Justin is a good friend of mine at work and a Six Sigma Black Belt. Over lunch the other day we were talking and he explained how sometimes people will tune past their optimum. While the conversation was casual the lesson in life seems to be worth sharing.
Firstly, let’s introduce Six Sigma’s distribution curve courtesy of Michael Galarnyk’s post at towardsdatascience.com.
What this curve does is enable the ability to predict the outcome of a particular operation. In manufacturing that operation might be coating a particular widget. Not all widgets get coated equally and some will fall outside of the quality specifications. This curve would allow you to predict how much of those widgets have to get tossed due to poor quality. Used properly you can use it to help manage the process to reduce the number of wasted widgets. That’s a very basic description, but for this post it will need to suffice.
On the graph you can see the percentage of good output and the Greek letter Sigma symbolizing the deviation from the optimum output. Ideally if you can manage a process down to only 6 Sigma (three plus and three minus) then the process is considered to be stable. Stable processes can be improved. Unstable processes cannot consistently be improved.
An optimum output would look like the graph above, but the curve would be taller and skinnier in the middle.
The time period used to create this graph also matters. When Justin was using it for a particular part of the manufacturing process (bagging) he was taking a daily average. This meant both shifts were combined. He had a wide curve and wanted to make it skinnier. When he reworked the data for each shift he noticed that neither shift was working their optimum.
Each shift would tune this particular machine to where they knew the performance was good enough. In the process they would inadvertently tune past where the machine could work at its optimum performance.
Justin explained how the machine was complex and each shift had recorded different settings in their notebooks to know how to set it for good-enough performance. You could image that after fiddling with the machine for hours on a difficult shift that once you finally figured out settings that worked for you, you stuck with those settings. Think of the pressure. All the other parts of the manufacturing are stopped because the thing you’re working on isn’t performing right. Everyone on your team would know you (rather your machine–but sometimes it’s hard to separate people from the problem) were the one holding things up. Once you finally got it *working* you’d probably feel relieved and just want to move on.
While we don’t all work in manufacturing we’ve probably all had those experiences where others were waiting on us and the pressure that comes from that attention. Inevitably we do have those parts of our personality and how we perform that are based on experiences that were hard–experiences that we don’t want to live again. What if those experiences taught/encouraged us to tune our habits close to our optimum while still missing it entirely?
Six Sigma falls in the discipline of continuous improvement and while we may get some things right or right enough to succeed I’d like to believe that all of us in some ways have tuned our habits and processes past our optimum. As challenging as it was to get to your comfort zone, maybe it’s time to step out of it to see if good enough really is how you want to operate going forward.