Synopsis of a Paper to address Complexity

I have been invited by the Institution of Engineers, India, as a keynote speaker, for a seminar to be held in April 2015.

The synopsis of the paper follows.

Title of the paper:

Vibration Analysis as a tool to Simultaneously Improve Industrial Performance, Productivity and Profitability


In industries, throughout the world, for the last fifty years or so, vibration analysis and monitoring  have been extensively used for Condition Based Maintenance (CBM). Proper application of CBM can  result in 50% reduction in downtime and 25% reduction in maintenance costs from a plant’s previous level of performance. It has now reached the desired level of technical and professional maturity to be well poised to evolve to the next stage of its evolution, i.e. IOT (Internet of Things).

However, in the meanwhile, “complexity” has also evolved to pose as a major challenge to industrial performance, productivity and profitability. Both industrial equipment and systems have grown in complexity, which is often manifested as multiple interrelated problems of machine failures, quality, performance and wastage that are difficult to address by traditional tools and techniques that are presently being used in industries.

This paper aims to highlight, through two case studies, the use of vibration analysis as one of the powerful tools to address such multiple problems in a simultaneous fashion, which solves multiple problems in one go rather than address each problem individually over a long period of time as done in the present. Present approaches to address prevalent “complexity” often turn out to be unsuccessful and frustrating for both engineers and managers. Application of vibration analysis along with appropriate understanding of design principles would help industries achieve dramatic improvement of performance, productivity and profitability with minimum interventions, time and resources as demonstrated by the cases. What is more — once such minimal changes are implemented industries continue to gain ongoing benefits for years to come. 


Ruthless Honesty & Integrity

Excerpt from my forthcoming book — Solving Complex Problems through Vibration Analysis; An introduction to Non-linear Dynamics


One last thing before we move on to the next chapter. We have had a glimpse into the fundamental process of seeing a problem. We start with an open mind. Then we ask lots of questions out of curiosity, being mindful that we don’t know the answer. We intently observe. We then make intelligent guesses to come up with a hypothesis that relates all the problems in the system, interdependent as they are. Finally, we should still doubt as to whether we understood the system to sufficient depth. We simply can’t get rid of this nagging doubt till our solutions are proven effective through practical implementation. If proven, we learn. If not proven, we learn to unlearn our ignorance and set out to learn again. Either way we learn something useful.

This reminds me of an incident. Long back when I was studying vibration analysis under my Guru, Tim Henry of the University of Manchester, I was working on a small experiment with accelerometers. I was bit upset about the funny results I got and was ashamed about the wasted effort at the end of my week-long experiment. Tim asked me as to why I look sullen on a fine English morning.

When he came to know the reason for my long face, he just said, ‘Well, there is nothing to feel bad about. What you got is also useful knowledge. It would tell others that this method of finding out of what you wanted to find out does not work and this is why it doesn’t work and here are the results as evidence. There is no question of shame. Don’t you think it would save a lot of time and energy for other researchers who would come after you by choosing to avoid the path you just found to be incorrect? And you just learned more about the subject. Haven’t you?”

What a relief that was. Thanking him, I promptly went out to soak in the rare sun on a cold English morning. And that is where I was hit by the truth — all human search must be based on honesty – ruthless honesty — so that integrity of human learning can be preserved since quality of that integrity decides whether we survive better or not. I vowed — from now on, I must learn to take things as they are.

I also think: by learning to be honest with our failures and vulnerabilities and to resiliently respond to uncertain outcomes and situations is a vital step to get rid of fear and shame that hold us back.



This book acts as a further expansion and exploration of my previous book – Winning Anywhere – the Power of ‘See’

Perception, Sense-making, Enlightened Action

Right Perception and RIght Sense making are the fundamental outcomes of our cognitive ability that enable effective leaders take enlightened action. 

Possibly, most problems that we create through our actions are a result of wrong perception and wrong sense-making.

To me, Perceiving, Sense-making and Enlightened Action in life is something like this:

“Any real life System about which we care to perceive, make sense and take enlightened action, comprises of a meaningful set of ever changing and self transforming objects, diverse in form, complexity, state and function, interacting in periodic and aperiodic manner with each other and inter-related through multiple network of interdependencies through mutual feedbacks and signals thereby generating variable amplitudes of energy exchanged/transferred within variable/flexible space(s), mostly operating far from its equilibrium conditions; not only exchanging energy and matter with its environment but also generating internal entropy to undergo discrete transformation triggered by the Arrow of Time forcing it to behave in a dissipative but self organizing manner to either self destruct itself in a wide variety of ways moving towards void or create new possibilities in performance and/or behaviour from the void of creative potential owing to presence of ‘attractors’ and ‘appearance of bi-furcations’; thereby making it impossible to predict the future behavior of the system in the long term or trace the previous states of the system with any high degree of accuracy other than express it in terms of probabilities or possibilities since only the present state of the system might be observable to a certain extent and only a probabilistic understanding may be formulated as to how a system has arrived at its present state and what would keep it going, change or destroyed thus triggering creative human responses through right insights (not grossly based on emotions or thinking or memory) to manage, maintain and enhance system conditions, functions and purposes with minimal intervention to create superior systems of the future through enhancement of self organized interactions within and without the system interfaced with other connected, unconnected and overlapping systems operating within larger envelopes of human activity.”

Such a representation of an Perception, Sense-making and Enlightened Action looks quite involved.

Perhaps it might be stated in a much simpler ways but I would not attempt to do so since it would make it more complex that it should be.

Perhaps more can be said about resilience, agility, etc but I would not do so since those are really superfluous.

Perhaps more can be said about Black Swans and not so ‘black swans’ and predictions but I would care less to say so since saying more would be ‘redundant’.

The whole gamut of Perception, Sense-making and Enlightened Action takes place within five envelopes of human cognition, which are as follows:

1. Physical envelope

2. Energy envelope

3. Mental envelope

4. Wisdom envelope

5. Enlightened Action envelope


However, the crux of the matter is

1) how we ‘see’ reality (Darshan/Notice)?

2) how do we understand what the system is telling us (Sadhana/Engage)?

3) how do we create and choose our responses (Bhavana/Mull)?

4) how do we develop the necessary intention to implement our choices to life and living (Shankalpa/Exchange)?



1. Darshan, Sadhana, Bhavana and Shankalpa are Sanskrit words

2. The above post is an excerpt from or notes of a forth coming book “Leadership – The Nemetics Way!”

Models as Purposeful Representation

Models may be of various types, such as:

a) working scaled, something solid or concrete, like a miniature, which might be termed as a prototype.

b) an abstract set of equations that represent or describe the interactions and inter-dependencies among various system elements or variables.

c) a computer simulation that tries to simulate a given situation on the ground.

d) simple drawings and a bit more complex animation of a process depicting movement.

e) graphical description of a system’s characteristics or a description of a design. It might be two or three-dimensional representation.

In Nemetics the idea of having a model (any of the above representation) is to fulfill two purposes, which are as follows:

a) Help in understanding one complex adaptive system (CAS)

b) Transfer the understanding of one CAS to another and understand the changes that would happen to a CAS over time.

The other part of Nemetics is the RGB waves, where:

a) R waves represent the events that occur in a CAS  It is temporal in its character allowing various events to happen over time.

b) G waves represent the behavior that triggers the R waves. It is spatial in nature. Repetitive behavior characterizes the space, enlivens it and lends meaning.

c) B waves depicts the design or the collective understanding that informs and shapes the G waves. It is space-time taken together, which shapes both space and time to initiate flow of certain behavior operating under certain constraints, which in turn leads to various events.

So, this is the concept of Unity in Diversity. The essential unity is B waves, which allows various manifestations to happen in the form of G and R waves.

It follows that if B waves are changed modified or redesigned then G and R waves would also be changed automatically.

It may so happen, as usually is the case, that two systems are interacting with each other and therefore influencing each other. It might happen to the machines and equipment. It might happen between persons. And it might also happen between organizations and nations.

To illustrate this case let us use the Richardson Arm’s Race Model:

While you might enjoy going through the link let us point out the RGB waves in this interactions between two systems or two nations.

The level of Arms build up of each nation = R waves (denoted by dx/dt and dy/dt). Interesting to note the temporal nature of the R waves.

The Reaction coefficients for each nation, indicating the degree to which each party feels threatened by the armament level of its rival, which then leads to the decision to build more arms = G waves (denoted by the coefficients a and b). Note the spatial nature of the G waves through exhibition of repetitive behavior; interdependent on each other.

The Expense coefficients, generally reflecting the economic costs of increased production and the grievance factors (indicating that the nations are competitors) or goodwill factors (indicating that the nations are friendly towards one another). = B waves (denoted in the equations by r and s). Interesting to note the space-time nature of the B waves, which governs both G and R waves.

The model captures the interactions and inter-dependencies between R, G and B waves and the adaptive behavior of the complex system along with the diversity of manifestation that it produces from the underlying unity.

This model and its variations can now be applied to two competitors, be they nations or companies highlighting the concept of Nemetics that understanding of one CAS can be smartly applied to another CAS to speed up understanding and decision-making.

In doing so, it allows us to predict certain types of behavior depending upon the values of the system parameters.

Similar but usually more complex models might be used to describe the behavior of other social and economic indicators.

Predicting Black Swans – Part II

In the earlier post we dealt with the concept of predicting a ‘black swan‘.

In this post, I intend to explore the concept a bit more: what exactly we monitor to notice a ‘black swan’ in time?

In doing so we would be forced to consider the natural response of a system.

The starting point of our exploration would be to understand how any system, as a whole, whether natural or engineered, would disturbed by a ‘black swan’.  A system is disturbed in three possible ways, which are as follows:

a) A system loses energy till it reaches a tipping point

b) A system gains more and more energy till it crosses the point of system resilience

c) A part of a system emits more energy than it is normally supposed to, that is going beyond the linear response of the part. 

So the natural way to watch a system to expect a ‘black swan’ in time, is to keep a tab on the ‘energy’ of a system in the following ways:

a) Monitor the entropy of a system. As a system functions the entropy of a system gradually rises till it hits a threshold limit indicating the appearance of a ‘black swan’ or an outlier. 

b) Monitor the energy gain of a system till it crosses the ‘resilience’ point to give birth to a ‘black swan’, outlier or a ‘wicked problem’. 

c) Monitor critical parts of a system for excess emission of energy till it goes beyond the linear response of a part. 

It is useful to remember that energy is transferred in ‘quanta‘ or in packets of energy. Therefore, it is natural to expect jumps of energy levels as we record by capturing the different manifestation of energy levels on monitoring trend charts. So when a ‘jump’ is big enough to cross a threshold limit or resilience point or linear response level indicated by its presence outside the Gaussian distribution range  we can be quite sure that a ‘black swan’ or an outlier or a ‘wicked problem’ would soon arrive on the scene. We call such an indicator as a signal.

Therefore, the central idea is to capture such signals in time, just before a ‘black swan’ makes it way to appear on the scene to dominate and change the system.

However, the question is how early can we detect that signal to effectively deal with the inherent ‘black swan’ in a system, which is yet to appear on the scene?

That would be explored in the next post.

Listening and Innovation

When I was learning the tricks of my trade from my teacher, I used to listen.

Since I hardly ever questioned him or expressed any doubt about what he said he asked me one day, “Do you understand what I say?”

“Yes”, I said “since I intently listen to what you say.”

“And how is that?” he asked.

“When I am listening to you there are no longer two persons like you and I. There are no longer the speaker and the listener. I just become one with you. That is the only way I understand what you say. The whole responsibility is mine”

Even today, I remember not only what my teacher spoke but also remember the manner in which he spoke, wrote, explained, gestured — every small detail.

Soon some of my friends remarked that when I delivered a talk or a lecture or engaged in a conversation, I spoke and behaved exactly the way my teacher did.

Needless to say, that this skill of listening soon helped me surge ahead with rapid speed in what became my profession and my hobby.  Soon I innovated many special techniques and methods of innovation that can be easily applied to complex situations.

Listening is an indispensable skill in innovation.

It is so very important when innovation is a response to an existing complexity.

The goal of listening is to merge with what is being ‘listened’ to and be one with it.

Then only true understanding emerges.

All good innovations take birth from such true understanding.



How to create an Incentive scheme to boost Self Organized Productivity?

The Issue

Creating an incentive plan or scheme in an organization is a tricky affair. Most don’t seem to get it right. As a result the desired goals are left unmet. So the top Management feels that they lost in the bargain. And surprisingly the employees also think the same that they have been unfairly treated or cheated by the scheme. Nonetheless it leaves behind a bitter taste that isolates Management from its most vital resource — the employees.

What is known or desired by Management?

However, the aims of any well-meant incentive plan is clear; some of those being —

a) Improve productivity through self organized improvement of efficiency and effectiveness of a production system.

b) Improve self managed quality as an inbuilt factor into any production system

c) Enables employees to quickly discover systemic faults in the production system and self correct those through self-initiated interactions.

What is Unknown by Management? 

a) Management does not yet have a model to work out an incentive plan/scheme that is not only systemic but also self organizing to improve the system.

b) Presently management looks at bits and pieces of data to create work wise incentive plan that is applicable to an individual or a group or a department at most. It does not know how to create an incentive plan that would map and address both interdependence of different departments and their independence too.

c) The same goes for correctly evaluating or assessing the contribution of different types and grades of employees who work in various departments.

d) Management is also unaware of the type of data to look for that would not only help them create the right type of incentive scheme but also keep the inherent dynamics of the system, where the central idea is to create a dynamic incentive plan that helps the production system to be resilient rather than a static one, which can prove to be quite anti-resilient and limiting.

What is needed? 

a) A clear understanding of the system dynamics.

b) The maximum and minimum potential of the system

c) What would be the stability zone to operate in and how to predict when instability sets in?

d) The inherent potential for the system to improve without any additional investment

e) The limit beyond which only additional investment can improve productivity.

f) The right parameters to be selected

The Resolution

The resolution to the above issue is depicted by the conceptual model as shown below:

incentive Plan
Incentive Model


This model (based on science of complexity) was applied to one relatively large Indian multi-national unit and the results were the following:

1. Productivity improved by 1.75 times within 2 months of implementation of the scheme.

2. Self organized improvements took place

3. Real time communication increased between employees

4. Quality improved and sustained.

5. The improvements were self-sustaining without any other capital investment.

Learning Complexity — Leadership Series … 3

In one of the many tanks in the fish farm lived three big fishes who were great friends. They were Silvy, Goldy and Platy. All the while they were practically moving together and gossiping for hours on end.  They would discuss for hours on the problems of life and how such complex problems might be resolved. They were very intelligent too. They devised an underwater Twitter like system, called ‘Fishter’, to continue their long conversations through the day.

However, they did not seem to have any problem of their own. Food was not a problem since they were well fed by their owners. So there was no need to hunt for food. In the process they forget the art of hunting for food. Life was otherwise comfortable, without a care in the world. Their joy of living was greatly enhanced by the presence of many beautiful female fishes to flirt with. The only regret they ever had was their inability to take a monsoon holiday in the nearby paddy fields as owners quickly drained off excess water fearing loss of fishes from their tanks.

Days passed by merrily with nothing much ado. Only in the evenings they swam up to the surface to pounce upon the tiny tasty morsels gleefully thrown at them by children living in nearby localities.

Then one day, terror struck. Silvy, Goldy and Platy overheard their owners discussing on the banks of their tank, ‘Tomorrow morning we are going to farm this tank for our order from Japan‘. It meant the end of the world for our three friends. They would be caught and kept alive to be sold in the Japanese market. The Japanese are terribly fond of buying fishes live. They abhor buying dead fishes.

On hearing this, Silvy was anxious and nervous. His blood pressure shot up. He began to fret and think of his fate – death at such a young age? He thought it to be unfair of the owners. He thought it unfair of God to have sentenced him to death for no fault of his. Then he thought of all the losses he would incur. He thought of the comfortable life he would lose forever. He thought as to why he was born in this tank. And he blamed his fate. After a few hours his body was slowly turning blue. His breath was labored. And he was light in the head, unable to think any further. His throat ran dry though he gulped gallons of water. And his body shivered. He felt his brains bursting out.

Meanwhile Goldy was adamant. He thought, “All these years we have mastered solving complex problems through our innumerable discussions and dialogs. So we can definitely think about this and solve it. Why not?”

Goldy sat in a corner like Rodin’s ‘Thinker’ and thought of ways and means to save himself and others from the impending danger. He asked himself questions and tried hard to find answers to those. He predicted possible scenarios and how to tackle them if they were to come up. He ‘tweeted’ about this to his world. But he received no response. Everyone seems to be worried about their impending fate of doom. Then Goldy thought of ways to hide from the fishermen or escape from the net. He thought about the tools and techniques that might come in handy during the process. He carefully worked out and rehearsed his designed processes. He also thought of taking up the matter with the ‘Fishes Right Commission’….

But Platy seemed to be himself looking cool and composed without a fish-scale being ruffled. He was a bit philosophical too, ‘Death is inevitable  None can escape the jaws of death. But didn’t Shakespeare say, ‘Cowards die many times before their death’. I shall meet it when it really comes.”

With this nonchalant view he went about with his flirting with young females, drinking, love making, perhaps for the last time and merry making in his usual spontaneous carefree attitude.

Night passed with its uncanny shivering. At the break of dawn fishermen were all round the tank. They cast their net. Finding themselves caught in the net, poor Silvy died of a heart attack. Goldy with his eyes wide open, was still trying to figure out ways from this dreadful situation. His plans were not working. But Platy kept observing what was going on in the most relaxed mood ever, possibly conserving vital energy for the very last moment.

After a few minutes the net was hauled up. The fishermen were delighted by the big catch — all big fishes were neatly rounded up. That meant more money for them. Then they started doing something queer. They started sifting the ones in poor health or the ones that were long dead from the healthier ones.

Platy saw that they put Goldy into a smaller tank which had water in it so that he could be kept alive for the Japanese market. Then he saw them throw out Silvy to the far end of the narrow strip of land that separated two fish tanks.

Immediately, Platy feigned death and lay as still and inflated as possible. Sure the fishermen took him to be long dead and threw him to the far corner of the narrow strip of land. That brought him very close to the other tank. Sensing opportunity he leaped for his dear life and jumped into the adjacent tank as soon as fishermen looked the other way.

There he did not waste a moment further. He gleefully glided towards the beautiful young female fishes.

(An ancient Indian story retold in the modern context)

Learning Complexity – Leadership Series .. 2

Root cause analysis
Root cause analysis

Fair to say I love Root Cause Analysis (RCA) and I deeply hate RCA.

That doesn’t look very helpful or fair enough. Isn’t it?

I shall try to explain my love and my concern..

For a simple and complicated systems RCA is rather helpful. No doubt about it.

But for Complex Systems RCA is a complete disaster.

I try explaining this but people seem to have a hard time getting it. They keep trying to apply RCA to all sorts of applications such as a) Project Management b) Equipment Failures in industries c) Human-Machine interfaces and a host of other things..

The funny thing is they consistently fail to explain why something happens in a complex system through RCA but then they consistently keep trying to apply the famous Why-Why…technique till they “ungorgeously” drop dead.

And why not?

I suspect we internally feel very insecure if we fail to find the cause or predict an outcome with any degree of accuracy when something either goes wrong or goes for a booming success. Otherwise we seem to feel insulted. Our intellect is insulted. Our imagined self esteem gets a shock of its life. Our education, training and mastery over many subjects are questioned…..

So why does RCA which works so well for simple and complicated systems miserably fail when dealing with complex systems or for that matter complex adaptive systems?

The distinction is deceptively simple —

1) For simple and complicated systems — events that happen are due to resultants.

2) For complex systems — events that happen are due to emergence

And there is a gulf of difference between resultant and emergence. Resultants lead to specific outcomes. Whereas Emergent events are simply depictions of the varied outcomes that might arise from a set of interactions. How? In a complex system change in quantity of any parameter within a set of interactions changes the emergent behavior of the system.

Therefore, for simple and complicated systems we ask ‘Why‘. That is we predict the outcome as accurately as possible. Perfectly simple.

But for a complex system we ask ‘How might this happen?’ In this case we anticipate or foresee possible outcomes (in the short term only). We must have patience and courage to Explain. Might go wrong at times. Perfectly simple too.

So I become cautious and worried whenever I hear anything close to these:–

a) Ah! Here is the root cause of….

b) This is a simple case of human neglect…

c) Oh! This is a plain case of lubrication failure…

d) Fatigue is the reason for this failure…

e) The supplier was at fault for…

f) We don’t know how to manufacture or operate or construct…..

This is because more than 90% of events that keep happening around us are neither simple nor complicated. They are complex.

Do you share my caution or worry?

Want to read more on this?

Here are some related articles-



The cartoon is taken from Facebook page: Life is a Bitch:

Note: This is a part of the course on Rapidinnovation:

Learning Complexity — Leadership Series – 1

Here is one of many toys I use in my classes on Leadership in Complexity to demonstrate complexity through play. It is a simple and common toy – a double pendulum. It is interesting to see how interactions between few elements really produce complexity. So, the question that I ask at the beginning of a session – ‘Can we predict what is going to happen?

We have made a video demonstration of it. It is about 5 mins. Hope you would find it engaging. You may choose to skip it if you like. I suggest a try. While you are viewing it mentally start predicting what might happen the next instant…

Predicting Complexity? ( <– click on the adjoining link to view the video)

What do you find?

Is complexity predictable or not?

On the face of it it appears that it isn’t predictable at all. The movements of the loose limbs of the double pendulum simply go crazy. It is not or nearly not possible to predict. Every time we think something like this might happen it usually turns out to be something else. It appears that there are no definite patterns about it. It is too random to make sense. No doubt this is what always happens in complex adaptive systems.

But then I show how complexity can be predicted along with many of its principles.

At first it feels rather strange to realize how all complex systems or complex adaptive systems are inherently predictable as an ensemble in the short run and how they all follow the same rules of the game.

That is really fascinating. It gives us tremendous hope to embrace complexity with faith. There is no point in ignoring complexity since we are entangled with it every moment of our lives. But once we embrace it knowing fully well how to read, learn and go about it —  life is simple indeed. The objective of learning about complexity and applying its principles is to make life simpler; not more complex.

That promises us an alternative way to lead our own lives through creativity and adaptation.

This alternative Leadership path can be summed up by three simple rules, which are —

1. Explain what is happening.

2. Institute methods to Foresee what might happen in the short term

3. Envision desired Interventions to make the system flow in the right direction.

Three of the best designed interventions that I found are a) Education b) Interactions c) Design. These give long term ongoing benefit for many.

So what do you feel and think about it?


(I personally thank my colleague Trichur for prototyping complexity through this model. )