Building AI-Ready Operations in Advanced Manufacturing

Summary

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Podcast Summary

Episode Overview

Podcast: Supply Chain Now
Episode Title: Building AI-Ready Operations in Advanced Manufacturing
Date: February 2, 2026
Host(s): Scott Lewton, Wy Jones
Guest: Garuth Acharya (8090 Industries)

This inaugural episode of the "Enterprise Unleashed" series explores what it truly means to build AI-ready operations in advanced manufacturing. The hosts are joined by Garuth Acharya, an investor and operator with deep technical pedigree and hands-on experience at companies like SpaceX and GE. The conversation delves into cultural and operational foundations, the realities of leveraging AI in manufacturing, and practical steps to modernize business operations.

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Key Discussion Points & Insights

1. The Rise of AI in Manufacturing & the Power of Data

Timestamps: 00:00–01:56, 15:22–20:07

• Data Quality is Critical:

  • “Data is the new oil, right? If you don’t have good data your models don’t matter... how do we generate data that is clean, correct and actionable?” — Garuth [00:00, 20:07]
    AI applications can’t deliver value when master data is poor or incomplete. Culture and disciplined processes must support clean, actionable data collection across the shop floor and beyond.

• AI as a Productivity Booster, Not a Job Killer:
  “People have this preconceived notion that AI is going to displace a lot of folks. I think in manufacturing it will just make people a lot more productive. There’s never a shortage of work in supply chain and production.” — Garuth [15:54]

• Real-World Use Cases:

  • Automated work instructions (e.g., Duro, Dirac) are reducing manual effort in capturing complex assembly steps.
  • AI-driven quality analytics and computer vision are surfacing defects and enabling upstream design improvements.
  • AI must be applied where it brings leverage—identifying bottlenecks, predicting shortages, and helping people prioritize effort.

2. Culture & Mission Alignment: The Bedrock of Transformation

Timestamps: 01:56–14:35, 19:27–23:09

• Mission-Driven Teams:
  At elite organizations like SpaceX, a deep-seated mission alignment fuels teamwork, precision, and attention to detail.
  “One of the more important things [in hiring] is… you have to write a paragraph or a short essay as to why you’re passionate about space tech and human space exploration. …Everybody there wants to be part of this mission.”—Garuth [13:24]

• Culture Over Tools:
  The success or failure of AI or data initiatives typically traces back to culture and willingness to change:
  “AI fails if you don’t have the culture to A, implement the infrastructure pipelines and then B, ask the right questions.” — Garuth [20:53]

• Humility Is Essential:
  “If you have a high ego in supply chain, you’re not going to make it, brother.” — Garuth [24:46]
  Success in manufacturing depends on cross-functional respect and open-mindedness—solving problems together and not being too attached to current methods.

3. Master Data Management—The Hidden Hero

Timestamps: 19:27–27:22

• Clean Data Starts with Collaborative Culture:
  Master data hygiene is “a team effort, plain and simple.” Garuth explains how part attributes, taxonomy, and deviations/redlines must be managed collaboratively across engineering, supply chain, and quality.

• Operational Clarity Drives Upstream Improvements:
  Redlines and deviations on the floor, when well-tracked and analyzed, often highlight design or process changes that should be made upstream. Systems and culture need to allow ideas to flow back up the value chain.

• Reality Check:
  Widespread “messy machine” processes—where transactional sloppiness creates ongoing oil spills of bad data—are still pervasive, and fixing this is often the best place to start digitization/AI journeys.

4. Preparing for AI: Red Flags, Best Practices, and Prioritization

Timestamps: 30:24–41:45

• Operational Red Flags:

  • Lack of clarity about current processes/builds.
  • Disconnected teams (“Is accounts payable coming out of their office to talk to procurement?”).
  • No single point of ownership for critical data (e.g., material planners being responsible for master data at SpaceX).
  • Jumping to AI before fixing data and process fundamentals: “AI is not even a band aid. It's probably going to be a net negative because you don't have the clean data.” — Garuth [32:08]

• Prioritization Framework:

  • "Build vs. Buy vs. Partner": Assess core competencies, resource constraints, and where true leverage or competitive advantage lies.
  • “Resources are precious... what’s the core competency of your company?” — Garuth [34:19]
  • Don’t attempt to boil the ocean; focus first where transformation will compound value and don’t hesitate to partner or buy when others have solved the problem better.

• Large Organization Advice:

  • Sometimes, if needed solutions can’t be bought, organizations must build their own (e.g., SpaceX’s internal “Warp Drive” system). But only if there’s adequate scale/reason, and full ownership over data responsibility.

5. On-the-Ground Leadership: Walk the Floor

Timestamps: 41:45–43:27

• "Go and See" Philosophy:
  Operators and tech innovators who don't spend time on the shop floor will miss the reality of how things actually work.
  “If you are building anything manufacturing or hardware-related, you fundamentally have to spend time on the shop floor and you have to know who your end user is.” — Garuth [41:45]
  • Firsthand exposure to problems and user needs is essential for building the right solutions and effective AI systems.
  • Don't let ego or pride in old solutions prevent necessary change.

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Notable Quotes & Memorable Moments

| Timestamp | Quote | Speaker | |---|---|---| | 00:00, 20:07 | “Data is the new oil, right? Like, if you don’t have good data, your models don’t matter, right? Fundamentally, that's what matters is how do we generate data that is clean, correct and actionable.” | Garuth | | 10:57 | “These are zero fail missions. If you’re launching astronauts into space… everything has to be perfect… These are mission critical... to fail means that you are setting the United States back and then on top of that, like, lives can be lost.” | Garuth | | 13:24 | “One of... the more important things is... you have to write a paragraph or like a short essay as to why you’re passionate about space tech and human space exploration... application process itself just weeds you out because like everybody there wants to be part of this mission.” | Garuth | | 15:54 | “People have this preconceived notion that AI is going to displace a lot of folks. I think in manufacturing, it will just make people a lot more productive. There’s never a shortage of work in supply chain and production.” | Garuth | | 19:27 | “Culture is actually at the center of all of this. It’s the kernel that everything grows out from.” | Wy Jones | | 24:46 | “If you have a high ego in supply chain, you’re not going to make it, brother.” | Garuth | | 32:08 | “If you don’t have that, then AI is not even a band aid. It’s probably going to be a net negative because you don’t have the clean data.” | Garuth | | 41:45 | “If you are building in anything manufacturing or hardware related, you fundamentally have to spend time on the shop floor and you have to know who your end user is and you have to spend time in the trenches with them.” | Garuth | | 45:53 | “Whenever we talk about technology innovation, we end up talking a lot about how we can work together better as people.” | Wy Jones |

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Important Segment Timestamps

• Opening & Series Introduction – 00:00–01:56
• Garuth's Background & SpaceX/Blue Origin Experience – 04:31–10:57
• Mission Alignment & Culture at SpaceX – 13:24–15:22
• Pre/Post AI Operations Shift – 14:42–19:12
• The Realities of Data and Master Data Challenges – 20:07–27:22
• Operational Red Flags & Readiness for AI – 30:24–34:19
• Build vs. Buy Frameworks – 34:19–39:32
• Big Company Deviations/Custom Platforms – 39:32–41:45
• Advice for Operators Building AI-Ready Operations – 41:45–43:27
• Main Takeaways (Collaboration, People, and Change) – 45:15–45:53

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Flow & Takeaways

• The episode weaves practical stories from the shop floor with high-level strategy, underscoring that cultural alignment and high-quality master data are prerequisites for any successful AI or digitization initiative in manufacturing.
• The panel stresses working across functions, with humility, and a relentless focus on the fundamentals of manufacturing science.
• Modernization is not about chasing tools and technology for their own sake. Instead, it requires commitment to change, deep process understanding, and clear-eyed prioritization.
• Ultimately, leveraging AI is about helping people work together better—technology amplifies people, not replaces them.

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For listeners:

If you want to successfully build AI-ready operations, focus on master data, mission-aligned teams, clear process ownership, and relentless operational clarity—starting not with tech, but with people and culture.

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Transcript

[A]

Data is the new oil, right? Like, if you don't have good data, your models don't matter, right? Fundamentally, that's what matters is how do we generate data that is clean, correct and actionable.

[B]

Welcome to Supply Chain now the number one voice of Supply Chain. Join us as we share critical news, key insights and real supply chain leadership.

[C]

From across the globe.

[B]

One conversation at a time.

[C]

Hey, good morning, good afternoon, good evening wherever you may be. Scott Lewton and special guest host Wy Jones with you here on Supply Chain now. Welcome. Hey Wy, how you doing today?

[D]

I am doing awesome. It's nine o' clock here and I'm ready to rock.

[C]

We are. You are ready to rock. I think you stay ready around the clock. And we got a great one here today, folks. We're kicking off an exciting new series for 2026, Enterprise Unleashed. Powered by WY and all of our friends at DOS who's been organization on the move for years now. Now throughout the year 2026, this series is going to be focusing on real leadership conversations with individuals, dynamos that are truly unleashing the power of the enterprise and beyond. Liberating their people, their operations and their performance from old fashioned technologies and approaches that we all, let's face it, we all cling to.

[A]

Right?

[C]

You're going to hear stories and perspective from innovative individuals that have led transformative change, not just talk about it all to help you drive real targeted outcomes driven change in your own organizations. And today we're going to be focusing on building truly AI ready operations and advanced manufacturing, one of our favorite favorite sectors. So W, we've really come to appreciate your point of view and expertise, all the great work you and the DOS team have been doing out in the marketplace. Delighted to partner with you on this series here. What else would you add to what we're after? This series here in 2026, we're experiencing.

[D]

A really unique time where human intelligence is something that machines are able to, you know, really operate on. Now all the advancements in AI are allowing us to completely re, you know, reframe and challenge a lot of assumptions we've had over the last decades. And so it's a cool time to get, you know, all these vignettes collected from people out in the field that are operating on the cutting edge because we just have a lot to learn. There's so much new stuff that we can actually go in and work on today that wasn't possible for. So really excited to bring all this to light.

[C]

Actionable, innovative vignettes to still One of W's many, many terms there. And folks, get ready. We're working on an incredible calendar of interviews and we start today with an outstanding business leader you can learn more about. So, Wy, you ready to get to work?

[D]

Let's do it.

[C]

All right, I'm going to introduce our esteemed guest here today. Garuth Acharya is a member of 8090s industry investment team focused on the organization's deep tech thesis across space, defense and decarbonization. Grooth has lived and worked across the world. Get this, wrestling rattlesnakes in Texas, dune bashing in the Middle east and building rockets destined for Mars in Silicon Beach. Growing up the son of a computer science professor, he's been surrounded by quantum computing, AI and machine learning since he was a kid. Garuth believes in deep tech because he's seen it change lives. From Starlink providing Internet to victims in war zones, to leveraging innovative grid systems to electrify Sub Saharan Africa. Now keep in mind, Garuth holds a Bachelor's in nuclear engineering and mechanical Engineering from Penn State, a Master's in Systems Engineering from Cornell, and an MBA from Wharton with a background at innovative companies such as SpaceX and Blue Origin. Currently he's an investor and part of the founding team at 8090 Industries, where they're building space, defense and advanced manufacturing technology through a robust portfolio of innovative companies. So join me and Wally and the rest of the gang in welcoming Garuth acharya, investor with 8090 Industries. Hey. Hey, G. Good. Good morning, good afternoon, good evening. Welcome to Enterprise Unleashed. How you doing?

[A]

Howdy, brother. I'm doing well. Once again, please don't hold a suit against me. Don't hold the NBA against me either. I'm a Penn State guy through and through.

[C]

Well, I love that. And Wiley, I love what growth Shared in the pre show that he's also a certified forklift operator. He's very proud of that credential. Is that right, Wiley?

[D]

Yes, it is. That's amazing.

[C]

So let's do this. I want to start with a fundamental question before we get into a slew of things that I can't wait to learn from you both. Really. But I got to learn more about your rattlesnake wrestling in Texas, the earlier part of your career. Groot, did you really wrestle with some rattlesnakes?

[A]

Look, you know, you gotta rumble in the jungle with some rattlesnakes and you know, you got a tango with some tarantulas. You know, it's. It's what you do when you're out in West Texas. So, look, I'm from Central. Well, I was born in Buffalo. My dad was a professor at SUNY Buffalo, and then he became a professor at Penn State. I grew up in Happy Valley. I'm a Central PA guy, and I graduated undergrad.

[C]

Yeah.

[A]

And I was supposed to go travel the world with General Electric doing cool stuff in power, electrification, energy, the whole nine yards. And then in 2014, the price of oil cratered, if you guys remember, Marcella Shale and kind of like some of that geopolitical stuff going on in the Middle East. And I got a call after I finished my last final, and the person in charge of my program, the OMLP Operations Management Leadership Program, called me and said, hey, you're ready to. You're ready for your first job. I'm like, hell, yeah, brother. Where are you sending me? I'm thinking offshore oil rigs in, you know, off the coast of Angola. I'm thinking I'm about to live expat life in UAE or do some cool stuff in Southeast Asia. Like, brother, we're sending you to East Texas and West Texas. So I, you know, from Central pa, and I went down from, from, you know, my, my fraternity at Penn State, all the, the, you know, eating cactus chimichangas out in the Permian Basin when we weren't, you know, putting pumps down whole. I was an artificial guy. And So I remember 5am being on the site. I've seen, I've seen a bed of tarantulas and quite a bit of snakes just, you know, rummaging around.

[C]

Groot. What an outstanding opening, Wiley. That's got to be one of the more unique openings we've had in recent memory. What? You're not scared of snakes, are you, Wiley?

[D]

I'm a big fan of snakes. I am a absolute wuss when it comes to spiders.

[C]

Okay.

[D]

You know, I'm not gonna lie. I'm not gonna lie. I don't, I, I don't like them. I like them.

[C]

At least. We got lots of problems in global supply chain, but snakes and tarantulas, maybe that's not one, so. And I'm very thankful of both of those things. All right, Grooth, I love how you kind of baked in some of your personal history and journey with my rattlesnake question. I want to dive in a little bit deeper, kind of set the table for a deep conversation we're having. Tell us more about your background, including your time in operations and technical program management at the Incredible organizations known as SpaceX and Blue Origin.

[A]

Listen, look, I had a great time. I would say the fundamentals of manufacturing supply chain I learned at my time at GE and the OMLP program. I started my first job off as a supplier quality engineer. Then I went in Lean Six Sigma, like how do you 5s a shop? That's where I actually got forklift certified. And then my third is I actually ran a shop out in West Texas which was fantastic. Artificial lift. It was an incredible opportunity and I think there was a big cohort of XGE guys, in fact a lot of my friends who are the heads of ops or COOs of all these NEO primes that are coming up. A lot of them started off at GE and then we all went to SpaceX together, which was incredible. So I was a supply chain Ops guy at SpaceX. My job was to manage the interstage. It has the four grid fins that help stabilize the landing and on top of that it's the large composite structure that connects the first and the second stage together. And then I owned the two nose cones that go on top of the side, which is Falcon Heavy. And that was incredible because we were going from 2x reusability to 10x plus reusability. And that meant working directly with research and development teams on, you know, the material science teams, your propulsion teams, the know we call it, you know, the plumbing guys. Right. And it was the definition of design build and qualify in parallel, which was anthetical, right. The exact polar opposite to the prime mentality, which is you design, then you qualify and then you build. Right. Which is fundamentally why it takes 15 plus years for the Primes to build something in SpaceX. I wouldn't say does it overnight, but the lead times are, are much shorter.

[C]

I'm big old space nerd garut. So I'm fascinated what you're sharing here.

[A]

Yeah, I mean listen, like these guys, like these SpaceX materials guys would come up with like new novel ways to leverage composite technology, right? And we had to be super nimble because this was pre boosters every week, right. We were still building quite a bit of new boosters. And the goal was, is in order to go at scale, you need to be able to do reusability. So we didn't have the spend that a Lockheed or Northrop did. Right, because like when you go out it's all about your spend, your demand forecasting stable supply. Right. In procurement 101. I don't know if you can tell. The reason why I'm almost bald is because like we were trying to spool up suppliers and you know, we were like, hey, we will have more spend, right? Or on top of that, where, where is my part? You know, is it have good quality, right? You know, can I get it faster? Those are the three big questions every person supply chain is asking. Because the delay to the production line is what's hairy? That's the most hairy part. But it was incredible. And then being operating in unison, right, that I would say one of the people who leads the show is the material planner. The material planner there is the face of the part or the face of the structure. So what they'll do is they'll say, you know, hey, engineering, where is your engineering bill of materials and manufacturing engineer and quality guys, where's my manufacturing bill of materials? Are we reconciling the two together? Are we doing DFM designed for manufacturability? Are we demand forecasting, demand planning? Are we saying that hey, this is going to be a super lead time, long lead part? Are we spooling up the supplier with a unreleased drawing so that they know what to expect ahead of time? Right. This is something that I would say most organizations don't have the sophistication to do because everybody's in their own office or nobody's really communicating. Supply chain fundamentally ops guys are. You have, I wouldn't say you have to be an extrovert, but I think you do a little bit better as an extrovert. But more importantly, it's a culture thing, it's a people problem.

[C]

So G, before I turn over to Wiley and dive a little deeper on more of a technical side, I want to ask you one thing. Because those organizations you're describing, some of the processes, some of the uniqueness of those of space sectors, space manufacturing sectors and failure can't be tolerated. Oftentimes, you know, human lives are on the line. So what's it like? Two part question. What's it like operating that environment and how has that shaped your mindset moving forward about, especially about execution in other companies?

[A]

These are zero fail missions. If you're launching astronauts in a space like these entities are human rated, so everything has to be perfect. I would say the secondary element is that you're launching oftentimes national security assets into space. Whether it's for space force, whether it's for the intelligence community, it could also be for NASA, right? Or it's just like commercial assets that you're launching as well. Fundamentally these are mission critical to the United States, to our allies, and also for broader humanitarian Applications. So to fail means that you are setting the United States back and then on top of that, like lives can be lost. These are very serious programs that we're working on. Everybody's got to be on board, everybody's got to be on the same page. Everybody's got to live and breathe this, which is what you get. Like at a SpaceX and blue. The people live for this, a rocket lab. The same culture exists. Like people live for this stuff. Everybody on the shop floor, all the way up to the boardroom lives and breeds, you know, human space exploration. They live and breathe frontier technology. And so being in that environment, you're just, your, your sophistication goes up, your level goes up, you're attuned to different things. You work 10x harder because you're bought into the mission. I mean, and listen, like the quality for these parts are very intense. Like the cleaning that it goes through, the specifications that you go through. If you're looking with suppliers, right? Like the barrier to entry to be a supplier to these entities are super intense. Like AS9100, ISO9001, sometimes NAD cap certifications. These are not that easy to get yet and maintain.

[C]

All right, so Wiley, I think you want to explore about some of these demanding operational environments, huh?

[D]

I'm always open minded about where these conversations go. And I love just the reframing of part of this, which is that when you look at some of the world's most high competency organizations, the thing that you're describing when people talk about SpaceX, SpaceX is fundamentally an R and D and cutting edge technology company and it's also a manufacturing business and it's one of the best ones in the world. And when we look at how these organizations operate, I think a lot about Tempo, you know, an incredible operating tempo and cadence and creating, creating incredible leverage and then just being right a lot. And it sounds like the thing that you're describing, growth is that at the core is actually a deep philosophical mission alignment that everyone is bought into this shared vision and that then the decisions and approach flows outwards from that. What are the concentric circles that you would draw around that mission that you think determine a lot of what you saw was successful inside of these companies.

[A]

Yeah, listen, I think like the hiring practice for these guys are like super intense. I would say there is probably one of the more important things in the application process is like, look, the case study is cool, right? Like, of course, can you triage these problems? How do you approach these problems? Like that is standard. I Think one of the more important things is there's a section and I don't know if they still do it. I'm sure, I'm positive that they do is that you have to write a paragraph or like a short essay as to why you're passionate about space tech and human space exploration. Right. So just like the application process itself just weeds you out because like everybody there wants to be part of this mission. It's part of this kind of like grandiose plan to take humankind from just, you know, one planet to an interplanetary species. I would say centric circles, is that everybody works hard and everybody works smart. Right. And I think being able to work together, teamwork makes the dream work, is like a very serious thing. And it's also do the small things right, because they add up. There's a phrase, slowest mood, smoothest, fast. I think a lot of folks in the military community use it. It's very apropos. And in production and manufacturing, because if you can't get the fundamentals right, then like bro, you can't, you can't design and launch a rocket.

[C]

Yep. That would be the U.S. navy SEALs, I believe, Garruth and Wiley, if I'm not mistaken.

[D]

Yeah, we use that a lot internally as well. The question that I think is then the, you know, where a lot of my mindset is right now is that we had these operating cadences, the leverage, this correctness mindset inside of operations and the pre AI version of it looked one way and the post generative AI version of it now is going to look another way. It does feel like there is a material shift happening in the way that people are going to be adopting these technologies in the next decade. What is one of the big areas that you're excited about? How are you seeing things fundamentally change already? Where are you seeing real value unlocks in your day to day already? Like walk me through your framework on the before and after shift here.

[A]

Yeah, listen, I think there's a couple great, you know, there's a couple great examples of this. But just, just look. Most recently I think Andrew and Durock, Phil shout out to him. He's building an incredible tech stack where you can have automated work instructions. One of my, one of like the beans of my existence was all right, now I have my engineering bill of materials that's been released. But like you need to make it all your consumables, the work order, steps one through 20, screwing, you know, fastener A into this thing. Right. How do you actually do this Right. That's the, that's the work order, right. That's the work instructions. Those take forever to make, especially if it's a complicated part. Right. And so areas that you can use to make humans more productive. I think people have this preconceived notion that AI is going to displace a lot of folks. I think in manufacturing will just make people a lot more productive. I mean there's never a shortage of work in supply chain and production. Let's get that clear, right? There is no shortage of work. I can work 24 hour days if I wanted to. Right. The whole point here is that you can do so much more with AI. An example would be if I need to build more products, I need the work instructions to do that. How do you leverage physics based AI models to go and do that? Dirac is solving that. Right. If I want to have, for example, understand what are my shortage reports that are going on, what work orders or what material planning do I have, let's say two or three months from now that I'm not looking at that I might have raw material issues for. Right. Where am I seeing, for example anomalies in procurement? What are my biggest quality defects? Right. There's so much data, right? The question is a, is the data good? Right. So the master data is probably one of the most important functions that if you are going from analog to digital, you got to do it the right way. That's the most important part. You know, once again, it's a culture issue, right? Master data, it comes down to culture. Are people inputting the right things into the system? And I think the second thing is what's the takeaway? What's the tldr? What's the bluff of what's going on? An example would be, hey, we're seeing major issues across parts, across this material with these complex geometries, right? Like here's what we are seeing, right? Here are the quality defects that we are seeing. Like these are the types of analytics that enable, let's say a design engineer to go back up the flagpole and say hey, maybe we should take a look at DFM designed for manufacturability. Maybe as a quality manager, if I need to load share, where am I going to be allocating resources? What can we do? Computer vision. Computer vision is now being used for defect detection versus just 100% in person inline inspection as 100 certs require a lot of quality control methods. But when we're talking about high rate production, a lot of this stuff, how can we Ensure that things don't get missed. A double odd, right? Like identify really the 8020 rule. What should a human be doing versus what can infrastructure and systems or IT systems do for us? So I think there is, we are beginning, I would say there are very few factories that you know are super sophisticated in how they do this. Like even Tesla tried to build a fully automated entity and like they had to rip a lot of stuff out and say first principles, what should we automate? What should we have a human do? So I think in this case it's AI is a tool but really identifying what are the big bottlenecks, what are the big things that we're seeing, right? You have atomic industries, right. Aaron is doing incredible work both through re industrialization. Right. And broader some of those major, major organizations that he's starting. But more importantly, right for injection molds. If you want a plastic part, you have to go to China to get this stuff half the time because the lead times take forever to build a mold, to clean a mold, to find a mold, right? How do we bring in AI to design walls, right, that help a proctor and gamble or help a Lockheed if they need certain plastic components that go in or they're tier two, tier three suppliers. So what I have seen as an investor is what are the big problems and culturally what are you doing to solve this? And I think AI is a tool. AI is not the solution. The solution will always be a problem.

[D]

Yeah, that all that resonates and it, it does tail back into the thing we were talking about which is probably actually a really good segue into Scott's question. I know he's going to ask but it's that culture is actually at the center of all of this. It's the kernel that everything grows out from.

[C]

It's so true. I've heard culture. I bet Grooth's mentioned it probably 13 times thus far and there's so much truth in that. But I want to move forward to your point while you're reading my brain here because I think amongst the value we're going to get from talking with Garruth here and share with our SCN Global fam is talking about what it really means to build AI ready operations because Garuth has been there and done that. So I want to ask you guru from your operator lens, right? From your all that that means including operating those forklifts, which I love, I love driving back in my day, why does AI on top usually fail in physical world environments?

[A]

Your thoughts, Eric Ruth, listen, look garbage in Garbage out, right? That's the number one thing. AI like you can have incredible models, but data is the new oil, right? Like, if you don't have good data, your models don't matter, right? Fundamentally, that's what matters, is how do we generate data that is clean, correct and actionable? So whether we're plugging in a PLC SCADAS to output all this machinery, all this data that's coming out of your lathe or your CNC machine, right? Like, that's great. But then I take out and say, all right, what's going on with my raw material? Are there certain issues that I'm seeing with raw material suppliers? It could be quality defects, it could be lead time issues. It could be a myriad of different things. How do you then say, what are the big problems that I'm looking to solve? Fundamentally, the customer cares about three things. A, where's my part for transparency? Right? Because you want to prevent baldness, right? Receding hairlines. And you do that by figuring out, where's my part? B, am I getting it on time? Can I get it the right way? Right. Is it actually accurate? Right. And, and, and finally, can I get it faster? Right? Like on time delivery, good quality, Can I get it faster? These are the big three things. And you can leverage AI by saying, hey, like, there's potential raw material shortages that are coming in, right? Like all of these things that are there. AI fails if you don't have the culture to A, implement the infrastructure pipelines and then B, ask the right questions. What does the customer care about? Whether, you know, there's plenty of people who are creating orchestration layers for a Domino's Pizza tracker, but is there anything to API into? And even if you API into it, is the data correct? That's a culture issue, right? So fundamentally, how do you take a factory from analog to digital, right? That's a major endeavor. Like General Electric spends billions of dollars on MRP ERP integrations. And they're very hairy onboarding palance here. Is that the right thing for a small business to do? It might be pretty expensive, right? Can they use it? Do they want to use it? What are they going to use it for? Right? Like crafting these strategies, I think is very interesting because we're at a time where people know that you have to innovate. But at the same time, the Vikings were building ships, right? You know, the Chinese were building ships back in the day, before there was AI or before there was an autocad, right? Before SolidWorks existed. So ship building is in the. Is in the DNA. Manufacturing is in the DNA of humankind. It's what we do. But the question is, we can always do it better. I mean, the Japanese taught us tps, the Toyota production system. That's where the lean Six Sigma stuff came from. At least that's what I was trained on. So culture is number one.

[C]

Yes, yes. Yeah. Back tape. That's number 16. I'm counting each time you mention the culture word there. Garruth. I love it. Big culture guy. Wally, really quick, I'm about to ask Garruth. You already touched on a few things that have to be true operationally before AI can really drive outcomes. Your quick comment, Wally, and what we just heard there from Grooth.

[D]

Yeah, just to react to one part of it, the part that I see so much in our day to day, is that people a lot of times will speak to us as a company about capabilities and they're speaking in terms of revenue and profitability and risk. Risk being a lot of stuff that Garuth was talking about related to do I get my part? Do I get it on time? Can I get it faster? Is it actually correct? And their ability to then go in and deliver on the revenue downstream of that, when we trace through those capabilities of can we deliver on all those things, the thing that actually ends up being the most common problem on all of it at the very bottom is we don't actually have clean master data. And we don't have a culture inside of our organization or operating system and operating principles that enforces that people have reconciled clean, accurate master data and that we don't have this, you know, let's call it messy machine that continues to generate and spit out basically make an oil spill all over the business every time we go and do anything transactionally. And that just what Groot's describing is something that we see in practice and you pull that thread all the way through, somehow you always end up back at master data.

[C]

Don't go out and get the latest and greatest Corvette and then put a V6 in it. Right. I love both yalls points around data. All right, so two part question, Garuth, you touched on a few things already that have to be true operationally before leadership can turn to AI to drive real outcomes. So I want to see if there's anything else you'd suggest there. And second part here, workflows, data discipline and decision making. How does all of that have to evolve for companies that really are primed and are delivering big, big results with AI? Your thoughts?

[A]

Listen Wiley, you, you, you hit the, you hit the nail on the head, right? It's Master Data. Master data is a team effort, right? Like no one Manufacturing in ops probably are the most humble folks because you have to work with everybody. Like, if you have a high ego and supply chain, you're not going to make it, brother. Right? Like you're just not going to make it, right? I mean, the amount of times I go up to someone's desk and be like, hey, can we like do abc? And they're like, well, like, can I get a deviation to alodyne instead of anodized? That will reduce my lead time, right? Like you have to go convince folks to go do things. But let me take a couple steps back. I think we all have some war wounds about this. But you know, listen, Master Data or just clean hygiene is a team effort, plain and simple. I'll give you this example. It starts off with your engineering PLM system, right? Like your part data, right? In I have my engineering part. What are the commodities around that part? Right? Tier one, tier two, tier three, commodities. A, it's a machine part B, it's small, medium, large. C, it's, you know, some other taxonomy, right? The more data that you can parse and once again, you don't boil the ocean and do stuff unnecessarily for the sake of just doing it. It's got to have value to the organization or it's got to, you know, there's got to be a business case for everything. It starts off with something as simple as that. What are the commodities of the part? How do you break down that part? And then how do I correlate those things to make decisions off of that is not easy to do. And in an organization, to require a supply chain to work with quality, to work with engineering, to work with manufacturing, engineering, and then all in all that, then the BI guys in the background, right? And then all these SQL or Python wizards can go ahead and start building all these dashboards. I mean, I'll give you this example we were looking at. What are the number of red lines that are going on? A war quarter and a red line for those who don't know it's a deviation. Right? And so I, I don't know many people who have ever built something without a deviation. If you have, good for you. I love that and I applaud you. But we were always saying, okay, right, we're on the shop floor. Even if it's Rev 5, right? Right. If it's rev D, rev E on the Drawing or on the work order, we're saying, oh, we can do this better. Oh, let's substitute this for this. Like those are what you call engineering approved red lines that says we can deviate to go do something. Well then maybe after you do this like 20 times across 20 different things, you can now start saying, well I'm looking at all this taxonomy and all this data that I have. Maybe if we made these three, five changes upstream, we wouldn't have to redline and have to delay the production floor and get engineering approval or quality approval to go do things. Right. So at the end of the day what matters most is how do I execute clean and fast, my production guys on the shop floor. All of that comes down to master data at the end of the day. Right, but it's a culture thing and.

[C]

Everybody has to be bought in 1818 Gruth. All right, so, so we're going to get Gru's vester hat put on. We've been talking really from operational lens for the first half the conversations. We're going to get some of his perspective from an investor here in just a second. But wy clearly our kindred spirits and, and the immense value of master data and probably amongst other, many other things, your quick call out of anything that GRU just shared there a mix of.

[D]

A call out and maybe a, you know, tee up the next, the next section on this is I think what you described as that process of refinement and the building a dimensional understanding of what's happening in a work order where you say, hey, we did these five red lines and four red lines and three red lines over the last 20 of these builds. They end up being the same or roughly the same every single time. If the business has a framework to pass that back upstream in the organization, then you can make changes inside of the company if you don't have that. And it's to your point is, you know, the, the salesmanship inside of manufacturing organizations is that you have to go around and socialize and coalesce ideas around the company. You just say, hey guys, like we don't want to keep getting handed these work orders with a million different deviations. How do we as a business come up with a plan for the ability for people that are receiving problems downstream to go in and you know, make it so that organization members upstream of that, that they're not affected by any of this, that, that they can actually be delivered a very clear, you know, rule of, rules of engagement for how we drive change in their work streams. Even if they have no reason to actually improve something that comes back up to the, you know, the leadership point of view that we were talking about and how the, you know, culturally leadership has to be able to establish that. But those are the things that we also see. And I think the way you call that out and the kind of the pipeline building of that process is something that is immensely valuable for organizations to understand. How does their version of this operate?

[C]

Yes, well said. Clarity, gosh, clarity is always underrated. Operational clarity, oh my gosh, operating through these supply chain ecosystems. Where to your point Garruth, why deal with problems that we could completely eliminate? And when I think we bring lots of clarity to these product development, design development conversations, we can really get out on a magic wand and make certain problems disappear. And imagine all the hours we get back to drive more practical innovation or 100%.

[A]

I mean, look, a clear example is, you know, I have a friend of mine, Sam Hoffman, guiller Legend X SpaceX, he's at Violet and they're, they're the backbone, right? So like I have all these siloed individual systems, I have a PLM system, I have a manufacturing execution system, I have a bunch of stuff. These guys are building the orchestration layer that's API ing and integrating and fusing this data together so that I can make these decisions, right? As you said, culture is great, but like infrastructure and tools are a functional culture, right? And that's where entities like Violet, right, can go in and parse that information and fuse that data together. So shout out to Sam, if anybody's listening, go give them a go, go give them a holler.

[C]

G. Let me ask you, red flags, right? We were just talking a second ago and we've been talking a good bit last chunk about AI ready organizations. When you see certain operational red flags that say, hey, we're not ready. What are a couple that really concern each and every time?

[A]

Listen, I, I think the first and foremost question is like I never ask about AI, I never ask about tools. I, I just say like talk to me about what you're building. How do you build it, right? Talk to me like value stream map 101, right? I, I mentioned before, I think we all agree, right? One is we were building ships right before we had computers, right? I mean people didn't have autocad, they were old school on, you know, on the desk with large sheets of paper. So I think it goes back to fundamentals manufacturing science. A, what is your mission? What is the culture, right? Like are you going to fundamentally be a Design, build and qualifying series or a parallel culture. How do you do it today? Where do you want to be? Right. What are your big challenges? Do they know the right questions to ask about themselves? Right. Like do they know what, what their issues are and are they open? Right. It's almost as if it's a therapy session, I think, when I speak to a lot of organizations, right. Whether it's small to medium sized businesses or mega corporate entities, right. Like all of these massive corporations have an AI strategy, but it's not an AI strategy, it's a culture strategy. Like, what exactly do you want to do? And automating it is the easy part. You just got to have all the backend, infrast setup so that you can do it right. Slowest, smoothest, smoothest, fast. So I think the red flags are a. Have you done the work to know what your own, you know, roadblocks are? Are people coming out of their offices? Right. Is accounts payable? Coming out of their office to talk to procurement and is procurement going out to talk to the shipping and receiving guys? Right. At the shipping and receiving guys, going and talking to your shop floor guys to make sure that they're not just going to the dock, picking stuff out then issuing it to a work order? Like maybe you don't even have any work orders. Like, is everybody on the same page to know where they all flow and how they're executing? And if you don't have that, then AI is not even a band aid. It's probably going to be a net negative because you don't have the cleaning data. So that's kind of my thoughts. Wylie, what do you think?

[D]

I mean, this is exactly the conversation. I think Scott and I have had this conversation multiple times on the show in the past, which is just the willingness and desire and courage to change the business and the courage to have conviction about what you're going to go do. The courage to know yourself as a company. That's the foundation that allows us to establish the culture, that allows us to establish the process, which allows us to establish the tooling. It's all just this very deep Socratic thing that starts with, do you want to do these things or not? And do you have the willpower to drive that through the company?

[C]

Yes. Wiley, well said. And conviction is one of my favorite words of what it communicates. When you know you're ready to hang everything you have on one thing happening or, or moving into one big new chapter or one big new initiative. Love that. And when you've got the conviction of a team, kind of going back to what you and Carruth both have spoken to, and you can really move mountains. So, Wiley, I think up next we're going to be talking about prioritizing, modernizing operations and where folks start. Take it away, Wally.

[D]

So let's say you've done all of this. You've done the internal work, you've done the value stream mapping. As a business, you understand that there are, you know, especially people who are in your position as well, Ruth, where you've seen a bunch of these patterns. There are so many things you can go in and fix. There are so many things that you can go in and decide to work on. The bottlenecks are innumerable often, and they only appeared, you know, depending on the stuff you push through the system. You find them in different, you know, shapes and sizes. How do you think about how to prioritize? Where do you go first? What do you focus on? Build versus buy partners in house. Like, what is your framework for this?

[A]

I mean, this is. This is the quintessential question on how you don't boil the ocean, right? Like, yeah, even if you have infinite resources, your build by partner strategy is probably the most important because that's where you dedicate resources to. And resources are precious, right? Like if I'm taking a manufacturing engineer or a supply chain guy off the shop floor or off like demand planning, right? And triaging supply chain shortages or some defect issues or whatever it is, or, you know, if they're not on the phone calling your supplier saying, where are my parts? Or issuing POs, then, you know, they have to be returning incredible value back to the business, right? That could be, you know, sitting up and saying, do I want to build my own internal ERP MES system, right? Can I use a dos? Can I use the first resonance, right? Like, what do I want to do? Do I want to go and build up my own robotic infrastructure to automate my warehouse or to automate my packaging? Like, is that necessary? Should I build that myself or can I go partner with a Reflex Robotics, right? Or a Tutor Intelligence, right? All of these guys are leveraging AI. They're all incredible engineers. They are building great products. Do I want to compete with them? Do I want to build that internally or can I just go partner, right? Do I want to go build my internal orchestration layer or do I go leverage a violet, right? Do I want to build up my own PLM system from scratch or do I use a duro, right? All of these things are a who do I Want to hire Slash, who do I have on my, on my company, you know, HR sheet, Like who's in my business and what is most valuable for them to do. And more importantly, what's the core competency of your company? I think like this is the sole searching every executive needs to do that says, you know, is SpaceX or Blue Origin or Lockheed. These are fundamentally systems integrators, right? Like they're doing incredible engineering design, incredibly system, you know, complex systems engineering. They do manufacture certain things like for example, a lot of their composite structures, like they lay up themselves, but they're systems integrators, right? At the end of the day, does that mean that they should go and develop, you know, buy 100 CNC machines and do all of that in house? Maybe, maybe not. That's the core competency that they got to figure out who do they want to be and what do they want to do. And that's something that a company like Saronic, right, which is backed by an incredible table, has an incredible lineup of X Bas and Ex Andro folks, right? Software is an internal core competency. So they're going to hire a bunch of software engineers. Manufacturing is going to be a core competency. What types of things are they going to do? Right. Varda, right. Which is an in space manufacturing business. Build by partner, what are they going to do? Right. All of these hardware intensive businesses, but even for software as a whole is critical. Right. I invested in a company called Galvanic. Right. Josh Steinman, former director of cyber at the nsc, now is building an industrial cybersecurity company, right? OT cyber. Like should I be building that? If I go start buying an operating factory, should I be building my own OT cyber security platform, maybe? Or do I partner with the right people? So I think as a business executive is taking two steps back and saying, what does my business do? What is the core competency? Build that and then partner slash buy with everybody else. That's how I approach when I'm talking with founders and we mess in their business. That's exactly the types of conversations that we have like, do you want to be a software ERP business? If so, then build it internally. If not, then buy a dos, then buy a first residence.

[D]

Yeah. And where do you see the leverage? Yeah, a lot of it also is where do you see competitive advantages for the company or durable advantages that can compound through further investment? Great example of this being, you know, all the work that I know that SpaceX as an organization has put into internal tooling and internal platforms. That I think a lot of people, you know, externally look at with awe and wonder. People internally see. They see the pros and cons. And so I think that's where there are ways for organizations to have an intellectually honest discussion about build versus buy and what are the incentives as well in that build versus buy relationship with the partners that you work with. I think that is another really massive thing to consider.

[C]

Yep, well said there. W before I got a couple of questions that we're going to kind of use as a bookend to our fasting. I think I've got 27 pages of notes G. Ruth so I'm going to try to act on all of that. But it's been fascinating and, and equally as fascinating is your ecosystem, the companies and the really cutting edge technologies that you're plugged into. We're going to have to have you back soon. But Wally, anything else? Especially when it comes to, you know, building buying partners versus in house execution, even that modern, you know where to start when it comes to modernizing operations. Anyone else you want, anything else you want to pose to G here today?

[D]

Yeah, I think on the other end of it actually, now that I consider this, I think that the advice, a lot of what we've been talking about is quite specific to certain types of businesses and manufacturing businesses. What would you say in just from what you've seen, especially the really large organizations like the big, you know, the big global, global mega cap companies, anything distinct that you've seen around those, especially from what you saw at ge, you know, the, your operations program time, any distinct advice like where maybe they would be deviating from some of the advice that you've just been outlining.

[A]

I think it's. If you can't find something that you really need to help scale, then you just got to do it yourself, right? I mean I think like internally SpaceX built warp drive because they're like, hey, like SAP is no bueno, right? It's not going to work. But be very mindful that the data took some time for it to be really useful. When you look at, when you look at it like the material planner was in charge for master data and that's why it's clean because you had a single point of contact. You said like they're the face of the part and when new parts created and pushed from your PLM system into your supply chain systems, then they would have to go do the whole supply chain taxonomy and say like hey, if it's a composite part, then like here is like the, the Commodity codes which then translate down to supplier quality codes that the purchase order has to go, you know, flow onto. And also like material inventory management. Right. If the refrigerated part then like, and there's all this like stuff that you have to do with it that flows down. So moral story is if you can't build or partner with something, then just do it yourself. But the question is, do you really want to invest that many resources? It could be alpha. Right. And if it's alpha, then do it.

[D]

Yeah, yeah. And I think the, the, the substrate of what you're describing as well is, is making sure that the organization is set up in terms of a human capital perspective and the way that the actual org chart functions so that functional group owners can also be tied to as system group owners. It's not like a pop fly where people are looking at each other and they're like the ball lands the middle of the field and they go oh, that was supposed to be yours. And they're like no, that was supposed to be yours. But in the thing you're describing in this case is the material planner, the buck stops at them whether or not the part actually ends up being correct or not.

[C]

Yes. Ownership, ownership, ownership, ownership. And hey, we got to revise Major league Baseball's infield pop up rule. By the way, reminds me, while Atlanta Braves been burned on that I know past seasons. Okay, so Garuth, I wish we had a couple more hours with you here today, but I know you've got, you spend your morning in our nation's capital meeting with many other industry leaders, but you've got some more afternoon meetings as we start to kind of come down the home stretch, I want to ask you this, what advice would you give the operators trying to build AI ready operations today, especially in manufacturing or industrial environments beyond what you've already shared with us?

[A]

Carruth, I think if you are building in anything manufacturing or hardware related, you fundamentally have to spend time on the shop floor and you have to know who your end user is and you have to spend time in the trenches with them. My first job out of college, right, was supplier quality and lean Six Sigma at ge. My OMLP manager, my plant manager put me in the trenches and said like, hey, for the first one month you are just going to spend time on the shop floor figuring out what's going on, what are the sins. You know, these guys probably already have a solutions. You can work with them to just figure it out. And I think when you're building AI, AI is a tool. It's not the solution. I think that's like very important. You have to really have firsthand experience of what it is. I think there's a lot of people who think that the customers will come but in reality there's a lot of back end infra that requires you to plug into the API into and if that's not there then you're going to have to pivot your strategy. So be on the shop floor.

[C]

That's right. And Wiley, one other thing, I'll get your reaction to that. But one other thing I loved that we heard from G earlier in today's episode is ego. Right. We've got to be open to better solutions, especially if we own the solution you know we're using today. Right. And because we all probably feel immense pride and ownership with things we build and organizations use. But gosh, we got folks in that position may need to be the most humble as folks. Sometimes we got to break what we've been using and build back bigger and stronger and newer. And ego oftentimes from what I've seen prevents us from doing all of that or at least makes it a lot tougher. But Wiley, react to that, react to what we heard there from Garuth in terms of his advice.

[D]

Hey, you're preaching to the the Taiichi Ono's number one fan here Toyota production system. Go and see, you know, Genshike and but right. It's like if you cannot go and physically be where the problems are, how can you understand them? How can you get to clarity and then how can you drive a culture the downstream of that that deeply understands how to operate and so we espouse that inside of our own organization, worked with our customers. You know, what we see in the market is that it all begins there. Deep, profound understanding of what is physically happening.

[C]

That's right. Deep profound understanding of what exactly is happening. There's no fooling. We can't fool each other, we can't fool ourselves and humans are really including myself. We're all good at doing that sometimes. G spent as much time talking about the intangibles as he spent talking about high end technologies today. And I really can appreciate that angle of attack. Okay so gth we will have you back on a future show. Love what you're up to. Here's some things or some cool initiatives you're up to that you can't share publicly here today. We'll have to get an update later. But how can folks follow your work or connect with you and the team over at 8090 industries growth.

[A]

Absolutely. You can just shoot me a note on LinkedIn. We're pretty responsive. And then. Or you can just shoot me a DM on X and we can take it from there. I think always, if you're an investor, you should be open to just chatting with folks, because that's your job.

[C]

That's right, that and do your job, folks. Do your job. Good stuff there, Garruth. Now, Wiley, we're going to talk about Garruth as if he's not here. And you get probably the toughest question of the whole last hour because Garuth has really shared some good stuff with us. What's your biggest takeaway here today from our conversation?

[D]

It's, you know, and this is our first of these installments on, on this show that we've, we've started here, Scott. And I think I'm curious to see if this is the takeaway every time, but it's that when every time we talk about technology, we talk about ways that we can unlock more value and get organizations moving better and faster, we always come back to how we can get people to have more leverage, how we work together as an organization, as people using tools and technology. And so the unsurprising surprising takeaway is that our, you know, when we talk about technology innovation, we end up talking a lot about how we can work together as petter, as people.

[C]

Yes. W. I love it. I think it's common sense. And to your point, it is a common theme in some of these conversations and I bet by extension, in a lot of operational round tables, war rooms. I know I've been in, in my career where it comes down to how we work together. And that kind of sounds Pollyannish, but it is such a big truth. Okay, well, Garruth is. It's wonderful to have you here. We got to have you back. We're going to wrap, though. Groot, get this. We're going to wrap with a couple things about Wiley and the DOS team. And I want to start Wiley with, you know, tell you on February 24th, we're having the second installment of Enterprise Unleash. And Guru, thank you so much because you've set the bar way up here. So whoever's coming on the second episode, oh, my gosh, they're gonna have their work cut out for them. Garruth, did you do that intentionally?

[A]

You're too kind. You're too kind. But shout out to dos. My brother over at Mazcla uses them. So it's a. It's a small world and clearly it's a testament to their product.

[C]

How about that Wiley man? We get, we get a little bit of an endorsement there, huh?

[D]

It was very funny. His brother sent me a message on in our, you know, you know, kind of customer support thing. He was like, hey, didn't know you're interviewing my brother. I was like, wait, what, what are we talking about?

[C]

It is a small world. A small world. But hey, that's. Blessed be the ties that bond. And folks, mark your calendars for the next installment of Enterprise Unleashed, February 24th at 12 noon Eastern Time. We also want to encourage you folks go over to dos.com where you can just heard it from GTH. You can check out all the cool things that Wy and the team were up to. You can even get a demo right? Learn more, doing some really cool things. And better yet, they're hiring a ton of team members. I think this is Katherine and Ranel here. Wy, I love this, these Polaroid approach you use. Is that right?

[A]

Yes.

[D]

Yeah, we were, we were laughing about it that we're like, we need to.

[A]

Just, we need to zoom in a little more.

[D]

Got to see their beautiful faces a little more. But no, it's, it's been amazing. You can actually see behind Catherine in that photo there's a wall of Polaroids.

[A]

Love it.

[D]

As we're adding more team members. So we hope to fill that wall out entirely soon.

[C]

Folks, go to dos.com. check out all that stuff, including a new big time, lots of career opportunities there. Okay? This has been terrific. Garruth. I really appreciate all of your time. I know you're out on the road. Really appreciate you carving some time out with us here today. Garruth Acharya with 8090 Industries. Thank you, G. Thank you so much for being here, my friend.

[A]

Thank you for having me. It was a pleasure, gentlemen.

[C]

No doubt.

[D]

Thank you.

[C]

Wally Jones. Really appreciate you and Ashan and the whole DOS team appreciate you. Not only what you're doing in industry, but, but thank you for enabling conversations with the one and only GTH here today. So thank you, Wally. Of course, folks, again, as Joshua. Big thanks to Joshua. Amanda behind the scenes, go check out dos.com. learn a whole bunch more. But folks, you learned a lot from Groot and Wiley. Your homework is this. You got to take one thing, one thing out of all the actionable advice we heard here today. Take one thing put into practice. Because you know, it's all about deeds, not words. That's how we're going to continue to unleash technologies, unleash teams and unleash enterprises in the months ahead, exciting months ahead. So with all that said, Scott L. Here on behalf of the Supply Chain now team challenge, you do good, get forward, be the change that's needed. We'll see you next time right back here on Supply Chain Now. Thanks, everybody.

[B]

Join the Supply Chain now community. For more supply chain perspectives, news and innovation, check out supply chain now dot com, subscribe to supply chain now on YouTube and follow and listen to Supply Chain now wherever you get your podcasts.