Tag Archives: Data

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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The Importance of Critical Thinking and Challenging Assumptions

There are many factors that are important to effectively practice the management improvement ideas I have discussed in this blog for over a decade. One of the most important is a culture that encourages critical thinking as well as challenging claims, decisions and assumptions.

I discussed this idea some in: Customers Are Often Irrational. There is a difference between saying people wish to have their desires met and people act in the manner to maximize the benefits they wish to receive.

It is important to study customer’s choice and learn from them. But being deceived by what their choice mean is easier than is usually appreciated. Often the decision made is contrary to the ideal choice based on their beliefs. It is often poor decision making not an indication that really they want a different result than they express (as revealed versus stated preference can show). People that ignore the evidence behind climate change and condemn coastal areas to severe consequences don’t necessarily prefer the consequences that their decision leads to. It may well be that decision to ignore the evidence is not based on a desire to suffer long term consequences in order to get short term benefits. It may well be just an inability to evaluate evidence in an effective way (fear of challenging ourselves to learn about matters we find difficult often provides a strong incentive to avoid doing so).

Knowing the difference between choosing short term benefits over long term consequences and a failure to comprehend the long term consequences is important. Just as in this example, many business decisions have at the root a desire to pretend we can ignore the consequences of our decisions and a desire to accept falsehoods that let us avoid trying to cope with the difficult problems.

photo of me with a blackboard in my father's office

Photo of me and my artwork in my father’s office by Bill Hunter

It is important to clearly articulate the details of the decision making process. We need to note the actual criticism (faulty logic, incorrect beliefs/assumptions…) that results in what some feel is a poor conclusion. But we seem to find shy away from questioning faulty claims (beliefs that are factually incorrect – that vaccines don’t save people from harm, for example) or lack of evidence (no data) or poor reasoning (drawing unsupported conclusions from a well defined set of facts).

Critical thinking is important to applying management improvement methods effectively. It is important to know when decisions are based on evidence and when decisions are not based on evidence. It can be fine to base some decisions on principles that are not subject to rational criticism. But it is important to understand the thought process that is taken to make each decision. If we are not clear on the basis (evidence or opinion regardless of evidence) we cannot be as effective in targeting our efforts to evaluate the results and continually improve the processes in our organizations.

Describing the decision as “irrational” is so imprecise that it isn’t easy to evaluate how much merit the criticism has. If specific facts are called into question or logical fallacies within the decision making process are explained it is much more effective at providing specific items to explore to evaluate whether the criticism has merit.

When specific criticisms are made clear then those supporting such a decision can respond to the specific issues raised. And in cases where the merits of one course of action cannot be agreed to then such critical thought can often be used to create measures to be used to evaluate the effectiveness of the decision based on the results. Far too often the results are not examined to determine if they actually achieved what was intended. And even less often is care taken to examine the unintended consequences of the actions that were taken.

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Technological Innovation and Management

Technological innovation brings great opportunity for improving results and our quality of life. But transforming potential benefits into real results comes with many challenges.

ASQ has asked their Influential Voices to explore the idea of the fourth industrial revolution: “this new era is founded on the practical use of technological innovations like artificial intelligence, big data, robotics, and the Internet of Things (IoT).”

For many years GMs huge investment in robotics in the 1980s ($billions) has been an example of how pinning hopes on technology often doesn’t produce the desired results. I think that a capable management system is needed to make technological innovation as successful as it needs to be.

In this decade we are finally reaching the point where robotics is really making incredible strides. Robotics has provided huge benefits for decades, when used appropriately, but the ease of use and benefits from robotics have greatly increased recently.

I think robotics is going to be an incredibly powerful source of benefits to society in the next 20 years. Amazon is very well placed to profit in this area. Several other companies (Toyota, Boston Dynamics*, Honda, SoftBank…) are likely to join them (though which will be the biggest winners and which will stumble is not obvious)

Cliff Palace historical ruins

Photo by John Hunter of Cliff Palace (built in the 1190s), Mesa Verde National Park.

I am less confident in the Internet of Things. It seems to me that much of the IoT effort currently is flailing around in ways similar to GMs approach to robotics in the 1980s and 1990s. There is huge potential for IoT but the architecture of those solutions and the impact of that architecture on security (and fragile software that creates many more problems than it solves) is not being approached wisely in my opinion. IoT efforts should focus on delivering robust solutions in the areas where there is a clear benefit to adopting IoT solutions. And that needs to be done with an understanding of security and the lifecycle of the devices and businesses.

I think it will be much wiser to have an internet hub in the business or home that has all IoT traffic route through it in a very clear and visible way. Users need clear ways to know what the IoT is trying to do and to have control to determine what is and what is not sent out from their system. Having devices that share information in a non-transparent way is not wise. This is especially when those devices have cameras or microphones.

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Podcast: Building Organizational Capability

The Software Process and Measurement Cast 420 features an interview with me, by Thomas Cagley, on Building Organizational Capability (download podcast).

John Hunter in the podcast:

Changing how organizations are managed makes a huge difference in people’s lives, not all the time and I understand most of the time it doesn’t. But when this is done well people can go from dreading going to work to enjoying going to work, not every single day – but most days, and it can change our lives so that most of the time we are doing things that we find valuable and we enjoy instead of just going to work to get a paycheck so we can enjoy the hours that we have away from work.

photo of John Hunter

John Hunter, Zion National Park, Utah, USA

Here are some links where I go into more detail on some of the topics I discuss in the podcast:

Thomas Cagley: If you have the power to change any 2 things that affect decision making what would they be and why?

John Hunter:

First that results are evaluated. Make decisions then evaluate what actually happens based upon what you do. Learn from that, improve how you make future decisions and keep iterating.

That idea of evaluating what actually happens is extremely powerful and will reinforce going in the right direction because if you evaluate most decisions many organizations make nothing got any better. And after doing that many times you can learn this isn’t working, we need to do something better.

And the second would be more prioritization. Make fewer decisions but take more time to make those decisions, implement those decisions, evaluate those decisions, learn from those results and iterate again.

I hope you enjoy the podcast.

Related: Software Process and Measurement Podcast With John Hunter on my book Management MattersDeming and Software Development

Understanding Design of Experiments (DoE) in Protein Purification

This webcast, from GE Life Sciences, seeks to provide an understanding Design of Experiments (DoE) using an example of protein purification. It begins with a good overview of the reason why multi-factorial experiments must be used while changing multiple factors at the same time in order to see interactions between factors. These interactions are completely missed by one-factor-at-a-time experiments.

While it is a good introduction it might be a bit confusing if you are not familiar with multi-factorial designed experiments. You may want to read some of the links below or take advantage of the ability to pause the video to think about what he says or to replay portions you don’t pick up immediately.

I have discussed the value of design of experiments in multiple posts on this blog in the past, including: Introductory Videos on Using Design of Experiments to Improve Results by Stu Hunter, Design of Experiments: The Process of Discovery is Iterative and Factorial Designed Experiment Aim.

He also provides a good overview of 3 basic aims of multivariate experiment (DoE):

  • screening (to determine which factors have the largest impact on the results that are most important)
  • optimization (optimize the results)
  • robustness testing (determine if there are risks in variations to factors)

Normally an experiment will focus on one of these aims. So you don’t know the most important factors you may choose to do a screening experiment to figure out which factors you want to study in detail in an optimization experiment.

It could be an optimized set of values for factors provides very good results but is not robust. If you don’t have easy way to make sure the factors do not vary it may be worthwhile to choose another option that provides nearly as good results but is much more robust (good results even with more variation within the values of the factors).

Related: YouTube Uses Multivariate Experiment To Improve Sign-ups 15% (2009)Combinatorial Testing for Software (2009)Marketers Are Embracing Statistical Design of Experiments (2005)

Unintended Consequences

Using data to understand your processes and improve them is very useful.

But using data often results in unintended consequences. If you don’t have a good understanding on the pressures collecting data will bring to bear on the system you can create pressure for results that damage the delivery of value to customers.

In this example there are requirements to take action if certain conditions are present. In this case, if the airplane is pushed back from the gate for more than 3 hours without taking off passengers must be given the opportunity to get off.

The Tarmac Delay Rule in 2010 has led to a jump in the rate of flight cancellations

Indeed, to avoid the fines, airlines are now far more likely to cancel flights that are sitting at the gate or on the tarmac than they once were, explains Vikrant Vaze, an assistant professor of engineering at Dartmouth and a co-author of the study. That means you’re now more likely to board your plane, sit there, and then still have the flight canceled.

It doesn’t seem the conditions imposed are unreasonable to me. But the expectation was for airlines to make sensible adjustments and not force customers to wait so long in the airplane sitting on the ground. The system could be improved by having more gates in operation, not pushing loading planes if you knew plane wasn’t going to leave for more than 30 minutes, etc.. But when customer value is taken very lightly (as USA airlines do) it isn’t surprising the USA airlines would take a very customer unfriendly method to avoid the issue that was the source of the new rules.

Distorting the system or distorting the data are often the result, instead of the process improvement that is desired and expected.

Related: Bad Weather is Part of the Transportation SystemPoor Customer Service at USA AirlinesData is Important and You Must Confirm What the Data Actually SaysUnited Breaks GuitarsRespect for Employees at Southwest Airlines

Most Popular Management and Leadership Quotes on Our Site in 2015

These were the most popular quotes on the Curious Cat Management and Leadership Quotes web site in 2015 (based on page views). Follow the link on the quote text for the source and more information on the quote.

  1. Having no problems is the biggest problem of all.Taiichi Ohno
  2. Managers who don’t know how to measure what they want settle for wanting what they can measure.Russell Ackoff
  3. Don’t look with your eyes, look with your feet. Don’t think with you head, think with your hands.Taiichi Ohno
  4. The answer to the question managers so often ask of behavioral scientists “How do you motivate people?” is, “You don’t.”Douglas McGregor
  5. Performance appraisal is that occasion when once a year you find out who claims sovereignty over you.Peter Block
  6. A bad system will beat a good person every time.W. Edwards Deming
  7. People who can’t understand numbers are useless. The gemba where numbers are not visible is also bad. However, people who only look at the numbers are the worst of all.Taiichi Ohno
  8. We believe customer number one, employee number two, shareholder number three… Because you’ve take care of the customer, take care of the employees, shareholder will be taken care of.Jack Ma
  9. A leader is a coach, not a judge.W. Edwards Deming
  10. Real benefits come when managers begin to understand the profound difference between “cost cutting” and “eliminating the causes of costs.”Brian Joiner
  11. Standards should not be forced down from above but rather set by the production workers themselves.Taiichi Ohno
  12. A problem never exists in isolation; it is surrounded by other problems in space and time. The more of the context of a problem that a scientist can comprehend, the greater are his chances of finding a truly adequate solution.Russell Ackoff
  13. 95% of changes made by management today make no improvement.Peter Scholtes
  14. blame the process not the person. We need to ask, “how did the process allow this to happen?”Brian Joiner
  15. Good execution of performance appraisal is not the solution. More people are realizing that improving how performance appraisal are done is an attempt to do the wrong thing better. If you insist on doing the wrong thing, I suppose you might as well do it better but how about just not doing the wrong thing at all?John Hunter
  16. A system is more than the sum of its parts; it is an indivisible whole. It loses its essential properties when it is taken apart. The elements of a system may themselves be systems, and every system may be part of a larger system. – Russell Ackoff
  17. All Models Are Wrong But Some Are UsefulGeorge Box
  18. It is wrong to suppose that if you can’t measure it, you can’t manage it – a costly myth.W. Edwards Deming
  19. the aim of leadership should be to improve the performance of man and machine, to improve quality, to increase output, and simultaneously to bring pride of workmanship to people. Put in a negative way, the aim of leadership is not merely to find and record failures of men, but to remove the causes of failure: to help people to do a better job with less effort. – W. Edwards Deming
  20. There are three ways to get better figures… Improve the system… Distort the system… Distort the figuresBrian Joiner

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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Transforming a Management System – A Case Study From the Madison Wisconsin Police Department

This post in an excerpt from The Quality Leadership Workbook for Police by Chief David Couper and Captain Sabine Lobitz (buy via Amazon).

cover image of the New Quality Leadership Workbook for Police

The New Quality Leadership Workbook for Police

Transformational Steps
A Case Study Madison, Wisconsin (1981-1993)

Step 1: Educate and inform everyone in the organization about the vision, the goals, and Quality Leadership. This step must be passionately led by the top leader.

  • Begin discussion with top management team and train them.
  • Discuss and ask employees; get feedback from them.
  • Share feedback with the chief and his management team.
  • Get buy-in from top department managers.
  • Survey external customers—citizens; those who live and work in the community.
  • Create an employee’s advisory council; ask, listen, inform, and keep them up to date on what’s going on.
  • The chief keeps on message; tells, sells, and persuades, newsletters, meetings and all available media.

Step 2: Prepare for the transformation. Before police services to the community can be improved, it is essential to prepare the inside first — to cast a bold vision and to have leaders that would “walk the talk.”

  • Appoint a top-level, full-time coordinator to train, coach, and assist in the transformation.
  • Form another employee council to work through problems and barriers encountered during implementation of the transformation and Quality Leadership.
  • Require anyone who seeks to be a leader to have the knowledge and ability to practice Quality Leadership.

Step 3: Teach Quality Leadership. This begins at the top with the chief and the chief’s management team.

  • Train all organizational leaders in Quality Leadership.
  • Train all employees as to what Quality Leadership is, why the transformation is necessary, and what it means for them.

Step 4: Start practicing Quality Leadership. If top managers within the organization are not authentically practicing Quality Leadership neither will anyone else.

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Manufacturing Outlook and History In the USA and Globally

I write primarily about management improvement on this blog – which makes sense given the title. In the very early days I had more on investing, economic data, science, engineering and travel. Then I created three new blogs (Curious Cat Investment and Economics Blog, Curious Cat Science and Engineering Blog, Curious Cat Travel Photos blog) and that made this blog more focused.

Even so the lines of what fits where can be a bit fuzzy and I continue to write about manufacturing, and health care, with a focus on economic data, occasionally. And that is what I am doing today while touching on management related to manufacturing a bit.

As I have written before the story of manufacturing in the USA, and globally, is greatly increased quality of processes and output as well as greatly improved productivity over the last few decades. Manufacturing output also increased, including in the USA, as I have written consistently for a decade now. For example: (Top 10 Countries for Manufacturing Production from 1980 to 2010.

Still many people have the notion that USA manufacturing has been declining, which hasn’t been true, and certainly isn’t true now (the last couple of years have been especially strong and even the general public seems to realize the idea of the USA losing manufacturing is a myth).

Chart of Manufacturing Output fro 1992 to 2012 - USA, China, Japan and Germany

Based on data from the UN. See my blog post on my economics for more details on the data (to be posted next week).

The chart is impressive and illustrates the point I have been hammering home for years. The USA manufacturing base is growing and far from crumbling (job losses are deceiving as they are global and not an indication of a USA manufacturing decline). China’s manufacturing growth is incredible. China and the USA are far away the top 2 manufacturing countries. Japan and Germany make out the top 4 before a large gap which then is followed by a group of countries that are very close (Korea is 5th with less than half the production of Germany).

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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)

Management Improvement Blog Carnival #190

The Curious Cat Management Carnival is published twice each month. The posts selected for the carnival focus on the areas of management improvement I have focused on in the Curious Cat Management Improvement Guide since 1996: Deming, evidence based management, systems thinking, respect for people, applied statistics, etc..

photo of George Box, John Hunter and Peter Scholtesphoto of (from right to left) Peter Scholtes, John Hunter and George Box in Madison, Wisconsin at the 2008 Deming Conference
  • George Box (1919 to 2013) by John Hunter – George Box was a very kind, smart, caring and fun person. He was a gifted storyteller and writer. He was also one of the most important statisticians of the last 100 years. He had the ability to present ideas so they were easy to comprehend and appreciate…
  • George Box: A remembrance by Bradley Jones – “His greatest contribution to my life was the wonderful book, Statistics for Experimenters, which he wrote with William G. Hunter and Stu Hunter and published in 1978, the same year he served as president of the American Statistical Association. I remember the excitement I felt on reading the description of how the attainment of knowledge is an endless spiral proceeding alternately from deduction to induction and back. Even now, I recall with pleasure the discussion of the randomization distribution early in the book.”
  • Getting Started with Factorial Design of Experiments by Eston Martz – “When I talk to quality professionals about how they use statistics, one tool they mention again and again is design of experiments, or DOE. I’d never even heard the term before I started getting involved in quality improvement efforts, but now that I’ve learned how it works, I wonder why I didn’t learn about it sooner. If you need to find out how several factors are affecting a process outcome, DOE is the way to go.”
  • Brian Joiner Podcast on Management, Sustainability and the Health Care System – Recently Brian has shifted his focus to the health care system (while maintaining a focus on quality principles and sustainability). “Our health care system is an economic tsunami that is about to overwhelm us if we don’t do something very significant, very soon.”
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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

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

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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My New Book: Management Matters

Image of the book cover of Management Matters by John Hunter

Management Matters by John Hunter is now available.

I have a new book in progress: Management Matters. It is now available in “pre-release format” via leanpub. The idea I am experimenting with (supported by leanpub) is pre-publishing the book online. The ebook is available for purchase now, and comes with free access to the updates.

My plan is to continue working on the book for the next few months and have it “release ready” by October, 2012. One of the advantages of this method is that I can incorporate ideas based on feedback from the early readers of the book.

There are several other interesting aspects to publishing in this way. Leanpub allows a suggested retail price, and a minimum price. So I can set a suggested price and a minimum price and the purchaser gets to decide what price to pay (they can even pay over suggested retail price – which does happen). The leanpub model provides nearly all the revenue to the author (unlike traditional models) – the author gets 90% of the price paid, less 50 cents per book (so $8.50 of a $10 purchase).

They provide the book in pdf, mobi (Kindle) and epub (iPad, Nook, etc.) formats. And the books do not have any Digital Rights Management (DRM) entanglements.

Management Matters covers topics familiar to those who have been reading this blog for years. It is an attempt to put in one place the overall management system that is most valuable (which as you know, based on the blog, is largely based upon Dr. Deming’s ideas – which means lean manufacturing are widely covered too).

I hope the book is now in a state where those who are interested would find it useful, but it is in what I consider draft format. I still have much editing to do and content to add.

Leanpub also provides a sample book (where a portion of the content can be downloaded to decide if you want to buy). If you are interested please give it a try and let me know your thoughts.

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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Management Blog Posts From November 2006

I have selected a few great posts from the Curious Cat Management Blog back in November 2006.

  • What Could we do Better? – There are many important ideas to improve management. This is one of the most important tips to aid improvement that I know of: it is easy to do, brings huge benefits and most organizations fail to do it. Ask your customers: “What one thing could we do to improve?”
  • Ackoff’s F-laws: Common Sins of Management presents 13 common sins of management, such as: Managers who don’t know how to measure what they want settle for wanting what they can measure
  • Common Cause Variation – “Every system has variation. Common cause variation is the variation due to the current system. Dr. Deming increased his estimate of variation due to the system (common cause variation) to 97% (earlier in his life he cited figures as low as 80%). Special cause variation is that due to some special (not part of the system) cause.”
  • Sub-Optimize by Interrupting Knowledge Workers – “The general consensus is that the loss from interrupting [software] developers is much greater than for interrupting most other forms of work and therefor a great deal of effort is placed on improving the system to allow developers to focus.”
  • Amazon Innovation – “I believe Amazon uses technology very well. They have done many innovative things. They have been less successful at turning their technology into big profits. But I continue to believe they have a good shot at doing so going forward (and their core business is doing very well I think).” [Amazon announced great sales numbers today, continuing their long term tread. They are also continuing to be very slow to grow profits (CEO, Jeff Bezos remains willing to challenge common practices – such as his willingness to build business and sacrifice current profits)].

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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