Flawed Mining Projects – No Such Thing as Perfection

Recently I read a post on LinkedIn where somebody was asking what key metrics companies are looking for in order to develop (or provide financing to) a new mining project. It’s more than just a project having a good NPV or IRR.  They are also looking at how difficult it is to achieve the targeted NPV.

Mining companies are always on the hunt for new projects to grow their cashflows. They would all like to find the “perfect” project; one with ideal conditions and great attributes.

However those perfect projects likely don’t exist anymore, if they ever even did.

Consequently companies must be willing to accept some potential flaws (or risks) in their go-forward projects.

The question is what flaws are they willing to accept and how far away from the ideal situation are they willing to go.

What makes a perfect project?

If one could envision a perfect mining project, what might it look like?   Here are some attributes that one would want to see (in random order). If a project had 100% of these, it would be a fantastic project.

• • A high grade ore orebody

  • A large reserve and long mine life to ride out commodity price cycles

  • Low operating cost

  • Low cash cost, in the bottom quartile of costs

  • Well defined ore zones, allowing simple mining with low dilution

  • A geotechnically competent rock mass

  • Clean and straightforward metallurgy

  • Consistent and straightforward permitting regulations

  • A stable government and stable fiscal regime

  • Safe security conditions for site personnel

  • High NPV and high IRR

  • No acid runoff issues from waste products

  • Stable tailings disposal conditions

  • Readily available local workforce / local power supply / good water supply

  • Favorable local community and stakeholder support

Other readers may have more attributes that they would like to see if asked to theorize “What constitutes a perfect mining project?”

Take off the promoter hat

Now take an honest look at some recent (or past) projects that you have been involved with. How many of the perfect attributes listed above would be represented? It would be surprising to see them all checked off. Unfortunately that means certain flaws (risks) must be accepted when developing a project.

Each company (or financier) will have their vision as to which attributes are “must have” and which ones are “nice to have”.

But we have risk tools

There are many risk tools available to help in evaluating the potential flaws in a project. Unfortunately these tools don’t make the decisions for management.

Risk based Monte Carlo analysis requires management to pre-define the magnitude of the risks and then decide upon what probability of success is acceptable. Real option analysis or decision trees or Kepner-Tregoe are examples of other tools that can help in the decision making process.

Ultimately risk is risky.  Management must make the go/no-go decision regardless of how many probabilistic histograms and tables they have generated. A 90% chance of success still means there is a 10% chance of failure. The probability of failure may be low, but it is not zero.

It would be interesting to examine recent failed projects to define the cause(s) of failure. One could then see if the cause was something that was pre-determined as a risk, either as a small risk or a large risk. Perhaps the cause was something that management felt could be mitigated or perhaps it was something viewed as highly unlikely. No doubt that successful projects also had risks, which were either mitigated or which (luckily) never occurred.

Conclusion

The bottom line is that 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.

In my career I have sat in on such management discussions and it’s never been a simple process, mainly because no project is perfect. Management know all the flaws (at least they think they do) and thus have to decide whether to push forward knowing the flaws exist.

I fully expect that future mining project risk will increase due to the complexity of project designs and broadening of stakeholder dynamics. Hence decision making in the mining industry isn’t going to get any easier regardless of the decision tools being used.  Look at your own situation, are your projects getting easier or harder?

One should examine the impact of the project flaws on the project economics.  The most common way to do this is the sensitivity chart (i.e. spider graph).  However this itself is a flawed way to evaluate the flaws.  You can read why at this blog post “Mining Cashflow Sensitivity Analyses – Be Careful“.

A better approach is to use a probabalistic analysis, like Monte Carlo simulation, that can examine multiple factors at the same time.   I discuss the benefits of this approach in a blog post ” Mining Financial Modeling – Make it Better!“

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