By Phil La Duke
In the last two months or so I have been engaged by multiple parties to talk about lagging and leading indicators. I don’t know what’s driving this conversation, however, I know there is a lot of confusion around this topic and so here I am most probably adding to the confusion. Let’s start by level setting by establishing a common lexicon for this context.
Lagging Indicator: A element of quantitative data that provides information on safety performance, or a calculation designed to normalize injury data such that it is not skewed by the size of the population or differences in the hours worked.
Leading Indicator: An element of qualitative data that provides insight into potential failure modes that are likely to cause an incident.
There are, of course, some data elements that fall into that gray area between lagging and leading indicators. For example, near miss reporting. The number of reported near misses is a lagging indicator, while the number of reported near misses per employee (an indication of worker engagement in safety) is a leading indicator, as it has been shown in numerous research study that there is a positive correlation between worker engagement and improved safety performance.
There are some essential questions regarding safety indicators that you should consider before drawing any specious conclusions:
What is the corresponding leading indicator for the lagging indicator I am considering?
Every lagging indicator should have a corresponding leading indicator because leading indicators tell us how we are likely to perform (and to a lesser extent how we are performing) while lagging indicators tell us how we have performed in that same area in the past. Without this correlative connection, we are dealing with unrelated or (at best tangentially related) elements; it’s like connecting your lucky rabbit’s foot with weather patterns—any correlation is simply coincidence.)
So what might be some leading indicators for Total Injuries, Injury Rates, or Day Away or Restricted Time? To answer this you have to consider the activities that directly correlate to injuries. Certainly, there are other lagging indicators that can help us, for example, the number of Safety Observation Tours, but we could also analyze the quality of the tours, by tracking the time it took to complete a tour, the average number of hazards found on the tour, and the time it took for a hazard to be corrected. The leading indicators, in this case, are tracking the quality of the tour.
Do I understand the expected total population of what I am tracking?
One major issue in using Near Miss reporting is that you are dealing with a huge unknown, and that is, how many near misses actually occur and go unreported. If you have two sites and one of them has 463 reported near misses out of 10,000 total near misses how do you compare that to the site with only 20 reported near misses out of a total population of 35 total near misses? Since we can never truly know the exact total population of the unreported near misses, I personally question the value of using near-miss reporting as an indicator at all. Without an accurate understanding of the full population, we cannot correlate the number of near misses to the risk of injuries, but we CAN make useful inferences regarding worker involvement using a population that we DO know, that is, the total number of employees. So the value in tracking the number of near-miss reporting is questionable, the number of reported near misses by employees is a very good indicator of worker engagement which strongly correlates to fewer injuries and in some cases a decline in the severity of injuries.
What am I really trying to learn?
Far too often we collect data and present it without context, interpretation, or even a point. We splash a chart on the screen and yell “viola!” and leave the audience to cipher out what the disjointed information means. What exactly does this month’s injury figures compared to last year’s figures tell us and why is that useful? One year does not a trend make. Yet I have sat in meetings where one month’s uptick in injuries is scrutinized and analyzed as if there were some great revelation lurking just below the surface. Sometimes numbers don’t tell us anything. And unless we have a large enough data pool indicators don’t mean much of anything,
To paraphrase Stephen Covey, we have to start with the end in mind, what are we trying to learn about our process—forget predicting outcomes there are just too many variables and even if we could measure them all there would still only produce probability and depending on the population size (which, as I’ve said, we probably don’t really know anyway) we could have a very large ± margin of error, and despite our best efforts, as any good gambler can tell you, longshots sometimes pay off.
I’m not bad mouthing lagging and leading indicators, but I am questioning how many people really understand what they are telling us about our safety performance or the level of robustness of our safety processes.
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Of course my first book is still for sale…
Did you like this post? If so you will probably like my book which can be ordered here I Know My Shoes Are Untied. Mind Your Own Business or on Barnes & Nobel.com. Did you hate this post? Did it offend you deeply? Maybe you should organize a book burning (minimum of 150 books) but be sure you are only burning my book, I don’t want you to go to a used book store and buy a bunch of cheap books and stack mine on top.
The book is a compilation of blog posts, guest blogs, magazine article (from around the world) and new material. Much of it is hard to find unless you know where to look. A second and third book has already been green-lighted by the publisher (expect fewer reprints and more new material).
In all seriousness, I have been blogging for free (without sponsors or advertising) for over 11 years and I think I have earned a bit of revenue so buy a damned book.