Tag Archives: Statistics

Don’t Expect Short Quotes to Tell the Whole Story

When people try to use a short quote as an accurate encapsulation of a management concept they will often be disappointed.

It is obvious that Dr. Deming believed that organizations failed to use data effectively to improve needed to change and use data effectively in order to thrive over the long term. He believed that greatly increasing the use of data in decision making would be useful. He also believe there were specific problems with how data was used, when it is was used. Failing to understand variation leads to misinterpreting what conclusions can appropriately be drawn from data.

Using data is extremely useful in improving performance. But as Deming quoted Lloyd Nelson as saying “the most important figures that one needs for management are unknown or unknowable.”

I believe Dr. Deming would have said something like “In God we trust, all others bring data” (I haven’t been able to find a source verifying he did say it). Others don’t believe he would referencing the Lloyd Nelson quote and all Deming’s other work showing that Dr. Deming’s opinion that data isn’t all that matters. I believe they are correct that Dr. Deming wouldn’t mean for the quote to be taken literally as a summation of everything he ever said. That doesn’t mean he wouldn’t use a funny line that emphasized an important message – we need to stop relying so much on unsubstantiated opinion and instead back up opinion with data (including experiments).

Quotes can help crystallize a concept and drive home a point. They are very rarely a decent way to pass on the whole of what the author meant, this is why context is so important. But, most often quotes are shared without context and that of course, leads to misunderstandings.

image of quote - "It is wrong to suppose that if you can’t measure it, you can’t manage it – a costly myth."

A funny example of this is the Deming quote that you often see: “if you can’t measure it, you can’t manage it.” Deming did actually say that. But without the context you get 100% the wrong understanding of what he said. Deming’s full statement is “It is wrong to suppose that if you can’t measure it, you can’t manage it – a costly myth.” Now normally the much more context is required to truly understand the author’s point. But this is a funny example of how a quote can be even be accurate when passed on to you and yet completely misleading because it is taken out of context.

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All Data is Wrong, Some is Useful

From my first blog post on this blog – Dangers of Forgetting the Proxy Nature of Data

we often fail to explore whether changes in the numbers (which we call results) are representative of the “true results” of the system or if the data is misleading.

Data is meant to provide us insight into a more complex reality. We need to understand the limitations when we look at “results” and understand data isn’t really the results but a representation we hope is close to reality so we can successfully use the data to make decisions.

But we need to apply thought to how we use data. Lab results are not the same are what happens in the field. It is cheaper and faster to examine results in a lab. But relying on lab results involves risk. That doesn’t mean relying on lab results is bad, we have to balance the costs and benefits of getting more accurate data.

But relying on lab results and not understanding the risk is dangerous. This is the same idea of going to the gemba to get an accurate understanding instead of relying on your ability to imagine reality based upon some data and ideas of what it is probably like.

photo of a Modified Yellow VW Beetle

VW Beetle (in Bangkok, Thailand) has some sort of modification along the back bumper but I don’t know what it is meant to do. Any ideas? More of my photos from Bangkok.

Volkswagen Drops 23% After Admitting Diesel Emissions Cheat

Volkswagen AG lost almost a quarter of its market value after it admitted to cheating on U.S. air pollution tests for years

During normal driving, the cars with the software — known as a “defeat device” — would pollute 10 times to 40 times the legal limits, the EPA estimated. The discrepancy emerged after the International Council on Clean Transportation commissioned real-world emissions tests of diesel vehicles including a Jetta and Passat, then compared them to lab results.

Obviously VW was managing-to-test-result instead of real world value. It seems they were doing so intentionally to provide misleading data. Obviously one of the risks with lab test results (medical trials etc.) is that those with an interest in showing better results could manipulate the data and lab procedures (or systems) to have the data show their product in the most favorable light.

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George Box Webcast on Statistical Design in Quality Improvement

George Box lecture on Statistical Design in Quality Improvement at the Second International Tampere Conference in Statistics, University of Tampere, Finland (1987).

Early on he shows a graph showing the problems with American cars steady over a 10 years period. Then he overlays the results for Japanese cars which show a steady and significant decline of the same period.

Those who didn’t get to see presentations before power point also get a chance to see old school, hand drawn, overhead slides.

He discusses how to improve the pace of improvement. To start with informative events (events we can learn from) have to be brought to the attention of informed observers. Otherwise only when those events happen to catch the attention of the right observer will we capture knowledge we can use to improve. This results in slow improvement.

A control chart is an example of highlighting that something worth studying happened. The chart will indicate when to pay attention. And we can then improve the pace of improvement.

Next we want to encourage directed experimentation. We intentionally induce informative events and pay close attention while doing so in order to learn.

Every process generates information that can be used to improve it.

He emphasis the point that this isn’t about only manufacturing but it true of any process (drafting, invoicing, computer service, checking into a hospital, booking an airline ticket etc.).

He then discussed an example from a class my father taught and where the students all when to a TV plant outside Chicago to visit. The plant had been run by Motorola. It was sold to a Japanese company that found there was a 146% defect rate (which meant most TVs were taken off the line to be fixed at least once and many twice) – this is just the defect rate before then even get off the line. After 5 years the same plant, with the same American workers but a Japanese management system had reduced the defect rate to 2%. Everyone, including managers, were from the USA they were just using quality improvement methods. We may forget now, but one of the many objections managers gave for why quality improvement wouldn’t work in their company was due to their bad workers (it might work in Japan but not here).

He references how Deming’s 14 points will get management to allow quality improvement to be done by the workforce. Because without management support quality improvement processes can’t be used.

With experimentation we are looking to find clues for what to experiment with next. Experimentation is an iterative process. This is very much the mindset of fast iteration and minimal viable product (say minimal viable experimentation as voiced in 1987).

There is great value in creating iterative processes with fast feedback to those attempting to design and improve. Box and Deming (with rapid turns of the PDSA cycle) and others promoted this 20, 30 and 40 years ago and now we get the same ideas tweaked for startups. The lean startup stuff is as closely related to Box’s ideas of experimentation as an iterative process as it is to anything else.

Related: Ishikawa’s seven quality control tools

He also provided a bit of history that I was not aware of saying the first application of orthogonal arrays (fractional factorial designs) in industry was by Tippett in 1933. And he then mentioned work by Finney in 1945, Plackett and Burman in 1946 and Rao in 1947.

George Box Articles Available for a Short Time

A collection of George Box articles have been selected for a virtual George Box issue by David M. Steinberg and made available online.

George E. P. Box died in March 2013. He was a remarkably creative scientist and his celebrated professional career in statistics was always at the interface of science and statistics. George Box, J. Stuart Hunter and Cuthbert Daniel were instrumental in launching Technometrics in 1959, with Stu Hunter as the initial editor. Many of his articles were published in the journal. Therefore we think it is especially fitting that Technometrics should host this on-line collection with some of his most memorable and influential articles.

They also include articles from Journal of the American Statistical Association and Quality Engineering. Taylor & Francis is offering these articles freely in honor of George Box until December 31st, 2014. It is very sad that closed science and engineering journals block access to the great work created by scientists and engineers and most often paid for by government (while working for state government universities and with grants organizations like the National Science Foundation[NSF]). At least they are making a minor exception to provide the public (that should be unlimited access to these works) a limited access to these articles this year. These scientists and engineers dedicated their careers to using knowledge to improve society not to hide knowledge from society.

Some of the excellent articles make available for a short time:

The “virtual issue” includes many more articles.

Related: Design of Experiments: The Process of Discovery is IterativeQuotes by George E.P. BoxThe Art of DiscoveryAn Accidental Statistician: The Life and Memories of George E. P. Box

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Analysis Must be Implemented by People to Provide Value

Guest Post by Bill Scherkenbach

photo of W. Edwards Deming with a cat

Every time I look at this picture, I think of Dr. Deming’s words to drive out fear and take joy in your work. We were talking in my home office when Sylvester saw a good lap and took it. Our conversation immediately shifted when both Dr. Deming and Sylvester started purring.

The greatest statistical analysis is nothing if it can’t be implemented by people. But people learn in different ways. Some like good stories, others like pictures. Only a few like equations. Dr. Deming always liked a good laugh; and a good purr.

By what method do you get your analyses implemented?

Bill Scherkenbach taught with Dr. Deming at the Deming 2 day seminars and received the Deming Medal and the author of several books on Deming management principles.

Related: How to Get a New Management Strategy, Tool or Concept Adopted part 1 and part 2Getting Known Good Ideas AdoptedRespect People by Creating a Climate for Joy in WorkPlaying Dice and Children’s Numeracy

Stu Hunter Discussing Bill Hunter, Statistics for Experimenters and EVOP

In this clip, Stu Hunter talks about Bill Hunter (my father, and no relation to Stu Hunter), Statistics for Experimenters and EVolutionary OPerations (EVOP).

Stu mentions Bill Hunter’s work with the City of Madison, which started with the First Street Garage (Out of the Crisis included a short write up on this effort by Dad, which, I believe, was the first application of Deming’s ideas in the public sector).

There was also a great deal of work done with the Police department, as the police chief, David Couper, saw great value in Deming’s ideas. The Police department did some great work and David’s blog shares wonderful ideas on improving policing. I don’t think Dad was that directly involved in what happened there, but it is one of the nice benefits of seeding new ideas: as they take root and grow wonderful things happen without any effort on your part.

As to why Dad got involved with the city, he returned from a summer teaching design of experiments and quality improvement methods in China (this is just before China was really open, a few outsiders were let in to teach). We had also lived overseas several other times, always returning to Madison. He decided he wanted to contribute to the city he loved, Madison, and so he talked to the Mayor about helping improve performance of the city.

The mayor listened and they started with a pilot project which Dad work on with Peter Scholtes. Dad talked to Peter, who he had know for years, and who worked for the city, before talking to the mayor. Read more about the efforts in Madison via the links at the end of this post.

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Design of Experiments: The Process of Discovery is Iterative

This video is another excerpt on the design of experiments videos by George Box, see previous posts: Introduction to Fractional Factorial Designed Experiments and The Art of Discovery. This video looks at learning about experimental design using paper helicopters (the paper linked there may be of interest to you also).

In this example a screening experiment was done first to find those factors that have the largest impact on results. Once the most important factors are determined more care can be put into studying those factors in greater detail.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician, and many of his other books.

The importance of keeping the scope (in dollars and time) of initial experiments down was emphasized in the video.

George Box: “Always remember the process of discovery is iterative. The results of each stage of investigation generating new questions to answered during the next.”

Soren Bisgaard and Conrad Fung also appear in this except of the video.

The end of the video includes several suggested resources including: Statistics for Experimenters, Out of the Crisis and The Scientific Context of Quality Improvement.

Related: Introductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)Why Use Designed Factorial Experiments?brainstormingWhat Can You Find Out From 12 Experimental Runs?

Stated Versus Revealed Preference

My father provided me a good example of the flawed thinking of relying on stated preference when I was growing up. Stated preference is, as you might deduce, the preferences voiced by customers when you ask. This is certainly useful but people’s stated preference often do not match there actions. And for a business, actions that lead to customers are more important than claims potential customers make about what will make them customers.

His example was that if you ask people if clean bathrooms in a restroom is required for a restaurant they will say yes. Potential customers will say this is non-negotiable, it is required. But if you eat at many “ethnic restaurants,” as we always did growing up, you would see many popular restaurants did not have clean restrooms. If the food at atmosphere was good enough clean restrooms were negotiable, even if customers stated they were not.

Now I think clean restrooms is a wise move for restaurants to make; it matters to people. Instead of creating a barrier to repeat customers that has to be overcome with much better food and atmosphere it is wiser to give yourself every advantage by giving the customers what they want. But I think the example is a simple example of stated versus revealed preferences.

McDonald’s gets a great deal of success by doing certain things well, including clean bathrooms, even if they miss on things some people think are important for a restaurant. McDonald’s really gets a fair amount of business for people driving a long distance that really want a clean bathroom and a quick stretch of their legs and quick food. This is a small percentage of McDonald’s customer visits but still a very large number of visits each day I am sure. Understanding, and catering to, the problem your customers are trying to solve is important.

The point to remember is what your potential customers say they will do is different than what they do. It is sensible to listen to stated preferences of customers just understand them for what they are.

We need to pay more attention to revealed preferences. Doing so can require putting in a bit more thinking than just asking customers to fill out a questionnaire. But it is worth the effort. A simple restaurant based example would be to have wait staff pay attention to what people leave on their plate. If you notice certain side dishes are not eaten more often, look into that and see what can be done (improving how it is prepared, substituting something else…).

Related: Voice of the CustomerThe Customer is the Purpose of Our WorkCustomers Are Often IrrationalPackaging Affects Our Perception of TasteBe Careful What You Measure

The Art of Discovery

Quality and The Art of Discovery by Professor George Box (1990):

Quotes by George Box in the video:

“I think of statistical methods as the use of science to make sense of numbers”

“The scientific method is how we increase the rate at which we find things out.”

“I think the quality revolution is nothing more, or less, than the dramatic expansion of the of scientific problem solving using informed observation and directed experimentation to find out more about the process, the product and the customer.”

“It really amounts to this, if you know more about what it is you are doing then you can do it better and you can do it cheaper.”

“We are talking about involving the whole workforce in the use of the scientific method and retraining our engineers and scientists in a more efficient way to run experiments.”

“Tapping into resources:

  1. Every operating system generates information that can be used to improve it.
  2. Everyone has creativity.
  3. Designed experiments can greatly increase the efficiency of experimentation.

An informed observer and directed experimentation are necessary for the scientific method to be applied. He notes that the control chart is used to notify an informed observer to explain what is special about the conditions when a result falls outside the control limits. When the chart indicates a special cause is likely present (something not part of the normal system) an informed observer should think about what special cause could lead to the result that was measured. And it is important this is done quickly as the ability of the knowledgable observer to determine what is special is much greater the closer in time to the result was created.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician: The Life and Memories of George E. P. Box, and many of his other books.

Related: Two resources, largely untapped in American organizations, are potential information and employee creativityStatistics for Experimenters (book on directed experimentation by Box, Hunter and Hunter)Highlights from 2009 George Box SpeechIntroductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)

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.

photo of George EP Box

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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Podcast Discussion on Management Matters

I continue to record podcasts as I promote my new book – Management Matters: Building Enterprise Capability. This the second part, of 2, of my podcast with Joe Dager, Business 901: Management Matters to a Curious Cat. The first part featured a discussion of 2 new deadly diseases facing companies.

image of the cover of Managmenet Matters by John Hunter

Management Matters by John Hunter

Listen to this podcast.

Links to more information on some of the topics I mention in the podcast:

More podcasts: Process Excellence Network Podcast with John HunterBusiness 901 Podcast with John Hunter: Deming’s Management Ideas Today (2012)Leanpub Podcast on Management Matters: Building Enterprise Capability

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.

Related: One factor at a time (OFAT) Versus Factorial DesignsThe purpose of Factorial Designed Experiments

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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.

photo of Mark Anderson

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.

Related: 2009 Curious Cat Management Blog Carnival2010 Management Blog Review: Software, Manufacturing and Leadership

Dr. Deming in 1980 on Product Quality in Japan and the USA

I posted an interesting document to the Curious Cat Management Library: it includes Dr. Deming’s comments as part of a discussion organized by the Government Accounting Office in 1980 on Quality in Japan and the United States.

The document provides some interesting thoughts from Dr. Deming and others; Dr. Deming’s statements start on page 52 of the document. For those really interested in management improvement ideas it is a great read. I imagine most managers wouldn’t enjoy it though (it isn’t giving direct advice for today, but I found it very interesting).

Some selected quotes from the document follow. On his work with Japan in 1950:

This movement, I told them, will fail and nothing will happen unless management does their part. Management must know something about statistical techniques and know that if they are good one place, they will work in another. Management must see that they are used throughout the company.
Quality control must take root with simple statistical techniques that management and everyone in the company must learn. By these techniques, people begin to understand the different kinds of variation. Then quality control just grow with statistical theory and further experience. All this learning must be guided by a master. Remarkable results may come quick, but one has no right to expect results in a hurry. The learning period never ends.

The statistical control of quality is not for the timid and the halfhearted. There is no way to learn except to learn it and do it. You can read about swimming, but you might drown if you had to learn it that way!

One of the common themes at that time was Deming’s methods worked because Japanese people and culture were different. That wasn’t why the ideas worked, but it was an idea many people that wanted to keep doing things the old way liked to believe.

There may be a lot of difference, I made the statement on my first visit there that a Japanese man was never too old nor too successful to learn, and to wish to learn; to study and to learn. I know that people here also study and learn. I’ll be eighty next month in October. I study every day and learn every day. So you find studious people everywhere, but I think that you find in Japan the desire to learn, the willingness to learn.

You didn’t come to hear me on this; there are other people here much better qualified than I am to talk. But in Japan, a man works for the company; he doesn’t work to please somebody. He works for the company, he can argue for the company and stick with it when he has an idea because his position is secure. He doesn’t have to please somebody. It is so here in some companies, but only in a few. I think this is an important difference.

At the time the way QC circles worked in Japan was basically employee led kaizen. So companies that tried to copy Japan told workers: now go make things better like the workers we saw in Japan were doing. Well with management not changing (and understanding Deming’s ideas, lean thinking, variation, systems thinking…) and staff not given training to understand how to improve processes it didn’t work very well. We (those reading this blog) may all now understand the advantages one piece flow. I can’t imagine too many people would jump to that idea sitting in their QC circle without having been told about one piece flow (I know I wouldn’t have), and all the supporting knowledge needed to make that concept work.

QC circles can make tremendous contributions. But let me tell you this, Elmer. If it isn’t obvious to the workers that the managers are doing their part, which only they can do, I think that the workers just get fed up with trying in vain to improve their part of the work. Management must do their part: they must learn something about management.

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Management Improvement Carnival #139

comic showing the dangers of drawing false conclusion based on statistical significance

Randall Munroe illustrates RA Fisher point that you must think to draw reasonable conclusions from data. Click the image to see the full xkcd comic.

The Curious Cat Management Improvement Carnival has been published since 2006. We find great management blog posts and share them with you 3 times a month. We hope you find these post interesting and find some new blogs to start reading. Follow John Hunter online: Google+, Twitter, LinkedIn, more.

  • Questioning the Value of the P-Value by Jon Miller – “Father of modern statistics Ronald A. Fisher invented the p-value as an informal measure of evidence against the null hypothesis. Although often overlooked, Fisher called on scientists use other types of evidence such as the a priori plausibility of the hypothesis and the relative strengths of results from previous studies in combination with the p-value.”
  • Teachers Cheating and Incentives by Dan Ariely – “they began to do anything that would improve their performance on that measure even by a tiny bit—even if they messed up other employees in the process. Ultimately they were consumed with maximizing what they knew they would be measured on”
  • It’s About The Journey and Sometimes It Starts With Failure by Tim McMahon – “If we allow ourselves to become discouraged during the learning process we may give up right before we reach our goal. Anytime we learn from our efforts we are in the process of succeeding. Each lesson brings us closer to our intended result.”
  • When Patents Attack – “as many as 80 percent of software engineers say the patent system actually hinders innovation. It doesn’t encourage them to come up with new ideas and create new products. It actually gets in their way.” (I added “An outdated intellectual property system” as deadly management/economic disease number 9 – building on Deming’s 7 deadly
    diseases a few years ago – John). Also from NPR: The Patent War
  • 3 Things You Can Do When Your Manager Doesn’t Support Continuous Improvement by Ron Pereira – “So keep fighting… keep learning… keep improving. If you do this, one thing is for certain, you and the organization you work for will be better off even if they don’t realize it.”
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Best Selling Books In the Curious Cat Bookstore

The most popular books in July at Curious Cat Books were, Statistics for Experiments (1st edition), followed by Statistics for Experiments (2nd edition) and the Leader’s Handbook by Peter Scholtes. These books are great, I am happy others have been finding them and reading them. Statistics for Experimenters is co-authored by my father.

Top sellers so far this year (adding together all editions, including Kindle):
1) The Leader’s Handbook
2) Statistics for Experimenters
3) New Economics
4) Abolishing Performance Appraisals
5) The Team Handbook
6) Out of the Crisis

The Leader’s Handbook is far away in the lead. The order of popularity on Amazon overall: 1) Out of the Crisis, 2) New Economics, 3) The Team Handbook, 4) Abolishing Performance Appraisals, 5) Statistics for Experimenters and 6) The Leader’s Handbook. The only thing that surprises me with the overall numbers is the Leader’s Handbook. The Amazon rankings are hugely biased by recent activity (it isn’t close to a ranking of sales this year). Still I expected the Leader’s Handbook would rank very well. It is the first book I recommend for almost any situation (the only exceptions are if there is a very specific need – for example Statistics for Experimenters for multi-factorial designed experiments or The Improvement Guide for working on the process of improvement.

My guess is Curious Cat site users (and I am sure a fair number of people sent by search engines) are much more likely to buy those books I recommend over and over. Still many books I don’t promote are bought and some books I recommend consistently don’t rack up many sales through Curious Cat.

I started this as a simple Google+ update but then found it interesting enough to expand to a full post. Hopefully others find it interesting also.

Related: Using Books to Ignite ImprovementWorkplace Management by Taiichi OhnoProblems with Management and Business BooksManagement Improvement Books (2005)

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.

Related: The Purpose of Factorial Designed ExperimentsUsing Design of Experimentsarticles by R.A. Fisherarticles on using factorial design of experimentsDoes good experimental design require changing only one factor at a time (OFAT)?Statistics for Experimenters

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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How to Manage What You Can’t Measure

In Out of the Crisis, page 121, Dr. Deming wrote:

the most important figures that one needs for management are unknown or unknowable (Lloyd S. Nelson, director of statistical methods for the Nashua corporation), but successful management must nevertheless take account of them.

So what do you do then? I am a strong advocate of Deming’s ideas on management. I see understanding system thinking, psychology, the theory of knowledge and variation as the tools to use when you can’t get precise measures (or when you can).

Even if you can’t measure exactly what you want, you can learn about the area with related data. You are not able to measure the exact benefit of a happy customer but you can get measures that give you evidence of the value and even magnitude. And you can get measures of the costs of dis-satisfied customers. I just mention this to be clear getting data is very useful and most organizations need to focus on gathering sensible data and using it well.

Without precise measure though you have to use judgment. Judgment will often be better with an understanding of theory and repeated attempts to test those theories and learn. Understanding variation can be used even if you don’t have control charts and data. Over-reaction to special causes is very common. Even without data, this idea can be used to guide your thoughts.

The danger is that we mistake measures for the thing itself. Measures are a proxy and we need to understand the limitation of the data we use. The main point Deming was making was we can’t just pretend the data we have tells us everything we need to know. We need to think. We need to understand that the data is useful but the limitations need to be remembered.

Human systems involve people. To manage human systems you need to learn about psychology. Paying attention to what research can show about motivation, fear, trust, etc. is important and valuable. It aids management decisions when you can’t get the exact data that you would like. If people are unhappy you can see it. You may also be able to measure aspects of this (increased sick leave, increased turnover…). If people are unhappy they often will not be as pleasant to interact with as people who are happy. You can make judgments about the problems created by internal systems that rob people of joy in work and prevent them from helping customers.

For me the key is to use the Deming’s management system to guide action when you can’t get clear data. We should keep trying to find measures that will help. In my experience even though there are many instances where we can get definite data on exactly what we want we fail to get data that would help guide actions a great deal). Then we need to understand the limitations of the data we can gather. And then we need to continually improve and continually learn.

When you have clear data, Deming’s ideas are also valuable. But when the data is lacking it is even more important to take a systemic approach to making management decisions. Falling back into using the numbers you can get to drive decision making is a recipe for trouble.

Related: Manage what you can’t measureStatistical Engineering Links Statistical Thinking, Methods and Toolsoutcome measures

Actionable Metrics

Metrics are valuable when they are actionable. Think about what will be done if certain results are shown by the data. If you can’t think of actions you would take, it may be that metric is not worth tracking.

Metrics should be operationally defined so that the data is collected properly. Without operationally definitions data collected by more than one person will often include measurement error (in this case, the resulting data showing the results of different people measuring different things but calling the result the same thing).

And without operational definitions those using the resulting data may well mis-interpret what it is saying. Often data is presented without an operational definition and people think the data is saying something that it is not. I find most often when people say statistics lie it is really that they made an incorrect assumption about what the data said – which most often was because they didn’t understand the operational definition of the data. Data can’t lie. People can. And people can intentionally mislead with data. But far more often people unintentionally mislead with data that is misunderstood (often this is due to failure to operationally define the data).

In response to: Metrics Manifesto: Raising the Standard for Metrics

Related: Outcome MeasuresEvidence-based ManagementMetrics and Software DevelopmentDistorting the System (due to misunderstanding metrics)Manage what you can’t measure