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...

x control chart for consistency

How Can We Trust Our Measurement Process?

Measurements tell us many things. They tell us if a product is within specification, if we met our goal, if we are staying the same, if we are improving, if we are getting worse, etc. What makes a good measurement process? This is essentially asking the following question:

What does it take for us to trust our measurement process?

Think about trust. Why do we trust some people? One reason is that they are consistent – we know what to expect from them because they are consistent. The same is true of our measurement processes. We begin...

How to Analyze a Cause and Effect Diagram

Our previous blog, What is a Cause and Effect Diagram , introduced the technique and presented the cause and effect diagram below on why a car will not start.

This blog addresses how you analyze a cause and effect diagram. The first step is to examine each idea and determine the degree to which the idea is an actual cause of the problem. For example, how likely is no gas to be the reason the car won’t start. Is it very likely? Is it somewhat likely? Or is it...

What is a Cause and Effect (Fishbone) Diagram?

A cause and effect diagram is a tool that shows the relationship between a quality characteristic (effect) and possible sources of variation (causes). As shown below, the effect could be a problem that needs to be solved or the goal of the process. The effect would then be listed on the cause and effect diagram. The causes involve everything that might trigger the problem. Cause and effect diagrams are also called fishbone diagrams (because of their shape) and Ishikawa diagrams (because of their developer).

The cause and effect diagram is one of...

How Do I Analyze a Scatter Diagram?

Our previous blog ( What is a Scatter Diagram? ) included an example of overtime in a warehouse. You are a warehouse manager, and your boss is concerned about overtime. You think that overtime is caused by the work level – the more lines picked in the warehouse, the more overtime. You constructed a scatter diagram to see if that is true. That scatter diagram is shown below.

How do you analyze this scatter diagram? There are several things you can do. First, you can simply look at the scatter diagram...

What is a Scatter Diagram?

A scatter diagram shows the relationship between two variables. For example, you might want to compare the speed you drive with the time it takes you to get to work, or to compare the heights and weights of children, or to compare the steam usage in a plant to the outside temperature. This is what scatter diagrams do.

Suppose you are a warehouse manager. Overtime is a concern to you since it is something your boss watches closely. There is overtime every day. You have a theory that the overtime is simply caused by the work level – the number of lines that are picked each day in the...

What Do These Histograms Tell You? - The Answers [VIDEO]

What Do These Histograms Tell You? - The Answers [VIDEO]

Our recent blog, entitled What Do These Histograms Tell You?, showed you five different histograms. The blog challenged you to figure out what the histograms were telling you about a supplier’s process. This is the background information we gave in our blog:

“A supplier sends in weekly shipments of one raw material to you. There is one raw material characteristic that is key to how it performs in your process. The supplier provides you with a Certificate of Analysis with each shipment that includes the measured value for this raw material...


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