E6S-195 Kai-Zen and the Art of Everything Part 1 - Scientific Method

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Intro:  Welcome to the E6S-Methods podcast with Jacob and Aaron, your weekly dose of tips and tricks to achieve excellent performance in your business and career.  Join us as we explore deeper into the practical worlds of Lean, Six Sigma, Project Management and Design Thinking.  In this episode number 195,  "Kai-Zen and the Art of Everything - Part 1" I read passages from Robert Pirsig's iconic book, "Zen and the Art of Motorcycle Maintenance," and elaborate on how his message applies in business and Lean Six Sigma.  If you're just tuning in for the first time, find all our back episodes at our podcast table of contents at e6s-methods.com. If you like this episode, be sure to click the "like" link in the show notes.  It's easy.  Just tap our logo, click and you're done. Tap-click-done!  Here we go. http://bit.ly/E6S-195 Leave a Review! http://bit.ly/E6S-iTunes

*** Kai-Zen and the Art of Everything Part 1 - Scientific Method ***                                                                         


I            Asking the right questions

a.       Pg 105 - Robert Pirsig - Zen and the Art of Motorcycle Maintenance

      "The real purpose of scientific method is to make sure Nature hasn't misled you into thinking you know something you don't actually know.  There's not a mechanic or scientist or technician alive who hasn't suffered from that on so much that he's not instinctively on guard. That's the main reason why so much scientific and mechanical information sounds so dull and so cautious. If you get careless or go romanticizing scientific information, giving it a flourish here and there, Nature will soon make a complete fool out of you. It does it often enough anyway even when you don't give it opportunities.  One must be extremely careful and rigidly logical when dealing with Nature: one logical slip and an entire scientific edifice comes tumbling down. One false deduction about the machine and you can get hung up indefinitely. 

      In Part One of formal scientific method, which is the statement of the problem, the main skill is in stating absolutely no more than you are positive you know.  It is much better to enter a statement 'Solve Problem: Why doesn't cycle work?' which sounds dumb but is correct, than it is to enter a statement 'Solve Problem: What is wrong with the electrical system?' when you don't absolutely know the trouble is in the electrical system.  What you should state is 'Solve Problem: What is wrong with cycle?' and then state as a first entry of Part Two: 'Hypothesis Number One: The trouble is in the electrical system.'  You think of as many hypotheses as you can, then you design experiments to test them to see which are true and which are false.

      This careful approach to beginning questions keeps you from taking a major wrong turn which might cause you weeks of extra work or can even hang you up completely.  Scientific questions often have a surface appearance of dumbness for this reason. They are asked in order to prevent dumb mistakes later on. 


i.      This is the essence of the Define phase as well as any exploratory phase of a new project or design

1.      Good problem statements are difficult to craft.  Too often they contain a bias for a root cause or even a solution.

a.       E6S-009          DMAIC Define Project Definition part 1

b.      E6S-010          DMAIC Define Project Definition part 2

2.      The dangers of bad problem statements:

a.       Waste time solving the wrong problem

i.      Even worse, never realize or admit it and continue throwing good money after bad.  Stuck in the "sunk cost" fallacy.  - The Sunk Cost Fallacy

ii.      It is also the basis of good coaching and consulting. Good consultants ask the dumb questions, and continue to follow up on these questions until they are exhausted.  Often the dumb questions are too often overlooked by those who "know better."

iii.      Similar approach also used during an 8D, "Is/ Is-Not" analysis, sorting only what we know for sure before embarking on hypotheses. 

iv.      Inferential Statistics and Hypothesis Testing "hedges" their bets. No more absolutes or "proof," only probabilities, including the probability of being wrong.  We cannot accept the null hypothesis, only fail to reject it.  We can reject the null hypothesis, but still have a 5% risk of being wrong. (assuming alpha risk = 0.05).  More on hypothesis testing in future episodes.

II         Testing hypotheses

a.       Pg 107 - Robert Pirsig - Zen and the Art of Motorcycle Maintenance

      To test properly the mechanic removes the plug and lays it against the engine so that the base around the plug is electrically grounded, kicks the starter lever and watches the spark plug gap for a blue spark. If there isn't any he can conclude one of two things: (a) there is an electrical failure or (b) his experiment is sloppy. If he is experienced he will try it a few more times, checking connections, trying every way he can think of to get that plug to fire. Then, if he can't get it to fire, he finally concludes that A is correct, there's an electrical failure, and the experiment is over. He has proved that his hypothesis is correct.

      In the final category, conclusions, skill comes in stating no more than the experiment has proved. It hasn't proved that when he fixes the electrical system the motorcycle will start. There may be other things wrong. But he does know that the motorcycle isn't going to run until the electrical system is working and he sets up the next formal question: "Solve problem: what is wrong with the electrical system?"

      He then sets up hypotheses for these and tests them. By asking the right questions and choosing the right tests and drawing the right conclusions the mechanic works his way down the echelons of the motorcycle hierarchy until he has found the exact specific cause or causes of the engine failure, and then he changes them so that they no longer cause the failure.

      An untrained observer will see only physical labor and often get the idea that physical labor is mainly what the mechanic does. Actually the physical labor is the smallest and easiest part of what the mechanic does. By far the greatest part of his work is careful observation and precise thinking. That is why mechanics sometimes seem so taciturn and withdrawn when performing tests. They don't like it when you talk to them because they are concentrating on mental images, hierarchies, and not really looking at you or the physical motorcycle at all. They are using the experiment as part of a program to expand their hierarchy of knowledge of the faulty motorcycle and compare it to the correct hierarchy in their mind. They are looking at underlying form.


i.      Root Cause Analysis using  PDSA (Plan-Do-Study-Adjust) or PDCA (Plan-Do-Check-Act) cycle - Iterative cycle until you've exhausted the options.  Following the bread-crumbs

ii.      Continue filling out the "Is/Is-not"

iii.      Similar basis for Shainin, Red-X methods

III      The novice advantage

a.       Pg 313 - Robert Pirsig - Zen and the Art of Motorcycle Maintenance

      The first time you do any major job it seems as though the out-of-sequence reassembly setback is your biggest worry. This occurs usually at a time when you think you're almost done. After days of work you finally have it all together except for: What's this? A connecting-rod bearing liner?! How could you have left that out? Oh Jesus, everything's got to come apart again! You can almost hear the gumption escaping. Pssssssssssssss.

      There's nothing you can do but go back and take it all apart again -- after a rest period of up to a month that allows you to get used to the idea. There are two techniques I use to prevent the out-of- sequence-reassembly setback. I use them mainly when I'm getting into a complex assembly I don't know anything about.

      It should be inserted here parenthetically that there's a school of mechanical thought which says I shouldn't be getting into a complex assembly I don't know anything about. I should have training or leave the job to a specialist. That's a self-serving school of mechanical eliteness I'd like to see wiped out. That was a ``specialist'' who broke the fins on this machine. I've edited manuals written to train specialists for IBM, and what they know when they're done isn't that great. You're at a disadvantage the first time around and it may cost you a little more because of parts you accidentally damage, and it will almost undoubtedly take a lot more time, but the next time around you're way ahead of the specialist. You, with gumption, have learned the assembly the hard way and you've a whole set of good feelings about it that he's unlikely to have.

i.      Similar approaches in Design Thinking (Empathize, Define, Ideate, Test, Prototype) and Lean Startup- Build Measure Learn - iterative problem solving, based on PDSA i.e. failing forward

1.      This is how new entrants disrupt an existing market. They take the leap to learn something and find a new innovation that eluded the incumbents. 

2.      Or be one who clung to the old model: E6S-180 Parable for our times-AP - QP July 2007

ii.      Don't be this guy: E6S-136 Jonny B Ermuda -Tale of a Project Lost                       

IV      The ego disadvantage

a.       Pg 321- Robert Pirsig - Zen and the Art of Motorcycle Maintenance

      If you have a high evaluation of yourself then your ability to recognize new facts is weakened. Your ego isolates you from the Quality reality. When the facts show that you've just goofed, you're not as likely to admit it. When false information makes you look good, you're likely to believe it. On any mechanical repair job ego comes in for rough treatment. You're always being fooled, you're always making mistakes, and a mechanic who has a big ego to defend is at a terrific disadvantage. If you know enough mechanics to think of them as a group, and your observations coincide with mine, I think you'll agree that mechanics tend to be rather modest and quiet. There are exceptions, but generally if they're not quiet and modest at first, the work seems to make them that way. And skeptical. Attentive, but skeptical, But not egoistic. There's no way to bullshit your way into looking good on a mechanical repair job, except with someone who doesn't know what you're doing.

      -- I was going to say that the machine doesn't respond to your personality, but it does respond to your personality. It's just that the personality that it responds to is your real personality, the one that genuinely feels and reasons and acts, rather than any false, blown-up personality images your ego may conjure up. These false images are deflated so rapidly and completely you're bound to be very discouraged very soon if you've derived your gumption from ego rather than Quality.


i.      Ego kills all that is good. 

1.      Destroys relationships, causes wars, ruins businesses

2.      Ego is the underlayment for all failed continuous improvement deployments

E6S-038    Why LSS Projects Fail

a.       Deployments implode when false successes are celebrated but turn up empty

b.      Program detractors put their egos, their own image, above what is best for the company.  Will wage war.

c.       Or program only targeted to make a "big splash" for a new executive, but not really meant the make a lasting difference

ii.      Ego-driven leaders will continue to double-down on a bad decision, even long after they know it's bad, all to protect their legacy/ego

E6S-063 Zombie Projects

iii.      Good consultants, Black Belts, and leaders must take a step back from their egos, or at least recognize if their actions are ego-driven

V         Unanswered questions - red herrings and dead ends

a.       Pg 327 - Robert Pirsig - Zen and the Art of Motorcycle Maintenance

      I want to talk now about truth traps and muscle traps and then stop this Chautauqua for today. Truth traps are concerned with data that are apprehended and are within the boxcars of the train. For the most part these data are properly handled by conventional dualistic logic and the scientific method talked about earlier.... But there's one trap that isn't...the truth trap of yes-no logic.

      Yes and no -- this or that -- one or zero. On the basis of this elementary two-term discrimination, all human knowledge is built up. The demonstration of this is the computer memory which stores all its knowledge in the form of binary information. It contains ones and zeros, that's all.

      Because we're unaccustomed to it, we don't usually see that there's a third possible logical term equal to yes and no which is capable of expanding our  understanding in an unrecognized direction. We don't even have a term for it, so I'll have to use the Japanese mu.

      Mu means ``no thing.'' Like ``Quality'' it points outside the process of dualistic discrimination. Mu simply says, ``No class; not one, not zero, not yes, not no.'' It states that the context of the question is such that a yes or no answer is in error and should not be given. ``Unask the question'' is what it says.

      Mu becomes appropriate when the context of the question becomes too small for the truth of the answer. When the Zen monk Joshu was asked whether a dog had a Buddha nature he said ``Mu,'' meaning that if he answered either way he was answering incorrectly. The Buddha nature cannot be captured by yes or no questions.

      That mu exists in the natural world investigated by science is evident. It's just that, as usual, we're trained not to see it by our heritage. For example, it's stated over and over again that computer circuits exhibit only two states, a voltage for ``one'' and a voltage for ``zero.'' That's silly!

      Any computer-electronics technician knows otherwise. Try to find a voltage representing one or zero when the power is off! The circuits are in a mu state. They aren't at one, they aren't at zero, they're in an indeterminate state that has no meaning in terms of ones or zeros. Readings of the voltmeter will show, in many cases, ``floating ground'' characteristics, in which the technician isn't reading characteristics of the computer circuits at all but characteristics of the voltmeter itself. What's happened is that the power-off condition is part of a context larger than the context in which the one zero states are considered universal. The question of one or zero has been ``unasked.'' And there are plenty of other computer conditions besides a power-off condition in which mu answers are found because of larger contexts than the one-zero universality.

      The dualistic mind tends to think of mu occurrences in nature as a kind of contextual cheating, or irrelevance, but mu is found throughout all scientific investigation, and nature doesn't cheat, and nature's answers are never irrelevant. It's a great mistake, a kind of dishonesty, to sweep nature's mu answers under the carpet. Recognition and valuatian of these answers would do a lot to bring logical theory closer to experimental practice. Every laboratory scientist knows that very often his experimental results provide mu answers to the yes-no questions the experiments were designed for. In these cases he considers the experiment poorly designed, chides himself for stupidity and at best considers the ``wasted'' experiment which has provided the mu answer to be a kind of wheel-spinning which might help prevent mistakes in the design of future yes-no experiments.

      This low evaluation of the experiment which provided the mu answer isn't justified. The mu answer is an important one. It's told the scientist that the context of his question is too small for nature's answer and that he must enlarge the context of the question. That is a very important answer! His understanding of nature is tremendously improved by it, which was the purpose of the experiment in the first place. A very strong case can be made for the statement that science grows by its mu answers more than by its yes or no answer. Yes or no confirms or denies a hypothesis. Mu says the answer is beyond the hypothesis. Mu is the ``phenomenon'' that inspires scientific enquiry in the first place! There's nothing mysterious or esoteric about it. It's just that our culture has warped us to make a low value judgment of it.

      In motorcycle maintenance the mu answer given by the machine to many of the diagnostic questions put to it is a major cause of gumption loss. It shouldn't be! When your answer to a test is indeterminate it means one of two things: that your test procedures aren't doing what you think they are or that your understanding of the context of the question needs to be enlarged. Check your tests and restudy the question. Don't throw away those mu answers! They're every bit as vital as the yes or no answers. They're more vital. They're the ones you grow on!

i.      Dead end?  Keep following up on unanswered questions.  Expand scope, and ask again.

1.      Check the data.  Red herring's exist - data that is false, but appears real and contradicts other data. 

a.       These include assumptions and anecdotal data or faulty recall

b.      The "Is/Is-not" method helps identify conflicting data

2.      Check the measurement system.  When you cannot measure the effect you are looking at, perhaps it is the measurement that is flawed.  Expand the scope of your investigation

E6S-049 Rule out the Ruler - MSA Part 1

E6S-050 Rule out the Ruler - MSA  Part 2

E6S-053 Rule out the Ruler - MSA Part 3                         

E6S-055 Rule out the Ruler - MSA Part 4A          

E6S-056 Rule out the Ruler - MSA Part 4B          

E6S-057 Rule out the Ruler - MSA Part 5A                      

E6S-058 Rule out the Ruler - MSA Part 5B                      

E6S-059 Rule out the Ruler - MSA Part 5C

a. Example: When testing chemical concentration in the laboratory.  It was learned that the variation in the process could be completely explained by the variation in the instrument  (meaning, it was the instrument, and not the process), which if undiagnosed would give is
"mu" or really complete noise without a signal.  When tested further, it was learned that the variation in readings could be correlated to temperature swings in the laboratory.  In essence, the instrument was not only measuring variations in chemical concentration, it was also measuring the "concentration of heat" in the room, i.e. temperature. 

3.      Check your alpha risk.  Sometimes "mu" can look like both rejecting and accepting the null hypothesis, depending on the alternate hypothesis you choose (one-tailed vs. two-tailed).  More on this in a future episode.

VI      Recap

a.       Ask the right questions

b.      Test hypotheses

c.       Take the advantage - be a novice

d.      Part ways with ego

e.       Follow up on unanswered questions - expand scope and ask again.


Outro: Thanks for listening to episode 195 of the E6S-Methods podcast. Don't forget to click "like" or "dislike" for this episode in the show notes. Tap-click-done!  If you have a question, comment or advice, leave a note in the comments section or contact us directly. Feel free to email me "Aaron," aaron@e6s-methods.com, or on our website, we reply to all messages.  If you heard something you like, then share us with a friend or leave a review.  Didn't like what you heard? Join our LinkedIn Group, and tell us why.  Don't forget you can find notes and graphics for all shows and more at www.E6S-Methods.com. "Journey Through Success. If you're not climbing up, you're falling down."    Leave a Review! http://bit.ly/E6S-iTunes

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