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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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. […]
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 […]
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 […]
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. […]
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 […]
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 […]
Have you ever heard someone say “You can’t measure what I do!” Or maybe this one: “We are a job shop – we don’t do the same thing over and over – so we have nothing to measure.” Most likely you have if you are involved in process improvement, in using SPC, or getting people […]
This week’s blog concludes our series on process capability and asks which is better: Cpk or Ppk? Your supplier has sent you the process capability chart you requested. Looks like your supplier is really performing for you. You note that his process has a Ppk = 1.14 and a Cpk = 2.07. Why are those […]
Our first two blogs in this process capability series answered two questions: What is process capability? – and – What is Cpk? This blog answers the next question: What is Ppk? The answer is quite simple. Just to refresh your memory, Cpk is expressed as the following: Cpk = Minimum (Cpu, Cpl) Cpu=(USL-X)/3? Cpl=(X-LSL)/3? where Cpu […]
Our first blog in this process capability series explored the simple question “what is process capability?”. There were two parts to the answer: Process capability is the range of values that you can expect to get from the process over an extended time period. A process is capable if the range of expected values fall […]
You hear the term process capability a lot these days. Customers want to know your process capability. You want to know your supplier’s process capability. So, what is meant by the words process capability? For a process: Process capability is the range of values that you can expect to get from the process over an […]
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. […]
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 […]
This is the final blog in a four-part series on Gage R&R studies. The first blog addressed what a gage R&R study is. The second blog addressed Gage R&R studies and process variation and determine the % of total variance due to the measurement system. The third blog examined how good your measurement system is […]
You just completed your ANOVA Gage R&R analysis. The results indicate that your measurement system is responsible for 40% of the process variance (GRR%). You look up the guidelines on the internet and this is what you see: Less than 1%: The measurement system is acceptable. Between 1% and 9%: The measurement system is acceptable […]
This is the second in a four-part series on Gage R&R. The first blog explained what a Gage R&R study is. This blog examines the relationship between the Gage R&R results and the process variation and answers the question: Is the measurement system capable of telling the difference between the parts or samples taken the […]
This is the first of a four-part blog on gage R&R. It answers this basic question: What is a Gage R&R study? If you google this question, you will get several answers including this one from the www.isixsigma.com dictionary: “Gage R&R, which stands for gage repeatability and reproducibility, is a statistical tool that measures the […]
Sigma – an important statistic for us to know. Sigma, or the standard deviation, is a measure of how much dispersion there is in a process. There are numerous ways to estimate sigma. One way, of course, is simply to calculate the value using the formula for the standard deviation: where Xi are the individual […]
This is the fourth and last blog in our four-part series introducing control charts. The first blog addressed the question of what a control chart is. The second blog explored the relationship between variation and control charts. The third blog addressed the purpose of a control chart. This blog answers the following question: Where are […]
This is the third in a four-part series introducing control charts. The first blog addressed the question of what a control chart is. The second blog explored the relationship between variation and control charts. This blog begins to answer the following question: What is the purpose of a control chart? We will continue with the […]
This is the second in a four-part series introducing control charts. The first blog addressed the question of what a control chart is. This blog will answer the following question: What is variation and how does it relate to a control chart? Understanding variation is the key to effectively using a control chart. A control […]
This is the start of a four-part blog on control charts. The blogs will answer the following questions: What is a control chart? What is variation and how does it relate to a control chart? What is a control chart used for? Where is a control chart used? Control charts are at the heart of […]
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 […]
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 […]
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 […]
Our previous blog introduced histograms. Histograms tell you four things about your process. Remember what those were? If not, see our previous blog, What is a Histogram?. Histograms also can give you insights to things you might not normally see. The example below demonstrates this. Here is the background information for you. A supplier sends […]
A histogram is a snapshot in time of your process. It tells you four things: Which result (or range of results) occurs most frequently How much variation there is What the shape of the variation looks like If any results are out of specifications Remember, all processes have variation. Processes in statistical control tend to […]
A Pareto chart helps you answer questions. What is the reason for the most rework or scrap in your organization? Which customers complain the most? What keeps us from getting the books closed after the end of the month. These questions are common in organizations, but we don’t always agree on the answer. This is […]
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