Nick Dedekian, founder of Industry Corps, Blaine Mathieu, Pratexo CEO, discuss industrial and manufacturing digital transformation
Transcript:
Blaine Mathieu:
Joining me today is Nick Dedekian from manufacturing industry-focused solution provider Industry Corps. Thanks for the time, Nick. We’re going to have some fun today.
Nick Dedekian:
Yes we are. Thanks for having me.
Blaine Mathieu:
Excellent. You bet. So why don’t you start by telling us a little bit about Industry Corps. What is it focused on?
Nick Dedekian:
Sure. So we’re a dedicated technology partner. We’re focused on serving industrial manufacturing and other process industries, like energy. So these are all folks that struggle with aging facilities, retiring experts. A lot of their best and brightest are getting to retirement age, and they might be the only folks who understand how the systems that are in place work, so that’s a challenge. And then, on top of that, they’ve got probably the most difficult labor market when it comes to trying to hire new talent that they’ve seen in a while.
So a great customer and friend told me that it used to be that in industry, it wasn’t considered a sexy destination for an engineer, but it paid better than the alternatives, whereas now, it’s neither. It pays worse than, let’s say, a Google or another alternative that an engineer would have, while also still having that unfortunate perception of being dirty, or unsafe, or just old and antiquated. So, that’s unfortunate. So, our customers are facing a lot of these headwinds. And so, we just help them improve in whatever steps they can.
So number one, just helping them quickly define the problems that they have in their operations and what the requirements would be of a new solution. Number two would be to recommend and implement real solutions. So instead of just talking, we actually have a whole suite of things that we can deploy for them. So we’re vendor agnostic, but we do spend a lot of our time researching, testing, validating, architecting, what we think are some of the best components to solutions for the modern factory. So we allow them to actually say, “Okay, well we have a problem. You guys can come solve it.” And we use very fast iterative cycles. Rather than this long and heavy, we do short, fast, see results.
And then number three is, once we have a footprint and we have success, then we provide these long term managed solutions, which each customer might need us for a slightly different long term partnership, whether that’s just maintaining the systems themselves, whether that is providing ongoing expertise and pointers, or just fully maintaining the whole solution. And then, an ongoing basis, we’ll provide advisory services to help them make foundational changes. So all of these things, they’re all centered around usually wanting to measure something, measure it better, and then take some immediate action based on the measurement.
And then, long term, once we have something that we can confidently measure and we can improve, in terms of engineering competence, then we have the ability to actually help our customers go get funding for more foundational improvements, like robotics or enhanced process automation.
All these use cases center around wanting to be able to measure something typically, measure it better, being able to take immediate action based on the measurement. So we’ll start, oftentimes, at the sensor level, introducing wireless sensors or other novel sensors to help them actually get data that they didn’t have before.
Next layer up would be the data processing. So a lot of times, they’ll have sensors and they’ll have PLCs, but they don’t really have a way to harmonize all that data together and see it. So we do a lot of work around edge computing and data acquisition. And then analytics, which is just creating rules based on measurements.
And then finally, the human interface, which is the most important part, is taking all these measurements, and these insights, and things that we can do with some combination of sensors and computers, and making that make somebody’s job better through the use of some intuitive software. So that’s probably the most fun part of it, but also potentially the most challenging. And then, from there, once we have that ability to measure, then we can start improving. So once we’ve pinpointed some of the areas and bottlenecks, then we can advise on other areas, like robotics or process automation.
Blaine Mathieu:
Sounds like an incredible amount of value you’re able to deliver to these industrial manufacturing clients. What’s your background? How did you get into this space?
Nick Dedekian:
Yeah. Didn’t expect necessarily to be in this space. Didn’t know the space existed, going back. When I first started my career, I guess nobody did, because it’s sort of a new space and a new moniker. But I was fortunate enough, early in my career, right out of the gate, to join two very experienced founders as an early team member, a very exciting novel startup software company. And we happened to be bootstrapped, so it didn’t have any outside funding, and so, as such, I think… I know our whole team, but I personally developed probably more scrappiness and creativity than I would have otherwise, which has been very valuable. So I think that is the DNA of this company.
But how I got there, we ended up selling that company, and I spent a couple years as part of the new, bigger company. And then I got a pretty exciting opportunity at an early stage industrial IOT company. And so, with that role, I was able to shift my focus and learn a lot about some of the manufacturing challenges out there, just industrial challenges in general, and got to meet a lot of great people, learn about a lot of exciting innovations solutions out there, and really just got a firsthand experience of what the challenges are with industry and why the current marketplace, at that time, and still today, was not positioned well to address the needs.
So I saw it as a lot of smart people working on individual parts of the problem in a very disparate way, with no real mechanism to help a customer. As I mentioned before, these are already people that are short-staffed, they are working as hard as they can to meet production targets, for example, and they don’t have the time to go and develop multiple subject matter experts around industrial IoT, let alone implement it.
So I saw that we needed a way to create a common language of how to talk about these things, help the customer see what’s possible, understand what their problems are, and then have a real no-nonsense approach to solving them. So there’s not really, I guess, a moniker out there for this type of company yet. I call it a dedicated technology partner. But around mid-2019, I had the idea, and just following in the footsteps of the founders who taught me most of what I know, I forked some of my modest means into the venture, and will ride it as long as it allows.
Blaine Mathieu:
Well, I think this digital transformation with the manufacturing and industrial space more generally is going to be going on for a long time, so I think you’ve got a long ride ahead of you. So I guess my next thought is, what particularly, I guess, excites you about what’s going on at the intersection of business and technology in the manufacturing space? And maybe as part of that, you can tell us about some of the more interesting projects you’re working on.
Nick Dedekian:
Yeah. I mean, I love that we can collaborate now across functions, across geographies, across companies. So the intersection of business technology and industry, I think, for one thing, this has been the last market segment to really be a focus of digital transformation. I think early on in my career, we heard a lot about it with medical, healthcare companies, financial services, other firms. But the manufacturing and really the core infrastructure was somewhat neglected. But now, it seems like that has risen to the top of consciousness and a lot of exciting… There’s a big, exciting ecosystem that’s working on the problem.
One of our most exciting applications we have right now happened very organically. It’s a company here in Northern California that does metal injection molding. And so, this is actually similar to plastic injection molding, only you’re using a feedstock that’s metal and binder. And they’re making very small, but intricate parts, so arterial clamps for surgical devices, they’re making little keys for handcuffs, they’re making just all sorts of small, metal, intricate components that would be really expensive to mass-produce in your traditional process.
And I just love it, because I walked in and I saw… Amazed by the process, but just saw such an opportunity for us to add value. They were completely pen and paper, completely whiteboard driven. So when you walk in there, you see punch cards and you see work order cards being moved from one section of the plant to another. And it’s exciting because, first of all, there’s great platforms to digitize the actual worker experience. So there were infinite opportunities to come in and add value on day one, by just digitizing procedures, digitizing PMs, automating a lot of these things, and the people liked it. So something that could add value on day one.
And on day two, in the different stages of the process, we’re able to capture data from the existing machines, the existing PLCs. So things that they could only see in very limited narrow views with the software on each individual machine, now those are converging into one analytics platform that can see the whole plant, that can see what’s going on, and can take some action. And that’s tied back into that connected worker platform.
So it brings together all those facets that I was talking about, edge computing, because there’s a lot of data coming off a lot of these machines, so you need to be able to process that and take some action without too much complexity. And really, the entire project now is starting to evolve into, “Okay, how do we automate certain… How do we do more with the existing workforce? How do we eliminate some of the mundane, repetitive tasks, like stacking trays, using robotics,” for example.
So it’s a perfect example of how you come in, you look for the easy wins, the low hanging fruit, you do that, and then that provides the framework to measure things. So now everyone’s looking at the same equation, and then we can see, here’s the bottleneck, and then we can really focus on making big improvements for that.
Blaine Mathieu:
So, a lot of this seems to be about using technology to break down silos in the organization, areas that used to be disconnected or not efficiently connected with each other? Now you’ve got from the machine all the way, to the work order, to improving the process itself. Is that a good way to think about it? Or one way to think about it?
Nick Dedekian:
It is. Yeah. It is. Yeah. Each machine has its own little screen before – you have to be an expert even to go look at the fault codes on it. And now it’s one intuitive platform that everybody knows how to use and operate.
Blaine Mathieu:
Interesting. Yep. Makes perfect sense. Now, Industry Corps is a partner of Pratexo. Maybe tell us a little bit more about how you use Pratexo in your practice, and I guess maybe why you chose Pratexo as a solution to help achieve these goals that you were just talking about.
Nick Dedekian:
Sure. I mean, it was an easy choice, just looking at the talent of Pratexo, and just having that common philosophy, I would say. From the first time we spoke, just being aligned with the fact that the customer is the one that should be determining what solutions look like. They’re the ones that know the problems, they’re the ones that generally know how a solution should look. So it’s less about us prescribing something so specific, more as us giving them the means to solve it, fill whatever gaps there were.
So that, from a philosophical standpoint, I mean, Pratexo provides us with a platform to innovate against the customer’s problems and innovate against their use cases. So a lot of what we’re doing is just capturing data from a disparate amount of sources, breaking down those silos, and then giving the customer a canvas to say, “What do you want to see? How do you want to see it? Who needs to see it? On what device,” without requiring them to be the system architect, but be the vision of what that solution should look like.
Blaine Mathieu:
Makes sense. So you’re using Pratexo in combination with other technologies to stand up, really, a flexible solution versus a pre-built, this is your pre-defined solution for manufacturing, automation, or predictive maintenance. It’s more about, what you’re able to do is figure out precisely what the customer’s challenges are, what the use cases are they want to support, and then you can use these platforms and technologies to really get exactly at their problem?
Nick Dedekian:
Exactly. And we can show them what their architecture looks like now. Here’s what the traditional architecture looks like. They can see why it’s usually not wise to try to overbuild the solution and go with older, antiquated models. And we can show them, here’s a way to build that same thing with a much leaner footprint and a much more effective and better projected outcomes going forward.
Blaine Mathieu:
Yeah. Makes perfect sense. Where do you see your customers on the evolution of the use of AI or machine learning in these applications? Is it really early days, or are some already doing it? What are you seeing there in the real world?
Nick Dedekian:
I’m seeing it’s like late-early days. So most of them have deployed some form of ML, machine learning, using usually third parties. And most of them are running up against the limits of them. Because there’s only so much value you can typically get from an out-of-the-box solution. So I would say they’re in adolescence. They’re now getting to the point where they can see, obviously, the benefits and they can see the wins that it can give them, but they also see some of the implications and limitations of various deployment models. So, it’s not a coincidence that so many of them have asked about AI on the edge and machine learning on the edge, because almost all of the solutions that they had started with were in the cloud.
Blaine Mathieu:
Right, right. Yep. And it’s all about fundamentally reducing complexity while providing the capability. You got to have two of those things. Just either one is not enough. You need them both. Well, Nick, this is great stuff, and it’s a pleasure to speak to a real-world practitioner like you, because I know you’re involved with a wide range of industrial manufacturing clients, and we’re pleased to be involved with you on some of those projects ourselves. So this is one of my favorite parts of the conversation. Let me give you a chance to maybe talk about where you buck conventional wisdom, where the market is saying X, but you’re saying Y. What do you think? Have you got something like that for us today?
Nick Dedekian:
I think so. I think the conventional wisdom… I’m looking at this in a customer’s perspective, someone who is, let’s say, a VP of corporate engineering at a manufacturing company, or director of process improvement, somebody that’s really looking at how to drive improvement forward. And conventional wisdom says, read some analyst reports, research the market, find out who the industry-leading vendors are in a particular area.
What I say is don’t be obsessed with the maker of the product, or don’t even be obsessed with the name of it, or the magic quadrant, or anything like that. Be obsessed with what does a 21st century, what does a modern best in breed framework look like? What is the solution architecture? None of us are going to be able to master each layer of this puzzle when it comes to industrial IoT, just because it spans so many disciplines, but we can figure out the general flow and, based on current technology, how things can work.
And once we’ve established that, once we’ve established, “Okay, here’s our standards, here’s our ideal framework,” then the rest of it will fall into place. You’ll be able to be much more precise when you go and evaluate new solutions. You’ll be able to be much more dangerous when it comes to negotiating, and all sorts of other benefits. So I would say, take the time to figure out what the framework and the structure looks like, and less so about just being obsessed with who’s the visionary or the leader on the magic quadrant.
Blaine Mathieu:
Interesting. Well, as a former Gartner analyst myself, I concur 100% with that assessment, having been involved in the development of these things in the past. Right on. So, any technology or business predictions for 2022? My last question.
Nick Dedekian:
I think it’s going to be simple. I think IoT, I mean, this is the focus here, or innovation at the edge, is going to be simpler and more complex at the same time. So I think you’re going to see cheap out-of-the-box… I mean, Amazon has a great example with their vibration sensors that you can buy on Amazon, of course. But the bigger picture is going to be ever more complicated, because you’re going to not only have more projects taking place.
Now that it’s cheap, you’re going to have, at an organization, more unilateral department level, or even plant level decisions about different technologies that get put into place. So it’ll be a little bit murkier about how do we wrap our arms around all of this and try to get to a cohesive data strategy where we can at least measure everything the same way across the board. So I think it’ll be easier to make it harder in 2022.
Blaine Mathieu:
Yeah. Well, I know what you’re talking about. And obviously, that’s the focus of Industry Corps, to help clients make it through that complexity in a successful way, and Pratexo is very happy to be helping you, help your clients do that, Nick, definitely. With that said, I think that wraps it. Nick, thank you so much for joining us today. It was a really insightful conversation, speaking to somebody like you.
Nick Dedekian:
Yes, indeed. Right back at you.
Blaine Mathieu:
So for those that are interested in hearing more of Nick’s thoughts, you can go to industrycorps.com, C-O-R-P-S, dot com. Also follow Nick on LinkedIn, and I guess even on Instagram at industrycorps.0, where you can see photos of some of their implementations. For those on Instagram, check that out.
Nick Dedekian:
Yeah. It’s getting a little stale. I got to refresh that. But yeah, it’s cool. You’ll see deployment photos and very cool machines.
Blaine Mathieu:
Excellent. Well, I’m looking forward to that myself. And of course, you can reach out to me on LinkedIn or via email at edge@pratexo.com. Thank you, Nick.
Nick Dedekian:
Thanks, Blaine.
