Using EHS Leading Indicators to Play Detective

Someone in the plant just got injured.

The EHS manager immediately sees the cause, and assigns the appropriate corrective action.

But what if a growing number of workers have suffered injuries recently, and the cause isn’t obvious? That’s when the EHS manager gets to play detective. Like any sleuth, the key is to gather as much information as possible, then somehow tie the threads together to arrive at a culprit. Hopefully, before anyone else gets hurt.

For the curious safety pro, the first thread is simply a history of what has already happened for a given period, let’s say a month. That collected data and any trends it reveals are considered Lagging Indicators.

If we were to carry that trending curve into next month, it might predict a likely further increase in similar incidents. With that knowledge, hazard controls can be established to eliminate the incident before it happens. Unfortunately, Lagging Indicators are ‘post event’ indicators requiring an injury to identify a workplace problem.

That’s where EHS analytics come in.

Data analytics are typically valuable for being more scientifically rigorous than guesswork. This is perhaps most essential in the use of EHS Leading Indicators.

For example, let’s say that the Leading Indicators reveal that the escalating injuries are chiefly among food processing workers. And the determined safety professional then broke down the metrics by type of injury, department and shift, employees’ age and gender, and whatever else might be going on in that division.

Where are injuries occurring? A filtered data graphic might show that the bulk of the incidents are occurring on the potato line. What types of injuries are most frequent? Cuts on their hands. Based on trending data, leading indicator predictions include continued growth of that department, with an increasing percentage of female hires.

The EHS manager now has some clues, but not the complete picture yet. For that, they first need to peel their face from their computer screen.

It’s time for boots on the ground.

Analytics can point us in the right direction, but sometimes there’s no substitute for in-person observation. Those aren’t abstract statistics out there doing the work, but real live humans who in most cases are doing their best, but like humans in general, aren’t perfect.

The EHS manager has now arrived at the potato production line, but unlike a random assessment, they have some idea of what they’re looking at.

The first thing they check is whether workers are wearing the Kevlar gloves that are supposed to prevent hand cuts. They all are.

So what’s going on here?

The safety pro sits back and continues to observe. What they eventually notice is that the females in the crew keep tugging at the Kevlar gloves to keep them all the way on. The males, not so much.

After using analytics to narrow the target, some old-fashioned eyeballing reveals the obvious problem: The Kevlar gloves are simply too large for the women, so they keep falling off.

Between that, the constant interruption of pulling up those gloves, and the rapid pace of the work, sharp knife blades keep coming into contact with soft skin.

Once the mystery is solved, the fix is obvious. Smaller Kevlar gloves are ordered for female line workers. And next month’s analytics show a marked decrease in hand cuts on the potato line.

EHS Leading Indicators really can prevent injuries.

As more and more safety professionals are finding, the volumes of data they’ve been collecting are valuable for more than just internal reports and external compliance.

And just as in our example, it often takes a combination of EHS Lagging Indicators, EHS Leading Indicators, and in-person observation. It’s the middle one that requires specialized analytics tools, and knowing how to use them to their fullest.

Of course, it’s impossible to exactly predict what will happen in the future, only what is likely to happen. But as we’ve seen, that can be enough to make a genuine difference. And isn’t that the whole point of workplace safety?

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