One of the requirements of NI 43-101 for Item 22 Economic Analysis is “sensitivity or other analysis using variants in commodity price, grade, capital and operating costs, or other significant parameters, as appropriate, and discuss the impact of the results.”
The typical result of this 43-101 requirement is the graph seen below (“a spider graph”, which is easily generated from a cashflow model. Simply change a few numbers in the Excel file and then you get the new economics. The standard conclusions derived from this chart are that metal price has the greatest impact on project economics followed by the operating cost. Those are probably accurate conclusions, but is the chart is not telling the true story.
I have created this same spider graph in multiple economic studies so I understand the limitations with it. The main assumption is that all of the sensitivity economics are based on the exact same mineral reserve and production schedule.
That assumption may be applicable when applying a variable capital cost but is not applicable when applying varying metal prices and operating costs.
Does anyone really think that, in the example shown, the NPV is $120M with a 20% decrease in metal price or 20% increase in operating cost? This project is still economic with a positive NPV.
In my view, a project could potentially be uneconomic with such a significant decrease in metal price but that is not reflected by the sensitivity analysis. Reducing the metal price would result in a change to the cutoff grade. This changes the waste-to-ore ratio within the same pit. So assuming the same size mineral reserve is not correct in this scenario.
Changes in economic parameters would impact the original pit optimization used to define the pit upon which everything is based.
A smaller pit size results in a smaller ore tonnage, which may justify a smaller fleet and smaller processing plant, which would have higher operating costs and lower capital costs.
A smaller mineral reserve would produce a different production schedule and shorter mine life. It can get quite complex to examine it properly.
Hence the shortcut is to simply change inputs to the cashflow model and generate outputs that are questionable but meet the 43-101 requirements.
The sensitivity information is not just nice to have. Every mining project has some flaws, which can be major or minor. Management understandably have a difficult task in making go/no-go decisions. Financial institutions have similar dilemmas when deciding on whether or not to finance a project. You can read that blog post at this link “Flawed Mining Projects – No Such Thing as Perfection“
So if the spider chart isnt he best way to tackle the risk issue, what way is better? In another blog post I discuss an different approach using the probabilistic risk evaluation (Monte Carlo). Its isn’t new but now well adopted yet by the mining industry. You can learn more at “Mining Financial Modeling – Make it Better!“
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