Averages... a pretty average performance metric...

Averages have the ability to express an overview... but, if used incorrectly, can hide a lot of variations that can have a serious impact on performance... lets dig in

Willie Jacobs

7/24/20232 min read

This is the second article on concepts to consider when looking at optimizing fleet performance. The previous article focused on Truck Factor accounting. Enjoy!

Averages may well be the best way to hide inconsistent performance... but we use it every day. How many of us can honestly say we have not used averages to blotch out instances of poor performance? Well, if you can do it, so can those who provide you with feedback... not so?

We mine in real time, load by load

You see, if we lose track of the fact that what we deliver at the end of a measuring period, ultimately, is the sum of a myriad of small actions... The collective outcome of all these small actions are totalled up into what is ultimately delivered at the end of a measurement period. You see, we do not drill at average penetration rates, load at average loading rates or haul at average hauling rates... we drill at instantaneous rates, load at instantaneous rates and haul at instantaneous rates. And unless we get our heads around managing these small, incremental instantaneous actions, our industry performance will remain, well, average...

In the past, it was simply impossible to record (never mind manage) all of these small actions in the average mining operation. Tens, if not hundreds of thousands of drill holes, shovel and truck loads, and a myriad of supporting activities. But things have changed, and we need to probe how technology can assist us in dipping down to a level where we can make sense and use the incremental instantaneous data to improve our performance.

In soccer, if the striker misses one shot to the left and then another to the right, what was his average performance? The average of the two shots were spot on, right? Reality is... he wasn't... He missed both shots...

Two wrongs, on average, don't make a right

Let us use the example of truck payloads. Picture the following scenario - truck payload has been a key discussion point for some time on an operation, and the feedback at today's meeting is an easy one... we have an average payload of 98% of rated payload. How do we get to that number? Easy!! We take the tonnes moved and divided it by the number of truckloads - simple!! With cheers all round, job well done, right? Well, lets consider...

As we have said previously, trucks do not load average payloads... we carry instantaneous loads. When we understand this, we can analyze our data accordingly. In both the scenarios above (histograms showing occurrence of specific payloads over a measured period), the "average" payload is spot on... but in Scenario 1, we have under-loaded 26% of the loads (wasting resources) and we have overloaded 14% of the loads above the 10:10:20 guideline... this has some serious safety implications, as the truck braking system is not designed to stop a truck overloaded by 30%, frame life is compromised, tires overloaded... the list goes on.

But... on average we did okay, right? Well, maybe not so after all... If we want to get real about performance, we need to dive deeper into the individual actions, and apply simple statistical tools to assist us in understanding how things REALLY look. We could then create measurement systems that gives the feedback to the operators, real time, and assist them in making decisions on the fly. It is being done elsewhere, and it can be done in mining.

As always, thoughts and comments most welcome!