Shift Excellence Scorecards

Mine sites have two major pieces of equipment they think about constantly, shovels and haul trucks. Shovels dig the dirt. Haul trucks carry and dump it. And every site in the world can tell you whether they are shovel-constrained or truck-constrained, because it's always one or the other. Back in 2013 Kennecott was truck-constrained, and because the mine was in a financial pickle after the Manefay slide, it really needed clever ways to speed up production. Since trucks were the bottleneck, they got all of the focus, and in a very particular way.

Heavy equipment operations tend to use what's known as a Time Usage Model. You have a multi-million dollar piece of equipment and you want to know what it's doing, so you can decide whether it's being used well. How often is it having maintenance work done? Was that maintenance planned or unplanned? Was it doing something useful? Was it operational but waiting on something else? I bring this up because leadership at the mine had landed on a critical metric, effective utilization, which is basically how much productive operating time you got as a percentage of scheduled time. It's a pretty good measure of how often the equipment was bottlenecked by something outside its control. Sitting in a queue, waiting on a dozer, that kind of thing.

I'm a data guy, so I started looking at the data, and pretty quickly one thing stood out like a sore thumb. Effective utilization on night shifts was way better than on day shifts, which I found curious. I looked back through the history and it held up, night shifts consistently beat day shifts. Talking to operations quickly got me to the bottom of why. "Engineers and managers aren't here, so we can actually move rock without constant interruptions." And this totally turns out to be true. In a traffic time study a few months earlier, we'd found that during the day, two-thirds of all traffic on the haul roads isn't heavy equipment. It's all the ancillary activity of a mine, which, necessary as it is, causes friction in the system. The vehicles congest things and they are full of people going out to interact with operations.

The shift learnings were interesting, but when I zoomed out in time, I saw an even more interesting pattern. Kennecott ran four crews across two shifts, and their effective utilization varied quite a bit. I started digging into why and eventually landed on a satisfying answer. If you took just the tonnage moved during a shift and divided it by the time the equipment was under the operator's control doing useful work- digging, swinging, tramming- you got a new metric I dubbed operator efficiency. And this is where things got fun. The top-performing shovel operators had twice the operator efficiency of the worst-performing ones. Remember how the whole mine was trying to shrink truck queues? It turns out that if you load trucks faster, the queues get shorter. That's why the shifts differed so much in effective utilization.

Nobody had ever shown the shift crews their own numbers. Kennecott generated plenty of reporting, but it flowed up, to planners and managers and monthly reviews. The people actually running a shift, the ones who could change tonight what a report would complain about next month, worked blind.

So I built dashboards nobody asked for. The data already existed in the dispatch and payload systems. I shaped it into scorecards a shift supervisor could read at a glance, and one of the first things the numbers surfaced was that gap nobody had seen. Some shovel operators were moving twice the material of others on the same equipment in the same ore. Not a rounding-error difference. Double.

So this is the part where I tell you how I released the data, got lifted into the air by cheering crowds, and was greeted with a ticker-tape parade. Not quite. Corporations are funny machines. They create incentive structures, and the incentives here were clear. Move effective utilization above all else. My numbers got attention from the shift crews, and the gap pointed straight at technique and training rather than machinery. But remember how the mine wasn't shovel-constrained? At a corporate level that meant shovels couldn't be the answer, so leadership pushed hardest on other metrics, and the managers balanced my numbers against the goals they were actually being held to. It was a bummer, but it taught me a valuable lesson. How you package the information you present is every bit as important as the information itself. I still believe to this day that I could have presented things in a way that would have given the project more legs. What it got instead was quiet adoption.

The people running the shifts looked at those scorecards every single day. They checked how the shift performed, how the numbers trended, where they stood. I think there's a real key metric for the utility of a tool. If you lost access, would you complain? If someone didn't get their preshift email, would they miss it? The honest truth is that most reporting doesn't meet that threshold.

Just after finishing that project I decided it was time to move on and pursue a career as a developer. There's a lot to polish up when you leave a company, and in a fugue state of busyness I forgot to send the morning report. When I got back to my desk I had an email. "Hey, we missed the report this morning."

I was not there long enough to see the culture change all the way through. But it was starting. Give people data they want to see every day and they begin managing their own performance before anyone asks them to. That is what a scorecard is actually for.