The dynamics of demand and price fluctuations for mining enterprise products is marked by high volatility. At the same time, the dynamics of fluctuations in the volume of consumption of mineral products and their prices are characterized by significant inertia, but almost never the graphs of the curves of these dependent indicators are consistent over time. Due to traditional inertia, mining enterprises often lag in adjusting production systems to external economic conditions. Adaptation of mining operations in open-pits to these changes is critically important for ensuring stability and competitiveness of enterprises in dynamic conditions.
The paper analyzes the experience of mining enterprises in adapting to changes in uncontrollable factors and reveals a mostly unsystematic approach to the implementation of adaptation mechanisms. This study provides a structural analysis of mining enterprises and proposes a concept of viewing them as anthropogenic mining complexes of various levels. The operating conditions of mining enterprises are marked by highly dynamic external factors, which retrospective analysis suggests will intensify in the future. The study systematizes adaptation tools for production systems according to the complex level. Extraction enterprises are recommended to use an adaptive management mechanism based on the systematic decomposition of extraction units. The proposed multi-level structure enables the justified selection of adaptation tools depending on the nature of the stimulating factors, allowing maximum effective response to them. The division of the mining system into complexes of different level allows, in accordance with the nature of the influence factor, to reasonably choose the optimal adaptation tools: changing the technological scheme of equipment operation, implementing a geoinformation system or changing the open-pit schedule etc.
It has been established that dividing the system into complexes of different levels allows for a justified selection of an adaptation tool depending on the nature of the stimulating factor. This approach enables adaptation at the necessary level, enhancing overall system resilience with lower costs, which is critically important for successful operations under uncertainty and constant change.