Quality Improvement

Blog posts and articles about using statistics and data analysis to improve quality through methodologies such as Lean and Six Sigma.

Last week, a customer called with an issue related to running a Gage R&R nested design in Minitab Statistical Software.  Everything initially looked okay, as he had the three columns necessary to perform a successful study: one for parts, one for operators, and another for the measurements.  However, when he tried to analyze his data using Stat > Quality Tools > Gage Study > Gage R&R (Nested), he... Continue Reading
“How do you write your blogs?" someone asked me the other day. “It’s really simple," I replied. "I just apply the infinite monkey theorem.” According to the infinite monkey theorem, if enough monkeys type randomly on a keyboard for a long enough time (infinity), they will be almost certain to produce any given text: a play by Shakespeare, the U.S. Constitution, or Minitab Help.A key premise is the... Continue Reading


Chicago, IL | 11-12 September, 2017


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Variation is everywhere. It’s in your daily commute to work, it’s in the amount of caffeine you drink every day, in the number of e-mails that arrive in your inbox, etc. Whether you’re monitoring something as ordinary as caffeine consumption or something more important like a multi-million dollar manufacturing process, you can use one simple tool to monitor variation and determine whether the... Continue Reading
It’s always interesting to see how people are using Minitab, and the successes they see! We just posted a case study that features the U.S. Defense Logistics Agency (DLA), and their use of Minitab for the data analysis of spare parts data.The DLA is responsible for nearly 100% of the necessary food, fuel, medical supplies, and equipment U.S. military forces stationed worldwide need to operate.... Continue Reading
So this time, we finally have the fun of getting to look at the full gage R&R results. Here’s how the data turned out, and some of the exciting stuff that went along with getting it.   RunOrder Operators Parts Measurements 1 J1 Red 37.9375 2 J1 Green 28.375 3 J1 Yellow 72.25 4 J2 Yellow 72.0625 5 J2 Red 38.25 6 J2 Green 28.4375 7 Kevin Red 41.0625 8 Kevin Green 22.25 9 Kevin Yellow 72.1875 10 J3 Green 22.1875 11 J3 Yellow 72.3125 12 J3 Red 40.9375 13... Continue Reading
I ran my first full marathon in November and as I was completing my training, I came across this quote about quality improvement from V. Daniel Hunt, quality management improvement author and CEO of Technology Research: "Quality is not a sprint; it is a long-distance event."I never thought to make the comparison between training and running a marathon and establishing a company’s quality... Continue Reading
When I get to apply tools like the P’ Chart, I get a thrill. What can I say? I’m a huge fan of Minitab® Statistical Software. I won’t claim to be its biggest fan, but let’s just say I’m definitely up there. Probably in the top 50; definitely the top 100. Admittedly, I’m biased. I have a pretty good gig here. But my history with Minitab started more than a decade before I came to work here. As a... Continue Reading
Now that we’ve explored using gummi bears to do a gage Linearity and Bias Study and a Type I Gage Study, it’s time to use gummi bears to practice the third and final type of measurement systems analysis that I’m planning to demonstrate: the gage R&R study."R&R" stands for Repeatability and Reproducibility, which are the two sources of variation we typically evaluate in a gage R&R study.... Continue Reading
You know that song "Once in a Lifetime," by Talking Heads?  The one where David Byrne keeps repeating "Same as it ever was"?   "Same as it never was" is more like it. We confront variation constantly.    Think about it. Did you spend exactly the same amount of time commuting to work today as you did yesterday? No? That's variation.    While these planes look identical, some variation exists. But... Continue Reading
In my post Assessing Variability for Quality Improvement, I showed how measuring variability is as important as measuring the mean for a product or service in a quality improvement initiative. The mean, by itself, often tells an incomplete story. Additionally, quality management veterans know that controlling the variability is often more difficult than controlling the mean. If you want to change... Continue Reading
Last time, we set up a worksheet for doing a Gage Linearity and Bias Study in Minitab Statistical Software. This time, we’ll take a look at my sample data and see what we might learn from a Gage Linearity and Bias Study. Getting comfortable with the variation present in measurement systems will go a long way towards building your confidence with quality statistics. Remember too that the... Continue Reading
It looks like I may have gotten my wish: NBA games should start Christmas day, which will be a huge present to a lot of fans. So while I ponder whether to proceed with a second post about statistics related to the lockout, let's shed some light on a different subject. From time to time, Minitab users will call in needing assistance with control charts.  One common problem people encounter... Continue Reading
Manufacturers need to make items that meet a customer’s standards, or they’ll soon be out of business. That’s why quality engineers devote a good deal of time to making sure that processes are able to meet those standards.  The first step is to make sure your process is stable. After all, you can’t predict the performance of an unstable process. But you can predict and improve on a stable process.  I... Continue Reading
Today, we’re going to get ready to do a Gage Linearity and Bias Study with gummi bears. But to do the linearity and bias study, you first have to talk more about how to collect the data. The Gage Linearity and Bias study has a complication that wasn’t present in the Type 1 Gage Study.The point of the gage linearity and bias study is to assess the bias of a gage across its operating range, not just... Continue Reading
We often see an average (a.k.a. the mean) used to summarize a population or a process. For example, a pizza restaurant might state that their mean delivery time is 20 minutes. Deceptively, this seems like a very intuitive measure.While the mean is important, people often react to the variability even more. Variability is a measure of how spread out the data are from the mean. How many times have... Continue Reading
Multivariate statistics can be used to better understand the structure of large data sets, typically customer-related data. Suppose you have a large amount of data about your customers (preferences, degree of satisfaction, expectations, dislikes etc…), and a large number of variables you need to analyze. Your data might seem somewhat chaotic at first, and you might consider the use of many different... Continue Reading
Gummi bears have more to teach us about measurement systems analysis.Today, we’ll look at doing a Type 1 Gage Study, which compares the measurement variation to the specifications for your process, to judge whether a gage is measuring well enough. The Type 1 Gage Study is a starting point because it evaluates accuracy, precision, and consistency, but only for a single case. Later, we’ll look... Continue Reading
What can gummi bears tell us about measurement system analysis?  We're still practicing statistics with gummi bears, because they don't bounce or slide off of popsicle sticks. In my last post, we looked at some factors that might affect how far a gummi bear flies off of a popsicle stick catapult. Next, what we'd really like to do is to pick some factors to study and do a designed experiment to... Continue Reading
When you're conducting a Capability Analysis, do you know which statistic to look at?  We get a fair number of questions about this, so let's explore the question.  In the graph below, you’ll see that there are two categories for Capability Statistics, Potential and Overall:  The Potential (Within) Capability statistics(Cp, CPL…) are based off of the estimate for within standard deviation. Well,... Continue Reading
So I’m standing at my desk last Tuesday, when my legs suddenly turn to Jell-O. Then I notice the overhead lights gently waving. Soon folks are popping out of their offices with quizzical, sometimes queasy expressions. A few phone calls and Internet searches confirm that we have just experienced a very rare, East Coast earthquake. One centered in Virginia but felt from Georgia to Maine. Wow.We were... Continue Reading