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Harish Jose

Management

Course-Correcting for Long-Term Success

A cybernetic view of quality control

Published: Wednesday, August 14, 2019 - 12:03

After reviewing Mark Graban’s wonderful book, Measures of Success (Constancy, 2018), I started rereading Walter Shewhart’s books, Statistical Method From the Viewpoint of Quality Control (Dover reprint 1986, originally edited by W. Edwards. Deming), and Economic Control of Quality of Manufactured Product (Martino Fine Books, 2015 reprint). Both are excellent books for any quality professional.

One of the themes that stood out for me while reading the two books was the concept of cybernetics. This column is a result of studying Shewhart’s books as well as articles on cybernetics by Paul Pangaro.

The term “cybernetics” originated from the Greek word κυβερνήτης, which means “navigation.” Cybernetics is generally translated as “the art of steering.” In 1948, Norbert Wiener, the great American mathematician, wrote the book, Cybernetics: Or Control and Communication in the Animal and the Machine (The MIT Press, second edition, 1965). He made the term cybernetics famous. Wiener adapted the Greek word to evoke the rich interaction of goals, predictions, actions, feedback, and response in systems of all kinds.

Loosely put, cybernetics is about having a goal and a self-correcting system that adjusts to the perturbations in the environment so that the system can keep moving toward the goal. This is referred to as the “first order of cybernetics.”

Remaining true to the Greek origin of the word, an example we can use is a ship sailing toward a destination. When there are perturbations in the form of wind, the steersman adjusts the path accordingly and maintains the course.

Another common example is a thermostat. It is able to maintain the required temperature inside a house by adjusting according to the external temperature. The thermostat kicks on and cools or heats the house when a specified temperature limit is tripped.

Another important concept that is used for cybernetics is the “law of requisite variety” promulgated by Ross Ashby. This states that only variety can absorb variety. If the wind is extreme, the steersman may not be able to steer the ship properly. In other words, the steersman lacks the requisite variety to handle or absorb the external variety. The main mechanism of cybernetics is the closed-feedback loop that helps the steersman adjust accordingly to maintain the course. This is also the art of a regulation loop—compare, act, and sense.

Warren McCulloch, the American cybernetician, explained cybernetics as follows:
Narrowly defined it is but the art of the helmsman, to hold a course by swinging the rudder so as to offset any deviation from that course. For this the helmsman must be so informed of the consequences of his previous acts that he corrects them—communication engineers call this ‘negative feedback’—for the output of the helmsman decreases the input to the helmsman. The intrinsic governance of nervous activity, our reflexes, and our appetites, exemplify this process. In all of them, as in the steering of the ship, what must return is not energy but information. Hence, in an extended sense, cybernetics may be said to include the timeliest applications of the quantitative theory of information.”

Walter Shewhart’s ideas of statistical control work well with cybernetic ideas. Shewhart purposefully used the term “control” for his field. Control or regulation is a key concept in cybernetics, as explained above.

Shewhart defined control as:
“A phenomenon is said to be controlled when, through the use of past experience, we can predict at least within limits how the phenomenon may be expected to vary in the future. Here it is understood that prediction within limits means that we can state, at least approximately, the probability that the observed phenomenon will fall within the given limits.”

Shewhart expanded further:
“The idea of control involves action for the purpose of achieving a desired end. Control in this sense involves both action and a specified end.... We should keep in mind that the state of statistical control is something presumable to be desired, something to which one may hope to attain; in other words, it is an ideal goal.”

Shewhart’s view of control aligns very well with the teleological aspects of cybernetics. From here, he develops his famous Shewhart cycle as a means to maintain statistical control. He wrote:
“Three steps in quality control. Three senses of statistical control. Broadly speaking, there are three steps in a quality control process: the specification of what is wanted, the production of things to satisfy the specification, and the inspection of things produced to see if they satisfy the specification.

“The three steps (making a hypothesis, carrying out an experiment, and testing the hypothesis) constitute a dynamic scientific process of acquiring knowledge. From this viewpoint, it is better to show them as a forming a sort of spiral gradually approaching a circular path to what would represent the idealized case, where no evidence is found in the testing of the hypothesis to indicate a need for changing the hypothesis. Mass production viewed in this way constitutes a continuing and self-corrective method for making the most efficient use of raw and fabricated materials.”

The Shewhart cycle as he proposed is shown below:

One of the criterions Shewhart developed for his model was that it should be as simple as possible and adaptable in a continuing and self-corrective operation of control. The idea of self-correction is a key point of cybernetics as part of maintaining the course.

Shewhart’s brilliance was in providing guidance on when we should react and when we should not to variations in data. He stated that:
“A necessary and sufficient condition for statistical control is to have a constant system of chance causes.... It is necessary that differences in the qualities of a number of pieces of a product appear to be consistent with the assumption that they arose from a constant system of chance causes.... If a cause system is not constant, we shall say that an assignable cause is present.”

Shewhart continued:
“My own experience has been that in the early stages of any attempt at control of a quality characteristic, assignable causes are always present, even though the production operation has been repeated under presumably the same essential conditions. As these assignable causes are found and eliminated, the variation in quality gradually approaches a state of statistical control as indicated by the statistics of successive samples falling within their control limits, except in rare instances.

“We are engaging in a continuing, self-corrective operation designed for the purpose of attaining a state of statistical control.

“The successful quality control engineer, like the successful research worker, is not a pure reason machine but instead is a biological unit reacting to and acting upon an ever-changing environment.”

James Wilk defined cybernetics as “the study of justified intervention.” This is an apt definition when we look at quality control, as understood by Shewhart. We have three options when it comes to quality control:
1. If we have an unpredictable system, then we work to eliminate the causes of signals, with the aim of creating a predictable system.
2. If we have a predictable system that is not always capable of meeting the target, then we work to improve the system in a systematic way, aiming to create a new a system whose results now fluctuate around a better average.
3. When the range of predictable performance is always better than the target, then there’s less of a need for improvement. We could, however, choose to change the target and then continue improving in a systematic way.

Source: Mark Graban, Measures of Success.

Final words

Shewhart wrote Statistical Method From the Viewpoint of Quality Control in 1939, nine years before Wiener’s cybernetics book. The use of statistical control allows us to have a conversation with a process. The process tells us what the limits are, and as long as the data points are plotted randomly within the two limits, we can assume that whatever we are seeing is due to chance or natural variation. The data should be random and without any order. When we see some manner of order, such as a trend or an outside data point, then we should look for an assignable cause. Then the data points are not necessarily due to chance anymore. As we keep plotting, we should improve our process and recalculate the limits.

I will finish off with Deming’s enhancement of Shewhart’s cycle. This is taken from a presentation by Clifford L. Norman. This was part of the evolution of the plan-do-study-act (PDSA) cycle, which later became famous as the plan-do-check-act (PDCA) cycle. This showed only three steps with a decision point after step three.

The updated cycle has lots of nuggets in it, such as experimenting on a small scale and reflecting on what we learned.

Always keep on learning....

First published May 27, 2019, on Harish's Notebook.

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About The Author

Harish Jose’s picture

Harish Jose

Harish Jose has more than seven years experience in the medical device field. He is a graduate of the University of Missouri-Rolla (U.S.), where he obtained a master’s degree in manufacturing engineering and published two articles. Harish is an ASQ member with multiple ASQ certifications, including Quality Engineer, Six Sigma Black Belt, and Reliability Engineer. He is a subject matter expert in lean, data science, database programming, and industrial experiments. Harish publishes frequently on his blog harishnotebook. He can be reached on LinkedIn.