Optimizing mining operations through cost analysis and comparative assessment

Financial analysis of earnings and expenditures in mining operations is essential for the economic evaluation of commodities, including non-metallic and industrial minerals. The costs associated with mining activities notably affect a company’s profitability and competitiveness within the sector. This research aims to identify potential optimisation strategies by conducting a comparative mining cost analy-sis of a company’s silica sand operations.
Cost accounting was performed across thirteen mining sites by systematically reviewing and categorising all expenditures to focus solely on those directly at-tributable to mining activities. Unit mining costs and operational performance metrics were calculated for the years 2023 and 2024, with three company quarries identified as cost outliers in both years. In line with the economies of scale hy-pothesis, a simple linear regression (SLR) model was used to assess the relation-ship between unit cost and production volume, revealing a statistically significant inverse relationship (p < 0.01), where a 1% increase in production corresponds to a 0.25% decrease in unit cost. A multiple linear regression (MLR) model was then applied for improved predictive capability, yielding strong statistical significance (p < 0.0001) across multiple variables. Four operations were found to have costs up to 60% higher than predicted by the model. To evaluate internal efficiency, data envelopment analysis (DEA) was conducted using mining cost as the input, and profitability, productivity, and production achievement as outputs. DEA re-sults identified three operations as efficiency frontiers, while three others were deemed inefficient for both 2023 and 2024. Further benchmarking with technical reports from international silica sand producers revealed that cost estimates at three of the company’s operations exceeded those of several projects in Australia and India.
Based on these findings, four operations were prioritised for optimisation. Proposed strategies include reducing energy consumption, improving mine plan-ning and pit design, assessing automation potential, and enhancing fleet man-agement. Overall, this study demonstrates the effectiveness of cost-based com-parative analysis for identifying optimisation opportunities in mining operations.