Augmented Ops

Tulip

Augmented Ops is a podcast for industrial leaders, shop floor operators, citizen developers, and anyone else that cares about what the future of frontline operations will look like across industries. We equip our listeners with the knowledge to understand the latest advancements at the intersection of manufacturing and technology, as well as actionable insights that they can implement in their own operations. This show is presented by Tulip, the Frontline Operations Platform. read less
TechnologyTechnology
Episode 120: Digital Manufacturing in Turkey and Beyond with Efe Erdem
Aug 9 2023
Episode 120: Digital Manufacturing in Turkey and Beyond with Efe Erdem
Efe Erdem, Executive Director of the MEXT Technology Center takes us on a journey through Turkey's manufacturing landscape and its pivotal role in advancing digitalization across the MENA region. We delve into the motivation behind establishing the MEXT Technology Center, its unique approach in providing end-to-end services to manufacturers, and the impact of their initiatives on digital transformation in various sectors, including automotive, steel, and textiles. Efe shares valuable insights on the importance of upskilling the workforce to drive innovation on the shop floor, and how technology can augment human capabilities leading to increased efficiency and productivity. As the region embraces sustainability, we discuss how digitalization becomes a critical enabler for achieving decarbonization goals and fostering growth in an increasingly competitive global market. If you like this show, subscribe at AugmentedPodcast.co (https://www.augmentedpodcast.co/). If you found this episode interesting, you might also like Episode 104: A Scandinavian Perspective on Industrial Operator Independence (https://www.augmentedpodcast.co/104) with Johan Stahre, or Episode 40: Israel Meets New England on Industry 4.0 (https://www.augmentedpodcast.co/40). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Special Guest: Efe Erdem.
Episode 119: Industrial Design with Kimberly Andersson
Jul 26 2023
Episode 119: Industrial Design with Kimberly Andersson
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is Industrial Design. Our guest is Kimberly Andersson, Head Of Design at Tulip Interfaces. The conversation between Trond and Kimberly revolves around the topic of design, particularly in the context of industrial software and digital tools for manufacturing processes. Kimberly shares her insights on the power of design, the tension between user-centric design and industrial constraints, and the importance of understanding user needs to create better solutions. She provides examples of how digital tools can simplify and improve processes in manufacturing, such as automating data recording and graphing tasks. She also touches on the challenges and opportunities in enterprise software UX and the potential of AI-generated design tools. The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co (https://tulip.co/). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Special Guest: Kimberly Andersson.
Episode 118: Digital Manufacturing in the Cloud with Jon Hirschtick (Rebroadcast)
Jul 12 2023
Episode 118: Digital Manufacturing in the Cloud with Jon Hirschtick (Rebroadcast)
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is: Digital Manufacturing in the Cloud. Our guest is Jon Hirschtick, Head of SaaS, Onshape and Atlas Platform, PTC.In this conversation, we talk about the story of SolidWorks, using agile methods, listening to the market, charting the evolution of CAD into SaaS, and its emerging and future iterations in the open source cloud and beyond After listening to this episode, check out PTC, Solidworks, as well as Jon Hirschtick's social media profiles:PTC (@ptc): https://www.ptc.com/enSolidworks (@solidworks): https://www.solidworks.com/ Jon Hirschtick (@jhirschtick): https://www.linkedin.com/in/jonhirschtick/Trond's takeaway: Digital manufacturing is moving to the cloud and that means a whole lot more than office software moving to the cloud. In fact, establishing a real-time digital thread, through next generation low-code and no-code systems, will reshape industry. The notion of factory production, distributed teams, product development, will all evolve significantly, and will enable personalization across industry and across any and eventually all of manufactured goods. The ramifications will be huge, but they won't automatically happen tomorrow, and the benefits will spread unevenly depending on who--be it corporations, nations, startups, or small- and medium enterprises--grabs the gauntlet first.Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 43, Digitized Supply Chain, episode 24, Emerging Interfaces for Human Augmentation, or episode 21, The Future of Digital in Manufacturing. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Special Guest: Jon Hirschtick.
Episode 115: Bridging the Physical-Digital Divide in Industrial Tech with Rony Kubat
May 31 2023
Episode 115: Bridging the Physical-Digital Divide in Industrial Tech with Rony Kubat
In episode 115 of the podcast, the topic is: Bridging the Physical-Digital Divide in Industrial Tech. Our guest is Rony Kubat (@kubat), CTO and co-founder, TulipIn this conversation, we talk about the complexity of the shop floor and programming a physical-digital environment. What does Digital Lean mean to you? What is augmentation? What's next in industrial tech?Augmented is a podcast for industrial leaders, process engineers and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), presented by Tulip (@tulipinterfaces), the frontline operations platform.Trond's takeaway: The physical-digital environment is no joke. When you speak with a real technologist who not only has imagined what the future would look like, but who is involved in building it, integrating software and hardware on the factory floor, you realize how difficult it will be to transform industrial work. It is not just about industrial tech, it is about people. It is not just about neat software, or fancy hardware. It all has to work together. And, more importantly, it has to fit into the overall context of what people are already doing.Thanks for listening. If you liked the show, subscribe at Augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like episode 44, No-code for IoT in the Cloud, episode 47, Industrial Machine Learning or episode 29, The Automated Microfactory. The Augmented podcast is created in association with Tulip, connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Special Guest: Rony Kubat.
Episode 114: The Industry 4.0 Journey with Scott Phillips
May 17 2023
Episode 114: The Industry 4.0 Journey with Scott Phillips
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Scott Phillips, founder of i4Score, joins us in this episode for a deep-dive conversation about the journey towards a data-driven culture. We discuss the three big challenges small- to medium-sized manufacturers face when trying to adopt new technology; the core principles of Industry 4.0; and how to use technology to automate, autonomize, and augment. If you like this show, subscribe at AugmentedPodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 93: Industry 4.0 Tools (https://www.augmentedpodcast.co/93) with Carl B. March, or Episode 109: Augmenting Workers With Wearables (https://www.augmentedpodcast.co/109) with Andrew Chrostowski. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond’s Takeaway: Industry 4.0 is indeed a journey, and there is a lot to potentially care about, a lot of places to start, and a lot of options that won't always lead firms to scale in a healthy manner. As long as the roadmap is owned by the organization itself, at least, the mistakes, which undoubtedly will be made, will be real lessons, not externally imposed. First among the challenges is to avoid transforming only to discover that you are yet again locked into solutions that you cannot fully make use of. Special Guest: Scott Phillips.
Episode 113: The Business Model of Lean with Jim Huntzinger
May 3 2023
Episode 113: The Business Model of Lean with Jim Huntzinger
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Jim Huntzinger, President of Lean Frontiers, joins us in this episode for a deep dive into the lean business model and all things lean accounting. We explore value stream versus product costing, the importance of lean coaching, the principles of Toyota Kata, and how these strategies can drive processes improvement and product development simultaneously. Throughout the conversation, we examine the value of transforming traditional business practices and the potential impact on organizational decision-making and growth. If you like this show, subscribe at AugmentedPodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 108: Lean Operations (https://www.augmentedpodcast.co/108) with John Carrier, or Episode 84: The Evolution of Lean (https://www.augmentedpodcast.co/84) with Torbjørn Netland. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond’s Takeaway: The lean business model is attractive to many manufacturing firms and still elusive to some of them, despite many examples of the principles in action popping up constantly. The business community should still spend more time on the interface between tech, logistics, and IT, and how all of that might interface with lean accounting. Strikingly, what we might think of as lean companies don't necessarily use lean practices across their business. Special Guest: Jim Huntzinger.
Episode 111: Operator 4.0 with David Romero
Apr 5 2023
Episode 111: Operator 4.0 with David Romero
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Operator 4.0. Our guest is David Romero, Professor of Advanced Manufacturing at Tecnológico de Monterrey University in Mexico (https://tec.mx). In this conversation, we talk about the emergence of a smart and skilled operator who is helped by cognitive and physical augmentation, how this trend emerged, and how it will shape the future where we need more resilience. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre (https://www.augmentedpodcast.co/104). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: The operator is again at the center of the industrial process. This is a curious thing that seems to happen a few years after every major technological breakthrough or implementation once we realize how adaptable and capable a human workforce can be. That does not mean that technology is irrelevant but only that training humans to know every step of the work process is important in order to capture value by addressing and fixing errors and suggesting improvements. Special Guest: David Romero.
Episode 110: Executing on Manufacturing Technology with Jane Arnold
Mar 22 2023
Episode 110: Executing on Manufacturing Technology with Jane Arnold
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "Executing on Manufacturing Technology" and our guest is Jane Arnold, board member at Aperio.ai (https://aperio.ai/about/) and former VP of Manufacturing Technology at Stanley Black & Decker (https://www.stanleyblackanddecker.com/). In this conversation, we talk about advanced manufacturing technology, the importance of material flow, transparency, throughput, cost cutting, and captivating users with digital tools. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 100: Innovating Across the Manufacturing Supply Chain (https://www.augmentedpodcast.co/100). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Execution is everything in manufacturing. You can have any technology you want, but it's only going to be as good as the execution, both among executives and among managers all along the supply chain and all across the factory. Special Guest: Jane Arnold.
Episode 109: Augmenting Workers With Wearables with Andrew Chrostowski
Mar 1 2023
Episode 109: Augmenting Workers With Wearables with Andrew Chrostowski
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "Augmenting Workers With Wearables." And our guest is Andrew Chrostowski, Chairman and CEO of RealWear (https://www.realwear.com/). In this conversation, we talk about the brief history of industrial wearables, the state of play, the functionality, current approaches and deployments, use cases, the timelines, and the future. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 92: Emerging Interfaces for Human Augmentation (https://www.augmentedpodcast.co/92). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Industrial wearables have come a long way. There is a big need for assisted reality in many workforce scenarios across industry. There are now companies taking good products to market that are rugged enough, simple enough, and advanced enough to make work simpler for industrial workers. On the other hand, we are far away from the kind of untethered multiverse that many imagine in the future, one step at a time. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind the new era of industrial operations where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Augmenting Workers With Wearables. And our guest is Andrew Chrostowski, Chairman and CEO of RealWear. In this conversation, we talk about the brief history of industrial wearables, the state of play, the functionality, current approaches and deployments, use cases, the timelines, and the future. Augmented is a podcast for industrial leaders, for process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Andrew, welcome to the show. How are you? ANDREW: Hi, Trond. Great to be here. I'm doing great. TROND: You know, you are a poster child entrepreneur engineer, Oregon State, University of Southern California. You are actually an expert on the future of work. There are so many people that say they talk about the future of work. You are implementing and, selling, and evangelizing a true future of work product, not just a story. We're going to be talking about augmented, assisted all kinds of reality and collaboration, Andrew, because that's, I guess, what it's all about. And you lead the industrial wearable company RealWear. But first, I want to get to the fact that you're a certified firefighter. Now, how does that fit into this? ANDREW: That's really a great question. And one of the things that's been passionate for me from the beginning is being close to the customer. It was true when I was an Air Force officer designing for systems that would support our warfighters and putting myself in their situations in life and death. Certainly, I think about it in terms of customers, and we were dealing with other lines of business and trying to understand the customers' perspective, and especially the frontline workers that create those products. And when I took over the Scott Safety business when I was part of Tyco, their particular market was firefighters. They were the leading provider of air tanks, cylinders, respirators, what we call SCBAs, self-contained breathing apparatus for firefighters. Now, I know a lot of things about a lot of areas of technology. But I didn't know anything about firefighting. And so when I took over that business, the first thing I did was go to Texas A&M and actually get trained and certified as an interior firefighter. So I actually put on all the bunker gear, timed donning just like you do when you're in the fire station, fought real fires that were built, and to understand really the challenges they faced. And I came out of that training really having a greater appreciation for just how challenging that work is. And I know it's shocking to your listeners, but everything we ever see on TV and movies about firefighting is wrong. Basically, firefighting, besides being terrifying, and difficult, and dangerous, is basically blind. You're in the smoke. You're in the dark. And my background in the Air Force thermal imaging systems and multispectral systems came back to me. And I said, "You know what we need to do is give predator vision to firefighters and give them the chance to see the unseen in the dark." And so, coming out of that training, I initiated an in-mass thermal imaging system for firefighters that went to the market about 14 months later at Scott site. TROND: Wow, that's some real background there. I'd like to start with that story because it reminds me that what we're about to talk about here, you know, wearables, it's not a joke. These are, you know, in industrial environments, these are not optional technologies once they really, really start working. And you can sort of say that they're first-line technologies. They better work every time. So this is not a case where you could kind of, well, you know, let's install another version and restart and whatnot. These are eventually going to be hopefully systems that the modern industrial worker really starts to trust to perform their job efficiently. Before we get into the nitty-gritty of all of the different things that RealWear is trying to do, I wanted to just ask you a basic question, what is assisted reality? It's a curious phrase. It's like, why does reality need assistance? [laughs] You know, where does that even come from? ANDREW: You can deny reality, but you can't deny the effects of denying reality. When we talk about assisted reality, it's a point on the spectrum what we call XR, the extended reality. It starts with reality and ends when that virtual reality, the fully immersive digital environment that we experience and what we talk about a lot in the metaverse. Then coming from reality forward, you have assisted reality, which is a reality-first, digital-second environment, which is what we focus on. It is the idea that this is the technology available now that allows a worker to be productive and work safely in a real-world environment. When you get into augmented reality, which is something that we think of when we think of products like HoloLens and other similar types of products, that's where this digital environment begins to overlay the actual environment. It imposes a cognitive load on the brain so that you're now having to focus on things that aren't really there while there are things that are really around you that could hurt you. This is great when you're in a safe environment, in a classroom, in a design area, when you're collaborating in the office to be able to immerse yourselves in these three-dimensional digital objects. It's much different when you're walking on the deck of an oil rig or you're potentially working around a cobot that can hurt you when your attention is distracted. And then we have sort of that virtual reality game that we started with in the metaverse where people are now kind of transposing themselves into a fully digital atmosphere. We at RealWear have focused on making a difference for the future of work and focusing on those 2 billion frontline workers who could work more safely and more productively if they were connected. And it makes perfect sense to us. If we learned anything from the COVID lockdowns, we learned that this idea of working from anywhere, the idea of the office worker working from home, working from the coffee shop, all of this now has become just a given. We know that we need these digital tools to collaborate remotely. What we only have begun to just crack the code on is that there are, again, 2 billion people working with their hands on the front line who could work more productively and more safely if they were connected workers, if they had access to information, if they had access to collaborating in a hands-free way with their counterparts across the world. And so RealWear, our focus is this mission of engaging, empowering, and elevating the performance of those frontline workers by giving them an assisted reality solution that is extremely low friction and easy to use. TROND: I like the distinction there. Even though this podcast is called augmented, I like the distinction between AR and assisted reality. Because there's really, I guess, you can see it more clearly in the consumer space where it sounds so fascinating to enter these virtual worlds. But in industry, the virtual is really subservient and needs to be subservient to the very reality. So I guess assisting reality is the point here. It's not the endpoint that is necessarily the virtual. You're using the technologies, if I understand it, to strengthen the ability to survive and be very, very efficient in reality as opposed to entering some sort of virtual space where you are simulating more. You're talking about critical applications in the physical industrial reality, so that's now clear to me. Having said that, this is not easy to do, is it, Andrew? ANDREW: No. I mean, there's a lot that comes into this idea of making technology that's human-centric. And all the things you were just talking about really bring us back to this idea that this kind of assisted reality solution is about helping the human being at that nexus of control operate more safely and effectively in a variety of environmental conditions. It is really important that we think about the technology serving the person and not so much technology that is imposing itself on people, which is oftentimes what we see as we try to roll out different kinds of technical solutions. The folks who are doing work with their hands who are daily exposing themselves to risk have a very low tolerance for things that waste their time, are difficult to use, or distract them from reality. And so all of those things are factors we took into account as we developed this first head-mounted tablet computer that now is in the market as the Navigator 500. TROND: Andrew, can you tell me a little bit about the history and evolution of these kinds of technologies? Because there is so much hype out there. And you did a pristine job as to making these concepts fairly distinct. But how long has there even been an industrial product? I guess a lot of us remember the first Google Glass, but partly what we remember is the hype in the consumer market, which then kind of fell flat. And then they reemerged, I guess, as sort of a light competitor to you guys and then has since somewhat disappeared. But, anyway, there are a lot of attempts in the near history of technology to do this kind of thing. I mean, it corresponds pretty neatly to various sci-fi paradigms as well. But what are the real prototypes that go into the inspiration for the technology as you have it today? ANDREW: Well, I'm glad you mentioned science fiction because really the way I would start this, otherwise, is, say, a long time ago, in a galaxy far, far away, we had Star Wars. And if you think back to that show, science fiction has been part of how people work in modeling, how people work for decades and more, from Jules Verne all the way through to Star Trek and the like. And so when you think about these technologies, you go back to processes and technologies that support humans collaborating. And back in Star Wars, we had a character called Boba Fett who famously has, and now you see it in the Mandalorian, a little device that comes down from his helmet in front of his eyes and acts as a rangefinder and computer screen. Actually, one of the founding engineers that were part of the design of the first RealWear device came out of designing Boba Fett's helmet. And so there is really a connection there about how people have imagined people work and how people actually work. And the actual part really started with Dr. Chris Parkinson and spending over ten years working on what is the right ergonomics. What's the right way to shift the balance, the weight, the size, and manner of the display? How do you control the windows and amount of information displayed? And how do you suppress the outside noise so that you can have a voice control system that makes it truly hands-free? So it began with this idea of all great things start with a spark of imagination. And then bringing that to a very practical point of view of solving the problem of being able to give someone information and collaboration tools hands-free in an environment where they can work safely but connect to all the value and information that's out there that we enjoy every day working as office knowledge workers with the internet. TROND: Andrew, what are some of the technical challenges you had to overcome? I can imagine; first, you have to design something that is probably bulkier than you wanted, and then eventually reducing its size is one thing. But I can imagine the algorithms apply to, I mean, there's imaging here, and there's a bunch of design techniques to make this work. And then you said ruggedized, right? I mean, this stuff cannot break. ANDREW: That's right. TROND: What are the kinds of things that went into and is going into your next-generation products? ANDREW: Well, I think that's a great question. And, of course, as new products evolve and we build on the learnings we've had from having one of the largest install base of wearable computers in the world, we can sit there and say, look, it starts with ruggedization. Because, frankly, these frontline workers, when they're wearing these devices on their hard hat, at the end of the day, that hard hat gets tossed into the back of the truck. It gets tossed in the van. It gets dropped on the ground, or in the mud, or out in the rain. So we knew right away that we had to build a device that was able to hold up to that, things that a lot of similar kinds of products that are out there just can't hold up to. So we started with this idea that it had to be extremely rugged. It had to be lightweight enough to wear all day. And our first version did that very well. The Navigator 500 has come now just as rugged but now 30% lighter. So we've learned how to make that ruggedness, even in a lighter form factor. You have to trade-off on how you see that display in bright sunlight, in dim settings. You have to think about how you operate in a noisy environment. So you can imagine if you're trying to use a voice-driven assistant, whether it's on your phone or a little microphone device in your home, you use a wake-up word, and then you have to try to talk clearly. And if you don't talk clearly, you end up having it not do what you want. That's very frustrating for a frontline worker, and it's just downright distracting and dangerous at times. So we chose to have a system and voice control that does not require a wake-up word. It's always listening. And it listens in context to what's on the screen. Literally, what we say is you say what you see. And that's about all the training you need to learn how to use the Navigator 500 effectively. And because it's so easy and intuitive, people get used to it quickly. And they go gravitate towards how it's making their work easier to get to, how it's easy to launch a collaborative meeting in any number of key applications, whether it's Microsoft Teams, Cisco, Webex on demand, whether it's Zoom, whether it's TeamViewer, any number of other partners that we have in terms of the types of collaborations. TROND: Well, I want to get into some of the use cases in a second, but just briefly, so you were founded as a company in 2016. And you're now, I guess, 140-some employees. I mean, it's fairly recent. This is not something that you've been doing since the '70s here. But on the other hand, this is also very challenging. It's not like you produce something, and all of industry immediately buys into it. So I just wanted to address that, that this particular market, even though it's always been there as this potential, there doesn't seem to have been kind of a killer application like there is in some other hardware markets. And maybe you're thinking you will be one. But I just wanted you to address this issue. Recently, the IBC the analysts came out with this prediction that they're forecasting a decline actually year over year in units sold. And they're also saying a lot of new vendors are going to come into this market, but the market is not very mature right now. What do you say to that kind of an argument? ANDREW: There's a lot to unpack there, so forgive me if I miss some of the things you brought up there. But I'd start really with RealWear and how we develop this. The Navigator 500, the product we have on the market today, is highly modular, lightweight, does all these types of things, and that's really the eighth generation. Even though we only have been around since 2016, the thinking behind this form factor has gone on for eight generations. So we've got a lot more maturity than some of the other folks who might be thinking about entering this market. We've also focused entirely from the beginning on that industrial frontline worker. It's a niche of over 2 billion people but very different from the consumer aspect and what people have gotten used to in terms of dealing with a piece of glass that they might carry in their pocket all day long. We think that A, we've kind of created this assisted reality space. We've won in so many of these industrial cases because of the way we make work safer and more productive. We've now passed applications where we've had installations over 3,500 units with a single use. We've got, in multiple cases, over 1,000 deployments. We've got 75-80 deployments of over 100 units. So we really have broken through. And what we see is whenever we talk about the assisted reality market, or we can talk more broadly, we usually only see data on augmented reality. They put all these smart glasses in sort of a category. And we're really only a portion of what they count as smart glasses. So when they start saying there's downward pressure on that market or it's not growing as fast, it goes back to something I just read in a book about builders in terms of how innovation happens. And the author described augmented reality as a solution looking for a problem. We came at it with a particular problem we were solving, and that's I think the big difference between us and a lot of how people have come into this space. We knew exactly the problem we're trying to solve. We knew that we wanted to make the human the central part of that control Nexus. And we knew that we wanted to be in a space where others would find it difficult to succeed. And so, as we've been successful here and as we continue to grow and expand these deployments and getting into larger and larger deployments, we know that others will kind of begin to look into this space and try to compete. But most of them are bridging over from that consumer side where a lot of the fundamental design trade-offs they've made do not well-support all shift use in a ruggedized environment and with the ease of use that we've designed into our products. TROND: Andrew, that makes a lot of sense to me. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you. TROND: Let's talk about some of these bigger deployments. So I don't know if you can mention names, but the biggest one, I'm assuming, is in the automotive industry because they are at the forefront of a lot of automation technology. So I'm just going to make that assumption. Tell me a little bit about that deployment. What is it all about? What are they using it for? What can you tell me about what they're using it for? ANDREW: Thank you, Trond. And I'm super excited about our success in the automotive sector, not only just because of what it represents but because, as an industry, it's so central to economies across the globe. And when we think about the transformation of that industry going to electrification, that change creates opportunity for us as well. So today, with our partner TeamViewer we're in over 3,500 dealerships. Virtually every dealership in America now has a RealWear product in it. For those technicians, when they're dealing with a particularly tough problem, they're able to put on our device as simple as what I'm doing here, just putting on their Navigator, their HMT-1. And they can call and connect with a technical assistance center in Detroit and have a first-person conversation with an expert who can help walk them through that repair, whether it's pushing diagrams to them to, illustrating over the video that they're getting but helping them solve that problem faster. And why is this so significant? Well, because from the customer point of view, you're happy that your problem is being solved quicker. You've got your car back. The dealer is happy because now they've been able to invoice the customer or invoice for it in this particular case to get their warranty repair dollars back. And Ford is happy because now they've got a happy customer, and they've got a better reputation and user experience. So it's a very positively reinforced system. And so when you think about that application alone of just being able to solve problems of existing cars, now think about the introduction of all of these electric vehicles to dealers, not only with Ford but anybody else you can think of is moving into electrification. There are a lot of technicians who know how to work on a gasoline engine, but very few who maybe know how to really solve those electricals. So this is a way that these dealers can bridge the skills gap that exists between what they have and what they need to be able to do in the near future. And that skills gap, by the way, is recognized not just in the automotive industry, but you and I experience it every day when we deal with restaurant industry, service industries, trucking. You think about any kind of skilled labor situation; we know demographically we've got a big gap. And that's going to be persistent for decades. And so a tool, a knowledge transfer platform that lets people move up that learning curve more rapidly to do more meaningful work, to be more self-actualized as they do that not only helps people but it helps industry serve their customers. And so we see ourselves really at the forefront of transforming work as we know it. TROND: I'm so glad you went to the skills, and it's so exciting that that's the main application right now because I think there's a lot of discussion, obviously, in the industry across sectors about the skills gap; they say, right? That the gap...we have to train people, or they have to go to school. They have to learn. It's an endless complexity. But, I mean, you're sort of saying the opposite. You're sort of saying cancel the training, put the headset on. Some of these things, very advanced training, very advanced advice, real-time support, can happen without going aside, looking at a computer, calling someone up, talking to you, you know, see you next week with your car. And then, meanwhile, what you're doing is scratching your head for a while, trying to figure out what's wrong. But you're saying this creates a much more dynamic scenario both for delivering the service and actually for the human worker who's trying to deliver some sort of service here and is plugged into an information ecosystem. I'm just wondering, is that a very, very typical use case? And do you foresee that that is the use case for assisted reality? Or are there wildly different use cases just depending on, I mean, pick another industry. I was just imagining the medical industry, famously remote surgery, or whatever it is. Some sort of assistance during surgery is obviously the big use case. I could imagine that there's something to be done here also with RealWear. ANDREW: Yeah, I mean, this is such an exciting area and topic to talk about, education, how people are educated, how that education plays to their employment and their employability, and how they add value and have careers. And we all have talked about whether university work is preparing people for the kinds of careers there are today or whether or not we need to be considering other kinds of applications, going direct to coding or whatever else. So when you talk about frontline workers, it's absolutely a matter of specific knowledge. It's not just general knowledge that matters. It's very specific things that can happen. And so by connecting people to experts, you do two things: you get the job done right away, but you also mature that worker because they learn from those experiences. And they can use our device to actually, while they're doing the work, film it. It can be curated and then used as training videos for the next generation of work that goes with it. So I think that alone is really exciting. There are so many use cases, though, beyond this, remote experts see what I see that we've been talking about. That's really...I'd say the predominant deployment today that people think about is how do I collaborate remotely on the front line? And that's super valuable. But what becomes even more interesting is when that device becomes a solution for how you do your daily work. As an example, if you're a heavy engine manufacturer and you have an end-of-line inspection, and that inspector is using a clipboard and a checklist to look at how the engine is functioning, imagine replacing that. For one of our particular customers, that takes about 30 minutes. When they implemented workflow using hands-free Navigator, they were able to reduce that time to about 12 minutes because now the person is not wasting time going back and forth to a clipboard, or to a table, or writing things down. They're absolutely hands-free, immersed in the work, being presented the next inspection point in their display, being able to photograph it, work through it, look at a comparison, document it. And the important thing is not just that they're doing it faster; they're finding three times as many defects because they're not distracted. We know there's no such thing as actually dual processing as human beings. If we think that we can listen to a Zoom call and do emails, we're doing neither very well. We know that we're just quickly switching. And that's the same thing that a lot of frontline workers experience. When you make it immersive and hands-free with workflow, now you begin to expand the value that this technology begins to support so much greater. As we move along, the implementations and the deployments are going to move from sort of this collaboration centric to workflow centric to then being able to be with our partner, IBM. IBM has actually created something they call Inspector Wearable, where they're giving a superpower inspection to an operator who might be standing at the end of an assembly line watching a car roll by. It stops in front of them. The camera knows, because of machine learning with Watson up in the cloud, that, hey, this is what a good wheel should look like and immediately highlights the operator with a telestration that's the wrong nut. There's a scratch on this rim or whatever defect we might be talking about. So then you start actually using these technologies that are inherent with the system to be able to augment the capabilities of these workers. And that starts to get really exciting. I'll add one of the points to that is in Q4, we're going to be introducing a thermal imaging camera that can easily be just snapped on on the part of our modular solution for Navigator to be able to then snap on a thermal imaging camera and give that person predator vision to be able to see if they're walking around their plant. They can see that an electrical panel is overheating or that a motor is hot, or they can use it in any of the hundreds of thermography industrial programs that people use today. So I think part of that transition goes from just being collaboration to how we work and do workflows to actually augmenting the capabilities of the folks who are wearing these wearable computers. TROND: Yeah, and that's so interesting. And, I guess, correct me if I'm wrong, but that's where it ties into not only IBM but a bunch of your other software partners too where Tulip being one of them, where now that you're providing a device, it actually is the end client that can put that device to use in their own scenarios. And they can build, I guess, apps around it and find their own use cases that may not be the ones that are super apparent to any of those who deliver it, whether it is you delivering the hardware, IBM, you know, delivering perhaps the machine learning capabilities or some other knowledge, or it is Tulip delivering kind of a frontline software platform that's adaptable. It is actually the end client that sits there and knows exactly how they want to explore it, and then in a second iteration, change that around. Or am I getting this ecosystem wrong here? ANDREW: No, I think you're onto something there very powerful, Trond. And there are three specific dots we have to connect when we think about a sustainable solution that can be deployed broad-spread across an industrial base, and the first one is the device. The device has to be right. It has to work for the user. It has to meet the requirements of the environmental conditions they're operating in. And so the device is critical. And that's really where RealWear started our journey with that focus on the user and the user experience with our device. But the next step is really the data that comes with it. That's that part where it's both accessing data and creating data through applications that they use to feed the data lakes above and to feed back into this IoT world where there's information coming up from our equipment and being fed back to us that we can take action on. And then, ultimately, we have to connect to systems of record. And this is where Tulip, for instance, one of our partners, plays such an important role. It's that connection between all of these things that talk together, the device, the data, and these decision-making systems of record, that now when they talk and connect, it's a very sticky situation. Now you've created more than just a point solution. You've created a system solution where you've changed the way people work, and you reduce friction in interacting with those systems. And I think that that's a real clear case. I'll give an example that RealWear did in a very simple way. We recently acquired a small company called Genba AI. Their whole purpose in life was to be able to take a CMMS system, which is done for maintenance purposes, and working with eMaint, which is a division of Fortive, and be able to then say, "We can take that currently operating device that requires a worker to print out a work order, go do something, and then put it back into a computer, we can now do that with voice only." So, again, you take friction out of that interaction and allow them to do things easier but with the systems of record. And so that's why I get so excited about partners like Tulip that are making and connecting the dots between all of these disparate systems that we find in fourth-generation industrial complexes and making them work together seamlessly to give information to make better decisions by the folks who manage that work. TROND: This makes me think of something that I promise we'll get back to in a second talking about the industrial metaverse, which I think is far more interesting than the consumer metaverse. And we'll get to that because you were starting with this whole ecosystem that starts to develop now. But before we get there, I just wanted you to comment a little bit on COVID, COVID-19. Massive experience; no one is untouched by this. And there clearly was a future of work dimension to it. And people have made a lot out of that and prognosticate that we will never show up in the office again, or hybrid is here forever. What did COVID do to RealWear? ANDREW: Well, you know, it's an interesting perspective. I've been with RealWear in one capacity or another since almost the beginning, starting off as a Strategic Advisor and Chairman of the Advisory Board to, stepping in as the COO during the series A, and ultimately becoming the CEO and Chairman of the board in 2020 just as COVID was happening. So a lot of that immediate experience of RealWear was at a time when the whole world was starting to shut down and realize that we had to work differently. So I literally had one meeting with my direct staff as the new CEO before Washington State was shut down. And all the rest of the year was done via remote work. So it's not a dissimilar story to what a lot of people went through in recognizing that, hey, what used to be done in the office and was deemed important to be done in the office had to now be done elsewhere. And we came quickly with this adoption of digital tools that supported this digital transformation. And what it really did was act as a catalyst because before, you could have a conversation about the value of remote collaboration software, laptop to laptop, and that sort of thing, but nobody was thinking about the front line as much. That was a really tall connection for RealWear to make. We'd go in and talk about the value of a hands-free remote connected worker. But when you suddenly had millions of displaced workers all contributing, in some cases with productivity increasing, it now said, hey, by the way, do you want to take this great hybrid environment you just created, and do you want to extend it to those important people who don't get to stay home, who don't get to dodge the risk of being exposed to COVID, who have to go out and serve the public or serve your customers? And now, if we talk about giving those people connectivity and extending that with technology that exists today using familiar platforms...RealWear runs on an Android 11 platform. That means imaginations are limitation, not technology. All those solutions we're talking about can be done in an Android environment, can be imported very quickly, and provide a solution for those users. And so it acted as a catalyst to say that remote experts at smart glasses, as it were, were here, and it was now, and this technology was ready. And the deployments took off. It probably shortened our deployment cycle. Our sales cycle probably contracted by 70% during COVID as people began to realize this is how we can get work done. This is how we can continue to serve our customers. And so it was a huge change, not only in terms of the demands that we were able to meet thanks to the great teamwork of our whole RealWear ecosystem and supply chain partners, but it also made a difference because it changed the thought processes of leaders who now realized that creating a connected worker not only was feasible, that it had a real, recognizable ROI to it. TROND: Andrew, you're really speaking to me here because eons ago, in my Ph.D., I was working on this very visionary idea back in 1999, the early internet heydays. Again, the future of work people and tech companies were saying, "We are soon unleashing the situation where no one has to come into the office. We will sit all separately on these islands and work together." So I would say I guess what has happened now is there's a greater awareness of the need for hybrid solutions meaning some people are physically there, others are not. But the powerful thing that you are enabling and demonstrating visually and physically is that remote is one thing and that it remains challenging, but it can now, in greater extent, be done. Physical presence is still really, really powerful. But what's truly powerful is the combination of which. It is the combination of physically being there and being amplified or assisted, or eventually perhaps in a fruitful way augmented but without losing touch with reality if it can be done safely. That's really the power. So there's something really interesting about that because you can talk about it all you want. You can say, well, with all the technology in the world, you know, maybe we don't want to meet each other anymore. Yeah, fine. But there's a powerful argument there that says, well if you combine the world's biggest computer, the human being, with some secondary computers, you know, AIs and RealWears and other things that have other comparative advantages, the combination of that in a factory floor setting or perhaps in other types of knowledge work is really, really hard to beat, especially if you can get it working in a team setting. I guess as you were thinking more about this as a futuristic solution, Andrew, what kind of changes does this type of technology do to teamwork? Because we've been speaking about the simple, remote expert assistance, which is sort of like one expert calling up another expert at headquarters. And then, you move into workflow, which is powerful product workflow in industry. But what about the group collaboration possible with this kind of thing? Have you seen any scenarios where multiple of these headsets are being used contemporaneously? ANDREW: Yeah, I mean, I think there's the application of not only people using them broadly in doing their work but also then being connected to a broad number of users. There's a great video that Microsoft put out when they built Microsoft Teams to run specifically on our RealWear platform. And in it, we talk about a plant where, you know, Honeywell was certifying a very large deployment technology in a plant that normally would take 40 workers to go to this facility and physically sign off all the things that need to be done for this large automation system. But using Microsoft Teams and RealWear devices, Honeywell was able to do that completely remotely. They were able to have the folks who were on site wearing the devices going through. And all of these people who would travel to it are now wherever they happen to be, in the office, at home, somewhere else, being able to see what was happening in the factory and sign off and validate the work remotely. So it's like this world where we've taken away the borders, these artificial borders between the office, not the office, and then the front line. And I think that the biggest thing that we can take away from this conversation today, Trond, is that we all probably accept that some form of hybrid work is here to stay with office workers. We've just proven over the last two years that you can work extremely productively as a remote team. And we've also validated there are times when we just got to come together from a human point of view to accomplish even more in terms of some of the cultural and emotional intelligence and teaming things that happen. But what we've also learned is that those frontline workers don't have the
Episode 108: Lean Operations with John Carrier
Feb 15 2023
Episode 108: Lean Operations with John Carrier
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "Lean Operations." Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: The core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. And our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Lean Operations. Our guest is John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talk about the people dynamics that block efficiency in industrial organizations. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. John, welcome to the show. How are you? JOHN: Trond, I'm great. And thank you for having me today. TROND: So we're going to talk about lean operations, which is very different from a lot of things that people imagine around factories. John, you're an engineer, right? JOHN: I am an engineer, a control engineer by training. TROND: I saw Michigan in there, your way to MIT and chemical engineering, especially focused on systems dynamics and control. And you also got yourself an MBA. So you have a dual, if not a three-part, perspective on this problem. But tell me a little bit about your background. I've encountered several people here on this podcast, and they talk about growing up in Michigan. I don't think that's a coincidence. JOHN: Okay, it's not. So I was born and raised in the city of Detroit. We moved out of the city, the deal of oil embargo in 1973. I've had a lot of relatives who grow up and work in the auto industry. So if you grew up in that area, you're just immersed in that culture. And you're also aware of the massive quote, unquote, "business cycles" that companies go through. What I learned after coming to MIT and having the chance to meet the great Jay Forrester a lot of those business cycles are self-inflicted. What I do is I see a lot of the things that went right and went wrong for the auto industry, and I can help bring that perspective to other companies. [laughs] TROND: And people have a bunch of assumptions about, I guess, assembly lines in factories. One thing is if you grew up in Michigan, it would seem to me, from previous guests, that you actually have a pretty clear idea of what did go on when you grew up in assembly lines because a lot of people, their parents, were working in manufacturing. They had this conception. Could we start just there? What's going on at assembly lines? JOHN: I'm going to actually go back to 1975 to a Carrier family picnic. My cousin, who's ten years older than I, his summer job he worked at basically Ford Wayne, one of the assembly plants. He was making $12 an hour in 1975, so he paid his whole college tuition in like a month. But the interesting point was he was talking about his job when all the adults were around, and he goes, "Do you know that when they scratch the paint on the car, they let it go all the way to the end, and they don't fix it till it gets to the parking lot?" And I'll never forget this. All the adults jumped on him. They're like, "Are you an idiot? Do you know how much it costs to shut the line down?" And if you use finance, that's actually the right answer. You don't stop the line because of a scratch; you fix it later. Keep the line running. It's $10,000 a minute. But actually, in the short term, that's the right decision. In the long term, if you keep doing that, you're building a system that simply makes defects at the same rate it makes product. And it's that type of logic and culture that actually was deeply ingrained in the thinking. And it's something that the Japanese car companies got away from. It's funny how deeply ingrained that concept of don't stop the line is. And if you do that, you'll make defects at the same rate that you make product. And then, if you look at the Detroit newspapers even today, you'll see billion-dollar recalls every three months. And that's a cycle you've got to get yourself out of. TROND: You know, it's interesting that we went straight there because it's, I guess, such a truism that the manufacturing assembly line kind of began in Detroit, or at least that's where the lore is. And then you're saying there was something kind of wrong with it from the beginning. What is it that caused this particular fix on keeping everything humming as opposed to, I guess, what we're going to talk about, which is fixing the system around it? JOHN: There's a lot of work on this. There's my own perspective. There's what I've read. I've talked to people. The best I can come up with is it's the metrics that you pick for your company. So if you think about...the American auto industry basically grew up in a boom time, so every car you made, you made profit on. And their competitive metric was for General Motors to be the number one car company in the world. And so what that means is you never miss a sale, so we don't have time to stop to fix the problem. We're just going to keep cranking out cars, and we'll fix it later. If you look at the Japanese auto industry, when it arose after World War II, they were under extreme parts shortages. So if one thing were broken or missing, they had to stop. So part of what was built into their culture is make it right the first time. Make a profit on every vehicle versus dominant market share. TROND: Got it. So this, I guess, obsession with system that you have and that you got, I guess, through your education at MIT and other places, what is it that that does to your perspective on the assembly line? But there were obviously reasons why the Ford or the Detroit assembly lines, like you said, looked like they did, and they prioritized perhaps sales over other things. When you study systems like this, manufacturing systems, to be very specific, how did you even get to your first grasp of that topic? Because a system, you know, by its very nature, you're talking about complexity. How do you even study a system in the abstract? Because that's very different, I guess, from going into an assembly and trying to fix a system. JOHN: So it's a great question. And just one thing I want to note for the audience is although we talk about assembly lines, most manufacturing work is actually problem-solving and not simply repetitive. So we need to start changing that mindset about what operations really is in the U.S. We can come to that in the end. TROND: Yeah. JOHN: I'll tell you, I'm a chemical engineer. Three pieces of advice from a chemical engineer, the first one is never let things stop flowing. And the reason why that's the case in a chemical plant is because if something stops flowing for a minute or two, you'll start to drop things out of solution, and it will gum everything up. You'll reduce the capacity of that system till your next turnaround at least. And what happens you start getting sludge and gunk. And for every class I was ever in, in chemical engineering, you take classes in heat transfer, thermodynamics, kinetics. I never took a class in sludge, [laughs] or sticky solids, or leftover inventory and blending. And then, when I first went to a real factory after doing my graduate work, I spent four to six years studying Laplace transforms and dynamics. All I saw were people running around. I'm like, that's not in the Laplace table. And, again, to understand a chemical plant or a refinery, it takes you three to five years. So the question is, how can you actually start making improvement in a week when these systems are so complex? And it's watch the people running around. So that's why I focus a lot on maintenance teams. And I also work with operations when these things called workarounds that grow into hidden factories. So the magic of what I've learned through system dynamics is 80% to 90% of the time, the system's working okay, 10% or 20% it's in this abnormal condition, which is unplanned, unscheduled. I can help with that right away. TROND: So you mentioned the term hidden factories. Can you enlighten me on how that term came about, what it really means? And in your practical work and consulting work helping people at factories, and operations teams, and maintenance teams, as you said, why is that term relevant, and what does it really do? JOHN: Great. So I'm going to bring up the origin. So many people on this call recognize the name Armand Feigenbaum because when he was a graduate student at the Sloan School back in the '50s, he was working on a book which has now become like the bible, Total Quality Management or TQM. He's well known for that. He's not as well known for the second concept, which he should be better known for. Right after he graduated, he took a job in Pittsfield, Massachusetts, for one of the GE plastic plants. Here he comes out of MIT. I'm going to apply linear equations. I'm going to do solving, all these mathematics, operation constraints, all these things. When he gets into that system, he realizes 30% of everything going on is unplanned, unscheduled, chaotic, not repeated. He's like, my mathematical tools just break down here. So he did something...as important as marketing was as an operational objective, he named these things called hidden factories. And he said, 30% of all that work is in these hidden factories. And it's just dealing with small, little defects that we never ever solve. But over time, they actually erode our productivity of systems that can eat up 10% to 20% of productivity. And then, finally, it's work that I'm doing. It's the precursor to a major accident or disaster. And the good side is if you leave the way the system works alone, the 80%, and just focus on understanding and reducing these hidden factories, you can see a dramatic improvement quickly and only focus on what you need to fix. TROND: So, for you, you focus on when the system falls apart. So you have the risk angle to this problem. JOHN: Exactly. And so just two things, I'm like a doctor, and I do diagnosis. So when you go to the doctor, I'm not there to look at your whole system and fix everything. I'm like, here are first three things we got to work at, and, by the way, I use data to do that. And what I realized is if everyone just steps back after this call and thinks about today, right? When you get to the end of the day, what percent of everything in that factory or system happened that was in your schedule? And you'll start to realize that 30% of the people are chasing symptoms. So you need data to get to that root cause, and that will tell you what data to collect. And second, look for time because what you're doing is these hidden factories are trying to keep the system running because you have a customer. You have your takt time, and so people are scrambling. And if you put that time back into the system, that's going to turn into product. TROND: John, I'm just curious; when you say data, I mean, there's so much talk of data and big data and all kinds of data. But in manufacturing, apart from the parts that you're producing, I mean, some of this data is hard to come by. When you say data, what data will you even get access to? JOHN: I come from the Albert Einstein School is. I need a ruler, and I need a stopwatch. Go into any system that you work in, whether it be your factory or your house, and ask the last time someone measured how long something took, and you will find a dearth of that data. And the reason why I love time data is it never lies. Most data I see in databases was collected under some context; I can't use it. So I go right in the floor and start watching 5 or 10 observations and looking at all the variation. The second point I ask is, what's a minute worth in your system or a second? So if we're in an auto assembly plant, in a chemical plant, if we're in a hospital, in an operating room, those minutes and seconds are hundreds of thousands of dollars. So within about 20 minutes, not only have I measured where there's opportunity, we're already on the way to solving it. TROND: So, so far, you haven't talked much about the technology aspects. So you work at a business school, but that business school is at MIT. There's a lot of technology there. It strikes me that a lot of times when we talk about improvements, certainly when we talk about efficiencies in factories, people bring up automation machines as the solution to that tool. And I'm sure you're not against machines, but you seem to focus a lot more on time, on organizational factors. How should people think about the technology factor inside of their operations? JOHN: So, first, you brought up...my nickname is Dr. Don't. And the reason they call me Dr. Don't [laughs] is because they'll go, "Should we invest in this? Can we buy these robots?" I say, "No, you can't do that." And I'm going to tell you why. First is, I was quote, unquote, "fortunate enough" to work in a lot of small and mid-sized machine shops during the 2009 downturn. And I was brought in by the banks because they were in financial trouble. And the one thing I noticed there was always a million-dollar automation or robot wrapped in plastic. And large companies can get away with overspending on technology, small and mid-sized companies can't. And so what you really want to do is go and watch and see what the problem is, buy just as much technology as you need, and then scale that. First is, like I just said, I was just in a plant a few weeks ago, and they just implemented several hundred sensors to basically listen to their system. That's all good. It's data we need. Two problems, why'd you put in several hundred and not put in 20? And second, when we inspected it, about 15% were either not plugged in or weren't reading. So what happened was if we would have started with 20 and put the resource in analyzing that data, then when we scaled to the several hundred, we'd have had our systems in place. Instead, we overwhelmed everyone with data, so it really didn't change the way they work. Now we fixed that. But your question was, why am I skeptical or slow to invest in technology? Technology costs money, and it takes time. If you don't look at the system first and apply the technology to solve the system problem, you're going to end up with a million-dollar piece of equipment wrapped in plastic. If you go the other direction, you will scale successfully. And no one's better at this than Toyota. They only invest in the technology they need. Yet you can argue they're at least as technologically sophisticated as all the rest. And they've never lost money except in 2009 so that is a proof point. TROND: What are some examples of places you've been in lately, I don't know, individual names of companies? But you said you're working kind of mid-sized companies. Those are...[laughs] the manufacturing sector is mid-sized companies, so that sounds very relevant. But what are some examples in some industries where you have gone in and done this kind of work? JOHN: I work for large companies and small and mid-sized. And I'm a chemical engineer, but I love machine shops. So I sit on the board of a $25 million machine shop. They make parts for a diesel truck and some military applications. They make flywheels. So one of their big challenges is in the United States and in the world, we're suffering with a problem with castings. We received our castings. Interesting thing is there are void fractions. One of the things I do want to share is as a systems guy, I'm not an expert in mechanical engineering or any of that, but I can add value by helping look for defects. Let me tell you what their challenge is. So, first of all, more of their castings are bad. Then this surprised me...I learned from asking questions. If you've ever been in a machine shop, one thing I learned about when you're making casting is that there are always bubbles in it. You can't avoid it. The art of it is can you put the bubbles in the places where they don't hurt? You minimize the bubbles, and you move them to the center. So one is we're getting bad castings, but the second part was when we made some of these castings, and they had a void problem in the center. So that doesn't cause a problem with your flywheel. The customer sent them back because they're becoming oversensitive to the defects that don't count. And it's because they switched out staff. So I guess what I'm trying to say here is our supply chain is undergoing this new type of stress because we're losing the type of expert system expertise that we've had from people that have worked in this industry 20 to 30 years. That's a really important aspect. The second is we're in their line balancing all the time. I think a lot of the things you learn in class, you spend one class on load balancing or line balancing, operation and manufacturing, and then you go into a factory, and no one's doing it. So I just wanted to share two points. My one factor is doing that they cut 30% of their time. Another system I'm working in they have one experienced supervisor managing four new people on four different setups. What I realized is there's not enough of that supervisor to go around. We're like, why don't we shoot videos like the NFL does [laughs] and watch those films of how people do their work? Because when you're an expert, Trond, and you go to do a task, you say, "That has five steps." But if I sent you or me new, we'd look and go, "There are really about 80 steps in there." And you explained it to me in 15 minutes. How am I going to remember that? So shooting film so people can go back and watch instead of bothering your supervisor all the time, which they won't do. So what I do think, to wrap up on this point, is when you talk about technology, the camera, the video that you have in your pocket, or you can buy for $200, is the best technology you can probably apply in the next three to six months. And I would greatly encourage everyone to do something like that. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you. TROND: I wanted to ask you then, derived from this, to what extent can some of these things be taught as skills on a systemic level in a university or in some sort of course, and to what extent? Do you really just have to be working in manufacturing and observing and learning with data on your own? By extension, to what extent can a manager or someone, anyone in the organization, just develop these practices on their own? And to what extent do you need mentorship from the outside to make it happen or see something in the system that is very difficult to see from the inside? JOHN: So it's interesting you ask that because that's very much the problem I'm dealing with because as good as our universities are, the best place to learn operations in manufacturing is on the factory floor. So how do you simulate that approach? I teach lean operations at MIT Sloan. And what I do with my students is I ask them to pick a routine task, video two minutes of it, and reduce that by 30%. And I've done this two years in a row. When you look at these projects, the quality of the value streams and the aha moments they had of time that they were losing is stunning. You know what the challenge is? They don't yet always appreciate how valuable that is. And what I want them to realize is if you're washing dishes or running a dishwasher, why is that any different from running a sterilization process for hospital equipment? Why is that any different from when you're actually doing setup so that maintenance can get their work done 30% faster? I've given them the tools, and hopefully, that will click when they get out into the workspace. But I do have one success point. I had the students...for some classes, they have to run computers and simulations during class. So that means everyone has to have the program set up. They have to have the documentation. So you can imagine 5 to 10 minutes a class, people getting everything working right. One of my teams basically said we're going to read...it took about five minutes, and they said, we're going to do this in 30 seconds just by writing some automated scripts. They did that for our statistics class, and then they shared it with their other classmates, beautiful value stream, video-d the screens, did it in about four or five hours. The next class they took later I found out they did that for a class project, and they sold the rights to a startup. So first is getting them that example in their own space, and then two, helping them make analogies that improving things in your own house isn't all that much different than the systemic things in a factory. TROND: Learning by analogy, I love it. I wanted to profit from your experience here on a broader question. It takes a little bit more into the futuristic perspective. But in our pre-conversation, you talked about your notion on industry 4.0, which, to me, it's a very sort of technology, deterministic, certainly tech-heavy perspective anyway. But you talked about how that for you is related to..., and you used another metaphor and analogy of a global nervous system. What do you think, well, either industry 4.0 or the changes that we're seeing in the industry having to do with new approaches, some of them technology, what is it that we're actually doing with that? And why did you call it a global nervous system? JOHN: When I graduated from school, and I'm a control systems skilled in the arts, so to speak. And the first thing I did...this is back in the '90s, so we're industry 3.0. When you're in a plant, no one told me I was going to spend most of my time with the I&C or the instrumentation and control techs and engineers. That's because getting a sensor was unbelievably expensive. Two, actually, even harder than getting the budget for it was actually getting the I&C tech's time to actually wire it up. It would take six weeks to get a sensor. And then three, if it weren't constantly calibrated and taken care of, it would fall apart. And four, you get all those three workings, if no one's collecting or knows how to analyze the data, you're just wasting [laughs] all your money. So what was exciting to me about industry 4.0 was, one, the cost of sensors has dropped precipitously, two, they're wireless with magnets. [laughs] So the time to set it up is literally minutes or hours rather than months and years. Three, now you can run online algorithms and stuff, so, basically, always check the health of these sensors and also collect the data in the form. So I can go in, and in minutes, I can analyze what happened versus, oh, I got to get to the end of the week. I never looked at that sensor. And four, what excited me most, and this gets to this nervous system, is if you look at the way industries evolved, what always amazes me is we got gigantic boilers and train engines and just massive equipment, physical goods. Yet moving electrons actually turns out to be much more costly in the measurement than actually building the physical device. So we're just catching up on our nervous system for the factory. If I want to draw an analogy, if you think about leprosy; a lot of people think leprosy is a physical disease; what it is is it's your nerves are damaged, so because your nerves are damaged, you overuse that equipment, and then you wear off your fingers. And if you look at most maintenance problems in factories, it's because they didn't have a good nervous system to realize we're hurting our equipment. And maintenance people can't go back and say, "Hey, in three months, you're going to ruin this." And the reason I know it is because I have this nervous system because I'm measuring how much you're damaging it rather than just waving it. And now it becomes global because, let's say you and I have three pumps in our plant, and we need to take care of those. They are on the production line, very common. What if we looked at the name of that pump, called the manufacturer who's made tens of thousands of those? There's the global part. So they can help us interpret that data and help us take care of it. So there's no defect or failure that someone on this planet hasn't seen. It's just we never had the ability to connect with them and send them the data on a platform like we can with a $5,000 pump today. So that's why I look at it, and it's really becoming a global diagnosis. TROND: It's interesting; I mean, you oscillate between these machine shops, and you had a medical example, but you're in medical settings as well and applying your knowledge there. What is the commonality, I guess, in this activity between machine shops, you know, improving machine shops and improving medical teams' ability to treat disease and operate faster? What is it that is the commonality? So you've talked about the importance, obviously, of communication and gathering data quicker, so these sensors, obviously, are helping out here. But there's a physical aspect. And, in my head, a machine shop is quite different from an operating room, for example. But I guess the third factor would be human beings, right? JOHN: I'm going to put an analogy in between the machine shops at the hospital, and that's an F1 pit crew. And the reason I love F1 is it's the only sport where the maintenance people are front and center. So let's now jump to hospitals, so the first thing is if I work in a hospital, I'm talking to doctors or nurses in the medical community. And I start talking about saving time and all that. Hey, we don't make Model Ts. Every scenario we do is different, and we need to put the right amount of time into that surgery, which I completely agree to. Where we can fix is, did we prepare properly? Are all our toolkits here? Is our staff trained and ready? And you'd think that all those things are worked out. I want to give two examples, one is from the literature, and one is from my own experience. I'd recommend everyone look up California infant mortality rates and crash carts. The state of California basically, by building crash carts for pregnancies and births, cut their infant mortality rate by half just by having that kit ready, complete F1 analogy. I don't want my surgeon walking out to grab a knife [laughs] during surgery. And then second is, I ran a course with my colleagues at MIT for the local hospitals here in Boston. You know what one of the doctor teams did over the weekend? They built one of these based on our class. They actually built...this is the kit we want. And I was unbelievably surprised how when we used the F1 analogy, the doctors and surgeons loved it, not because we're trying to actually cut their time off. We're trying to put the time into the surgery room by doing better preparations and things like that. So grabbing the right analogy is key, and if you grab the right analogy, these systems lessons work across basically anywhere where time gets extremely valuable. TROND: As we're rounding off, I wanted to just ask you and come back to the topic of lean. And you, you use the term, and you teach a class on lean operations. Some people, well, I mean, lean means many things. It means something to, you know, in one avenue, I hear this, and then I hear that. But to what extent would you say that the fundamental aspects of lean that were practiced by Toyota and perhaps still are practiced by Toyota and the focus on waste and efficiency aspects to what extent are those completely still relevant? And what other sort of new complements would you say are perhaps needed to take the factory to the future, to take operational teams in any sector into their most optimal state? JOHN: As a control engineer, I learned about the Toyota Production System after I was trained as a control system engineer. And I was amazed by the genius of these people because they have fundamentally deep control concepts in what they do. So you hear concepts like, you know, synchronization, observability, continuous improvement. If you have an appreciation for the deep control concepts, you'll realize that those are principles that will never die. And then you can see, oh, short, fast, negative feedback loops. I want accurate measurements. I always want to be improving my system. With my control background, you can see that this applies to basically any system. So, in fact, I want to make this argument is a lot of people want to go to technology and AI. I think the dominant paradigm for any system is adaptive control. That's a set of timeless principles. Now, in order to do adaptive control, you need certain technologies that provide you precision analysis, precision measurement, real-time feedback loops. And also, let us include people into the equation, which is how do I train people to do tasks that are highly variable that aren't applying automation is really important. So I think if people understand, start using this paradigm of an adaptive control loop, they'll see that these concepts of lean and the Toyota Production System are not only timeless, but it's easier to explain it to people outside of those industries. TROND: Are there any lessons finally to learn the way that, I guess, manufacturing and the automotive sector has been called the industry of industries, and people were very inspired by it in other sectors and have been. And then there has been a period where people were saying or have been saying, "Oh, maybe the IT industry is more fascinating," or "The results, you know, certainly the innovations are more exciting there." Are we now at a point where we're coming full circle where there are things to learn again from manufacturing, for example, for knowledge workers? JOHN: What's driving the whole, whether it be knowledge work or working in a factory...which working in a factory is 50% knowledge work. Just keep that in mind because you're problem-solving. And you know what's driving all this? It is the customer keeps changing their demands. So for a typical shoe, it'll have a few thousand skews for that year. So the reason why manufacturing operations and knowledge work never get stale is the customer needs always keep changing, so that's one. And I'd like to just end this with a comment from my colleague, Art Byrne. He wrote The Lean Turnaround Action Guide as well as has a history back to the early '80s. And I have him come teach in my course. At his time at Danaher, which was really one of the first U.S. companies to successfully bring in lean and Japanese techniques, they bring in the new students, and the first thing they put them on was six months of operations, then they move to strategy and finance, and all those things. The first thing that students want to do is let's get through these operations because we want to do strategy and finance and all the marketing, all the important stuff. Then he's basically found that when they come to the end of the six months, those same students are like, "Can we stay another couple of months? We just want to finish this off." I'm just saying I work in the floor because it's the most fun place to work. And if you have some of these lean skills and know how to use them, you can start contributing to that team quickly. That's what makes it fun. But ultimately, that's why I do it. And I encourage, before people think about it, actually go see what goes on in a factory or system before you start listening to judgments of people who, well, quite frankly, haven't ever done it. So let me just leave it at that. [laughs] TROND: I got it. I got it. Thank you, John. Spend some time on the floor; that's good advice. Thank you so much. It's been very instructive. I love it. Thank you. JOHN: My pleasure, Trond, and thanks to everybody. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean operations, and our guest was John Carrier, Senior Lecturer of Systems Dynamics at MIT. In this conversation, we talked about the people dynamics that block efficiency in industrial organizations. My takeaway is that the core innovative potential in most organizations remains its people. The people dynamics that block efficiency can be addressed once you know what they are. But there is a hidden factory underneath the factory, which you cannot observe unless you spend time on the floor. And only with this understanding will tech investment and implementation really work. Stabilizing a factory is about simplifying things. That's not always what technology does, although it has the potential if implemented the right way. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like other episodes on the lean topic. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, and devices, and systems. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co. Please share this show with colleagues who care about where industrial tech is heading. And to find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: John Carrier.
Episode 107: Explainability in AI with Julian Senoner
Feb 1 2023
Episode 107: Explainability in AI with Julian Senoner
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is "Explainability and AI." Our guest is Julian Senoner, CEO and Co-Founder of EthonAI (https://ethon.ai/). In this conversation, we talk about how to define explainable AI and its major applications, and its future. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 103: Human-First AI with Christopher Nguyen (https://www.augmentedpodcast.co/103). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Explainability in AI, meaning knowing exactly what's going on with an algorithm, is very important for industry because its outputs must be understandable to the process engineers using it. The computer has not and will not use the product. Only a domain expert can recognize when the system is wrong, and that will be the case for a very long time in most production environments. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented reveals the stories behind a new era of industrial operations where technology will restore the agility of frontline workers. Technology is changing rapidly. What's next in the digital factory, and who's leading the change? In this episode of the podcast, the topic is Explainability and AI. Our guest is Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talk about how to define explainable AI and its major applications, and its future. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip. Julian, welcome to the show. JULIAN: Hello, Trond. Thank you for having me. TROND: I'm excited to have you. You know, you're a fellow runner; that's always good. And you grew up in the ski slopes.; that makes me feel at home as a Norwegian. So you grew up in Austria; that must have been pretty exciting. And then you were something as exciting as a ski instructor in the Alps. That's every man and woman's dream. JULIAN: Yeah, I think it was very nice to grow up in the mountains. I enjoyed it a lot. But, you know, times have passed, and now I'm happy to be in Zurich. TROND: You went on to industrial engineering. You studied manufacturing and production at ETH. And you got interested in statistics and machine learning aspects of all of that. How did this happen? You went from ski instruction to statistics. JULIAN: Yeah, I was always impressed about watching stuff being made. I think it's a very relaxing thing to do. And I always wanted to become an engineer. When I was five years old, I wanted to become a ship engineer. So it was always clear that I wanted to do something with manufacturing and mechanical engineering. So I started actually doing my bachelor's in Vienna at Technische University. And for my master's, I moved to Zurich and studied Industrial Engineering. ETH has historically been very strong in machine learning research. Every student, no matter if you're interested or not, gets exposed to machine learning, statistics, and AI. It caught my attention. I thought there were very interesting things you can do when you combine both. So that's how I ended up doing research on interface and becoming an entrepreneur in this area. TROND: Yeah, we'll talk about your entrepreneurship in a moment. But I wanted to go to your dissertation, Artificial Intelligence in Manufacturing: Augmenting Humans at Work. That is very close to our interests here at the podcast. Tell me more about this. JULIAN: There is a lot of hype about AI. There's a lot of talk about self-aware factories and these kinds of things. These predictions are not new. We had studies in the 1970s that predicted there won't be any people in factories by the 1980s; everything will be run by a centralized computer. I never believed in these kinds of things. During my dissertation, I was interested in looking into how we can develop useful AI tools that can support people doing their jobs more effectively and efficiently. TROND: Right. But you already were onto this idea of humans at work. Where did you do your case studies? I understand ABB and Siemens were two of them. Give me a little sense of what you discovered there; pick any one of them. JULIAN: Sure. I'd love to start with the case that we ran with Siemens. I've worked quite a lot with Siemens in different use cases, but one of them was supporting frontline workers in complex assembly tasks on electronic products. So the aim was to help the worker check if the product has been assembled correctly. There are many connectors that could be missing or assembled in the wrong way. So the idea was to have a camera mounted to the workstation, and the worker would put the final product under the camera and get visual feedback if it has been assembled correctly or not. What we did here is really studying the psychological aspect of that. I would say most of my Ph.D. was really math-heavy and about modeling, but here we were interested in the psychological aspect. Because, in the beginning, we thought perhaps andon lights with green or red signals would be enough. But we got intrigued by the research question of does the worker actually follow these recommendations if it's just the green or red light? So we did an experiment, which I'm very excited about. So we got 50 workers that volunteered within Siemens to participate, which I'm very grateful for. We basically divided factory workers in two groups. We looked into the effect of explainability in the decisions that the AI makes. So we had one group that got basically just a recommendation to reject or to pass the product. And we had another group that got the exact same recommendations. But in addition to that, we provided visual feedback indicating the area where the AI believes that there could be an error. And the results of this experiment were perhaps not too surprising, but the effect size clearly was. We found that the people that did not get explanations for these recommendations were more than three times more likely to overrule the AI system, although the AI was correct. And I think this is a really nice finding. TROND: Well, it's super interesting in terms of trust in AI. And this topic of explainability is so much talked about these days, I guess, not always in manufacturing because people overlook that sometimes, people who are not in the industry. And they think about whether machines will take over and what decisions they're taking over and, certainly, if the machines are part of the decision making, what goes into that decision making? But as you were discovering more about explainability, what is explainability? And how is it different from even just being able to...it starts, I guess, with the decision of the AI being very clear because if that's not even clear, then you can't even interpret the decision. But then there's a lot of discussion in the industry, I mean, in the AI field, I guess, about interpretability. So can you actually understand the process? But you did this experiment, and it became very clear, it seems, that just the decision is not enough. Was it the visual example that was helping here? Or what is it that people want to know about a machine decision to make them trust the decision and trust that their processes, you know, remains a good process? JULIAN: I think I kind of see two answers to this question; one is the aspects of interpretability and explainability; perhaps I start with that. So these terms are often used interchangeably, and academics are still arguing about the differences. But there is now a popular opinion that I also share that these two things are not the same. So when we talk about interpretable AI, we think about models that have basically an interpretable architecture or functional relationship, so an example would be a linear regression. You have a regression line; it has a slope, it has an intercept. And you know how an X translates into a Y or an input to an output. Explainable AI is a fairly new research branch, which it's slightly different to that. It looks into more complex AI models like deep neural networks and ensembling techniques, which do not have this inherently interpretable model architecture. So a human, just by looking at it, cannot understand how decisions are formed. And what explainable AI methods do is basically reverse engineer what the model is doing by approximating the inner behavior of the model. So, in essence, we're creating a model of a model. Coming to your second question, so why might this be important in manufacturing? Basically, what I discovered during my research is that AI is still not trusted in the manufacturing domain, so people often do not understand what AI does in general, and I think explanations are a very powerful tool to simplify that. And a second use case of explainability is also that we can reduce complexity. So we can use more powerful methods to model more complex relationships. And we can use explainability on top of that to, for example, conduct problem-solving. TROND: Wow, you explain it very easily, but it's not easy to explain an actual AI model. Like, if you were to say, you know, here is the neural network model I used, and it had eight layers, good luck explaining that to a manufacturing worker or to me. JULIAN: So I think that explaining what a model is is also a different topic, and perhaps it's not even needed. You can still treat this as a mathematical function. I think it's really more about the decisions. We need to understand how decisions are formed, and there are different techniques to that. So when we talk about vision models, heat maps are a very interesting application. So we cannot really tell how the algorithm came up with a decision, but we can try to visualize the areas of an image where the neural network focused on to inform its decision. And we can, for example, see that certain areas are more highlighted than others, and perhaps that goes with the human intuition and creates more trust. TROND: You know, this topic is, for me, so fascinating because when we think about frontline workers or, indeed, engineers or quality managers on the shop floor, previously, they didn't have perhaps the tools available to open up the boxes, to open up the machines and look at the decisions that were being made. And, of course, that doesn't lead you to an enormous amount of confidence that what you're doing is good or bad or mediocre. You're not getting enough feedback. But it does seem to me that as machines and tools and algorithms on the shop floor become more and more complex, this is going to be a big effort. It doesn't sound very easy. And maybe you can characterize, you know, with the process today. These methods are just being applied on the shop floor. Do you have a sense that this general idea that things have to be explainable is a shared commodity on shop floors that are starting to use these techniques? Or would you say that it's enough of a challenge just to start experimenting with them, let alone trying to explain them to anybody around? I guess it's called a black box problem, right? JULIAN: Sure. I think in any use case where you have some kind of interaction between humans and AI, you've got to have explainability. It's going to be key. And there are also some less obvious use cases around explainable AI that I would also be happy to share. I think everywhere where you're going towards full automation, you perhaps don't need it, perhaps only if it's very high-stakes decisions being made. If you are rejecting products based on an AI that are very expensive, you might want to know why your line scrapped the products. In general, if you're going for automation, I would say explainability is nice to have. When we talk about augmentation, I think it's absolutely key. TROND: Yeah, it's absolutely clear. But we were talking before, and you were reminding me that in semiconductor production on an average production line, a large percentage of those components tend to be scrapped for quality reasons. And each of those components might be very, very expensive to manufacture. And it's a big problem. You have to recycle the part again, and they're made out of rare earth metals or whatnot. And it's a complicated thing, so it's not like you're just making wooden parts that you can just do over or plastic, and you can mold it again. These are like you said, they're expensive decisions that you're trusting machines to make. JULIAN: Yes. Talking about semiconductor industry, we have been also working on a different use case using explainable AI in semiconductors which I'm really excited about, and that is root cause analysis. In semiconductor manufacturing, as you mentioned, it's common that manufacturers throw away 15% to 20% of the chips they produce. We have car production lines that are standing still because of a chip shortage, so, obviously, this is a problem. What explainable AI can do here is we can try to model relationships in the manufacturing system to try to understand what causes these problems in the first place. So actually, when I was still a researcher back at ETH, I worked together with ABB semiconductors who exactly had this problem, so they had costly yield losses. And process engineers were struggling to find out where these losses were coming from. Because in semiconductor manufacturing, you often have hundreds of process steps, and each process, you can have even hundreds of process parameters such as temperatures and pressures. And you would like to know which of these process parameters do I need to adjust to avoid my yield losses. And if you have thousands, in this case, we had 3,600 different parameters that could have been suspects for yield losses. It's very hard to kind of track where yield losses are coming from. And the methods that are still used in industry are often based on linear methods. So we find this big effect, but we can't find the more hidden ones. And I think this is a very neat application of AI because you can use more complex models like neural networks or tree-based methods to model these relationships. So, in essence, we try to imitate the physical processes to learn the physical processes as they are. But since neural networks are very complex and we cannot really understand what they have been modeling, we need kind of explanations for that. And using these explanations, we can inform process engineers who are domain experts about what the model might have found. We're still acting upon correlations, not causation. But still, we can point towards certain areas that are interesting. And in the case of ABB, the AI guided the process engineers to suspicious processes, and the domain experts were able to come up with two improvement actions based on that input. Then they were able to reduce the scrap by more than 50% in one of their lines, which was, of course, substantial. And I think it's a very nice example of how humans and AI can collaborate and get more out of it. TROND: So, Julian, is that what augmentation of workers means to you? The augmented workforce is essentially a collaboration between man and machine in a deeper way than before? JULIAN: I think it's about expanding capabilities and getting teams of humans and machines that perform better and provide more value than either alone. I think that is what augmentation is about. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you. TROND: You ended up commercializing an idea around this. Tell me more about that and how that came about. JULIAN: Exactly. So at some point in my Ph.D., I decided I'm not going to become a professor; many PhDs have a similar experience. But I saw that 50% scrap reduction, for example, at one partner company that's quite substantial, and these tools kind of scale. And together with a friend of mine who did a Ph.D. in the same department, we decided let's found a company around this. So we founded a startup called EthonAI. And we are developing a software platform that helps manufacturers improve their quality management. So we offer five different products in different application areas, three computer vision products that help manufacturers in detecting defects, one product around process monitoring and anomaly detection, and one product for AI-based root cause analysis. So we cover this entire continuous improvement loop that manufacturers need, and we provide tools for that. And I think one thing that really stands out for us, and this is our key hypothesis, is that all of our products can be used by process engineers without writing a single line of code. We're not building fancy toolboxes for data scientists. We really build the tools for the people in the factory. We want to empower them to do their jobs better or help them do it better because we believe these are the people that drive the improvements on the shop floor. And those people should be the ones that use the tools. TROND: I'm just curious; in this process, you've talked to a lot of process engineers. How are they reacting to these new opportunities? Are they excited that they get to do some programming or do advanced analysis without getting deeply into software? Or are they a little bit weary that they have to still jump into a new domain? There's so much discussion these days about the need for re-skilling. How have you found them to react to these new changes? JULIAN: I think the experience is really mixed. Sometimes you see skepticism; sometimes, you see great excitement. I think at the end of the day, you need to solve problems. It's not about bringing AI to the factory. It's rather to solve the problems of the people that have worked in those factories. And if you have a tool that can be used very easily...so anyone that can operate a computer can operate our tools, and it solves problems. People are often happy to use it or mostly happy to use it. Of course, if you are coming in with this marketing thing of AI is going to change everything, then you experience more skepticism. So this is why we really talk about problems and not about the technology. Technology kind of is in the back end. And people don't care about how fancy your algorithms are; it has to work. What I think is so rewarding working on a startup in the manufacturing space is that the outcomes are so binary; it either works or it doesn't. And that's really cool to see in the physical world. Our entire team is working really hard to bring very useful tools to the market. TROND: You're a little bit of a hybrid yourself between a manufacturing engineer, basically, and now a little bit of a software engineer or at least an analytics perspective here with statistics and machine learning. Where do you find the expertise that you need at Ethon to move forward? Because it's a very rare thing still to combine knowledge of shop floor real-world challenges, systems that cannot fail, or at least when they fail, it has a bigger consequence than when a software needs to patch because the whole idea of why your software tentatively is in the shop floor is to reduce these kinds of stops and starts at production lines. How do you find people that really managed to combine the perspective of building software with this reality of how it's going to work in a physical environment? JULIAN: We have hired three kinds of people, first being brilliant scientists from the area of AI that have never been in a factory before but have been publishing at the highest level, NeurIPS, ICML, all those top outlets in the AI field. And they can really help to push the state of the art. The second category of people is process engineers. We've hired process engineers from companies that could be potential clients. They know about the problems and have been conducting all this data analysis in a quite manual way. So they're coming into our company to kind of guide us in building the product of their dreams. Then we have some people that have been working on the interface, such as Bernhard and myself. So Bernhard is the CTO in our company, people that have been on the interface between AI and manufacturing. So I think you really need basically experts and generalists in a company, and then it typically goes well. And then the other thing that you need is really customer-centricity. So you really need to be close to your customers and try to understand their pains. And you also need to bring the machine learning engineers to the factories so that they can see for themselves, and that's usually very helpful. TROND: What's your experience with frontline operators themselves? In the experiments that you've done with your Ph.D. and with your early products with Ethon, how are they reacting to these new opportunities for augmentation? JULIAN: I mean, typically, they're quite positive about it because it helps them in their jobs. But when we're talking about augmentation, it's not about replacing people; it's about helping them do things better. And often, they're quite critical about user interfaces, but you learn so much interacting with them, and you improve it. And then they get something that they really need. And the biggest learnings here have really been around how do you visualize or represent things or content to the workers? This is so crucial because, in factories, you have such a diverse workforce of people in their 60s who will retire soon. And you have young people who just started their first job and basically were raised with iPads. So it's really that's a challenge, but it's a great one to work on. TROND: Well, I guess that brings me to the important question that a lot of people want to answer about the factory of the future. There's fear about the factory of the future. There's this idea of a 24/7 automated factory, autonomous, working without humans. And then there's this idea that factories will look very different and people will do different things, but there are still people in there. And then there's anything in between. You could also imagine a world without factories but where industrial production happens somewhat distributed. Because I guess what a factory is, at the heart of it, is it's a mass of things, of people and machines concentrated in one location. JULIAN: Yes, it's a social, technical system. As I mentioned in the beginning, there is a lot of hype about AI, and these predictions are not new. This lights-out factory has been around for a very long time, and I still haven't seen it, at least on a productive level. You can also over-automate. Companies have seen that Tesla, for example, has cut back on automation. Elon Musk tweeted, "Humans are underrated." So I think in the future, people will still shape the image of factories. Of course, we will see more automation that is enabled through AI because, with AI, we can also now model stochastic processes. So we don't need deterministic outcomes like a robot that always picks the same things. We can have more self-organized, isolated systems. But I think it's really about isolated systems and not an entire factory that is operated by one single artificial brain. But I think the most intriguing use cases will be those where we augment humans rather than replacing them. So I think that's really where the magic happens. It's giving process engineers, for example, the tools to conduct more effective problem solving, finding things that were unknown to them previously, and couple that with their domain expertise. I think that's certainly something that we're going to see more. I don't think that AI will regulate production processes in a fully automated manner. At the end of the day, you always need the process and product expert that has intuition about the processes, creative problem-solving. So I think process engineers will always be around and be integral to any manufacturing system, but I think AI we will see more AI tools that enable these people and empower them. And I think this is an exciting development. TROND: Yeah, it's so interesting that sometimes I feel like these futuristic discussions fail to take into account innovation. So it's a very basic problem with this discussion because you're assuming that automation and factory production overall is all about squeezing out tiny, little efficiencies, and if that's all it is, then machines might be the better way to go. But if you're talking about incrementally improving and sometimes radically improving a process or changing even what you are producing based on feedback from a market and stuff like that, it would seem to me that we are quite far into the future before a socio-technical system like that with complete feedback, long supply chain, and understanding what all of these things mean and the decisions that go into it, and costs. And that all can be managed by one network algorithm. It's really a little bit hard to envision how those futurists really have been thinking about it, or maybe they were just considering isolated use of robots that looked very cool. JULIAN: The thing with AI is that AI is incredibly lazy; that's one of the problems. It always tries to learn shortcuts. And we need to understand the things that it learns. You always have the example of correlation and causation. And I think if you provide outputs from an AI to a human expert who can judge the validity of the results, that's where you can generate value. If an AI is supposed to make fully automated decisions, then I think it would relatively quickly turn out to be a mess because it might just be a correlation and not a causal relationship. There's always this famous example of shark attacks and ice cream consumption, both have a very high correlation, but it's not because sharks like ice cream. So I think it's very similar in manufacturing. When you produce stuff, you might find a correlation with a certain process parameter that can't causally have an effect on your quality, for example, or downtime. And if you would have a system that operates itself, it very likely would try to tweak that parameter with perhaps a bad outcome. So I think the human in the loop still remains crucial here. TROND: What excites you about the future of manufacturing? Everyone's always worried about manufacturing because it's such a big part of the economy. A lot of people lose their jobs if things go badly. And, historically, it's gone up and down. And maybe for a while, in some countries, it's gone mostly down, and it hasn't been the most exciting place to be. Are you excited about the future of manufacturing? JULIAN: Yes, I mean, definitely. I've always been, and I think, I will always be excited about manufacturing. And obviously, at EthonAI, we will also try to leave a mark on the industry. We are helping big companies to improve everything around quality but also helping them improve their CO2 footprint by reducing production waste. And this is something that really excites me to help these companies to provide useful tools and also see that these tools have an impact in the physical world at big corporations like Siemens. That's a very exciting place to be. I think we will see very, very interesting developments over the next couple of years. TROND: Well, Julian, it's exciting to jump in and hear a little bit about your world here. I certainly wish you best of luck with Ethon, and it was fascinating to hear. Thank you so much for sharing your perspective. JULIAN: Thank you very much, Trond. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Explainability in AI. Our guest was Julian Senoner, CEO and Co-Founder of EthonAI. In this conversation, we talked about how to define explainable AI, its major applications, and its future. My takeaway is that explainability in AI, meaning knowing exactly what's going on with an algorithm, is very important for industry because its outputs must be understandable to the process engineers using it. The computer has not and will not use the product. Only a domain expert can recognize when the system is wrong, and that will be the case for a very long time in most production environments. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 103: Human-First AI with Christopher Nguyen. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects people, machines, devices, and systems. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring, and you can find Tulip at tulip.co. Please share this show with colleagues who care about where industrial tech is heading. You can find us on social media, and we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Julian Senoner.