It’s not surprising that, if given the choice between lagging and leading, most would gravitate toward leading. “Leading” sounds more progressive—and it is.
In a recent Harvard Business Review podcast, Sue Ashford, a University of Michigan professor, took it one step further by explaining “Why Everyone Should See Themselves as a Leader.” Interestingly, she said in the interview, “We need people taking leaderlike actions in more places so that they can react more quickly, react in a way that allows more voices to be heard to handle some of that complexity and ambiguity.”
That train of thought makes total sense in that context. However, in the Environmental Health & Safety space, leading has a different connotation. Instead of being able to “react more quickly” as in the workplace example, leading indicators in risk allow for predictive analytics that help prevent incidents before they happen. It’s the difference between being proactive vs. reactive.
Lagging indicators for risk mitigation such as incident rate have been the norm in the EHS space. They are an important component of all incident reporting and performance improvement initiatives, but they cannot change the past. What’s done is done.
What if the focus was flipped and companies could start looking toward the future? Sounds compelling, right? For example, software that offers predictive maintenance that tells you when your car will likely need an oil change is more useful than just basing it off the typical manufacturer-suggested 3,000-mile range, right? It would save time and money if you knew you could wait an extra 2,000, 3,000 or even 4,000 miles before having to take the car in.
This is the beauty of focusing on leading indicators: They help show companies what the right actions are to prevent incidents before they happen.
But it’s easier said than done.
Without recent technological innovations—such as the Internet of Things and all the valuable data that it produces—true Integrated Risk Management strategies were not a possibility.
IoT and electronic systems give us a means to measure inputs and create predictive models for outputs.
So it all boils down to a three-step process for success:
1. Making sure that you have good measurement capabilities.
2. Having a subject-matter expert who can figure out how to model the data.
3. Getting the leading indicators from the model to help prevent big incidents.
Actually, I’ll add a fourth component to that list as well: not settling for the convenience of applying lagging indicators. There’s a certain “What, me worry?” mentality that prevails when it comes to unlikely incidents. If a certain outcome has never materialized in an organization, then, at some companies, it’s hard to get buy-in for implementing preventive measures to change what they deem an unlikely scenario.
Unfortunately, unlikely incidents do occur. Being prepared for such events can help mitigate those risks or, minimally, lessen their impact on the organization or even the community at large.
It’s well past time to take the lead when it comes to incident management.