Category Archives: Six sigma

George Box

I would most likely not exist if it were not for George Box. My father took a course from George while my father was a student at Princeton. George agreed to start the Statistics Department at the University of Wisconsin – Madison, and my father followed him to Madison, to be the first PhD student. Dad graduated, and the next year was a professor there, where he and George remained for the rest of their careers.

George died today, he was born in 1919. He recently completed An Accidental Statistician: The Life and Memories of George E. P. Box which is an excellent book that captures his great ability to tell stories. It is a wonderful read for anyone interested in statistics and management improvement or just great stories of an interesting life.

George Box by Brent Nicastro.

George Box was a fantastic statistician. I am not the person to judge, but from what I have read one of the handful of most important applied statisticians of the last 100 years. His contributions are enormous. Several well know statistical methods are known by his name, including:

George was elected a member of the American Academy of Arts and Sciences in 1974 and a Fellow of the Royal Society in 1979. He also served as president of the American Statistics Association in 1978. George is also an honorary member of ASQ.

George was a very kind, caring and fun person. He was a gifted storyteller and writer. He had the ability to present ideas so they were easy to comprehend and appreciate. While his writing was great, seeing him in person added so much more. Growing up I was able to enjoy his stories often, at our house or his. The last time I was in Madison, my brother and I visited with him and again listened to his marvelous stories about Carl Pearson, Ronald Fisher and so much more. He was one those special people that made you very happy whenever you were near him.

George Box, Stuart Hunter and Bill Hunter (my father) wrote what has become a classic text for experimenters in scientific and business circles, Statistics for Experimenters. I am biased but I think this is acknowledged as one of (if not the) most important books on design of experiments.

George also wrote other classic books: Time series analysis: Forecasting and control (1979, with Gwilym Jenkins) and Bayesian inference in statistical analysis. (1973, with George C. Tiao).

George Box and Bill Hunter co-founded the Center for Quality and Productivity Improvement at the University of Wisconsin-Madison in 1984. The Center develops, advances and communicates quality improvement methods and ideas.

The Box Medal for Outstanding Contributions to Industrial Statistics recognizes development and the application of statistical methods in European business and industry in his honor.

“All models are wrong but some are useful” is likely his most famous quote. More quotes By George Box

A few selected articles and reports by George Box

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Software Process and Measurement Podcast With John Hunter

In my podcast with Tom Cagley, Software Process and Measurement Cast: John Hunter on Management Matters, as you might expect there was a bit of a focus on software development and agile software development as related to the ideas I expressed in Management Matters: Building Enterprise Capability.

Deming: management system, continual improvement, respect for people (the 3rd point I said I would mention, but didn’t)
Why do so many management improvement efforts fail?
The proxy nature of data
Building Adoption of Management Improvement Ideas in Your Organization

photo of John Hunter at the Borobudur Temple

John Hunter at the Borobudur Buddhist Temple in Indonesia.

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Special Cause Signal Isn’t Proof A Special Cause Exists

One of my pet peeves is when people say that a point outside the control limits is a special cause. It is not. It is an indication that it likely a special cause exists, and that special cause thinking is the correct strategy to use to seek improvement. But that doesn’t mean there definitely was a special cause – it could be a false signal.

This post relies on an understand of control charts and common and special causes (review these links if you need some additional context).

Similarly, a result that doesn’t signal a special cause (inside the control limits without raising some other flag, say a run of continually increasing points) does not mean a special cause is not present.

The reason control charts are useful is to help us maximize our effectiveness. We are biased toward using special cause thinking when it is not the most effective approach. So the control chart is a good way to keep us focused on common cause thinking for improvement. It is also very useful in flagging when it is time to immediately start using special cause thinking (since timing is key to effective special cause thinking).

However, if there is result that is close to the control limit (but inside – so no special cause is indicated) and the person that works on the process everyday thinks, I noticed x (some special cause) earlier, they should not just ignore that. It very well could be a special cause that, because of other common cause variation, resulted in a data point that didn’t quite reach the special cause signal. Where the dot happened to land (just above or just below the control limit – does not determine if a special cause existed).

The signal is just to help us systemically make the best choice of common cause or special cause thinking. The signal does not define whether a special cause (an assignable cause) exists of not. The control chart tool helps guide us to use the correct type of improvement strategy (common cause or special cause). But it is just a signaling device, it isn’t some arbiter of whether a special cause actually exists.

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Management Improvement Blog Carnival #179

The Curious Cat Management Blog Carnival has been published since 2006. The carnival links to great management blog posts; it is published twice a month. I hope you find these post interesting and find some new blogs to start reading. Follow me online: Google+, Twitter and elsewhere.

Lean Versus Historical TPS by Art Smalley – “identify what are your impediments to improvement and work on those. In particular as what are barriers to higher equipment uptime, higher process capability, safer equipment, higher capital and labor productivity without adding cost, more highly trained personnel, and you will be on the right track. I call this building better process stability and it is an essential yet often ignored element of the historical Toyota Production System.”
5 Critical Control Chart Characteristics You May Not Be Aware Of by Ron Pereira – “No matter if you call yourself a ‘lean practitioner’ or ‘six sigma practitioner’ or some combination of the two… one ‘tool’ you should have a deep understanding of is the control chart.”

Mount Merapi, Yogyakarta, Java, Indonesia by John Hunter.

Adaptability vs Evolutionary Change by David J. Anderson – “Organizations with evolutionary capability have resilience – they remain relevant despite changing circumstances and maintain high levels of effectiveness as the environment around them changes. Kanban is a means to install evolutionary capability and deliver on higher level agility. Evolutionary capability defines second generation Agile methods.”
Completion: Limiting WIP Post II by Jim Benson – “When we limit work-in-progress, we not only limit the number of projects we are working on, but also the number of tasks. This helps us complete tasks efficiently and effectively. When we are done, we understand what we did. While we are doing the tasks we are fully aware of how long they are taking.”

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Introductory Videos on Using Design of Experiments to Improve Results

The video shows Stu Hunter discussing design of experiments in 1966. It might be a bit slow going at first but the full set of videos really does give you a quick overview of the many important aspects of design of experiments including factorial designed experiments, fractional factorial design, blocking and response surface design. It really is quite good, if you find the start too slow for you skip down to the second video and watch it.

My guess is, for those unfamiliar with even the most cursory understanding of design of experiments, the discussion may start moving faster than you can absorb the information. One of the great things about video is you can just pause and give yourself a chance to catch up or repeat a part that you didn’t quite understand. You can also take a look at articles on design of experiments.

I believe design of experiments is an extremely powerful methodology of improvement that is greatly underutilized. Six sigma is the only management improvement program that emphasizes factorial designed experiments.

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Why Use Lean if So Many Fail To Do So Effectively

If less than 1% of companies are successful with Lean, why are we doing it?

Lots of us are not. I would say the efforts I see “fail” are because they don’t do it. They have something they call TQM, six sigma, lean management or whatever and try out 10-30% of it in some half-measures, with big doses of Dilbert’s pointy haired boss methods and then don’t get great results. Wow.

The biggest complaint (with some merit) I see is why is lean/Deming/six sigma… so hard to actually do. If companies constantly fail to do it at all (even when they use the name) isn’t that an issue. Isn’t that a weakness of the “solution.” My answer is: yes. The caveat is, until someone comes up with the management system that both gets the results using Deming’s management ideas can, and is super easy for organizations to actually fully adopt (and have the great success that doing so provides) I know of nothing better than trying to do these things.

Certainly I believe you are much better off attempting to use Deming, lean or six sigma than listen to someone that tells you they have management instant pudding that will give you great results with no effort.

My belief is that a partial success rate is much higher than 1%. While many organization never go beyond slapping a few good tools on a outdated management system those few tools actually have good results. Maybe 50% of the implementations are so lame they have almost no positive results (not even getting improvement worth the time and effort). They could be seen as “failures,” to me. Those that actually have a right to say they are practicing “lean” I would say is a pretty small number but still above 1%?

There is also an advantage to this stuff being hard to do. You really don’t have to invent anything new. If you just have persistence and keep continually improving along the path applying ideas proven over decades from Deming, Ohno, McGregor, Christensen, Drucker, Scholtes, Womack, Roger Hoerl (six sigma)… you have a great advantage over all those organizations that ignored the ideas or made a bit of effort and then gave up.

2011 Management Blog Roundup: Stats Made Easy

The 4th Annual Management blog roundup is coming to a close soon. This is my 3rd and final review post looking back at 2001, the previous two posts looked at: Gemba Panta Rei and the Lean Six Sigma Blog.

I have special affinity for the use of statistics to understand and improve. I imaging it is both genetic and psychological. My father was a statistician and I have found memories of applying statistical thinking to understand a result or system. I also am comfortable with numbers, and like most people enjoy working with things I have an affinity for.

Mark Anderson

Mark Anderson’s Stats Made Easy blog brings statistical thinking to managers. And this is not an easy thing to do, as one of his posts shows, we have an ability to ignore data we don’t want to know. Wrong more often than right but never in doubt: “Kahneman examined the illusion of skill in a group of investment advisors who competed for annual performance bonuses. He found zero correlation on year-to-year rankings, thus the firm was simply rewarding luck. What I find most interesting is his observation that even when confronted with irrefutable evidence of misplaced confidence in one’s own ability to prognosticate, most people just carry on with the same level of self-assurance.”

That actually practice of experimentation (PDSA…) needs improvement. Too often the iteration component is entirely missing (only one experiment is done). That is likely partially a result another big problem: the experiments are not nearly short enough. Mark offered very wise advice on the Strategy of experimentation: Break it into a series of smaller stages. “The rule-of-thumb I worked from as a process development engineer is not to put more than 25% of your budget into the first experiment, thus allowing the chance to adapt as you work through the project (or abandon it altogether).” And note that, abandon it altogether option. Don’t just proceed with a plan if what you learn makes that option unwise: too often we act based on expectations rather than evidence.

In Why coaches regress to be mean, Mark explained the problem with reacting to common cause variation and “learning” that it helped to do so. “A case in point is the flight instructor who lavishes praise on a training-pilot who makes a lucky landing. Naturally the next result is not so good. Later the pilot bounces in very badly — again purely by chance (a gust of wind). The instructor roars disapproval. That seems to do the trick — the next landing is much smoother.” When you ascribe special causation to common cause variation you often confirm your own biases.

Mark’s blog doesn’t mention six sigma by name in his 2011 posts but the statistical thinking expressed throughout the year make this a must for those working in six sigma programs.

2011 Management Blog Roundup: Lean Six Sigma Blog

For my contribution to the 4th annual management blog roundup I am taking a look at 3 management blogs. In this post I look back at the year that was at the Lean Six Sigma blog.

We are lucky to have so many great management blogs to read all year. They provide inspiration and great advice to managers. Though, one of my frustrations is how few good six sigma resources there are online. In this area we are unlucky. The disparity between the amazingly high number of very high quality lean blogs and agile software development blogs compared to almost nothing of similar quality for six sigma content is dramatic (and unfortunate).

Ron Pereira

Ron Pereira is the managing partner of Lean Six Sigma Academy and the Gemba Academy which provide high quality online lean manufacturing training. One of the ways Ron stands out are his posts that make continuous improvement a family affair (which I appreciate given that I grew up in such an environment).

In Let’s Dance he looks at understanding psychology as it relates to working with groups/teams (in this case his daughters soccer team): “my coaching style and my assistant coach’s style had become a bit too intense and, as a result, the girls were playing tight and scared to make mistakes… We kept this ‘dancing’ theme alive for the rest of the season. During warm-ups before games I, and the girls, would dance like fools. The other teams watched us like we were nuts… but we didn’t care. We kept right on laughing and dancing.” Take a look at this post, it really packs in a ton of great thoughts for managers.

Another way Ron stands out is with his webcasts on discussion lean terms (the gemba glossary). In this webcast he looks at the topic of standardized work processes.

One of the great things about blogs is the focus on what people really deal with day in and day out. It is nice to read about a great management system in a book like the Leader’s Handbook by Peter Scholtes. But what do you do when you are in a much more common situation, where others don’t share your desire to reshape the management system into something new and better? Ron took a look at this in his post: 3 Things You Can Do When Your Manager Doesn’t Support Continuous Improvement: “The best way to combat this is to demonstrate the value without them asking you to. In other words, make something better and let them know about it. And when I say make it better I mean it. Do something to positively impact the business.”

Another wonderful family related post by Ron this year was Training Wheels – “Like most young people my boy was itching to take the training wheels off his bicycle… The best part of all is he’s learning to solve his own problems. He’s not waiting for people to hand him things on a platter… How many times do we continuous improvement practitioners moan and groan about the lack of management support when, in actuality, even though they may not care they won’t stop you from making things better?”

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One factor at a time (OFAT) Versus Factorial Designs

Guest post by Bradley Jones

Almost a hundred years ago R. A. Fisher‘s boss published an article espousing OFAT (one factor at a time). Fisher responded with an article of his own laying out his justification for factorial design. I admire the courage it took to contradict his boss in print!

Fisher’s argument was mainly about efficiency – that you could learn as much about many factors as you learned about one in the same number of trials. Saving money and effort is a powerful and positive motivator.

The most common argument I read against OFAT these days has to do with inability to detect interactions and the possibility of finding suboptimal factor settings at the end of the investigation. I admit to using these arguments myself in print.

I don’t think these arguments are as effective as Fisher’s original argument.

To play the devil’s advocate for a moment consider this thought experiment. You have to climb a hill that runs on a line going from southwest to northeast but you are only allowed to make steps that are due north or south or due east or west. Though you will have to make many zig zags you will eventually make it to the top. If you noted your altitude at each step, you would have enough data to fit a response surface.

Obviously this approach is very inefficient but it is not impossible. Don’t mistake my intent here. I am definitely not an advocate of OFAT. Rather I would like to find more convincing arguments to persuade experimenters to move to multi-factor design.

Factorial Designed Experiment Aim

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
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