dcsimg
 

Process Capability

Blog posts and articles about tools for assessing the capability of a process in manufacturing and service situations.

We often receive questions about moving ranges because they're used in various tools in our statistical software, including control charts and capability analysis when data is not collected in subgroups. In this post, I'll explain what a moving range is, and how a moving range and average moving range are calculated. A moving range measures how variation changes over time when data are collected as... Continue Reading
After my husband’s most recent visit to the dentist, he returned home cavity-free...and with a $150 electric toothbrush in hand.  I wanted details. It began innocently. His dreaded trip to the dentist ended in high praise for no cavities and only a warning to floss more. That prompted my programming-and-automation-obsessed husband, still in the chair, to exclaim, "I wish there was a way to automate... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

Get the facts >
While the roots of Lean Six Sigma and other quality improvement methodologies are in manufacturing, it’s interesting to see how other organizational functions and industries apply LSS tools successfully. Quality improvement certainly has moved far beyond the walls of manufacturing plants! For example, I recently had the opportunity to talk to Drew Mohler, a Lean Six Sigma black belt and senior... Continue Reading
If you need to assess process performance relative to some specification limit(s), then process capability is the tool to use. You collect some accurate data from a stable process, enter those measurements in Minitab, and then choose Stat > Quality Tools > Capability Analysis/Sixpack or Assistant > Capability Analysis. Now, what about sorting the data? I’ve been asked “why does Cpk change when I... Continue Reading
How deeply has statistical content from Minitab blog posts (or other sources) seeped into your brain tissue? Rather than submit a biopsy specimen from your temporal lobe for analysis, take this short quiz to find out. Each question may have more than one correct answer. Good luck! Which of the following are famous figure skating pairs, and which are methods for testing whether your data follow a... Continue Reading
Having delivered training courses on capability analyses with Minitab, several times, I have noticed that one question you can be absolutely sure will be asked, during the course, is: What is the difference between the Cpk and the Ppk indices? Ppk vs. Cpk indices The terms Cpk and Ppk are often confused, so that when quality or process engineers refer to the Cpk index, they often actually intend to... Continue Reading
Control charts are a fantastic tool. These charts plot your process data to identify common cause and special cause variation. By identifying the different causes of variation, you can take action on your process without over-controlling it. Assessing the stability of a process can help you determine whether there is a problem and identify the source of the problem. Is the mean too high, too low,... Continue Reading
Don't be a grumpy cat when something on your capability report doesn't smell right. After pressing that OK button to run your analysis, allow your inner cat to understand how and why certain statistics are being used. To help you along, here are some capability issues that customers have brought up recently. Cp is missing You’ve generated a capability analysis report with the Johnson transformation... Continue Reading
By Matthew Barsalou, guest blogger A problem must be understood before it can be properly addressed. A thorough understanding of the problem is critical when performing a root cause analysis (RCA) and an RCA is necessary if an organization wants to implement corrective actions that truly address the root cause of the problem. An RCA may also be necessary for process improvement projects; it is... Continue Reading
Did you know that November is World Quality Month? The American Society for Quality is once again heading up this year’s festivities. Throughout the month of November, ASQ will be promoting the use of quality tools in businesses, communities, and institutions all over the world. You can check it out at http://asq.org/world-quality-month/. Here at Minitab, we’re also pretty excited about World... Continue Reading
By Matthew Barsalou, guest blogger Teaching process performance and capability studies is easier when actual process data is available for the student or trainee to practice with. As I have previously discussed at the Minitab Blog, a catapult can be used to generate data for a capability study. My last blog on using a catapult for this purspose was several years ago, so I would like to revisit... Continue Reading
I read trade publications that cover everything from banking to biotech, looking for interesting perspectives on data analysis and statistics, especially where it pertains to quality improvement. Recently I read a great blog post from Tony Taylor, an analytical chemist with a background in pharmaceuticals. In it, he discusses the implications of the FDA's updated guidance for industry analytical... Continue Reading
September 17 marked the release of new information from the American Community Survey (ACS) from the U.S. Census Bureau. Here’s a bar chart of what the press releases looked like for that day: Clearly there was a theme in play, one that was great news for major metropolitan areas. The Census Bureau even released a graph showing that the percentage of people within the 25 most populous metropolitan... 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
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... Continue Reading
Last time, I told you how I had double-checked the analysis in a post that involved running the Johnson transformation on a set of data before doing normal capability analysis on it. A reader asked why the transformation didn't work on the data when you applied it outside of the capability analysis.  I hadn't tried transforming the data that way, but if the transformation worked when performed as... Continue Reading
I don't like the taste of crow. That's a shame, because I'm about to eat a huge helping of it.  I'm going to tell you how I messed up an analysis. But in the process, I learned some new lessons and was reminded of some older ones I should remember to apply more carefully.  This Failure Starts in a Victory My mistake originated in the 2015 Triple Crown victory of American Pharoah. I'm no... Continue Reading
Before I joined Minitab, I worked for many years in Penn State's College of Agricultural Sciences as a writer and editor. I frequently wrote about food science and particularly food safety, as I regularly needed to report on the research being conducted by Penn State's food safety experts, and also edited course materials and bulletins for professionals and consumers about ensuring they had safe... Continue Reading
When data are collected in subgroups, it’s easy to understand how the variation can be calculated within each of the subgroups based the subgroup range or the subgroup standard deviation. When data is not collected in subgroups (so the subgroup size is 1), it may be a little less intuitive to understand how within-subgroup standard deviation is calculated.  How does Minitab Statistical Softwarecalcu... 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