Ubuntu At the Gemba:


“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:

Is Lean the Medium or the Message?


In today’s post, I am looking at the profound phrase of Marshall McLuhan, “The medium is the message.” Marshall McLuhan was a Canadian philosopher and a media theorist. McLuhan noted that: [1]

Each medium, independent of the content it mediates, has its own intrinsic effects which are its unique message… The message of any medium or technology is the change of scale or pace or pattern that it introduces into human affairs… It is the medium that shapes and controls the scale and form of human association and action.

The simplest understanding of the phrase, “the medium is the message”, is that it does not matter what we say, it matters how we say it. However, this is a simplistic view. McLuhan’s insight was that any medium is an extension of ourselves. For example, the telephone is an environment, and it affects everybody. The smartphone, which is a further advancement of the telephone, has a much larger impact on us and what we do. McLuhan realized that as we shape the media, the media shapes us. It is a complex interactive phenomenon. McLuhan said that it does not matter what you print, as long as you keep going with that activity. Every medium helps us to do much more that what we can do physically. For example, McLuhan talked about language being an extension of our thoughts, and written language is a further extension of our speech. The ability to print replaced the need for us to be there physically to extend our thoughts via speech. The ability to print had a profound impact on us much more than all the printed media combined. The medium is the message simply because the impact the media has on our social life.

McLuhan realized that media has an impact on our environment, and sadly we are most of the time unaware of our changing environment. He noted that people in any environment are less privileged to observing themselves than those slightly outside. McLuhan explained this phenomenon with a catchy phrase – the fish did not discover water. He postulated that fish may not be aware of the water, the very thing their life depend on. Another way to look at this is by looking at tweets from a politician. The tweets themselves are beside the point. The medium of Twitter has a far reaching impact on our social media. McLuhan would ask us to look beyond the obvious content in a tweet and look at the social impact the medium is generating.

I wanted to view this idea with Lean. As Lean Leaders, we are trying to propagate the good messages of Lean – “Banish waste”, “respect for Humanity”, “kaizen” etc. We need to realize that the message is not the content, but the medium and the context of our actions. As the aphorism goes, our actions speak louder than our words. The medium, as extensions of us, reaches into our lives and shape ourselves. We should concentrate on the medium to make a larger favorable impact. A good example is kanban. Kanban is a simple mechanism for a pull system, a paper slip that triggers production in a quantity that is needed at the time it is needed. However, the use of kanban leads to an awareness of the problems at the gemba, which leads to a need for a kaizen culture.

The ideas of revealing waste as it occurs, challenging ourselves to continuously improve by elimination of waste and develop people as part of a value adding function are integral to any Lean implementation. This complex intermingled set of ideas cannot be made understood by an edict top down from the CEO – “implement Lean.” What is needed is an understanding of the medium and the environment. The medium of daily board meetings for example has an impact on the social aspects in an organization because of involvements at different levels. The medium of QC circles or daily or weekly kaizen groups are another example. The content of fixing problems is not as important as the medium itself and the long-lasting impact it has by developing people to see wastes and improving their own ability to fix problems.

Sometimes we focus more on the content of the message, as in implementing “Lean”, without trying to understand what is the need that we are trying to address. McLuhan explained this focus on the content as a juicy piece of meat carried by the burglar to distract the watchdog of the mind. We are focusing on the wrong thing. The top down push for lean, six sigma etc. without changing medium may not have a lasting effect. The medium itself has to be changed to change the meaning and impact. The medium is the message, which is context driven! If you want to make “change”, don’t just change the message, change the medium itself. Hence, the title of this post – Is Lean the Medium or the Message?

Final Words:

It is said that the typesetters mistakenly printed, “The medium is the massage” on the cover of his book [2]. McLuhan loved the changed phrasing because it had additional interpretations that he appreciated. He said, “Leave it alone! It’s great, and right on target!” [3]

I will finish with a great insight that McLuhan made in 1964 [1], that foreshadowed the medium of internet and social media:

Archimedes once said, “Give me a place to stand and I will move the world.” Today he would have pointed to our electric media and said, “I will stand on your eyes, your ears, your nerves, and your brain, and the world will move in any tempo or pattern I choose.” We have leased these “places to stand” to private corporations.

Always keep on learning…

In case you missed it, my last post was Purpose of a System in Light of VSM:

[1] Understanding Media, Marshall McLuhan

[2] The Medium is the Massage, Marshall McLuhan

[3] https://www.marshallmcluhan.com/common-questions/

Herd Structures in ‘The Walking Dead’ – CAS Lessons:


The Walking Dead is one of the top-rated TV shows currently. The show is about survival in a post-apocalyptic zombie world. The zombies are referred to as “walkers” in the show. I have written previously about The Walking Dead here. In today’s post, I want to briefly look at Complex Adaptive Systems (CAS) in the show’s backdrop. A Complex Adaptive System is an open non-linear system with heterogenous and autonomous agents that have the ability to adapt to their environment through interactions between themselves and with their environment.

The simplest example to get a grasp of CAS is to look at an ant colony. Ants are simple creatures without a leader telling what each ant should do. Each ant’s behavior is constrained by a set of behavioral rules which determine how they will interact with each other and with their environment. The ant colony taken as a whole is a complex and intelligent system. Each ant works with local information, and interacts with other ants and the environment based on this information. The different tasks that the ants do are patrol, forage, maintain nest and perform midden work. The local information available to each ant is the pheromone scent from another ant. As a whole, their interactions result in a collective intelligence that sustains their colony. In presence of perturbations in their environment, the ants are able to switch to specific tasks to maintain their system. The ants decide the task based on the local information in the form of perturbation to their environment and their rate of interaction with other ants performing the specific tasks. The ants go up in the ranks eventually becoming a forager in the presence of need. A forager ant always stays a forager. The ant colony carries a large amount of “reserve ants” who do not perform any function. This reserve allows for specific task allocation as needed based on perturbations to their environment.

To further illustrate the “self-organizing” or pattern forming behavior of ants, let’s take for example, their foraging activity. The ants will set out from the colony in a random fashion looking for food. Once an ant finds food, it will bring it back to the nest leaving a pheromone trail on its way back. The other ants engaged in the foraging activity will follow the pheromone trail and bring back food while leaving their pheromone scent on the path. The pheromone scent will evaporate over a short amount of time. The ants that followed the shortest path would go back for more food and their pheromone trail will stay “fresh” while a longer path will not remain as “fresh” since the pheromone has more time to evaporate. This means that the path with the strongest pheromone trail is the shortest path to the food. The shortest path was a result of positive feedback loops from more and more ants leaving pheromone at a faster rate. Here the local information available to each ant is the rate of pheromone release from the other ants. The faster the rate, the stronger the trail. This generally corresponds to the shortest trail to the food source. Once the food source is consumed, another food source is identified and a new short path is established. This “algorithm” called as Ant Colony Optimization Algorithm is utilized by several transportation companies to find the shortest routes.


In the show, The Walking Dead, a similar collective behavior is shown by the zombies. The zombies exhibit a herding behavior where a large number of zombies will move together as a herd in search for “food”. The zombies in The Walking Dead world are devoid of any intelligence and there is no one in charge similar to the ants. The zombies however do not have a nest. They just wander around. The zombies in the show are attracted by sound, movement and possibly absence of “zombie smell”. The zombies do not attack each other possibly due to the presence of “zombie smell”. In fact, in the show several characters were able to survive zombie attack by lathering themselves in the “zombie goo”.

The possible explanation for the formation of herd structures is the hardwired attribute that we all have – copying others. We tend to follow what others are doing when we are not sure what is happening. We go with the flow. A good example is the wave we do in a sports stadium. We could develop a model where a few zombies are attracted by a stimulus and they walk toward the stimulus. The other zombies simply follow them, and soon a large crowd forms due to the reinforced loops with more and more followers. This is similar to the positive reinforcing feedback of pheromone trail in the example of ants.

The show recently introduced an antagonist group called the “Whisperers”. The Whisperers worship the dead and adorn the zombie skins and walk amongst the zombies. They learned to control the herd and make them go where they want. The Whisperers themselves a CAS, adapted to survive by being with the walkers. Possibly, they are able to guide the walkers by first forming a small crowd themselves and then getting more walkers to join them as they move as a group. Since they have the “zombie smell” on them, the walkers do not attack them.

How Does Understanding CAS Help Us?

We are not ants and certainly not zombies (at least not yet). But there are several lessons we can get from understanding CAS. We all belong to a CAS at work, and in our community. The underlying principle of CAS is that we live in a complex world where we can understand the world only in the context of our environment and our local interactions with our neighbors and with the environment. Every project we are involved in is new and not identical to any previous project. This could be the nature of the project itself, or the team members or the deadlines or the client. Every part of the project can introduce a new variation that we did not know of. Given below are some lessons from CAS.

  1. Observe and understand patterns:

Complex Adaptive Systems present patterns due to the agents’ interactions. You have to observe and understand the different patterns around you. How do others interact with each other? Can you identify new patterns forming in the presence of new information or perturbations in your environment? Improve your observation skills to understand how patterns form around you. Look and see who the “influencers” are in your team.

  1. Understand the positive and negative feedback loops:

Observe and understand the positive and negative feedback loops that exist around. A pattern forms based on these loops. The awareness of the positive and negative loops will help us nurture the required loops.

  1. Be humble:

Complexity is all around us and this means that we lack understanding. We cannot foresee or predict how things will turn out every time. Complex systems are dispositional, to quote Dave Snowden. They may exhibit tendencies but we cannot completely understand how things work in a complex system. Edicts and rules do not always work and they can have unintended consequences. Every event is possibly a new event and this means that although you can have insights from your past experiences, you cannot control the outcomes. You cannot simply copy and paste because the context in the current system is different.

  1. Get multiple perspectives always (reality is multidimensional and constructed):

Get multiple perspectives. To quote the great American organizational theorist, Russell Ackoff, “Reality is multidimensional.” To add to this, it is also constructed. The multiple perspectives help us to understand things a little better and provide a new perspective that we were lacking. Systems are also constructed and can change how it appears depending on your perspective.

  1. Go inside and outside the system:

We cannot try to understand a system by staying outside it all of the time. Similarly, we cannot understand a system by staying inside it all of the time. Go to the Gemba (the actual workplace) to grasp the situation to better understand what is going on. Come away from it to reflect. We can understand a system only in the context of the environment and the interactions going on.

  1. Have variety:

Similar to #4, variety is your friend in a complex system. Variety leads to better interactions that will help us with developing new patterns. If everybody was the same then we would be reinforcing the same idea that would lack the requisite variety to counter the variety present in our environment. Our environment is not homogenous.

  1. Aim for Effectiveness and not Efficiency:

In complex systems, we should aim for effectiveness. Here, the famous Toyota heuristic, “Go slow to go fast” is applicable. Since each event is novel, we cannot aim for efficiency always.

  1. Use Heuristics and not Rules:

Heuristics are flexible and while rules are rigid. Rules are based on past experiences and lack the variety needed in the current context. Heuristics allow flexing allowing for the agents to change tactics as needed.

  1. Experiment frequently with safe to fail small experiments:

As part of prodding the environment, we should engage in frequent and small safe to fail experiments.  This helps us improve our understanding.

  1. Understand that complexity is always nonlinear, thus keep an eye out for emerging patterns:

Complexity is nonlinear and this means that a small change can have an unforeseen and large outcome. Thus, we should observe for any emerging patterns and determine our next steps. Move towards what we have identified as “good” and move away from what we have deemed as “bad”. Patterns always emerge bottom-up. We may not be able to design the patterns, but we may be able to recognize the patterns being developed and potentially influence them.

Final Words:

My post has been a very simple look at CAS. There are lot more attributes to CAS that are worth pursuing and learning. Complexity Explorer from Santa Fe institute is a great place to start. I will finish with a great quote from the retired United States Army four-star general Stanley McChrystal, from his book, Team of Teams:

“The temptation to lead as a chess master, controlling each move of the organization, must give way to an approach as a gardener, enabling rather than directing. A gardening approach to leadership is anything but passive. The leader acts as an “Eyes-On, Hands-Off” enabler who creates and maintains an ecosystem in which the organization operates.”

Always keep on learning…

In case you missed it, my last post was Conceptual Metaphors in Lean:

The Confirmation Paradox:

albino raven

In today’s post I will be looking at Confirmation Paradox or Black Raven Paradox by Carl Hempel. Let’s suppose that you have never seen a raven in your life. You came across a raven one fine morning, and observe that it is black in color. Now that you have seen one, you suddenly start paying more attention and you start seeing ravens everywhere. Each time you see a raven, you observe that its color is black. Being the good scientist that you are, you come to a hypothesis – All ravens are black. This is also called induction, coming to a generalization from many specific observations.

Now you would like to confirm your hypothesis. You ask your good friend, Carl Hempel, to help. Carl suggests that you start looking at things around his house that are not black and not raven, like his red couch, the yellow tennis ball etc. He suggests that each of those observations support your hypothesis that all ravens are black. You are rightfully puzzled by this. This is the confirmation paradox. Carl Hempel was a German born philosopher who later immigrated to America.

Carl Hempel is correct with this claim. Let’s look at this further. All ravens are black can be restated as “Whatever is not black is not a raven”. This is a logical equivalence of your hypothesis. This would mean that if you observe something that is not black and is not a raven, it would support your hypothesis. Thus, if you observe a red couch, it is not black and it is also not a raven, therefore it supports your hypothesis that all ravens are black.

How do we come in terms with this? Surely, it does not make sense that a red couch supports the hypothesis that all ravens are black. The first point to note here is that one can never prove a hypothesis via induction. Induction requires the statement to be provided with a level of confidence or certainty. This would mean that the level of “support” that each observation makes depends upon the type of information gained from that observation.

I will explain this further with the concept of information from Claude Shannon’s viewpoint. Information is all around us. Where ever you look, you can get information. Claude Shannon quantified this in terms of entropy with the unit as a bit. He described this as the amount of surprise or reduction of uncertainty. Information is inversely proportional to probability of an event. The less probable an event is, the more information it contains. Let’s look at the schematic below:


The black triangle represents all the black ravens in our observable universe. The blue square represents all of the black things in our observable universe. The red circle represents all the things in the observable universe. Thus, the set of black ravens is a subset of all black things, which in turn is a subset of all things. From a probability standpoint, the probability of observing a black raven is much smaller than the probability of observing a black thing since there are proportionally a lot more black things in existence. Similarly, the probability of observing a non-black thing is much higher since there are lot more non-black things in existence. Thus, from an information standpoint, the information you get from observing a non-black thing that is not a raven is very very small. Logically, this observation does provide additional support, however, the information content is miniscule. Please note that, on the other hand, observing a black raven is also supporting the statement that all non-raven things are non-black.

When you first saw a black raven, you had no idea about such a thing existing. The information content of that observation was high. After you started observing more ravens, the information you got from each observation started diminishing. Even if you made 10,000 observations of black ravens, you cannot prove (100% confirm) that all ravens are black. This is the curse of induction. This is where Karl Popper comes in. Karl Popper, an Austrian-British philosopher, had the brilliant insight that good hypotheses should be falsifiable. We should try to look for observations that would fail our hypothesis. His insight was in the asymmetry of falsifiability. You may have 100,000 observations supporting your hypothesis. All you need is a single observation to fail it. The most popular example for this is the case of the black swan. The belief that all swans are white was discredited when black swans were discovered in Australia. To come back to the information analogy, the observation of a white raven has lot more information content that is powerful enough to break down your hypothesis since the occurrence of a white raven(albino) is very low in nature. Finding a white raven is quite rare and thus have the most information or surprise.

This also brings up the concept of Total Evidence. The concept of Total Evidence was put forth by Rudolf Carnap, a German born philosopher. He stated that in the application of inductive logic to a given knowledge situation, the total evidence available must be taken as basis for determining the degree of confirmation. Let’s say that as we learned more about ravens and other birds, we came across the concept of albinism in other animals and birds. This should make us challenge our hypothesis since we know that albinism can occur in nature, and thus it is not farfetched that it can occur in ravens as well. The concept of Total Evidence is interesting because even though it has the term “Total” in it, it is beckoning us to realize that we cannot ever have total information. It is a reminder for us to consider all possibilities and to understand where our mental models break down. In theory, one could also make whimsical statements such as “All unicorns are rainbow colored”, and say that the observation of a white shoe supports it based on the confirmation paradox. Total evidence in this case would require us to have made at least one observation of a rainbow colored unicorn.

I will finish with another paradox that is similar to the confirmation paradox – the 99-foot (feet) man paradox by Paul Berent. Up to this point, we have been looking at qualitative data (black versus not black, or raven versus not raven). Let’s say that you have a hypothesis that says all men are less than 100 feet. You surveyed over 100,000 men and found all of them to be less than 100 feet. One day you heard about a new circus company coming to town. Their main attraction is a 99-foot man. You go to see him in person and sure enough, he is 99 feet tall. Now, your hypothesis is still intact since the 99-foot man is technically less than 100 feet. However, this adds doubt to your mind. You realize that if there is a 99-foot man, then the occurrence of a 100-foot man is not farfetched. The paradox occurs since the observation of a 99-foot man strengthens your hypothesis, but at the same time it also weakens it.

Always keep on learning…

In case you missed it, my last post was Know Your Edges:

Know Your Edges:


In today’s post I will start with a question, “Do you know your edges?

Edges are boundaries where a system or a process (depending upon your construction) breaks down or changes structure. Our preference, as the manager or the owner, is to stay in our comfort zone, a place where we know how things work; a place where we can predict how things go; a place we have the most certainty. Let’s take for a simple example your daily commute to work – chances are high that you always take the same route to work. This is what you know and you have a high certainty about how long it will take you to get to your work. Counterintuitively, the more certainty you have of something, the less information you have to gain from it. Our natural tendency is to have more certainty about things, and we hate uncertainty. We think of uncertainty as a bad thing. If I can use a metaphor, uncertainty is like medicine – you need it to stay healthy!

To discuss this further, I will look at the concept of variety from Cybernetics. Variety is a concept that was put forth by William Ross Ashby, a giant in the world of Cybernetics. Simply speaking, variety is the number of states. If you look at a stop light, generally it has three states (Red, Yellow and Green). In other words, the stop light’s variety is three (ignoring flashing red and no light). With this, it is able to control traffic. When the stop light is able to match the ongoing traffic, everything is smooth. But when the volume of traffic increases, the stop light is not able to keep up. The system reacts by slowing down the traffic. This shows that the variety in the environment is always greater than the variety available internally. The external variety also equates with uncertainty. Scaling back, let’s look at a manufacturing plant. The uncertainty comes in the form of 6M (Man, Machine, Method, Material, Measurement and Mother Nature). The manager’s job is to reduce the certainty. This is done by filtering the variety imposed from the outside, magnifying the variety that is available internally or looking at ways to improve the requisite variety. Ashby’s Law of Requisite Variety can be stated as – “only variety can absorb variety.

All organizations are sociotechnical systems. This also means that in order to sustain, they need to be complex adaptive systems. In order to improve the adaptability, the system needs to keep learning. It may be counterintuitive, but uncertainty is required for a complex adaptive system to keep learning, and to maintain the requisite variety to sustain itself. Thus, the push to stay away from uncertainty or staying in the comfort zone could actually be detrimental. Metaphorically, staying the comfort zone is staying away from the edges, where there is more uncertainty. After a basic level of stability is achieved, there is not much information available in the center (away from the edges). Since the environment is always changing, the organization has to keep updating the information to adapt and survive. This means that the organization should engage in safe to fail experiments and move away from their comfort zone to keep updating their information. The organization has to know where the edges are, and where the structures break down. Safe to fail experiments increases the solution space of the organization making it better suited for challenges. These experiments are fast, small and reversible, and are meant to increase the experience of the organization without risks. The organization cannot remain static and has to change with time. The experimentation away from the comfort zone provides direction for growth. It also shows where things can get catastrophic, so that the organization can be better prepared and move away from that direction.

This leads me to the concept of “fundamental regulator paradox”. This was developed by Gerald Weinberg, an American Computer scientist. As a system gets really good at what it does, and nothing ever goes wrong, then it is impossible to tell how well it is working. When strict rules and regulations are put in place to maintain “perfect order”, they can actually result in the opposite of what they are originally meant for. The paradox is stated as:

The task of a regulator is to eliminate variation, but this variation is the ultimate source of information about the quality of its work. Therefore, the better job a regulator does, the less information it gets about how to improve.

This concept also tells us that trying to stay in the comfort zone is never good and that we should not shy away from uncertainty. Exploring away from the comfort zone is how we can develop the adaptability and experience needed to survive.

Final Words:

This post is a further expansion from my recent tweet. https://twitter.com/harish_josev/status/1055977583261769728?s=11

Information is most rich at the edges. Information is at its lowest in the center. Equilibrium also lies away from the edges.

The two questions, “How good are you at something?” and “How bad are you at something?” may be logically equivalent. However, there is more opportunity to gain information from the second question since it leads us away from the comfort zone.

I will finish with a lesson from one of my favorite TV Detectives, D.I Richard Poole from Death in Paradise.

Poole noted that solving murders were like solving jigsaw puzzles. One has to work from the corners, and then the edges and then move towards the middle. Then, everything will fall in line and start to make sense.

Always keep on learning…

In case you missed it, my last post was Bootstrap Kaizen:

Bootstrap Kaizen:


I am writing today about “bootstrap kaizen”. This is something I have been thinking about for a while. Wikipedia describes bootstrapping as “a self-starting process that is supposed to proceed without external input.” The term was developed from a 19th century adynaton – “pull oneself over a fence by one’s bootstraps.” Another description is to start with something small that overtime turns into something bigger – a compounding effect from something small and simple. One part of the output is feedback into the input loop so as to generate a compounding effect. This is the same concept of booting computers, where a computer upon on startup starts with a small code that is run from the BIOS which loads the full operating system. I liked the idea of bootstrapping when viewed with the concept of kaizen or “change for the better” in Lean. Think about how the concept of improvement can start small, and eventually with iterations and feedback loops can make the entire organization better.

As I was researching along these lines, I came across Doug Engelbart. Doug Engelbart was an American genius who gave us the computer mouse and he was part of the team that gave us internet. Engelbart was way ahead of his time. Engelbart was also famous for the Mother of All Demos, which he gave in 1968 (way before Windows or Apple Events). Engelbart’s goal in life was to help create truly high performance human organizations. He understood that while population and gross product were increasing at a significant rate, the complexity of man’s problems were growing still faster. On top of this, the urgency with which solutions must be found became steadily greater. The product of complexity and urgency had surpassed man’s ability to deal with it. He vowed to increase the effectiveness with which individuals and organizations work at intelligent tasks. He wanted better and faster solutions to tackle the “more-complex” problems. Engelbart came up with “bootstrapping our collective IQ.”

He explained:

Any high-level capability needed by an organization rests atop a broad and deep capability infrastructure, comprised of many layers of composite capabilities. At the lower levels lie two categories of capabilities – Human-based and Tools-based. Doug Engelbart called this the Augmentation System.

Augmentation system

The human-based capability infrastructure is boosted by the tool-based capability infrastructure. As we pursue significant capability improvement, we should orient to pursuing improvement as a multi-element co-evolution process of the Tool System and Human System. Engelbart called this a bootstrapping strategy, where multi-disciplinary research teams would explore the new tools and work processes, which they would all use immediately themselves to boost their own collective capabilities in their lab(s).

Doug Engelbart’s brilliance was that he identified the link between the human system and the tool system. He understood that developing new tools improves our ability to develop even more new tools. He came up with the idea of “improving the improvement process.” I was enthralled by this when I read this because I was already thinking about “bootstrap kaizen.” He gave us the idea of “ABC model of Organizational Improvement.” In his words:

    A Activity: ‘Business as Usual’. The organization’s day to day core business activity, such as customer engagement and support, product development, R&D, marketing, sales, accounting, legal, manufacturing (if any), etc. Examples: Aerospace – all the activities involved in producing a plane; Congress – passing legislation; Medicine – researching a cure for disease; Education – teaching and mentoring students; Professional Societies – advancing a field or discipline; Initiatives or Nonprofits – advancing a cause.

    B Activity: Improving how we do that. Improving how A work is done, asking ‘How can we do this better?’ Examples: adopting a new tool(s) or technique(s) for how we go about working together, pursuing leads, conducting research, designing, planning, understanding the customer, coordinating efforts, tracking issues, managing budgets, delivering internal services. Could be an individual introducing a new technique gleaned from reading, conferences, or networking with peers, or an internal initiative tasked with improving core capability within or across various A Activities.

    C Activity: Improving how we improve. Improving how B work is done, asking ‘How can we improve the way we improve?’ Examples: improving effectiveness of B Activity teams in how they foster relations with their A Activity customers, collaborate to identify needs and opportunities, research, innovate, and implement available solutions, incorporate input, feedback, and lessons learned, run pilot projects, etc. Could be a B Activity individual learning about new techniques for innovation teams (reading, conferences, networking), or an initiative, innovation team or improvement community engaging with B Activity and other key stakeholders to implement new/improved capability for one or more B activities.

This approach can be viewed as a nested set of feedback loops as below:


Engelbart points out that, Bootstrapping has multiple immediate benefits:

1) Providers grow increasingly faster and smarter at:

  • Developing what they use – providers become their own most aggressive and vocal customer, giving themselves immediate feedback, which creates a faster evolutionary learning curve and more useful results
  • Integrating results – providers are increasingly adept at incorporating experimental practices and tools of their own making, and/or from external sources, co-evolving their own work products accordingly, further optimizing usefulness as well as downstream integratability
  • Compounding ROI – if the work product provides significant customer value, providers will start seeing measurable results in raising their own Collective IQ, thus getting faster and smarter at creating and deploying what they’re creating and deploying – results will build like compounding interest
  • Engaging stakeholders – providers experience first-hand the value of deep involvement by early adopters and contributors, blurring the distinction between internal and external participants, increasing their capacity to network beneficial stakeholders into the R&D cycle (i.e. outside innovation is built in to the bootstrapping strategy)
  • Deploying what they develop – as experienced users of their own work product, providers are their own best customers engaging kindred external customers early on, deployment/feedback becomes a natural two-way flow between them

2) Customers benefit commensurately:

  • End users benefit in all the ways customers benefit through outside innovation
  • Additionally, end users can visit provider’s work environment to get a taste and even experience firsthand how they’ve seriously innovated the way they work, not in a demo room, but in their actual work environment
  • Resulting end products and services, designed by stakeholders, and rigorously co-evolved, shaken down and refined by stakeholders, should be easier and more cost-effective to implement, while yielding greater results sooner than conventionally developed products and services

Final Notes:

I love that Engelbart’s Augmentation System points out that tools are to be used to augment the human capability, and that this should be ultimately about the system level development. His idea of bootstrapping explains how the “kaizen” thinking should be in Lean.

Interestingly, Engelbart understood that the Human side of the Augmentations System can be challenging. A special note on the Human System: Of the two, Engelbart saw the Human System to be a much larger challenge than the Tool System, much more unwieldy and staunchly resistant to change, and all the more critical to change because, on the whole, the Human System tended to be self-limiting, and the biggest gating factor in the whole equation. It’s hard for people to step outside their comfort zone and think outside the box, and harder still to think outside whatever paradigm or world view they occupy. Those who think that the world is flat, and science and inquiry are blasphemous, will not consider exploring beyond the edges, and will silence great thinkers like Socrates and Gallileo.

As I was researching for this post, I also came across the phrase “eating your own dog food.” This is an idea made famous by the software companies. The idea behind the phrase is that we should use our own products in our day-to-day business operations (Deploying what they develop). In a similar vein, we should engage in improvement activities with tools that we can make internally. This will improve our improvement muscles so that we may be able to tweak off-the-shelf equipment to make it work for us. This is the true spirit of the Augmentation System.

When you are thinking about getting new tools or equipment for automation, make sure that it is to strictly to augment the human system. Unless we think in these terms, we will not be able to improve the system as a whole. We should focus more on the C activities. I highly encourage the reader to learn more about Doug Engelbart. (http://www.dougengelbart.org/)

Always keep on learning…

In case you missed it, my last post was A “Complex” View of Quality:

Distrust Simplicity:


In today’s post, I will be looking at the famous quote from the famous English mathematician and philosopher, Alfred Whitehead.

Seek simplicity, and then distrust it.

This quote comes from his 1920 collection of lectures, The Concept of Nature. The quote is embedded in the paragraph below:

Nature appears as a complex system whose factors are dimly discerned by us. But, as I ask you, Is not this the very truth? Should we not distrust the jaunty assurance with which every age prides itself that it at last has hit upon the ultimate concepts in which all that happens can be formulated? The aim of science is to seek the simplest explanations of complex facts. We are apt to fall into the error of thinking that the facts are simple because simplicity is the goal of our quest. The guiding motto in the life of every natural philosopher should be, Seek simplicity and distrust it.

I like this idea a lot. We are all asked to keep things simple, and to not make things complicated. Whitehead is asking us to seek simplicity first, and then distrust it. Whitehead talks about “bifurcation of nature” – nature as we perceive it, and the nature as it is. Thus, our perception of reality is an abstraction or a simplification based on our perceptions. We need this abstraction to start understanding nature. However, once we start this understanding process, we should not stop. We should build upon it. This is the scientific method – plan the prototype, build it, assess the gap, and continue improving based on feedback.

As I was reading The Concept of Nature, several other concepts came to my mind. The first one was Occam’s razor – the idea that Entities should not be multiplied unnecessarily. Seek the simplest explanation, when all things are equal. At the same time, we should keep Epicurus’ Principle of Multiple Explanations in mind – If more than one theory is consistent with the observations, keep all theories. I also feel that Whitehead was talking about systems and complexity. As complexity increases, our ability to fully understand the numerous relationships decreases. As the wonderful American Systems thinker Donella Meadows said:

We can’t impose our will on a system. We can listen to what the system tells us and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will alone.

Seeking simplicity is about the attempt to have a starting point to understand complexity. We should continue to evolve our understanding and not stop at the first abstraction we developed. One of the famous Zen story is about the teacher pointing his finger at the moon. I have talked about this here. We should not look at the finger and stop there. We should look at where the finger is pointing. The finger is the road sign and not the destination itself. The simplicity is a representation and not the real thing. We should immediately distrust it because it is a weak copy. Seeking simplicity is not a bad thing but stopping there is. Simplicity is our comfort zone, and Whitehead is asking us to distrust it so that can keep improving our situation – continuous improvement. Whitehead in his later 1929 book, The Function of Reason, states:

The higher forms of life are actively engaged in modifying their environment… (to) (i) to live, (ii) to live well, (iii) to live better.

Final Words:

In seeking simplicity, we are trying to be “less wrong”. In distrusting our simplified abstraction, we are seeking to be “more right”. I will finish with a Zen story.

A Zen master lay dying. His monks had all gathered around his bed, from the most senior to the most novice monk. The senior monk leaned over to ask the dying master if he had any final words of advice for his monks.

The old master slowly opened his eyes and in a weak voice whispered, “Tell them Truth is like a river.”

The senior monk passed on this bit of wisdom in turn to the monk next to him, and it circulated around the room from one monk to another.

When the words reached the youngest monk he asked, “What does he mean, ‘Truth is like a river?’”

The question was passed back around the room to the senior monk who leaned over the bed and asked, “Master, what do you mean, ‘Truth is like a river?’” Slowly the master opened his eyes and in a weak voice whispered, “OK, truth is not like a river.”

Always keep on learning…

In case you missed it, my last post was Cannon’s Polarity Principle:

Cannon’s Polarity Principle:


I recently read the wonderful book “On the Design of Stable Systems”, by Jerry Weinberg and Daniela Weinberg. I came across a principle that I had not heard of before called “Cannon’s Polarity Principle”. Cannon’s Polarity Principle can be stated as the strategy that a system can use to overcome noise by supplying its own opposing actions. If a system relies on an uncertain environment to supply the opposing factor to one of its regulatory mechanisms, that mechanism must have a much more refined model. By supplying its own opposing factor, it can get away with a much simpler model of the environment.

This principle is one of those things that is profound yet very simple. The Weinbergs give the example of a sticky knob on a gas stove to explain this idea. If the knob is sticky then it is tricky to raise the flame to the precise point we would like it to be. Due to the “stickiness” we will try to apply much more force than needed and inadvertently overshoot, going past the desired point. The result is that the flame is at a much higher setting. When we try to turn the flame down we are still in the same situation and again go past the point where we would like to be.

What we can do instead is to use one hand to push against the direction we would like and then slowly try to turn the knob with our other hand. With this approach we can be much more refined and be at our desired position. By working “against” our own goal, we make precise adjustment possible in the face of an unknown, but small, amount of stickiness.

This got me thinking. There are several times where we apply opposing forces to slow us down, to take the time to reach the correct decision (precise adjustment). One of my favorite Toyotaism is – Go slow to go fast. This makes a lot of sense in the light of the Polarity Principle. Any time we are doing a root cause analysis, we are prone to a plethora of biases including confirmation bias – selectively looking for ideas that reinforce our thinking, and availability bias – latching on to the first idea because that was the immediate idea we came up with. These biases might make us jump to unwarranted conclusions to address symptoms, and not addressing the root problem(s). The Polarity Principle would advise us to slow down.

I will finish this short and sweet with an apt Zen saying:

The one who is good at shooting does not hit the center of the arrow.

Always keep on learning…

In case you missed it, my last post was Contextual Why:

Contextual Why:


One of the scientists that I have referenced in my posts a lot is the American physicist Richard Feynman. I particularly love his imaginary depiction of Mayan astronomy. Feynman went to Mexico for his second honeymoon and came across a copy of the Dresden Codex (one of the oldest surviving books from the Americas). He was particularly interested in the bars and dots in the codex. He was able to decipher the number system that the Mayans used to depict Venus’ trajectory in the solar system. He was so good at it that he was able to find that some of the versions were actually fakes. Feynman imagined the Mayans counting and putting nuts in a pot to make predictions of where Venus would be on a given day. Feynman was curious whether the Mayans actually knew what was happening (why it was happening) or whether they were going by the rules and making predictions based on a rule-based system of counting and manipulating numbers. Feynman stated that the Mayans may have gotten really good with counting but they must not have understood how the celestial bodies worked.

The push for following rules without understanding the context is unfortunate. Yet this is very prevalent. The rigidity of the rules cannot be sustained when a complex situation arises. The rigidity of rules indicates a direct linear relationship where cause and effect are clearly noted. This is the push for standardization and having one best way of doing things. This leads to stagnation, since this stymies creativity and the push for innovation. Rigid rules always break. Another way to look at this is as the push for robustness – avoiding failure by any means. We will put redundant steps, perform multiple inspections and implement punishments for not following rules. In the complex world, we should accept that things will fail – the push should be for resilience, getting back up in a short time. The rules are dictated top-down since the rules are created by the experts. These rules do not have the requisite variety to tackle the uncertainties of day-to-day dealings. The contexts of these rules do not match the actual context where the action takes place – the context at the gemba. Context is what brings out the meaning in a situation. The focus on rules and efficiency through best practice does not lead to having the requisite variety to change the context as needed to address a problem when it arises. We are involved in complex adaptive systems on a day-to-day basis. We need to change context as needed and adapt to respond to unanticipated events. Evolution requires that we have variety. This response is not always rule-based and is developed depending upon the context. We should allow room for bottom-up heuristics, since these are based on experience and local context.

As a simple example, let’s look at 5S, one of the most commonly identified lean tools, to look into this more. 5S is translated from Japanese as Sort, Straighten, Shine, Standardize and Sustain. The rules are provided to us and they are clear cut. Similar to the Mayan story, do we actually know the context for 5S? Toyota did not have 5S. The last few S’s were added on later. This has now changed into 6S and even 7S. The “sort” step in 5S is to have only the required tools needed at the station. The “straighten” step is to identify/label the tools so that operators from other shifts or job rotations can easily find the tools. The third step is “shine” where the work station is cleaned by the operator. This allows the operator to find any spills or other signs of wear and tear that may not be seen by a cleaning crew. These three steps help the operator to identify problems as they occur, raises awareness and helps to take pride in the work. The fourth step is “standardize” and this is mainly a regulatory function to ensure that the first three steps are followed. The last step is “sustain”, which means to integrate the first three steps so that they become the normal routine and if they are not followed, one feels like something is missing. The context is to help the operator do his or her job better and be effective. The context is that a problem is made visible immediately so that it can be addressed and people can be developed. The context is not following rules. The context is not applying 5S in areas where it does not make sense. The context certainly is not policing people. When the context of what the operator does is not made clear, they do what makes sense to them in their context – at that time with the limited information they have. Empty actions do not have context and are thus meaningless and non-value adding.

Seek to understand the perspectives of your employees. Seek to understand their local context. Seek to make them understand your context, and the context of the shared goals and objectives. Heed to their stories. Develop your employees to see problems.

I will finish with an interesting question that was posed by some French researchers in the late 1970’s.

“On a boat, there are 26 sheep and 10 goats. What is the age of the captain?”

Perhaps, you might see this as a trick question. Perhaps, you may use the two numbers given and come up with the answer as 36. The answer 36 sounds right. The answer that the researchers expected was “I do not have enough information to give the answer.”

To the researchers’ surprise, very few subjects challenged the question. Most of them reasoned in their context and came up with a number that made sense in their mind. We are not trained to ask the contextual questions.

Always keep on learning and ask contextual questions…

In case you missed it, my last post was MTTF Reliability, Cricket and Baseball:

Mismatched Complexity and KISS:



In today’s post, I will be looking at complexity from the standpoint of organizational communication and KISS. For the purpose of this post, I am defining complexity as a measure of computational effort needed to describe your intent. This idea of complexity is loosely based on Kolmogorov’s definition of “Complexity” from an algorithm standpoint.

To give a very simple example, let’s say that I would like to convey two messages, M1 and M2:

M1 = 010101

M2 = 100111

From the complexity standpoint, M2 requires more effort to explain because there is no discerning pattern in the string of numbers. M1, on the other hand, is easier to describe. I can just say, “Repeat 01 three times.” For M2, I have no choice but say the entire string of numbers. In this regard, I could say that M2 is more complex than M1.

Let’s look at another example, M3:

M3 = 1415926535

Here, it may look like there is no discerning pattern to the string of numbers. However, this can be easily described as “first 10 decimal values of pi without 3. Thus, this message also has low complexity. We can easily see a direct linear relationship or know the content just by observation/empirical evidence.

The examples so far have been examples of low complexity messages. These are easy to generate, diffuse and convey. From the complexity standpoint, these are Simple messages. If I were to create a message that explained Einstein’s relativity, it may not be easily understood if the receiver of the message does not have a good grasp of Physics and advanced math. This is an example of medium complexity or a complicated topic. The relationship is evident with all of the information available.

Now let’s say that I would like to create a message about a complex topic – solve poverty or solve global warming. There is no evident relationship here that can be manipulated with an equation to solve the problem. These are examples of wicked problems – there are no solutions to these problems. There are options but none of the options will fully solve the many intricate problems that are entangled with each other. Such a topic is unlikely to be explained in a message.

The common thread in communication or solving problems is the emphasis on KISS (Keep It Simple Stupid). However, in an effort to keeping things simple, we often engage in mismatched complexity. Complex ideas should not be exclusively conveyed as simple statements. The ideal state is that we use the optimal message – adjust complexity of the message to match the complexity of the content. This is detailed in the schematic below. The optimal message is the 45 degree line between the two axes. A highly complex topic should not be expressed using a low complex message such as a slogan or policy statement. In a similar fashion, a low complexity topic does not need a high complexity message method such as an hour-long meeting to discuss something fundamental.

message diagram

The highly complex topic can use both low and medium message methods to ensure that the complex idea is conveyed properly. The diffusion of the highly complex topic can build upon both low and medium message methods. The diffusion of a highly complex topic also requires redundancy, which means that the message must be conveyed as many times as needed and use of metaphors and analogies. One definition of “communication” from the great Gregory Bateson is – Communication is what the receiver understands, not what the sender says.

A good example to explain this is Toyota Production System. The concept of a production system for the entire plant is a complex concept. Toyota Production System was once called “the Ohno method” since it was not implemented company-wide and there was doubt as to the success of the system being a long-term plan. Ohno’s message was not written down anywhere and the employees did not learn that from a manual or a video. Ohno conveyed his ideas by being at the gemba (actual work place), implementing ideas and learning from them. He also developed employees by constantly challenging them to find a better way with less. Ohno used to draw a chalk circle on the floor for supervisors/engineers to make them see what he saw. He developed the Toyota Production System and with continuous mentoring, nurtured it together with the employees. Today there are over 1000 books at Amazon regarding “Lean Manufacturing”. When top management is looking at implementing lean, the message should match the complexity of the content. Low complex message methods like slogans or placards will not work. Medium complex message methods like newsletters, books etc will not work. This will require constant on-the-floor interactive mentoring. At the same time, slogans and newsletters can aid in the diffusion process.

Final Words:

I have always felt that KISS and Poka-Yoke have a similar story to tell from a respect-for-people standpoint. Poka-Yoke (Error proofing) was initially termed as Baka-Yoke to indicate “fool proofing”. Shigeo Shingo changed it to Poke-Yoke to indicate error proofing after an employee asked him “have I been such a fool?” In a similar fashion, KISS was initially put forth as “Keep It Simple Stupid” (without the comma). Nowadays, this has been changed to “Keep It Short and Simple” and “Keep It Simple Straightforward”.

It is good to keep things simple and to view at a problem from a 10,000 feet level. However, we should not stop there. We need to understand the context and complexity of the problem and then create this information in such a manner that it can be diffused across the organization. This can be repeated as many times as needed. Do not insist on simplicity without understanding the complexity of the problem. Asking to keep things simple is an attempt to keep round pegs in familiar square holes. When there is a mismatch of complexity it leads to incorrect solutions and setbacks. As Einstein may have said,everything should be as simple as it can, but not simpler”.

We can also view the complexity/message diagram in the light of the Feynman (Nobel-prize winning physicist Richard Feynman) technique of studying hard subjects. Feynman came up with a method where he would start studying and making notes pretending to prepare a lecture for a class. He would use simple terms and analogies to explain the subject. When he got stuck he would go back and try to understand it even better. He would then proceed with making notes. He would repeat the steps many times until he got the concept thoroughly. Moving from High to Medium to Low in the diagram, and going back-and-forth helps to connect the dots and gain a better understanding.

I will finish with another quote, attributed to Lotfi Zadeh (father of Fuzzy Logic):

“As complexity rises, precise statements lose meaning and meaningful statements lose precision.”

Always keep on learning…

In case you missed it, my last post was Flat Earth Lean: