Content By Steven Ouellette

Steven Ouellette’s picture

By: Steven Ouellette

Why is improving quality so important? Why not spend our money on something else in the business? I know it seems a little odd to ask this, especially to readers of Quality Digest, but could those not initiated into the mysteries of the quality gurus be right? Is getting it “out the door” the only thing that matters? Or is there a pragmatic reason why we work so hard on improving quality? Give me a few moments of your time, and I think I can prove to you why making quality better makes you more money.

But first, let’s talk about a little thing called “Bayes’ theorem.” (You know me; I couldn’t pass up an opportunity to bring stats into the discussion.)

Now Bayes’ theorem is pretty simple in terms of probabilities but has far-reaching implications for those of us who live in reality (note that I specifically exclude most politicians from this clade). It is also almost never applicable in solving problems in industry, for reasons that will become obvious soon. That does not mean it is unimportant—the principle underlies how science actually works, even if is not going to help you design an experiment in industry.

Steven Ouellette’s picture

By: Steven Ouellette

“It is what it is.” I’m hearing that a lot now. I’m OK with it if someone is using it as a shortcut to mean something like the Serenity Prayer. But more and more, I’m hearing people use it in a way that sounds like an expression of helplessness and futility.

As “it is what it is” permeates into casual speech, it molds our thoughts into a state of pliable acceptance, rather than an eagerness to undertake and overcome challenges. It’s the language of a fading colonial power realizing that the “winds of change” beyond its control will blow it about like a floppy rag doll. It’s not the language of innovation and fortitude we have come to expect from modern societies creating and experiencing the best human conditions in history.

What does that have to do with quality? I think the history of quality offers an interesting parallel, and maybe a way through this paradigmatic funk.

Steven Ouellette’s picture

By: Steven Ouellette

Last article, I wrote about the importance of correctly classifying variables as part of the research design process, and discussed the benefits of the hugely useful, but oft-neglected, blocked variables. As part of my ongoing crusade against poor experimental designs, and the people who love them, let’s finish this one up.

Steven Ouellette’s picture

By: Steven Ouellette

Yesterday in “Blocking Out the Nuisance, Part One,” we saw the results of an experiment done by That Guy Over There that didn’t control for the environmental variable of humidity. There was a lot of variability in that experiment, so we didn’t see a whole lot of improvement to be made. I also gave you the data from a blocked design that you created which controlled for humidity. Let’s take a look at the analysis and see if we can find out anything more.

You perform the analysis at α = 0.05, test and pass the assumptions for analysis of variance (ANOVA), and find the following:

Figure 1: Analysis of variance (ANOVA) with humidity blocked

 

In the immortal words of the philosopher Neo, “Whoa.”

You see an entirely different result than That Guy Over There, don’t you? First off, with an adjusted R2 of 0.996 and an overall ω2 of 0.9979, you properly conclude that you have nailed it. You see a significant three-way interaction between all of the factors:

Steven Ouellette’s picture

By: Steven Ouellette

No, I am not writing about going, “Lalalalala! I can't hear you!” when someone is trying to tell you bad news. In the last couple of articles, we have been exploring how to properly perform research in industry. In this one, we will take a look at how you can handle variables in the real world that just get in the way, but still need to be dealt with. And there is a Really Important Thing partway through this article that will save you a lot of money.

So far we have done a good chunk of the research planning. When last we interfaced, we were planning an experiment to see how manipulating different process variables (the “treatments”) would affect the expansion of a complex polymer. But since it is the real world we are working in, we had also identified humidity as a variable that could affect expansion, a variable over which we had little control.

 

actstudyplando identifyandclassify

Steven Ouellette’s picture

By: Steven Ouellette

Let's face it—many industrial researchers, including Six Sigma Black Belts, do a terrible job of planning the research they need to do to perform their jobs efficiently. See that guy over there? Yeah, he is the one I am talking about, so you should read this article so you can help the poor bloke. In my last couple of articles I have been covering a process to plan a research study—in this one we continue with the planning phase and confront an often neglected step for good experimental design—nuisance variables.

In my previous article, I covered the first part of how to plan a study. Remember, this can be any type of study—anything from the plan for a mega project spanning an entire company to a sub-sub-project where we are investigating a small component of that process.

Again, here is the cycle I'll be using.

Steven Ouellette’s picture

By: Steven Ouellette

Last month I showed you a process to use to save money, time, and sanity when doing any type of research, including applied problem solving and quality improvement (“Don’t Design the Experiment Until You Research the Process”), However, I didn’t have room to go through the steps to show you how it works. That’s exactly what I am going to do now.

Again, here is the cycle I’ll be using.

actstudyplandonoarrow

Figure 1: The research design process (from Design of Experiments in Quality Engineering, by Jeffrey T. Luftig and Victoria S. Jordan, McGraw-Hill, 1998)

Steven Ouellette’s picture

By: Steven Ouellette

Although we may use the define, measure, analyze, improve, control (DMAIC) mnemonic to help guide us through our problem solving, that doesn’t really give us a lot of specific direction (as I bemoan in my Top 10 Stupid Six Sigma Tricks No. 4). Good experimental design technique is critical to being able to turn problems into solutions, and in my experience Black Belts have not been introduced to a good process to do this. If you know someone whose first thought is, “Let’s go collect some data to see what is going on,” then read on to avoid losing millions of dollars in experimental mistakes.

Steven Ouellette’s picture

By: Steven Ouellette

“Come and listen to a story ‘bout a man named Ned / a poor Texas Sharpshooter barely kept his family fed. Then one day he was shootin’ at his barn / and he came up with a plan to spin a silly yarn. ‘Specifications,’ he said, ‘making of… the easy way.’ ” What do a Texas sharpshooter and specifications have to do with each other? And what do you do when your humble author has an old TV show theme song stuck in his head? Let’s find out…

Long-time readers (of two months or so) will know that I found a website with logical fallacies all organized into a snazzy tree diagram.

“Geeky,” says you? Like a fox, says I.

This month I thought I would explore a fallacy that we see all the time in industry, and which coincidentally has the funniest non-Latin name of them all: The Texas Sharpshooter Fallacy. (The Latin ones are only funny if you are into Latin double-entendres… then they are hilarious. Trust me. Te audire no possum. Musa sapientum fixa est in aure.)

Steven Ouellette’s picture

By: Steven Ouellette

With the announcement of another Toyota recall, it seems that everyone and their dog have an opinion about Toyota, and some of them might even be drawing the right conclusions. While everyone is allowed to have opinions (not the dogs—on quality matters I don't trust entities that consider cat poo a delicacy), it’s interesting to note that Toyota’s was not the biggest recall, not even the biggest in recent memory. So why do they get all the bad press—and what does it mean for quality?

ADVERTISEMENT

My students started me thinking about this in an online discussion. First off, let’s put the Toyota recall into historical perspective: