In 1968, Bob Gibson, a pitcher for the St. Louis Cardinals started 34 games. He finished the season with 22 wins and 9 loses, with an earned run average (ERA) of 1.12 and 13 shutouts. He had 268 strikeouts. He was one of the best pitchers ever in the major leagues.

Most amazingly, he had 28 complete games in that season – he finished 28 of the 34 games he started. Last year the National League (NL) had a total of 17 complete games while the American League (AL) had 25 complete games – both less than Gibson’s total by himself in 1968. Where did the complete game go?

Our last blog - What is the Analysis of Means(ANOM)? - explained how ANOM is used to determine if there are differences in treatment means from an experiment. ANOM answers the question:

“Are there any significant differences in the treatment averages?”

The Analysis of Ranges (ANOR) is very similar to ANOM, but ANOR focuses on the within treatment variations. ANOR answers this question:

There are times we want to compare things, e.g., different machines, different raw materials or different shifts. There are several ways to do this statistically, but visual is always best. And the best visual technique for comparing things is the Analysis of Means (ANOM) method.

You have just finished a pilot production run for a new product. You want to know if the data you have are homogeneous, i.e., they come from one process and there are no outliers. How do you do that? In the past, I simply put the data on an individuals (X-mR) control chart. If the chart was in statistical control, I would assume that the data were homogeneous.

You are a front-line operator. Who gets the blame when something goes wrong? Far too often, you do – the operator. You are doing the work. Of course, it is your fault! Have you heard these reasons for why it is the operator’s fault?

Have you heard of Dr. W. Edwards Deming? Do you remember who he was? Dr. Deming was one of the great “quality gurus” back in the 1980s and 1990s.  (Photo courtesy of The W. Edwards Deming Institute®.)

Our last blog, The Red Bead Experiment, described a simple process to teach variation to everyone. This blog describes another simple experiment that shows the danger of not using the knowledge of variation you learned from the red beads.

Dr. Deming said, "If anyone adjusts a stable process for a result that is undesirable, or for a result that is extra good, the output that follows will be worse than if he had left the process alone." This is often called tampering with or over-controlling the process.

Our last blog, A True Story about Variation, showed how leadership was blaming operators for process problems. They did not understand variation. The most powerful method I know for developing an understanding of variation is the red bead experiment. Dr. W. Edwards Deming often referred to it as a stupid experiment that you'll never forget. I had the privilege to see Dr. Deming perform this experiment in a seminar in 1984.

To really understand common and special causes of variation, many people must change their paradigm. The following is a true story. A plant produced several different powdered products. Each of these products was run through the same production equipment at different conditions and put into unique silos (one or more for each product type).