What is the Analysis of Means (ANOM)?

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.

Suppose you have five different treatment methods you are considering for coating parts. For each treatment method, you coat four parts and measure the coating weight. For each treatment method, you calculate the average and the range. Of course, the treatment averages are not usually the same. This is not...

ANOX Chart

What is the Analysis of Individual Values (ANOX)

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.

That changed when I read a 2017 article by Dr. Donald Wheeler and James Beagle III that described a new test for homogeneity. The problem with using a control chart to do a test for homogeneity is that the number of data...

hardness tester

What is Evaluating the Measurement Process (EMP)?

It is time to replace the average/range method and ANOVA method for Gage R&R studies. Evaluating the Measurement Process (EMP) is a collection of techniques that allows you to discover much more about your measurement process than if you just use those traditional techniques. EMP has been developed over the years by Dr. Donald J. Wheeler.

The experimental process is essentially the same. One or more operators, measure one or more parts, multiple times. The difference is that EMP immediately places the results on a control chart to assess how consistent, reproducible, and...

It is Your Fault!

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?

  • He didn’t follow our job work instructions. These are written out and the operator has been trained.
  • He wasn’t paying close enough attention.
  • The equipment is down because the operator didn’t run it correctly.
  • She didn’t take time to re-check her work.
  • He simply doesn’t care.

Blaming the...

knowledge

Dr. W. Edwards Deming and Profound Knowledge

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®.)

He is often credited with helping the Japanese rebuild their economy after World War II. Dr. Deming spent years developing a methodology to help companies move forward. It is still valid today. He called this methodology his “System of Profound Knowledge. This system is composed of four bodies of knowledge:

  • Appreciation for a system
  • Knowledge of variation
  • ...
Funnel

Variation and the Funnel Experiment

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.

For example, a...

red-beads

The Red Bead Experiment

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.

The red bead experiment requires a sampling bowl that contains 80% white and 20% red beads -- hence the...

operator in plant

A True Story About Variation

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).

To ensure that the product went to the correct silo, an operator had to set up the lines from the process to the correct silo. If product was introduced into the wrong silo, that silo was cross-contaminated and the entire silo had to be sold as...

normal probability plot

Part 2: Are My Data Normally Distributed?

Our last blog - Are My Data Normally Distributed? - involved using a normal probability plot to answer that question. Essentially, if the data fell along a straight line, we would conclude that the data came from a normal distribution. If it did not fall along a straight line, we would conclude that the data did not come from a normal distribution.

If you and I look at a normal probability plot, we might not agree if something “falls along a straight line or not.” For this reason, it is much easier if we have a statistic to...

Are My Data Normally Distributed?

You have a set of data. You would like to know if the data comes from a normal distribution. How do you do that? A normal probability plot can be used to determine if sets of data come from a normal distribution. This involves using the probability properties of the normal distribution. The data are compared to a normal distribution in such a way that will result in a straight line if the data are normally distributed.

For example, consider the normal probability plot below. This is a data set of the forearm lengths for males. We want to know if the data come from a normal...

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