When Monitoring A Process Distribution Both The
So, you’re watching something happen. Maybe it’s baking cookies. Or perhaps it’s a bunch of people trying to assemble IKEA furniture. Whatever it is, you’re keeping an eye on it. You’re monitoring it. And you’re looking at the distribution. You know, how spread out things are. Are the cookies all perfectly golden brown? Or do some look like charcoal briquettes and others like pale, sad discs? Is the furniture built straight? Or is it leaning like the Tower of Pisa?
This is where things get interesting. Because when you’re monitoring a process distribution, both the average and the variability matter. Now, some folks might get all excited about the average. They’ll nod sagely and say, "Ah yes, the average is just where we'd expect it to be." And sure, that's fine. That's like saying, "Most of my socks are in the drawer." It's a statement of fact. It tells you the general neighborhood of where things are. But it doesn't tell you the whole story, does it?
Let’s talk about variability. This is the wild card. This is the quirky cousin who shows up to every family gathering. Variability is the difference between your cookies. It’s the wobbly leg on that bookshelf. It’s the reason why sometimes your coffee is just right, and other times it tastes like hot dirt.
Think about it this way. Imagine you’re at a carnival, playing a ring toss game. The average number of rings you land on the bottle might be… well, let’s not get too optimistic here. Let’s say it’s zero. But the variability? Oh, the variability is HUGE! You might have one glorious toss that lands perfectly. And then you have a hundred wild misses that land in the next county. The average is depressing, but the sheer range of outcomes is… impressive, in its own chaotic way.
Now, what happens when you only look at the average? You miss all the drama! You miss the spectacular successes and the spectacular failures. It’s like judging a movie by its IMDb rating without watching it. Sure, the average might be a 7 out of 10, but what if that 7 is made up of a few 10s and a lot of 4s? Not exactly a balanced experience, is it?
We often get so focused on hitting the bullseye, we forget about the scattered shots around the target.
And sometimes, the variability is the real problem. Let’s say you’re baking those cookies again. The average baking time is 12 minutes. Sounds reasonable. But then you notice that half the cookies are done in 8 minutes and are perfectly golden, while the other half are still pale and doughy after 16 minutes. That’s not a good cookie distribution, is it? The average is fine, but the spread is a disaster. You’ve got some winners and some losers, and nobody likes a cookie lottery.
It’s like a race. The average finishing time might be respectable. But if one runner finishes in a blur of speed and another takes a leisurely stroll, stopping to admire the scenery, the average doesn’t really tell you much about the effort or the consistency, does it? You might have a few Olympic medalists and a lot of people who are just happy to be there, maybe even enjoying a picnic at the halfway mark.
This is where my unpopular opinion comes in. I think we, as humans, have a slight bias towards the average. We like neat, tidy numbers. We like things to be… well, average. It feels safe. It feels predictable. But predictability isn’t always the goal. Sometimes, you need the unexpected. Sometimes, you need the outliers. And sometimes, you need to understand why those outliers are happening.
Consider a restaurant. The average waiting time for a table might be 20 minutes. Okay. But what if half the people wait 5 minutes and are seated immediately, and the other half wait 35 minutes? That’s a lot of frustrated customers staring daggers at the host. The average is misleading. It masks the frustration of the long waits. The variability is telling a much more dramatic story.
So, when you’re watching something unfold, whether it’s the production line at a widget factory or the speed at which your teenagers get out of bed on a Saturday morning, don’t just look at the middle. Don’t just fixate on the average. Look at the spread. Look at the extremes. Look at the emwhy behind the variation.
Because often, the real story, the really useful information, the stuff that makes you go, "Aha!" is lurking in those edges. It’s in the things that are a little bit different. It’s in the cookies that are too dark, or the ones that are too light. It’s in the ring toss that lands in the parking lot. It’s in the runner who stops for a nap. That’s where the learning happens. That’s where the improvements are made. That’s where the fun is, if you’re willing to look beyond the plain old average.
So next time you’re monitoring a process distribution, remember this. The average is just the headline. The variability is the investigative report. And you, my friend, are the discerning reader who reads both.