Borrowing Innovation from Aerospace Technology

Sina Golshany is the Director of Technology at Fabricated Extrusion Company. He previously spent 8 years at Boeing in various aerospace engineering roles.

Sina established and manages the computational engineering function responsible for supporting the custom extrusion business and product design needs of customers in various industries including, heavy machinery, aerospace, automotive, medical devices and more.

Over the course of his career, he's developed a unique framework for innovation. In this episode I invited him to share the four pillars of his system.

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Sina Golshany | Fabricated Extrusion Company
Director of Technology
Sina Golshany started his career at the advanced concepts group at Boeing Commercial Airplanes in 2009. For the following 8 years, he held various technical positions in the product development organization at Boeing, primarily specializing in propulsion aerodynamics, airplane performance, and aircraft design. During his time at Boeing he led multiple design projects and became the primary author of 11 US patents and three dozen international patents.  He has been a significant contributor to the design of Boeing 787-10, Boeing 777X, and various developmental programs.In 2017 he joined the management team at Fabricated Extrusion Company as the director of technology, where he currently leads efforts related to implementation of new technologies into the engineering and manufacturing workflow, including simulation driven product development, optimization, and production automation. 
 
 
Sina is the holder of BSc and MSc degrees from University of Southern California in aerospace engineering with specialties in aero/fluid dynamics and aircraft design, as well as a complimentary management education from MIT.  
 
 
In addition to his pursuits in engineering, Sina is an accredited investor and serves as an independent consultant to the investment management industry. 
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Transcript

 

DAVID LEDGERWOOD:  Sina, thanks for joining us. It's really great to have you on.
 
SINA GOLSHANY:  It's great to be here, David.
 
LEDGE:  Fantastic! Please, if you would introduce yourself to our listeners with maybe two or three minutes about your history and your work.
 
SINA:  Sure thing! I moved to the U.S. about thirteen years ago. I started school at USC for Aerospace Engineering. I went there for undergrad and grad school and, somewhere in between, I fit in a program at MIT for management. And then, I started with the Boeing company in 2009 in the commercial section where I worked on various projects and roles primarily in new airplane development and aerodynamics engineering.
A little more than a year ago, I moved to a new leadership role. I'm currently the director of technology at Fabricated Extrusion Company located in the Central Valley, about an hour outside of San Fran.
I basically direct a number of projects related to computational simulations, basically engineering optimization and product design for customers and our own internal needs.
 
LEDGE:  You and I had a really interesting conversation before we got on the recording about the framework that you use both in the current simulation work and prior to that that you had developed a framework of thinking around innovation and the major pillars of doing that.
I would love if you would share that with the listeners.


SINA:  I've been involved in a lot of innovation initiatives and contests and, in general, just a lot of innovative work. I basically have four principles that I think determine relevance when it comes to innovating, and I think it can be generalized into pretty much any technical field.
I call the first one “design competence” and this primarily has to do with people doing the work. A competent team makes all the difference between making or breaking a project. If you can gather together a very good competent team, you're seventy percent of the way home to potentially having a successful project that leads to some innovative product.
The second component is basic computational physics and this is something that has been around for a little while but just recently in the past five to ten years, you've seen a lot of development in computer simulation software and low-cost, high-performance computers and very capable accelerators; and all these synergistically have enabled engineers and designers to do new types of analyses before they ever build anything and be able to create extraordinary performance out of whatever it is that they're designing.
So pretty much the way I see it is if you're dealing with any complex process or product design problems, computational physics would be the way to go absolutely. Payoff is there. Technology has been developed to the point that it can be used readily. I definitely recommend it to be incorporated in whatever project you're working in if it fits the framework.
The third component is the high velocity team and, by that, I mean a fairly small team of experts who work very well together; and, most important, they're enabled and allowed to operate with speed without being hampered by a corporate bureaucracy or something like that.
There are many cases of high-velocity teams in different industries that I've studied and they've all exhibited certain traits that we can actually go into if there's interest.
But, basically, you need a highly motivated team that is enabled, has authority, and, ultimately, is held accountable for results and rewarded accordingly. And they're given a limited time to do this ─ hence, the high velocity.
So that would be the structure for a small team to do relevant innovations, I think, pretty much in any field.
Larger teams, in my opinion, create a lot of management overhead and the performance doesn't increase proportional to this team size above a certain number of people.
And the other one, perhaps, one of the more important ones is product strategy. I see a lot of innovation happening in absence of a lot of evidence on the demand side since a lot of people just look at the market and there's the absence of a certain type of product or service; and they interpret that as “Oh, there is a gap there and there is absolutely a commercial demand.” And they automatically make a connection that because there's a gap, there's commercial demand and they go and develop a product for service in place of that gap only to realize that the gap was there for a reason and the demand is simply not there or the physics simply just doesn't add up.
So anyone involved in innovation has to be very careful. Before you undertake any sort of significant development, you need to sit down and look at your own proposed product or service space very closely and very critically and determine if there are very good reasons why such thing doesn't exist. Either the market isn't there, the economics doesn't work, the physics doesn't add up, or technology is just very far off ─ things of that sort.
So that prevents a lot of white elephants from being developed and a lot of people spending a lot of money and time developing them in vain.
That's about it. Those are the core principles.
 
LEDGE:  Fantastic! I wonder, over the course of your evolution of this framework, I assume you didn't sit down and write that down all on one day; and there's quite a lot of experience and study there. How has it evolved from early versions of the mental model and what have you added and subtracted as you did more study and had more practical experience?


SINA:  I think this has just been growing since I started my career in aerospace, in general ─ a little bit maybe before that ─ because I had studied a lot of biographies and case studies of successful designers and projects and tried to come up with a common denominator of what makes a great design team work and what is behind a great product.
Originally, I was very focused on aerospace because that's what I specialize in; and, over time, I realized that there are parallels in many other fields of endeavor that you can actually fold into this.
It's been added to it. I didn't start with a much longer list and subtract out. As I went along, as I saw more and more common denominators, I just added them in. That's how this came to be.
You're absolutely right. Each of these is highlighted by a significant experience in my own career or a significant observation in my own career. And in the order I just mentioned, that's how I basically put it together gradually.
 
LEDGE:  We're in the business of largely software engineering which doesn't involve a lot of physical product. I wonder how you would adopt Item #2, Pillar #2 into that space because you'd have to get pretty meta to sort of computationally model what software might do before developing the software.
Do you see that happen?
 
SINA:  Yes. There's a lot of that happening in the autonomous space because, there, you have a very complex combination of optical systems, very complex software and AI in the loop that is commanding a physical machine moving around being either drawn or a vehicle.
So I see a lot of that actually happening in the field that the embedded software is being designed in parallel to the optical system, the sensor system, and the physical vehicle itself.
 
So I do see a lot of interaction between the pure software space and the rest of the parts of the machine. I guess, you could put that in that context.
Simulation per se, you can look at it from different angles but just being able to predict accurately the optical performance of an optical sensor that you have in an autonomous vehicle is a very big deal and being able to do that accurately can be important.
And since we project that we won't have enough time to reasonably field test in the autonomous system just being able to run holistic simulation involving all the sensors and the software and the physical digital twins of the vehicle just in the virtual environment becomes paramount. You won't be able to afford to test the vehicle for ten years before you put it on the market. Just makes sure the AI and the rest of the components work well.
However, if you've got enough compute power, you can actually do that with fair reasonable accuracy in the virtual environment. And I think that's where that fits within the software ecosystem ─ being able to do that interaction correctly.
 
LEDGE:  You work with a lot of different clients solving a lot of different problems. What's the ideal client experience?
And I ask the question because a lot of freelancers work in a structure where they're working with a lot of different clients and they have a lot of different personalities and different value structures and things like that.
So there's a parallel there that you are working with people who are trying to develop really new groundbreaking innovative things and I wonder what you've learned about dealing with the different personalities and leadership types particularly of what I call the “creative class,” the inventors and the innovators because they can be, sometimes, opaque or esoteric or many of the other types of adjectives that we could come up with?
 
SINA:  One of the interesting trends I'm seeing is ─ when I consult, for instance. There are typically two classes of clientele that I deal with. The majority class are very educated and versed in the topic that they're seeking information about and they basically enter the discovery phase with a very open mind. They're open to all arguments. They're going to engage a number of consultants so they can get different views and they just digest that and come to a decision.
Whereas a minority of the clientele is simply looking for a verification of their own ideas. And I think in order for anyone to avoid making big mistakes in whatever field it is, if they're making a decision, they need to be able to do a lot of synergistic thinking and involve a lot of different views.
The best thing they can do is not enter a decision-making process with an ideal outcome in mind. Basically, just don't get married to an idea entering the exploratory phase. That is very important because I've seen a lot of people ignore tremendous amount of evidence and just enter development only to realize after having spent large amounts of money and time that certain ideas just simply doesn't pan out.
And they would have realized that if they were just open-minded going in. Keeping an open mind is probably the prime trait that I appreciate in a client and I think it is a big contributor to the success of any technical endeavor in general.
And that kind of fits in my fourth pillar under product strategy. You should not enter basically an exploratory effort with already an expected outcome that you're attached to and just simply pick and choose evidence to support that outcome. That can be very toxic.
 
LEDGE:  I wish many bootstrapping entrepreneurs would listen to that advice because it, sometimes, is a very expensive lesson to learn with your own money.
 
SINA:  Yes ─ and painful.
 
LEDGE:  Yes ─ very painful although it ends up being a good education if you stay at it. Sometimes, you need to learn that experientially with the burns and all.
Let me ask you one last question. You have been in and around engineering and sort of highly technical work and that can be stressful and you need to ─ you'll probably agree ─ free the mind, sometimes, and think about something else and think about a structure that doesn't adhere to innovative pillars and such.
What do you recommend to people to get out of their sort of system, get out of their mental model? What's been successful for you there?
I would like to use and present that knowledge to our engineers who are listening to think about “Hey, how do you get out of that chair and stop writing brilliant algorithms and do something else?”


SINA:  The ultimate goal would be to become a better person, in general. Am I correct to understand that?
 
LEDGE:  What's the position of open-mindedness and maybe work-life balance and those things?
You've got some of the ideas that say that we should work a hundred hours a week on our craft and that's all we should do; on the other side, you've got ideas that say, “Hey, we should take ample amount of breaks and we should go stare at a tree for some of our time.”
I wonder where you land on that.
 
SINA:  Personally, I'm the advocate of the second approach ─ kind of being very organic in terms of how the work gets done especially if you're seeking new things. I personally think that human thinking has evolved primarily around that.  The concept of working extreme long hours just grinding at it is really not basically our default way of functioning. And although you can actually do that for a limited period of time and see a gain in efficiency, I think, over time, if you make that same person do that same thing for years on end, you're hampering his ability to be creative, to be productive, and to be happy, in general.
I'm very much a fan of being organic about how you arrange that balance. The truth is, for those of us who are not in the manual labor business, for those of us who do primarily thinking and tinkering and developing, I seldom think that any of us really truly a hundred percent are able to stop our minds when we go for a run or when we do yoga or any other activity, a hundred percent shut off the train of thought that's related to the algorithm you're working on.
I think there's always some background process that's happening and, sometimes, you realize it more than others.
I think we should respect that. I think that's a part of the process. Personally, when I'm working on some very difficult problem at work and I'm not happy with my progress, I personally really like to just stop, go take a walk. And when I take my walk, I think about it. I haven't stopped thinking about it. I just think about it and nine out of ten times, when I get back to my machine, I implement a solution and work.
I've seen that happen so many times and I just recently realized that some pretty famous scientists arrived at that same process ─ Albert Einstein being one of them. That's how he actually organized and tackled when he had a roadblock in some derivation.
Just take a ten- to fifteen-minute walk. Think about it and be back at your desk and things work out. I seriously doubt that he just locked himself in a room for ten days to be able to tackle those problems because you get tired and a tired brain is not able to think creatively.
And that's what you need when you're facing a problem. You need to engage that side of the brain that deals with creativity and it becomes increasingly more difficult if you're tired.
 
LEDGE:  Absolutely, great advice! I've also found the same thing myself. Sina, thanks so much for your insights. I know you're a busy guy and we appreciate having you on. I know the listeners are going to love it.

SINA:  Thanks. I really enjoyed it. I wish all the entrepreneurs out there the best of luck and more useful products for the rest of us to use.

 

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