A dynamic simulation approach to support operational decision-making in underground mining

Abstract

Underground mining is a highly competitive industry that is often singularly focused on material production as a key performance indicator. However, maximizing material production does not always maximize industry profitability. Equipment utilization in support of underground mining operations is a significant factor that affects both material production rates and industry profitability. Specifically, uncertainties related to equipment fleet acquisition, operations, and maintenance can challenge the predictability of profit margins based on material production. With a focus on maximizing industry profitability, a methodology to support operational decision-making in underground mining based on dynamic simulation was developed. First, data related to underground mining operations from a medium-sized mine in southern Peru were collected and processed. Based on them, relevant operational indicators were selected and defined for inclusion in the simulation model. Next, the simulation model was formulated and applied to four scenarios with distinctive equipment fleets and associated material production rates. Simulation results indicated that the scenario with the lowest equipment fleet costs and the lowest associated material production rate (base case) had a profitability margin that was nearly 13 times higher than the scenario with the highest equipment fleet costs and the highest associated material production rate. This dynamic simulation methodology, which was successfully demonstrated in this study, can be broadly applied to support operational decision-making in underground mining to maximize industry profitability based on a wider and holistic array of factors beyond material production.