Perceptions Dictate Actions
Abraham Maslow proposed a theory on human motivation outlining an hierarchy of needs we all move through. Whether the hierarchical structure strictly applies may be questionable, but his categories of needs can be accepted as defined with an appeal to personal experience.
At various points in our lives we have felt a need for food, water and shelter, safety and security, stability, friendship, love, respect, and growth. Maslow grouped these needs into five categories: physiological, safety, love/belonging, esteem, and self-actualization. While he organized these categories in an hierarchy, I find it more meaningful to think of them as factors that combine and interact to give rise to various mental states.
Personal experience has led me to believe mental states affect perception. A given situation will be perceived differently under different mental states. And, how you perceive affects how you react. If your mental state is dominated by a sense of insecurity, you will perceive your situation as threatening and react accordingly. Futhermore, no other category of need will feel worthwhile until the one dominating your mental state has been attended to.
It has also been my experience that people with dissimilar mental states have difficulty relating to one another in a productive fashion. Because mental states affect perception, dissimilar states result in different perspectives of the same issue. Differing perspectives are not conducive to forming the common understanding necessary to cooperatively face a situation. People talk past one another. Messages are misunderstood. Communication doesn’t take place.
Given that it’s unlikely two people will share similar mental states at any given time, how does one move across this emotional minefield? Meditation and self-reflection have helped me to characterize my mental state. This awareness has opened up a choice for me unavailable before: how do I want react to things? At the very least I have an option to not make things worse. I can also attempt to ascertain the other person’s mental state and work towards common understanding.
Failure
Most of us have been taught to live our lives singularly focused on outcomes. We are a result oriented society. We worship winners and revile losers. With that as the context, is it any wonder that the majority of us are risk averse? Why do something if there is a chance we might not achieve our desired end? Who would want to be labeled a failure or a loser?
I remember an incident back in middle school. I had got a ‘D’ in one of my classes. It generated such anxiety that I doctored my report card. With a couple of well placed dots penciled in on the dot matrix printout I changed the grade from the ‘D’ to a ‘B’. I knew it was wrong, but the fear of failure was dominating. Even though my parents had never set any expectations for grades – they had always wanted me to just do my best – I had nevertheless internalized the stigma of failure from other social contexts.
The message we all, student or professional, get every waking moment is clear even if it is just implied: hit the target. It does not matter how. Anything less is not only worthless but it will bring negative consequences. What we fail to grasp is the fact that just as you cannot inspect quality into a product, you cannot test knowledge into a student. Trying to do it just fosters our present culture wherein we are all afraid to take chances and risk failure.
We must shift our focus upstream to the learning process. We must be taught how to learn beyond just what to learn. All of us have to understand how knowledge is built and then use that process to grow our own knowledge. That process, the learning cycle, is elegantly expressed by Dr. Shewhart and Dr. Deming: PDSA or Plan-Do-Study-Act. Its deceptive simplicity hides the profound impact it has had in growing human knowledge.
Make a guess at the solution to the problem you’re facing and plan a way to test your guess. Following the plan, actually do the test; carry it out. Study the result by comparing it against your guess. Do they match each other? How you act, depends on the answer. If the result matches your guess, you have confirmation of your guess. If not, you will need to modify your guess with the new found data and run through the cycle once more. Either way, you have increased your knowledge. There is no failure!
How many cycles it takes to understand the nature of the problem we face depends on how good our initial guess is. We must afford everyone the opportunity to run through as many cycles as they need without judging them as a success or a failure based on the outcome of any given iteration of the cycle. Doing so is a way to kill intrinsic curiosity and stop the learning process cold. If we want people to take risks, then we have to foster an environment of experimentation and cooperation.
Life is a journey, not a destination. — Ralph Waldo Emerson
Entropy and Assignable Causes
Left alone, a process in the Ideal State will migrate toward the State of Chaos over time. Wear and tear of parts in use will lead to breakdowns and failures. Even mothballed machines cannot escape deterioration and decay caused by the environment. Entropy affects everything.
However, the effects of entropy can be repaired. Signatures of deterioration and failure made apparent by process control charts provide clues as to what repairs need to be made when. But, a process isn’t affected just by entropy. Other assignable causes keep it from operating in a predictable fashion. Replacing worn out parts might pull a process out from the State of Chaos back to the Brink of Chaos, but it’s just a matter of time before it returns to it.
Process managers, through the proper use of process control charts, must counteract the effects of entropy and assignable causes to help their processes achieve the ideal state and stay there. This is a never ending cycle.
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Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
The State of Chaos
When a process is out of control and it is producing nonconforming product it is in a state of chaos. The State of Chaos is one of the four states a process can be in as shown in “What State Is Your Process In?“. The manufacturer cannot predict how much nonconforming product his process will produce in any given hour or day. At times the process will produce nothing but conforming product. Then without warning it will produce nothing but nonconforming product. It might seem as if there were ghosts in the machine.
A process in such a state is affected by assignable causes that are easily identified through the use of process control charts. The effects of these assignable causes have to be eliminated one at a time. Patience and perseverance are necessary. It is essential that the process be brought under statistical control and made predictable. Once the process has achieved stability further improvement efforts can be made to reach the ideal state.
Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
The Brink of Chaos
Of the four states a process can be in (see “What State Is Your Process In?“) the most sinister state is the one where it is producing 100 percent conforming product but is operating in an unpredictable way. That is, the process is not under statistical control. Such a process is, in fact, on the brink of chaos. But, hold on. There is no nonconforming product, therefore there is no problem, right? It is easy to get lulled into complacency by this happy circumstance.
But because the process is not under statistical control it is impossible to predict what it will do in the next instance. Various assignable causes are affecting the process in an unpredictable fashion. The effect of these causes could very well be the production of nonconforming product without any warning. When that happens the process has moved into a state of chaos.
The only way to address a process on the brink of chaos is to use process control charts to identify assignable causes and eliminate their effects one-by-one and bring the process under statistical control. You can then start other improvement efforts like moving the process mean to the process aim and reducing the process variation by minimizing the influence of common causes affecting the process.
Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
The Threshold State
A process that is predictable or in a state of statistical control, but producing nonconforming product can be described as being in the Threshold State. This is one of the four possible states that a process can be in as noted in “What State Is Your Process In?“. But, what might such a process look like?
A process in the threshold state might be operating with its mean higher than the process aim,
or it might be operating with its mean lower than the process aim,
or it might be operating with a process dispersion greater than the product specification window,
or it may be operating with some combination of a shift in its mean and breadth of its dispersion.
Nevertheless, the fact that such a process is in statistical control means that it will continue to produce consistent product so long as it stays in control. This in turn means that the producer can expect to produce a consistent amount of nonconforming product hour after hour day after day until a change is made in the process or a change is made to the specifications.
It is important to say here that exhorting your workers to work harder or to “Do it right the first time” or to show them the examples of nonconforming product from a process in the threshold state will not lead to improvements. They are not the cause for the failures. The causes for the nonconforming product are systemic and must be dealt with at the system level. Focusing on the worker will only serve to demoralize and frustrate them. It may lead to tampering with the process turning a bad situation worse.
You can always share your process data with your customer to demonstrate its stability and ask for a change in the product specifications. However, if specifications cannot be changed your only recourse is to modify your process to shift it from the threshold state into the ideal state. Adjusting the process mean to match the aim is usually relatively simple. In comparison, reducing the process variation requires an understanding of the common causes affecting the process and their respective effects – a much more involved activity.
While you are working on improving your process you are still producing nonconforming product. Until such time as you achieve the ideal state for your process, you must screen every unit or lot before shipping product to your customer – a 100 percent inspection and sort. This should be treated as a temporary stop-gap measure. You must recognize it as an imperfect quality control method and be mindful that defectives will escape.
Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Deming, W. Edwards. Out of the Crisis. Cambridge, MA: MIT CAES. 1991. Print. ISBN 0-911379-01-0
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
The Ideal State
In “What State Is Your Process In?” I noted that a process can be in one of four possible states. Here I write about the Ideal State wherein a process is predictable and is producing 100 percent conforming product.
A process that is predictable is one that is in a state of statistical control. The variability of the product from one unit to the next is randomly distributed about the average and bounded within statistically established limits – its natural limits (red solid lines in the figure below). So long as the process remains “in control”, it will continue to produce units within these limits.
Complete product conformity comes about when the process’s natural limits fall within the product’s specification limits (blue solid lines in the figure below). This depicts the ideal state for a process.
In order for a process to achieve this ideal state
- The process must be inherently stable over time. This means that in the absence of external disturbances – what Dr. Shewhart referred to as assignable causes – the process’s natural variability does not change over time. (Note: There are processes that are inherently chaotic. An excellent reference to such processes is “Nonlinear Dynamics And Chaos” by Professor Steven Strogatz)
- The process must be operated in a stable and consistent manner. The operating conditions cannot be selected or changed arbitrarily. Often machine parameters are tweaked in response to natural fluctuations in the process’s output. These actions add to the process’s natural variation disrupting its stability. Dr. Deming demonstrated the effects of such tampering through the “Nelson Funnel Experiment”. (Bill Scherkenbach has an excellent discussion of it in “Deming’s Road to Continual Improvement”.)
- The process average must be set and maintained at an appropriate level. If you refer to the charts above, you can imagine the consequence of moving the process average up or down from its aim. The result is the production of nonconforming product either on the high or low side.
- The natural tolerance of the process must be less than the specified tolerance for the product. This is obvious upon a first glance at the second chart above.
A process that satisfies these four conditions will be in the ideal state and the manufacturer can be confident that he is shipping only conforming product. In order to maintain the process in the ideal state he must use process control charts. He must act on the signals they provide to promptly identify assignable causes and eliminate their effects.
Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Moen, Ronald D., Thomas W. Nolan, Lloyd P. Provost. Improving Quality Through Planned Experimentation. McGraw-Hill. 1991. Print. ISBN 0-07-042673-2
- Scherkenbach, William W. Deming’s Road to Continual Improvement. Knoxville, TN: SPC Press, Inc. 1991. Print. ISBN 0-945320-10-8
- Strogatz, Steven H. Nonlinear Dynamics And Chaos: With Applications To Physics, Biology, Chemistry, And Engineering. Reading, MA: Addison-Wesley. 1995. Print. ISBN 0-201-54344-3
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
What State Is Your Process In?
You can take one of two approaches to controlling the quality of your product. Once manufactured, you can compare it against its specifications and sort it as either conforming or nonconforming. This, however, will guarantee the greatest degree of variability between units as anything within specifications is considered acceptable. Alternatively, you can work to run the manufacturing process as consistently as possible to produce units that vary as little as possible from one another.
There is no path to improving performance with the first approach where you sort manufactured product as conforming or nonconforming. However, there is a clear path to improving performance through the use of process control techniques introduced by Dr. Walter Shewhart. But, how do you gauge improvement? One measure is achieving a state of statistical control for the process so that its behavior is predictable. Another measure is the manufacture of 100 percent conforming product by the process.
When these two measures are taken together, there are four possible states a process can be in (see figure below): The process is
- predictable and producing conforming product – the ideal state
- predictable but producing nonconforming product – the threshold state
- not predictable but producing conforming product – the brink of chaos
- not predictable and producing nonconforming product – the state of chaos
Note: I learned this material from reading Dr. Wheeler’s writings. My post is intended to reflect my current understanding. None of the ideas herein are original to me. Any errors are my failures alone.
References
- Wheeler, Donald J. and David S. Chambers. Understanding Statistical Process Control. Knoxville, TN: SPC Press, Inc. 1986. Print. ISBN 0-945320-01-9
- Wheeler, Donald J. The Four Possibilities for Any Process. Quality Digest. 1997. Web. http://www.qualitydigest.com/dec97/html/spctool.html
Great Generals
As to the influence and genius of great generals — there is a story that Enrico Fermi once asked Gen. Leslie Groves how many generals might be called “great.” Groves said about three out of every 100. Fermi asked how a general qualified for the adjective, and Groves replied that any general who had won five major battles in a row might safely be called great. This was in the middle of World Wat II. Well, then, said Fermi, considering that the opposing forces in most theaters of operation are roughly equal, the odds are one of two that a general will win a battle, one of four that he will win two battles in a row, one of eight for three, one of sixteen for four, one of thirty-two for five. “So you are right, general, about three out of every 100. Mathematical probability, not genius.”
– John Keegan, The Face of Battle (Viking, New York, 1977)
Whirlpools…
We are rather like whirlpools in the river of life. In flowing forward, a river or stream may hit rocks, branches, or irregularities in the ground, causing whirlpools to spring up spontaneously here and there. Water entering one whirlpool quickly passes through and rejoins the river, eventually joining another whirlpool and moving on. Though for short periods it seems to be distinguishable as a separate event, the water in the whirlpools is just the river itself. The stability of a whirlpool is only temporary. The energy of the river of life forms living things – a human being, a cat or dog, trees and plants – then what held the whirlpool in place is itself altered, and the whirlpool is swept away, reentering the larger flow. The energy that was a particular whirlpool fades out and the water passes on, perhaps to be caught again and turned for a moment into another whirlpool.
Nothing Special : Living Zen – Charlotte Joko Beck with Steve Smith
