Episode Transcript
[00:00:00] Speaker A: Manufacturing has spent years investing in digital tools, connected systems, and now, obviously, AI. But let's be honest, if the underlying data still lives in silos, a lot of that value never shows up.
So today we're getting into interoperability, why it matters, why it's been so hard to solve, and why this may be the issue that determines how fast smart manufacturing actually moves from concept to. To reality.
This is Ctrl Alt Manufacturing.
Hello, hello. Hello, everybody. Welcome back to the Kontrol Alt Manufacturing podcast, Resetting and Rethinking Manufacturing, where we help explore some of the people, technologies, and strategies that are driving the digital transformation of manufacturing.
I'm not Stephanie Neal. I'm Gary Cohen. That's Stephanie Neal right over there.
[00:00:57] Speaker B: I'm Stephanie Neal. Happy to be here. Good to see you, Gary.
[00:01:01] Speaker A: How are you? I actually realized, by the way I've been doing this, this is control Alt Manufacturing. I think I realized this morning I stole that from Marketplace on npr. So apologies to all the people on mar. I just was thinking about it and I was like, where have I heard that before? And they always do. This is Marketplace. And I think that's where I took that cadence from. So.
[00:01:20] Speaker B: That's all right. It's. It sounds cool. I mean, I hope you don't get some sort of.
[00:01:25] Speaker A: I know I probably shouldn't have admitted that.
[00:01:28] Speaker B: That's all right.
[00:01:29] Speaker A: I can change it. It's not a big deal. So how are you doing today, Stephanie?
[00:01:32] Speaker B: I'm good. How are you? Every time. I feel like every time I talk to you, you're on the way to a badminton tournament.
[00:01:37] Speaker A: I am off. I wish I could say I was the one playing badminton now. My daughter plays badminton and it's been. The season just started and the last two nights we have gotten home and eaten dinner after 9 o' clock because they've been long, long evenings of badminton. But she's doing really well this year. So I've got a son who is in baseball and a daughter who is in badminton, and they both start at the same time. And apparently I've just chosen that my children can only play sports that start with B. So badminton, baseball, basketball, bocce, base jumping.
[00:02:12] Speaker B: Wow.
[00:02:13] Speaker A: No, I think I'm out of Beast boys. That's all I got.
[00:02:16] Speaker B: I didn't know that. I didn't even know that badminton was student sport.
I was so impressed when I heard that.
[00:02:22] Speaker A: But it is. It's kind of funny, too, because it's. You know, there's the backyard sport where people are just popping it up and then you watch people who are really good at it, and it's. It's impressive. It's. It goes fast. They're hitting it hard.
I grew up playing tennis, and so I'll try to play badminton with my daughter, but I try to hit it like a tennis ball, which is not what you're supposed to do. And the birdie does not go where you want it to when you hit it like that. So, yeah, she had to teach me how to hit it. It's basically like underhand or overhand seems to work well, but if you try to hit like a forehand or a backhand, it's no good.
[00:02:57] Speaker B: Well, you guys, you're a sports family. If we go back to the. Something about Gary, I don't know if our listeners know that he used to write for the Chicago Cubs magazine. Is that right?
[00:03:09] Speaker A: Yep. I ran their publications department for about a decade from 2011 to 2018, continued working with them through 2020. So, yeah, I was there in the press box most days, in the clubhouse most days, and was there for the World Series run. And, yeah, it was great.
And I grew up playing baseball, and my son plays baseball. And so it was.
I used to say, even when I was having bad days, you'd look around and I'd be up in the press box and it'd be 85 degrees and sunny, sitting at Wrigley Field, and I was like, you know, I can't complain. This is about as good as it gets.
[00:03:47] Speaker B: So, I don't know, is it better than, you know, writing about technology?
[00:03:52] Speaker A: No, of course not. No. But the whole, like, touch grass movement that's going on right now, I always felt that way. I was like, I'm standing on grass outside in the middle of a work day. That's not a bad way to be.
[00:04:02] Speaker B: Yeah, not bad at all.
[00:04:04] Speaker A: Yeah.
This actually, I think, should be a really interesting podcast, a. Because we've got an old friend of yours on, but we have John Deutsch, CEO of Smart Manufacturing Institute, John Harrington, chief Product Officer of High Bite. Have a conversation about, as I said in the intro, interoperability and why that might kind of be the missing piece in smart manufacturing and what it will take to turn all this industrial data that we have into actual business value.
[00:04:34] Speaker B: Yeah. And I mean, in all the podcasts that we do are always, you know, we obviously are always talking about digital transformation, and we're talking about, you know, how we're getting the data, but there's a lot of data silos. And so being able to have that interoperability is critical to the process and I think it's been overlooked and maybe, or maybe not overlooked, maybe it's just been really hard to do. And I think that's what we're going to discuss here today.
[00:05:01] Speaker A: So I was going to say I think for a while with the data that was coming in and maybe pre AI companies could kind of power through, you know, they custom integrations or one offs or frankly just a lot of heavy lifting and heroics on the part of the internal team. But that's pretty hard to scale and hard to build on.
And now that smart manufacturing is coming to fruition and you know, people want real time visibility and better decision making and AI that's grounded in real data, I don't know that that works as well anymore. So yeah, interoperability I think used to be just kind of a technical headache and now it really is a business issue which I think brings it to the forefront.
[00:05:48] Speaker B: So that's why we have such a great conversation today because John Dyke, who's an old friend of mine, we've known each other since way back when, I won't say how many years because I don't want to age myself or John, but let me read their bios and we can, and then we can introduce these guys and have this real conversation so. John dyke was appointed CEO of SET, the Smart Manufacturing Institute in June of 2018.
John is known globally as a domain expert on digital transformation in manufacturing operations and supply chains, understanding the intersect between strategy, business outcomes and the innovation required to advance them. John was recently awarded a number of patents for the application of AI and analytics and manufacturing workflows and business processes. And prior to joining Sesame, John held senior leadership positions at GE and Rockwell Automation and was effective in raising VC funding and building a successful software startup called ActivePlant. John currently serves on the Board of Directors for the Manufacturing Leadership Council and the Cyber Manufacturing Innovation Institute. Welcome John. Thanks for joining us today.
And then I'm going to also introduce John Harrington.
John is the Chief Product Officer of Highbite, focused on defining the company's business and product strategy. His areas of responsibility include product management, customer success, partner success and go to market strategy. John is passionate about delivering technology that improves productivity and safety in manufacturing and industrial environments. John received a Bachelor of Science in Mechanical Engineering from Worcester Polytech and Master of Business Administration from Babson College. Welcome John Harrington. Thank you both for joining us.
[00:07:29] Speaker C: Thank you.
[00:07:29] Speaker D: It's good to be here. Thank you.
[00:07:31] Speaker A: I'm going to start just by laying out our problem here. Which is we've invited two johns onto the podcast, which I think we probably shouldn't have done.
So we're have to go full names here with John Dyke and John Harrington so we know who we're talking to before we dive in here. I say we just start at the start and we'll go. John Dyke, can you give us a quick overview of the Smart Manufacturing Institute, how it came to be and what its purpose is?
[00:08:00] Speaker D: Yeah, thank you. Appreciate the chance to be here with you and to have this conversation.
The Smart Manufacturing Institute was a glimmer in some really, really smart people's eyes. Fifteen plus years ago, and then probably closer to eight years ago, the US government, the federal government, kind of figured out that advanced manufacturing, smart manufacturing was a really important part of how we become and stay more competitive and more productive as a manufacturing nation.
As one of the many Manufacturing USA Institutes being roughly 17 at this point, Manufacturing USA Institutes created in various domains to drive that or support that cause, the idea that we need to become more competitive and more productive. And so we were stood up and at a macro level, that is our objective. Specifically in the domain of smart manufacturing, which we're going to talk more about, we have a stated goal to reduce the time to implement smart manufacturing and the cost to implement smart manufacturing by 50%.
That's our goal. By 2030, we need to be able to demonstrate that the cost and time to implement smart manufacturing and the complexity to sustain smart manufacturing is reduced. So I could go on for some time about the specific objectives that are in front of us as an institute. But fundamentally that's what we are. We're a consortia, we're a gathering of the great thinkers, the leaders, the organizations, the individuals in this space, including my friend John Harrington from High Byte as a great example of one of the great software providers in this space engaging with us to collaborate. And that's a really important principle here. As a, as us, as US citizens, we're individualistic as people. We tend to be more individualistic as companies as well. And so unlike Europe and much of Asia, where collaboration is part of their DNA, it's really not part of ours. And so I bring that up just because it's kind of a new thing. And we're super excited to see the growth in great individuals, great organizations like Highbite and many others that, that are actually willing to collaborate to solve really, really hard but strategic and important problems.
[00:10:34] Speaker A: Absolutely, we talked about that in a recent podcast. This idea that we want to have all of our information in silos and not give away our special sauce, which obviously is very important for businesses. But the more collaboration happens, the faster things tend to move forward.
[00:10:49] Speaker D: You bet, yeah.
[00:10:51] Speaker A: On this podcast, we talk a lot about digital transformation.
John Dyke, is smart manufacturing a synonymous term with digital transformation, or is this the foundation upon which to build that digital transformation strategy?
[00:11:06] Speaker D: So I could give you the purest answer or response and say there are distinctions between digital transformation, smart manufacturing, even Industry 4.0, but I've learned over the years that those are nuances and that manufacturers generally see these categories, these spaces, these names that we attribute to or tie to an initiative like this as a means whereby they create initiatives within their organizations. Some call it industrial transformation. Some will say this is our digital manufacturing strategy. Some say smart manufacturing. Some call IT Industry 4.0 or Manufacturing 4.0. My friend David Brucelle at MLC, they've sort of double clicked on the idea of manufacturing 4.0. So in my mind they are synonymous because most manufacturers see them as ideas or initiatives around which to bring their community together to invest in and to drive these strategic capabilities forward.
[00:12:09] Speaker B: I just want to say I know your friend David Bristol. I used to work with him. You'll have to tell him I said hello.
[00:12:16] Speaker D: Would love to, absolutely.
[00:12:19] Speaker B: So, switching gears a little bit, John Harrington, tell us a little bit more about Highbite, what the company does and how you got involved with the Smart Manufacturing Institute. What does it bring to the table for your company and your customers?
[00:12:32] Speaker C: Yeah, thanks, Stephanie and Gary, thanks for having me on.
So Highbite is a data integration platform specifically designed for industrial data for manufacturing companies.
And, you know, we recognize when we founded the company that the integration challenges are what's actually, you know, similar to what John Dyke and Sesame found out, that data integration and data utilization is what's holding companies back, it's what's slowing down the adoption. Now, we founded the company back in 2018 and AI wasn't a big topic. It was a topic, but a lot of people were talking about IoT, a lot of people were talking about the cloud and how do we leverage industrial data. And what we realized is for all these projects, we've got all these great technologies to develop applications, but no one could get access to data that they could use.
And so we really founded the company around the premise of accelerating companies through smart manufacturing OR, or Industry 4.0 or digital transformation. By enabling them to get access to good data, high quality data, contextualized data, and being able to do that at scale. It's easy to do for one data point is easy to do even for one pump or one asset. It's really hard when you're doing it for a thousand assets within a facility, for multiple lines, for multiple work cells. And then you want to do it across your enterprise, and you want to be able to analyze the data across the enterprise. You want similar consistent data across the enterprise, so you can really drive the adoption of smart manufacturing. So that's really what we focus on. And so, you know, partnering with Sesame was kind of a match made in heaven from the perspective that, you know, we're a technology platform focused on the same general business problem that Sesame, as a industry group that's funded by the federal government to drive manufacturing, is also focused on.
[00:14:47] Speaker B: So, interesting. So you did already say, you know, I had a question for you about, like, the lack of integration between technology stacks. Does that stall progress in the industry? You basically said yes. So your company is.
The platform itself is like a data architecture, like industrial Data Ops. Is it. Does it come from the IT side and you're implementing it for OT or how does that work?
[00:15:12] Speaker C: Great question. So DataOps has been a concept on the IT side, and integration platforms have been a concept on the IT side for a number of years.
In the OT side, we didn't really have them.
A number of years ago, the big focus was on, you know, protocols like opc, where we could get the data into the SCADA system or the MES system very efficiently.
And there were a number of platforms that were focused on that problem.
But with this new, you know, ever since probably 2015 until now, the focus isn't so much moving the data into the SCADA or the MES platform. The focus is extracting the data, leveraging the telemetry, but extracting the data out of those platforms and merging it with the MES or the asset maintenance or the quality system or the power consumption system, or the providing the data to the design engineering team, and the list kind of goes on the supply chain team. And how do we leverage data across the business? Not just on the plant floor, we're still working on solving that problem, but how do we leverage it across the business? And you talk about silos. It's funny. Every time people put in a new system to help with this, it effectively creates another silo. And so how do we seamlessly move data across these different applications so that we can leverage the people that need the data, can leverage the data, and then they can share their knowledge with others? So, for instance, we may be moving data into the cloud for a data scientist to perform some analytics on it, but then they want to move their learnings back, back onto the factory floor to an HMI where an operator can respond to the learning. So, you know, there's a lot of data moving and whereas. I'll say Whereas with Industry 3.0, the focus was just on the SCADA and MES. Now we're seeing, and there was maybe one to five applications that wanted telemetry data. Now we're seeing, you know, 10 to 50 applications that want access to that data. And with AI, we expect that number to probably explode by a factor, factor of 100, because we're going to be utilizing and seeing AI agents.
So there's just a lot of, you know, data consumers. They all have their own needs, and that's what we're really focused on, being able to deliver that data.
[00:18:00] Speaker D: Can I, can I respond to that as well?
[00:18:03] Speaker A: Yeah, please.
[00:18:03] Speaker D: It's almost.
Thank you. It's almost like in the IT community, it's taken them four decades to kind of go from a data analyst back in the 70s and 80s to the CIO office with the rigor and the focus on standardization and sustainability of applications and everything that they've learned over those decades. It's like the OT community has essentially avoided all of that because of the fundamental need to keep the lines running, keep the shop running, keep, keep. You know, it's Bob and Sue and Fred down on the shop floor doing anything they can. We kind of talk in just a little bit about the duct tape and the bailing twine mentality that, that just gets these, get.
These people are highly innovative, but, but in the process of innovating and fixing problems, there has been no opportunity to standardize. No, no opportunity, in fact, nobody even pushing them to drive towards some level of interoperability because it's just about the moment and solving this problem right now. But I think that's why we've reached this sort of.
If we look at manufacturing productivity data here in the US by worker, we've been plateauing and declining for the first time in a recorded history. And I think it's in part it's this issue, the systems, the methodologies, from what John, rightfully, John Harrington, rightly referred to as the industry 3 of 3.0 approach, these data silos and these stovepipe architectures, that is no longer sufficient to solve the next generation of problems, to drive AI at scale, to solve the productivity challenges that manufacturers are really struggling with right now.
[00:19:51] Speaker B: Wait, I just want to jump in. Did you say bail and twine mentality.
Did I hear that correctly?
[00:19:57] Speaker D: Bail and twine. Yeah, I'm sorry, I'm a farmer by bail and buddy.
[00:20:01] Speaker B: I love it.
[00:20:03] Speaker A: I caught that one too.
You know, I love a good turn of phrase. Yes, to both of your points. Obviously interoperability is a huge issue right now. So let's talk John Dyke, a little bit more about what Sesame is doing here with the recent launch of the Industrial Information Interoperability Exchange and how that is helping us overcome this interoperability issue.
[00:20:28] Speaker D: Yeah, so we announced this initiative in the fall of 2024, recognizing that this is a really, really important area for US manufacturers to engage and to solve something in a pretty competitive way that literally doesn't prevent, that drives our capabilities forward, but doesn't get in the way of their desire to differentiate themselves and to sort of manage their intellectual property.
We were pleasantly surprised to see an incredible response from US manufacturers and the more forward looking manufacturing technology providers like Highbite and a handful of others as we'll discuss here in a moment, who said, wow, there has been no center of gravity willing to tackle this problem.
Let's get together and solve this. And so probably I'm going to say 10 to 12 companies, some of the brightest minds in the world from 10 to 12 companies, manufacturers and technology providers, including High Byte, agree to sit down, meet every two weeks, which is what they've done for a year and a half now, to build a specification that solves this problem in a way that no other organization can or certainly has tried to tackle. And so it's been an extraordinary,
[00:21:52] Speaker C: it's
[00:21:52] Speaker D: been an extraordinary thing to see great companies that compete head to head day after day, but really working together in a genuine way to solve this problem.
[00:22:04] Speaker A: With that we'll jump back over to John Harrington. So you're on the launch working group for this organization. How and why did you get involved with this initiative? Why did you think this was so important?
[00:22:14] Speaker C: So we integrate with a lot of different applications and we have a platform, it's a no code platform so that people can create these integrations. But even with that there's still a lot of work. And you take five different applications that are all consuming industrial data or all producing industrial data and they all have their own APIs and they all have their own data structures and they all have their own way of doing things and they all have limitations and advantages and it is what slows things down. And so when you know, and we recognize there are different needs, there's needs to structure the data properly there's needs to be able to understand those structures, be able to see what data is available and then to be able to get access to that data.
And so, you know, we were recognizing that there was these challenges and when Sesame started talking about, you know, we see this challenge and we're trying to solve for it, we just thought this, this would be a perfect solution to this. It would accelerate our customers and it would accelerate the ability of them to, you know, leverage this data and put it to use much, much faster. And so we got on board and helped as, as John said, our CTO joined the steering committee and in driving the, the definition of the specification based on all of our learnings because we have over customers who are all doing this sort of thing and they've deployed at hundreds of sites. And so all of those learnings we're able to put together with, as John said, a number of other players in the industry to build this specification up. And yeah, I guess we'll probably get into some of the results that we've seen recently. But yeah, it's exciting for us.
[00:24:13] Speaker B: So just to follow up, I mean, where are you with the implementation of the specific and you know, what problems do you eventually see it solving?
[00:24:24] Speaker C: So in terms of. So the spec is in an alpha state and I think it's very soon going to be released. But we are releasing the next version of our product, which will include the current version of the spec next week. So that's going to be widely available to all of our customers and we've seen some other applications be able to adopt it very quickly and then we're able to just integrate the system so much faster the customers can move data across the different systems so much faster.
[00:24:59] Speaker D: If I can jump in briefly to add that interoperability sort of allows the separation or an abstraction between the platforms that provide the data, the rules, the structures, really the, the context along with the data and the actual applications themselves.
A lot of the current technology providers out there, in fact most of them, if not all of them, represent sort of the one monolithic structure where they are the platform, the data ingestion store, contextualization platform, plus the application, all hardwired together.
The reality is any industry that has successfully scaled has created an abstraction between that infrastructure layer, the platform call it, and the application layer. And so what's exciting here is that for the first time in this space, we get to provide that abstraction layer using a standard, using this API, this new standard, Open API and Empower, Inspire, create, see the facilitate the creation of a Whole new community of application developers who can innovate, build AI, model, commercialize them, bring them to the marketplace.
Apps and models that are built against an API, not against any one vendor stack. And so we believe this will have an extraordinary impact on the pace of innovation in this space and also the overall adoption of smart manufacturing, because this is a significant reduction in cost and complexity and an accelerator for the innovation for the great innovators in this space.
[00:26:48] Speaker B: So, John Dike, I want you to maybe expand on that a little bit more, especially when we're talking about these monolithic platforms that the vendors roll out, which probably helps them sometimes because they have a captured audience when they are implemented into a manufacturing site. So I totally understand why end users and manufacturers are excited about this, but it seems like the reaction has been significant in both the manufacturing community and the software providers. Can you explain why the software providers are so excited about this?
[00:27:22] Speaker D: Well, in part. So you're exactly right.
I grew up in the technology provider vendor space with Rockwell and GE and activeplant, as noted. And so I've been part of this community building what we kind of call vendor lock in into our stack. Not that we had any choice. Like I said before, there is no center of gravity up to this point around any effort to really, truly address that. And so the technology providers, the vendors in this space, some will do this because it's the right thing to do for their, for their marketplace. Others are much more focused on kind of a, call it a legacy mindset that says, yeah, we know our platforms are sticky. We know that vendor lock in is kind of a way to drive recurring revenue. And that's been our bread and butter. But on the flip side of this, we have this incredible set of manufacturers that are part of our community here across all industries, all sizes of manufacturers.
But it's been the collaboration between those large manufacturers, large and small, medium manufacturers, formed as the Smart Manufacturing Executive Council, which have stepped up, stepped to the podium to say that kind of Industry 3.0 approach to building data silos and vendor lock in, no longer sufficient. We recognize it's going to take some time to transform the way these big companies work.
But we want to work with companies with technology providers, consultants, integrators that recognize that there's a new way to do this, that interoperability and openness has to be part of our mindset as we develop architectures and solutions and capabilities going forward. And so Highbite's one of the former organizations with the mindset I mentioned up front.
This is A young company, an incredibly smart set of people that recognize this is what their customers want.
But the dawning realization across the industry, and this is kind of the latter part of your question.
Since we soft launched or pre launched i3x at Pruvit four weeks ago on the stage with us there was obviously High bytes, Inductive Automation, AWS, Siemens and ACE Technologies. Six different vendors. We've had almost 30 vendors, including virtually every company that matters in this space come to us and say how can we be part of this? How can we contribute? How can we build this into our roadmap, into our portfolio? And so that to me has been unbelievable. It's been gratifying on a whole new scale to say, yeah, the industry has finally figured out that there is a better way and that there is an opportunity for every vendor. If we increase the size of the pie, we believe that every vendor is going to be able to succeed and grow their business with this new mindset.
[00:30:37] Speaker B: Yeah, just quickly, John Harrington, I wanted to see if you had a reaction as well.
[00:30:42] Speaker C: Yeah, thank you.
I think with people talk about it and OT coming together and what that really means is that companies are leveraging their data outside, like I said earlier, outside of the factory floor. They're leveraging it across many different business functions, engineering functions, purchasing functions, quality functions.
And so you almost can't have that full stack, one player because now we've got, you know, Amazon is in the mix and Microsoft are in it. And in fact both of them are partners in this.
You've got AI models, so the AI players are in the mix. So like technology has gotten, it used to be much more contained and you could have someone who's providing the full piece and is so wide now that we have to recognize that we all live in an ecosystem and that you can't just, no one is providing one thing and you have specialists. So some people are very good at quality and some people are very good at discrete manufacturing with robots and some people are very good at process manufacturing for pharma.
So we've got all these specialists.
Some are very good at focusing on controlling the line and some are great at providing data and analytics and leveraging that. So it's really about being able to enable these ecosystems to operate within our customer base, right within the manufacturers, so that they can take advantage of the premium products that they want and take advantage of the non premium products, the products that are run of the mill inexpensive because it's not a key function for them. So it really gives the decision making capabilities back into the manufacturer to decide what systems do I want to pay up for what systems do I need as core technology and be able to easily integrate and move data across them and not have to be beholden to the amount of effort that that used to take.
[00:32:53] Speaker A: Sure, because no control Alt Manufacturing podcast is complete without a question about AI.
John Harrington, I'll ask you, do you think this i3x initiative will facilitate a more effective approach to scaling AI in manufacturing operations?
[00:33:09] Speaker C: Yes.
And the reason is what AI does is it's taking the number of applications and is dramatically increasing it. Because AI agents work best when they're highly focused. And so what you end up with is a lot of many really highly focused agents. In fact, sometimes we'll tell people like imagine every work cell has a quality agent, every work cell has a inbound supply agent, every work cell has an asset maintenance agent, and they're all looking to collect data. Well, all of a sudden, the quantity of applications and agents that need access to data is dramatically increased.
And i3x enables us to just integrate all these applications so much faster and so much easier. And they know exactly what data they have access to and how to leverage that data. The data is contextualized and it's usable, which just accelerates it.
Whether those agents are running in the cloud or whether they're running at the edge, it just really scales up the ability to manage all that.
[00:34:24] Speaker A: Sure.
John Dyke, you've mentioned in the past that the manufacturing industry is at an inflection point on how innovation is approached right now. What did you mean by that?
[00:34:35] Speaker D: So because we've kind of created this technical debt, this mess, I'll refer to it as a mess. The reality that every manufacturing organization has these literally dozens of, on average, fortunately, 1,000 manufacturers, over 50 sanctioned corporate sanctioned data silos, and still pipe architectures built up over three decades, you1 use case at a time, one problem solved at a time by some really, really smart, innovative people.
The inflection point today, now that interoperability is a genuine production ready capability, is the next use case. Is it going to be another data data silo, another stovepipe architecture? Or will we do the work and move towards a modern, interoperable, standardized set of architectures that facilitate the kind of innovation that we're now capable of enabling? And so it truly is an inflection point that black and white to say, because we will be solving the next 5, 10, 15, 20, 50 use cases over the next five years, will we do it the old way? The industry 300 way will we do it the new smart, interoperable way? And frankly, the cost is going to go down. So it really becomes a matter of will you as an organization make this a strategic priority or are you going to revert back to what you've done for the last three decades?
[00:36:10] Speaker B: So, a couple of final questions.
You know, Sesame does a lot more than what we've been talking about today on the podcast. So, John Dyke, are there other working groups or other things that are happening within the coalition that will help move the industry forward?
[00:36:27] Speaker D: Yeah, I appreciate that. Interoperability is often confused with kind of the thing. At the end of the day, it's an enormous accelerator and enabler.
But what it drives, what it necessitates is the solving of problems, the acceleration of decision support, the velocity of decision making at scale, AI adoption at scale. AI and interoperability in my mind are fundamental capabilities that enable problem solving. So what problems do you solve?
How do you justify, what's the cost justification for interoperability?
It's in solving those problems and accelerating the ability to solve those problems.
We talk about shadow. I Talked about the 50 systems on average sanctioned OT IT systems in the average Fortune 1000 company.
Back in the plants, there are on average over a thousand shadow IT systems that the plants have innovated and created. And this Excel worksheet, that access database still running on Windows NT somewhere, those shadow IT systems are there for a reason.
Can we now take shadow IT and make it sanctioned innovation, but doing so in a scalable, secure, sanctioned way enable those plants to do what they need to do? So all of this is the and story. It's the interoperability and innovation. What problem are we going to solve? How are we going to solve it? How are we going to do it in a way that now scales so that thing that Fred or Sue made in plant one can be lifted and carried into plant two through 200 with very little extra work. That is, that is the opportunity in front of us.
[00:38:14] Speaker B: Yeah, and I think we, I mean we've talked about that a lot in the past. Like how do you take what's been, you know, implemented successfully in one plant and move it to another, but it required a lot of work to do it, so you're alleviating that. So my last question for both of you, with all this great work going on behind the scenes, what will a smart factory look like in five to ten years from now?
And do you think it would surprise people?
Are we talking about a very different looking factory in five to 10 years?
[00:38:47] Speaker D: I'm happy to start.
First of all, I do think that autonomous or semi autonomous manufacturing is a must for most manufacturers here in this country.
I think the need to drive productivity, the fact that we're reshoring near shoring front shoring a lot of our operations with a critical worker shortage.
As much as we've kind of talked in decades gone by about will automation change the workforce, will it allow us to take our workforce and drive it to, to where we can be more productive, the reality is we, we're projected to be 2.3 million jobs short by the year 2030. And so above and beyond what AI is going to do and what automation's gonna do, I think that in and of itself, rep represents an urgent need for the idea of autonomous manufacturing or semi autonomous manufacturing. So that's something we see most large manufacturers driving towards today. I think lights out for most organizations is not a reality, but I think semi autonomous or something approaching semi autonomous manufacturing is a must see. Elon Musk said recently that if you're not moving atoms, your job won't exist in five years.
I'm struggling to see what that looks like in manufacturing because we're fundamentally about moving atoms.
So while AI will have a dramatic impact on manufacturing, I do think it's, you know, if you're making stuff, if you're making things and making stuff, you're going to be well and gainfully employed in five to 10 years, continuing to make it in a more productive way
[00:40:39] Speaker B: here in the U.S.
john Harrington, any final thoughts? What are you talking to your customers about and what do you projecting will the factory of the future look like?
[00:40:53] Speaker C: So, you know, I certainly agree with what John said.
I think, you know, the goal is that it'll be more efficient, it'll be cleaner, there'll be fewer people. But it's not fewer people because we're stripping people out. We certainly, you know, I agree with the semi autonomous, but also because various business functions can stay in their office and get access to high quality data, it'll be much more organized because they'll be able to come down when the factory is stopped, they'll be able to repair the equipment that needs to be repaired.
And then when the line is running, when the factory is running, there won't be breakages, there'll be less waste created, there'll be less leakage, less line down, where all of a sudden you have to pull 10 people all together to figure out what's going on. How can we react, oh, we need to fly these parts in. It'll be much more disciplined because people will be able to get access to information quicker. We'll be able to predict, you know, problems before they do, and therefore, we can react to them. And so it just, it's a less hectic, reactionary environment and much more planned and execution focused environment, which sounds like
[00:42:18] Speaker A: a very positive thing. Yeah, that's a great way to wrap things up.
John. And John. John Harrington, John Dyke. Thank you gentlemen, so much for being on here. Great information. Really appreciate you guys.
[00:42:29] Speaker B: Thank you so much.
[00:42:30] Speaker C: Appreciate the time.
[00:42:31] Speaker B: Good to see you.
[00:42:32] Speaker C: Thank you. Likewise.
[00:42:34] Speaker A: All right, guys. Bye, everybody.
[00:42:36] Speaker C: Bye.
[00:42:37] Speaker A: All right, Stephanie, There we go.
I think we safely navigated the John Quagmire.
[00:42:43] Speaker B: Yeah, yeah. It was a little bit of a maze, but we did it.
Listen. Such a great conversation and I walked away with a lot of good information. I know you walked away with a good soundbite.
[00:42:55] Speaker A: I saw you writing.
[00:42:56] Speaker B: Yeah.
The bale and twine mentality soundbite. That's going to live on forever.
[00:43:03] Speaker A: I know. Yeah, absolutely. One of the things that John Dyke said early on, and, you know, this is one of my little pet topics, but just that idea that in the US we are trained to be so individualistic and to protect, to guard our secrets closely. And I think this, the, you know, what they're doing there, the kind of i3x idea of getting these major manufacturers and there are big companies in that list that he rattled off to work together to move the industry forward in a better way, to build better decisions to make a better business case.
I just think that's such a useful thing versus that kind of fear of we can't talk to other people or they'll, whatever it is, we'll lose our place in the market or they'll steal our secrets or. I just think it works so much better when people are collaborating. Obviously, you've got to protect, as I said, your special sauce, whatever the, you know, your herbs and spices are. But, yeah, I do love this idea of collaboration, which is why sesame is of interest to me.
[00:44:14] Speaker B: Yeah.
And John Harrington said we all live in an ecosystem and we can all live there together because there's specialists. Right. Like you said, everybody's got their special sauce that they bring to the table, but we all can work collaboratively and there's room for everybody at the table. If we're going to move this industry forward, we have to start talking, we have to start collaborating, we have to start sharing.
So, yeah, let's, let's keep this going.
[00:44:43] Speaker A: Sounds absolutely.
Yeah. Perfect. All right, Stephanie, let's get the bailing twine out and wrap up this podcast.
[00:44:50] Speaker B: Good.
[00:44:51] Speaker A: So just want a little credit for that one. Thanks so much for joining us as always. Please follow like subscribe to the podcast. Always happy to have you guys listening. We'll have more great conversations if you know people. If you want to talk to Stephanie or I, please look for us on LinkedIn.
Or you can go to the Control Alt Manufacturing LinkedIn page. We're checking that as well.
If you have topics you'd love us to cover, people you think that we should be talking to, should we be talking to you? Let us know.
[00:45:21] Speaker B: Yeah, we love the feedback. So please keep in touch, drop us a note and we will continue the conversation until next time.
[00:45:30] Speaker A: Absolutely.
[00:45:32] Speaker B: All right. Have a wicked good day.
[00:45:34] Speaker A: Bye everybody.
[00:45:35] Speaker B: Bye.
