Latest / ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence / Sridhar Ramasamy on How Digital Factories are Transforming the Future of Manufacturing (#042)

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Sridhar Ramasamy on How Digital Factories are Transforming the Future of Manufacturing (#042) · ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence

0:00
SPEAKER_02: Lights out manufacturing.
0:02
SPEAKER_02: Uh, what does that mean?
0:04
SPEAKER_02: Uh it means um physically the plant is completely automated, raw material is loaded on uh one side of the dock, uh comes from our trucks and whatnot, and the finished product comes out on the other end.
0:20
SPEAKER_02: Uh you don't need even need any lights, meaning you don't have anyone in the plant, um uh physical human beings there doing the manufacturing, it's completely automated.
0:38
SPEAKER_00: Welcome to another episode of Contraminds.
0:41
SPEAKER_00: In this episode, I am talking to Sridhar Ramaswamy.
0:44
SPEAKER_00: Sridhar is an engineer who moved to the US and is a veteran in building digital factories and is an expert in industry 4.0.
0:54
SPEAKER_00: Sridhar took a career path not taken by many.
0:58
SPEAKER_00: He did not go and join an IT or a software company, but he went into manufacturing.
1:04
SPEAKER_00: The fact that he went into manufacturing helped him look at manufacturing processes which were completely manual, and the whole set of digital transformation was happening to manufacturing industries, and he learned those skills, and that's what he shares with us in this episode.
1:24
SPEAKER_00: The fact that you need to have interdisciplinary thinking, learning from other industries, have curiosity, have the discipline and the rigor to learn every time, ask stupid questions, ensure that you don't have the solutions, observe people, observe processes are some of the things that he shares in this episode.
1:47
SPEAKER_00: Let's get into the conversation.
1:55
SPEAKER_00: Hi Shree, lovely talking to you and uh really great to have a conversation with you on digital factories.
2:03
SPEAKER_02: Yeah, me too.
2:04
SPEAKER_02: Uh it's been almost 30 years uh since we met uh uh after college, so it's been a long time.
2:11
SPEAKER_02: Uh and and thanks for the invite and uh hopefully you find some uh material that's uh uh useful and uh make it into the podcast.
2:20
SPEAKER_00: Absolutely, Shri.
2:21
SPEAKER_00: I think uh having done uh you know electrical engineering, uh today uh you are actually uh into uh building digital factories, right?
2:31
SPEAKER_00: So uh can you uh tell me uh you were you didn't take the normal path of going into software engineering and getting into IT and so where did this interest in manufacturing uh you know come about?
2:45
SPEAKER_02: Yeah, um you know after undergrad, uh you know, I worked a year, year and a half uh uh in India working for a TBS uh uh washing machine plant.
2:55
SPEAKER_02: And um so they trained me on uh lean manufacturing just in time production.
3:01
SPEAKER_02: And uh so I I got uh my interest uh in the more on the manufacturing side.
3:06
SPEAKER_02: Uh I actually wanted to do being operations.
3:09
SPEAKER_02: So when I came to the US, um I wanted to actually do MBA, but I ended up uh deciding uh okay, I'll uh do actually grab um electrical engineering, I continue with my electrical engineering, get my graduate degree.
3:22
SPEAKER_02: So when I graduated, it was in Wisconsin, um the you know, um because of the visa and everything else, and I I could was able to do one year of uh um more like a uh uh trainee program or with the steel company, and uh it was a much smaller company, and uh um you know again, um I get to do a lot of different things, uh not just engineering.
3:46
SPEAKER_02: And uh the first opportunity was it's heavy manufacturing, and uh, that's how I got into manufacturing rather than uh uh going into programming.
3:55
SPEAKER_02: And obviously, you know, it was an automation engineering job.
3:58
SPEAKER_02: You get to do a little bit of a programming as well.
4:00
SPEAKER_02: It was not like uh you stayed away from it, but it's also traditional engineering, um, you know, designing control systems and things like that.
4:09
SPEAKER_02: So it's a mix of both.
4:11
SPEAKER_02: Um yeah, that that's where it came about.
4:15
SPEAKER_00: So uh so one thing uh that I see is uh you know uh you got a career uh which is very interesting, which is you know, you got into smart manufacturing, you got into uh digital transformation, you've been doing digital transformation in aerospace, pharma.
4:34
SPEAKER_00: Uh so one thing that I can see is that uh you know uh you've been a curious learner all through your uh yeah, you know, your career.
4:43
SPEAKER_00: Uh so how do you really prepare yourself for such a diverse career given the fact that uh you know you started off uh you know in electrical engineering, but now uh you're really talking of uh you know a whole different industry.
4:57
SPEAKER_00: So how did you go about uh preparing preparing yourself for such careers?
5:03
SPEAKER_02: Yeah, you know, uh uh obviously this is uh uh my based on my experience.
5:09
SPEAKER_02: So uh necessity made me to uh learn different things.
5:13
SPEAKER_02: So when I was in the steel industry, it was a much lower uh profit margin industry.
5:18
SPEAKER_02: So we you know, the engineering and the automation, that's the easy part.
5:24
SPEAKER_02: You can get it done.
5:25
SPEAKER_02: But uh eventually uh it came to a point.
5:28
SPEAKER_02: Uh we wanted to be more efficient, uh, we wanted to make it right the first time, things like that.
5:33
SPEAKER_02: So then you're going beyond that, uh, you started looking at how how do I use uh you know or uh make product uh uh the right way.
5:43
SPEAKER_02: Uh you know, in steel industry, most of your cost is in conversion, uh the energy you put in.
5:49
SPEAKER_02: And once you know you put in there's no recovery, and if you don't make the particular grade, you you know you lose your money, right?
5:56
SPEAKER_02: Um so so from being just being an engineer, now I'm talking about more of an operation.
6:03
SPEAKER_02: How do how can we make more profit?
6:05
SPEAKER_02: How do we make quality products?
6:06
SPEAKER_02: So that's where the data analytics part comes in.
6:09
SPEAKER_02: And when I started uh a lot of company, steel industry, we have very uh limited uh automation, uh, we didn't do a lot of uh data analysis and things like that.
6:20
SPEAKER_02: So we started building uh some even those days we didn't call it machine learning or artificial intelligence.
6:26
SPEAKER_02: We had a find it a little uh you know, uh consign uh problem.
6:30
SPEAKER_02: So we simplified it and we tried to solve the issues in the controllers.
6:34
SPEAKER_02: That's what we had.
6:36
SPEAKER_02: Um so kind of from uh going from being just an engineer, now you're thinking more of uh how do I optimize my process?
6:44
SPEAKER_02: So necessity made me to do that.
6:47
SPEAKER_02: Uh and then after 15 years working there, um, I get a chance to build a brand new plant.
6:54
SPEAKER_02: Um, you know, so I get to do a lot of my, you know, use my experience to design it in a way I wanted to design it, and I definitely had a lot of uh external vendors uh uh to partner with.
7:07
SPEAKER_02: Uh so we build a really uh you know state-of-the-art facility in the Cleveland area.
7:13
SPEAKER_02: And uh and uh you know, I'm then I'm getting bored uh in the steel, and uh and at the same time when I was uh uh you know we started up the plant and our utilization was like about 40, 45 percent on a brand new plant, and should be uh anywhere from 80 to 90 percent, and we're not getting it.
7:32
SPEAKER_02: And uh there's a lot of uh issues, uh startup issues, premature failures.
7:37
SPEAKER_02: Um, all of a sudden I'm being an engineer from operations, now I'm looking at reliability issues.
7:43
SPEAKER_02: Uh so it's the circumstances, you know, different circumstances and different things I was going through uh during my career uh made me to learn new things.
7:52
SPEAKER_02: So I started working on reliability engineering, manufacturing support, how do we improve uh my plant um in our life?
8:00
SPEAKER_02: And uh so that kind of dictated my next job.
8:04
SPEAKER_02: Um so Swagelock as a local company uh makes a lot of CNC machine fittings, and they got some automotive parts also.
8:13
SPEAKER_02: They hired me as a reliability engineering manager uh and manufacturing support manager, and I started managing you know at the beginning, it was just two plants, and then they ended up being six plants.
8:25
SPEAKER_02: Uh and um, it's uh so I completely off of you know, it's more of a management, managing people, um, and and then I also learning how to improve equipment reliability.
8:37
SPEAKER_02: I never worked on a CNC machine in my whole life up to that point, and all of a sudden I'm learning about CNC machines.
8:44
SPEAKER_02: So definitely, you know, you have to take advantage of what is in front of you, what is available, be curious, um, you know, continuously learning, and uh, and and and also you know, if you think it is just a job, then it is a job, uh, and then you know you had to think about it as a career.
9:01
SPEAKER_02: You know, um my career is uh you know, the way I like to define it is how can I help uh companies, how can I help individuals, you know.
9:11
SPEAKER_02: Um definitely uh I I mentioned uh during our uh the beginning, uh I'm not a great programmer, so I I had a lot of support.
9:21
SPEAKER_02: Um, you know, engineers they throughout my career, they supported me, uh my teams um to make me look good.
9:29
SPEAKER_02: Uh so uh in turn, uh their uh success uh became my success.
9:34
SPEAKER_02: Uh uh so facilitating helping them be successful.
9:38
SPEAKER_02: So that that's what I try to do.
9:45
SPEAKER_00: So when you hear Shri speak, he talks about the importance of an engineer knowing the business problem.
9:52
SPEAKER_00: Why are plants making such low profits?
9:56
SPEAKER_00: What do we do to improve utilization?
9:58
SPEAKER_00: And then they find a solution.
10:01
SPEAKER_00: So if you are an engineer, the need to be curious about a business, about a business problem, and the importance of working with people seems to be a very, very important critical aspect of how you can be successful in your engineering job.
10:21
SPEAKER_00: Every time you tell me, uh I get an interesting perspective, uh Shri.
10:25
SPEAKER_00: One thing is uh you actually talked to me about the fact that you went and discovered things that you never done before.
10:34
SPEAKER_00: Okay.
10:35
SPEAKER_00: Uh that's not how probably the engineers of the past were, right, in the last uh few decades.
10:42
SPEAKER_00: So therefore, when you uh when you wanted to get there and start looking at reliability engineering or smart manufacturing or using uh you know uh automation of plants and things like that, uh what was your uh you know uh method of learning?
10:59
SPEAKER_00: Okay.
10:59
SPEAKER_00: Uh was it uh was because it's a very important thing.
11:02
SPEAKER_00: One is to actually get uh you know uh completely uh you know stuck saying that I won't take any risks and make mistakes.
11:11
SPEAKER_00: So how did you go about learning, uh reducing your mistakes and uh building a career around probably the most in-the-day, which is digital factories?
11:20
SPEAKER_00: So, what are your key tenets that you can share with us?
11:24
SPEAKER_02: Yeah, uh learning.
11:26
SPEAKER_02: Uh you know, I'm not a patient uh book reader.
11:28
SPEAKER_02: Uh I I you know, anything I uh if I buy a new toy, um, you know, a lawnmower, for example, uh I you know, I wouldn't even read a book and I I'll try to figure it out myself.
11:41
SPEAKER_02: It's like assembling an IKEA furniture.
11:43
SPEAKER_02: Um you know, a few times I screwed up or put the wrong bolt and wrong screw uh and so I'm more of a tinkerer and trying to be hands-on.
11:53
SPEAKER_02: Uh and early on, on the one thing I learned to do is uh go watch the process, go listen to the people, uh, and they want to tell you and they uh help you learn things, and uh, you learn quite a bit by watching and observing.
12:09
SPEAKER_02: And uh so uh I'll give you an example.
12:12
SPEAKER_02: Um I didn't know much about reliability engineering, and I got hired.
12:16
SPEAKER_02: Uh Peter Scheid, uh uh my boss at the time at Swayzark.
12:19
SPEAKER_02: I don't know what he saw in me, but uh, he did uh hire me and uh gave me an opportunity and hired me.
12:24
SPEAKER_02: Um and uh so uh I I had a I hired this uh maintenance planner and um he came from an automotive company.
12:33
SPEAKER_02: He taught me everything about uh doing uh maintenance uh planning in uh in any company.
12:40
SPEAKER_02: Um you know I used to get mad at him and say, Why why are we not putting all the equipment in preventive maintenance?
12:46
SPEAKER_02: And uh he walked me through the process and said, look, without plans and without parts, uh a preventive maintenance plan is not a very good, it's just a paper uh uh plan.
12:56
SPEAKER_02: Uh it doesn't work and you don't get the uh you know efficiencies and yields.
13:00
SPEAKER_02: So you taught me all the nuances.
13:02
SPEAKER_02: So that's kind of what my uh MO when it comes to learning.
13:06
SPEAKER_02: Um you go talk to the people, learn from people, and you know, obviously you do have to do some book learning and some uh you know, even uh you know, coding and things like that.
13:17
SPEAKER_02: You have to do a little bit on your own.
13:19
SPEAKER_02: So that's kind of my how I will go about learning, um, uh listening to the people and asking some questions.
13:26
SPEAKER_02: Um, you know, sometimes uh they're not the very smartest of the questions, but uh that's how I learn.
13:32
SPEAKER_02: Yeah.
13:37
SPEAKER_00: Srida here talks about the importance of observing and watching.
13:43
SPEAKER_00: Think back and look at what you do every day, and then uh reflect on how deeply do you watch and observe people, processes and business problems.
13:55
SPEAKER_00: I think that provides a host of ideas for you to solve business problems.
14:03
SPEAKER_00: And as an engineer, it's very critical to ask stupid questions and you don't need to worry about why it's not so intelligent.
14:15
SPEAKER_00: I think the fact that you need to ask those stupid questions and keep asking them again and again to understand the core business problem and therefore get deep into why this problem is occurring, and therefore, you will find a solution at the end of the deep pit.
14:35
SPEAKER_00: That I think is the most important, and you don't need to get embarrassed asking idiotic questions.
14:42
SPEAKER_00: That I think is a very key message that he gives out in this discussion.
14:53
SPEAKER_00: Uh, you know, in schooling, in college, uh, we are all taught about uh saying the right thing, right?
14:59
SPEAKER_00: But suddenly you go to an industry which is uh, you know, you got to set up a plant, you basically have to go and uh do an industry uh you know uh category where probably you are not uh uh you know uh not knowing.
15:13
SPEAKER_00: So how did you deal with ambiguity?
15:14
SPEAKER_00: So how did you uh how did you break it down?
15:17
SPEAKER_02: Yeah, oh that's a great question.
15:19
SPEAKER_02: Ambiguity, uh and and the culture, right?
15:22
SPEAKER_02: Uh uh where we come from.
15:24
SPEAKER_02: Uh you don't question uh the professors or uh uh you know uh and I I I think the professors, I don't know, maybe uh my my diet uh was a professor, so I shouldn't kind of generalize it.
15:35
SPEAKER_02: Uh um, you know, you don't yeah, that's a great question.
15:38
SPEAKER_02: Uh so I think uh in the US, so that's one of the things um they teach you in schools and uh uh and um you know they challenge you on everything.
15:46
SPEAKER_02: Uh you can't just assume that your view is the right view.
15:50
SPEAKER_02: Uh you you better have uh good answers and good explanations uh before you propose anything, right?
15:57
SPEAKER_02: So uh so you had to do your homework, uh uh definitely.
16:00
SPEAKER_02: Uh and a lot of times uh you know um being you know, I think when I started out as an automation engineer, a lot of times uh I had to help with the maintenance troubleshooting and things, and you walk in, um equipment is down, uh plant is down, and uh you know, every mil uh I think uh hour uh every hour the steel plant was down, we were losing like a hundred dollars or something like that.
16:25
SPEAKER_02: This is almost 30 years ago, so it's a lot of money.
16:27
SPEAKER_02: And so this you know, um every uh the company was a private company, and there was a lot of pressure, uh, and everybody got benefit if we made good tons.
16:36
SPEAKER_02: So there's a lot of always a pressure when you walk in.
16:39
SPEAKER_02: Uh and so early on, um, you got to have good discipline.
16:44
SPEAKER_02: Uh and so you know, lean teaches you.
16:46
SPEAKER_02: Uh that's one thing I learned.
16:48
SPEAKER_02: Um, so I I that's why I go back to my lean training, um, to listen to your people.
16:54
SPEAKER_02: Uh, and uh that's one thing.
16:56
SPEAKER_02: Um you know that's important for me.
16:59
SPEAKER_02: Um so you walk in in any situation, could be uh yeah, you know, you don't know what you're going to deal with, whether it's a maintenance troubleshooting or a problem you're trying to solve.
17:10
SPEAKER_02: Um later on uh when I was working for Alcova uh or an aerospace company or supplying parts, and I was in the digital transformation team, we'll go to different plants and different sites, and we walk in, they had different problems.
17:25
SPEAKER_02: One had efficiency issues, they were not making fast enough, others had quality issues, uh, so different issues you had to walk in.
17:33
SPEAKER_02: So you you you go interview, you spend time um in the uh field, so to speak, and learn uh before you open your big mouth to uh start suggesting things.
17:46
SPEAKER_02: That's important, um, you know, when you're dealing with the ambiguity.
17:49
SPEAKER_02: Um also you try to apply uh instances and cases from parallel uh industries and parallel uh observation that helps uh when you're dealing with the ambiguity.
18:01
SPEAKER_02: And there's no substitute doing homework and uh reading up on things, uh, those are the things.
18:07
SPEAKER_02: And uh um in so to me it's a multiple different things.
18:11
SPEAKER_02: There's no like a black and white uh when it comes to ambiguity, uh, and also eventually your experience also comes into play.
18:19
SPEAKER_02: They they call it gut feeling.
18:20
SPEAKER_02: Uh you know, gut feeling is based on an experience in my mind.
18:24
SPEAKER_02: So those are the things I generally use.
18:29
SPEAKER_00: So if you look at this section, Shriza talks about how he deals with ambiguity.
18:37
SPEAKER_00: Most often in our education systems and in whatever we do over the first 20 years of our life, we actually know what questions are going to be asked and we are prepared for answering those questions.
18:54
SPEAKER_00: When you get into a business and when you get into a job, most often the problem is not known.
19:02
SPEAKER_00: Therefore, the importance of observing, understanding the business, looking at the business problem, and if you look at some of the points that he says, he does not talk about the solution first.
19:16
SPEAKER_00: He talks about listening to people, observing the process, understanding the business problem, and that I think is very, very important.
19:25
SPEAKER_00: He talks about the importance of discipline and not jumping to conclusions by first looking at each problem in a most modular manner, and then you go back and then say, okay, this is how problem you may need to solve.
19:42
SPEAKER_00: So the importance and the criticality of looking at the business problem, observing people, observing the process, and learning from it is very, very important.
20:00
SPEAKER_00: Uh you talk a lot about uh you know lean and uh you know maybe you are leaning on lean, right?
20:07
SPEAKER_00: In solving problems.
20:08
SPEAKER_00: So, what are the key tenets of lean that you carry with you even today?
20:14
SPEAKER_02: Oh um uh even uh you know uh dealing with my kids and my family, uh kind of uh uh try to uh you know the one thing I learned early on is I'm always not right.
20:26
SPEAKER_02: Uh I think I know the answers, but uh uh a lot of times uh 50-50 chance uh maybe wrong.
20:34
SPEAKER_02: So uh so you walk in and uh uh you know that that's one thing I you know I learned to do, observe.
20:41
SPEAKER_02: And then the third thing for me is uh you know data.
20:46
SPEAKER_02: Um if you have good data, there is no substitute for having a data and letting the data dictate uh the terms, and you know, uh so um often uh you don't have good data available.
21:00
SPEAKER_02: Um in one instance, I I just took a job.
21:04
SPEAKER_02: Um uh you know, they gave me two plants uh at Stragel, and we didn't have good uh uh data to go by or how the plant was performing.
21:16
SPEAKER_02: All I had was uh XF spreadsheet I had where I was logging um the spend uh you know the PO's purchase orders we were writing, and I started using that for the first uh month or so and started looking at uh so that's you know, it gave me a patent, things I mean, we we were uh calling a lot of external help uh and um uh to troubleshoot and fix our machines.
21:42
SPEAKER_02: Uh and then when the help came, I started focusing on what they were doing, and uh and they were uh working on equipment uh unsupervised.
21:52
SPEAKER_02: So some behaviors we needed to change.
21:54
SPEAKER_02: So I started assigning uh um technicians to work with them, and that helped them to.
22:00
SPEAKER_02: To keep an eye on what they're doing, and also they were learning uh on the job, so to speak.
22:05
SPEAKER_02: So, so you have to start with the data.
22:08
SPEAKER_02: So, those are very basic fundamental things.
22:10
SPEAKER_02: Um, have an open mind and understand that you're not always right.
22:15
SPEAKER_02: That's one thing.
22:16
SPEAKER_02: Observe and listen, talk to people.
22:19
SPEAKER_02: Um third thing, um, use any data uh that's available for you to you know dictate uh uh what you will do.
22:28
SPEAKER_00: Fantastic.
22:32
SPEAKER_00: So in this section, uh if you look at what Sridhar is talking, he talks about the importance of an open mind and data.
22:43
SPEAKER_00: Most often this aspect of an open mind is not given so much of an importance, especially when you're working with people, when a business problem is thrown at you, and if you have all the experience, you pretty much look at how to solve the problem.
23:02
SPEAKER_00: But when you have an open mind, your ability to look at different perspectives, question your own biases, and therefore understand the business problem better is very very important when it comes to looking at how you will suggest a solution.
23:22
SPEAKER_00: The other thing he talks about is looking at every piece of data.
23:27
SPEAKER_00: Often, this is a misnomer that I see where people come in and then say, Where is the data or I have too much of data?
23:37
SPEAKER_00: In fact, what he really talks about is picking up small pieces of data and trying to understand what these small data elements mean.
23:48
SPEAKER_00: It could be in the plant where a simple sheet is being maintained where data is picked up, patterns are being looked at, and when you look at these patterns, you realize that there is a business problem that can be solved, and therefore, having an open mind and looking at any data element that you have access to can throw up interesting ideas and solutions.
24:36
SPEAKER_00: So in a way you are uh you know you did something contra, right?
24:39
SPEAKER_00: Which is because you are actually uh you know all manufacturing pretty much moved out of the US.
24:46
SPEAKER_00: Uh you still you know pursued with the manufacturing uh you know uh uh you know uh domain that you started in.
24:55
SPEAKER_00: Uh was there any specific reason you saw opportunity?
24:58
SPEAKER_00: What did you see which others did not see?
25:04
SPEAKER_02: That's a great question, I guess.
25:06
SPEAKER_02: Um so uh I was always fascinated by manufacturing early on.
25:11
SPEAKER_02: Um all my cousins and uh they're all in manufacturing, and uh so um that influenced me definitely.
25:19
SPEAKER_02: Um so most of my career I was uh uh lucky enough uh to work for private companies.
25:25
SPEAKER_02: Uh my first job was with a steel manufacturing company, Charter Steel, in uh uh headquartered in Wisconsin, a privately owned, family-owned company, and uh the Mellows family they had a philosophy, no layout policy.
25:41
SPEAKER_02: Uh and uh and even when the uh business was down, uh they guaranteed 35 to 40 hours of paid uh uh work for their employees.
25:52
SPEAKER_02: Uh, when other companies and steel mills were laying off the people, the airports will come in, they may paint or clean up, do something, they may not produce steel, but they got paid.
26:03
SPEAKER_02: And what it allowed them to do was uh be ready when the upturn came.
26:09
SPEAKER_02: And uh, you know, uh they actually invested, they bought new equipment, and we did a lot of project work during downturns.
26:16
SPEAKER_02: Um, it's a fascinating philosophy.
26:18
SPEAKER_02: And when I started uh that company was like 150, 200 million dollar company, and I think they were now uh one or two billion dollar company.
26:28
SPEAKER_02: I I don't keep track of them you know anymore.
26:32
SPEAKER_02: Uh you know, uh so it's organically they grew and still is uh maintained as a family-owned business.
26:38
SPEAKER_02: Um, so to me, um, you know, there is uh always a way uh to be successful um if you have you know if you uh have a clear path and a clear thought.
26:50
SPEAKER_02: In a lot of ways, uh I think uh Indian businesses uh you know I've seen it over the years.
26:56
SPEAKER_02: Um, you know, they don't do a lot of my mass layoffs and things like that.
27:01
SPEAKER_02: They still run it as a um uh family uh you know uh started companies.
27:06
SPEAKER_02: I I go back to my TBS days, uh I see uh notes about on the Kilosker and Tartos and uh um you know some of the Indian companies, they do the same thing.
27:16
SPEAKER_02: And I think uh culture has to do with it.
27:18
SPEAKER_02: And uh some of the Indian uh US companies are also the same way.
27:22
SPEAKER_02: And another company is Sweswalk, again, it's a family-owned company, and they acted the same way.
27:28
SPEAKER_02: Um and they thrive and and they still thrive, um, even when other uh industries and their peer groups were um suffering, they always thrived.
27:37
SPEAKER_02: So, you know, I was uh fortunate enough to be in those companies, and I always moved up uh in those companies, so I didn't really see a need to jump and say, Oh, I'm going to be an IT person.
27:49
SPEAKER_02: And then after 15 years, you're uh you know, sometimes it's kind of a little uh typecast, right?
27:55
SPEAKER_02: Um it's like acting, you know, the actors are typecast into a role.
27:59
SPEAKER_02: Um that did happen.
28:01
SPEAKER_02: Uh, but you know, going back to your question, how do you change your career?
28:05
SPEAKER_02: So that's why you know, curiosity.
28:08
SPEAKER_02: I also got bored and doing the same job after a few years, and I always wanted to do something different, better.
28:15
SPEAKER_02: Um one thing I always kind of attract to do is how do I do my boss's job?
28:21
SPEAKER_02: Uh this is something uh I learned somewhere, and if I can do my boss's job, you know, my idea is so they will see me, value, and they may uh they will promote me to those roles.
28:33
SPEAKER_02: So and uh that did happen.
28:35
SPEAKER_02: Uh so yeah, that that's why I stayed with the manufacturing.
28:39
SPEAKER_02: I still love it.
28:40
SPEAKER_00: Uh in this section, uh Sri talks about doing your boss's job.
28:50
SPEAKER_00: That's pretty different from what I've heard.
28:54
SPEAKER_00: Most often, what I've heard is, hey, this is my role, this is my description of what I have to do, and this is what I'm supposed to do.
29:05
SPEAKER_00: So when you take up something, when you really start saying, How do I do my boss's job?
29:12
SPEAKER_00: There are a couple of things that you're putting in the talk.
29:17
SPEAKER_00: One is your knowledge of how you can solve a problem far ahead of your capability.
29:24
SPEAKER_00: The second one is preparing for it, and the third one is ensuring that you become successful in that.
29:31
SPEAKER_00: And he also talks about another important philosophy of his value systems and his beliefs aligning with some of the family-owned business in the US where there are no mass layoffs, there are times when uh you know they're being paid when there is a downturn, uh, they go and clean up factories, get prepared for an upturn in the business cycle.
29:57
SPEAKER_00: So there was this philosophy that he believed in, having come from a culture like India, and therefore his alignment of the value systems and beliefs, and therefore the kind of job that he started picking up and then doing the boss's job.
30:19
SPEAKER_00: I think if you want to build successful careers, you need to look at all these anomalies and all these interconnections, and that allows you to build a career with a purpose.
30:38
SPEAKER_00: Do you believe uh you know US uh has uh you know moved away uh from its competitive space space of manufacturing that it had?
30:49
SPEAKER_00: And uh uh has that been one of the reasons for uh you know uh I would say relatively uh you know slower adoption of digital and transformation uh in manufacturing?
31:03
SPEAKER_02: Um you know it depends on the industry.
31:06
SPEAKER_02: Uh and uh so automotive uh been doing this almost uh 20, 35 years ago.
31:13
SPEAKER_02: Um they're highly automated, not just a physical automation, digital automation also.
31:19
SPEAKER_02: Um you know, they were collecting a lot of data and uh there, you know, uh some other quality improvements.
31:26
SPEAKER_02: Um, you know, when you talk about Six Sigma, um, you know, automotive industry truly follows, and uh as uh tier two suppliers, we had to uh learn to when we supply to automotive, we had to learn to do those things.
31:42
SPEAKER_02: And so they've been doing it for a long time.
31:45
SPEAKER_02: Other industries like aerospace, um, you know, it's a high value but not a high volume products they were making.
31:54
SPEAKER_02: Uh so you you make, for example, uh maybe uh 10 uh F-35 bulkheads, these are big uh structural components where engine gets mounted.
32:04
SPEAKER_02: Uh each one was about uh uh I think a half a million dollars, something like that.
32:09
SPEAKER_02: They will make about 10 per month, and that's how many they were assembling.
32:14
SPEAKER_02: And uh so this is you know, and it is a very involved product, it will take uh um a couple of weeks to make one of these things, right?
32:22
SPEAKER_02: Uh so those industries they didn't have a real incentive to really uh you know uh take uh uh digital, go digital all the time.
32:32
SPEAKER_02: And also pharma is another example.
32:36
SPEAKER_02: The profit margins are really high, um, you know, even from 20 to 40, 50, 60 percent profit margins, and a lot of times it's the mindset.
32:45
SPEAKER_02: Um, you know, they don't want to kind of break something that's working, even though it is paper-based.
32:52
SPEAKER_02: Uh, so uh that's the you know, that's one of the things I tell when I do digital workshops.
32:58
SPEAKER_02: Um, you know, it's not the tools, it's not the skill set that you need to do digital, it's the cultural mindset.
33:05
SPEAKER_02: Um, do you have enough lean uh ideas mindset within the plants, uh within your thinking uh to go digital?
33:14
SPEAKER_02: That is very important.
33:15
SPEAKER_02: Um that's one of the biggest barriers I see uh to go to a digital factory mindset.
33:21
SPEAKER_02: And uh in the contrast of uh when you look at the newer companies like Madonna, for example, or BioNTech, and some of the vaccine companies, uh, you know, they didn't have any product uh, so to speak, uh uh until three, four years ago.
33:38
SPEAKER_02: Uh their first product is COVID uh vaccine, and but they were ready for it.
33:43
SPEAKER_02: And and they'll uh look at uh they have some papers that's on uh uh AWS, uh you know, it's readily available.
33:50
SPEAKER_02: They show you uh what they do.
33:53
SPEAKER_02: Uh it's not like a secret or anything like that.
33:56
SPEAKER_02: Um you know end to be end uh they are connected uh enterprise.
34:02
SPEAKER_02: Um you know, a batch release in a pharma industry typically takes anywhere from uh on average two to four weeks.
34:09
SPEAKER_02: Um Madonna can review their batches within a couple of hours.
34:13
SPEAKER_02: Um because it's digitized and uh they're not looking at everything they don't have to their system, so they help them.
34:21
SPEAKER_02: They don't uh they the in each and every stage of their manufacturing, they have checks and balances electronically uh being looked at and they proceed uh uh you know um from step to step without making a lot of mistakes, and uh so that that you know that's a pretty uh typical in a uh automotive.
34:42
SPEAKER_02: Whereas farming is now waking up to that reality and they're starting setting up that so it it depends on the industry.
34:49
SPEAKER_02: Uh and on the other extreme, you look at uh someone like Apple um making uh you know iPhones, FoxCon is highly connected, highly um automated industry.
34:59
SPEAKER_02: And you know they are coming to uh manufacture in India, they're setting up plants in uh right.
35:05
SPEAKER_02: So you you're going to see that happening uh quite a bit.
35:09
SPEAKER_01: Okay.
35:12
SPEAKER_00: So here uh if you observe what Shri talks about, is this importance of not whether manufacturing is done in a country like US or it's moved to some other country, but the importance of the mindset of how you build a completely connected factory.
35:33
SPEAKER_00: So he talks about the criticality of having a mindset for digital, and he really says that I may be a traditional industry like aerospace where it's high value, low-volume business, or it could be a pharma business where traditionally uh you know it takes you know a couple of weeks to really look at batches, while uh you know there are companies, new new age companies which can do it in a couple of hours, right?
36:04
SPEAKER_00: So, therefore, when it comes to digital, it's a cultural mindset of looking at how have you been operating over the last you know many decades or many years, and you are looking at how you should move into a digital first mindset, and there you need to break your traditional thinking, look at interconnected patterns, look at interconnected processes, and that's really how you start building a digital factory, and that is important when you do digital transformation.
36:42
SPEAKER_00: Uh, so that uh gives me a good uh segue to ask you a question on what is a digital factory?
36:49
SPEAKER_02: Oh um, so digital factory, uh, you know, there are uh you know, I mentioned earlier, uh depending on who you ask and which industry you ask, uh the you you're going to hear a lot of different answers and a lot of different uh uh ideas about digital factory.
37:05
SPEAKER_02: So uh you know digital transformation exists in every uh uh sector, every industry, uh, whether it's a retail or manufacturing, um you know, it means uh different things to different industries.
37:20
SPEAKER_02: So in manufacturing, that's my uh you know, I have years of experience, almost 30 years of experience there.
37:26
SPEAKER_02: Um, you know, uh on one extreme uh lights out manufacturing.
37:30
SPEAKER_02: Uh what does that mean?
37:32
SPEAKER_02: Uh it means um physically the plant is completely automated, raw material is loaded on uh one side of the dock, uh comes from our trucks and whatnot, and the finished product comes out on the other end.
37:48
SPEAKER_02: Uh, you don't need even need any lights, meaning you don't have anyone in the plant, um, uh physical human beings there uh doing the manufacturing.
37:57
SPEAKER_02: It's completely automated, robots and material handling equipment uh and equipment running, and uh so there are factories like that.
38:07
SPEAKER_02: Um, the famous example I heard about, I've never seen it, is a Fanuk robot making plant somewhere in Japan.
38:14
SPEAKER_02: One of the sales engineers they always uh invited, hey, if you want to go see, you know, or if you are in Japan, you can go see.
38:21
SPEAKER_02: That's the extreme end of it.
38:22
SPEAKER_02: Um, you know, oftentimes what you see is uh um you know some form of uh physical automation uh and uh you know late uh a lot of digital automation.
38:35
SPEAKER_02: Um you know um you know manufacturing generates ton of information.
38:41
SPEAKER_02: Um you know the data is collected and stored uh locally or sometimes fed into corporate uh servers.
38:49
SPEAKER_02: Um almost 80-90% of the data that it goes unused, uh the data never gets translated into actionable information.
39:01
SPEAKER_00: What kind of data gets uh uh you know stored in corporate servers in manufacturing?
39:07
SPEAKER_02: Um every millisecond uh uh um there is something collected.
39:12
SPEAKER_02: Yeah, if an operator pushes a button, if a temperature changes, if a robot moves, everything is observed and uh collected as data and gets stored.
39:23
SPEAKER_02: And uh so uh uh it could be equipment uh status data, um, you know, how equipment is working, or a product uh uh related data, like uh at what temperature a product was made, or at what speed was it made, things like that.
39:40
SPEAKER_02: So um anything uh you can see, observe, uh it is getting collected and stored.
39:47
SPEAKER_02: And most of the time it gets stored and it's sitting there uh not being used.
39:52
SPEAKER_02: Um often uh when my first my my question is uh how is it collected?
39:57
SPEAKER_02: Uh so you know at a modern digital factory, uh you have sensors of the uh all these uh how it is collected and everything is defined by uh a standard called ISA 95.
40:11
SPEAKER_02: Um it was developed uh way 30, 40 years ago at the Purdue University, used to be called Purdue model, and it describes uh how different systems and different levels of systems uh classified and how they talk to each other, and from all the way from the sharp road, the sensors like a temperature sensor or a uh speed sensor or anything, all the way to the corporate uh systems like the enterprise resource planning, ERP systems, product lifecycle management systems, customer uh CRM uh tools.
40:46
SPEAKER_02: Um the ISA 95 model describes that.
40:49
SPEAKER_02: And um, so at the shop load level, at level zero, level one, you have sensors, you have programmer logic controllers talking to the sensors, um, and the programmer logic uh controllers in turn are talking to SCADA systems or MES tools.
41:08
SPEAKER_02: SCADA is a supervisory control and the data acquisition systems, um, these are all uh more of a software systems.
41:15
SPEAKER_02: Um the lowest level, the data is collected at uh milliseconds and microseconds.
41:22
SPEAKER_02: As you go up, uh the uh data gets consolidated, uh the PLC uh still milliseconds, uh, the MES or a scale systems maybe uh seconds, minutes.
41:33
SPEAKER_02: Uh as you go into ERP, it's more of a days and weeks, and that's how its data gets collected, gets filtered.
41:41
SPEAKER_02: Um, you know, for an ERP system, they don't really need to know how fast a product ran, rather they want to know when the product will be done, uh production will be ready to be shipped out so they can give that information to their customers.
41:57
SPEAKER_02: So that's how the systems work.
41:58
SPEAKER_00: Yeah.
41:59
SPEAKER_01: Okay.
42:02
SPEAKER_00: So if you carefully observe what uh Srida talks about, he says that in most companies and especially in manufacturing, which is the example that he talks about, there's a lot of data that often gets collected, but often not used.
42:18
SPEAKER_00: I think this is something that all of us need to think, saying, Hey, uh, you know, is data available?
42:26
SPEAKER_00: Of course, data is available.
42:28
SPEAKER_00: Is it being used?
42:30
SPEAKER_00: Most often it's not being used.
42:32
SPEAKER_00: That's one part of the problem that I think uh you know you need to think.
42:37
SPEAKER_00: The other important point that uh you know I see here in this uh point that he makes is that if you look at uh you know industrial transformation, you did physical goods, the physical goods were not automated, then you started looking at hey, how do I do a light sword manufacturing process where there is no hands touching right from the time when you know the products, uh the raw materials come from the stockyard to actually becoming a finished good.
43:13
SPEAKER_00: So that's really how physical factories really started changing, and then you started saying, you know, how do you really do a lights out manufacturing process?
43:24
SPEAKER_00: Now that is probably exactly what's happening to knowledge factories where you know you had physical people, you you know, you are doing the programming, you are now doing the programming, you get a lot of business problem, and then you start looking at saying, Hey, how do I really automate the process?
43:43
SPEAKER_00: So, you know, there is a requirement that's coming, there's a solution that needs to be built, there is a software that needs to be done.
43:49
SPEAKER_00: So now you have the AI and the machine learning really looking at saying, Okay, how do I solve this problem?
43:56
SPEAKER_00: So, the fundamentals of how from an industrial Economy to a knowledge economy.
44:02
SPEAKER_00: The change happened over the last 50 years.
44:05
SPEAKER_00: The tenets of how do you build a digital factory for a knowledge industry pretty much can be learned from the manufacturing industry and how the transition from uh industrial economy to a knowledge economy happened and therefore how digital factories were built in the manufacturing sector, and that's really how I think the knowledge factories of today will become digital factories of tomorrow.
44:34
SPEAKER_00: So, therefore, there are these bunch of components uh you know sitting in the shop floor and they are throwing the data from the sensors to the PLCs to the Scala systems, and uh and then they get into some kind of a server and they remain as raw data, and uh that's really how uh you know this whole uh data gets collected.
44:55
SPEAKER_00: So so when you look at a digital factory, uh uh you know, I think uh very interesting uh data point that I read was uh most companies are moving into becoming digital factories, right?
45:10
SPEAKER_00: Because uh uh they like what you talked about, Moderna, right?
45:14
SPEAKER_00: Basically it takes a couple of hours to uh you know produce a vaccine.
45:18
SPEAKER_00: So uh so real digital transformation has to happen uh if manufacturing needs to get uh and uh needs to get digitally connected.
45:28
SPEAKER_00: And uh so therefore uh the my question is in the digital uh you know uh factory, uh how do you define uh if you want to become a digital factory, what are the steps that you need to take to say, okay, uh how do I define maturity of a digital factory?
45:48
SPEAKER_00: And how do I then move from one level to the other?
45:51
SPEAKER_00: And how do you really think through this?
45:53
SPEAKER_00: Because there are components that are, you know, uh uh um if I go to Amazon Cloud, they say I also have a manufacturing uh you know cloud, and then you can get in there kind of a thing.
46:03
SPEAKER_00: So, what are the key components for this digital factory becoming really valuable to an enterprise?
46:11
SPEAKER_02: Yeah, um that that gets into the heart of uh uh digital factory, uh that question right there.
46:17
SPEAKER_02: Um so uh this is where uh the uh when you go into any factory these days, uh they are somewhat connected.
46:26
SPEAKER_02: Uh they're collecting data.
46:28
SPEAKER_02: Um you're hardly going to see uh any uh modern factory.
46:32
SPEAKER_02: Um, you know, the sensors are uh you're not going, you know, they're already there.
46:38
SPEAKER_02: Uh it's not like it they don't have anything.
46:41
SPEAKER_02: Uh there is a some uh resemblance of a digital factory is there.
46:45
SPEAKER_02: And uh so um uh typically uh uh to do a digital factory, uh the process you know I'm familiar with, and most uh people will follow uh is uh there are some tools available, digital tools and industries, um digital uh plan maturity uh metrics, and some tools are available.
47:05
SPEAKER_02: Um they are XOS spreadsheet based, and there's even some software tools that are available, and they giving you a template of questions to ask.
47:13
SPEAKER_02: Um you know, um the uh pharma industry for uh, for example, uses a tool from um uh um you know that's recommended by ISP, uh International Society of Pharma Engineering and a few of these uh uh trade groups, um, you know, they have defined these tools and say this is how a pharma industry should look like.
47:36
SPEAKER_02: And so these tools are available, so you could use them.
47:39
SPEAKER_02: Um but at the end of the day, each tool is uh has to be personalized a little bit uh to the industry, to the company, and uh that's where uh um uh an assessment comes into uh play.
47:50
SPEAKER_02: Uh somewhat you know, uh employing someone like me or someone with my background and experience, uh there are several uh consulting companies, uh they do this kind of work.
48:01
SPEAKER_02: Uh uh you know, McKinsey does it, uh Price Waterhouse does it, uh you know, pretty much every consulting company they have a practice now to come in and help you with an assessment of uh where you are in the maturity index.
48:18
SPEAKER_02: Uh and they will compare you to your peer groups, and uh and you can also even compare to other industries, uh, you know, from automotive, you can compare to pharma and things like that, vice versa, and you can benchmark yourself.
48:30
SPEAKER_02: And so you can do that, and that takes uh three to six months of assessment at the beginning.
48:35
SPEAKER_02: Um, that kind of helps you to level set where you are in your digital maturity.
48:40
SPEAKER_02: Um, so there is a scale one to five.
48:43
SPEAKER_02: Um, so you can put yourself in, you know.
48:46
SPEAKER_02: Um, you know, typically I tell for most companies you're either around uh two.
48:51
SPEAKER_02: Um that's my experience when you go through this assessment.
48:55
SPEAKER_02: But that uh uh index or that score comes uh based on these tools that you can use to do assessment.
49:01
SPEAKER_02: So that's the first step, that's very important.
49:04
SPEAKER_02: And then based on this assessment, you do want to have hot-to-heart uh discussion with your leadership team and say, look, where do you want to go?
49:13
SPEAKER_02: Uh what are you trying to do?
49:15
SPEAKER_02: What is the business case?
49:16
SPEAKER_02: What do you want to do digital?
49:18
SPEAKER_02: Um, you know, um, is it uh because you want to be number one in your uh industry, or um are you losing money, you know, or you're uh you know, that's uh the real uh you know, you got to spend some time uh coming up with a vision uh for the company, what the digital vision is going to look like.
49:38
SPEAKER_02: And someone may come in and say, Well, I want lights out uh manufacturing for every plant, and but that's not realistic, uh realistic.
49:45
SPEAKER_02: And uh you know, you got to have a pragmatic view of, okay, yeah, that lights out uh vision is uh what we want, and that may be five years, ten years from now, that's what we were going to be.
49:58
SPEAKER_02: Uh so you know, some sort of a vision like that, you can define it.
50:02
SPEAKER_02: And then what can I do in the next uh in the short term, next two years, three years, five years, what are the things I need to do?
50:10
SPEAKER_02: Um that's a roadmap you want to build next.
50:14
SPEAKER_02: Uh in a lot of companies, uh they go company wide uh site, or they're going to do some level one type work uh on that roadmap.
50:26
SPEAKER_02: Some companies uh they go uh take a pilot plan, uh they pilot uh uh uh you know lighthouse projects, uh, and they take one plant uh that maybe you're close to uh um where they are in their digital maturity, and they say we are going to do these four or five different things.
50:44
SPEAKER_02: Uh you know, we're going to set up uh some we are going to collect some data.
50:48
SPEAKER_02: Uh and you know, or maybe we are already collecting data, we're going to build some dashboards that converts the data, raw data into actionable information that the sites can use.
50:59
SPEAKER_02: Uh so those are some of the projects you come up with.
51:02
SPEAKER_02: And uh typically companies do uh proof of concept pilot uh at one site and then you know make sure everything works the way you like it, and then maybe you try to uh lean it out, use some agile uh methodologies, uh, and build some playbooks, and then you can give it to every site and then tell them to go implement it.
51:24
SPEAKER_02: So there's a lot of different ways to build that roadmap.
51:27
SPEAKER_02: So that's a typical uh process.
51:29
SPEAKER_02: So when you're doing that, we also have to have good assessment and also good report back to your leadership.
51:36
SPEAKER_02: And the key in this whole process is the buy-in from the highest level of the company, uh, CEO on down.
51:46
SPEAKER_00: So here Srida talks about mixing pragmatism with innovative thinking and balancing it with business realities.
52:00
SPEAKER_00: That's really how I think most often innovative ideas such as building a digital factory, getting into transformative work like digital takes shape.
52:14
SPEAKER_00: What I believe he's saying, and is very important, is there are tools and tools and tools, but there is a need to customize it to your environment, your business challenges, and your objective of where do you want to be.
52:32
SPEAKER_00: A tool cannot take you to where you know uh you want to go.
52:39
SPEAKER_00: It's important to know where you want to go, and a tool is a catalyst.
52:45
SPEAKER_00: So when you're looking at something like a digital factory, the important point that he makes here is the fact that your vision, your leadership, buying into the philosophy of lights of manufacturing, uh looking at profitability.
53:05
SPEAKER_00: So the business issue comes to the fore.
53:08
SPEAKER_00: So if you're an engineer having access to the best of the technologies, the fact that you need to put it back into how a business looks at the problem and therefore connect it back to any of the technologies that you have.
53:26
SPEAKER_00: And therefore, which of these tech platforms, which of these tech solutions in this case it could it could be a scatter system, it could be something that is a PLC that's connected to machines, there's a lot of desperate data that's available.
53:41
SPEAKER_00: What's important is to have a clarity on where do you want to go and then apply a digital transformation process and a roadmap, and that I think will make it extremely successful.
53:58
SPEAKER_00: So uh so when you say uh you know there are tools, what I'm uh what I'm uh you know learning from this conversation is uh you know there are tools, but you have to get your strategy right, right?
54:10
SPEAKER_00: So uh I think that's really a very important point that you're talking about.
54:14
SPEAKER_00: So can you give me an example of uh you know uh how do you uh you know get a strategy that was right and therefore it became a digital factory, and what therefore, what kind of outcomes and returns have some companies seen?
54:29
SPEAKER_00: And therefore, can you give me a real life example of how to define that?
54:33
SPEAKER_00: Because when I look at a manufacturing plant, at least what I have seen is that oh, you know what, uh you know, my productivity should increase, okay, my efficiency should get better, my uh you know asset uh asset lifecycle uh you know should be better, okay.
54:49
SPEAKER_00: Uh I've heard these kinds of things, okay.
54:51
SPEAKER_00: But when I'm actually looking at a digital factory, are there some larger business goals which are very different from the traditional uh you know analog factory to a digital factory?
55:02
SPEAKER_00: How do you uh assess what kind of business goals can I set for myself?
55:07
SPEAKER_00: Well, I I wouldn't say they are too different.
55:10
SPEAKER_02: Uh um, you know, at the end of the day, uh any business or their end goal is to make money, right?
55:16
SPEAKER_02: Um uh you know, you know, um I you see a scene, uh slogans, uh you know, we want to save people's lives, uh, we want to do good, uh, do no evil, uh, you know, all those things are slogans at the end of the day.
55:32
SPEAKER_02: But uh, you know, bottom line is you are there to make um uh you know uh return on investment for your investors.
55:39
SPEAKER_02: Um you know, um so how you go about doing is what uh differentiates the companies.
55:46
SPEAKER_02: Um, you know, digital factories, um, you know, there's a hard way to do and there's an easy way to do.
55:52
SPEAKER_02: And analog factories, they do it in the hard way.
55:55
SPEAKER_02: Um, you know, um you can inspect every bolt and nut that you manufacture.
56:01
SPEAKER_02: Uh and even in China, uh with a low uh uh labor cost, it it's becoming prohibitively costly to do that kind of a work.
56:11
SPEAKER_02: Uh and that's where uh the uh six sigma, the million uh pots per uh uh you know you know PPD uh reducing a defect comes into play.
56:23
SPEAKER_02: Uh they want to make sure you produce it without inspection.
56:26
SPEAKER_02: You can either inspect quality or you can produce quality.
56:30
SPEAKER_02: And so that's a mindset you need, you know.
56:32
SPEAKER_02: This is goes back to the lean uh mindset.
56:35
SPEAKER_02: Uh that's where it comes from, all these things come from.
56:37
SPEAKER_02: So to me, that's uh uh thinking in a digital factory.
56:41
SPEAKER_02: Um uh Madonna, for example, decided uh they're going to be completely cloud-first uh manufacturing, and uh they hardly have any servers or anything in their factory.
56:52
SPEAKER_02: So that's uh you know, they can do that because it's uh all greenfield.
56:58
SPEAKER_02: Uh you know, they were building a factory, so that's how they built the factory.
57:03
SPEAKER_02: So how do I how do I become a cloud-first factory?
57:07
SPEAKER_02: Uh on a greenfield site, it's much easier because you're starting from a you know empty canvas white uh uh sheet, so you can design it the way you want to do.
57:17
SPEAKER_02: Uh and uh, you know, you connect everything, you make sure you have good uh internet connection and everything, your network is up to speed.
57:24
SPEAKER_02: So uh so you design everything uh to that effect.
57:28
SPEAKER_02: Uh you know, I'm running everything in AWS, that's what Madonna does.
57:32
SPEAKER_02: But 90% of the companies, uh, they're not greenfield plants, they're uh what we call the brownfield sites.
57:38
SPEAKER_02: They have existing assets, they're high-performing assets right now.
57:42
SPEAKER_02: You're not going to abandon it.
57:44
SPEAKER_02: There's no business case to rip and replace.
57:46
SPEAKER_02: Um you still have to bring them uh to uh the uh digital factory uh side of it, and that's the biggest challenge, and that's why some of the uh uh you know um the the some of the challenges really is to uh go about and say, okay, I'm going to be a digital factory.
58:05
SPEAKER_02: So how do you go about doing it?
58:06
SPEAKER_02: So that that's where the in the roadmap I was talking about comes into play.
58:10
SPEAKER_02: Um if it is a greenfield, yeah, you start from scratch, that's the easy uh easiest way to do it.
58:16
SPEAKER_02: Brownfield, um, you obviously want to do it gradually.
58:20
SPEAKER_02: And so uh each site may be you know in the same company, maybe different.
58:24
SPEAKER_02: Um they may be assembling cars, one site may be 40-year-old site, another site may be 15-year-old site, so they may be in a different uh maturity level.
58:35
SPEAKER_02: Uh so that's why you you do have to individualize and you have to adjust your strategy.
58:40
SPEAKER_02: Uh that's where the strategy becomes a more of a uh when I say strategy, you think it will be set in stone.
58:47
SPEAKER_02: It never is, it is a dynamic strategy, and it is going to change uh uh side to side.
58:54
SPEAKER_02: And so you definitely need a corporate strategy to say this is how I'm going to implement, this is how I'm going to pay for it.
59:02
SPEAKER_02: And uh, you know, at the corporate level, you may say, I do see a value uh doing everything on this side because that's already uh um performing at 90% level, and I'm trying to get the remaining 10%, and that probably is the hardest because you have uh you know got all the low-hanging fruits with the 90% performance, uh, and then the remaining 10% performance is the hardest one to get, so you may want more data, and and that site may provide you more data because it's already high-performing sites.
59:35
SPEAKER_02: So that's one way of approaching it.
59:37
SPEAKER_02: The other way of approaching it is that the low-performing site may be may have a lot of low-hanging fruits that may be easy to solve, also.
59:46
SPEAKER_02: So that's where the strategy comes in, and you have to make up your mind based on case by case and say, this is how what my approach is going to be.
59:54
SPEAKER_02: Uh, you know, if I did a million dollars on a low performing site, I may bring up the performance 20-30%.
1:00:02
SPEAKER_02: Or I may have to spend$10 million to improve the performance of a site that's already performing 90% level, uh, and I may improve only one or two percent.
1:00:13
SPEAKER_02: So that's where uh you know use the data to help uh form your strategy.
1:00:18
SPEAKER_01: Okay.
1:00:21
SPEAKER_00: You'll observe uh in this conversation, Sridhar spent the last couple of minutes talking about strategy.
1:00:30
SPEAKER_00: So here is an engineer trained in math science talking business strategy.
1:00:38
SPEAKER_00: So as an engineer, as you move up an organization, the value you provide to an organization is understanding the business strategy, the business realities, and ensuring that the technology and the platforms and your engineering skills come to the fore.
1:00:59
SPEAKER_00: This balance is very very important.
1:01:02
SPEAKER_00: Life is not just going to be about just solving some engineering problems.
1:01:08
SPEAKER_00: As you move up, your ability to look at the big picture, look at what the industry needs, so that becomes more and more important.
1:01:18
SPEAKER_00: Therefore, what is in what is critical and therefore what is important for you is to look at saying, okay, what's the business problem I'm trying to solve?
1:01:30
SPEAKER_00: How many different kinds of business problems are there?
1:01:33
SPEAKER_00: Which of my engineering skills and engineering problem solving skills can I use to solve this business problem?
1:01:42
SPEAKER_00: What are the high maturity problems that require top engineering skills?
1:01:47
SPEAKER_00: What are the low maturity problems that require low-level engineering skills?
1:01:52
SPEAKER_00: But what's not going to go away is your ability to mix your business strategy, understanding of the business problem with your engineering skills.
1:02:02
SPEAKER_00: That is going to remain forever.
1:02:07
SPEAKER_00: So uh if I were to move from say a low-performing site to a high performing site, uh, let's take the example of pharma or automotive or whatever.
1:02:17
SPEAKER_00: You know, we can take an industry.
1:02:19
SPEAKER_00: Uh uh, normally, uh, you know what KPIs really drive this change?
1:02:26
SPEAKER_00: Because uh, you know, I would I want the audience to get a sense of how do I get that change?
1:02:32
SPEAKER_00: Because normally if I'm in an automotive factory, I'll say how many cars, how many more cars would I produce?
1:02:38
SPEAKER_00: Is that what is important?
1:02:40
SPEAKER_00: Or how many uh you know different models of cars should I be producing?
1:02:45
SPEAKER_00: Then the customization and personalization becomes very, very uh different.
1:02:50
SPEAKER_00: Okay, or if I'm in a pharma factory, I'll say, you know, I can produce uh you know 20 lines of different pharma products in uh per hour.
1:03:01
SPEAKER_00: Okay.
1:03:01
SPEAKER_00: So therefore, uh I just want them to get a sense of how they define the strategy and therefore the importance of the digital factory in scaling it, right?
1:03:10
SPEAKER_00: That's very important.
1:03:11
SPEAKER_02: Yeah.
1:03:12
SPEAKER_02: So the business case, uh coming back to the business case, right?
1:03:15
SPEAKER_02: And uh the business case is going to be depending on what you're trying to improve.
1:03:20
SPEAKER_02: Uh so even within the same company, one factory versus the next factory, the business case may be different.
1:03:26
SPEAKER_02: Uh, and uh uh so all those things you listed, reducing the cost, improving the operations efficiencies, improving the uptime of the equipment, uh product mix, uh improving the quality, doing it right the first time, um, improving safety, another one of them.
1:03:46
SPEAKER_02: And off late, uh, we also started adding zero uh uh um net zero and uh sustainability, um, you know, that's also getting added to those are the some of the key uh uh performance targets that the companies are using it.
1:04:01
SPEAKER_02: And you know, when you're doing an assessment with uh some of these digital tools, those are also part of the questions you will start asking to side by site uh to say, okay, for this site it may be efficiency.
1:04:14
SPEAKER_02: So during my work at Alcoa, when we used to go to different sites, each one had a different issue.
1:04:20
SPEAKER_02: One side had a quality issue, another side had a throughput issue, and so each one has to do things differently and had to focus on different KPIs, and so that's becomes a part of the site level assessments.
1:04:35
SPEAKER_02: So you know we talked about vision, uh corporate strategy, and then uh we build a corporate uh roadmap.
1:04:42
SPEAKER_02: Now each site had to build their site level roadmap, and uh, while you're defining it, you also have to define what is important to the site, and uh so uh in pharma, one site may be producing bulk materials, another site may be taking that bulk material, filling them into small wires.
1:05:01
SPEAKER_02: So two different processes, even though it's the same company, they may have two different needs and requirements.
1:05:08
SPEAKER_02: So that's where site level assessments come into play.
1:05:11
SPEAKER_02: And the KPIs at the end, the high level, you still are going to look at the reducing cost, quality, uh, improving quality, improving efficiencies, uh, throughput, availability, uh, so each one will drive different behaviors within the site.
1:05:30
SPEAKER_00: If you look at uh KPIs when you define them, it's directly related to what the business needs are and what the business challenges are.
1:05:41
SPEAKER_00: So if you are somebody in technology or in engineering, the fact that when you design KPIs, the need to understand the business problem, the business challenge, and therefore how do you define the KPI becomes a very important skill that you need to build over time.
1:05:58
SPEAKER_00: The KPI stack.
1:06:00
SPEAKER_00: He talks about go back and listen, and you will realize that most of them first are business issues.
1:06:05
SPEAKER_00: The business issues then get converted into data, and the data then gets massaged and analyzed to deliver that KPI.
1:06:15
SPEAKER_00: And he also talks about the importance of linking a corporate strategy with a vision at the higher end and to a site level vision, which is to say, you know what?
1:06:28
SPEAKER_00: Hey, I want to really improve my throughput, I want to improve my efficiency, I want to really build uh you know one product at a time, and that I need to do at scale.
1:06:40
SPEAKER_00: Now, each site, depending on the investments that have been made over the past many years, because it talks only about few green fields getting set up, la lot of them being brown fields.
1:06:52
SPEAKER_00: So in many projects, you enter when they are brown fields, and when it's a brown field, and when you want to innovate, you incrementally chip your way through, and that's really how you'll be able to show a difference and value to the enterprise.
1:07:08
SPEAKER_00: See, this is an interesting problem that uh you know that hit my mind.
1:07:12
SPEAKER_00: I have uh, you know, in an automotive industry or in a pharma industry, uh, if I have say 10 manufacturing plants, or if take alcova, you know, I may actually be making uh you know products in 10 different plants.
1:07:25
SPEAKER_00: Uh 10 different plants having 10 different problems.
1:07:29
SPEAKER_00: Okay, but when I am a consumer or when I'm a customer, I may be a B2B customer, I don't care from which plant it comes from, right?
1:07:38
SPEAKER_00: So uh if you solve a problem of quality in one uh you know factory and you are talking about uh demand for uh you know uh demand or customization, which is really what the market need is.
1:07:53
SPEAKER_00: Now, how do I really balance all this?
1:07:55
SPEAKER_00: Because at the end of the day, I have to measure all this against uh you know uh some uh you know metric which says is uh you know uh is Toyota a reliable car manufacturer?
1:08:09
SPEAKER_00: Because I mean, I have hundreds of factories, hundreds of different problems.
1:08:12
SPEAKER_00: So how do I then assess and actually say that digital factory has added value to the you know the customer uh you know integrity or trust score or whatever that I want to call it?
1:08:24
SPEAKER_02: Yeah, uh you know the same company in Toyota's example is uh perfect one because uh um you know Toyota uh has thousands of thousands of vendors supplying products to them, uh just in time type uh production environment.
1:08:40
SPEAKER_02: And that's what the Toyota uh uh manufacturing system, uh the production system uh TPM came about, product manufacturing system I say uh came about, right?
1:08:52
SPEAKER_02: Um so you you know one of the things uh some companies the way they do it is each plant and each site is being operated as its own profit center.
1:09:02
SPEAKER_02: Um a lot of times one uh factory is supplying to the next factory within the same company.
1:09:08
SPEAKER_02: Uh so if my raw material is coming coming from plant A, um I should, you know, plant B, I'm plant B.
1:09:17
SPEAKER_02: I need to make sure, and I had to treat plant A, even though it's the same company, same family, I need to treat them as my supplier.
1:09:26
SPEAKER_02: And I had to be expect from my supplier the same uh thing I would uh expect quality and efficiencies and productivity that I would expect from an external supplier.
1:09:38
SPEAKER_02: So uh one way to handle it is you treat each uh uh plant, each uh line or a sub-profit center, uh, and um you know that's one way of addressing uh that question.
1:09:51
SPEAKER_02: Um, you know, another way is at the end of the day, the corporation is responsible for the product, uh, the product quality overall.
1:09:59
SPEAKER_02: Uh you you are exactly right.
1:10:01
SPEAKER_02: Uh when I'm going to go buy a Toyota car, uh I don't care uh who made the bolt and not.
1:10:08
SPEAKER_02: Um I'm you know, it's Toyota's name on the plate.
1:10:11
SPEAKER_02: And so it is uh they are responsible.
1:10:14
SPEAKER_02: So um, you know, um you know if I had to take a guess, uh Toyota ensures every part of the manufacturer uh um meets that requirement, uh the quality Six Sigma requirement they may have, and that's how they are going to uh ensure uh and uh you know they don't you know when a product is shipped to Toyota's plant, they're not inspecting the plant at their receiving end.
1:10:37
SPEAKER_02: Um the expectation is it has already come in the quality, uh, and if it has to be inspected, it is done at the supplier's end, right?
1:10:45
SPEAKER_02: Um that's one way of ensuring um you know my product uh in every stage it is um it is uh in quality.
1:10:53
SPEAKER_02: Another way is with the modern uh digital factory, um every step of the way, you know, that's one of the expectations when you're setting up a system to ensure it is meeting the spec.
1:11:06
SPEAKER_02: So, for example, if a product has to be within a certain tolerance, let's say temperature, um, I'm going to use uh Fahrenheit here, so that's what I'm these days I'm familiar with.
1:11:18
SPEAKER_02: Um say if if a product is uh uh has to be made at a 80 degree plus or minus 10 percent, and doing your manufacturing process, that's what the one of the expectations was in the past.
1:11:33
SPEAKER_02: Uh you know, if a specification says make sure the product is at room temperature, to me, that's not a good, very good specification.
1:11:42
SPEAKER_02: Room temperature in the US may be different in winter versus summer.
1:11:48
SPEAKER_02: So if an engineer, process engineer, or a quiet you know, uh product designer says, make sure uh the cake is baked at room temperature, that's not a very good specification.
1:12:00
SPEAKER_02: I want a very precise specification.
1:12:04
SPEAKER_02: And so this is where digital factory, when you say I'm going to do set up a digital factory, it's not you know installing a bunch of uh devices, connecting controllers and systems.
1:12:18
SPEAKER_02: It's everything that I do from product design all the way to inspection, making an inspection.
1:12:27
SPEAKER_02: Do I have uh design factory mindset apply principles applied to that process?
1:12:34
SPEAKER_02: So it's not about uh operation doing something, engineers doing in the shop floor something that's going to make you a digital factory.
1:12:44
SPEAKER_02: It is the entire uh supply chain doing these things.
1:12:47
SPEAKER_02: And so, in the case of a baking a cake, I precisely want 80 degrees Fahrenheit plus or minus 10% over a certain time.
1:12:59
SPEAKER_02: It has to be baked.
1:13:00
SPEAKER_02: That is the specification I'm expecting.
1:13:03
SPEAKER_02: That I can turn that into a recipe that I can give it to a PLC programmologic controller, it can use the thermocouple RTD to monitor it and report back to saying, Yes, I maintained that temperature within that tolerance for the two hours you asked me to make, uh bake the cake, don't have to inspect it since I met those requirements.
1:13:28
SPEAKER_02: That cake should come in at that quality that you want and it won't be burnt.
1:13:35
SPEAKER_00: So when you look at what uh he talks about in terms of specs, a digital factory or any automation process, the importance of being precise is something that that's extremely critical to being a successful digital factory.
1:13:57
SPEAKER_00: In the past, uh, if you didn't automate and if you didn't have the data, you would probably give a certain large policy decision saying that hey, this is how I want.
1:14:10
SPEAKER_00: But today, any process that you look at, whether it you're building uh software or whether you're building a product, the importance of devices being tagged on from the time you start designing a product to the time you deliver the product until the time you service a product, the fact that you can use multiple devices to collect this data to a specification that you actually have on blueprint and then start measuring it, and then finally you are able to connect all of them up, and therefore, the fact that interconnected processes and looking at data from different processes feeding into the next is very very important, and therefore, siloed thinking is something that's not going to be uh important need that uh that businesses have.
1:15:07
SPEAKER_00: Siloed thinking is something that will not succeed.
1:15:12
SPEAKER_00: Siloed thinking will create you know islands of excellence, but what digital factories do is to build a chain of excellence across processes, and that is something that's going to be adopted by even the knowledge industry.
1:15:29
SPEAKER_00: So so is that where probably the concept of a digit digital twin comes in?
1:15:35
SPEAKER_00: Because uh, you know, uh I am actually doing it, you know, I I have to do it on uh you know on a system, uh, see how the product comes out so that finally when I put it out in the factory where I'm send uh selling millions of products, uh you know, the investment is far more larger, and therefore is so is is that what is called as a digital twin?
1:15:57
SPEAKER_00: That's that's a great uh segue.
1:15:59
SPEAKER_02: Uh yeah, uh yeah.
1:16:01
SPEAKER_02: A lot of ways it is.
1:16:02
SPEAKER_02: Uh uh you know, digital twin is used in a lot of different ways.
1:16:06
SPEAKER_02: One of the ways is to uh um you know, when you're designing a product, uh you want to know how that's going to uh be produced.
1:16:15
SPEAKER_02: Uh and one of the ways to do it is uh uh model your process or your equipment using a digital twin model and run simulations and adjust your temperatures.
1:16:26
SPEAKER_02: Now you know um this you can do it safely and without blowing up anything and cost effectively because you're not wasting a product, uh right?
1:16:36
SPEAKER_02: You know, the going back to my steel uh industry uh experience, uh the most uh cost incurred in steel making is the conversion cost, um, the energy you put in to make the steel.
1:16:47
SPEAKER_02: And so you you're not uh you can't do experimentation, um, you know, but with a digital twin, you can do those kind of uh uh trailer and error type uh experimentation.
1:16:58
SPEAKER_02: Um and and and with the digital twin also, um you know, um with the machine learning and artificial intelligence, um, it takes you to the next level.
1:17:08
SPEAKER_02: Um, for example, uh uh vaccine manufacturing uh or uh you know tablet uh you know uh medication and they're uh trying to create.
1:17:20
SPEAKER_02: Um, you know, either you mix up a bunch of chemicals and uh you know try to see trial and error, you can do that.
1:17:27
SPEAKER_02: Uh that's how they used to do.
1:17:29
SPEAKER_02: Um you know, vaccine uh manufacturing took anywhere from 12 to 17 years because they were literally performing those tests physically.
1:17:40
SPEAKER_02: They're using digital twins and uh a lot of automated machine learning and artificial intelligence to build these uh models to simulate those things really fast and get to 18, 90 percent of the way, and then tweak the remaining 10, 30 percent in the actual plant.
1:18:00
SPEAKER_00: So, how does that work, Sri?
1:18:02
SPEAKER_00: Uh so uh I don't have a physical plant, or uh, you know, I have a composition for a tablet.
1:18:08
SPEAKER_00: Um, where do I deploy it?
1:18:10
SPEAKER_00: Because normally they do uh it tests on uh you know, uh they use on uh you know what they call as uh target patients or whatever, and then that's really then they see the uh you know impact that's the way they uh build over 17, 18 years.
1:18:25
SPEAKER_00: Now in a digital twin, uh you know, how do I assess how it works?
1:18:30
SPEAKER_00: So uh is that something which is completely uh uh machine learning and uh or uh you know if it is a product like what you're talking about, uh 80 degrees or whatever, how uh uh you know it's a completely simulated environment, is it?
1:18:45
SPEAKER_02: Yeah, I I mean a phase one, two, uh, three trial for a drug product, you still have to go through with uh uh human-based trials before uh FTA or any regulatory authority is going to approve your product.
1:18:58
SPEAKER_02: You still have to go through that.
1:19:00
SPEAKER_02: But to get to the point, uh they typically did 10, 12 years of uh uh you know creating these concoctions, uh different uh uh products uh by physically doing it.
1:19:14
SPEAKER_02: Uh so originally uh they automated it using machines, and then they moved to the next step and say, look, uh the chemicals are going to react this way, that way.
1:19:23
SPEAKER_02: Um you know, this is a subject I'm not really an expert on uh when it comes to uh drug products, uh, how they do this.
1:19:31
SPEAKER_02: Um I can go back and uh give you a simulation of uh uh manufacturing uh in the aerospace part, please, please, which I'm really very familiar with.
1:19:40
SPEAKER_02: So um so you know, when you when you're building an uh say F-35 fighter uh plane, um one of the things, you know, but maybe we shouldn't talk about F-35.
1:19:52
SPEAKER_02: There may be some um, you know, uh that may be classified and stuff like that, which I don't know anything about.
1:19:58
SPEAKER_02: So um I've been out of the industry almost five years.
1:20:02
SPEAKER_02: But um, let's say if I'm building a product for a new uh Boeing or an Airbus uh uh plane, um it's all about light weight.
1:20:11
SPEAKER_02: Uh so steel is heavy, uh, you can't really put steel in airplanes, so they started using aluminum.
1:20:19
SPEAKER_02: Aluminum is uh lightweight, but it is not as strong as uh steel.
1:20:24
SPEAKER_02: So they started mixing alloys.
1:20:26
SPEAKER_02: And uh so uh to produce a new product uh used to be you you mix up literally physically, you mix up a bunch of different alloys and uh to figure out uh how uh strong it is, uh, come up with the different strengths uh and how it is going to perform in different conditions, temperatures, is it going to break if I'm launching this, putting it in a rocket, how is it going to perform in uh in uh outer uh in space?
1:20:55
SPEAKER_02: Uh those are the things that they literally they were used to test.
1:20:58
SPEAKER_02: So it would take anywhere from five to ten years to do these kinds of testing.
1:21:03
SPEAKER_02: Uh so now what they do is uh instead of physically testing these things, um, they have uh evidence of uh some of these uh products, how they behave, uh characteristics of different alloys and stuff.
1:21:17
SPEAKER_02: So they have those uh things, uh data available.
1:21:20
SPEAKER_02: So they use those data, uh they do finite elemental analysis and a bunch of other uh computational tools, they build those into uh a digital twin model and they simulate how a product will perform.
1:21:34
SPEAKER_02: And uh and so uh what it allows them to do is they can eliminate some of the other combinations effectively so they get you around 50-60% of the weight.
1:21:48
SPEAKER_02: Uh so that reduces the time uh frame much much less.
1:21:53
SPEAKER_02: Uh and then based on those simulations, they then they go back and they do uh create those combinations physically, test it, and further refine their models.
1:22:05
SPEAKER_02: So the constant model refining goes on.
1:22:09
SPEAKER_02: Uh so it never is straight, you know, 80% of the way I'm going to simulate myself.
1:22:14
SPEAKER_02: Um, you know, it's a step process.
1:22:17
SPEAKER_02: Um you you simulate, eliminate some of the possibilities, and then make a prototype.
1:22:23
SPEAKER_02: And so you're reducing in the meantime, you're reducing that cycle time that needs to make that part.
1:22:31
SPEAKER_00: So thinking back, um is there some learning to take from the digital twins that the manufacturing industry uses for the software and IT industry?
1:22:44
SPEAKER_00: Most often uh what we really see is uh you know when you deploy uh tech or a software, the business benefits are what they call as you know uh the return on investment or uh you know the uh TCO as they call it and therefore the ROI that happens out of uh IT products uh is pretty much uh I believe in a manufacturing era where you know you build products, you deploy them, uh, you know, do you often measure efficiency?
1:23:19
SPEAKER_00: Do you measure productivity?
1:23:21
SPEAKER_00: Uh do you really look at uh you know when a release happens, uh does that release really help the customer?
1:23:29
SPEAKER_00: I think there's a lot of learning that the manufacturing industry provides because they've built physical products, they've got data, they then build the digital twins and then simulate them in a structured environment and therefore in a restricted environment, and therefore they're able to see some results and then they're able to build a product.
1:23:50
SPEAKER_00: So similarly, the IT industry is, I believe, the software industry is in a manufacturing era where you know there are lots of uh you know hypothesis and biases and solutions that you know come in a software engineer's mind, but when it gets deployed, is it easy to use?
1:24:10
SPEAKER_00: Does it save time?
1:24:12
SPEAKER_00: Does it give me better productivity?
1:24:14
SPEAKER_00: Are we able to collaborate better?
1:24:16
SPEAKER_00: These are themes that are on the basis on which products are built, but do we collect data and therefore can we then start to fine-tune it?
1:24:27
SPEAKER_00: I think there's a lot of learning that can be picked up from the manufacturing industry and from digital factories because today's knowledge industry will become digital factories of tomorrow, and therefore there's a lot of learning to pick up and apply them here.
1:24:42
SPEAKER_00: You probably were responsible or one of the team members uh to set up the three billion dollar uh you know Sanofi uh you know vaccine greenfield site, probably the largest.
1:24:56
SPEAKER_00: Uh you are amongst the uh guys there.
1:25:00
SPEAKER_00: So, how was it to set up as large as uh greenfield site?
1:25:04
SPEAKER_00: It must have been completely automated.
1:25:06
SPEAKER_00: What was the kind of scale that they were operating?
1:25:09
SPEAKER_02: Well, so the the Sanofi site is actually a 120-year-old site, so it was not a greenfield site, uh, that particular site.
1:25:16
SPEAKER_02: Uh so uh uh it's a$3 billion biggest vaccine site.
1:25:20
SPEAKER_02: I think it is$3 billion.
1:25:22
SPEAKER_02: Um, I may have to go check on it.
1:25:24
SPEAKER_02: Uh I I put it on my resume, so it got to be.
1:25:27
SPEAKER_02: So uh but anyway, uh so I you know this site is uh a close to a thousand-acre site, and uh and it's a huge uh vaccine manufacturing site over there, one of their biggest vaccine manufacturing sites.
1:25:43
SPEAKER_02: Um within the site, they were building a$500 million uh uh new biologics building uh to uh increase their capacity, and it was one of those uh US government-sponsored projects uh to uh build a new capacity in the US.
1:26:00
SPEAKER_02: Um, you know, typically um I was not in uh involved in the beginning of that site, so that's kind of a genesis when it comes to the pharma.
1:26:10
SPEAKER_02: Whereas I can talk about the 130 million dollar uh site um uh steel plant site, but I was involved from day one.
1:26:19
SPEAKER_02: Uh so I yeah, I worked uh in an existing plant almost 10 years, uh, the Brownfield site in an old steel plant.
1:26:28
SPEAKER_02: So I had a pretty good idea of what I wanted to do on a greenfield site.
1:26:34
SPEAKER_02: And uh so uh one of the things uh in any project, uh when you started doing a project, um, is what product you are going to manufacture.
1:26:44
SPEAKER_02: Uh um, you know, uh that's the basis for everything you do right after that.
1:26:50
SPEAKER_02: And uh so uh this particular instance, uh, you know, steel making is uh there's two kinds of steel making.
1:26:57
SPEAKER_02: You have carbon steel making and stainless steel uh steel making, they're both completely different products.
1:27:03
SPEAKER_02: Uh uh melt shop that makes a carbon steel making doesn't make stainless steel making.
1:27:08
SPEAKER_02: Uh, you cannot make those.
1:27:10
SPEAKER_02: But this company decided to do both on the same plant.
1:27:13
SPEAKER_02: So that became uh uh the basis for how we do uh uh so there is a mechanical design aspect of it.
1:27:21
SPEAKER_02: Um so someone else was doing it, uh, the different equipment they had to buy, different uh furnaces they had to buy, different power requirements uh they had to have for each one.
1:27:31
SPEAKER_02: So they were doing it.
1:27:33
SPEAKER_02: Whereas my uh job was uh more related to uh you know raw material moving through the plant, how we uh receive it, how do we do the conversion, what kind of uh uh automation we need, what kind of a data we need to collect.
1:27:51
SPEAKER_02: So we're uh doing a data mapping of the entire process.
1:27:55
SPEAKER_02: So at the beginning, you you do that process and then you work with the different.
1:28:00
SPEAKER_02: Suppliers, you know, steelmaking is uh almost a 200, 300 year old industry.
1:28:06
SPEAKER_02: Um, they're making steel forever.
1:28:09
SPEAKER_02: And so, you know, you also look at the industry, what other companies are doing, and then you come come up with a concept design.
1:28:17
SPEAKER_02: Uh, and and then uh you go about and say these are the different uh uh you know, I'm going to melt it, I'm going to refine it, uh, I'm going to uh take the different impurities out, and then I'm going to cast it.
1:28:31
SPEAKER_02: Uh and so those are the three or four different areas.
1:28:34
SPEAKER_02: And within each area, we started looking at uh what we want in terms of equipment.
1:28:40
SPEAKER_02: So overall, I also had a philosophy of what kind of a control system I needed.
1:28:46
SPEAKER_02: So I had some corporate standards written uh for each area.
1:28:50
SPEAKER_02: Uh saying I want this particular uh uh brand of PLC, this particular brand of uh human machine interface.
1:28:59
SPEAKER_02: I want this particular uh SCADA system.
1:29:02
SPEAKER_02: And so those are some of my corporate standards.
1:29:05
SPEAKER_02: So that's becomes a basis for any vendor to bid on the package to provide a proposal for you.
1:29:12
SPEAKER_02: So you know, a plant like this, it takes almost uh two to three years to design, develop, uh you know, get bid packages, and then uh use those bid packages to uh further or refine your processes.
1:29:30
SPEAKER_02: In the meantime, you're working your uh with uh your IT groups, your quality groups to say, hey, I need this kind of infrastructure.
1:29:38
SPEAKER_02: IT, can you provide me this kind of uh network, uh wireless network, whatever that is, uh servers and everything?
1:29:45
SPEAKER_02: Quality, what kind of a data that we need to collect?
1:29:48
SPEAKER_02: And how are we going to use this data?
1:29:52
SPEAKER_02: Work with your metallurgist, what kind of a chemistry is we are going to make?
1:29:56
SPEAKER_02: How can you predict when uh steel will be done?
1:30:00
SPEAKER_02: Can we build some models for it?
1:30:02
SPEAKER_02: So all those things become part of it.
1:30:04
SPEAKER_02: So it is a complex process, and obviously, I'm not the only one in this team, and you you surround yourself with a lot of smart people who know what they are doing, and then take the benefit of their wisdom, and uh you know, my role, and then it becomes a more of a um con you, you know, you know, you you you're facilitating uh different the communications and ideas, uh, and and then also uh being pragmatic, what is possible and what is our you know, CapEx needs and what is available.
1:30:40
SPEAKER_02: So you middle all that, um, and then have a good project manager's uh to help you um because it is a complex project, right?
1:30:49
SPEAKER_03: So yeah.
1:30:50
SPEAKER_00: And what is the what's the kind of outcome uh did you achieve out of this?
1:30:55
SPEAKER_00: Did you achieve the goal that you set out?
1:30:58
SPEAKER_00: And so what was it like and what were you really uh you know proud about?
1:31:04
SPEAKER_02: Yeah, um uh yeah, uh it was a hundred and uh twenty million dollar product, and uh we did build it and you know 15 years ago, it was one of the very modern uh steel making plants in the US at the time.
1:31:21
SPEAKER_02: And uh we did make this uh stainless steel and carbon steel, and uh the first time we made a stainless steel, it took almost like uh uh I would say 48 hours to make one batch of steel, uh whereas carbon steel every hour we would make one batch.
1:31:37
SPEAKER_02: So you can see the complexity, there was a lot of learning, and um and also uh our uh when we uh first started, our utilization uh rate was about 40-45 percent.
1:31:51
SPEAKER_02: Um a lot of learning, uh a lot of uh um uh changes we had to make.
1:31:57
SPEAKER_02: Um we got some reliability, premature uh failure issues.
1:32:01
SPEAKER_02: Uh so I had to adjust my hat, and uh we had a lot of teams.
1:32:05
SPEAKER_02: Uh you know, one of the teams I was on was how to improve the cash for reliability.
1:32:10
SPEAKER_02: I learned quite a bit, uh, and we brought the uh efficiencies and uh uh to almost a 70% uh by the time I left the company.
1:32:18
SPEAKER_02: Um so it was a you know, when you see the product coming out, um that's one of those the best satisfactions you can get out of.
1:32:28
SPEAKER_02: Um you know, um it's like uh you know, when I had my my kids, uh that was you know one of the best joys you can have.
1:32:39
SPEAKER_02: And probably it's uh not as quite as bad, but uh definitely it's a it's a great achievement.
1:32:46
SPEAKER_02: Um you can gain a lot of confidence.
1:32:48
SPEAKER_02: Um you know, it's a lot of hard work, you know, like it's uh 15-hour days uh nonstop for uh you know starting up a plan, six to eight months, uh you know, you're your uh you know, your walk you're using all kinds of hours, um, you're spending a lot a lot of sleep hours.
1:33:09
SPEAKER_02: Um you know, but at the end, it's all worth well worth it.
1:33:13
SPEAKER_02: Uh and uh I would say it's one of the very uh productive, efficient plans.
1:33:20
SPEAKER_02: Um I even took a visit uh four or five years later.
1:33:24
SPEAKER_02: Uh um twice I actually took uh two different companies that I worked for on a planned tour of it.
1:33:30
SPEAKER_02: I had a good relationship with the company.
1:33:32
SPEAKER_02: I haven't done a tour in five years of the site, uh, but um, you know, it's one of those family-owned companies.
1:33:38
SPEAKER_02: They welcomed you back.
1:33:40
SPEAKER_02: Um, you know, they uh you know, uh so I was grateful to take a tour back, uh, some of my um uh uh people in my team to show them what a digital plant would look like.
1:33:53
SPEAKER_02: Um you know, so yeah.
1:33:56
SPEAKER_02: Great.
1:33:56
SPEAKER_03: Yeah.
1:34:00
SPEAKER_00: So when you build uh the nature of what Shida talks about, the digital factory, a completely green field project like the steel plant that he talks about.
1:34:12
SPEAKER_00: What was interesting for me to observe and uh look at is the multiple functions that he had to coordinate and understand.
1:34:23
SPEAKER_00: So it's not just about his area and role of saying, let me build a digital factory, I am a manufacturing specialist, let me now bring my PLCs and scanner systems and you know all other kinds of human interface devices into the plant.
1:34:39
SPEAKER_00: Here to work with the IT team, understand, help them understand the networking principles that was required.
1:34:44
SPEAKER_00: Here to work with the metallurgy team and actually say, you know what, if you are really really looking at what kind of a data do you want from these devices to decide on your quality, what kind of data should I be capturing?
1:34:56
SPEAKER_00: The ability to think multifunction, to be interdisciplinary, to learn from other industries and apply them is a key, is a key skill that I think is very very important.
1:35:09
SPEAKER_00: And as you build new ideas, new factories, new thoughts, the fact that you need to interact and coordinate with multiple functions and help them understand your vision, seek their support, and ensure that their work becomes efficient.
1:35:30
SPEAKER_00: It's something that as an engineer, as a business head, as a uh you know, as an ongoing uh you know person who's actually building your career, these become very important.
1:35:42
SPEAKER_00: And if you are in a siloed, closed thinking job mode, you will never be able to expand and build large digital transformation projects like a digital factory.
1:35:59
SPEAKER_00: So uh, so one of the things that I uh read which is interesting for me was you talk about the interdisciplinary learning you had from different industries.
1:36:10
SPEAKER_00: Okay.
1:36:10
SPEAKER_00: Yeah, how important is it for an engineer, uh Sri for uh the interdisciplinary learning?
1:36:16
SPEAKER_02: Yeah, uh I think it is very important.
1:36:18
SPEAKER_02: Uh a lot of times when you uh go to a new plant, um engineers there uh working in that site uh 20 years, 30 years, uh knowing everything uh about it.
1:36:31
SPEAKER_02: Uh I'll give you an example.
1:36:33
SPEAKER_02: Um there was an application in one plant, I won't name the plants, um, I don't want to embarrass anyone.
1:36:41
SPEAKER_02: Um, you know, um so the process is uh before you can make a product, you have to oil the pot so it doesn't stick to the the mold.
1:36:53
SPEAKER_02: Um so uh you know uh seems like a simple uh thing, uh, but they have people uh manually uh uh doing this work, uh oiling the plan uh the pot.
1:37:07
SPEAKER_02: And uh you know it's a waste of uh talent.
1:37:10
SPEAKER_02: Uh you know, you're putting a guy, um it's not a value added uh function, he's standing there as a long uh hose with a uh metal wand at the end as a nozzle and sprays oil, and he's just you know going through.
1:37:27
SPEAKER_02: It's like you're making uh dosai uh in India.
1:37:30
SPEAKER_02: Uh you know, you oil it, and you look like you know every time I see it on YouTube, it's fascinating how they you know it comes off.
1:37:38
SPEAKER_02: Same thing.
1:37:38
SPEAKER_02: If you're making an aerospace parts, you're oiling it.
1:37:42
SPEAKER_02: Um and uh one of the mandates I really had was uh to uh automate this process.
1:37:49
SPEAKER_02: And it can be easily done.
1:37:50
SPEAKER_02: I can put a robot in and they I can teach it and you do that all day, every day.
1:37:56
SPEAKER_02: So you know uh a robot will cost anywhere from$20,000 to$100,000.
1:38:02
SPEAKER_02: An employee, uh, you know, those days it was a hundred thousand dollars per uh employee.
1:38:08
SPEAKER_02: Um when you're working working in a union shop, it's very hard to replace an employee because they're losing an employment, right?
1:38:15
SPEAKER_02: Um so that's one of the challenges.
1:38:19
SPEAKER_02: My point to them is look, I could use that person, and the data has shown every time you did an automation, uh it allowed the companies to expand and hire more people.
1:38:32
SPEAKER_02: Uh people think automation uh you will lose employment.
1:38:37
SPEAKER_02: You know, no, if a company is successful, um and if you're they're making profit, they are going to expand, they're going to hire more people.
1:38:45
SPEAKER_02: That's what I have seen.
1:38:46
SPEAKER_02: Um, there may be instances where automation replaced people, but when you dig deeper, there are other issues why the companies couldn't uh sustain and you know they did what they did, they closed up and things like that.
1:38:58
SPEAKER_02: But a well-done company, automation actually enhances and makes them more productive and expand.
1:39:04
SPEAKER_02: The private companies I worked uh in the two companies I worked in my career, they were always like that.
1:39:11
SPEAKER_02: They always expanded with the even with the automation.
1:39:13
SPEAKER_02: So this thing was uh the feedback from the software was well, you cannot automate this oiling process because the oiling is an art form and it's in the movement of the wrist.
1:39:26
SPEAKER_02: And that that's a mindset, uh, the control uh issue you uh run into.
1:39:32
SPEAKER_02: Uh, not being in other industries, you don't see how even a more complicated uh process than that is being automated.
1:39:41
SPEAKER_02: Um they have robots they can uh put a screw in an iPhone.
1:39:46
SPEAKER_02: Um if they can do that, I'm pretty sure you can automate an oil in a part.
1:39:51
SPEAKER_02: So that's why it's important uh for engineers.
1:39:54
SPEAKER_02: And this common it's in the wrist movement, came from an engineer.
1:39:58
SPEAKER_02: So that's why it is important uh tying all that back to see different industries have that uh wide experience and have an open mind.
1:40:06
SPEAKER_02: Uh, it's like a traveling, right?
1:40:08
SPEAKER_02: When you travel, you see different things and your mind expands.
1:40:15
SPEAKER_00: Here um Sridhar talks about the importance of interdisciplinary thinking, learning from other industries.
1:40:25
SPEAKER_00: If you're steeped in one industry, uh, you know, it could be IT, it could be software, it could be banking, it could be uh, you know, mutual funds, it could be insurance, uh, it could be uh you know healthcare.
1:40:40
SPEAKER_00: When you're steeped in an industry, you pretty much get into a cognitive bias of saying, you know what, here is the way it gets done here, this is the way to do it.
1:40:50
SPEAKER_00: But when you want to transform a digital process, what's important is to look at adjacent industries, different industries, how they've solved the problem, learn from them, and then start to apply it back in these processes.
1:41:08
SPEAKER_00: The other important point that uh he makes is the fact that automation increases more jobs.
1:41:16
SPEAKER_00: That's a very, very interesting perspective because there's a lot of furors, there's a lot of issues around, you know, you're trying to automate this, you know, you're gonna keep losing jobs.
1:41:27
SPEAKER_00: But the important takeaway that I see from what he's saying is that when you try to automate, you pretty much start looking at the company handling its demand far more efficiently, far more better, the business to start doing well, and therefore it gives more jobs to people.
1:41:48
SPEAKER_00: Therefore, what is important is to look at how automation can transform certain uh you know skills and therefore the need to constantly upgrade yourself to the new skills that may be required when an automation happens is something to think about.
1:42:12
SPEAKER_00: So Shri, uh one of the things that is coming out very, very strongly to me in this conversation is uh when I look at uh you know uh a heavy engineering manufacturing kind of a setup where deep engineering is required.
1:42:30
SPEAKER_00: Yeah, then uh the knowledge of the domain that you know you come with.
1:42:35
SPEAKER_00: In fact, as an engineer, you spoke more domain than many uh you know engineers would speak.
1:42:41
SPEAKER_00: You are not talking about you know uh architecture or you're not talking about some program or whatever, but you're talking about industry issues and then you went back to solving it.
1:42:51
SPEAKER_00: Okay.
1:42:52
SPEAKER_00: Um while in other industries other than manufacturing, uh many a times I see uh you know the knowledge of the industry uh is pretty much uh you know sometimes uh you know not there.
1:43:06
SPEAKER_00: They probably say that okay, just program this and give it to me.
1:43:10
SPEAKER_00: Okay.
1:43:11
SPEAKER_00: Uh so is that so therefore, if I'm an engineer, would you recommend that uh you know I do the hard work of working in say a manufacturing unit or a uh you know uh or an industrial unit if I were to call it, okay, for a few years, so that it just gives me a hard uh you know knowledge of what I can then apply and innovate in other industries?
1:43:35
SPEAKER_02: Absolutely, I think uh that that's a great uh suggestion to be honest.
1:43:39
SPEAKER_02: Uh um you put it uh in a better way uh light.
1:43:43
SPEAKER_02: Um yeah, uh I would say that that is a great thing to do.
1:43:47
SPEAKER_02: Uh having different uh backgrounds and uh experiences only enhances your uh uh work.
1:43:53
SPEAKER_02: Um you know um one of the things I learned uh you know in my tours, um uh thinking about um, you know, one time uh I was in uh in an alcova tech center and they were uh the original suppliers of parts for um uh Apple products.
1:44:18
SPEAKER_02: And um in the um some of the stories they um uh described uh how Steve Jobs was so personally involved in the because they were using aluminum uh uh cases and how he was involved in this aluminum parts making, even though some of these parts were not even exposed, um they were not even aesthetics parts.
1:44:43
SPEAKER_02: And uh so you know, the reason I'm you know I'm bringing that that up is people who are that successful, they are so focused and they are uh want to know everything.
1:44:57
SPEAKER_02: Uh so for me, the best way uh you know, everyone cannot be um you know you're not going to have that kind of opportunities and mindset like Steve Jobs.
1:45:06
SPEAKER_02: So, what is the best thing can you do?
1:45:08
SPEAKER_02: Um, the best thing you can do is I'm fortunate, for example, is uh to be in seal, to be in um automotive, to be in air space, now in pharma from life science, even within pharma is life science.
1:45:21
SPEAKER_02: There is uh tablets making, uh, cell and gene therapies, the next big thing, uh, individualized uh medication is coming, they take your genes, and you know, these are all fascinating.
1:45:33
SPEAKER_02: Every day I learn.
1:45:35
SPEAKER_02: And and and then also I'm able to leverage things so that I learn from one industry and I'm able to apply in another industry.
1:45:43
SPEAKER_02: Um and uh I I I feel that helps me to be a quick learner when I change jobs or uh take up a completely new industry, allows me to be, you know, I don't want to label myself as successful, that's kind of a self-consolatory thing.
1:46:00
SPEAKER_02: Uh, but um, it allows me to be uh to start up on any new jobs pretty quickly, I feel and I do.
1:46:08
SPEAKER_02: Uh and so for an engineer, so I'm going to uh bring this up.
1:46:12
SPEAKER_02: You you probably might remember it.
1:46:14
SPEAKER_02: Uh I was a civil engineer, uh, civil uh professor uh in the college.
1:46:18
SPEAKER_02: I forgot his name.
1:46:20
SPEAKER_02: Um, shame on me.
1:46:21
SPEAKER_02: Um, you know, the one thing he uh told us was look, I'm going to teach you uh uh you know, you're going to use some tools that's not even uh invented yet.
1:46:34
SPEAKER_02: Okay, you're going to use those tools.
1:46:37
SPEAKER_02: So what I am going to teach you is to how to think like an engineer so that you'll be ready to use those tools that it's not even created yet.
1:46:46
SPEAKER_02: Uh something like that.
1:46:48
SPEAKER_02: Okay.
1:46:48
SPEAKER_02: Um to this date I think about that.
1:46:51
SPEAKER_02: Um you know, I I should remember the professor's name, uh, but I don't.
1:46:56
SPEAKER_02: Uh it was a basics of civil engineering class.
1:46:59
SPEAKER_02: Um he taught us that.
1:47:01
SPEAKER_02: And uh to me, that that mindset comes when you have exposure to different uh areas.
1:47:07
SPEAKER_02: And if you're going to be a programmer in Google, um, you know, uh sometimes when I look at use some of the tools, um uh especially some of the Microsoft products, I uh you know, I say, why why don't they get some user input?
1:47:21
SPEAKER_02: Oh my god, you know, uh sometimes I think that.
1:47:24
SPEAKER_02: Uh yeah.
1:47:28
SPEAKER_00: Uh here Sridhar uh shares a very, very powerful thought of how do you really build a product uh that's not invented as yet?
1:47:39
SPEAKER_00: How do you solve a problem that's not invented as yet?
1:47:42
SPEAKER_00: How do you build a product for a problem that's not invented as yet?
1:47:46
SPEAKER_00: Therefore, as an engineer, what he suggests, and which I think is something that I totally agree with, is you need to first teach yourself how to solve a problem.
1:47:59
SPEAKER_00: And as you build your career as an engineer, as you know, as a doctor, as a uh, you know, as a musician, there are many, many tools and uh you know machines that are going to come your way over the next 30-40 years.
1:48:15
SPEAKER_00: And you need to understand that if you were to you know know how to solve the problem, break down the components into real issues, then any problem can be solved when these new devices uh you know come in.
1:48:31
SPEAKER_00: Therefore, the fact that you need to really understand core problem solving skills is something that he talks about.
1:48:39
SPEAKER_00: And if you are an engineer, the fact that you need to learn these are very, very interesting points that he makes.
1:48:47
SPEAKER_00: So, therefore, it's not just about you know doing the job that you do, but it's about learning from different industries, applying one industry learning to another industry, and also looking at things that have never been invented as yet, but you'll be thrown these problems in a couple of decades, and you should be able to pick them up and quickly find a solution for them.
1:49:12
SPEAKER_00: That's the key skill that you need to build for yourself.
1:49:19
SPEAKER_00: Beautiful.
1:49:20
SPEAKER_00: So I think uh that's a very interesting uh perspective, which is understanding manufacturing, you know, spending time in the shop floor, uh, you know, coming back and then applying it in services business or programming or software businesses is a great way of doing it.
1:49:36
SPEAKER_00: Uh, which uh brings me to my last few questions, Sri.
1:49:40
SPEAKER_00: Uh uh, these are going to be rapid fire questions.
1:49:43
SPEAKER_00: So uh, you know, uh what does success mean to you, Sri?
1:49:49
SPEAKER_02: Oh gosh, uh that's uh fully loaded question.
1:49:52
SPEAKER_02: Success means uh you know it's no straight path to success.
1:49:56
SPEAKER_02: Um you know, um there are a lot of uh disappointments on the face.
1:50:00
SPEAKER_02: ideas.
1:50:01
SPEAKER_02: But as long as to me, you know, for me in the family life, you know, that's what I try to tell my kids also.
1:50:09
SPEAKER_02: As long as you're trying and um be happy in what you're doing and then you're able to make a living out of what you're doing, to me that's successful.
1:50:19
SPEAKER_02: It's not money.
1:50:21
SPEAKER_02: Yeah, money is great and you know you get to enjoy some of the final things in life.
1:50:25
SPEAKER_02: But as long as you're happy and enjoy uh things uh that what you're doing.
1:50:30
SPEAKER_02: I look at you uh uh Swami you mentioned um you you you know um you're able to do what you like uh you know it came from engineering did complete something completely different um I couldn't imagine doing that and and then you're doing something different now you know completely different right to me that's uh you know you're able to do it on your own terms and to me that's to me success.
1:50:55
SPEAKER_00: Brilliant.
1:50:56
SPEAKER_02: What would be uh a piece of advice if you were to give to an 18 18 year old in an university today um for me um you know attitude is everything uh for me that's so important uh i that's what i tell my kids uh and also anyone i hire um especially uh fresh uh grads i you know over the years i had so many interns um yeah uh bring them in uh you don't have to know the tools and technology and you can always learn uh but if you have a great attitude and say i'm going to give my best um that is important and also uh you know don't expect uh rewards uh on day one uh you know attitude and put your uh time in uh do your best um the rewards will follow you uh even if it's not on day one uh eventually you will get the recognition and if those recognitions are not coming um after some time after you put in your attitude and you your effort if it is not coming also don't be afraid to make a change uh those three things to me is important you have worked with lots of bosses and lots of people lots of mentors uh what's one piece of advice that you got which probably you use even today or remember even today um I had a couple of bosses uh they said a very similar thing uh is uh uh be here do the right thing uh you're a professional uh be here do the right thing and as an engineer you know you don't time in time out uh but be available uh that uh and and that's one thing and the second one is uh this is something I learned on my own uh be loyal to your team and to your company as long as you are there do your best okay you're there you accepted you're going to be there uh do your best and be loyal uh what's one thing that you believe in that uh others don't agree with you oh there's a lot of things uh people don't agree with me especially my kids and my wife uh I don't know uh that's a difficult question to be honest uh um you know um uh personally uh I think uh there is uh uh you know especially in the in the world we live in there is a lot of uh um uh skepticism um you know I you know I I tend to think there is always good in people um you know uh there are uh um I try to practice now you know try to give the benefits of doubt not always uh uh successfully but uh um you know I hope everybody can believe that and uh you know just uh um give some benefit of doubt in when some when you run into some difficult situation or difficult people uh don't uh get too uh um you know turned off by some negative uh uh you know behaviors or negative feedbacks or negative uh experience brilliant if you were to uh have a dream dinner with a few people who would be on the table and why I don't know uh probably uh my dad um you know um yeah um uh you know he uh a lot of things I do practice and learn um kind of like getting emotional yes so sorry uh is I learned from him um it's not like I he told things or uh you know just watching him and uh he was a college professor he was a principal in a small uh time college uh in uh Sathur uh near Madurai uh uh in India um you know um he grew up with the um uh you know his aunt raised him uh he pretty much didn't have parents uh him and his brother uh he grew up very poor and for 35 years 40 years he spent his life uh teaching uh kids and uh you know you used to have a seven days a week and he will go to the college and um he will bike uh two miles uh back and forth to the college and um even though you know he could have uh got a car uh you know there was allowances and things like that but he never uh took anything uh like that because it will cost money for the college um you know every Saturday and Sunday he will travel around the uh different towns and villages collecting uh donations for the college um and I learned a lot from him and um he never um some people recognize and I don't know how you know yeah how much recognition he got for his effort but sometimes I run into people and they will tell hey I was a dad student uh some really older people so uh for me uh I want to wanted to be like him and um you know I wanted to tell them that how much uh influence he has on me.
1:56:28
SPEAKER_00: Fantastic.
1:56:29
SPEAKER_02: Yeah thanks thanks Sri uh a brilliant conversation perspectives which are very very different uh not normally what I hear uh topic that uh is not such a glamorous topic because people tend to talk of chat GPT AI uh you know software and you know that's what people want to talk about but I think we talked about some very hard stuff a lot of details uh great perspectives around how an engineer can learn from doing things and being in the shop floor and then taking that to different industries brilliant uh uh conversation and thanks a lot it was lovely talking to you uh thank you so much for those kind words and uh uh I I hope uh it it is uh uh after you look back uh some of this stuff is useful and maybe you can uh uh turn that into a useful podcast uh and uh and some of the questions he asked uh were brilliant made me think uh I was pretty nervous uh at the beginning I never done a podcast before um very thought provoking questions to be honest um and uh I I I think this is uh an art and uh asking questions uh that is uh and uh um uh I I really admire uh it was brilliant uh thank you so much uh thanks for the opportunity definitely thanks for listening to this episode for selected links and detailed show note visit www.contramines.com slash blog follow contramind on social media and let us know who you would like to see next on the podcast if you're listening to contraminds on Apple podcast do share your comments and give us a rating we are keen to know what you're thinking contraminds is also on YouTube if you're listening to the podcast on YouTube hit the subscribe button and stay up to date on all our releases thanks for listening and stay safe