Monte-Carlo Simulation - Forecast - Key Performance Indicators - Gold Mining Industry
Originally published: 24/04/2019 22:03
Publication number: ELQ-26477-1
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Monte-Carlo Simulation - Forecast - Key Performance Indicators - Gold Mining Industry

Monte-Carlo simulation to determine select KPI's for a Gold Mine.

The purpose of this model is to generate common Key Performance Indicators for a company involved in the Gold Mining industry. This model is not based on pin-point forecasts but based on a range of calculations using a Monte-Carlo Simulation.

The simulation is based on a number of variables with values that are generated using a normal distribution given a random number generator (for each variable), the expected forecast values and deviation values that are expected to represent one standard deviation.

The Monte-Carlo Simulation process is run using one hundred simulations with each result recorded within a system containing aggregate results.

Based on one hundred observations for each simulation variable, the minimum, maximum and mean are calculated. A histogram is also generated for each variable with the purpose of showing the distribution and risk associated with each variable.

Histograms are also generated for some of the key KPI's that are calculated from the simulated results. In this case, some of the key KPI's are generated using multiple input variables that are each subject to a range of values given the random number generator. All the results are displayed in the various sheets in the "KPI Section".

This Best Practice includes
Gold Mne - Simulation - Detailed Inventory Analysis

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Further information

The objective of this business tool is to show the distribution (risk) of key variables in a Gold Mining project and associated operations. A Monte-Carlo simulation is conducted on 46 variables simultaneously. The simulation is executed and recorded for 100 iterations. Simulation results are used to create histograms and high-lo charts for several select key indicators.

Gold Mine Industry / Industry

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