by Robb Richardson, guest blogger
One of the things that I love most about my job is that I get to help educate, coach, and develop others on topics such as continuous improvement and data analysis.
In that capacity, one of the most frequently seen challenges is that team members and managers want to react to every data point. Their intentions are noble – but doing so is almost always an unnecessary exercise since these variations are a normal part of how the process behaves.
I’ve used lots of different examples to illustrate this point, but few seemed to resonate deeply with them and get them to completely grasp the concept. That is, until I started to use something that we are all pretty familiar with: our car’s miles-per-gallon statistic.
Now, truth be told, I’m not exactly what you would call a “car guy.” I drive a ten-year-old Toyota Solara and simply follow the suggested maintenance directions. There is one other thing that I do – I use an app on my smart phone to track the gas mileage I get with each fill-up. With very few exceptions, I always fill up at the same gas station (and the same pump) and I also remove the nozzle when the pump first “clicks” that the tank is full. By taking these steps, I feel pretty comfortable that I’ve eliminated some of the basic items that could contribute to inconsistent measurement.
When working with others, I ask them if them if they get the same gas mileage every week. Their response is always a resounding “No.” At that point I show them my most recent miles-per-gallon control chart (shown below) from Minitab (Stat > Control Charts > Variable Charts for Individuals > Individuals) and ask them what they think. Most of the time, they say something along the lines of it looking like what they expected.
From there, I point out those individual data points that are lower than the previous value, are below the center line, or meet both conditions. I then ask them if I should take my car to the local Toyota dealership to be checked out, get the oil changed, tires replaced, or some other expensive service performed. Naturally, they answer in the negative and point out that “it’s just part of the process”…and at that point I know they understand the concept on a very personal level and will be less likely to go chasing after data points that are “in control” and are simply “normal cause” items.
One last thing that’s worth mentioning: occasionally someone will ask me about the one data point that comes very close to crossing the Lower Control Limit (LCL). I tell them that, when something comes so close, it may be worthy of a very brief double-check, but not much more than that. In the case of my one data point in the chart above, it should be pointed out that my wife was driving my car for that timeframe when she and our daughter went to a resort over on the western side of Florida. They literally had five suitcases weighing about 200 pounds. Additionally, my wife has a “lead foot” and then, when she was off the interstate, drove through two hours of stop-and-go traffic. So, during that period of time, the car was driven under much different conditions than it normally would be…and if it had crossed the LCL, I simply would have recognized it as a “special cause.”
I led off this item by mentioning that the best part of my job is to educate, coach, and develop others. With Minitab, it is much easier to drive home the important topics of discussion.
Would you like to publish a guest post on the Minitab Blog? Contact email@example.com.