Contextual Why:

Láminas_8_y_9_del_Códice_de_Dresden

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:

Advertisements

Mismatched Complexity and KISS:

mismatch

*work-in-process*

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:

Which Way You Should Go Depends on Where You Are:

compass

I recently read the wonderful book “How Not To Be Wrong, The Power of Mathematical Thinking” by Jordan Ellenberg. I found the book to be enlightening and a great read. Jordan Ellenberg has the unique combination of being knowledgeable and capable of teaching in a humorous and engaging way. One of the gems in the book is – “Which way you should go depends on where you are”. This lesson is about the dangers of misapplying linearity. When we are thinking in terms of abstract concepts, the path from point A to point B may appear to be linear. After all, the shortest path between two points is a straight line. This type of thinking is linear thinking.

To illustrate this, let’s take the example of poor quality issues on the line. The first instinct to improve quality is to increase inspection. In this case, point A = poor quality, and point B = higher quality. If we plot this incorrect relationship between Quality and Inspection, we may assume it as a linear relationship – increasing inspection results in better quality.

Inspection and Quality

However, increasing inspection will not result in better quality in the long run and will result in higher costs of production. We must build quality in as part of the normal process at the source and not rely on inspection. In TPS, there are several ways to do this including Poka Yoke and Jidoka.

In a similar fashion, we may look at increasing the number of operators in the hopes of increasing productivity. This may work initially. However, increasing production at the wrong points in the assembly chain can hinder the overall production and decrease overall productivity. Taiichi Ohno, the father of Toyota Production System, always asked to reduce the number of operators to improve the flow. Toyota Production System relies on the thinking of the people to improve the overall system.

The two cases discussed above are nonlinear in nature. Thus increasing one factor may increase the response factor initially. However, continually increasing the factor can yield negative results. One example of a non-linear relationship is shown below:

productivity

The actual curve may of course vary depending on the particularities of the example. In nonlinear relationships, which way you should go depends on where you are. In the productivity example, if you are at the Yellow star location on the curve, increasing the operators will only decrease productivity. You should reduce the number of operators to increase productivity. However, if you are at the Red star, you should look into increasing the operators. This will increase productivity up to a point, after which the productivity will decrease. Which Way You Should Go Depends on Where You Are!

In order to know where you are, you need to understand your process. As part of this, you need to understand the significant factors in the process. You also need to understand the boundaries of the process where things will start to breakdown. The only way you can truly learn your process is through experimentation and constant monitoring. It is likely that you did not consider all of the factors or the interactions. Everything is in flux and the only constant thing is change. You should be open for input from the operators and allow improvements to happen from the bottom up.

I will finish off with the anecdote of the “Laffer curve” that Jordan Ellenberg used to illustrate the concept of nonlinearity. One polical party in America have been pushing for lowering taxes on the wealthy. The conservatives made this concept popular using the Laffer curve. Arthur Laffer was an economics professor at the University of Chicago. The story goes that Arthur Laffer drew the curve on the back of a napkin during dinner in 1974 with the senior members of then President Gerald Ford’s administration. The Laffer Curve is shown below:

Laffer curve

The horizontal axis shows the tax rate and the vertical axis shows the revenue that is generated from taxation. If there is no taxation, then there is no revenue. If there is 100% taxation, there is also no revenue because nobody would want to work and make money, if they cannot hold on to it. The argument that was raised was that America was on the right hand side of the curve and thus reducing taxation would increase revenue. It has been challenged whether this assumption was correct. Jordan used the following passage from Greg Manikiw, a Harvard economist and a Republican who chaired the Council of Economic Advisors under the second President Bush:

Subsequent history failed to confirm Laffer’s conjecture that lower tax rates would raise tax revenue. When Reagan cut taxes after he was elected, the result was less tax revenue, not more. Revenue from personal income taxes fell by 9 percent from 1980 to 1984, even though average income grew by 4 percent over this period. Yet once the policy was in place, it was hard to reverse.

The Laffer curve may not be symmetric as shown above. The curve may not be smooth and even as shown above and could be a completely different curve altogether. Jordan states in the book – All the Laffer curve says is that lower taxes could, under some circumstances, increase tax revenue; but figuring out what those circumstances are requires deep, difficult, empirical work, the kind of work that doesn’t fit on a napkin.

Always keep on learning…

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

Who is right?

132

I came across a great graphic that I thought I should share.

right

The graphic above shows the importance of understanding the perception of the other party involved. This helps us in understanding their viewpoint.

It is also important as a leader in your organization that when you are trying to spread your vision, to make sure you understand how your employees view your vision. The view at the top of the organization may not match the view at the bottom of the organization.

The view at the top of the organization may not match the view at the bottom of the organization.

Alexander the Great and the monk:

There is a great story I heard from Devdutt Pattanaik, that explains this really well. I have paraphrased it.

Alexander the great reached India after conquering a lot of nations. On his path to conquer India, he met a monk. The monk was sitting on a rock enjoying the beauty of nature. The monk was naked, and belonged to a sect of Jainism.

Alexander watched the monk for a while. The monk was just sitting and smiling, totally oblivious of Alexander watching him.

“What are you doing?” Alexander asked the monk.

“I am enjoying being nothing.” the monk looked at Alexander, and said.

“What a fool to sit there and do nothing?” Alexander laughed at him. Alexander saw the monk as wasting his life away, doing nothing.

“What are you doing?” the monk asked Alexander.

“I am conquering the world”, Alexander replied with great pride.

Now the monk started laughing at Alexander.

“What a fool to pursue such a futile effort?” the monk thought to himself.

The next time, you face an opposing view; try to understand where the other party is coming from. What is his viewpoint? Are you the monk or Alexander?

Always keep on learning…

In case you missed it, my last post was Lean and the Mountain.