A synergetic approach to open-pit mine management using big data and intelligent predictive analytics systems

DOI: https://doi.org/10.30686/1609-9192-2025-1-154-160

Читать на русскоя языкеYu.N. Shedko , K.V. Kharchenko, S.A. Zudenkova, E.I. Moskvitina, L.K. Babayan
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №1 / 2025 p. 154-160

Abstract: A synergetic approach based on integration of big data and intelligent predictive analytics systems opens up new opportunities for optimizing open-pit mine management. The purpose of this study is to develop a conceptual model of synergistic management of open-pit mines and empirically evaluate its efficiency. The methodology includes statistical analysis of production data sets (N = 1500), scenario modeling and expert interviews (n = 25). The results demonstrate that implementation of predictive analytics helps to reduce operating costs by 22.5% (p < 0.01), improve production prediction accuracy by up to 94.7% (p < 0.05), and reduce the decision-making time by 47.3% (p < 0.01). The discussion focuses on the theoretical relevance of the findings for the development of the concept of synergy in management, as well as on practical recommendations for implementation of intelligent systems in the open-pit mining sector. The research prospects are related to upscaling of the proposed model and to assessment of its cross-cultural applicability.

Keywords: synergetic management, big data, predictive analytics, process optimization, open-pit mining sector, intelligent systems

For citation: Shedko Yu.N., Kharchenko K.V., Zudenkova S.A., Moskvitina E.I., Babayan L.K. A synergetic approach to openpit mine management using big data and intelligent predictive analytics systems. Russian Mining Industry. 2025;(1):154–160. (In Russ.) https://doi.org/10.30686/1609-9192-2025-1-154-160

Article info

Received: 19.12.2024

Revised: 22.01.2025

Accepted: 23.01.2025

Information about the authors

Yuriy N. Shedko – Dr. Sci. (Econ.), Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Konstantin V. Kharchenko – Cand. Sci. (Sociol.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Svetlana A. Zudenkova – Cand. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ekaterina I. Moskvitina – Cand. Sci. (Econ.), Assistant of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Levon K. Babayan – Assistant of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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