Theoretical and methodological foundations of proactive operational efficiency management in mining companies

DOI: https://doi.org/10.30686/1609-9192-2026-5-118-126

Читать на русском языке Yury V. Zabaikin1, Ekaterina V. Krasavina2, Dmitry V. Lyutyagin3,4, Ekaterina V. Novikova2, Dmitriy A. Lunkin3
1 National University of Oil and Gas “Gubkin University”, Moscow, Russian Federation
2 Plekhanov Russian University of Economics, Moscow, Russian Federation
3 Sergo Ordzhonikidze Russian State University for Geological Prospecting, Moscow, Russian Federation
4 LLC ANSGAR, Moscow, Russian Federation
Mining Industry Journal (Gornaya Promyshlennost) №5/ 2026 p. 118-126

Summary: The study justifies theoretical and methodological foundations of proactive operational efficiency management in mining companies within the Industry 4.0 paradigm. Its relevance is explained by a divergence between the following two processes. The physical volume of the gross value added in the Russian mining industry declined by 1.7% in 2025 with the mining and geological conditions deteriorating measurably, e.g. the stripping ratio at the Polyus Group rose from 1.5 to 2.4 m³/t in a single year, while the coal production cost grew from 2,234 to 3,400 Rubles per tonne. At the same time, the utilization capacity factor of the open-pit excavators remains at 0.45–0.49 against 0.80–0.90 in the record-high months. A hypothesis is made that in the cyber-physical production the measurement costs cease to be an exogenous parameter and become a design variable, which shifts the whole configuration of the in-company control. The objective is to justify a conceptual model combining noninvasive real-time monitoring, dynamic labour rate setting and minimisation of the in-house transaction costs. The methods used include comparative analysis, content analysis of corporate reporting, decomposition of the equipment time fund and theoretical modelling. A four-level closed cybernetic control loop is proposed; control costs are decomposed into measurement, monitoring and enforcement; an observability index and a proactivity reserve are introduced. It is demonstrated that the selective photochronometry detects on average 1.6% of working time losses against 14.0% when recorded continuously at the same facility.

Keywords: mining company, proactive management, operational efficiency, cyber-physical systems, digital twin, transaction costs, non-invasive monitoring, dynamic labour rate setting, overall equipment efficiency, Industry 4.0

For citation: Zabaikin Yury V., Krasavina Ekaterina V., Lyutyagin Dmitry V., Novikova Ekaterina V., Lunkin Dmitriy A. Theoretical and methodological foundations of proactive operational efficiency management in mining companies. Mining Industry Journal (Gornaya Promyshlennost). 2026;(5):118–126. https://doi.org/10.30686/1609-9192-2026-5-118-126


Article info

Received: 04.06.2026

Revised: 22.07.2026

Accepted: 28.07.2026


Authors info

Zabaikin Yury V. – Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Automation of Technological Processes, National University of Oil and Gas “Gubkin University”, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Krasavina Ekaterina V. – Dr. Sci. (Sociol.), Associate Professor, Professor of the Department of Political Analysis and Socio-Psychological Processes, Plekhanov Russian University of Economics, Moscow, Russian Federation

Lyutyagin Dmitry V. – Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Production and Financial Management, Sergo Ordzhonikidze Russian State University for Geological Prospecting, Moscow, Russian Federation; scientific supervisor LLC ANSGAR, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Novikova Ekaterina V. – Cand. Sci. (Econ.), Associate Professor of the Basic Department of the Chamber of Commerce and Industry of the Russian Federation “Human Resources Manage- ment”, Plekhanov Russian University of Economics, Moscow, Russian Federation; https://orcid.org/0000-0002-5347-108X; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Lunkin Dmitriy A. – Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Humanities, Sergo Ordzhonikidze Russian State University for Geological Prospecting, Moscow, Russian Federation; https://orcid.org/0000-0002-4553-006X; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


References

1. Zhuravlev A.G., Glebov I.A., Cherepanov V.A. Searching for reserves to increase productivity of excavator-automobile complexes using computer simulation. Russian Mining Industry. 2023;(6):148–154. (In Russ.) https://doi.org/10.30686/1609-9192-2023-6-148-154

2. Dolzhenko R.A., Malyshev D.S. Digitalization of approaches to labor organization and rationing in the industrial enterprise. Herald of Omsk University. Series “Economics ”. 2021;19(4):54–66. (In Russ.) https://doi.org/10.24147/1812-3988.2021.19(4).54-66

3. Абашкин В.Л., Абдрахманова Г.И., Бочаров М.Я., Вишневский К.О., Гохберг Л.М., Демидкина О.В. и др. Индикаторы цифровой экономики: 2026. М.: ИСИЭЗ ВШЭ; 2026. 304 с. Режим доступа: https://issek.hse.ru/mirror/pubs/share/1122445658.pdf (дата обращения: 21.02.2026).

4. Coase R.H. The nature of the firm. Economica. 1937;4(16):386–405. https://doi.org/10.1111/j.1468-0335.1937.tb00002.x

5. Alchian A.A., Demsetz H. Production, information costs, and economic organization. American Economic Association. 1972;62(5):777–795. Режим доступа: https://www.aeaweb.org/aer/top20/62.5.777-795.pdf (дата обращения: 21.02.2026).

6. Barzel Y. Measurement cost and the organization of markets. The Journal of Law and Economics . 1982;25(1):27–48. https://doi.org/10.1086/467005

7. Jensen M.C., Meckling W.H. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics. 1976;3(4):305–360. https://doi.org/10.1016/0304-405X(76)90026-X

8. Williamson O.E. Transaction-cost economics: the governance of contractual relations. The Journal of Law and Economics . 1979;22(2):233–261. https://doi.org/10.1086/466942

9. Lee J., Bagheri B., Kao H.-A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters . 2015;3:18–23. https://doi.org/10.1016/j.mfglet.2014.12.001

10. Nakajima S. Introduction to TPM: Total productive maintenance . Cambridge, MA: Productivity Press; 1988. 129 p. Organization . 2025;23(2):351–365. https://doi.org/10.1177/14761270241228674

11. Avdasheva S.B., Geliskhanov I.Z. Transaction cost economics: Lessons from past reforms and potential for the digital Russian Journal of Economics. 2025;11(3):237–268. https://doi.org/10.32609/j.ruje.11.156897

12. Cram W.A., Wiener M., Tarafdar M., Benlian A. Examining the impact of algorithmic control on uber drivers’ technostress. Journal of Management Information Systems . 2022;39(2):426–453. https://doi.org/10.1080/07421222.2022.2063556

13. Sullivan G.P., Pugh R., Melendez A.P., Hunt W.D. Operations & Maintenance Best Practices: A Guide to Achieving Operational Efficiency. Release 3.0. Richland, WA: Pacific Northwest National Laboratory; 2010. 321 p. Available at: https://www.energy.gov/sites/default/files/2020/04/f74/omguide_complete_w-eo-disclaimer.pdf (accessed: 21.07.2026).

14. Rojas L., Peña Á., Garcia J. AI-driven predictive maintenance in mining: a systematic literature review on fault detection, digital twins, and intelligent asset management. Applied Sciences . 2025;15(6):3337. https://doi.org/10.3390/app15063337

15. van Eyk L., Heyns P.S. A framework to define, design and construct digital twins in the mining industry. Computers & Industrial Engineering. 2025;200:110805. https://doi.org/10.1016/j.cie.2024.110805

16. Klebanov A.F., Eremkin I.V., Gabusu P.A. Digital twins of control processes for highly automated mining transport complexes. Russian Mining Industry . 2025;(4):61–70. (In Russ.) https://doi.org/10.30686/1609-9192-2025-4-61-70

17. Ashby W.R. An introduction to cybernetics. New York: John Wiley & Sons; 1956. 295 p. Acta Montanistica Slovaca . 2010;15(2):95–101. Available at: https://actamont.tuke.sk/pdf/2010/n2/1elevli.pdf (accessed: 21.07.2026). Manisa-Soma (Turkey) open pit mine. Journal of Scientific Reports-A . 2024;(056):45–55. https://doi.org/10.59313/jsr-a.1433960

18. Shibanov D.A., Ivanov S.L., Yemelyanov A.A., Pumpur E.V. Evaluation of working efficiency of open pit shovels in real operating conditions. Mining Informational and Analytical Bulletin . 2020;(10):86–94. (In Russ.) https://doi.org/10.25018/0236-1493-2020-10-0-86-94

19. Pelders J.L., Magweregwede F., Rupprecht S.M. Optimization of shift cycles in the South African mining sector. Journal of the 2021;121(8):427–436. https://doi.org/10.17159/2411-9717/1428/2021

20. Kharchenko K.V., Zubets A.Zh., Moskvitina E.I., Babayan L.K., Laffah A.M. Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies. Russian Mining Industry. 2024;(4):130–138. (In Russ.) https://doi.org/10.30686/1609-9192-2024-4-130-138

21. Meng X., Gong X. Digital transformation and innovation output of manufacturing companies – An analysis of the mediating role of internal and external transaction costs. PLoS ONE. 2024;19(1):e0296876. https://doi.org/10.1371/journal.pone.0296876

22. Ravid D.M., White J.C., Tomczak D.L., Miles A.F., Behrend T.S. A meta-analysis of the effects of electronic performance monitoring on work outcomes. Personnel Psychology . 2023;76(1):5–40. https://doi.org/10.1111/peps.12514