Process Capability Statistics: Cp and Cpk, Working Together

Capability statistics are wonderful things. These statistics tell you how well your process is meeting the specifications that you have. But there are so many capability statistics that it's worth taking some time to understand how they’re useful together.

Two capability statistics that are hard to keep straight are Cp and Cpk. Their names are different by only a single letter. A single letter that, by the way, doesn’t really explain anything about how these two statistics are different.

Definition of Cp

The equation for Cp is often written ET / NT. ET stands for Engineering Tolerance, which is...

The Three Coolest Things You Didn't Know about Histograms in Minitab

Not too long ago, I observed that one number is rarely adequate to describe data. Means and medians can disagree, and it’s important to know whether different groups of data have similar spreads. A great tool for displaying a more complete representation of the data is the histogram. Histograms are an easy way to summarize a lot of statistics. If you’re not convinced, take a minute to explore some. For example, Katherine Roswell can give you an example of how to use a histogram to identify the best opportunity for improvement in hospital patient readmissions. Histograms are great.

And the...

How Data Analysis Can Help Us Predict This Year's Champions League

by Laerte de Araujo Lima, guest blogger

A few weeks ago, my football friends and I were talking about the football in the UEFA Champions league (UEFA CL), and what we could expect for the 2013-14 season.

Some of us believe that the quality of the football played in the UEFA CL has improved in the last few years, as evidenced by more goals per match, more teams with strategies based in the attack and, finally, more show games. Others disagree, arguing that the teams were pursued defensive strategies with consequently fewer goals per match, more faults per game, and less effective use of game time...

Use Analysis of Means to Classify Baseball Parks

When I first got interested in looking at baseball park factors, I only wanted to know which parks benefited hitters and which benefited pitchers. Once I got started, I got interested in the difference between ESPN's published formula and its results and whether there were obvious reasons for the variation in park factors from year-to-year.

But today I’m returning to the original question: which parks are hitters’ parks, and which are pitchers’ parks?

We already know that the mean and median are inadequate by themselves. For example, consider AT&T Park, where the mean suggests a pitchers’...

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...

Kickoffs into the End Zone: To Return, or Not to Return?

In the world of Six Sigma, we’re always looking to improve our process. Whether it’s increasing the strength of building materials or improving the way calls are processed in a call center, it’s always a good idea to use a data-driven analysis to determine the best solution to your process.

The same is true for the NFL. Two years ago, the NFL decided to move kickoffs up from the 30 yard line to the 35. This has resulted in more kicks traveling into the end zone. So NFL coaches have a decision to make on their kick return process:

  • Should I have my player take a knee whenever he catches the ball...

Avoiding a Lean Six Sigma Project Failure, part 2

In a previous post, I discussed how to avoid a Lean Six Sigma project failure, specifically if the reason behind the failure is that the project solution never gets implemented.

In this post, let's discuss a few other project roadblocks that our customers cited when we asked them about the challenges they come across in completing projects. I’ll also go into detail about suggestions our industry-seasoned trainers at Minitab offer to avoid these failures.

Is the project scope too large?

One common reason quality improvement projects get started on the wrong foot is that their scope is too large.


Avoiding a Lean Six Sigma Project Failure

Failure. Just saying the word makes me cringe. And if you’re human, you’ve probably had at least a couple failures in both your work and home life (that you've hopefully been able to overcome).

But when it comes to Lean Six Sigma projects, there’s really nothing worse than having your entire project fail. Sometimes these projects can last months, involve a large project team, and cost companies a lot of money to carry out, so it can be very upsetting for all involved to know that the project failed (for whatever reason).

At Minitab, we’re always talking to our customers and practitioners in the...

Warning: Failing to Display a Pareto Chart May be Hazardous to Your Health

Defects can cause a lot of pain to your customer.

They can also cause a lot of pain inside your body. The picture at right shows my broken right clavicle. Ouch!

You might think of it as the defective output from my bicycling process, which needs improvement.

Sitting around all summer cinched up in a foam orthopedic brace hasn’t exactly been wild and wacky 50s-style fun at the beach.

But the injury has had its perks (a box of mouth-watering dark chocolate ganaches from kind Minitab coworkers, for example!)

It’s also provided me with a rare commodity in the year 2013: Plenty of time to think.


Variation Amplification: Even a 3-Year-Old Understands It...Do You?

This weekend my 3-year-old son and I were playing with his marble run set, and he said to me, "The marbles start together, but they don't finish together!"

It dawned on me that the phenomenon he was observing seems so obvious in the context of a marble run, and yet many practitioners fail to see the same thing happening in their processes.  I quickly made a video of me placing six marbles in simultaneously so I could illustrate to others what I will call "variation amplification:"

It is obvious in the video that there is little variation in the positions of the marbles in the beginning, but as...

Using Minitab Statistical Software to Analyze the Woeful Bengals

by Jeff Parks, guest blogger

Being a Cincinnati Bengals fan is tough. It's true that Bengals fans don't have it as bad as, say, long-suffering Chicago Cubs fans...nevertheless, the Bengals haven’t won a playoff game since January 1991. That's currently the longest streak in the NFL. In the 1990s they were voted the worst sports franchise by ESPN. Not the worst football team, mind you, but the worst franchise in all of sports.

Not the L.A. Clippers. Not the Cleveland Browns. Not the Pittsburgh Pirates.

The Cincinnati Bengals.

Why? Why must it be so? What separates the Bengals from the good teams in...

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...

Anderson-Darling, Ryan-Joiner, or Kolmogorov-Smirnov: Which Normality Test Is the Best?

Minitab Statistical Software offers three tests for Normality: Anderson-Darling (AD), Ryan-Joiner (RJ), and Kolmogorov-Smirnov (KS). The AD test is the default, but is it the best test at detecting Non-Normality? Let's compare the ability of each of these normality tests to detect non-normal data under three different scenarios.  We'll use simulated data for each, but they reflect common situations you're likely to encounter if you're analyzing data for quality improvement.

Scenario 1 – The manufacturing process produces large outliers from time-to-time. In this simulation, 29 values are...

The Odds of Finding a Four-Leaf Clover Revisited: How Do Some People Find So Many?!

Picture of four-leaf clover by Joe Papp.

This may seem to be an odd time to write about four-leaf clovers, the traditional Irish lucky charms. However, clovers are currently growing full-force in my yard!

I was out doing yard work when I noticed patches of clovers. I blame my neighbor for them because, while I have patches of clover in my grass, he has patches of grass in his clover filled yard! The clovers got me thinking about Carly Barry’s post about the odds of finding four-leaf clovers. It also prompted some fun, backyard science with my daughter!

In Carly’s blog, reader comments raise a...

Quartile Analysis for Process Improvement

The value of analyzing data is well established in industries like manufacturing and mining, but data-driven process and quality improvement is increasingly being adopted in service industries like retail sales and healthcare, too. In this blog post, I'll discuss how a simple data analysis may be used to improve processes in the service sector.

Suppose we want to improve the way incoming calls are processed in a call center run by a large insurance company. We are interested in analyzing duration metrics, which are very useful in assessing both the experience of customers—who always...

A correspondence table for non parametric and parametric tests

Most of the data that one can collect and analyze follow a normal distribution (the famous bell-shaped curve). In fact, the formulae and calculationsused in many analyses simply take it for granted that our data follow this distribution; statisticians call this the "assumption of normality."

For example, our data need to meet the normality assumption before we can accept the results of a one- or two-sample t (Student) or z test. Therefore, it is generally good practice to run a normality test before performing the hypothesis test.

But wait...according to the Central Limit Theorem, when the...

A Brief Illustrated History of Statistics for Industry

by Matthew Barsalou, guest blogger

The field of statistics has a long history and many people have made contributions over the years. Many contributors to the field were educated as statisticians, such as Karl Pearson and his son Egon Pearson. Others were people with problems that needed solving, and they developed statistical methods to solve these problems.

The Standard Normal Distribution

One example is Karl Gauss and the standard normal distribution, which is a key element in statistics. The distribution was used by Gauss to analyze astronomical data in the early nineteenth century and is...

Analyzing a Process Before and After Improvement: Historical Control Charts with Stages

We tend to think of control charts only for monitoring the stability of processes, but they can be helpful for analyzing a process before and after an improvement as well. Not only do control charts allow you to monitor your process for out-of-control data points, but you’ll be able to see how your process mean and variability change as a result of the improvement.

You might create separate before and after control charts for each phases of the improvement project, but making comparisons between those charts can be difficult. You could also try analyzing all of the data over the course of your...

Using Design of Experiments to Minimize Noise Effects

All processes are affected by various sources of variations over time. Products which are designed based on optimal settings, will, in reality, tend to drift away from their ideal settings during the manufacturing process.

Environmental fluctuations and process variability often cause major quality problems. Focusing only on costs and performances is not enough. Sensitivity to deterioration and process imperfections is an important issue. It is often not possible to completely eliminate variations due to uncontrollable factors (such as temperature changes, contamination, humidity, dust etc…).


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...