Gauging Gage Part 3: How to Sample Parts

In Parts 1 and 2 of Gauging Gage we looked at the numbers of parts, operators, and replicates used in a Gage R&R Study and how accurately we could estimate %Contribution based on the choice for each.  In doing so, I hoped to provide you with valuable and interesting information, but mostly I hoped to make you like me.  I mean like me so much that if I told you that you were doing something flat-out wrong and had been for years and probably screwed somethings up, you would hear me out and hopefully just revert back to being indifferent towards me.

For the third (and maybe final) installment, I...

Gauging Gage Part 2: Are 3 Operators or 2 Replicates Enough?

In Part 1 of Gauging Gage, I looked at how adequate a sampling of 10 parts is for a Gage R&R Study and providing some advice based on the results.

Now I want to turn my attention to the other two factors in the standard Gage experiment: 3 operators and 2 replicates.  Specifically, what if instead of increasing the number of parts in the experiment (my previous post demonstrated you would need an unfeasible increase in parts), you increased the number of operators or number of replicates?

In this study, we are only interested in the effect on our estimate of overall Gage variation. Obviously,...

Gauging Gage Part 1: Is 10 Parts Enough?

"You take 10 parts and have 3 operators measure each 2 times."

This standard approach to a Gage R&R experiment is so common, so accepted, so ubiquitous that few people ever question whether it is effective.  Obviously one could look at whether 3 is an adequate number of operators or 2 an adequate number of replicates, but in this first of a series of posts about "Gauging Gage," I want to look at 10.  Just 10 parts.  How accurately can you assess your measurement system with 10 parts?

Assessing a Measurement System with 10 Parts

I'm going to use a simple scenario as an example.  I'm going to...

Applying Six Sigma to a Small Operation

Using data analysis and statistics to improve business quality has a long history. But it often seems like most of that history involves huge operations. After all, Six Sigma originated with Motorola, and became adopted by thousands of other businesses after it was adopted by a little-known outfit called General Electric.

There are many case studies and examples of how big companies used Six Sigma methods to save millions of dollars, slash expenses, and improve quality...but when they read about the big dogs getting those kind of results, a lot of folks hear a little voice in their heads...

Use the Minitab Assistant to Choose a Graph

Everyone loves Minitab’s Assistant. My favorite bit, as I’ve shown with the Gage R&R Study, is the way that the Assistant puts all the results you need into reports that are easy to understand and present. But it’s also pretty neat that before you ever choose what to do in Minitab, the Assistant is ready to help you. Let’s take a closer look at the Assistant's Graphical Analysis tools.

Help Me Choose

Choose Assistant > Graphical Analysis and the most prominent thing you’ll see is a question:

But you’re not left with just the three objectives. Select "graph variables over time," and before you...

Itchy, Sneezy, Stuffy: Delivering Relief with Nasal Spray and DOE

Recently, a customer called our Technical Support team about a Design of Experiment he was performing in Minitab Statistical Software. After they helped to answer his question, the researcher pointed our team to an interesting DOE he and his colleagues conducted that involved using nasal casts to predict the drug delivery of nasal spray.

The study has already been published, and you can read more about it here, but I wanted to highlight this use of the DOE tools in Minitab in this blog post.

Using Nasal Casts to Predict Nasal Spray Drug Delivery

The nose is a convenient route of administration...

Avoiding a Lean Six Sigma Project Failure, part 3

In previous posts, I’ve outlined some reasons why a Lean Six Sigma project might have been deemed a failure. We’ve gathered many of these reasons from surveying and talking with our customers.

I’d like to present a few more reasons why projects might fail, and then share some “words of wisdom” from Minitab trainers on how you can avoid these project failures.

Forcing Projects into DMAIC

Certain quality improvement projects were never meant to be Six Sigma projects that fit neatly into the DMAIC (Define – Measure – Analyze – Improve – Control) methodology. Examples include:

1. Selecting a vendor...

Doing Gage R&R at the Microscopic Level

by Dan Wolfe, guest blogger

How would you measure a hole that was allowed to vary one tenth the size of a human hair? What if the warmth from holding the part in your hand could take the measurement from good to bad? These are the types of problems that must be dealt with when measuring at the micron level.

As a Six Sigma professional, that was the challenge I was given when Tenneco entered into high-precision manufacturing. In Six Sigma projects “gage studies” and “Measurement System Analysis (MSA)” are used to make sure measurements are reliable and repeatable. It’s tough to imagine doing that...

Quality Improvement in Financial Services

Process improvement through methodologies such as Six Sigma and Lean has found its way into nearly every industry. While Six Sigma had its beginnings in manufacturing, we’ve seen it and other process improvement techniques work very well in the service industry—from healthcare to more service-oriented business functions, such as human resources.

However, Six Sigma seems to have had a slower rate of adoption in financial services. I recently came across a great article about the challenges faced in the financial industry when it comes to successfully implementing a process improvement...

How to “Expand” Your Gage Studies

As we said in yesterday’s post, it’s been exciting for Minitab to be a supporter of the ASQ World Conference on Quality and Improvement taking place this week in Indianapolis. There have been many great sessions and an abundance of case studies shared that highlight how quality teams worldwide are improving the performance of their businesses.

One session that generated a lot of interest from the conference participants was conducted by Minitab trainers Lou Johnson, Daniel Griffith and Jim Colton.

Their presentation, Sampling Plan for Expanded Gage R&R Studies, covered Gage R&R studies and how...

Explaining Quality Statistics So My Boss Will Understand: Measurement Systems Analysis (MSA)

As a teenaged dishwasher at a local eatery, I had a boss who'd never washed dishes in a restaurant himself. I once spent 40 minutes trying to convince him that forks and spoons should go in their holders with the business end up, while knives should go in point-down. Whatever I said, he didn't get it. We were ordered to put forks and spoons in the holders with the handles up.

The outraged wait staff soon made clear what I hadn't: you can't immediately tell the difference between a fork and a spoon when all you can see is the handle! Explaning that in the right way would have minimized wasted...

Evaluating a Gage Study With One Part

Recently, Minitab News featured an article that talked about how to perform a Gage R&R Study with only one part. This prompted many users to contact our technical support team with questions about next steps, like these:

  • What can I do with the output of a Gage study with only one part? 
  • How can I use the variance component estimates to obtain meaningful information about my measurement system?

By themselves, the variance component estimates from the ANOVA output for a Gage study with just one part are not particularly useful. However, if we combine what we’ve learned about the variance for...

A Simple Guide to Gage R&R for Destructive Testing

Measurement systems analysis (MSA) is essential to the success of any data analysis. If you cannot rely on the tool you’re using to take measurements, then why bother collecting data to begin with? It would be like trying to lose weight while relying on a scale that doesn’t work. What’s the point in weighing yourself?

Minitab Statistical Software offers many types of tools that you can use to assess your measurement system, including:

  • Gage R&R Study (Crossed)
  • Gage R&R Study (Nested)
  • Attribute Agreement Analysis

Destructive Testing Defined

In MSA studies for continuous measurements (e.g. weight,...

Orthogonal Regression: Testing the Equivalence of Instruments

I recently got a request from one of our Facebook fans to do a post about orthogonal regression, which I admit is not a subject I’m very familiar with. However, with a little help from Minitab’s help resources and by consulting a few Minitab experts, I think I came up with a post that will be useful. I thought it would help to discuss orthogonal regression with an example, but first...

What the Heck Is Orthogonal Regression?

Orthogonal regression is also known as “Deming regression” and examines the linear relationship between two continuous variables. It’s often used to test whether two...

My Work with Minitab

The Minitab Fan section of the Minitab blog is your chance to share with our readers! We always love to hear how you are using Minitab products for quality improvement projects, Lean Six Sigma initiatives, research and data analysis, and more. If our software has helped you, please share your Minitab story, too!

Throughout my 15 years as a Six Sigma Initiative Leader, Consultant, Trainer, Black Belt and master Black Belt I have been enthusiastic about Minitab Statistical Software, starting from release 11 to now.

Minitab's graphics are outstanding in their ability to present messages and...

Minitab and Excel: Which Should I Use, and When?

Have you ever found yourself switching back and forth between a Microsoft Excel file and Minitab Statistical Software just to complete a single analysis? Which software will give me the accurate results I need quickly?

I decided to put a few important factors to the test—workflow, organization, quality focus, and help. The review below provides my own two cents on which software seems to work best in a different situations.

Creating Graphs with Raw Data Easily

Microsoft Excel is a general spreadsheet software program. It is great for compiling, sorting, and highlighting large amounts of data....

Summer Fun! Statistics in Your Backyard

There are some sounds that are quintessential summertime…the whir of the lawnmower, shrieks of children splashing in the pool, the crackle of a campfire. I’m sure we could think of a hundred more. For me, one sound that comes to mind in particular is the chirp of crickets in the evening. In this blog we'll recreate an old country trick using cricket chirps and, I hope, learn some new Minitab tricks along the way!

Our story starts with Amos Dolbear, an American physicist and inventor who lived in the late 1800s. Dolbear invented several elements of the telephone and was, therefore, attuned to...

Accuracy vs. Precision: What’s the Difference?

I remember sitting in my ninth-grade chemistry class when my teacher mentioned that the day’s lesson would include a discussion about accuracy and precision, and how both relate to making experimental measurements. I’ve always been more of a liberal-arts-minded individual, and I initially thought, Is there really a difference between the two terms? In fact, I even remembered using the words interchangeably in my writing for English class!

However, as I continued through more advanced science and math courses in college, and eventually joined Minitab Inc., I became tuned in to the important...

Unbalanced Designs and Gage R&R (Expanded)

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 would get this error message:

Gage R&R Study - Nested ANOVA

* ERROR * Design is not balanced; execution aborted.

One of the assumptions in performing a Nested Gage analysis is that the data is balanced,...

Gummi Bear Design of Experiments: Choosing Factors to Study

In recent posts, we’ve reviewed a number of Measurement Systems Analysis (MSA) studies: Type I Gage Studies, Linearity and Bias Studies, and Gage R&R Studies. Before that, we took a look at a cause and effect diagram, also called a fishbone diagram. And we did all this because we were getting ready to practice a designed experiment.


Remember the fishbone diagram?

On the fishbone diagram, I came up with 24 variables that might affect how far the gummi bears go. I’d like to study as many of these factors as possible, but first need to decide which ones I can both manipulate and measure. Here...