Category Archives: Data

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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Quality Processes in Unexpected Places

This month Paul Borawski asked ASQ’s Influential Voices to explore the use of quality tools in unexpected places.

The most surprising example of this practice that I recall is the Madison, Wisconsin police department surveying those they arrested to get customer feedback. It is obvious that such “customers” are going to be biased. Still the police department was able to get actionable information by seeking the voice of the customer.

photo of a red berry and leaves

Unrelated photo from Singapore Botanical Garden by John Hunter.

Certain of the police department’s aims are not going to match well with those they arrest (most obviously those arrested wish the police department didn’t arrest them). The police department sought the voice of the customer from all those they interacted with (which included those they arrested, but also included those reporting crimes, victims, relatives of those they arrested etc.).

The aim of the police department is not to arrest people. Doing so is necessary but doing so is most similar in the management context to catching an error to remove that bad result. It is better to improve processes so bad results are avoided. How the police interact with the public can improve the process to help steer people’s actions away from those that will require arrests.

The interaction police officers have with the public is a critical gemba for meeting the police department’s aim. Reducing crime and encouraging a peaceful society is aided by knowing the conditions of that gemba and knowing how attempts to improve are being felt at the gemba.

All customer feedback includes bias and personal preferences and potentially desires that are contrary to the aims for the organization (wanting services for free, for example). Understanding this and how important understanding customer/user feedback on the gemba is, it really shouldn’t be surprising that the police would want that data. But I think it may well be that process thinking, evidence based management and such ideas are still not widely practiced as so the Madison police department’s actions are still surprising to many.

Quality Leadership: The First Step Towards Quality Policing by David Couper and Sabine Lobitz

Our business is policing, our customers are the citizens within our jurisdictions, and our product is police service (everything from crime fighting and conflict management to safety and prevention programs.)

If we are to cure this we must start to pay attention to the new ideas and trends in the workplace mentioned earlier that are helping America’s businesses; a commitment to people, how people are treated — employees as well as citizens, the development of a people-oriented workplace, and leadership can and does make a difference.

If we change the way in which we lead the men and women in our police organizations, we can achieve quality in policing. However, wanting to change and changing are worlds apart. The road to change is littered by good intentions and short-term efforts.

This article, from 1987, illustrates the respect for people principle was alive and being practiced 25 years ago; most organizations need to do a great deal more work on applying practices that show respect for people.

Related: Quality Improvement and Government: Ten Hard Lessons From the Madison Experience by David C. Couper, Chief of Police, City of Madison, Wisconsin – SWAT Raids, Failure to Apply System Thinking in Law EnforcementMeasuring What Matters: Developing Measures of What the Police DoThe Public Sector and W. Edwards DemingDoing More with Less in the Public Sector – A Progress Report from Madison, Wisconsin

The Market Discounts Proven Company Leadership Far Too Quickly

Developing a strong executive leadership culture is not a short term effort. It isn’t based on one person. It almost never deteriorates quickly. Yet markets continually overact to minor blips on the long term success of companies. I think this is mainly due to a failure to appreciate systems and a failure to appreciate variation along with plenty of other contributing factors.

The market’s weakness does provide investment opportunities. Though taking advantages of them is much more difficult than spotting a general weakness. While excellent management almost never becomes pitiful overnight (regardless of how often talking heads would have you believe) business can change very quickly due to rapidly changing market conditions. Avoiding the purchases when the underlying business has sustained a significant blow that excellent management will deal with but which will reduce the value of the enterprise going forward is key to taking advantage of the market’s silly overreaction to bad news (or even calling things “bad news” that are not actually bad just not as awesome as some were hoping for).

My positive opinion of Toyota’s management has continued for a long time. A few years ago an amazing number of people were all excited about the “decline of Toyota” and wrote about how Toyota’s ways had to change. I wrote at the time was this is needless hysteria and if Toyota just focused a bit more on applying the Toyota’s management methods they would be in great shape. The problems were due to Toyota’s mistakes in practicing the Toyota Production System not in a weakness of those practices.

Looking at a chart of Toyota’s stock price from 2007 to today it peaks at about $137 in January 2007 and bottoms at $58 in early 2009 and now is at $96. Toyota’s stock price has been priced richly due to respect for management and consistently strong cash flow. As it fell below $75 there you no longer had to pay a premium for excellent management, but that management was still there. I like getting bargains when I buy stocks. One of the things I have learned I am too focused on bargains and I should be more willing to accept less of a bargain to get great management systems – so I have adjusted, and have improved my results. When I can get a great bargain and great management it is wonderful, though sadly a rare occurrence. Toyota’s price now seems reasonable, but not a huge bargain.

The market continually gets overly excited by either actual problems or perceived problems. I wrote about this happening with Netflix 2 years ago. Netflix made some mistakes and faced some tough business issues. The evidence of sound, sensible, effective management vastly outweighed the evidence for management failure – yet there were hundreds of articles about the pitiful failure of Netflix management.

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Leanpub Podcast on My Book – Management Matters: Building Enterprise Capability

image of the cover of Managmenet Matters by John Hunter

Management Matters by John Hunter

I recently was interviewed for a podcast by Len Epp with Leanpub: Leanpub Podcast Interview #9: John Hunter. I hope you enjoy the podcast (download the mp3 of the podcast).

In the podcast we cover quite a bit of ground quickly, so the details are limited (transcript of the interview). These links provide more details on items I mention in the podcast. They are listed below in the same order as they are raised in the podcast:

The last 15 minutes of the podcast I talk about some details of working with Leanpub; I used Leanpub to publish Management Matters. I recommend Leanpub for other authors. They don’t just have lean in their name, they actual apply lean principles (focusing on the value chain, eliminating complexity, customer focus, etc.) to operating Leanpub. It is extremely easy to get started and publish your book.

Leanpub also offers an excellent royalty plan: authors take home 90% of the revenue minus 50 cents per book. They publish without “digital rights management” crippling purchasers use of the books. Buyers have access to pdf, kindle (mobi) and epub (iPad, nook) format books and get access to all updates to the book. All purchases include a 45 day full money back guaranty.

Related: Business 901 Podcast with John Hunter: Deming’s Management Ideas TodayInterviews for Management Matters: Building Enterprise Capability

Indirect Improvement

Often the improvements that have the largest impact are focused on improving the effectiveness of thought and decision making. Improving the critical thinking in an organization has huge benefits over the long term.

My strategy along the lines of improving critical thinking is not to make that the focus of some new effort. Instead that ability to reason more effectively will be an outcome of things such as: PDSA projects (where people learn that theories must be tested, “solutions” often fail if you bother to look at the results…), understanding variation (using control charts, reading a bit of material on: variation, using data effectively, correlation isn’t causation etc.), using evidenced based management (don’t make decision based on the authority of the person speaking but on the merit that are spoken).

These things often take time. And they support each other. As people start to understand variation the silly discussion of what special causes created the result that is within the expected outcomes for the existing process are eliminated. As people learn what conclusions can, and can’t, be drawn from data the discussions change. The improvements from the process of making decisions is huge.

As people develop a culture of evidence based management if HiPPOs try to push through decision based on authority (based on Highest Paid Person’s Opinion) without supporting evidence those attempts are seen for what they are. This presents a choice where the organization either discourages those starting to practice evidence based decision making (reverting to old school authority based decision making) or the culture strengthens that practice and HiPPO decision making decreases.

Building the critical thinking practices in the organization creates an environment that supports the principles and practices of management improvement. The way to build those critical thinking skills is through the use of quality tools and practices with reminders on principles as projects are being done (so until understanding variation is universal, continually pointing out that general principle with the specific data in the current project).

The gains made through the direct application of the tools and practices are wonderful. But the indirect benefit of the improvement in critical thinking is larger.

Related: Dan’t Can’t LieGrowing the Application of Management Improvement Ideas in Your OrganizationBuild Systems That Allow Quick Action – Don’t Just Try and Run FasterBad Decisions Flow From Failing to Understand Data and Failing to Measure Results of Changes

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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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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Keys to the Effective Use of the PDSA Improvement Cycle

The PDSA improvement cycle was created by Walter Shewhart where Dr. Deming learned about it. An improvement process is now part of many management improvement methods (A3 for lean manufacturing, DMAIC for six sigma and many other modifications). They are fairly similar in many ways. The PDSA cycle (Plan, Do, Study, Act) has a few key pieces that are either absent in most others processes of greatly de-emphasized which is why I prefer it (A3 is my second favorite).

The PDSA cycle is a learning cycle based on experiments. When using the PDSA cycle prediction of the results are important. This is important for several reasons but most notably due to an understanding of the theory of knowledge. We will learn much more if we write down our prediction. Otherwise we often just think (after the fact); yeah that is pretty much what I expected (even if it wasn’t). Also we often fail to think specifically enough at the start to even have a prediction. Forcing yourself to make a prediction gets you to think more carefully up front and can help you set better experiments.

An organization using PDSA well will turn the PDSA cycle several times on any topic and do so quickly. In a 3 month period turning it 5 times might be good. Often those organizations that struggle will only turn it once (if they are lucky and even reach the study stage). The biggest reason for effective PDSA cycles taking a bit longer is wanting more data than 2 weeks provides. Still it is better to turn it several times will less data – allowing yourself to learn and adjust than taking one long turn.

The plan stage may well take 80% (or even more) of the effort on the first turn of the PDSA cycle in a new series. The Do stage may well take 80% of of the time – it usually doesn’t take much effort (to just collect a bit of extra data) but it may take time for that data to be ready to collect. In the 2nd, 3rd… turns of the PDSA cycle the Plan stage often takes very little time. Basically you are just adjusting a bit from the first time and then moving forward to gather more data. Occasionally you may learn you missed some very important ideas up front; then the plan stage may again take some time (normally if you radically change your plans).

Remember to think of Do as doing-the-experiment. If you are “doing” a bunch of work (not running an experiment and collecting data) that probably isn’t “do” in the PDSA sense.

Study should not take much time. The plan should have already have laid out what data is important and an expectation of what results will be achieved and provide a good idea on next steps. Only if you are surprised (or in the not very common case that you really have no idea what should come next until you experiment) will the study phase take long.

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Agile Story Point Estimation

In agile software development tasks are documented as user stories. Then the level of effort for those stores can be estimated by assigning each story points. The velocity that can be produced in a period (called a sprint, for us 2 weeks) can be estimated. Thus you can predict what can be delivered in the next sprint (which can help business managers make priority decisions).

I have found estimation to be worthwhile. In doing so, we accept there is a great amount of variation but points give a hint to scale. They can help prioritize (if you have 5 things you want but 1 is much harder you may well drop that to the bottom). I have always accepted a great amount of variation in the velocity, worry about the variation I don’t find worthwhile. I do think trying to act as though the velocity is precise can lead to problems. At the same time having a measure of velocity, even accepting understanding variation was present, was useful.

Over time reducing variation (probably largely through better estimation and perhaps a few better tools, reduced technical debt, better documentation, testing…) is helpful and laudable. We made improvement but still lots of variation existed. The biggest help in reducing the measured velocity was breaking down large stories to more manageable sizes. The challenge of estimating user stories, I suspect, has some fairly high variation (even with good system improvements that can help reduce variation).

Large stories just can hide huge variation in what is really required once getting into implementing it.

The way we did estimation (discussing in a sprint planning meeting) did take some time (but not a huge amount). It was agreed by those involved that the time spent was worthwhile. Sometimes we did slip and spend too much time on this, that was an area we had to pay attention to. The discussions were educational and helped provide guidance on how to approach the story. The value of discussions around estimations was probably the biggest surprise I have had in implementing any agile ideas. The value of those discussion was much higher than I imagined (I basically anticipated them just as non-value added time to get the result of an estimate, but they were a source of learning and consensus building).

Related: Assigning Story Points to Bug FixesMistake Proofing the Deployment of Software CodeChecklists in Software Development

These thoughts were prompted by: Story Points Considered Harmful – Or why the future of estimation is really in our past…

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Trust But Verify

The following are my comments, which were sparked by question “Trust, but verify. Is this a good example of Profound Knowledge in action?” on the Linked In Deming Institute group.

Trust but verify makes sense to me. I think of verify as process measures to verify the process is producing as it should. By verifying you know when the process is failing and when to look for special causes (when using control chart thinking with an understanding of variation). There are many ways to verify that would be bad. But the idea of trust (respect for people) is not just a feel-good, “be nice to everyone and good things happen”, in Deming’s System of Profound Knowledge.

I see the PDSA improvement cycle as another example of a trust-but-verify idea. You trust the people at the gemba to do the improvement. They predict what will happen. But they verify what does actually happen before they run off standardizing and implementing. I think many of us have seen what happens when the idea of letting those who do the work, improve the process, is adopted without a sensible support system (PDSA, training, systems thinking…). It may actually be better than what was in place, but it isn’t consistent with Deming’s management system to just trust the people without providing methods to improve (and education to help people be most effective). Systems must be in place to provide the best opportunity to succeed. Trusting the people that do the work, is part of it.

I understand there are ways to verify that would be destructive. But I do believe you need process measures to verify systems are working. Just trusting people to do the right thing isn’t wise.

A checklist is another way of “not-trusting.” I think checklists are great. It isn’t that I don’t trust people to try and do the right thing. I just don’t trust people alone, when systems can be designed with verification that improves performance. I hear people complaign that checklists “don’t respect my expertise” or have the attitude that they are “insulting to me as a professional” – you should just trust me.

Sorry, driving out fear (and building trust – one of Deming’s 14 points) is not about catering to every person’s desire. For Deming’s System of Profound Knowledge: respect for people is part of a system that requires understand variation and systems thinking and an understanding of psychology and theory of knowledge. Checklists (and other forms of verification) are not an indication of a lack of trust. They are a a form of process measure (in a way) that has been proven to improve results.

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