Episode Transcript

Mike Ambrose
But ultimately, it’s this ability to simulate and use all these tools to look at concepts or products before you actually design and build them.

Tan Deleon
On behalf of the members of the Connecticut Academy of Science and Engineering, welcome to this episode of Learning and Living STEMM in Connecticut, the podcast of the Connecticut Academy of Science and Engineering. My name is Tan Deleon and I’m an elected member of the Academy and serve on its governing council. For more information about the Academy, visit ctcase.org. That’s www.ctcase.org. I’m pleased to have as our guest, Mike Ambrose, CASE Member, and recently retired Vice President of Enterprise Business Transformation at Sikorsky, a Lockheed Martin Company. We’ll be talking about his experiences in digital transformation. Welcome, Mike.

Mike Ambrose
Hi, Tan. It’s really, really an honor to be here with you. I look forward to this discussion.

Tan Deleon
Yeah, Mike, really thank you for taking the time to do this with us and to share your experiences. Just to give our audience just a bit about yourself. Could you just let us know just a bit about yourself?

Mike Ambrose
Oh, of course. So as you said recently retired – almost 39 years – my entire career at Sikorsky Aircraft. And just an amazing career. I mean, you mentioned my last assignment was being in charge of enterprise business transformation. And that’s what we’re going to kind of talk about, but it took really 38 years to get there. Prior to that. I was the Chief Engineer and Vice President of Engineering and Technology at Sikorsky for a number of years and I had worked in operations as a general manager, I had worked in international government programs. Started my career as an engineer. I’m a local guy. I grew up in Bridgeport, Connecticut. Pretty humble beginnings. And we’ll talk a little bit more about that. Went to the University of New Haven, where I ran track, went on a track scholarship, and also got my engineering degree. I also got my Masters of Science in System Engineering from MIT. So that’s my background in 30 seconds. But there’s a lot in there.

Tan Deleon
Yeah, no, thanks, Mike. So speaking of humble beginnings, can you give our listeners just a bit about the inspiration and why you decided to become an engineer?

Mike Ambrose
Sure.

Tan Deleon
I think it’s a really good story.

Mike Ambrose
Sure. Sure. You know, it’s probably a testimony to our teachers and guidance counselors and really understanding students in ways that we don’t understand ourselves. So when I was in high school – went to high school at Bridgeport Central. I was a runner, and I was, I was even the state champion in the mile. In class LL, this was back in the 1970s. So senior year, I’m looking forward to graduating. I was going to be a runner. Back then there wasn’t all the focus on college athletics that there is today. I was going to be the first male in my family to graduate from high school. So that was a big honor. I was getting track scholarships, but I didn’t pay any attention to them because I was going to be a runner. So guidance counselor called me into his office one day in the fall of my senior year and said, “How come you’re ignoring all these track scholarships?” And I said, well, I’m going to be a runner, why do I need to pay attention to them? And he goes, he literally said to me, says, “You’re not going to make any money from that.” Again, the 1970s track was all amateur athletes. And, he said, “What else are you good at?” And I said, well, I like to draw. He’s like you’re not gonna make any money off of that, either. He says, “What else? Like what you like, you know, science or English?” And I said, well, I’m pretty good at math. Now, that’s relative. You know, I showed up for class. So he said, “Okay, you’re good at math. You like to draw? You’re going to be an engineer.”

I had no idea what an engineer was, again, much different 1970’s than it is in 2023. So my only response was, can I still run? And so what ended up happening is, is that I found out that wow, I really liked this math. And you know, I had tutors and people just helped me. And I found out very quickly that I’m pretty good at this. But I also realized that I wasn’t going to be able to run track at a collegiate level. So I ended up actually taking a year to be able to adjust to both being a good engineering student and getting ready to run collegiate sports. And so the University of New Haven provided me with the opportunity to do both. And the rest has been an amazing journey.

Tan Deleon
Yeah, I mean, collegiate athletes are, you know, people, people don’t realize the amount of effort it takes to be an athlete. And also, you know, and be a scholar-athlete, I should say, right? So, you know, being a scholar and an athlete, it takes a lot of work, it takes a lot of time, it takes a lot of discipline, and a lot of dedication. So, so kudos to you for being able to do both, because I know a lot of people aren’t able to do to go that path. But, from that perspective, let’s let’s try to let’s jump into some of the discussion for the topic today. So can you just help us understand what digital transformation is – or DX – is that the acronym?

Mike Ambrose
Yeah, and we’ll use DX, but, you know, digital transformation. And, you know, it really has a lot of meetings, if you were to Google it, you’d probably get like, you know, 300 million different interpretations of what it is not kidding. But in the end, it’s really about, you know, creating in revising, improving processes using digital tools, you know, really to achieve some customer goal better than, than if you were to do it using manual type processes at a really high level. Now, there’s a lot in that, in terms of when you talk about digital transformation, I usually tell people before you can even start talking about digital transformation, you need to understand the digital thread. And that’s a new term. And to understand the digital thread, you need to understand the product or the process. And so to help our discussion today, I’ve actually brought a show and tell. So I brought a show and tell.

Tan Deleon
I like show and tell.

Mike Ambrose
So I will make sure that for the five or six-year-olds out there, I think you’ll recognize this, this is a toaster. We should all be familiar with toasters. And I’m going to use this, I’ll put this over here, so we can explain. To be able to articulate what is a digital thread, what is the process that a product has to go through from the conception of a need, all the way to, its being used by a customer, and you have to maintain it, and it has a life to it. And so, the reason why that’s important from a digital thread is because of all the interfaces along the way. And for everyone out there to think about that as data. Now in the past, that would have been manual, that would have been drawings, that would have been calculations that would have been, you know, pieces of paper, feedback from customers. But more and more when we talk about digital transformation – and digital thread – all of that information, which we now call data, is an opportunity to have transformation. To be able to do it more efficiently, more smarter, cheaper, better, more sustainable. You know be able to last longer. That’s the essence of what a digital thread is that ultimately leads to how you take that information, and then be able to be able to make it better. So if we start with a toaster. So, toaster – it does one thing really, it heats bread, and bread in many different forms. And basically, what you see here was invented initially, it was invented in the 1890s. And back then it was just the way it would heat up one side of the bread, then you flip it over. But in 1921 came the patent that essentially is this model. Now there have been a lot of bells and whistles since then. But essentially what a toaster does is it uses infrared heating to go and heat the bread up. And it heats it up to over 300 degrees Fahrenheit. And the coils that run through it – actually our electric current is over 1000 – is actually over 1100 degrees. So but that’s the principle of a toaster. And it’s really it’s an elegant electromechanical for most of those last 100 years where you’ve got a couple of springs, you’ve got electric coils. You know originally it had some sort of timer that was tied to probably Wonder Bread – who knows back then – but since then, there’s been a lot of other different kinds of bread including gluten-free and bagels and waffles and you name it. And so, you know, having that timer got a little complicated. So many of the modern toasters actually have things like photocells to measure the light on the bread. And so as you start to think about all that, and you’ll look at, you know, and why am I using a toaster instead of the helicopter, because if you think of a toaster and you start to go through it, well, what’s so hard about this?

You start to talk about the process of what it takes to go from a customer need to requirements to design to build, to test to certifying to make sure that it doesn’t burn, and it meets safety requirements. To be able to go and put it out to customers, and then ultimately, to make sure that you understand the reliability and how long it’ll last. That is the process/product flow from the beginning of a product idea all the way to you throw it in the garbage at some point, or recycle it. And so as you go through all those you look at a toaster, well, it doesn’t look so complicated, there’s one thing, there are literally 10s of 1000s of interfaces, and I just described some of them in terms of the interface between the spring and the toast and the different kinds of bread and you know, the buttons and you know, the, the dials, you know, in terms of the amount of current that goes through there. There are materials that have to last, there’s a cost associated with this. So in the past, it would be all metal. Now, today thermoplastic, in some cases on the outside, but you have to understand the temperature variations. 300 degrees is pretty hot on the inside of here. And so you start to pull on all that information, that data, and you say, wow! You know, today more and more of that information – is data – that is electronically stored. And so here’s where we really start to get into the digital thread and understand how DX, or digital transformation, plays a role. Even as simple as something like the toaster. And we’ll talk about helicopters and ships and planes. And you know, towards the end on how is orders and orders of magnitude more opportunities sophisticated. But what ends up happening is is today, the more you have electronic data, you’re able to connect that information. And ultimately, what we’re attempting to do, whether it be a helicopter, ship, airplane, toaster, or whatever, is to derive that data into a single, what we would call, a digital warehouse. Somewhere, you know, hard drive, computer, wherever it may be, that enables us to have what we term in the industry as a single source of the truth. Very important concept –

Tan Deleon
Single source of the truth. But what is what does that mean – single source of the truth?

Mike Ambrose
Yeah, so that means that all that information that describes that product or that process is connected and stored. So when you have that single source of truth, whether you’re using that information to design, then you could use it to build and test, it allows you to do it concurrently, do it at the same time. You know, so you can you can do things at the same time. You can simulate things.

Tan Deleon
Okay.

Mike Ambrose
You can iterate or repeat designs and concepts very quickly to see how it behaves in the field, see how it fails, you know, maybe like a case of a toaster? What does it take to go and get overheated? And you can do all those things in a virtual environment before you even build or design anything. So by having that single source of the truth, you’re able to look at the entire product.

Tan Deleon
So this single source of the truth, I understand that concept. So how do you validate that that single source of the truth is the truth and maintain that truthfulness? Because I mean, once you’re connected, especially in today’s age, with, you know, cyber and everything, you could potentially compromise that. So is that am I on?

Mike Ambrose
Oh, absolutely, oh, my gosh, that is actually a really, really, it’s one of those foundational things is like, how do I believe it? How do I know it’s true? And so what we do in industry, then anyone who does anything with digital through a digital transformation has to correlate information to real testing. So ultimately, you need to validate it, we need to prove it. You know, digital information is interesting, but in the end, it has to be based on some sort of reality. And so ultimately what needs to happen is that you’re going to correlate your simulations with actual testing. And so at the beginning, you’re not able to really separate yourself entirely from the physical world. Now what happens over time, as you build that database of actual information, you’re able to then go in start to extrapolate or interpolate actually start with interpolating, which means that within the data set that you have, you can try points of the design or points of the manufacturing or points of, you know, any particular aspect of that product lifecycle. You’ll be able to test it because you can correlate it, and compare it to actual test data. What happens over time, and this is like, the really exciting part is now, as with the advantage, or the advancement of things like machine learning and artificial intelligence, you’re able to go and start making guesses, educated guesses or educated analysis on what happens if I now go and extend my design or extend my capabilities beyond what I have actual test data for. Now, eventually, you’re going to need to go and validate that but you can get ahead of the design by making those kinds of predictions based on the way you’re seeing in this goes back to maybe high school level type of mathematics in terms of how you’re seeing that curve project. And will the design or will the characteristics based on physics, mostly physics, chemistry, and biology, type things will continue to go and project along that curve, or based on our knowledge of physics, biology, and chemistry type things, will it deviate? And so that’s one of the amazing things about digital tools – think about all the digital tools that I mentioned, I mentioned things like computer-aided design and computer-aided manufacturing. We talked about predictive analytics, prescriptive analytics, we talked about machine learning, we talked about artificial intelligence, and now…

Tan Deleon
Additive – additive manufacturing, is that part of it?

Mike Ambrose
Yes, it is. Yeah, yeah, in terms of computer-aided design, or manufacturing. And so you start to pull this together. But ultimately, it’s this ability to simulate and use all these tools to look at concepts or products, before you actually design and build them. There’s another concept that is important. And that is, I call it we call it x before you x.

Tan Deleon
Okay, okay. And so can you expound upon that, because it sounds, I think I have an idea of what it means. But I just want, I would prefer if it if you just kind of elaborate a bit…

Mike Ambrose
For sure. And so when you talk about all these things that are connected, and you talk about the single source of truth, and you talk about being able to go into simulations, one of the first things that a company or industry will do is, as they start to connect the digital thread, as they start to get pieces of this data and show its correlation. They realize that they can start to simulate the design, simulate how things are going to be manufactured, and made, how they’re going to be assembled, how they’re going to be tested, and how they’re going to be supporting the field before they do it. And so x before you x, really, it means design before you design. Build before you build. Test before you test. All of that is done in a virtual environment, using this single source of data as you build the digital thread.

Tan Deleon
So now is that I guess the x before you x, would that be the same terminology as a digital twin? Or is that something completely different?

Mike Ambrose
It’s on a path to a digital twin, the digital twin I kind of look at it as the holy grail of digital thread, of digital transformation. Because it is something that you’re always driving towards – it is an objective, a goal. Because when you have a digital twin, which is a virtual representation of a product, or process, okay? And I always look at it as – it could always be better because you’re always getting new information, you’re always correlating data. So there should never ever be an end state to a digital twin. But as you go in and connect those pieces of a digital thread, then you’re able to go and use that information to do things like x before you x.

Tan Deleon
Okay, so, so it sounds kind of like, I mean, all this is based on probability, right? And making sure that you have the highest probability with respect to the data that you have and how it represents reality. Right? So from that perspective, right? When you say that you always have to continuously keep improving it, it makes a lot of sense, right? Because it’s, you’re never 100% Yes, this is this is it. So you always are adding more information to improve or increase the probability, or the percentage of probability that this is exactly like reality. Because as soon as you, as soon as you say it is, guess what reality just changed, right? Because its reality is always changing. Is that right?

Mike Ambrose
You know, it’s fascinating. This isn’t, you know, I’ve never really thought of it exactly that way. But probability is a very good word in this case, is because one of the reasons why I like engineering so much is because it’s always evolving, and we’re always getting smarter. And, you know, you talk about probabilities. And, you know, I hate to, for the business that I just spent 39 years, is that from an FAA perspective, you know, there are probabilities that there’s going to be an accident. But for the FAA, if you’re in a commercial airplane, that probability is really, really small. There are lots of zeros. And that’s why I feel very comfortable getting commercial aircraft because it’s…

Tan Deleon
Same here!

Mike Ambrose
A real lot of zeros that go in front of that probability. And so the same thing applies as you’re building this virtual representation of you know, what the toaster, whether it be, you know, the world’s greatest helicopter, you know, there’s a lot of, there’s, there’s a lot of ways to look at it. But engineers should always look at things in terms of probabilities, and recognize that there are always variables. And this is one of the fascinating concepts when you’re talking about digital threads and why digital transformation is such an exciting field. It’s because there’s always new information that’s coming, no matter how mature a product can be. And being able to go and build upon that foundation. Because you’re always you know, there are new materials, there’s new way of building things, there’s new way of analyzing, there’s more efficient ways of processing data. And, you know, and the interesting aspect about that, and this is something that is definitely worth mentioning is that I talked about a toaster that has 10s of 1000s of interfaces, well, a helicopter can have – will have – millions and 10s of millions of interfaces. And any one of those is generating a lot of data. For example, when we do a flight test, and we’re collecting data, we’re collecting terabytes of data on every single flight test. Now, this is where things like machine learning and artificial intelligence come in. Because as we enter more into this digital transformation, this digital thread, the amount of data and information that we’re collecting, is, even if you go back 10 years ago, beyond the capability, of us or anyone to be able to actually store and process this data. And this is why, again, it’s just so exciting time in here, because part of the challenge or the opportunity is well what’s the right data? And I don’t need to have all those terabytes of data. But I need to have the right data. And how do I find the right data? Well, that’s where things like machine learning and artificial intelligence come in, because they can, they can interrogate that data, to be able to understand what makes the most sense. Again, Tan to your point, is that all this has matured through correlating data to understand that building this model of re-educating the algorithms in the analysis that we’ve created.

Tan Deleon
And typically those models that, you know, once the AI or ML – or the machine learning – creates that model from all that data. Then technically, to store that model, it’s a lot, probably a smaller ask than, you know, the original data to begin with. So you’re getting, you’re getting reductions in costs for data storage, right? And then you’re still able to add new data without having to, you know, keep all this like humongous repository of information. That’s a that’s very profound. So as you mentioned, that the helicopter is millions upon, you know, probably millions of, of different pieces and data bytes. So it’s a system of systems, right? Is that is that is that a proper term to say like, It’s just like the toaster is a system is a component or it could be its own system I guess right? Whereas the helicopter is kind of like a system of systems. So you have that multiplicative effect, based on the number of data points that you would have in a toaster? Or is that appropriate or not?

Mike Ambrose
It actually is even more fun than that. So, you know, the interesting thing is, is that, yes, there’s complexity in different systems. But in the end, if you were to ask them, like a military customer, what they would say is that a helicopter is one node in an overall system, you know, a system of systems. It’s a node, similar to what a tank would be or a ship or satellite, radar, you know, all those will be considered parts of a system, a system of systems. And so there are levels. And that’s actually, if you take it there, Tan, is that, ultimately, there is all that connection between how all these different systems work. And, you know, and that’s all data. And that lends itself to many of the tools we talked about. And I think a phenomenal example, in terms of digital transformation. And you take a system, like a helicopter, is, well, how do you support it in battle? How do you support it in the field? You know, the same thing would apply to a toaster, but it’d be how you support it in your kitchen. And what ends up happening is you end up getting information. So it’s part of this node and the helicopters out, it’s doing a mission, something happens to the helicopter. It has to go down. You know, today or in the past, that helicopter would have to be taken to somewhere where it could be evaluated. An assessment was made of what was wrong. Parts would have to be ordered. Rebuilt. Sent back out to the field, maybe, and then be able to go in repair that helicopter. Know if it’s performing, if it’s in a big mission or something, boy, that really hurts, but with a digital thread that is enabled through this single source of truth. Okay, there’s sensors on that helicopter, and there’s a pedigree, or there’s a history to every part that went into that helicopter. So there’s, there’s this virtual representation of that specific helicopter, that tells you its vibration characteristics; it tells you the life on the parts; it tells you how the parts were made. And so you get all this and again, what is the right information, that’s part of the fun of this journey. But if that helicopter were to go down, you could have real-time assessment based on the sensors and information that you get from that platform, and say, there’s a damaged part, you know damaged frame or something. Today, you could get that information back to someone who could additively manufacture a part that is tuned to that specific helicopter, to make sure that not only will it allow it to continue operating with a way to go and do it in the field, but also make sure that it’s not changing its vibration characteristics, which is really important for helicopters, you know, there’s a lot of tuning from a vibration standpoint, that’s just one, that’s just one example. All of that, theoretically, not only theoretical, I mean, the science is there, the ability to do it is there, that can be done through a digital thread and digital transformation.

Tan Deleon
Okay, so let me just try to see if I can summarize this just a bit. So the digital transformation piece in the digital thread, so if so, if I can use, I’m gonna use a different analogy, I’m gonna actually use a clothing analogy if I can. Since, you know, it takes different pieces of thread to create my, my, my, my clothing, my piece of clothing. And so can, and based on what you said with the military thinking that helicopter is technically you know, a node in their larger system, right? It basically comes down to perspective. So, is the helicopter a thread onto itself? Or is it multiple threads in this digital transformation universe that we have here?

Mike Ambrose
Wow. Yeah, very perceptive. So, there’s probably, you know, you could think of it as one overall thread that you end up having the potential for an infinite number of threads within it, that’s one way to look at it. So, you know, going back to the toaster example, if you think about, you know, threads of cloth that feed into, you know, what does it take to virtually represent a toaster, and you think about those threads could be something like, you know, the physical characteristics of the toaster so that it can fit on your counter in the kitchen. Thread manufacturing has to be manufacturing. So as there’s a there’s a build thread. There’s a chemistry actually, yeah, chemistry thread that talks about how you heat the bread, which really would the smell, the way it’s charred way, you know, ultimately, that’s amino acids, the sugars that are in the bread, that give it that smell, that give it the right level of heating. You know, that’s a thread. You know, then you take it another level, then you there’s like, well, how does the, how does a toaster be able to be sustained or serve in, in in the kitchen? And what is the right life, um, you know, should the toasters last two years should the last 10 years your last 20 years? Because there’s a price point that goes so there’s a whole nother thread that goes into the type of materials and the way it’s serviced and the way it’s used, that be able to go in and determine that. And so all those come together, but that’s so that’s one way to look at it is each of those kind of like physics models kind of thing, and all of the model. But then there’s another way to look at these individual threads. And ultimately, that’s going to be, you know, I think the way you look at it for how it works in the overall environment.

And that’s where things like the helicopter, you know, have that additional level of, obviously, orders of magnitude level more complexity when you start to look at the different threads, and we typically look at it from a build thread. We look at it from a test thread. We look at it from a sustained sustainment or support thread. And if you were to look at just one of those threads, like the build thread, you look at the build thread. And you say, okay, that’s the objective, I want to build something I want to iterate on it, I want to make it as easy to build an assemble as possible. But it still has to be connected, just like a shirt in all the threads are connected, the build thread will take the design that will take the requirements that are required in the design. And then because it’s from a single source of truth because it’s part of this digital fabric. We do things like virtual, we use virtual reality, you know, the same thing with the goggles that you see. And we’re able to do simulations of that build thread. You know, to basically say, okay, here are the different designs, how would I build? How would I assemble each one of these? And you start to look at other, you know, like ergonomics, you start to look at costs, you start to look at permissibility. So you can really get an appreciation for the complexity and how there are many different threads that go into, you know, any, any even the simplest product. Does that help?

Tan Deleon
Yeah, no, certainly, Mike, that that definitely helps. So, I mean, we are, we’re coming down to the, to the time. So what I would like to do, and if this is okay with you is, you know, I know, we still have, as you mentioned, digital twin is the holy grail. And that’s kind of like the end state. But we still have you know, some time, we still have a couple of steps that we need to get to until we get to that digital twin. So if it’s okay with you, I would like to have you come back in and we can discuss those steps to get to that, that digital twin, right, the holy grail piece of it, which would kind of just build upon this this this present discussion that we’ve had thus far. Does that sound okay to you?

Mike Ambrose
Of course, yeah. And you did phenomenally leading the discussion. Yeah, there’s a lot there.

Tan Deleon
No, no, I appreciate that. But I mean, I think it’s very, it’s easy to lead a discussion when you have a great person that that’s answering the questions and, and that’s so insightful in those responses as well. So I give you more of the credit than myself. So thank you for that.

Mike Ambrose
Thank you.

Tan Deleon
So, folks, we are we will plan to try to have a part two because there’s much much more to discuss and there may even need to be a part three because this is not something that is easily digestible, and it really requires a good understanding of of how the manufacturing process really comes to be. So with that, I would like to thank our guest, Mike Ambrose. Thank you Mike for your time today,

Mike Ambrose
Thank you, Tan.

Tan Deleon
For those living in Connecticut and others tuning in from outside our state, we enjoyed learning about your digital transformation and its use at Sikorsky. I encourage you to subscribe to this podcast on Apple Podcasts, Google Podcasts, Spotify, Amazon Music, or YouTube, and visit the Academy’s website at www.ctcase.org. That’s www.ctcase.org to learn more about our guests, read the episode transcript, and access additional resources as well as to sign up for the CASE bulletin. Thank you again, Mike Ambrose. And we look forward to part two of this discussion.

Mike Ambrose
Thank you.