Challenges and effectiveness of IT tools used in mining for forecasting production parameters

Abstract

The research examines the challenges and effectiveness of IT tools used in the mining industry for forecasting production parameters, a crucial aspect as mining operations become increasingly complex. Accurate forecasting is essential not only for optimizing efficiency, safety, and profitability but also for managing the high risks associated with the construction and operation of mining plants, which require long-term financial security and flexible production management. The study explores various IT solutions, such as advanced data analytics, machine learning algorithms, and simulation models, employed to predict key production parameters like ore quality, equipment performance, and resource availability. However, significant challenges, such as data quality issues, the integration of diverse data sources, and the need for specialized expertise, pose obstacles to the effective use of these tools. Despite these challenges, the research finds that IT tools can lead to more accurate forecasting, improved decision-making, and enhanced operational planning, provided that technical and organizational hurdles are addressed. The paper also highlights the importance of modern IT tools in mining production scheduling, demonstrating their advantages over older tools like MS Excel. By presenting examples from coal and copper ore deposits, the study shows that these modern tools not only increase the accuracy of production forecasts but also enable the creation of multiple scenarios and the rapid modification of schedules, which are key to maintaining the competitiveness and liquidity of mining companies.