Regardless of how much of an all-star your geologist may be, it’s pretty hard to get around the fact that there is never 100% confidence in grade or tonnage estimates. This is often hard to stomach for those that rely on this information, particularly when the business focus is to meet key performance indicators (KPIs) which are highly sensitive to reserve variation. Without a doubt, the first line of defense in managing risk associated with geologic uncertainty is a better understanding of your position.

What is the typical approach?

Most mining schedules are based on point estimates for material quantities and qualities. This information is handily supplied by your local geostatistician in a block model format. Often times, these point estimates are generated from a more comprehensive conditional simulation method, however they are eventually translated into an average grade model in order to be used in typical planning processes. Your mine planner then grabs ahold of this beast and begins generating mining schedules to either set KPIs for strategic or budget level planning, or follow KPIs for tactical level planning.

When the reality hits you in the face and the grade isn’t quite what you had planned for, what happens then? A lot can happen. And it happens every day.

What is the typical result?

The specific conditions or style of the local management will often result in an operationally appropriate response for handling the actual grades. However, some simply continue mining despite this discrepancy, and attempt to manage the variation later through some type of downstream blending or stockpiling buffer. Other operations shift the mining focus to another available area in order to deliver the KPIs of ore tonnage or grade.

Yet in nearly every case, overcoming this unforeseen obstacle means a deviation from the original plan, and this registers as a mark against mine plan compliance. Further, these deviations cause ripple effects for the remainder of the operational schedule which then must be re-configured to maintain feasibility.

This often results in increased costs, an increased risk to the future plan, and additional grey hairs on mining engineers’ heads.

So what is the core of the problem?

Let’s spill the beans

Let’s face it: in mining, we’re not a manufacturing facility producing cans of beans in a controlled environment. No offense to our bean-packing friends here, but in mining we are dealing with something much more dynamic and complicated. We have many unknowns, and whether we like it or not, the few ‘knowns’ are actually not-that-well known either.

This uncertainty begins with our incomplete assessment of what is in the ground. Further, limitations in data, tools, and/or local processes often force mining businesses to make numerous assumptions in order to make planning and managing their operations more digestible. Without understanding where our original point estimates stand in relation to the range of possible realizations, our operation is walking a tenuously fine line.

What we can do about it

While the ultimate goal is to generate a robust mine plan with an optimal schedule given a range of possible future conditions, we should analyze our existing mine plan first to know where we stand. To do this, we can run a series of simulations on our existing schedule using an informed variation of the critical grade elements in the base data.

We can then compare the current KPIs of the mine plan with the possible solutions (simulations) and draw information from this analysis to indicate its level of robustness with respect to that statistical representation. This topic is discussed in more detail here.

So how does all this relate to mine plan compliance?

Understanding your position

The first line of defence in managing this type of risk is to be better informed about geologic uncertainly when generating (and approving) our mine schedule.

If we know that our mine plan is based on an average grade model that predicts only a 10% probability for some key parameter (ore quantity produced, metal produced, net present value, etc.), it’s quite likely that management will go back and revise the plan before establishing operational KPIs on it.

This then will fundamentally increase the likelihood that the plan will be followed, and overall compliance (not to mention stakeholder confidence in the plan) will be maintained. A good result for all involved.

What is your first step in increasing your mine plan compliance? Developing a process for aligning the different levels of your mine plan? Or maybe managing the geological uncertainty within your mine plan?