Abstract
Price uncertainty is one of the major uncertainties in the life of mine (LOM) planning process which can have a decisive effect on the overall profitability. Today’s mine planning software tools provide block-sequencing optimisation for a given static price assumption that is then used as a basis of managerial decision-making process. This paper proposes a complementary approach to this by introducing a simulation-based decision-making tool that, with the help of simulation, seeks for the optimal mine plan when a managerially estimated price development with minimum and maximum boundaries is used as a data input for the given period. To demonstrate the approach, a realistic gold mine case study is presented with five alternative and technically feasible mine plans calculated in a static optimiser from a commercial mine planning software package. These mine planning scenarios are then subjected to price uncertainty in simulation with and without a price trend assumption to highlight the effect of price on the mine’s expected performance. Based on the results, we derive and demonstrate a simulation-based system that automates the matching of optimal mine plan with the managerial insight of long-term price development.
Highlights
Metal mining investments are long-term, irreversible investments with high costs of development
We focus the quantitative investment analysis where, according to the surveys of the mining industry, valuation methods revolve around discounted cash flow (DCF) metrics such as net present value (NPV), internal rate of return and payback period
We extend our price range to cover the full range of 1–240 months data and the NPV for each mine planning scenarios calculated at each point
Summary
Metal mining investments are long-term, irreversible investments with high costs of development. We propose a simulation-based decision-making system which considers the long-term price uncertainty while selecting the initial mine plan This method addresses the gap identified by, e.g. Martinez 2010 that most of the current valuation methods and mine planning optimisation techniques are not integrated in the investment decision-making process. Supplementing the analysis with alternative price scenarios or random simulation provides additional managerial insights but are practically inhibited by the inability to alter the mine plan as well to match the selected scenario. Automating this matching process between the optimal mine plan for the managerially estimated market price development is in the core of this paper
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