets State University, Yelets, Russian Federation; https://orcid.org/0000-0001-7008-5319; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Larisa N. Rubtsova – Dr. Sci. (Econ.), Professor at the Department of Economics and Finance, Lipetsk Branch of the Financial University under the Government of the Russian Federation, Lipetsk, Russian Federation; https://orcid.org/0000-0003-1687-1890; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Ekaterina A. Lemm – Cand. Sci. (Econ.), Senior Lecturer at the Department of Industry Markets, Faculty of Economics and Business, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; https://orcid.org/0009-0004-0635-3090; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Olga S. Kirichenko – Cand. Sci. (Econ.), Assoc. Prof. at the Department of Industry Markets, Faculty of Economics and Business, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; https://orcid.org/0000-0001-6230-2280; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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