Strategic Mine Planning (SMP) is about solving complex problems with objectives like maximising of value, increase in reserves, or other corporate objectives. Along with the fulfilment of economic goals, it also considers operational, safety, and environmental restrictions. The traditional way of managing the planning process consists of executing some optimisation cycles in silos that consider the given parameters and constraints. However, in conventional SMP, there is not enough visibility or understanding of the variability of the parameters and their effect on the results.
To improve the SMP flow, we must differentiate external variables from design parameters. External variables are entered as rigid inputs where the planner has no control over their values: they may be deterministic, or they may have a certain degree of uncertainty and/or variability over time. On the other hand, the design parameters are essential in the planner's decision criteria as they define the ranges that sustain all the possible designs of a mine plan. A way to expand our possibilities and make the best decision is to open the design options. From there, we can evaluate the effect of each design parameter on the results of the simulation results, at the same time, contemplating the uncertainty of the external variables.
This approach is known as the Design of Experiments (DOE), it enables the trade-off the available options and reveal the solution space. With a correct understanding of the universe of design, we can effectively move towards optimal and robust results, which increases the reliability of our decisions and improves our control over the possibilities of the project. Added to a systematic methodology, promises to be a substantial change and improvement in the way we carry out planning and how we choose our best scenario.