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Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

As a member of Minitab's Technical Support team, I get the opportunity to work with many people creating control charts. They know the importance of monitoring their processes with control charts, but many don’t realize that they themselves could play a vital role in improving the effectiveness of the control charts.   In this post I will show you how to take control of your charts by using Minitab... Continue Reading
In the mid 1940s, Taiichi Ohno established the Toyota Production System, which is primarily based on eliminating non-value-added waste. He discovered that by reducing waste and inventory levels, problems get exposed and that forces employees to address these problems. To engage the workers and therefore improve processes, Ohno developed many exercises. One of his most popular exercises, “Stand in a... Continue Reading

MINITAB INSIGHTS CONFERENCE 2017

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One of the most memorable presentations at the inaugural Minitab Insights conference reminded me that data analysis and quality improvement methods aren't only useful in our work and businesses: they can make our home life better, too.  The presenter, a continuous improvement training program manager at an aviation company in the midwestern United States, told attendees how he used Minitab... Continue Reading
One highlight of writing for and editing the Minitab Blog is the opportunity to read your responses and answer your questions. Sometimes, to my chagrin, you point out that we've made a mistake. However, I'm particularly grateful for those comments, because it permits us to correct inadvertent errors.  I feared I had an opportunity to fix just such an error when I saw this comment appear on one of... Continue Reading
by Matthew Barsalou, guest blogger For want of a nail the shoe was lost,For want of a shoe the horse was lost,For want of a horse the rider was lostFor want of a rider the battle was lostFor want of a battle the kingdom was lostAnd all for the want of a horseshoe nail. (Lowe, 1980, 50) According to the old nursery rhyme, "For Want of a Nail," an entire kingdom was lost because of the lack of one... Continue Reading
In ancient times dragons were believed to be set by the gods to guard golden treasures. This is because dragons were the most fearsome creatures and would deter would-be thieves. Dragons typically lived in an underground lair or castle and would sleep on top of their gold and treasures.  They were terrifying and often depicted as large fire-breathing, scaly creatures with wings and a huge deadly... Continue Reading
"Data! Data! Data! I can't make bricks without clay."  — Sherlock Holmes, in Arthur Conan Doyle's The Adventure of the Copper Beeches Whether you're the world's greatest detective trying to crack a case or a person trying to solve a problem at work, you're going to need information. Facts. Data, as Sherlock Holmes says.  But not all data is created equal, especially if you plan to analyze as part of... Continue Reading
If you have a process that isn’t meeting specifications, using the Monte Carlo simulation and optimization tool in Companion by Minitab can help. Here’s how you, as a chemical technician for a paper products company, could use Companion to optimize a chemical process and ensure it consistently delivers a paper product that meets brightness standards. The brightness of Perfect Papyrus Company’s new... Continue Reading
As someone who has collected and analyzed real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd. How can you improve a real product with simulated data? In this post, I’ll help you understand the methods behind Monte Carlo simulation and walk you through a simulation example using Companion by Minitab. Companion by Minitab is a software platform that... Continue Reading
Choosing the right type of subgroup in a control chart is crucial. In a rational subgroup, the variability within a subgroup should encompass common causes, random, short-term variability and represent “normal,” “typical,” natural process variations, whereas differences between subgroups are useful to detect drifts in variability over time (due to “special” or “assignable” causes). Variation within... Continue Reading
Do your executives see how your quality initiatives affect the bottom line? Perhaps they would more often if they had accessible insights on the performance, and ultimately the overall impact, of improvement projects.  For example, 60% of the organizations surveyed by the American Society for Quality in their 2016 Global State of Quality study say they don’t know or don’t measure the financial... Continue Reading
Have you ever tried to install ventilated shelving in a closet?  You know: the heavy-duty, white- or gray-colored vinyl-coated wire shelving? The one that allows you to get organized, more efficient with space, and is strong and maintenance-free? Yep, that’s the one. Did I mention this stuff is strong?  As in, really hard to cut?  It seems like a simple 4-step project. Measure the closet, go the... Continue Reading
Grocery shopping. For some, it's the most dreaded household activity. For others, it's fun, or perhaps just a “necessary evil.” Personally, I enjoy it! My co-worker, Ginger, a content manager here at Minitab, opened my eyes to something that made me love grocery shopping even more: she shared the data behind her family’s shopping trips. Being something of a data nerd, I really geeked out over the... Continue Reading
If you regularly perform regression analysis, you know that R2 is a statistic used to evaluate the fit of your model. You may even know the standard definition of R2: the percentage of variation in the response that is explained by the model. Fair enough. With Minitab Statistical Software doing all the heavy lifting to calculate your R2 values, that may be all you ever need to know. But if you’re... Continue Reading
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... Continue Reading
Earlier, I wrote about the different types of data statisticians typically encounter. In this post, we're going to look at why, when given a choice in the matter, we prefer to analyze continuous data rather than categorical/attribute or discrete data.  As a reminder, when we assign something to a group or give it a name, we have created attribute or categorical data.  If we count something, like... Continue Reading
You run a capability analysis and your Cpk is bad. Now what? First, let’s start by defining what “bad” is. In simple terms, the smaller the Cpk, the more defects you have. So the larger your Cpk is, the better. Many practitioners use a Cpk of 1.33 as the gold standard, so we’ll treat that as the gold standard here, too. Suppose we collect some data and run a capability analysis using Minitab Statisti... Continue Reading
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... Continue Reading
by Kevin Clay, guest blogger In transactional or service processes, we often deal with lead-time data, and usually that data does not follow the normal distribution. Consider a Lean Six Sigma project to reduce the lead time required to install an information technology solution at a customer site. It should take no more than 30 days—working 10 hours per day Monday–Friday—to complete, test and... Continue Reading
"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... Continue Reading