Information at the Gemba:

Info

Uncertainty is all around us. A lean leader’s main purpose is to develop people to tackle uncertainty. There are two ways to tackle uncertainty; one is Genchi Genbutsu (go and see) and the other is the scientific method of PDCA. Claude Shannon, the father of Information Theory, viewed information as the possible reduction in uncertainty in a system. In other words, larger uncertainty presents a larger potential for new information. This can be easily shown as the following equation;

New Information gain = Reduction in Uncertainty

Shannon called the uncertainty as entropy based on the advice from his friend John Von Neumann, a mathematical genius and polymath. The entropy in information theory is not exactly the same as the entropy in Thermodynamics. They are similar in that entropy is a measure of a system’s degree of disorganization. In this regard, information can be viewed as a measure of a system’s degree of organization. Shannon recalled his conversation with Von Neumann as below;

“My greatest concern was what to call it. I thought of calling it ‘information’, but the word was overly used, so I decided to call it ‘uncertainty’. When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, ‘You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage.”

I loved the encouragement from Von Neumann that Shannon would have an advantage in a debate since “nobody knows what entropy really is”.

In this post, I am not going into the mathematics of Information Theory. In fact I am not even going to discuss Information Theory but the philosophical lessons from it. From a philosophical standpoint, Information Theory presents a different perspective on problems and failures at the gemba. When you are planning an experiment, and things go well and the results confirm your hypothesis, you do not learn any new information. However, when the results do not match your hypothesis, there is new information available for you. Thus, failures or similar challenges are opportunities to have new information about your process.

There are seven lessons that I have and they are as follows;

  • Information Gain ≠ Knowledge Gain:

One of the important aspects from the view of the information available at the Gemba is that information does not translate to knowledge. Information is objective in nature and consists of facts. This information gets translated to knowledge when we apply our available mental models to it. This means that there is potentially a severe loss based on the receiver. A good analogy is Sherlock Holmes and Dr. Watson at the crime scene – they are both looking at the same information available, but Holmes is able to deduce more.

  • Be Open:

When you assume full knowledge about a process, you are unwilling to gain knowledge from any new information available. You should be open to possibilities in order to welcome new information and thus a chance to learn something new. Sometimes by being open to others viewpoints, you can learn new things. They may have a lot more experience and more opportunities for information than you may have.

  • Go to the Gemba:

The majority of times, the source of information is the gemba. When you do not go to the source, the information you get will not be as pure as it was. The information you get has been contaminated with the subjective perspectives of the informer. You should go to the gemba as often as you can. The process is giving out information at all times.

  • Exercise Your Observation Skills:

As I mentioned above in the Holmes and Watson analogy, what you can gain from the information presented depends on your ability to identify information. There is a lot of noise in the information you might get and you have to weed out the noise and look at the core information available. One of my favorite definitions of information is by the famous Cerbernetician Gregory Bateson. He defined information as “the difference that makes the difference.” The ability to make the difference from the information given depends mostly on your skill set. Go to the Gemba more often and sharpen your observation skills. Ask “For what Purpose” and “what is the cause” more often.

  • Go Outside Your Comfort Zone:

One of the lessons in lean that does not get a lot of attention is – “go outside your comfort zone”. This is the essence of Challenge in the Continuous Improvement Pillar of the Toyota Way. When you stay inside your comfort zone, you are not willing to gather new information. You get stuck in your ways and trust your degrading mental model rather than challenging and nourishing your mental model so that you are able to develop yourself. Failure is a good thing when you understand that it represents new information that can help you with understanding uncertainties in your process. You will not want to try new things unless you go outside your comfort zone.

  • Experiment Frequently:

You learn more by exposing yourself to more chances of gaining new information. And you do this by experimenting more often. The scientific process is not a single loop of PDCA (Plan-Do-Check-Act). It is an iterative process, and you need to experiment frequently and learn from the feedback.

  • Challenge Your Own Perspective:

The Achilles’ heel for a lean leader is his confirmation bias. He may go to the gemba more often, and he may experiment frequently. Unless he challenges his own perspective, his actions may not be fruitful. My favorite question to challenge my perspective is “What is the evidence I need to invalidate my viewpoint right now, and does the information I have hint at it?” Similar questions ensure that the interpretation of the information you are getting is less tainted.

I will finish off with a funny story I heard about Sherlock Holmes and Watson;

Sherlock Holmes and Dr. Watson decide to go on a camping trip. All the way to the campsite, Holmes was giving observation lessons to Dr. Watson and challenging him. After dinner and a bottle of wine, they lay down for the night, and go to sleep.

Some hours later, Holmes awoke and nudged his faithful friend.

“Watson, look up at the sky and tell me what you see.”

Watson replied, “I see millions of stars.”

“What does that tell you?” Holmes asked.

Watson pondered for a minute.

“Astronomically, it tells me that there are millions of galaxies and potentially billions of planets.”
“Astrologically, I observe that Saturn is in Leo.”
“Horologically, I deduce that the time is approximately a quarter past three.”
“Theologically, I can see that God is all powerful and that we are small and insignificant.”
“Meteorologically, I suspect that we will have a beautiful day tomorrow.”
“What does it tell you, Holmes?” Watson asked.

Holmes was silent for a minute, then spoke: “Watson, you idiot. Someone has stolen our tent!”

Always keep on learning…

In case you missed it, my last post was The Pursuit of Quality – A Lesser Known Lesson from Ohno.

PDCA and the Roads to Rome:

Different roads to take, decision to make

In today’s post, I will be trying to look at the concept of equifinality in relationship to the scientific method PDCA. In Systems Theory, the concept of equifinality is defined as reaching the same end, no matter what the starting point was. This is applicable only in an open system. An open system is a system that interacts with its environment (external). This could be in the form of information, material or energy.

I wanted to look at the repeatability of the PDCA process. PDCA stands for the Plan-Do-Check-Act cycle, and is the framework for the scientific method. If three different people, with different ways of thinking, are facing the same problem, can all three reach the same end goal using the PDCA process? This would imply that equifinality is possible. This concept is shown below. Point A is the initial condition, and point B is the final desired condition. The three different colored lines depicts the three different thinking styles (the different thinking styles indicates the different starting points).

equi

Iterative Nature of PDCA:

The most important point about PDCA is the iterative nature of the cycle. Each cycle of PDCA leads to a new cycle that is more refined. The practitioner learns from each step of the PDCA cycle. The practitioner observes the effect of each step on the problem. Every action is an opportunity to observe the system more. The results of his experiments lead to more experiments, and yield a better understanding of multiple cause-effect chains in the system.

If the scientific method is followed properly, it is highly likely that the three different practitioners can ultimately reach the same destination. The number of iterations would vary from person to person due to different thinking styles. However, the iterative nature of the scientific method ensures that the each step corrects itself based on the feedback. This type of steering mechanism based on feedback loops is the basis of the PDCA process. This idea of multiple ways or methods to have the same final performance result is equifinality. This is akin to the saying “all roads lead to Rome”. This idea of “steering” is a fundamental concept of Cybernetics. I will be writing about this fascinating field in the future.

Final Words:

This post was inspired by the following thought – can a lean purist and a six sigma purist reach the same final answer to a problem if they pursued the iterative nature of the scientific method? There has been a lot of discussion about which method is better. The solution, in my opinion, is in being open and learning from the feedback loops from the problem at hand.

I will finish this post with a neat mathematical card trick that explains the idea of equifinality further. This trick is based on a principle called Kruskal Count.

The Effect:

The spectator is asked to shuffle the deck of cards to his heart’s content. Once the spectator is convinced that the deck is thoroughly shuffled, the magician explains the rules. The Ace is counted as 1, and all the face cards (Jack, Queen and King) are counted as 5. The number cards have the same values as the number on the card.

The spectator is asked to think of any number from 1 to 10. He is then directed to hold the cards face down, and then deal cards face up in a pile. He should deal the amount of cards equal to the number he chose in his mind. The spectator takes a note of the value of the final card dealt. The spectator is directed to deal those many cards face up on the already dealt cards.

deck_discardThis is repeated until the spectator has reached a card at which point there are not enough cards to deal. For example, the card was 8 of Hearts, and there are only six cards remaining. This card is his selected card. He then puts the face up cards on the table on top of the cards he has on his hand. They do all of this while you have your back turned. You easily find their selected card.

The Secret:

All roads lead to Rome. This trick has an over 80% success rate.

The secret is to repeat exactly what the spectator did. You also choose a random number between 1 and 10, and start dealing as described above. Just like the concept of equifinality, no matter which number you chose as your starting position, as you go through the process, you will choose the same set of cards at the end resulting in the same selected card! Try it for yourself. Here is a link to a good paper on this.

Always keep on learning…

In case you missed it, my last post was If the Learner Has Not Learned, Point at the Moon.

It’s Complicated

Cynefin final

It’s Complicated:

PDCA, the four letter acronym made famous by Dr. Deming stands for Plan – Do – Check – Act. It is a continuous cycle.

PDCA is said to be the framework for scientific thinking and continuous improvement. I have always thought of PDCA to have something missing in it. It looked so simplistic. Can it really be that simple?

I have come to realize that what was missing was context; the context behind PDCA. It cannot be that everything you see is a nail, if you only have a hammer. What happens before PDCA? The moment before you decided, “Hey, let’s do PDCA.” What makes you decide the “scope” for PDCA? How do you know if PDCA is even appropriate?

This post is an ode to the Cynefin framework. For those who do not know the Cynefin framework, it is a brainchild of Dave Snowden, and it is a sense making framework. Dave Snowden has stated that in the Cynefin framework, data precedes framework and it is valid to understand. The Cynefin framework is not a categorization framework, where framework precedes data.

The idea behind the Cynefin framework is that when you encounter a problem or a new project, your first step is to understand what domain you are in. This provides us a framework to proceed. As a learning organization, it is essential that our efforts and our methodologies match the type of change that we are planning. The Cynefin framework lays the groundwork for this exact intent.

The Cynefin framework has 5 domains and is dynamic. No problem with high complexity or chaos ever stays in the same domain at all times. The problem we had last year may have appeared to be complex, but now it may be in the complicated domain, or even the simple domain. Even a situation from the Simple domain can collapse into the Chaotic domain if there is complacency.

Screen shot 2010-07-07 at 23.33.02

The following definitions are taken from Cognitive Edge website;

The Cynefin framework has five domains. The first four domains are:

Simple (also called as Obvious), in which the relationship between cause and effect is obvious to all. The approach is to Sense – Categorize – Respond and we can apply best practice.


Complicated, in which the relationship between cause and effect requires analysis or some other form of investigation and/or the application of expert knowledge. The approach is to Sense – Analyze – Respond and we can apply good practice.


Complex, in which the relationship between cause and effect can only be perceived in retrospect, but not in advance. The approach is to Probe – Sense – Respond and we can sense emergent practice.


Chaotic, in which there is no relationship between cause and effect at systems level. The approach is to Act – Sense – Respond and we can discover novel practice.


The fifth domain is Disorder, which is the state of not knowing what type of causality exists, in which state people will revert to their own comfort zone in making a decision. In full use, the Cynefin framework has sub-domains, and the boundary between simple and chaotic is seen as a catastrophic one: complacency leads to failure. In conclusion, chaos is always transitionary and dynamics are a key aspect.

This is summarized in the following figure.

Cynefin final

The need for the Cynefin Framework:

Most of the methodologies, including PDCA, assume some form of order. Sometimes this leads to the misapplication of methodology that leads to failures. Only Simple and Complicated domains assume some form of order. The Cynefin framework helps us in being efficient and at the same time effective.

There are minimal resources needed for a situation in the Simple domain. The answer is fairly obvious, and best practice is already known in the form of SOPs (Standard Operating Procedures) or work instructions. For example, the light bulb burned out – replace the light bulb. Project management is certainly not needed for this domain. There is no true need for a PDCA methodology in this domain. The Cynefin framework recommends sense-categorize-respond for this domain. The assumption is that there is a known best practice available or that the best practice is fairly straightforward.

The Complicated domain needs guidance from experts. Multiple solutions can exist, and we need experts’ help to identify the optimal solution. For example, if the light bulb keeps going out, it may not be as easy as replacing a light bulb. This is a domain that works well with PDCA. One should not imitate and apply the best-practice in this domain. Dave Snowden refers to a phenomenon called “premature convergence” where we stop exploring how to make ideas better, thinking that we have found the answer. Cynefin framework recommends sense-analyze-respond. This is similar to a PDCA approach.

The Complex domain does not have order. It is an unordered domain. We need patience for patterns to emerge in this domain. Cause and effect relations are not directly visible in this domain. The recommended practice is probe-sense-respond. Multiple and different PDCA loops might be required for this domain to let the patterns emerge. Think of any root cause projects that you completed, where you did not see the solution in the beginning, but on hindsight it made sense. Dave Snowden gives the example of “Houston, we have a problem” scene from the movie “Apollo 13”.

As the name suggests, the chaos domain is indeed full of turbulence and chaos. This is not a domain where you search for answers. This is a domain for rapid decisions to regain control and stabilize the turbulence. The recommended approach is act-sense-respond. The act phase can be an attempt to stabilize the turbulence. As you can see, this is not an ideal candidate for the PDCA approach. If PDCA is used, the Plan phase will need to be quite short. The goal of this domain is to quickly move to the complex domain as soon as possible. Dave Snowden’s example for this domain is the unfortunate 9/11 incident.

Final words:

In the business world, there is no solution that is one-size-fits-all. Context is everything! Each domain of the Cynefin framework comes with its own burden. Being too complacent in the Simple domain can push you into the Chaotic domain. Trying to imitate what worked for one company can cause you to fail (the Complicated domain). Not waiting for patterns to emerge in the Complex domain, and trying to push for best practices can push you over to the Chaotic domain. The Cynefin framework provides you a thinking framework to understand the scope of your situation and helps you in being efficient and effective with your PDCA approach. This post was written based on my thoughts on my learning with the Cynefin framework. I encourage the reader to read upon the Cynefin framework more at Cognitive-Edge.com. The HBR article “A Leader’s Framework for Decision Making” is also an excellent place to start.

Always keep on learning…