Guest Episode 1: Beyond the Hype: Practical AI That Makes Manufacturers More Profitable, with Benjamin Karrasch

Manufacturers have more data than ever. Benjamin Karrasch with Corello AI

ERP reports. Production data. Inventory reports. Financial statements. Customer information. Dashboards everywhere.

But in the leadership meetings I sit in, the same questions still come up.

The problem is rarely a lack of data.

It is a lack of clarity.

In my first guest episode of The Manufacturing Money Room, I sit down with Ben Karrasch from Corello AI to have a practical conversation about what artificial intelligence can actually do inside a manufacturing business.

We move beyond the hype and discuss how AI can help manufacturers reduce administrative work, preserve tribal knowledge, improve quoting, support multilingual teams, expand capacity, and make better use of the information they already have.

We also talk about the concerns many owners have:

•  Will AI replace people? 
•  Is it only for large manufacturers? 
•  Does it require a major technology investment? 
•  How should manufacturers protect sensitive business data? 
•  Where should AI show up in profitability—not just productivity? 

As a CFO, I am not interested in technology simply because it is new.

I want to know whether it helps a business make better decisions, improve capacity, protect margins, and become more profitable.

So instead of asking:

“How do I use AI?”

I want you to ask:

“What important decisions am I still making with uncertainty?”

That is where the real opportunity may begin.

Connect with Benjamin:

Website
https://www.corello.ai/

LinkedIn
https://www.linkedin.com/in/benjamin-karrasch-00b4a4127/

Tolani Lawson, CPA is a finance leader with experience at KPMG, WestRock, and Air Lift Company, specializing in manufacturing finance, FP&A, and helping businesses improve cash flow visibility and decision-making.

Got a question about something you heard today? Have a great suggestion for a topic or know someone who should be a guest? Reach out to us:
Email:  tolani@fiscal12.com

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Transcript

Introduction

VoiceOver 0:01

Welcome to the Manufacturing Money Room with host Tolani Lawson. Tolani is an experienced CFO who works with manufacturing businesses to bring clarity to their numbers, especially when cash feels tight and decisions feel heavy. These are the conversations that usually stay behind closed doors. Until now, it's time to step into the Manufacturing Money Room.

Tolani 0:28

Welcome

Why manufacturers don't have a data problem

Tolani 0:29

back to the Manufacturing Money Room. I am your host, Tolani Lawson. One of the biggest meets in manufacturing today is that companies have a data problem. And really they don't. Manufacturers have more data than they've had ever before. We've got ERP systems tracking production, accounting systems generating financial reports, CRMs full of customer information, inventory reports, quality metrics, machine data, really just dashboards everywhere. Yet, despite all that information, I still walk into leadership meetings where the same questions come up over and over again. And the problem is not a lack of data, it's just the lack of clarity. And as a CFO, my job is to help manufacturers turn financial information into better decision making because reports don't grow businesses, but good decisions do. Today, this conversation is about the next evolution of that idea. Artificial intelligence has become one of the hottest topics in manufacturing, really, depending on who you ask. It's either going to revolutionize your business or it's going to be just another buzzword. So today we're going to cut through the hype. We're going to talk about what practical AI actually looks like inside a manufacturing company, where it creates real business value, and how we can apply that in practice. Today I have here with me Ben.

Meet Benjamin Karrasch and the story behind Corello AI

Ben 1:57

Hey, happy Friday, Tolani. Thanks for having me.

Tolani 2:00

Thanks for joining us today, Ben. Do you mind giving us a very brief introduction about yourself, who you are, who Corello AI is?

Ben 2:07

Yeah, absolutely. Glad to help. So I'm a fourth generation manufacturer. Uh metal's very much in my blood, as well as three of my limbs, and got two boys at home that I would love to have join the industry as well. So tried my hand at tech. I got to work with global teams at Dropbox, Instagram, Facebook, and manufacturing pulled me back in. I helped support a facility, Wolfram Manufacturing, with 5X growth. They were led by a solutions architect from Lockheed Martin that became very familiar with automation, basically internal CNC automation technology. Got to work with a lot of the primes and aerospace defense. A lot of people have heard of Saronic. They've got autonomous drone boats, even now in service. So they just saved some Apache pilots from being down in the Hormuz Strait. Also worked with uh United Rotorcraft for Blackhawk to Firehawk conversion and even some Mark 20 and bunker buster pump production. So got to do a lot of work with big facilities. Now my heart has always been with the tier two, the tier three, like the mom and pop family-owned shops. And I'm very excited that now I can help expand modern technology and ease of use, essentially just using less software each day to those types of people and those folks that are really holding up the American supply chain.

Tolani 3:27

Awesome. Awesome. We are absolutely excited to have you here today, Ben, and to talk more about AI and where Corolla is going with AI as well. Ben, I seem to hear about AI everywhere right now. Every software company claims to have AI. Every conference I attend is, you know, there is an AI set uh session, and every manufacturer is wondering whether they should be paying attention. But let's cut through all of that noise. What's actually changed over the last year that's making AI such a big topic for manufacturers today?

Separating AI hype from practical business value

Ben 4:00

There is a lot changing and it's changing very quickly. And before I jump in on the AI topic, I also want to admit that I myself was an AI doubter the last few years.

Tolani 4:10

I can imagine.

Ben 4:11

I mean, I got kids coming into the workforce. I don't want to hear that it's going to do everything for us. It's going to replace people, it's going to run society. So I want to provide very real perspective today. And I also want to be a part of drawing the line where we keep AI our assistant, not our replacement. Uh, we want to figure out how to use it to enable people, how to multiply human capital rather than it's the Dunn Kruger effect. Uh, ultimately, that a lot of like investment bankers and high-level corporate structures kind of lean into. It's a cognitive bias where people with limited knowledge or confidence in a specific domain greatly overestimate their own capabilities. So just basically stating no CEO can know the work capabilities of every employee at a large company, and some same with investors. So we should not be replacing people where we think their job is easier than we may understand. What we need to do is enable people. So yes, uh AI is everywhere. I got to attend an AI innovation conference uh run by TSTC. Uh it was ARM ARM, so they're a large British company making microchips that was leading the talks and the panel. They said that here coming shortly, AI is going to be as present as air, as Wi-Fi. It's going to be all around us. And what I see changing is currently every software, every company is trying to add their own chatbot, basic functionality. It's all kind of cloud oriented. But when you're working with private business processes, financial data, uh, any kind of proprietary manufacturing process information, you can't be throwing that into 12 different cloud access points. So instead of adding AI to every little thing, using a dozen different forms of AI on a daily basis, uh the Corello approach is data lakes. So we ingest everything from each system separately with context. Uh, we put together a proprietary algorithm that can work with smaller data sets, which does even contribute to solving the data center issue and the energy issue. Because by using public uh knowledge models, the large ones, the frontier models, those are touching millions or billions of data points when you put in a question. That's lots of compute. If you're only working with the data sets on a business, you're looking at far less data, far less electricity to reference all of it. And that's really all you need to do for simple administrative tasks within a business. If you're doing something with photos, yes, I will say lean into the public models that have millions more, all these images that pull things together. So if you say, hey, a picture of a pilot hitting the autopilot button in a plane, like it can do that. The public models are perfect for that. Lots of things changing. I I even like to bring in what large companies are doing to show how things are changing. Uh Samsung, they leaned into GPT a few years back, uh as an early adopter.

Tolani 7:16

Yeah.

Ben 7:17

And they found three separate data leaks. Uh, one of them was internal code uh being leaked out into public spaces and then some manufacturing process data. So then their solution was to build an internal proprietary model. Uh, I think it was Gauss or something like that. And yeah, they solved the public leak issue, and they have brought back GPT since then, but only for marketing work, things that are external-facing to begin with. So those are some of the changes that I've noticed in these past few years.

Tolani 7:50

And it's interesting that you did mention basically trying to put data into 10 different GPTs or 10 different AI tools to be able to achieve one thing. And as we said, the real problem isn't necessarily lack of data because manufacturers have data everywhere. They have ERP data, accounting, and all of this data. So when you walk into a manufacturing business, what are those inefficiencies that you notice? Are those usually technology problems or processes? Or where do people spend most of the time where they shouldn't? What are those things that tell you a company has more of a visibility problem and they're not using their data the

Common manufacturing inefficiencies AI can solve

Tolani 8:30

right way?

Ben 8:30

I love to tour facilities. So this is a great question. It's just every manufacturer is different. It's astounding. Like when you see trailer hitches, you think, oh, I'm sure tons of places make trailer hitches. And then when you get behind the scenes, you see there's only like two or three major trailer hitch companies across the whole US. So they're the only ones that have things in common on a large scale. You need to address every manufacturer differently, but there are some universal inefficiencies. So I'm from the metal world. So, yes, there's 20,000 CNC shops across the US. And of that 20,000, 19,000 of them are high mix. They're constantly taking in a new project, a new design, running some prototype design work, high changeover with their projects. And they know what their downtime is. It's having to retool everything, like reset up the manufacturing process for something. And then, yes, the thousand that are left of that number are all production shops, which are quite capable. They're making lots of money because if they're making tens of thousands, hundreds of thousands, millions of parts, they've got their own development staff. They've got their own automation staff. They may even have like very well-established tech stacks and cloud storage and things like that. So they don't really need the help. Everybody chases them because they've got money, but I want to help the people that are trying to get off the ground and grow from a more grassroots perspective. So these folks, I mean, you simply walk in, you see they're carrying either an idea through conversation across the floor. If they're having to walk over, like stop whatever they're doing, go find another employee, like go through that chain of command, or move through a piece of paper. Uh, sometimes you'll see a facility still doing maintenance off of the manuals themselves, where they go dig through a dusty closet full of all these manuals for their high-end machinery, trying to look through and like recapture, okay, how often do I do this? I mean, CNC machines, they're easier to manage than a car. But if I have a whole fleet of these things, like it's still hard to know, like, oh, oil change here, filter there.

Tolani 10:38

Absolutely.

Ben 10:39

And sometimes they write down dates on filters, but nobody's checking them, or the oil is washing it off. So it's like, yeah, we need to have systems that are checking all these little things, like doing that tedious, like timestamp stuff, like even running it against monitoring data, if there's a consumable that's based on hours of runtime or things like that. Bilingual employees, we have one customer, they've got Spanish-speaking, French-speaking, English-speaking employees across their floor. Uh, they can only work with certain points of contact because if they need to have what is standard English like process documentation translated into Spanish, they're at a loss. Like, and and they're just kind of hanging out, trying to stay busy until that person they can reach and converse with uh is able to help them with their next steps. So we can overcome that. Uh, Caralho can help translate to 125 plus languages. Uh yes, like bring people like our customer training materials in their native language for the first time in their life. And then I got to see firsthand what happens to owners as well is they really try to scale a manufacturing operation. Like six years ago when I stepped into Wolfram, it was amazing to see the owner like out participating, like helping manage people on the floor, like decide where materials are going. But as more equipment came into play, more employees came into play, that CFO, like really financial operations kind of oriented side of his work just really took precedence. And so that means lots of spreadsheets, lots of time reporting, lots of time just kind of being stuck in his office. And so we want to be out making parts. Like people don't love digging through spreadsheets and software all day. And that's where AI can really pull us out and allow us to do the things that we love. Go hang out with a customer, uh, go deal with a bottleneck on the shop floor instead of digging through maintenance manuals and things of that nature. Uh, even sales folks, like uh there's one company we're assisting that a hundred-year-old company. They've been building engines for Cummins engines, for Caterpillar. Uh, it took them four days to make a quote. 220 parameters to run through. We got that down to 30 minutes or a few hours instead of a few days. So sorry. A lot of areas to cover.

Tolani 13:09

Yeah, that's a lot, that's a lot to cover because these are a lot of the inefficiencies that we see in the business. You talk about stancing manufacturing, we're talking about smaller manufacturers, not your multi-billion dollar manufacturer, right? Where there's a lot of downtime just from changing tooling or not understanding something that went wrong on the shop floor when the data is available, the system is running the data. It's just that data is not translated into decision making or into information that you know the business can act on. The managers, the plant managers, quality managers, salespeople, they need to be able to act on this information. So it's more like converting data into decision making and not just having the data out there, but understanding how it impacts the business and what decisions can be made to improve the business immediately. And I think AI has become one of those topics where there's a lot of excitement, but also a lot of fear, especially with smaller manufacturers that don't have huge capital to invest in AI. So, what are some of those big misconceptions that manufacturers have about AI that you've seen? Uh, something that you said earlier, for example, was where people might fear that AI is going to replace jobs or that AI is just for these mega large manufacturers. What are some of those

The biggest misconceptions about AI in manufacturing

Tolani 14:30

other misconceptions that you're hearing out there?

Ben 14:32

Yeah, absolutely. So, yeah, some of the initial ones, like AI will give us options for decisions. It allows for more informed decision making. Like the answer before was let's add more dashboards, let's add more graphs, things of that nature. But then you're still spending time digesting those items, you're meeting over them. Like now we can have it use intent to take next steps and be like, okay, what are actionable things we can do? Not just the questions being answered, the data being organized. Like, what do we do to move this forward? And yes, a huge misconception. I mean, this is propagated by Elon Musk, Peter Thiel, Jeff Bezos, Stan Altman, uh, even Anthropic CEO, Dario Amade. Yes, they're all trying to say that AI is the end-all be-all because they're just trying to get people to be afraid to put their money anywhere else. Like even uh Dario Amade over Anthropic, which is valued at $985 billion right now, uh, about to go IPO here in the next, I don't know, two to six months.

Tolani 15:39

Yeah.

Ben 15:40

Uh he was replaced by co-founder Tom Brown and public policy chief Sarah Heck. Uh and as their partner said, uh, Tom Brown is not being a weirdo like Dario and can actually engage. So it's like these companies, even with their CEOs, are having trouble with them just like speaking English and being real with people about what this can or cannot do. So that's what I want to do is I want to cut through all that, like just speak it in plain English to everybody, reduce the amount of buzzwords. I guess I'll just go first person story like into Wolfram. Like, yeah, I was a little nervous when I was first selling automation. I was like, this runs a this technology like T Mac, like it's like autopilot principles from a plane applied to a CNC machine, which it's like we've trusted autopilot on planes for what, I don't know, 50 to 100 years now? Like, why are people afraid to put it on a machine? And at first I was like, am I replacing people's jobs? Like, am I being moral? What is this? But as the shop grew, I found that one, we were like bringing in more money to America because small parts projects are usually the first thing shipped overseas. As soon as someone says we want five or 10,000, 20,000 of this part, the next question is, okay, how much is a plane to get to China? So with Wolfram, we were able to prove that not only can you make like the big, clunky, high mix, like oil-filled parts for like drilling and stuff like that. We also proved that you could make parts for SWAT, for police officers, for firearms, all of these things. And you could do it cost effectively in the US because if you can multiply the amount of machines that a person manages, instead of having them like all in chain to one machine, scale that up to three to five machines, you can compete globally. Because we can't pay people 13 to 15 cents per minute like they do in China. We can't compete to the bottom. We have to compete on the efficiency game. We have smart people, we have amazing technology. So that's where the focus should be is how do we enable our people? Because we are looking at a shortage of about 100,000 engineers in manufacturing moving towards 2032. And I'm part of all these workforce initiatives where we're trying to resolve that. Uh, like TSTC is trying to bring in kids for technical programs. Uh, Dadamic is also looking to help with workforce. There's Swift in Austin helping with rockets. I'm even uh on the board for the Austin STEM Center where we're trying to get middle school kids exposed to CNC machines, laser, additive, woodworking, uh, textile. Like just show them there's cool things you can do, like stuff you can make. You don't have to only worry about going into a cubicle in an office somewhere. There are some things where maybe some of those kids that weren't planning on going to college can have some really cool opportunities. Like I would have been a millionaire by now if I had just gone and been a welder or learn how to program or something. I'd be running my own company instead of doing the whole corporate ladder thing. Like I love where I am and all the stuff I've gotten to do. But I mean, kids coming out of two-year technical programs, they're making 50 to 100 bucks an hour in some cases. And it's like that's what we need. If we're gonna rebuild in the States, because straight over moves, like tariffs, globalization is slowing down. That is a fact. And just here in Texas, we're about to make three submarines each year here. They're trying to bring in 2,500 people to that workforce. Uh definitely look out for Blue Forge Alliance events. They're really trying to spearhead the thing. And this is just opportunity for all kinds of kids coming out of college.

Tolani 19:23

I love what you said when you talked about just bringing back money into America, really. And then um, it goes beyond people losing their job. We do have a lot of opportunities here to upskill and reskill people. And let me put my CFO on for a minute here and say, at the end of the day, I don't really care whether AI saves someone 10 minutes writing an email. I do, but in the grand scheme of things, what I really care about is does it help businesses become more profitable? Do we truly bring, you know, some of that money back into uh the US? So for me, I'll say, would you agree that the real measure for AI is not just the productivity, but also profitability? And if I'm a business owner, where should I expect AI to show up first on my financial statement, for example? It's just looking at measuring the business outcome of AI. And I think you've talked a lot about those measurable business outcome of outcomes of AI. But for a smaller business that is thinking about this investment as this big, huge investment into their business, what can they expect in terms of not just productivity, but translating that into profitability?

Measuring AI through profitability, not productivity

Ben 20:35

Absolutely. Yeah, it's it's a bit of a learning process. So most people start with a personal use of like GPT, Gemini, uh, or sorry, Claude, Gemini and uh Microsoft Copilot. I have not heard good reviews or feedback on. So if you want to learn, I recommend Claude or GPT. Um and then as that scales, so yeah, they give you, I think it's only like 20% of the revenue that's made from anthropic open AI is from those personal subscriptions. Most of their money is made from enterprise rollouts. And then you're looking at about 50,000 to 250,000 for like pretty much anything over 30 to 50 employees for an organization. The more agents you build, the more that they're used on a daily basis, the more you're burning through credits. So that's where a lot of companies have had trouble proving profitability because they were just like, hey, let's just roll this out to everybody. Let's try to really dive in as heavy as we can, see what happens. And then it just turns into kind of this big science experiment, which is so common in manufacturing too, because there's just a lot of these people are kind of in their own world. Like, not many people around them are working on the exact same type of parts or are willing to talk about how they're working with these kind of things. Like they have associations and stuff like that, but yes, it's it's a lot of owners that want to multiply their own. Capabilities. So there is some excitement about AI where the benefits of Corello come in, and I'll even talk about some other AI companies here too, just to like help share about the whole landscape. So Corello, it's all manufacturing machine learning. So we partnered with Mass MEP. So that's what's key about our system is it's just manufacturing oriented. It's designed to be exact enough, like no hallucination, uh exact answers that work for engineers, for business owners. That like if you ask GPT an answer, it doesn't know, it will try to fabricate the closest thing it can come up with to an answer. So maybe harmful if it takes you down a rabbit hole. So Corello will say no if you try to ask it for information it cannot index or find. Um, but real-world scenarios we're seeing it can accelerate sales, like I was mentioning some of the quoting. There's even systems, uh like yeah, one called Ether that can take something from uh an RFQ to G-code in a machine. So if you're lacking like programmers on your team, it can provide benefits in that area. Yeah, really just trying to reduce the administrative work in as many ways, like paper-filled facilities. I mean, you can't sell a part unless you have all those traceability reports, uh, material reports, everything that goes with it. So with Corello, you can just hold it up, capture it, photo, video. Uh, you can just talk to the system if you're trying to train it on how to do a manufacturing process so someone else can learn it down the road. So, really trying to remove people's hands from keyboards where possible, uh, get some of that just like we're even looking into meta classes, applications, augmented reality systems where it can like see what you're doing and try to tell you the next steps in that process. Yes, just based on context of what part you're at or what part of the process or step between machines. Um, yeah, the tribal knowledge, like so this can be another key area, like not a focus for us really. But if anyone's ever selling a business, like you don't want to just sell the position, you want to sell the systems and processes exactly to run that business. Yeah, that's a huge multiplier on the value of your sale. So it being able to take all that owner knowledge, like we can capture spreadsheets, all their previous emails. So, like for proposals, like we'll keep that consistency on how they're reaching out to certain customers, pricing them, quoting them out. It's just so many companies when they lose an employee, there's ten thousand dollars at least of training in a new employee, doing the HR work, replacing them, so we can help minimize that, crunch that as much as possible, uh increasing value through the exit. It's just getting us time back in our days. I mean, it's one of the leaders of Greece was just speaking to the whole European Union and he was saying, like, look at what China's doing with robotics and AI. If we don't learn or take steps in this direction, they're gonna run away with this. We're not gonna catch up. And so that's that's a benefit of Corello is like, you don't have to learn anything about AI. We will forward deploy engineers to build everything you want. Like if you understand your bottlenecks, the specific issues in your uh business model, then we can address those. And it doesn't have to be any off-the-shelf dashboard. Some of our solutions have no dashboard or interface at all. It just does the work and brings you the end product, which is nice.

AI readiness for small and mid-sized manufacturers

Tolani 25:35

So basically, what I'm hearing is the companies that will win over the next decade wouldn't necessarily be the ones with the most sophisticated technology, but the ones that are willing to embrace and um explore with AI, explore with how you know these improvements can be brought directly into their business. They're not necessarily afraid of the uncertainties or the what-ifs. They're basically just open to newer ideas and newer technologies like Corello, but not just open to newer ideas, but zoning in on the ones on the type of technology that would expand their business the best, that will answer the key business questions that they have, not just any standard GPT anywhere, but actually agentique AI that will be very specific to their business use cases.

Ben 26:25

Yeah, so AI readiness. I mean, I guess this is almost a misconception here because a lot of people think like, oh, I need to be jumping into robotics or things like that first. I mean, a robotics project is $50,000 to $70,000, and then you're multiplying that by four by the time you bring in a consultant. And as we mentioned earlier, like 95% of these shops, they have limited applications for robots. I mean, there are new like vision systems that can do some bin picking, like change the process of a robot a bit on the fly, which can help them. Uh, but most of the time, you're really trying to set up the robot for a hard process, something that's repeating thousands and thousands of times. That's not really a requirement for a company to approach AI. Like AI is simply like the old style of the world we had to integrate one piece of software to another and do that dozens of times across the business to get some ERP fields flowing or things of that nature. Now we can digest context. I mean, every piece of software is just a database underneath. So we can pull all those databases together, ingest it with context so it knows how things are related. Like that's how you kind of build those brain maps that you'll see where it's like it'll situate things according to a workflow or according to importance, and that's how you start to get intent out of it. But yes, anybody using software or anybody that would like to go paperless can approach this. And it is a benefit if you're using Claude or GPT, because then you're starting to build out some systems or applications. And what we can essentially do is rebuild those on a Microsoft foundation

Protecting knowledge, improving decisions, and scaling smarter

Ben 28:00

so they live on your network. So you're not having to put any like aerospace or defense type of documentation into a public system like Samsung struggled with previously with GPT. Our system, it's as bulletproof as your private network, keeps all your data on premises, uh, again, works without data center use or contributing to those power or water issues that some areas are experiencing. And even if you're not using those systems, like if you can simply describe from a cost perspective, like where you need more money or a primary area, just if it's hard for you to hire. And I hear this across the board for manufacturers, it's hard for them to find talent, especially depending on their region. Because a lot of them just kind of build in the country, they're looking for cheap property, like where can I stand up a warehouse? So it's hard to get people to drive out there.

Tolani 28:53

So it And it's hard to find that specialized talent as well, and be able to so being able to expand

Final advice for manufacturing leaders considering AI

Tolani 29:00

that your capacity without necessarily always hunting for new talent, being able to document a lot of these and just basically taking a lot of those tribal knowledge that you mentioned that could really expand capacity in a different way. Thank you so much for joining us today. I really appreciate you sharing your perspective. And one thing that will continue to stand out to me about AI when people talk about AI, something you said is that it's really not about replacing people or chasing the latest technology. It's about helping leaders get to that better decision making with the information that they already have. And as finance leaders, we often say that what gets measured gets managed. So thank you so much for sharing your perspective today. If you are listening, don't ask yourself, how do I use AI? Instead, ask yourself, what are the biggest decisions in my business that I'm still making with uncertainty? If AI can help answer those questions more confidently, that's where the real opportunity begins. Thanks again for joining us on the Manufacturing Money Room. If you found today's conversation valuable, be sure to subscribe, share this episode with other manufacturing leaders and reach more business owners who are building stronger and profitable manufacturing business that just helps us all as a community. Thank you so much, Ben, for joining us and we'll see you all next time.

Ben 30:24

Absolutely. Thank you, Tolani. Happy Friday and a wonderful weekend, everyone.

Closing remarks

VoiceOver 30:29

Thanks for spending time in the Manufacturing Money Room. If this episode gave you something to think about, let us know. Drop Tolani a voice note, or leave a comment or review. And hey, if you like what you heard, share it with your friends. If you didn't like what you heard, share it with your enemies. You'll find the links in the show notes to connect with Tolani. And if you want to watch the episode on YouTube, that's there as well. Join us next time in the Manufacturing Money Room, where it's all about better numbers, better decisions, better manufacturing.