Too often today I hear people disparaging management tools/concepts (PDSA cycle, mistake proofing, flowcharts, design of experiments, gemba…). The frequently voiced notion is that tools are being applied and not helping improve management in the organization.
But it seems to me using these tools re-enforce the best practices of management improvement. Yes, ignoring the underlying principles (while applying tools and concepts) drastically limits how successful an organization will be in improving management practices (and limits the results the organization will achieve). But using the tools is not the problem. Using the tools is a necessary but not sufficient part of the process to improve.
What is needed is to use the tools with engaged people that are continually learning and adjusting the management system based on their increase understanding of the organization as a system. Using management tools effectively (if you are unsure of what those tools are, read the posts on this blog discussing many management improvement tools) supports gaining insight into the underlying management improvement principles.
It is important to understand there are fundamental concepts that connect and reinforce each other. And those organizations that are successful are using management tools and continually building their understanding of the underlying principles.
John Hunter, in a cave at Marble Mountain, Da nang, Vietnam. This is one of my last stops before returning home. See more of my travel photos
I have experience applying quality tools since I was a kid being guided by my father. Another influential voices author, that I met in Hong Kong when I presented a a Deming seminar, included a mention of that connection in his post: Quality Life and Succession.
In this blog I write about using management improvement thinking in my personal life. That extends from management concepts such as optimizing the entire system and not getting trapped by habit or convention, for example in: The Aim Should be the Best Life – Not Work v. Life Balance.
My father applied these ideas in our family life and so naturally they formed my way of thinking. At the core was a focus on experimentation and focusing on what was important. It is easy to spend a lot of time on things that really are not that important and questioning if the actions we are taking is really what we should be doing based on the most important aims was a natural part of how we thought growing up. In order to experiment effectively you need to be able to understand data and draw appropriate conclusions (post on an experience with my father as a child: Playing Dice and Children’s Numeracy).
Also we would look at what wasn’t giving the results we desired and experiment on how to improve. I include in “results” the happiness or frustration the process causes (so as a kid this was often the frustration my brother and I had in doing some task we didn’t want to do – cleaning our room, doing homework etc. and the frustration our parents felt at having to continually bring us back onto task). Much of this effort amount to setting the understanding and incentives and process to get better results (both the end results and increasing happiness and reducing frustration of all of us in the family).
A concept I use a good deal in my personal thinking on a more concrete level is mistake proofing (or at least mistake making less easy). Many people do this, without really thinking that is what they are doing. But by thinking of it consciously I find it helps you design processes to be most effective.
Why, well mainly I am kidding about it being the best, but if you don’t read his Gemba Panta Rei blog you should! Go add it to your RSS feed reader, before you continue with this post.
Ok, welcome back. In addition to thinking his blog is great the solution from his blog is very flexible and easy – though it isn’t quite a packaged solution (as asked for on Reddit). Also that post provides some good insight into the thinking behind the board (as well as how to create your own).
Another silly site, that sells some sort of solution, blocked my access because they don’t sell in the country my computer reported being located in. So I didn’t give them a free plug (assuming their product was decent which it might be?). Very dumb design if you ask me; well even though you didn’t ask, I told you anyway.
Localization that impedes users rather than helping them seems far far too common in my experience. Mapping (and related – find closest…) uses are about the only localization stuff I find useful – country based localization I nearly always find annoying or crippling. And showing my location on a map is totally awesome (especially as I travel around as a tourist – or really in whatever capacity). Such bad design and poor usability decisions cost companies money.
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.
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.
Bill Fox interviewed me and has posted part one of the interview on his web site: Predicting Results in the Planning Stage (sorry, the link has been hijacked to forward to an unrelated page [so obviously I removed the link], I have posted the interview which can now be reached here):
Bill: John, what is your best process improvement strategy or tactic that has worked well for you or your clients?
John: I would say the PDSA improvement cycle and a few key practices in using the PDSA properly like predicting the results in the plan stage—something that a lot of the times people do not do—to determine what would be done based on the results of that prediction.
People discover, especially when they’re new to this stuff, regarding the data that they’re collecting, that maybe even if they got the results they are predicting, they still don’t have enough data to take action. So you figure that even if that number is 30, they would need to know three other things before they make the change. So then, in the plan stage, you can figure that you need to address these other issues, too. At any time that people are collecting data is useful to figure out, for instance: “What do we need to do if the result is 30 or if the result is 3?” And if you don’t have any difference, why are you collecting the data?
Another important piece is the D in Plan, Do, Study, Act. It means “do the experiment”. A lot of times, people get confused into thinking that D means deploy the results or something like that, but thinking of D as ‘doing the experiment’ can be helpful.
A really big key between people that use PDSA successfully and those who don’t is that the ones that do it successfully turn the cycle quickly.
Another response:
Bill: What is the biggest misunderstanding about the Deming Management System you think people have?
John: I would say that there are a couple. The followers that want to pin everything to Deming tend to overlook the complexities and nuances and other things.
The other problem is that some of the critics latch on to a specific quote from Deming, something like a one-sentence long quote, and then they extrapolate from that one sentence-long quote what that means. And the problem is that Deming has lots of these one-sentence quotes that are very memorable and meaningful and useful, but they don’t capture every nuance and they don’t alone capture what it really means (you need to have the background knowledge to understand it completely).
They are sort of trying to oversimplify the message into these sound bites, and I find that frustrating. Because those individual quotes are wonderful, but they are limited to one little quote out of hours of videotape, books, articles, and when you don’t understand the context in which that resides, that’s a problem.
See the full interview for more details and other topics. I think it is worth reading, of course I am a bit biased.
Using the PDSA cycle (plan-do-study-act) well is critical to building a effective management system. This post provides some resources to help use the improvement cycle well.
“You are a fool if you do what I say. You are a greater fool if you don’t do as I say. You should think for yourself and come up with better ideas than mine.”
The best examples of Lean in healthcare are examples where leaders and organizations learned, but did not blindly copy. Sami Bahri DDS (the “lean dentist”) read Deming, Shingo, Ohno, etc. and had to figure this out himself, rather than copying some other dentist.
ThedaCare is the first to say “don’t directly copy what we do.”
We can learn from others, run our own experiments to see what works, and keep improving to make it better than even Ohno or Shingo would have imagined.
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 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.
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:
Every operating system generates information that can be used to improve it.
Everyone has creativity.
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.
I would most likely not exist if it were not for George Box. My father took a course from George while my father was a student at Princeton. George agreed to start the Statistics Department at the University of Wisconsin – Madison, and my father followed him to Madison, to be the first PhD student. Dad graduated, and the next year was a professor there, where he and George remained for the rest of their careers.
George died today, he was born in 1919. He recently completed An Accidental Statistician: The Life and Memories of George E. P. Box which is an excellent book that captures his great ability to tell stories. It is a wonderful read for anyone interested in statistics and management improvement or just great stories of an interesting life.
George Box 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.
