Developing Talent Pool: Every part of the fish has to be alive for the fish to be alive.

STIMS Institute and MICROMATIC Grinding Technology Ltd (MGT) have been collaborating for more than eight years on an initiative to develop Unique New Solutions (UNS). These are solutions for new machine tools and their auxiliaries for novel grinding processes for customers. The goal is to focus on unique outcomes
not available in India and, in some cases, first of its kind in the world. The focus is always on the end to end innovation (i.e.) from concept to commercially realized or implemented solutions.

This initiative also involves an innovative program designed to train and foster a few highly competent graduates into future leaders in manufacturing technology through System Thinking and Transformational Skills.

This team works in close collaboration with design, manufacturing and application departments at MGT, with end customers as well as research teams at the innovative University / Industry collaborative R&D center: Advanced Manufacturing Technology Development Center (AMTDC) at IIT Madras, India. Dr. Subramanian, President, STIMS Institute serves as the adviser to AMTDC.

Through the years, these teams at MGT and AMTDC have had several successes many of which are first of its kind for ‘Make in India’. Some of them are unique or novel in the world. The lessons learned from these collaborations are summarized below:

Recruitment and development of the members for this initiative is rather unique. Individuals, mostly recent graduates, are recruited and assigned to assume a range of responsibilities in a short period of time. The assignments include:
• Market assessment in close collaboration with Sales and Application Engineering to establish the ‘need’ or the customer’s interest and the reason behind;
• Concept development for new solutions as a system, pricing and commercial contract execution;
• Research, Design and analysis of critical sub-assemblies and components;
• Design validation through theoretical calculations using modern tools and methods of FEA/FEM/ Mechatronics as well as advanced software solutions;
• Development of the solution through Concept Validation (establish the ‘science’), Prototype Demonstration (refine the ‘Engineering’), develop the Complete Solution (driven by “Strategy”) and implement at the customer facility;
Complete ownership in the development of unique products (machines, software, process solutions) from Concept to Commercially Viable Solutions.
Thus, in a short period of a few years, the fresh graduate can grow into a thorough technology professional (with integrated skills across Science, Engineering and Management) in the manufacturing sector. All this shift requires constant training and mentoring on System Thinking and Transformational Skills. This experiment in human resource innovation has been very interesting to say the least! It requires
continuous engagement by the senior management as well as rigorous review and on-line mentoring. Location or time zones are not the barriers for such human resource innovation!

Developing a new solution requires an integration of knowledge across various disciplines. No one person can come with the knowledge from diverse fields such as Manufacturing Processes, Mechanical Engineering, Design, Materials, Electrical Engineering, Instrumentation, Testing, FEA, Mechatronics, Advanced Software
and CNC programming skills. Hence, recruiting the right talent with the required knowledge is a challenge and a starting point.

While graduates from well-known institutions have an edge in the beginning, this advantage is sustained more by those with a passion for continuous learning. After a few years of our experiment we find that true talent resides in those who excel at
three core capabilities: Knowledge, Experience and People skills.

Core Capabilities for Talent Development

Experience is not to be judged by the years of work in a given job or assignment. Instead it’s gained very quickly by those who are risk takers, willing to experiment with new ideas. Real life validation of their knowledge through working models, prototypes or sales contract builds self-confidence and a true sense of self-worth in young professionals, which is priceless. But this also requires a set of personal skills such as involvement, risk taking, collaboration and a result-driven attitude.

Core Capabilities of Professionals 
Tools or Enablers
    KnowledgeDeep and extensive learning; Well informed; Comprehension of various aspects of the subjectFormal Education, Reading, Learning from peers, Data driven, searching on-line data base, Observations
    Experience  Skill derived from actual participation or direct involvement; Accumulated wisdom from real life.Hands-on Activities, Involvement, Experiments, Risk-taking
    People Skills  Ability to seek out others and receive their support, help, and cooperation; Willingness to reciprocate, to achieve mutual benefitsHonesty, Integrity, Communication Skills, Collaboration, Team Spirit, Results driven, Emotional Intelligence.
Core Capabilities of professionals

People skills are those beyond the well-known attributes for inter-personal interactions to get along well with others. In some regards, the people skills we find valuable are grounded in factors such as honesty, integrity and emotional intelligence. These are the skills that not only impel one to personal success, but also helps others and the team to the same outcome.


End to End Innovation: Fish is not alive unless every part of the fish is alive!

In most companies, R&D and commercial efforts are run as two parallel silos. One is an internal driven approach where ideas are developed and pushed outside. In other cases it is the sales driven identification of customer needs being pushed into the company for further internal development. Most of the time the internal identified solutions are partial representation of the “system” largely driven by “science” based ideas and their “engineering” refinement. The externally identified marketing driven needs are also partial in that “sales potential” is translated into “engineering” parameters which may or may not be compatible with the internal core capabilities. Hence these partial descriptions of the solution are often incompatible. Also the science and engineering minded professionals show little interest in engaging with the end customers and their needs. Sales and marketing professionals also show little or interest in the graphs, charts and simulations proficient to the technical “experts” inside the company. Our experiment has been to find a seamless blend between the two. Typically, such seamless connection happens in small startup companies. But our goal through UNS and AMTDC is to bring about entrepreneurial teams, talent and outcome while leveraging the resources and facilities of a well-established enterprise and institutions. The talent development for this effort requires education and commitment from everyone, especially the young talented professionals who learn and believe that ‘Every part of the fish has to be alive for the fish to be alive!’.

Methodologies for “Problem Solving”

Recently we came across the article titled: “How to master the seven-step problem solving process: Structured problem solving can be used to address almost any complex challenge in business or public policy” from Mckinsey & Co.  This article makes a very useful and interesting reading material. Below is a table that shows the alignment of the STIMS Strategy for System Thinking and Transformational Skills as well the as the differences.

STIMS strategy emphasizes knowledge integration across the disciplines of Science, Engineering and Management, skills which are dependent on collective core capabilities of human resources involved in the project and problem solving. STIMS Strategy also requires treating every problem as a “System” and handling the solution at three levels: Awareness, Analysis and Synthesis.

The referenced article also makes reference to the “Design Thinking” methodology. The table below provides a comparison between this methodology and the STIMS methodology for System Thinking and Transformational Skills.

Figure 1
Figure 2.
McKinsey: seven-step process Institute: System Thinking and Transformational Skills.
1.Problem definitionDevelop a Common Language:  Define the “problem” as Input / Transformation / Output system. (See Figure 1)
2. Use logic trees to disaggregate the problem. (e.g.): “How can we save Pacific salmon?”Decompose the outputs between the “What?” – the deliverables (TECHNICAL Outputs) – and the “Why?”- value /benefit for the user (SYSTEM Outputs). This is the STRATEGY behind the solution. Transformation = The “SCIENCE”, the causal  connection between the inputs and the outputs;
ENGINEERING is the application of the “Science” to achieve the deliverables Problem and its solution = Knowledge Integration (Across relevant Science, Engineering and Management (Strategy) relevant to that problem.
3. Rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?Decompose the “inputs” in four categories: Investment / Expenses / Need / Constraints  or Platform (Hardware) / Tooling (Software) / Need / Specifications
Machine / Tools (Supplies) / Component / Parameters.
Develop tools and methods to validate the “Science” and the “Strategy”
4. Work plan: Depending on what you’ve prioritized: It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level one answer, there’s a level-two answer, there’s a level-three answer.  One can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.Complete the STIMS Diagram. Identify the gaps and find the data and resources to fill the gaps: This step leads to a natural formation of inter-disciplinary Team (Eco-system) across relevant resources inside the company, across companies and other players. Develop a “Hypothesis” – science based model – based on real life data. Demonstrate the hypothesis and its modifications: Using a controlled “testing” or “incubator” unit. More rigorous the understanding of relevant Science and Engineering more accurate and reliable is the testing and validation. At this stage the TECHNICAL Outputs and how to achieve them (in a scaled down version) are well established. Scale up the testing unit to achieve the desired outcomes – the SYSTEM Outputs.
Some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative.System Thinking involves three levels:
AWARENESS (of the problem as a whole) – Steps 1and 2 above.
ANALYSIS (of the problem in terms of relevant Science, Engineering and Strategy) – Steps 2, 3 and 4 (See Figure 2).).
SYNTHESIS (Knowledge integration and delivery of SYSTEM outputs) – Step 4
It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process.Bias is an outcome of subjective (task oriented) approach for problem solving; System Thinking (and related knowledge Integration) is non-personal and hence distances human bias from the solution process. However caution must be exercised with respect to bias due to lack of relevant knowledge (Science, Engineering and Management (Strategy / operations) – core capabilities) behind the problem and its solution.
5. AnalysisSee Step 4
6. and 7.
Synthesize the pieces that came out of the analysis and begin to weave those into a story; That helps people answer the question “What should I do?” Motivating people to action
Synthesize the solution at three levels or scales:  
Feasibility demonstration (Bench Scale) where the Science is validated.
Scale up where the Engineering (and its constraints) are established.
Full scale implementation where the SYSTEM outputs (from Item 2 above) are validated.
“Design Thinking”:
Start with an incredible amount of empathy for the user and use that to define the problem; go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.
System thinking and Transformational Skills (Seven steps):
1. Develop a common language: Define the problem as Input / Transformation / Output system
2. Decompose the “problem” into relevant Science / Engg. / Mgt.
3. Distinguish between “deliverables” and “Value/benefits” – end user experience (Technical Vs. System Outputs).
4. Emphasize on Science and Strategy;
Rely on diagnostic tools, in-process (real life) data and analysis.
5. Eco – system Development (Every solution requires many partners both inside the company as well as outside).All partners in the eco-system are connected through the common Science, Engineering and Strategy (System Outputs).
6. End to End Innovation (Awareness to Analysis to SYNTHESIS)
7. Emotional Intelligence (for innovation and problem solving)

Transformational Skills that are critical (and missing today) in many service engineering / science solutions.

           Take for example the customer experience when one calls the “Call Centers” for any help such as your cable TV service or phone bill or whatever. The automated systems are designed to minimize interaction with human respondents at all cost. More efficient and modern the system, more are ways of routing the customers internally to as many automated options as possible. Only the most ardent and persevering customer may get through. The rest will give up merely due to sheer fatigue! :-(         

In all of the above the “customer” is designed into the system as the “enemy” to get rid off as fast as possible. The myriad of options the company deals with have to be sorted through by the customer by listening to the endless pull down menu presented in so many ways. Poor application of data science on the part of the programmer is inflicted as the burden on the customer in the above interaction!          

In the mean time and when the customer finally gets through to a real live person, the poor call center workers are trained to say “Is there anything else I can help you with?” when the original problem is not resolved or left to the next agent. These call center workers are endless chain of people with responsibility to communicate without any authority to resolve the issue on hand.         

All of the above suggest a need for (a) Common Language (b) System thinking or integrated view of the problem from a larger perspective vs. task oriented actions seen as the “job”) (c) emphasis on End to End innovation and (d) Emotional Intelligence that treats customer as one with a need (instead of merely as a source of data). These are four of the seven Transformational Skills essential to be practiced by the service solution developers (who are in most cases technical professionals). For more details:

Ethics and AI

Recently I attended an on-line seminar on “Ethics and Artificial Intelligence (AI)” by Roberto Zicari (Prof. of DBIS) – Goethe University Frankfurt) on the need of an Ethical AI Due Diligence.

It was an interesting seminar that addressed the issues relevant to measurement and management of ethics in the development and use of AI Tools. As a result of this seminar, I was motivated to ask the question: Ethics is a matter of philosophy. Do we need a sustained education on Philosophy and the related topics on Subjectivity Vs. Objectivity as part of AI Technology education? The speaker acknowledged the need and pointed out some effort in this direction in the academic circles.

Following are some reflections on ethics and philosophy:

We have discussed earlier that Philosophy is an in-depth analytical study of any subject matter:

It is not a mere coincidence that the highest degree awarded in any field of study is called “Doctor of Philosophy”. The best researcher in any field has the best KNOWLEDGE of the laws of nature at play in that field, the limitations (IGNORANCE) of such knowledge and possible wrong or erroneous interpretations (BIAS) of the same! The same can be said of any true professional – the best engineer, doctor, surgeon, musician, carpenter, etc.  The knowledge, bias and ignorance are the three connectors through which we relate to the subject matter. The process of understanding these connectors, when it is explicit and analytical and quantitative, we call the process as “scientific”. The more intuitive and inferential the process, we call it as “Common sense”.  

The relative proportion of the three co-existing connectors – Knowledge, Bias and Ignorance – with respect to the subject on hand and the dominance of one is not always easy to identify and separate out. If we observe carefully, we find that our “education” of every kind is intended to facilitate our skills to identify these three connectors, their relative proportions and how to sort them out! One who is good at this skill (to identify these connectors in any given field) becomes “expert” in that field of study.

This process of search for the three connectors and their relative proportions can be precise, only when there is equal weight placed on all three connectors! Consideration of all evidences with equal weight and emphasis on all three connectors – knowledge, bias and ignorance – is called “Objectivity”. An objective person is not swayed by his/her knowledge nor tends to understate or diminish the evidences pertaining to bias and ignorance. An objective frame of mind treats all three connectors with equal weight or merit.

The characteristic features of Knowledge, Bias and Ignorance have been outlined earlier:

  Knowledge Bias Ignorance
Features or key characteristics of the three Connectors (Guna). Knowledge adds illumination and clarification of the situation or problem on hand Bias arises out of personal needs and wants and our attachment to them. Ignorance is driven by illusion, fantasy or irrational expectations.
  Knowledge binds a person through genuine sense of happiness Bias binds a person to endless chain of activities Ignorance binds one through lack of directions.
  Knowledge can be recognized through the happiness and contentment based on the well-being for all. Bias can be recognized through associated endless chain or recurrence of additional activities, without a sense of closure, satisfaction or fulfillment. Ignorance shrouds the knowledge and leads to lack of direction.
How can one perceive the dominance of each connector? When knowledge, illumination or comprehension is perceived in every aspect of the subject matter and its functions, one can recognize that through the tranquility that follows. When Bias is dominant the subject matter or activity is drawn into greed or desires of endless nature, driven by intense personal needs, initiation of innumerable activities due to a lack of satisfaction or contentment, unease and longing. When stagnation or inactivity prevails, the result is ineptness, lack of direction or sense of purpose and illusion (attraction born out of ignorance).
At a time of crisis or when a decision needs to be made,  the dominant connector leads to: True Knowledge transforms a person to a higher plane of existence (of total self-control and unattached active participation). Bias leads a person to more activities, merely as a means to satisfy growing personal wants and desires which continue to remain as unfulfilled. Ignorance leads one to the vicious cycle of being shrouded by ignorance
The result or fruit of dominance of each Connector: Proper or virtuous acts and purity or clarity  Sorrow. Depression and despair
Each Connector Leads to:  Knowledge and understanding  Greed Lack of direction and illusion
Accomplishment of the intended purpose by the persons under the influence of each connector: Rise to the higher level (through greater levels of engagement of self-control and the reasoning and logic that occurs as a result) Stay in the middle (due to the self and its reasoning being constantly over ruled by attachments and its insatiable needs and wants) Sink to the lower level (since the reasoning and logic of the self never occurs, like the fire being shrouded by the ashes eventually gets quenched).

Now let us discuss the relevance of these three connectors with respect to “Ethics”. Whatever we learn on this aspect would be applicable as it pertains to “ethics in any subject” including AI.

Let us begin our discussion by first defining the subject matter. AI is often thought of a solution using the tools of Machine Learning and Data Science. The AI proliferates depending on the data, its collection methods, tools for analysis, etc. To keep it simple let us state that the end result is a “solution” and its use. Ethics will be of concern during the development of the solution, its application / use, benefits achieved of value to the developer of the solution as well as the user and finally the impact of the solution and its benefits to the society at large. This chain of events is illustrated in the schematic diagram as noted above.

While ethics is often thought of as the impact of the solution on the society at large, such focus will be like attempting to lock the cattle inside after the barn door has been left open for a while! In many respects this might be the ignorance as it pertains to ethics and its management. In fact, ethics must be taken into account at every stage – from solution development, testing and USE or deployment – keeping in mind the impact to the developer, user and the society at large. This emphasis on benefit at large (which in turn also leads to the benefit to the self) may be described as the Emotional Intelligence for innovation. For details:

Focus on ethics at every stage of the innovation process also requires a passionate engagement of professionals in areas beyond their comfort zone. It is natural for engineers and technical professionals – often computer scientists – to think of their work as “technical” and leave all the rest to “others” to worry about. This bias and attachment to partial knowledge is often the source of problems that manifest as larger issues. The same can be said of ethics and how it is impacted by the bias or task oriented approach to solution development and implementation of AI solutions. To overcome such limitations of bias, it is imperative to teach and train professionals on “System Thinking” and its comprehensive understanding:

Once we have minimized the ignorance and bias as described above Knowledge pertaining to ethics permeates. Ethics is no longer seen as an afterthought, but built into every phase of the solution development and deployment. Such effort is preceded by comprehensive description and definition of the entire solution chain. Ethics is no longer a thought or task to be carried out. Instead ethics becomes a way of life, the life blood of every professional at every level engaged in the solution. Taken in abstract, this statement may sound Utopian. But, when efforts are made and education is provided to minimize the ignorance and bias as described above, ethics as a way of life, ethical solutions as the only acceptable solutions become a natural and accepted practice.

System Thinking and Emotional Intelligence are part of a set of seven Transformational Skills described in two books:

Rendering a human touch to smart manufacturing!

Rendering a human touch to smart manufacturing


If we can treat the Physical Processes in the manufacturing shop floor as human beings, then much of the information management practices may be applicable to the manufacturing sector as well. This humane treatment of machines and manufacturing processes may be the next generation Smart Manufacturing?