The Cybernetic View of Quality Control:

Shewhart cycle1

My last post was a review of Mark Graban’s wonderful book, Measures of Success. After reading Graban’s book, I started rereading Walter Shewhart’s books, Statistical Method from the Viewpoint of Quality Control (edited by Dr. Deming) and Economic Control of Quality of Manufactured Product. 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. Today’s post is a result from studying Shewhart’s books and articles on cybernetics by Paul Pangaro.

The term “cybernetics” has its origins from the Greek word, κυβερνήτης, which means “navigation”. Cybernetics is generally translated as “the art of steering”. Norbert Wiener, the great American mathematician, wrote the 1948 book, Cybernetics: Or Control and Communication in the Animal and the Machine. Wiener 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 towards the goal. This is referred to as the “First Order Cybernetics”. An example (remaining true to the Greek origin of the word), we can use is a ship sailing towards 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. The thermostat is able to maintain the required temperature inside the house by adjusting according to the external temperature. The thermostat “kicks on” when a specified temperature limit is tripped and cools or heats the house. An important concept that is used for cybernetics is the “law of requisite variety” by Ross Ashby. The law of requisite variety 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 (cybernetics) 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 works well with the cybernetic ideas. Shewhart purposefully used the term “control” for his field. The term 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, Shewhart develops his famous Shewhart cycle as a means to maintain statistical control. Shewhart 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 approach a circular path to which would represent the idealized case, where no evidence is found in the testing of hypothesis indicates 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:

Shewhart cycle1

One of the criterions Shewhart developed for his model was that the model 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.

The brilliance of Shewhart was in providing guidance on when we should react and when we should not react to the variations in the 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 everchanging environment.

James Wilk defined cybernetics as:

“Cybernetics is the study of justified intervention.”

This is an apt definition when we look at quality control, as viewed 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: Measures of Success (Mark Graban, 2019)

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 in the likes of a trend or an outside data point, then we should look for an assignable cause. 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 Dr. 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 PDSA (Plan-Do-Study-Act) cycle which later became famous as PDCA cycle (Plan-Do-Check-Act). This showed only 3 steps with a decision point after step 3.

Shewhart cycle2

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

Always keep on learning…

In case you missed it, my last entry was My Recent Tweets:

Note: The updated Shewhart cycle was added to the post after a discussion with Benjamin Taylor (Syscoi.com).

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My Recent Tweets:

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I will be posting soon… Meanwhile, here are some of my recent tweets that may be of interest to you.

#cybernetics #Lean #SystemsThinking #philosophy

Always keep on learning…

Book Review – Measures of Success:

Measures-of-Success-Cover-Dark-Green-Final-copy-1

In today’s post, I am reviewing the book, “Measures of Success”, written by Mark Graban. Graban is a Lean thinker and practitioner. Graban has written several books on Lean including Lean Hospitals and Healthcare Kaizen. Graban was kind enough to send me a preview copy of his latest book, Measures of Success. As Graban writes in the Preface, his goal is to help managers, executives, business owners, and improvement specialists in any industry use limited time available more effectively.

The book is about Process Behavior Charts or PBC (Statistical Process Control or SPC). Graban teaches in an easy way how to use Process Behavior Charts to understand a process, and truly see and listen to the process. The use of PBC is a strategy of prevention, and not a strategy of detection alone. PBCs help us see when a process is in control and whether what we see is indicative of normal noise present in a process in control or not. Walter Shewhart, who created and pioneered SPC, 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 proceeded to state 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.

Graban has written a great book to help us decide what is noise and what is meaningful data. By understanding how the process is speaking to us, we can stop overreacting and use the saved time to actually make meaningful improvements to the process. Graban has a great style of writing which makes a somewhat hard statistical subject easy to read. I enjoyed the narrative he gave of the CEO looking at the Bowling Chart and reacting to it in the third chapter. The CEO was following the red and green datapoints, and reacting by either pontificating as a means of encouragement or yelling “just do things right” at her team. And worse of all, she thinks that she is making a difference by doing it. Just try harder and get to the green datapoint! Graban also goes into detail on Deming’s Red Bean experiment that is a fun way of demonstrating the minimal impact a worker has on normal variation of the process through a fun exercise.

Similar to Deming’s line of questions regarding process improvement – How are you going to improve? By what method? And How will you know?, Graban also provides three insightful core questions:

  1. Are we achieving our target or goal?
  2. Are we improving?
  3. How do we improve?

We should be asking these questions when we are looking at a Process Behavior Chart. These questions will guide in our continual improvement initiatives. Graban has identified 10 key points that help us reflect on our learning of PBCs. They are available at his website. They help us focus on truly understanding what the process is saying – where are we and should we make a change?

Graban provides numerous examples of current events depicted as PBCs. Some of the examples include San Antonio homicide rates and Oscar Ratings. Did the homicide rate significantly go down recently? Did the Oscar ratings significantly go down in the recent years? These are refreshing because they help solidify our understanding. This also provides a framework for us to do our own analysis of current events we see in the news. Graban also provides an in-depth analysis of his blog data. In addition, there are several workplace cases and examples included.

The list of Chapters are as follows:

  • Chapter 1: Improving the Way We Improve
  • Chapter 2: Using Process Behavior Charts for Metrics
  • Chapter 3: Action Metrics, not Overreaction Metrics
  • Chapter 4: Linking Charts to Improvement
  • Chapter 5: Learning From “The Red Bead Game”
  • Chapter 6: Looking Beyond the Headlines
  • Chapter 7: Linear Trend Lines and Other Cautionary Tales
  • Chapter 8: Workplace Cases and Examples
  • Chapter 9: Getting Started With Process Behavior Charts

The process of improvement can be summarized by the following points identified in the book:

  • If we have an unpredictable system, then we work to eliminate the causes of signals, with the aim of creating a predictable system.
  • 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.
  • 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.

It is clear that Graban has written this book with the reader in mind. There are lots of examples and additional resources provided by Graban to start digging into PBCs and make it interesting. The book is not at all dry and has managed to retain the main technical concepts in SPC.

The next time you see a Metric dashboard either at the Gemba or in the news, you will definitely know to ask the right questions. Graban also provides a list of resources to further improve our learning of PBCs. I encourage the readers to check out Mark Graban’s Blog at LeanBlog.org and also buy the book, Measures of Success.

Always keep on learning…

In case you missed it, my last post was Ubuntu At the Gemba:

Ubuntu At the Gemba:

Ubuntu

“My humanity is tied to yours. I am because you are.” 

In today’s post I will be looking at the African philosophical concept of Ubuntu. The word “Ubuntu” is best explained by the Nguni aphorism – Umuntu Ngumuntu Ngabantu, which means “a person is a person because of or through others.” Ubuntu is a key African philosophy and can be translated as humanity. It emphasizes the group solidarity, sharing, caring and the idea of working together for the betterment of everybody. Ubuntu has many derivatives in Bantu languages and this concept is spread across the many nations in Africa.

Ubuntu is the humanness in us. It is said that a solitary human being is a contradiction. We remain humans as part of a community. We get better through the betterment of our community. Our strength comes from being part of a community. To quote Archbishop Desmond Tutu:

One of the sayings in our country is Ubuntu – the essence of being human. Ubuntu speaks particularly about the fact that you can’t exist as a human being in isolation. It speaks about our interconnectedness. You can’t be human all by yourself, and when you have this quality – Ubuntu – you are known for your generosity. 

We think of ourselves far too frequently as just individuals, separated from one another, whereas you are connected and what you do affects the whole world. When you do well, it spreads out; it is for the whole of humanity. 

An interesting part about African philosophy is that most of it was not written down. The ideas were transmitted through oral traditions, which depended upon having strong communal roots. Some of the key ideas that are part of the Ubuntu philosophy are:

  • Always aim for the betterment of the community over self.
  • When we treat others with dignity, all of us are able to perform and contribute better.
  • The strength of the community lies in the interconnectedness of the members.
  • The survival of one person is dependent upon the survival of the community.
  • Ubuntu philosophy aims for harmony and consensus in decision making.
  • Ubuntu requires us to be open and make ourselves available to others.
  • Ubuntu requires us to coach and mentor those younger than us. This also helps us become better at what we do.
  • Respect and dignity, as part of ubuntu, ensure that we provide an environment where everybody is able to contribute and bring value.
  • Ubuntu is a philosophy focused on people, and promotes working together as a team towards the common goal. At the same time, it promotes healthy competition and challenges people to keep growing.
  • Ubuntu points out that aiming for individual goals over common goals is not good. System optimization is the end goal.
  • Ubuntu facilitates a need to have a strong communication system.
  • As a management system, Ubuntu puts the focus on local conditions and context. How does what we do impact those around us? How does what we do impact our environment? How does what we do impact our society?
  • Another key concept is the Ubuntu philosophy is forgiveness or short memory of hate!

As I was researching and learning about Ubuntu, I could not help but compare it against the concept of “Respect for Humanity” in Toyota Production System.  I see many parallels between the two concepts. Respect for Humanity (People) is one of the two pillars of the Toyota Way. The other pillar being Continuous Improvement. Japan is an island with limited resources, and the concept of harmony is valued in the Japanese culture. Toyota Production System and Lean are famous for its many tools. Tools are easy to identify since they have physical attributes like kanban, Visual work place, standard work etc. However, respect for people was not understood or looked at by the Toyota outsiders. Most of the Japanese literature about Toyota Production System mentioned Respect for Humanity (people) while it took a while for the western authors to start discussing Respect for Humanity.

Toyota’s view of Respect for People is to ensure that its employees feel that they are bringing value and worth to the organization. Fujio Cho, the pioneer of the Toyota Way 2001, expressed Respect for People as:

Creating a labor environment “to make full use of the workers’ capabilities.” In short, treat the workers as human beings and with consideration. Build up a system that will allow the workers to display their full capabilities by themselves.

Toyota has built up a system of respect for human, putting emphasis on the points as follows: (1) elimination of waste movements by workers; (2) consideration for workers’ safety; and (3) self-display of workers’ capabilities by entrusting them with greater responsibility and authority.

Final Words:

Paul Bate, Emeritus Professor of Health Services Management in University College London, said:

Nothing exists, and therefore can be understood, in isolation from its context, for it is context that gives meaning to what we think and we do.

Our context is in the interconnectedness that we share with our fellow beings. It is what gives meaning to us. In this regard, Ubuntu sheds light on us as humans. Respect for people begins by developing them and providing them an opportunity to grow so that they can help with the common goal and causes.

I will finish with the great Nelson Mandela’s explanation of Ubuntu:

A traveler through a country would stop at a village and he didn’t have to ask for food or for water. Once he stops, the people give him food and attend him. That is one aspect of Ubuntu, but it will have various aspects. Ubuntu does not mean that people should not enrich themselves. The question therefore is:

Are you going to do so in order to enable the community around you to be able to improve?

Always keep on learning…

In case you missed it, my last post was Clausewitz at the Gemba:

Clausewitz at the Gemba:

vonClausewitz

In today’s post, I will be looking at Clausewitz’s concept of “friction”. Carl von Clausewitz (1780-1831) was a Prussian general and military philosopher. Clausewitz is considered to be one of the best classical strategy thinkers and is well known for his unfinished work, “On War.” The book was published posthumously by his wife Marie von Brühl in 1832.

War is never a pleasant business and it takes a terrible toll on people. The accumulated effect of factors, such as danger, physical exertion, intelligence or lack thereof, and influence of environment and weather, all depending on chance and probability, are the factors that distinguish real war from war on paper. Friction, Clausewitz noted, was what separated war in reality from war on paper. Friction, as the name implies, hindered proper and smooth execution of strategy and clouded the rational thinking of agents. He wrote:

War is the realm of uncertainty; three quarters of the factors on which action in war is based are wrapped in a fog of greater or lesser uncertainty.

Everything in war is very simple, but the simplest thing is difficult. The difficulties accumulate and end by producing a kind of friction that is inconceivable unless one has experienced war.

Friction is the only conception which, in a general way, corresponds to that which distinguishes real war from war on paper. The military machine, the army and all belonging to it, is in fact simple; and appears, on this account, easy to manage. But let us reflect that no part of it is in one piece, that it is composed entirely of individuals, each of which keeps up its own friction in all directions.

Clausewitz viewed friction as impeding our rational abilities to make decisions. He cleverly stated, “the light of reason is refracted in a manner quite different from that which is normal in academic speculation… the ordinary man can never achieve a state of perfect unconcern in which his mind can work with normal flexibility.” In a tense situation, as most often the case is in combat, the “freshness” or usefulness of the available information is quickly decaying and reliability of the information is also in question.

Friction is what happens when reality differs from your model. Although Clausewitz’s concept of friction contains other elements, I am interested in is the friction coming from ambiguous information. Uncertainty and information are related to each other. In fact, one is the absence of the other. The only way to reduce uncertainty (be certain) is to have the required information that counters the uncertainty. To quote Wikipedia, Uncertainty refers to epistemic situations involving imperfect or unknown information. If we have full information then we don’t have uncertainty. It’s a zero-sum game.

We have two options to deal with the uncertainty due to informational friction:

  1. Reduce uncertainty by making useful information readily available to required agents when needed and where needed
  2. Come up with ways to tolerate uncertainty when we are not able to reduce it further.

As Moshe Rubinstein points out in his wonderful book, Tools for Thinking and Problem Solving, uncertainty is reduced only by acquisition of information and you need to ask three questions, in the order specified, when acquiring information.

  1. Is the information relevant? (is it current, and is the context applicable?)
  2. Is the information credible? (is it accurate?)
  3. Is the information worth the cost?

How should we proceed to minimize the friction?

  1. We should try to get the total picture, an understanding of the forest before we get lost in the trees. This helps us in realizing where our epistemic boundaries might be, and where we need to improve our learning.
  2. We should have the courage to ask questions and cast doubts on our world views. Even with our belief system, we can ask whether it is relevant and credible. We should try to ask – what is wrong with this picture? What am I missing?
  3. We should always keep on learning. We should not shy away from “hard projects.” We should see the challenges as learning experiences.
  4. We should know and be ready to have our plan fail. We should understand what the “levers” are in our plan. What happens when we push on one lever versus pulling on another? We should have models with the understanding that they are not perfect but they help us understand things better. We should rely on heuristics and flexible rules of thumbs. They are more flexible when things go wrong.
  5. We should reframe our understanding from a different perspective. We can try to draw things out or write about it or even talk about it to your spouse or family. Different viewpoints should be welcomed. We should generate multiple analogies and stories to help tell our side of the story. These will only help in further our understanding.
  6. When we make decisions under uncertainty and risk, each action can result in multiple outcomes, and most of the times, these are unpredictable and can have large-scale consequences. We should engage in fast and safe-to-fail experiments and have strong feedback loops to change course and adapt as needed.
  7. We should have stable substructures when things fail. This allows us to go back to a previous “safe point” rather than go back all the way to the start.
  8. We should go to gemba to grasp the actual conditions and understand the context. Our ability to solve a problem is inversely proportional to the distance from the gemba.
  9. We should take time, as permissible, to detail out our plan, but we should be ready to implement it fast. Plan like a tortoise and run like a hare.
  10. We should go to the top to take a wide perspective, and then come down to have boots on ground. We should take time to reflect on what went wrong and what went right, and what our impact was on ourselves and others. This is the spirit of Hansei in Toyota Production System.

Final Words:

Although not all of us are engaged in a war at the gemba, we can learn from Clausewitz about the friction from uncertainty, which impedes us on a daily basis. Clausewitz first used the term “friction” in a letter he wrote to his future wife, Marie von Brühl, in 1806. He described friction as the effect that reality has on ideas and intentions in war. Clausewitz was a man ahead of his time, and from his works we can see elements of systems thinking and complexity science.

We propose to consider first the single elements of our subject, then each branch or part, and, last of all, the whole, in all its relations—therefore to advance from the simple to the complex. But it is necessary for us to commence with a glance at the nature of the whole, because it is particularly necessary that in the consideration of any of the parts the whole should be kept constantly in view. The parts can only be studied in the context of the whole, as a “gestalt.“

Clausewitz realized that each war is unique and thus what may have worked in the past may not work this time. He said:

Further, every war is rich in particular facts; while, at the same time, each is an unexplored sea, full of rocks, which the general may have a suspicion of, but which he has never seen with his eye, and round which, moreover, he must steer in the night. If a contrary wind also springs up, that is, if any great accidental event declares itself adverse to him, then the most consummate skill, presence of mind and energy, are required; whilst to those who only look on from a distance, all seems to proceed with the utmost ease.

Clausewitz encourages us to get out of our comfort zone, and gain as much variety of experience as we can. The variety of states in the environment always is larger than the variety of states we can hold. He continues to advise the following to reduce the impact of friction:

The knowledge of this friction is a chief part of that so often talked of, experience in war, which is required in a good general. Certainly, he is not the best general in whose mind it assumes the greatest dimensions, who is the most overawed by it (this includes that class of over-anxious generals, of whom there are so many amongst the experienced); but a general must be aware of it that he may overcome it, where that is possible; and that he may not expect a degree of precision in results which is impossible on account of this very friction. Besides, it can never be learnt theoretically; and if it could, there would still be wanting that experience of judgment which is called tact, and which is always more necessary in a field full of innumerable small and diversified objects, than in great and decisive cases, when one’s own judgment may be aided by consultation with others. Just as the man of the world, through tact of judgment which has become habit, speaks, acts, and moves only as suits the occasion, so the officer, experienced in war, will always, in great and small matters, at every pulsation of war as we may say, decide and determine suitably to the occasion. Through this experience and practice, the idea comes to his mind of itself, that so and so will not suit. And thus, he will not easily place himself in a position by which he is compromised, which, if it often occurs in war, shakes all the foundations of confidence, and becomes extremely dangerous.

US President Dwight Eisenhower said, “In preparing for battle I have always found that plans are useless, but planning is indispensable.” The act of planning helps us to conceptualize our future state. We should strive to minimize the internal friction, and we should be open to keep learning, experimenting, and adapting as needed to reach our future state. We should keep on keeping on:

“Perseverance in the chosen course is the essential counter-weight, provided that no compelling reasons intervene to the contrary. Moreover, there is hardly a worthwhile enterprise in war whose execution does not call for infinite effort, trouble, and privation; and as man under pressure tends to give in to physical and intellectual weakness, only great strength of will can lead to the objective. It is steadfastness that will earn the admiration of the world and of posterity.”

Always keep on learning…

In case you missed it, my last post was Exploring The Ashby Space:

Exploring The Ashby Space:

Ashby4

Today’s post is a follow-up to an earlier post, Solving a Lean Problem versus a Six Sigma Problem:

In today’s post, I am looking at “The Ashby Space.” The post is based on the works of Ross Ashby, Max Boisot, Bill McKelvey and Karl Weick. Ross Ashby was a prominent cybernetician who is famous for his “Law of Requisite Variety.” The Law of Requisite Variety can be stated as “Only variety can destroy/absorb variety.” Ashby defined variety as the number of distinguishable states of a system. Stafford Beer used variety as a measure of complexity. More variety a system has the more complex it is. An important concept to grasp with this idea is that the number of distinguishable states (and thus variety) depends upon the ability of the observer. In this regard, variety of a system may be viewed as dependent on the observer.

Max Boisot and Bill McKelvey expanded upon the Law of Requisite Variety and stated that only complexity can destroy complexity. In other words, only internal complexity can destroy external complexity. If the regulatory agency of a system does not have the requisite variety to match the variety of its environment, it will not be able to adapt and survive. Ashby explained this using the example of a fencer:

If a fencer faces an opponent who has various modes of attack available, the fencer must be provided with at least an equal number of modes of defense if the outcome is to have the single value: attacked parried.

Boisot and McKelvey restated Ashby’s law as – the range of responses that a living system must be able to marshal in its attempt to adapt to the world must match the range of situations—threats and opportunities—that it confronts. They explained this further using the graphical depiction they termed as “the Ashby Space.” The Ashby Space has two axes, the horizontal axis represents the Variety of Responses, and the vertical axis represents the Variety of Stimuli. Ashby’s law can be represented by the 45˚ diagonal line. The diagonal line represents the requisite variety where the stimuli variety matches the response variety. To adapt and survive we should be in on the diagonal line or below. If we are above the diagonal line, the external variety surpasses the internal variety needed and we perish. Using Ashby’s fencer example, the fencer is able to defend against the opponent only if his defense variety matches or exceeds that of the opponent’s offense variety. This is shown below.

Ashby1

Boisot and McKelvey also depicted the Ordered, Complex and Chaotic regimes in the Ashby space. In the ordered regime, the cause-effect relationships are distinguishable and generally has low variety. The complex regime has a higher variety of stimuli present and requires a higher variety of responses. The cause-effect relationships are non-linear and may make sense only in hindsight. The chaotic regime has the most variety of stimuli. This is depicted in the schematic below. Although the three regimes may appear equally sized in the schematic, this is just for representational purposes.

Ashby2

The next idea that we will explore on the Ashby Space is the idea of the Adaptive Frontier. Ashby proposed a strong need for reducing the amount of variety from the external environment. He viewed this as the role of regulation. Ashby pointed out that the amount of regulation that can be achieved is limited by the amount of information that can be transmitted and processed by the system. This idea is depicted by the Adaptive Frontier curve. Any variety that lies outside this curve is outside the “adaptation budget” of the system. The system does not have the resources nor capacity to process all the variety that is coming in, and does not have the capacity to allocate resources to choose appropriate responses. The adaptive frontier is shown in the schematic below as the red dotted curve.

Ashby3

Combining all the ideas above, the Ashby Space can be depicted as below.

Ashby Space

Boisot and McKelvey detail three types of responses that a living system might follow in the presence of external stimuli. Consider the schematic below, where the agent is located at “Q” in the Ashby Space, which refers to the stimuli variety, X.

  1. The Behaviorist – This is also referred to as the “headless chicken response”. When presented with the stimuli variety, X, the agent will pursue the headless chicken response of trying to match the high variety in a haphazard fashion and soon finds himself outside the adaptive frontier and perishes. The agent fails to filter out any unwanted stimuli and fails to process meaningful information out of the incoming data.
  2. The Routinizer – The routinizer interprets the incoming stimuli as “seen it all before.” They will filter out too much of the incoming data and fail to recognize patterns or mis-categorize them. The routinizer is using the schema which they already have, and their success lies in how well their schema matches the real-world variety-reducing regularities confronting the agent.
  3. The Strategist – An intelligent agent has to correctly interpret the data first, and extract valid information about relevant regularities from the incoming stimuli. The agent then has to use existing schema and match against existing patterns. If the patterns do not match, the agent will have to develop new patterns. As you go up in the Ashby space, the complexity increases, and as you go down, the complexity decreases. The schemas should have the required complexity to match the incoming stimuli. The agent should also be aware of the adaptive frontier and stay within the resource budget constraints. The strategist will try to filter out noise, use/develop appropriate schemas and generate effectively complex responses.

Ashby4

Final Words:

The Ashby Space is a great representation to keep in mind while coping with complexity. The ability of a system to discern what is meaningful and what is noise depends on the system’s past experiences, world views, biases and what its construes as morals and values. Boisot and McKelvey note that:

Not everything in a living system’s environment is relevant or meaningful for it, however. If it is not to waste its energy responding to every will-o-the wisp, a system must distinguish schema based on meaningful information (signals about real-world regularities judged important) from noise (meaningless signals). Note that what constitutes information or noise for a system is partly a function of the organism’s own expectations, judgments, and sensory abilities about what is important —as well as of its motivations— and hence, of its models of the world. Valid and timely representations (schema) economize on the organism’s scarce energy resources.

This also points to the role of sensemaking. As Karl Weick notes, “an increase in complexity can increase perceived uncertainty… Complexity affects what people notice and ignore… The variety in a firm’s repertory of beliefs should affect the amount of time it spends consciously struggling to make sense. The greater the variety of beliefs in a repertoire, the more fully should any situation be seen, the more solutions identified, and the more likely it should be that someone knows a great deal about what is happening.”

The models or representations we construct to represent a phenomenon do not have to be as complex as the phenomenon itself, just like the usefulness of a map is in its abstraction. If the map was as complex as the city it represented, it would become identical to city, with the roads, buildings etc., an exact replica. The system however should have the requisite variety. The system should be able to filter out unwanted variety and amplify its meaningful variety to achieve this. The agent must wait for “meaningful” patterns to emerge, and keep learning.

The agent must also be aware to not claim victory or “Mission Accomplished” when dealing with complexity. Some portion of the stimuli variety may be met with the existing schema as part of routinizing. However, this does not mean that the requisite variety has been achieved. A broken clock is able to tell time correctly twice a day, but it does not mean that you should assume that the clock is functional.

I will finish off with a great insight from Max Boisot:

Note that we do not necessarily require an exact match between the complexity of the environment and the complexity of the system. Afterall, the complexity of the environment might turn out to be either irrelevant to the survival of the system or amenable to important simplifications. Here, the distinction between complexity as subjectively experienced and complexity as objectively given is useful. For it is only where complexity is in fact refractory to cognitive efforts at interpretation and structuring that it will resist simplification and have to be dealt with on its own terms. In short, only where complexity and variety cannot be meaningfully reduced do they have to be absorbed. So an interesting way of reformulating the issue that we shall be dealing with in this article is to ask whether the increase in complexity that confronts firms today has not, in effect, become irreducible or “algorithmically incompressible”? And if it has, what are the implications for the way that firms strategize?

Always keep on learning…

In case you missed it, my last post was Nietzsche’s Overman at the Gemba:

I welcome the reader to read further upon the ideas of Ross Ashby. Some of the references I used are:

  1. An Introduction to Cybernetics, Ross Ashby (1957)
  2. Requisite variety and its implications for the control of complex systems, Cybernetica 1:2, p. 83-99, Ross Ashby (1958)
  3. Complexity and Organization–Environment Relations: Revisiting Ashby’s Law of Requisite Variety, Max Boisot and Bill McKelvey (2011)
  4. Knowledge, Organization, and Management. Building on the Work of Max Boisot, Edited by John Child and Martin Ihrig (2013)
  5. Connectivity, Extremes, and Adaptation: A Power-Law Perspective of Organizational Effectiveness, Max Boisot and Bill McKelvey (2011)
  6. Counter-Terrorism as Neighborhood Watch: A Socio/Computational Approach for Getting Patterns from Dots, Max Boisot and Bill McKelvey (2004)
  7. Sensemaking in Organizations (Foundations for Organizational Science), Karl Weick (1995)

Nietzsche’s Overman at the Gemba:

Overman

In today’s post, I am looking at Nietzsche’s philosophy of Übermensch. Friedrich Wilhelm Nietzsche is probably one of the most misunderstood and misquoted philosophers. The idea of Übermensch is sometimes mistranslated as Superman. A better translation is “Overman”. The German term “mensch” means “human being” and is gender neutral. Nietzsche spoke about overman first in his book, “Thus Spoke Zarathustra.” In the prologue of this book, Nietzsche through Zarathustra asks:

I teach you the overman. Man is something that shall be overcome. What have you done to overcome him?

Nietzsche provides further clarification that, “Man is a rope, fastened between animal and Übermensch – a rope over an abyss.” Übermensch is an idea that represents a being who has overcome himself and his human nature – one who can break away from the bondage of ideals and create new ones in place of the old stale ones.

Nietzsche came to the conclusion that humanity was getting stale by maintaining status quo through adhering to ideals based in the past. He also realized that the developments in science and technology, and the increase in collective intelligence was disrupting the “old” dogmatic ideals and the end result was going to be nihilism – a post-modern view that life is without meaning or purpose. Nietzsche famously exclaimed that; God is dead! He was not rejoicing in that epiphany. Nietzsche proposed the idea of Übermensch as a solution to this nihilistic crisis. Übermensch is not based on a divine realm. Instead Übermensch is a higher form on Earth. Overcoming the status quo and internal struggles with the ideals is how we can live our full potential in this earth and be Übermensch.

Nietzsche contrasted Übermensch with “Last Man”. The last man embraces status quo and lives in his/her comfort zone. The last man stays away from any struggle, internal or external. The last man goes with the flow as part of a herd. The last man never progresses, but stays where he is, clutching to the past.

Nietzsche used the metaphors of the camel, the lion and the child to detail the progress towards becoming an Übermensch. As the camel, we should seek out struggle, to gain knowledge and wisdom through experience. We should practice self-discipline and accept more duties to improve ourselves. As the lion, we should seek our independence from the ideals and dogmas. Nietzsche spoke of tackling the “Thou Shalt” dragon as the lion. The dragon has a thousand scales with the notation, “thou shalt”. Each scale represents a command, telling us to do something or not do something. As the lion, we should strongly say, “No.” Finally, as the child, we are free. Free to create a new reality and new values.

At the Gemba:

Several thoughts related to Übermensch  and Lean came to my mind. Toyota teaches us that we should always strive toward True North, our ideal state. We are never there, but we should always continue to improve and move towards True North. Complacency/the push to maintain status quo is the opposite of kaizen, as I noted in an earlier post.

I am reminded of a press article about Fujio Cho. In 2002, when Fujio Cho was the President of Toyota Motor Corporation, Toyota became the third largest automaker in the world and had 10.2% of share of world market. Cho unveiled a plan to be world’s largest automaker with 15% global market share. Akio Matsubara, Toyota’s managing director in charge of the corporate planning division, stated:

“The figure of 15 percent is a vision, not a target,” he said. “Now that we’ve achieved 10 percent, we want to bring 15 percent into view as our next dream. We don’t see any significance in becoming No. 1.”

The point of the 15 percent figure, he said, is to motivate Toyota employees to embrace changes to improve so they would not become complacent with the company’s success.

My favorite part of the article was Morgan Stanley Japan Ltd. auto analyst Noriaki Hirakata’s remarks about Fujio Cho. Toyota’s executives, he said, believe Toyota is “the best in the world, but they don’t want to be satisfied.”

It’s as if Cho’s motto has become “Beat Toyota,” Hirakata said.

I am also reminded of a story that the famous American Systems Thinker, Russel Ackoff shared. In 1951, he went to Bell Labs in Murray Hill, New Jersey, as a consultant. While he was there, all the managers were summoned to an impromptu urgent meeting by the Vice President of Bell Labs. Nobody was sure what was going on. Everyone gathered in a room anxious to hear what the meeting was about. The Vice President walked in about 10 minutes late and looked very upset. He walked up to the podium and everyone became silent. The Vice President announced:

“Gentlemen, the telephone system of the United States was destroyed last night.”

He waited as everyone started talking and whispering that it was not true. The Vice President continued:

“The telephone system was destroyed last night and you had better believe it. If you don’t by noon, you are fired.”

The room was silent again. The Vice President then started out laughing, and everyone relaxed.

“What was that all about? Well, in the last issue of the Scientific American,” he said, “there was an article that said that these laboratories are the best industrially based scientific laboratories in the world. I agreed, but it got me thinking.”

The Vice President went to on to state that all of the notable inventions that Bell Lab had were invented prior to 1900. This included the dial, multiplexing, and coaxial cable. All these inventions were made prior to when any of the attendees were born. The Vice President pointed out that they were being complacent. They were treating the parts separately and not improving the system as a whole. His solution to the complacency? He challenged the team to assume that the telephone system was destroyed last night, and that they were going to reinvent and rebuilt it from scratch! One of the results of this was the push button style phones that reduced the time needed to dial a number by 12 seconds. This story reminds me of breaking down the existing ideals and challenging the currently held assumptions.

Nietzsche challenges us to overcome the routine monotonous ideas and beliefs. Instead of simply existing, going from one day to the next, we should challenge ourselves to be courageous and overcome our current selves. This includes destruction and construction of ideals and beliefs. We should be courageous to accept the internal struggle, when we go outside our comfort zone. The path to our better selves is not inside the comfort zone.

Similar to what Toyota did by challenging the prevalent mass production system and inventing a new style of production system, we should also challenge the currently held belief system. We should continue evolving toward our better selves. As Nietzsche said:

“What is great in man is that he is a bridge and not an end.”

“I say unto you: One must still have chaos in oneself to be able to give birth to a dancing star.”

Always keep on learning…

In case you missed it, my last post was Solving a Lean Problem versus a Six Sigma Problem:

Solving a Lean Problem versus a Six Sigma Problem:

Model

I must confess upfront that the title of this post is misleading. Similar to the Spoon Boy in the movie, The Matrix, I will say – There is no Lean problem nor a Six Sigma problem. All these problems are our mental constructs of a perceived phenomenon. A problem statement is a model of the actual phenomenon that we believe is the problem. The problem statement is never the problem! It is a representation of the problem. We form the problem statement based on our vantage point, our mental models and biases. Such a constructed problem statement is thus incomplete and sometimes incorrect. We do not always ask for the problem statement to be reframed from the stakeholder’s viewpoint. A problem statement is an abstraction based on our understanding. Its usefulness lies in the abstraction. A good abstraction ignores and omits unwanted details, while a poor abstraction retains them or worse omits valid details. Our own cognitive background hinders our ability to frame the true nature of the problem. To give a good analogy, a problem statement is like choosing a cake slice. The cake slice represents the cake, however, you picked the slice you wanted, and you still left a large portion of the cake on the table, and nobody wants your slice once you have taken a bite out of it.

When we have to solve a problem, it puts tremendous cognitive stress on us. Our first instinct is to use what we know and what we feel comfortable with. Both Lean and Six Sigma use a structured framework that we feel might suit the purpose. However, depending upon what type of “problem” we are trying to solve, these frameworks may lack the variety they need to “solve” the problem. I have the used the quotation marks on purpose. For example, Six sigma relies on a strong cause-effect relationship, and are quite useful to address a simple or complicated problem. A simple problem is a problem where the cause-effect relationship is obvious, whereas a complicated problem may require an expert’s perspective and experience to analyze and understand the cause-effect relationship. However, when you are dealing with a complex problem, which is non-linear, the cause-effect relationship is not entirely evident, and the use of a hard-structured framework like Six sigma can actually cause more harm than benefit. All human-centered “systems” are complex systems. In fact, some might say that such systems do not even exist. To quote Peter Checkland, “In a certain sense, human activity systems do not exist, only perceptions of them exist, perceptions which are associated with specific worldviews.”

We all want and ask for simple solutions. However, simple solutions do not work for complex problems. The solutions must match the variety of the problem that is being resolved. This can sometimes be confusing since the complex problems may have some aspects that are ordered which give the illusion of simplicity. Complex problems do not stay static. They evolve with time, and thus we should not assume that the problem we are trying to address still has the same characteristics when they were identified.

How should one go from here to tackle complex problems?

  • Take time to understand the context. In the complex domain, context is the key. We need to take our time and have due diligence to understand the context. We should slow down to feel our way through the landscape in the complex domain. We should break our existing frameworks and create new ones.
  • Embrace diversity. Complex problems require multidisciplinary solutions. We need multiple perspectives and worldviews to improve our general comprehension of the problem. This also calls to challenge our assumptions. We should make our assumptions and agendas as explicit as possible. The different perspective allows for synthesizing a better understanding.
  • Similar to the second suggestion, learn from fields of study different from yours. Learn philosophy. Other fields give you additional variety that might come in handy.
  • Understand that our version of the problem statement is lacking, but still could be useful. It helps us to understand the problem better.
  • There is no one right answer to complex problems. Most solutions are good-enough for now. What worked yesterday may not work today since complex problems are dynamic.
  • Gain consensus and use scaffolding while working on the problem structure. Scaffolding are temporary structures that are removed once the actual construction is complete. Gaining consensus early on helps in aligning everybody.
  • Go to the source to gain a truer understanding. Genchi Genbutsu.
  • Have the stakeholders reframe the problem statement in their own words, and look for contradictions. Allow for further synthesis to resolve contradictions. The tension arising from the contradictions sometimes lead us to improving and refining our mental models.
  • Aim for common good and don’t pursue personal gains while tackling complex problems.
  • Establish communication lines and pay attention to feedback. Allow for local context while interpreting any new information.

Final Words:

I have written similar posts before. I invite the reader to check them out:

Lean, Six Sigma, Theory of Constraints and the Mountain

Herd Structures in ‘The Walking Dead’ – CAS Lessons

A successful framework relies on a mechanism of feedback-induced iteration and keenness to learn. The iteration function is imperative because the problem structure itself is often incomplete and inadequate. We should resist the urge to solve a Six Sigma or a Lean problem. I will finish with a great paraphrased quote from the Systems Thinker, Michael Jackson (not the famous singer):

To deal with a significant problem, you have to analyze and structure it. This means, analyzing and structuring the problem itself, not the system that will solve it. Too often we push the problem into the background because we are in a hurry to proceed to a solution. If you read most texts thoughtfully, you will see that almost everything is about the solution; almost nothing is about the problem.

Always keep on learning…

In case you missed it, my last post was Maurice Merleau-Ponty’s Lean Lessons:

Maurice Merleau-Ponty’s Lean Lessons:

Merleau-Ponty

In today’s post, I am writing about three great Lean lessons inspired by the late French philosopher, Maurice Merleau-Ponty. Merleau-Ponty was a phenomenologist who believed that our conceptual framework is inherently flawed. He wanted to develop a framework that accurately reflected the nature of things it described. His insight was that we perceive things by interacting with them. The more we interact, the deeper our perception becomes, and the more we can enjoy the richness of the object we are interacting with. Merleau-Ponty believed that being in the world is the embodied experience of perception. The world does not present itself “all at once” to the perceiver. The perceiver has to go through an ongoing process of exploration and discovery and a deeper understanding emerges gradually through this ongoing process.

The three lessons I have chosen are interrelated and are about perception. Lean teaches us the importance of Genchi Genbutsu or Go to See and Grasp the Situation. The following three ideas align really well with the idea of Genchi Genbutsu.

  • The philosopher is a perpetual beginner…

Merleau-Ponty’s point here is that a true philosopher does not take things for granted. I will replace the word “philosopher” with “Lean leader”. Thus, the Lean leader is a perpetual beginner. As Lean leaders, we are ready to learn everyday from the gemba. We are continually improving our perception from the gemba. We must resist the urge to feel that we have completed our learning and that there is nothing left to learn. To paraphrase Merleau-Ponty, we need to learn to see the world (and gemba) as something new every single day. We must start to “see” with a beginner’s mind to learn.

 

  • In order to see the world, we must break with our familiar acceptance of it:

Our ability to observe depends on our preconceived notions and biases. Understanding of a phenomenon lies under the surface in the nuances and the contradictions. Our familiarity based on our prior biases cloud our ability to “see”, and Merleau-Ponty advises us to break our familiar acceptance in order to see the world. We must put aside our assumptions and relearn to see the world with fresh eyes.

 

  • Nothing is more difficult than to know precisely what we see:

This idea to me is simply wonderful. When we are at the Gemba to see or observe, we jump to conclusions. We believe that we “see” the problem and know how to fix it. The act of observing and perceiving requires a vantage point. This vantage point comes with prejudices. We believe that what we see is quite simple and straightforward, and that we have a clear perspective. This actually hinders our ability to know and understand the phenomenon we are perceiving. From a philosophy standpoint, we believe that what we perceive is reality. This of course is incomplete and most of the time a faulty notion.

Final Words:

The three ideas of Merleau-Ponty advises us to go to the Gemba more and interact with it to improve our understanding. We should look at the real workplace with the eyes of a beginner, and keep interacting with an open mind without preconceived notions to learn. We should resist the urge to believe that we know precisely what we see.

Taiichi Ohno was famous for his Ohno circles. Taiichi Ohno drew chalk circles and made the supervisor or the engineer stand in the circle to observe an operation until he was able to “see” the waste that Ohno saw. Similar to Merleau-Ponty, Ohno also advises us to go and see without preconceived notions. Go and see a lot. This helps us to improve our perception. The more we do it, the better we get at it. And yet, we should strive to remain a perpetual beginner.

Always keep on learning…

In case you missed it, my last post was Toyota Physics:

Toyota Physics:

newton

In today’s post, I am looking at Factory Physics and Toyota Production System. My main references for the post are the 1977 paper coauthored by ex-Toyota president Fujio Cho [1] and key ideas from Factory Physics [2].

One of my favorite definitions of “Lean” comes from Wallace J. Hopp and Mark L. Spearman (Factory Physics). They defined Lean as:

Lean is fundamentally about minimizing the cost of buffering variability… Production of goods or services is lean if it is accomplished with minimal buffering costs.

Variability is the norm of life. Variability is all around us. Variability impacts the 6Ms of production – Man, Method, Machine, Material, Mother Nature (Environment) and Measurement. Variability degrades the performance of a system. Variability is anything that causes the system to depart from regular, predictable behavior. Variability can be internal in the form of quality issues, operator unavailability, material shortage, skill levels, equipment issues etc. Variability can also be external in the form of irregular flow of customer orders, requests for diverse products, supplier issues, new regulations etc.

Factory Physics teaches us that any system has three buffers to deal with variability – Inventory, Capacity and Time.

Regardless of its source, all variability in a production system will be buffered. A fundamental principle of factory physics is that there are three types of variability buffer: inventory, capacity, and time.

For example, safety stocks represent inventory buffers against variability in demand and/or production. Excess capacity can also provide protection (i.e., a capacity buffer) against fluctuations in demand and/or production. Finally, safety lead times provide a time buffer against production variability. While the exact mix of buffers is a management decision, the decision of whether or not to buffer variability is not. If variability exists, it will be buffered somehow.

A Capacity buffer in the form of overtime is quite familiar to any organization. If there is excess demand, use overtime to get out of the backorder situation. The Inventory buffer in the form of just-in-case or safety stocks is also easy to understand. The last form, time buffer, is unfortunately suffered by the customer. When an organization cannot produce products on time, the lead time goes up and the customer has to wait. The time buffer is automatically enforced by the system when the other two buffers are not used wisely.

Another way to look at these buffers is to see what is waiting to know what buffer is available to use:

                Inventory buffer – parts are waiting

                Capacity buffer – resources (labor, equipment etc.) are waiting

                Time buffer – customers are waiting.

A successful organization is able to swap the right buffer at the right time in the right amount. The success of Taiichi Ohno and Toyota was in developing a production system framework through decades of trial and error that excelled in minimizing the cost of buffering variability.

Toyota could not match Ford or any other competitor in carrying the inventory required by the mass production system. Toyota focused first on the capacity buffer. They modified equipment to match what they needed. They created the Just-in-Time system so that required product is made at the right time and in the right quantity. They also had operators manage more than one piece of equipment at a time. Toyota was also able to bring down the set-up times for their equipment which allowed them to run a variety of parts in smaller lots. They focused on the flow of parts and redid the factory layout to match the process flow. With the development of the kanban system, Ohno was able to create a full-fledged pull system to support the Just-in-Time concept. As Hopp and Spearman point out, Toyota utilized the capacity buffer wisely. [3]

At a time when automotive plants generally ran three shifts a day, Toyota went to a two-shift schedule, with 10-hour shifts separated by 2-hour preventive maintenance (PM) periods. These PM periods served as capacity buffers to allow shifts to make up any shortfalls on their production quotas. With these capacity buffers as backup, Toyota could afford to run much leaner with respect to inventory.

A key part of increasing capacity was also where Toyota shined, with the concept of Respect for Humanity. This is very well described in the 1977 paper – Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system (Y. Sugimori, K. Kusunoki, F. Cho & S. Uchikawa). The authors document that Toyota recognized the need for producing better quality goods having higher added value and at an even lower production cost than those of the other countries. Toyota focused on a system that would allow the workers to display their full capabilities by themselves. The authors detailed the “requirements” that existed at the time for the automotive industry – the need to carry large inventory of many different components.

The ordinary production control system in such an industry consists of fulfilling the production schedules by holding work-in-process inventory over all processes as a means of absorbing troubles in the processes and changes in demand. However, such a system in practice often creates excessive unbalance of stock between the processes, which often leads to dead stock. On the other hand, it can easily fall into the condition of having excessive equipment and surplus of workers, which is not conformable to Toyota’s recognition.

This section in the paper identifies the inventory buffer and capacity buffer quite well. Toyota was not keen on carrying inventory and having extra equipment and surplus of labor since that would increase the cost of production. Ohno realized that focusing on value added work would allow them to utilize the capacity buffer efficiently.

In order to improve their capacity buffer, Toyota focused on Respect for Humanity. The paper states:

The just-in-time production is a method whereby the production lead time is greatly shortened by maintaining the conformity to changes by having ” all processes produce the necessary parts at the necessary time and have on hand only the minimum stock necessary to hold the processes together”. In addition, by checking the degree of inventory quantity and production lead time as policy variables, this production method discloses existence of surplus equipment and workers. This is the starting point to the second characteristic of Toyota Production System (the first being Just-In-Time production), that is, to make full use of the workers’ capability.

Toyota clearly identified that they were not going to utilize the inventory buffer or the time buffer in the form of production lead time.

Toyota has succeeded in reducing the lot size through greatly shortening the· setup time, improving production methods including the elimination of in-process inventory within the process resulting from ordering of multipurpose machining equipment in accordance with the processing requirements for a product line, and improving conveyance resulting from repetitive mixed loading.

In fact, Toyota specifically called out not using the inventory buffer.

In the conventional production control system, existence of inventory is appreciated as a means to absorb troubles and fluctuations in demand and to smooth fluctuations in load of processes. In contrast to this, Toyota sees the stock on hand as being only a collection of troubles and bad causes.

Toyota went on to clearly state that carrying an inventory buffer goes against their need for respect for humanity.

Such latency of waste makes it difficult for workers to display their capability and it even becomes obstructive of an ever-lasting evolution of a company.

The paper also goes into detail on the formulation of number of the kanbans needed. They identify that the capacity buffer in the form of overtime and inventory buffer can be used initially while the plant focuses on making improvements.

Toyota defined themselves as an organization where conditions are enforced to make the necessity for improvement immediately visible. This is in a sense a pull system for improvements.

Any employee at Toyota has a right to make an improvement on the waste he has found. In the just-in-time production, all processes and all shops are kept in the state where they have no surplus so that if trouble is left, unattended, the line will immediately stop running and will affect the entire plant. The necessity for improvement can be easily understood by anyone. Therefore, Toyota is endeavouring to make up a working place where not only the managers and foremen but also all workers can detect trouble. This is called ‘ visible control ‘. Through visible control, all workers are taking positive steps to improve a lot of waste they have found. And the authority and responsibility for running and improving the workshop have been delegated to the workers themselves, which is the most distinctive feature of Toyota’s respect for human system.

Always keep on learning…

In case you missed it, my last post was My recent tweets…

[1] Y. SUGIMORI , K. KUSUNOKI , F. CHO & S. UCHIKAWA (1977) Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system, THE INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 15:6, 553-564, DOI:10.1080/00207547708943149

[2] Factory Physics, 3rd edition

[3] Wallace J. Hopp, Mark L. Spearman, (2004) To Pull or Not to Pull: What Is the Question? Manufacturing & Service Operations Management 6(2):133-148.