I was teaching a class and asked participants to create a control chart of an indicator that was important to them. A lab supervisor presented me with a chart on the number of procedures her department performed and told me that it wasn’t very useful.

She wanted to use the chart for staffing purposes, but the wide limits were discouraging to the point of being ridiculous:

I know that a lot of you have access to software with the classic Western Electric special cause tests programmed in. In this case, none of them were triggered. On the surface, the chart is exhibiting common cause. For what it’s worth (and as you will see, it isn’t worth much), the data also “pass” a Normality test with a p-value of 0.092. Lots of statistics. And the subsequent action is...?

Do you still insist on asking, “Which chart do I use for which situation?”

I’ve seen many flowcharts in books to help you answer this question. They’re all some variation of this:

I find them far too confusing for the average user and have never taught this in my work. Besides, you get no credit for choosing the technically correct chart or computing the right numbers—only for taking the right action.

Your ‘Swiss Army knife’

For any initial chart for process assessment, the data should be in its naturally occurring time sequence, preferably as a run chart. Next, the control chart of choice with which to start is virtually almost always the I-chart (individuals chart), which uses the moving range (MR) between consecutive points to determine its common-cause limits. The I-chart is the “Swiss Army knife” of control charts. As I have discovered time and time again, it usually approximates the “correct” chart under most conditions, and its use is easier to explain.

I can hear the chorus: “So, what are the conditions when it isn’t correct?”

In my last column, I considered two of the most common questions faced by a statistical educator and the deeper questions that need to be addressed. I encouraged people to consider their everyday reality for the necessary context. Predictably, some become frustrated by my lack of concise answers and try to distract me by asking, “Well, even though I can’t think of a situation, how many data points do I need to have a good chart?”

This predictable question is once again met by my predictably vague reply, “How much data do you have?” It’s usually not very much.

Despite the pedantic proclamations many of you have encountered in favor of waiting for anywhere from 15 to 25 data points, I have found that useful limits may be computed with much less data. As few as seven to 10 observations are sufficient to start computing limits, especially if it’s all you have—something that happens to me frequently. What else are you going to do? I dare you to find a more accurate way to assess the situation.

Critical thinking does not necessarily result from using a practitioner’s toolbox framed within a formal improvement structure such as Six Sigma, lean, lean Six Sigma, or the Toyota Production System. It’s very easy to get seduced by all the fancy tools, acronyms, and Japanese terminology—and promises. I’d much rather have far fewer tools used in conjunction with critical thinking to understand variation—in one’s native language. So again, it’s time to apply critical thinking to rapid-cycle PDSA, the plan-do-study-act method for learning and improvement developed by Walter Shewhart. If you find yourself confused and frustrated in your current applications, there’s good reason.