Podcasts about executive chairman

In this episode, recorded live on LinkedIn, YouTube, and Facebook, Geoff Slade and I discuss the year that was, analyzing the major job market trends that have been a Hallmark of 2022 post-pandemic. We also look at the year ahead; specifically, the in-demand skills and job market opportunities we believe can support professionals plan for a successful job-search strategy. Geoff Slade has worked at the forefront of the recruitment industry for over 50 years. He is the Executive Chairman of Slade Group, TRANSEARCH International Australia, Yellow Folder Research, and Skillstream Australia.  During the live recording of this episode, we had over 50 participants listening as we recorded, sending us comments and feedback. I hope you enjoy this chat with Geoff and that it helps you plan ahead for a successful job hunt in the months ahead. Timestamps: 04:37 - Geoff Slade discusses the 2022 job market 05:52 - Geoff Slade explains why the recruitment process is taking longer than pre-pandemic  07:09 - Sectors and professions at risk of redundancy in 2023 08:35 - Should you apply for a job this December or wait until next year? 10:45 - What has been different in the recruitment industry since the pandemic? 11:48 - Geoff Slade explains what it takes to be a good recruiter 13:20 - What makes people so antagonistic about sales? 14:47 - How recruiters handle the high volume of applications 16:08 - Are recruiters still keen to catch up with candidates? 19:00 - Are cover letters still important? 20:00 - Geoff Slade looks ahead at 2023 trends for jobs 24:00 - The top skills for job candidates in 2023 25:53 - Three factors that will shape the future labor supply globally 28:00 - How is the Slade Group keeping up with the technology change 33:21 - Geoff Slade explained the importance of being entrepreneurial when job hunting 36:58 - Renata Bernarde's client's biggest challenges 39:30 - The importance of LinkedIn for corporate professionals 41:25 - Flexibility and part-time opportunities  Links mentioned in this episode: Find My Talents: Learn about your strengths, and watch your career grow Reset Your Career Online Course The Job Hunting Podcast episode 52. Interview with Geoff Slade. Other episodes with members of Slade Group: Interview with Anita Ziemer (35) and Julian Doherty (50)  Other ways to enjoy this podcast: Read the full blog on the podcast website.  Click here to open the transcript of this episode. About your host: Hello, I'm Renata Bernarde, the Host of The Job Hunting Podcast. I'm also an executive coach, job-hunting expert, and career strategist. I teach corporate, non-profit, and public professionals the steps and frameworks to help them find great jobs, change, and advance their careers with confidence and less stress.  Subscribe to the weekly newsletter. A free resource for job hunters: The Optimized Job Search Schedule. Learn more about my career services: www.renatabernarde.com. Book a time to discuss 1-1 coaching and achieve your goals faster Or please email me at rb@renatabernarde.com.        --------- Host: Renata Bernarde Editing: Camille Cariño Music: Scott Holmes Contact us: rb@renatabernarde.com The Job Hunting Podcast is a podcast by Pantala Pty Ltd. Pantala acknowledges the Traditional Owners of the Land we have recorded this podcast on, the Bunurong people. We pay our respects to their Elders, past and present, and extend that respect to all Aboriginal and Torres Strait Islander cultures.

On November 28th Torq Resources (TSX.V:TORQ - OTCQB:TRBMF) announced another round of drill results from the Falla 13 Discovery on the Margarita Project in northern Chile. These results expand the copper-gold mineralization to 800 meters.     Shawn Wallace, Executive Chairman of Torq Resources and Michael Henrichsen, Chief Geologist join me to recap the phase 2 drill program and the latest results. There are a couple Figures below from the news release for you to follow along. The team is already planning a follow up program at the Falla 13 area as well as to test some new targets.   We wrap up with a quick comment on the Santa Cecilia gold-copper Project where field work has begun and drilling will start shortly.   If you have any follow up questions for Shawn or Michael please email me at Fleck@kereport.com.   Click here to visit the Torq Resources website to read over the drill results news release. 

On this edition - Are marine scientists getting their message across to the public? We talk to the Master of a fisheries patrol vessel about inspecting trawlers, and to the man who will write the history of Meitheal Mara. We also hear from the Executive Director of the European Fisheries Control Agency and the Executive Chairman of the Sea Fisheries Protection Authority. Presented by Tom MacSweeney - https://twitter.com/tommacsweeney Discover comprehensive maritime coverage at https://maritimeirelandradioshow.ie/ Stay up to date with Tom MacSweeney's Maritime Ireland by subscribing to the show on Apple Podcasts here: https://apple.co/3qfVLEr or on Spotify here: https://spoti.fi/2DX9F5FA Follow Tom MacSweeney's Maritime Ireland on Facebook: https://www.facebook.com/maritimeirelandradioshow/ Or on Twitter - https://twitter.com/tmmaritime

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 "A Scandinavian Perspective on Industrial Operator Independence." Our guest is Johan Stahre (https://www.linkedin.com/in/jstahre/), Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich (https://www.augmentedpodcast.co/84). 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: Human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen. 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. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is A Scandinavian Perspective on Industrial Operator Independence. Our guest is Johan Stahre, Professor and Chair of Production Systems at Chalmers University in Sweden. In this conversation, we talk about how the field of human-centered automation has evolved, the contemporary notion of operator 4.0, Scandinavian worker independence, shop floor innovation at Volvo, factories of the future, modern production systems, robots, and cobots in manufacturing. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Johan, Welcome. How are you? JOHAN: I'm fine, thank you, Trond. It's really nice to see you. TROND: Yeah, likewise. JOHAN: Fellow Nordic person. TROND: Fellow Nordic person. And I apologize for this very American greeting, you know, how are you? As you know, I'm from the Nordic region. I actually mean it, [laughs] you know, it was a question. So I do wonder. [laughs] JOHAN: I'm actually fine. It's just ending the vacation, so I'm a little bit sad about that because everyone...but it's a very nice time now because the rest of the world seems to be on vacation, so you can get a lot of work done. TROND: I concur; that is a wonderful time. Johan, I wanted to just briefly talk about your exciting background. You are an engineer, a mechanical engineer from Sweden. And you had your initial degree from Linköping University. Then you went on to do your Ph.D. a while back in manufacturing automation, and this was at Chalmers, the University in Sweden. And that's where you have done your career in manufacturing research. You are, I think, the first Scandinavian researcher certainly stationed currently in Sweden that we've had on the podcast. So I'm kind of curious, what is manufacturing like in Scandinavia? And what is it that fascinated you about this topic so that you have moved so deeply into it? JOHAN: Manufacturing in Sweden is the core; it's the backbone of our country in a sense. We have statistically too many large manufacturing companies in Sweden as compared to, I mean, we're only 10 million people, but we have like 10, 12 pretty large companies in the manufacturing area in automotive but also in electronics like Ericsson, you have Volvo, we have SKF. We have a lot of big companies. Sweden has an industrial structure that we have several small companies and a couple of large companies, not so many in the middle section there. This happened, actually, in the 1800s somewhere. There was a big growth of big companies, and there was a lot of effort from the government to support this, and that has been continued. So the Swedish government has supported the growth of industry in Sweden, and therefore we have a very strong industry and also quite good digital growth and maturity. TROND: So the Scandinavian background to me when I was there, I remember that one of the things that at least Scandinavian researchers think is distinct about Scandinavia is worker independence. And it's something that I kind of wanted to just tease out a little bit in the beginning of this podcast. Am I wrong in this, or is there something distinct about the relationship between, I guess, workers and managers in Scandinavia, particularly? One speaks about the Scandinavian model. Can you outline a little bit what that means in manufacturing if it still exists? It's an open question. JOHAN: From my perspective, Sweden usually ranks very high in innovation, also when it comes to international rankings. And I think some of that has to do with the openness and the freedom of thinking in a sense and not so hierarchical, more consensus-oriented, ability to test and check and experiment at work without getting repercussions from top management. And it is much easier. In fact, if you are at one department in a manufacturing company or in university as such and you want to collaborate with another colleague across the aisle, if you have a two hierarchical system, you need to go three levels up in order to be able to do that. But here, I think it's easier to just walk across the aisle to have this collaboration and establish a cooperative environment. I think that that's part of the reason. Also, we're not so many; I mean, I think historically, we needed to do a lot of things ourselves in Sweden. We were a country up north with not so many people, and we have harsh environments, and I think it's the same as Norway. I mean, you need to be self-sustainable in that sense, and that creates, I think, environmental collaboration. TROND: We'll go more deeply into your research on manufacturing and to what extent a question I asked here matters to that. But do you have a sense just at the outset here that this type of worker and operators sort of independence, relative independence, perhaps compared to other regions, is it changing at all? Or is this kind of a feature that is a staple of Scandinavian culture and will be hard to change both for good and for bad? JOHAN: I think that as everything...digitalization has sort of erased a lot of the cultural differences across the world in that sense. Because when I was a student, there was not this expressed digital environment, of course. The information environment was less complex. But I think now all the young people, as well as my mother, does her banking...she's 90, but she does her banking on her iPad; I mean, it's very well-spread. And I think that we are all moving towards a similar culture, and the technology is spreading so quick. So you cannot really have cultural differences in that sense. But I think that's still the way that we're using this. And I think that the collaborative sense I think that that is still there. The reason why Sweden is comparatively innovative still is that we still maintain our culture and use the technology to augment that capability. TROND: So, Johan, we'll talk about a bunch of your experiences because you obviously are based in Sweden. And because of Sweden's industrial situation, you have some examples, you know, Volvo, a world-famous company obviously, and also famous for its management practices, and its factory practices, we'll get into that. But you've also worked, and you're advising entities such as the World Economic Forum, and you are active on the European stage with the European Institute of Technology. Your activity clearly goes way, way beyond these borders. But why don't we maybe start with some of these Scandinavian experiences and research projects that you've done maybe with Volvo? What is it with Volvo that captured people's attention early on? And what sort of experience and research have you done with Volvo? JOHAN: I think that Volvo is very innovative, and Volvo today is two types of companies; one is the car company that has now gone fully electric. It was introduced at the stock market, most recently owned by a Chinese company, and before that, it was owned by Ford, and before that, it was also public. But you also have the other part, which is the Volvo Group, which is looking at trucks, and boats, and things like that. And they both share a high level of innovation, ambition, innovation, and power, I think, using the experiences already from the '60s, where you had a lot of freedom as an employee. And also very good collaboration with the union in investments and in all the changes in the company I think that has been very beneficial. And it's made them...what is now Volvo Cars was very, very early, for example, with digital twins. They were experimenting with digital twins already in the 1990s. And we work together with Volvo but also with SKF, which is a roller-bearing company here to look at how we can support frontline workers and augment their capabilities because they're very skilled and they're very experienced. But sometimes you need to have sensor input, and you need to have structures, and rules, and procedures, and instructions. So we worked quite early with them already, maybe in 2009, 2010, to see how can we transform their work situation, provide them with work instructions through wearable devices. It was very popular at that time. MIT was experimenting with cyborgs. And the people that were...I think it was Thad Starner; he was trying to put on a lot of computer equipment. Then he went through the security at the airport and had some problems there. But that's not the case for the operators. But it was a little bit too early, I think. We tried to experiment with some of the maintenance people at Volvo cars. And they were very interested in the technology, but the use for it was a little bit obscure. And this was at the time when you had the mobile connectivity was 9,600 kilobits through a mobile phone or in the modem, so Wi-Fi more or less did not exist. And the equipment: the batteries weighed two kilos, and the computer weighed one kilo. And then you had a headset that looked like you came from deployment in a war zone. So it was a little bit...it looked a little bit too spacy for them to be actually applicable. And then some 10 years later, we actually did a similar experiment with SKF, the roller bearing company where we deployed the first iPod touch, I think they were called. That was right before the iPhone. I think it was an experiment by Steve Jobs to see how can we create what then became the iPhone screen. And we put that on the arms of the operators and tried to see how can we give them an overview of the process situation. So they were constantly aware, and they were quite happy about this. And then, we wanted to finish the experiment. The operators actually said, "Well, we don't want to give the equipment back." And then we said, "Well, we need to have it back. Of course, you can use the software." So they brought their own phones, and they downloaded the software. And they're still using it, actually, not on their own phones anymore. But they use this kind of software that we developed at that time together with them. So that was quite interesting. TROND: That's fascinating. Extrapolating from some of these early experiences up until now, I wanted to just ask you this from a research perspective, but also, I guess, from a management perspective. So you work on production systems. What is really the goal here, or what has the objective been early on? You talked about these early MIT experiments. And I know control systems is a very old area of research. And from what I understand, in the early days, the use cases weren't just factories; they were also on spacecraft and things. But to your point, especially earlier, we were working with very, very different technology interfaces. But now, obviously, we are starting to roll out 5G, which gives a whole other type of richness. But does it really matter how rich the technology interface is? Or does it matter more what the objective is with these various types of augmentations that have been attempted really throughout the decades? Can you just give us a little sense of what researchers and yourself what you were trying to augment and how that depends or doesn't depend on the quality of technology? JOHAN: First, we need to realize that the manufacturing industry has always been a very, very early adopter. The first computers were used for war simulations and for making propellers for submarines to see how you can program the milling machines. This was in the 1950s. And the industrial robots in the '60s in the '70s were also very early applications of digitalization. Before anything else had computers, the manufacturing industry was using it, and that's still the case. That might surprise some people. When they walk out into a shop floor, they see no computers around because all the computers are built into the machines already. What is still missing is the link, perhaps to the people. So they are still using the screens. And they are the ones...people are the key components of handling complex and unforeseeable situations. So you need to provide them, I think...to be really productive, you need to provide the frontline staff with the equipment for them to avoid and to foresee and to handle unforeseen situations because that's what differs between the man and machine or a human and the machine. People are much more apt to solve a complex situation that was not programmed before. That's the augmentation part here; how can we augment the human capabilities? And people talk about augmented reality; I mean, I don't think it's the reality that needs to be augmented; it's the human to be handling the reality that needs to be augmented. TROND: Johan, this is so fascinating because, first of all, it's quite easy to dismiss manufacturing a little bit these days because, to the untrained eye, all the excitement is in the consumer space because that's where the new devices get released, and that's, obviously, where all the attention is these days unless you obviously are in manufacturing. But can you bring us back to those early days of computing when a lot of the use cases for computing were first explored with manufacturing? So you talked about MIT, and back at MIT and at Stanford, all the way back to the '60s, they were exploring this new and fascinating field of even artificial intelligence, but before that, just regular control systems, electronic interfaces. What fork in the road would you say happened there? Because clearly, the fascination has been with digitalizing everything and software kind of one for 30 years, but in manufacturing, it's more complicated. You say people, so it's people, and then it's kind of these production systems that you research. That's not the same as the use case of an individual with their phone, and they're sort of talking to people. There are many, many more variables in play here. What is the real difference? JOHAN: Last year actually the European Commission put forth industry 5.0, which should be the follower after industry 4.0. And they based that on three main challenges. One is sustainability, one is resilience, and the various kinds of resilience towards the shock of the war but also by climate, et cetera. And the third one is actually human-centeredness to see how can we really fully deploy human capabilities in a society and also in industry, of course. I think what you're referring to is the two guys at Stanford in the '60s; one was John McCarthy. He was the inventor of the artificial intelligence concept. His aim then was to replace human work. That was the ambition with the artificial intelligence because human work is not as productive as computing work, but it still has some drawbacks. But in the same place not so far away, in another department at Stanford, was a guy called Douglas Engelbart. And he was actually the father of...he called it intelligence augmentation. So it was AI and IA at that time. But his ambition was to augment human work to see how can you have this. And he was the one that invented hypertext and the mouse. And he put up the first hypermedia set in Silicon Valley. So this was a guy that inspired companies like Apple, and Xerox PARC, those kinds of institutions that had a huge bearing. There was a book by a research colleague at Oxford. He was comparing that over time, from the early industrial days and then forward, technology that replaces people always has more complications when introduced and scaled than technology that augments people. If you look at the acceptance and the adoption of the iPhone, that took months, or weeks, or whatever, seconds for some people, for me, for example. If you look at what happened in the industrial revolutions in the 1800s and the 1700s, you had a lot of upheaval, and already in the 1960s...I'm starting to sound like a university professor. But in '96, in the U.S., there was a Senate hearing about is automation taking the jobs from people or not? And the conclusion was that it is not, it is actually creating companies that then employ more people because of the productivity gains and the innovation gains. And you allow people to use the automation as augmentation, not only cognitive augmentation. We think a lot about augmentation as something that you do with your eyes and your brain. But robots are also augmenting people. It lifts heavy objects like cars or big containers, whatever. That's the kind of augmentation that maybe you don't consider when you look at it from an artificial or an augmented reality perspective. TROND: Well, so many things to pick up here. But the variety of meanings of augmentation are kind of astounding, aren't they? And you've written about this operator 4.0 several times. There's obviously cognitive augmentation, and then there's physical augmentation. Are there other types of augmentation that you can speak of? JOHAN: I really can't think of any. TROND: But those are the main ones. So it's either kind of your mentality or sort of your knowledge. So the work instruction parts go to the skills-based, I guess, augmentation, which perhaps is an additional one. Or I'm just thinking if manufacturing wants to make progress in these things, it would perhaps make sense to really verify what workers at any moment actually themselves express that they need. And I guess that's what I was fishing for a little bit here in this history of all of this, whether the technology developers at all moments really have a clear idea of what it is that the workers are saying themselves they're missing or that they obviously are missing. Because automation and augmentation, I mean, do you find them diametrically opposed, or are they merely complementary when it works well? JOHAN: I mean, automation traditionally has been the way to scale, and, I mean, in the beginning, you want to see what the machine is doing, right? And then you really don't want to see it. You just want it to work. So it's really helping you to scale up your work. And in that sense, automation, like collaborative robots, for example, which people are talking about robots, are something that is replacing jobs, but if you look at it, it is a very small portion of statistics. In Singapore, which is the highest user of robots installed, there were 950 maybe robots per 10,000 employees. And the average in the Americas is 100 robots per 10,000 employees, and that's not really a lot. And so there is plenty of space for robots to be the tools for people. So if you don't treat them as something that will replace you but something that will actually augment you, I think it would be much easier. What could happen, though, and I think that is maybe part of your question, is that, well, these tools are becoming so complex that you cannot use them unless you increase your skill. How do you do that? Because no company would like to end up in a situation where the tools that you have bought and invested a lot of money in are too complex for your employees to use. That's a lost investment. It's like you're building a big factory out in a very remote place, and you don't have enough electric power to run it. You don't want to end up in that situation. Like you expressed, I think that maybe what's missing and what's trending right now is that the upskilling of the workforce is becoming extremely important. TROND: And how do you do that, Johan? Because there's obviously...there's now an increased attention on upskilling. But that doesn't mean that everyone has the solution for it. And employers are always asking for other people to pay for it, for example, governments, or the initiative of the worker, perhaps. It seems like Europe has taken this challenge head-on. Germany, at least, is recognized as a leader in workforce training. The U.S. is a latecomer to the game from that perspective. But it typically shows up in a big way. So something is going to happen here in the U.S. when it comes to workforce training. What is the approach? I mean, there seems to be two approaches to me; one is to simplify the technology, so you need less training. And the other would be obviously an enormous reskilling effort that either is organized, perhaps ideally in the workplace itself, so it's not removed from the tasks. Or some enormous schooling effort that is highly efficient and perhaps online. What do you think are the winning approaches to re-skilling that entire manufacturing workforce continuously? Because it's not like you have to rescale them once, you have to rescale them every time. JOHAN: Well, I can only guess. I think that you need to do all of these, all of the above. One complicating factor is the demographics of, especially Japan; of course, we know that from a long time that, they have an aging population. But Europe is now becoming the new Japan in that sense. We have a very big problem in terms of aging populations, especially countries like Italy and perhaps Germany but also in northern countries. And we don't have perhaps...there's a lot of discussion on immigration right now. But actually, the workforce would need a lot of immigration to be able to respond to the needs of our industry in the forthcoming situation. I think that China is maybe 4 or 5 years behind Europe, and the U.S. is maybe 10-12 years behind Europe as well. So that will happen...the only non-affected regions right now are India and Africa. And that means that the European, and Chinese, and U.S. industries will have to compete with a rather young population in Africa and India. And so that will become over time, but it is a long time, so that means that it's not always on the political agenda. Things that take a long time are usually not the things that you speak about when you have election times that we have in Sweden right now. It's mostly what's on the table. So I think that how to do that is really complex. We had some collaboration within the World Economic Forum. It is a fantastic organization because it spans the whole globe. So that means that the information comes from different parts of the world, and you can see different aspects of this. And a country that has done a lot about this is Singapore, very good experiments, very nice projects, initiatives regarding upskilling. And Europe is now launching an innovation program where they want to go deeper into deep tech to try to...the commissioner for research and education in June launched a big initiative around innovation and how that can be supported by deep technology. So we'll see what comes out of that. It'll be very, very interesting to see. 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: Speaking about the World Economic Forum for a minute, Johan, you have been part of this group project called the Augmented Workforce Initiative. You told me when we spoke earlier that, in your opinion, this initiative couldn't have existed even just five years ago. Can you explain what you mean by that? Because augmentation, the way that you've been speaking about it now, is a perspective that was nascent, even in the early days of computing and manufacturing control systems. Yet, it seems to have disappeared a little bit, at least from the top end of the political and research agenda. Yet here we are and you said this initiative couldn't have existed five years ago. Can you explain what you meant by that? JOHAN: That is a very, very nice initiative by the World Economic Forum, and it's run by the forum and Cambridge University, who has a very, very good group on this and some very nice people. And I'm honored to be part of that group together with my colleague from Mexico, David Romero. You may know him as well. And I think that what they're looking at is the increased understanding. And that was actually one of the sessions at this World Economic Forum, you know, the Davos days that were run this year. And it was actually part of those days as a theme about how to engage, and how to support, and to augment the workforce, which has never happened before on that level. So it's really, really high on the agenda. The Forum has been running previous projects also on the future of work and how the demographic situation is affecting or how the skill situation is affecting the companies. They have come up with suggestions that more or less half the workforce needs to be upskilled within the next couple of years. And that's a huge undertaking. TROND: The novelty here is that the world's elite managers, I guess, who are represented at the World Economic Forum are increasingly aware of the complexity of workforce issues generally, and then specifically of upskilling, and maybe even upskilling in this very specific meaning of augmenting a worker which, I guess to my mind, is a little bit different from just generally speaking about robotic automation and hammering these efficiency points. But obviously, it's a much more challenging debate because it's one thing to find a budget for an automation effort and introduce a lot of computers or introduce a lot of whatever technology, usually hardware, but what we're talking about here is a lot more challenging because you need to tailor it to these workers. And there are many workers, obviously, so it's a complicated phenomenon. How is that going? What would you say are some of the findings of the Augmented Workforce Initiative? JOHAN: I think that companies like Tulip, companies like Black & Decker, and others have a lot of good use cases actually already, which may or may not before have been labeled augmentation. It might have been labeled as operator support, or decision-making support, or things like that, or upskilling. But I think that the findings are that there is a lot out there, but it's not emphasized as something that is really important for the company's survival in that sense. TROND: It wasn't so glorified before. A lot of the decision support systems were viewed as lower-level systems that were just kind of more like HR systems or just tinkering with necessary stuff that people had to know kind of a thing. And so you're saying it's been elevated now, yeah, as having a much more essential impact on the quality of work. JOHAN: It has a leveraging impact for the whole company, I would say, but that's also part of this industry 4.0 approach. And you have the hierarchical integration of companies where the CEO should be aware of what's going on on the shop floor and vice versa, as well as the horizontal integration where you have the companies up and down the supply chain and value chain knowing what's going on early. And that is really something that maybe stopped at mid-management level before, but now it needs to be distributed out to the places where the complexity is higher, and that's the frontline workers. Maybe...now I'm guessing, but I think that also the understanding that the investments done by this company in complex manufacturing equipment could be at risk if you don't have the right skills to use them is now penetrating, I think, a lot of the companies. In Europe, in 2019 or something like that, there were almost 30 million people employed in the manufacturing industry. And if you look at the number of...if you say that half of these need to be upskilled somehow over a period of three years...and I actually made a mock calculation that the re-skilling need for in-person months in Europe if we were to fulfill this is 50 million person-months, 50 million person-months, just the time for the people to participate in these trainings. So that's a huge undertaking. And I think that that scares companies as well as governments because just imagine taking 50 million person-months out of productivity or the production equation. But the alternative might be worse. If you lose your capability to use your equipment, that might even be worse. TROND: Wow, these are daunting things. I guess that brings me to the last section here and some thoughts from you on the future outlook. When it comes to technology and these tools for human augmentation, what are the timelines for, well, either making the improvements or, as you said, not losing competitiveness because of this skills crisis? What are we looking at here? Is there some imminent challenge and opportunity? Or is this going to play out over 25 years? JOHAN: I think that in 25 years, the demographic situations will have changed again, so I assume that they will look different. But right now, we have a problem with an aging population. And we have a lot of people going into retirement. A lot of knowledge will disappear unless we can store it somehow. A lot of people will not go into industry. I mean, when I talk to colleagues, they say, "Well, we need to make the manufacturing industry more sexy. It should be cleaner, or it should be nicer because young people don't go to industry." But if I go to the healthcare section, they will say the same thing, "Oh, we need to make it much better because people are not applying for these educations." TROND: [laughs] Where are people applying, the tech companies? JOHAN: No, that's the problem. They don't exist. They were never born. TROND: [laughs] Right. JOHAN: So the demographic bomb is that they are actually not there. So you cannot rely on employing young people because they are not existing in Europe and soon not in the U.S. to the extent that they were before. So therefore, you need to focus on the older people. So you need to re-upskill not only the middle-aged people but the people in their 50s and even in their 60s. That adds to the complexity. In the next 5 to 10 years, there will be a lot of discussions on how to fill the missing places in industry to remain competitive. I also think that you can see the augmentation here as a fantastic tool together with the upskilling because upskilling the new skills together with the augmented tools like collaborative robots, like cognitive support, like whatever you can put in an iPhone, or whatever phone, or tool, or watch, or whatever, you can add the capability to make decisions. And that's the augmentation you will see. And you will see a lot of digital twins try to foresee problems. You will see a lot of transversal technologies going from different high-tech industry into manufacturing industry to support especially the frontline people and to enable their innovation capabilities. TROND: Johan, you said earlier that the complexity is higher at the level of frontline workers. Did you mean that, basically, the complexity of frontline work of itself at an individual level is also underestimated? Or were you simply saying that because there are so many frontline workers and the various situations of various types of frontline workers is so different that it's obviously an underappreciated management challenge? Or were you truly saying that frontline work in and of itself is either complicated or becoming more complex? JOHAN: If a task was not automated, it is inherently complex. So you couldn't automate it, right? TROND: Right. JOHAN: Because if you can teach a robot or whatever to do tasks, then it's not difficult, and you can foresee the results. There was a lady called Lisanne Bainbridge. She put out The Paradox of Automation that the more you automate, the more dependent you become on the few people that are still there to handle the situations that are so complex that you could not foresee them. So everything that is programmed is programmed by a programmer, and the programmer tries to foresee every foreseeable situation, and to that extent, the robots and the automation works. But if these situations go out of hand, if they're too complex, and something happens, then there is no robot that can fix that. Unfortunately, AI is not there yet. TROND: Well, you said, "Unfortunately, AI is not there yet," but I would also conjecture that, fortunately, AI is not there yet because you're pointing to something missing, I think. And a lot of the AI debate is starting to come back now. And it was there in the '60s because people realized that for lots of different reasons, to have a human oversight over robotic processes is actually a good thing. And you talked to me earlier about the experiments with imagining a trip to Mars and having to execute robotic actions on Mars in a control system environment where you actually had to foresee the action and plan; it was always a supervised type of situation. So the supervisory control concept has been there from the beginning of computing. If you were to think of a future where AI actually does get more advanced, and a lot of people feel like that's imminent, maybe you and I don't, but in any case, let's imagine that it does become more advanced and becomes sort of a challenge, how do we maintain human control over those kinds of decisions? I mean, there are researchers that have imagined, you know, famously in Superintelligence, Bostrom imagines this paperclip factory that goes amok and starts to optimize for producing paperclips, and everyone is suddenly producing, you know, and the machine then just reallocates resources to this enormously ridiculous task of producing only paper clips. It's a very memorable example. But a lot of people feel that AI could soon or at some point reach that level. How do we, as a failsafe, avoid that that becomes an issue? Or do you see it as such a far-fetched topic in manufacturing that it would be decades, if not centuries, away? JOHAN: I think that AI has been seasonal if you allow the expression. There's talk about these AI winters every now and then, and they tend to come every 10 or 15 years, and that matches two Ph.D. lifetimes, Ph.D. development. I mean, people tend to forget the problems, and then they tend to use these Gartner curves. If you look at the Gartner curve, you have the expectation part. I'm not being arrogant towards the AI research. I think that AI is fantastic, but it should be seen, from my perspective, as what it is, as an advanced form of automation that can be used as an augmentation tool. I think it was Kasparov that started to collaborate with a chess computer maker or developer, and they won every tournament because the combination of the human and the chess computer was astounding. And now I think there are even competitions with chess computers plus chess experts comes with them. There was, I think, in the 1800s, there was a traveling exhibitionist where they had the Mechanical Turk, I think it was called. It was a chess player that was competing then against the people in the audience. And actually, inside this box, there was a small human that was making all the chess moves. And they were beating all the chess champions. So there was a man inside this. I think that there is still a man inside a lot of the automation. TROND: A man and a woman. I wanted to just lastly end on a more positive note because you told me earlier that you are more optimistic now than ten years ago on behalf of your industry that you've researched for so many years. Why is that? JOHAN: I think that the technology, I mean, I'm a techno-optimist. And I think that we have also the full scale, the full attention from the ICT industry on various industrial processes right now. It was a lot of service-oriented. And I think that that is playing out now in the platform wars, the different services, but these different services are actually making a lot of good in the manufacturing and the tougher industries. And so, there is a bigger focus now on creating CO2-less steel. And there's an exploration of different industries that are going across; you look at the electrification of vehicles which is cutting across several sectors in the industry, automotive industry, electronics industry. And I think that the problems in industry are becoming so complex. So the ICT attention is on industry now more than perhaps on consumers, as it were, and I think that that's promising. I see companies like Ericsson promoting 5G. I see companies doing the Amazon Web Services and such companies looking at services that are useful for industry. And that's also augmenting the people's capability in that sense, so that's why I'm so positive. I see all the sensors coming. I see all the computing power coming into the hands of the frontline operators. And I see also the use for the upskilling and the skilling technologies that are emerging. How do you do that? What they do in Matrix when the leading lady downloads the instructions for the helicopter or motorcycle or whatever it is. But how do you do that in real life? How do you prepare for something that's coming in the next few minutes? That is something that people are now looking at using technologies, augmenting technologies, digital twins, and things like that in a completely different way than they were five years ago. TROND: Wow. So these are exciting moments for learning in manufacturing with perhaps wide-ranging consequences if we succeed. Johan, I thank you so much for these reflections. You've spent a career investigating production systems, and manufacturing, and workers. And these are very rich debates. And it seems like they're not over, Johan. So, hopefully, we'll have you back when something happens. And we'll have you comment on some developments. Thank you very much. JOHAN: Thank you, Trond. Thank you for a very interesting discussion. You always learn a lot by being asked a lot of questions, so thank you so much for this learning experience. Thank you. TROND: You're very gracious. Thank you, Johan. You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was a Scandinavian Perspective on Industrial Operator Independence. Our guest was Johan Stahre, Professor and Chair of Production Systems at Chalmers University of Sweden. In this conversation, we talked about how the field of human-centered automation has evolved. My takeaway is that human-centered automation is the only kind of automation that we should be thinking about, and this is becoming more and more clear. Operators are fiercely independent, and so should they be. This is the only way they can spot problems on the shop floor, by combining human skills with automation in new ways augmenting workers. It seems the workforce does not so much need engagement as they need enablement. Fix that, and a lot can happen. 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 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. 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, devices, and systems 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, and you can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially about where industrial tech is heading. 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: Johan Stahre.

If you joined us for the Faith Driven Entrepreneur conference, you're in for a treat. This is the full interview with Strive Masiyiwa , Founder and Executive Chairman of the Econet, and one of Africa's most respected business leaders. In this episode, Strive speaks about Africa's role within the global marketplace. He also shares how faith has empowered him to operate in the world without fear and how other entrepreneurs can go bravely into the spheres God called them. If you weren't there for the live event, you can still access the conference recordings here.Check out more from Faith Driven Entrepreneur Africa at africa.faithdrivenentrepreneur.org.

Iger With Your Nose So Bright!             In today's My Disney Class Podcast episode, Howie and Ryan discuss the recent return of Bob Iger in his role as CEO of the Walt Disney CO. Bob Iger returned to The Walt Disney Company as Chief Executive Officer on November 20, 2022, shocking most all of us. He previously served as CEO and Chairman of The Walt Disney Company from 2005 to 2020, then Executive Chairman and Chairman of the Board through 2021.             Over Mr. Iger's 15 years leading Disney, he was the steward of one of the world's largest media companies and some of the most respected and beloved brands around the globe. His strategic vision focused on three fundamental pillars: generating the best creative content possible; fostering innovation and utilizing the latest technology, and expanding into new markets worldwide He, for some of us, most notably enhanced Disney's storytelling with acquisitions of Pixar, Marvel, Lucasfilm, and 21st Century Fox.             Ryan and Howie will discuss what they think this means for the company and how leaders can sometimes take the fall for things that are out of their control. They will also discuss what this potentially doesn't mean for the company and how working with our own school and district leaders can show parallels to what Disney is experiencing with the rehiring of Bob Iger into his previous role as CEO.  We hope you enjoyed the discussion and opinions we had about the recent announcement of Mr. Iger returning to his role as CEO of the Walt Disney Company. There is always something to learn from Disney that we can bring to our professions as educators and how they handle their leadership positions is no exception.  Don't forget, if you haven't already, to join the fun and supportive community we have in our Facebook group, Educators who love Disney, and don't forget to rate and review our podcast on Apple music. Your support and feedback will only make us better!  

Charlie Ruffle, co-founder and Executive Chairman of Kudo, a private equity firm that is changing the economics of the investing business by taking long term, passive minority stakes in successful, often boutique investment management firms. Kudu now has stakes in 23 businesses with assets under management, depending on the day, of about $60 billion. Before that, Charlie spent decades studying and reporting on the asset management and wealth management business as co-founder and CEO of Asset International. He successfully sold the media company in 2010. He's an incredibly astute observer. Perhaps it's his training as a journalist, or maybe it's his passion for fly fishing where he observes trends, context, conditions, and subtle changes in water and air in some of the more remote corners of the world.In this episode of Outside In, Charlie talks with Jon about the importance of trust, the future of finance and his passion for fly fishing.

HAPPY THANKSGIVING!!!! On today's episode, entreprenuer, author and work researcher Jordan Raynor talks with Alan about how we've gotten work wrong, why we must recover a theology of work and why he wrote his new book The Word Before Work... and don't miss the fun lightning round. Also, be sure to check out more from our episode sponsor Full Strength Network in the show notes below.  About Jordan Jordan Raynor helps Christians respond to the radical, biblical truth that their work matters for eternity. He does this through his bestselling books (The Creator in You, Redeeming Your Time, Master of One, and Called to Create), podcast (Mere Christians), and weekly devotional (The Word Before Work)—content that has served millions of Christ-followers in every country on earth. In addition to his writing, Jordan serves as the Executive Chairman of Threshold 360, a venture-backed tech startup which Jordan previously ran as CEO following a string of successful ventures of his own. Jordan has twice been selected as a Google Fellow and served in The White House under President George W. Bush. A sixth-generation Floridian, Jordan lives in Tampa with his wife and their three young daughters. The Raynors are proud members of The Church at Odessa. Connect with Jordan The Word Before Work Website: https://jordanraynor.com/ This episode is sponsored by: Full Strength Network Ministry is hard. Pastors and ministry leaders work long hours with little rest. Maybe your personal life feels exhausting because ministry drains so much energy from you. Often, we're so busy taking care of the people in our ministries, we don't spend the time we should taking care of ourselves. At Full Strength Network, we get it. That's why we have created a wellbeing membership program that gives you access to confidential coaching & counseling experts, relevant wellbeing resources, and a strong community of other pastors focused on living healthy lives. Head over to www.fullstrength.org to learn more and sign up. It's time to take back your wellbeing so you can live and lead well, and Full Strength is here to help. Connect with Full Strength: https://fullstrength.org/    

In the lead up to the 2022 AGM The Sash has opened the door to board members up for election to come on the show and pitch to the members why they should be re-elected. Today's guest is incumbent Andrew Muir. Andrew is the founder and director of The Good Foundation. He was CEO of The Good Guys from 1993 to 2005, and the group's Executive Chairman between 2005 to 2016. Muir lead The Good Guys during a period of rapid growth as it grew from to a national leader in household appliances and consumer electronics. Andrew has been on Essendon's board since 2015 when we was appointed to assist in the fall out from the saga. Visit Store Join PREMIUM Go Dons

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 Human-First AI. Our guest is Christopher Nguyen (https://www.linkedin.com/in/ctnguyen/), CEO, and Co-Founder of Aitomatic (https://www.aitomatic.com/). In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat (https://www.augmentedpodcast.co/80). 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: Physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations in industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Christopher, how are you? And welcome. CHRISTOPHER: Hi, Trond. How are you? TROND: I'm doing great. I thought we would jump into a pretty important subject here on human-first AI, which seems like a juxtaposition of two contradictory terms, but it might be one of the most important types of conversations that we are having these days. I wanted to introduce you quickly before we jump into this. So here's what I've understood, and you correct me if I'm wrong, but you are originally from Vietnam. This is back in the late '70s that you then arrived in the U.S. and have spent many years in Silicon Valley mostly. Berkeley, undergrad engineering, computer science, and then Stanford Ph.D. in electrical engineering. You're a sort of a combination, I guess, of a hacker, professor, builder. Fairly typical up until this point of a very successful, accomplished sort of Silicon Valley immigrant entrepreneur, I would say, and technologist. And then I guess Google Apps is something to point out. You were one of the first engineering directors and were part of Gmail, and Calendar, and a bunch of different apps there. But now you are the CEO and co-founder of Aitomatic. What we are here to talk about is, I guess, what you have learned even in just the last five years, which I'm thrilled to hear about. But let me ask you this first, what is the most formational and formative experience that you've had in these years? So obviously, immigrant background and then a lot of years in Silicon Valley, what does that give us? CHRISTOPHER: I guess I can draw from a lot of events. I've always had mentors. I can point out phases of my life and one particular name that was my mentor. But I guess in my formative years, I was kind of unlucky to be a refugee but then lucky to then end up in Silicon Valley at the very beginning of the PC revolution. And my first PC was a TI-99/4A that basically the whole household could afford. And I picked it up, and I have not stopped hacking ever since. So I've been at this for a very long time. TROND: So you've been at this, which is good because actually, good hacking turns out takes a while. But there's more than that, right? So the story of the last five years that's interesting to me because a lot of people learn or at least think they learn most things early. And you're saying you have learned some really fundamental things in the last five years. And this has to do with Silicon Valley and its potential blindness to certain things. Can you line that up for us? What is it that Silicon Valley does really well, and what is it that you have discovered that might be an opportunity to improve upon? CHRISTOPHER: Well, I learn new things every four or five years. I actually like to say that every four or five years, I look back, and I say, "I was so stupid five years ago." [laughs] So that's been the case. TROND: That's a very humbling but perhaps a very smart knowledge acquisition strategy, right? CHRISTOPHER: Yeah. And in the most recent five years...so before co-founding Aitomatic, which is my latest project and really with the same team...and I can talk a lot more about that. We've worked with each other for about ten years now. But in the intervening time, there's a four-and-a-half-year block when we were part of Panasonic. So we had a company called Arimo that was acquired by Panasonic for our machine learning AI skills and software. And I would say if you look at my entire history, even though I did start with my degree in semiconductor all the way down to device physics and Intel and so on, but in terms of a professional working career, that was the first time we actually faced the physical world as a Silicon Valley team. And anybody who's observed Silicon Valley in the last 15-20 years, certainly ten years, has seen a marked change in terms of the shift from hardware to software. And my friend Marc Andreessen likes to say, "Software is eating the world." If you look at education, you know, the degrees people are getting, it has shifted entirely from engineering all the way to computer science. And the punch line, I guess, the observation is that we Silicon Valley people do not get physical. We don't understand the manufacturing world. We don't know how to do HVAC and so on. And so when we build software, we tend to go for the digital stuff. TROND: Christopher, it's almost surprising given the initial thrust of Silicon Valley was, of course, hardware. So it's not surprising to me, I guess because I've been observing it as well. But it is striking more than surprising that a region goes through paradigms. CHRISTOPHER: Yeah. Yeah. And it's a global trend. It's the offshoring of low-end, shall we say, low-value manufacturing and so on. And we're discovering that we actually went a little too far. So we don't have the skill set, the expertise anymore. And it's become a geopolitical risk. TROND: Right. Well, a little bit too far, maybe, or not far enough. Or, I mean, tell us what it is that you're losing when you lose the hardware perspective, particularly in this day and age with the opportunities that we're about to talk about. CHRISTOPHER: Well, I can talk specifically about the things that touch my immediate spheres. Maybe you can think abstractly about the lack of tooling expertise and manufacturing know-how, and so on. But as part of Panasonic, the acquisition was all about taking a Silicon Valley team and injecting AI, machine learning across the enterprise. And so we were part of that global AI team reporting to the CTO office. And we found out very quickly that a lot of the software techniques, the machine learning, for example, when you think about people saying data is the fuel for machine learning and specifically labeled data, right? In the digital world, the Google place that I came from, it was very easy to launch a digital experiment and collect labels, decisions made by users. You can launch that in the morning, and by evening you're building examples. You can't do that in the physical world. Atoms move a lot more slowly. And so when you try to do something like predictive maintenance, you don't have enough failure examples to train machine learning models from. So all of the techniques, all of the algorithms that we say we developed from machine learning that seem to work so well, it turns out it worked so well because the problem space that we worked on has been entirely digital, and they all fail when it comes to manufacturing, the things that you can touch and feel, you know, cars that move and so on. TROND: I want to ask you this, Christopher, because the first company you helped co-found was, in fact, a contract manufacturer. Do you think that reflecting on this long career of yours and these various experiences, what was it that convinced you before others? I mean, you're not the only one now in the Valley that has started to focus on manufacturing and including hardware again, but it is rare still. What does it require to not just think about manufacturing but actually start to do compute for manufacturing? Is it just a matter of coming up with techniques? Or is it a whole kind of awareness that takes longer? So, in your case, you've been aware of manufacturing, acutely aware of it for decades. CHRISTOPHER: I would say there are two things, one is obvious, and the other was actually surprising to me. The obvious one is, of course, knowledge and experience. When we work on sonar technology that shoots a beam down an echogram that comes back to detect fish in the ocean, it's very necessary, not just convenient, but necessary for the engineers that work on that to understand the physics of sound waves travel underwater, and so on. So that education, I have long debates, and it's not just recently. When we were trying to structure a syllabus for a new university, I had long debates with my machine-learning friends, and they said, "We don't need physics." And I said, "We need physics." That's one thing. But you can concretely identify you need to know this. You need to know this. So if you're going to do this, learn the following thing. The thing that was more unexpected for me in the last five years as I sort of sound this bell of saying, hey, we need to modify our approach; we need to optimize our algorithms for this world, is a cultural barrier. It's kind of like the story of if you have a hammer, you want to go look for nails. So Silicon Valley today does not want to look for screwdrivers yet for this world. TROND: So you're saying Silicon Valley has kind of canceled the physical world? If you want to be really sort of parabolic about this, it's like software is eating the world, meaning software is what counts, and it's so efficient. Why go outside this paradigm, basically? If there's a problem that apparently can't be fixed by software, it's not a valuable problem. CHRISTOPHER: Or I can't solve that problem with my current approach. I just have to squint at it the right way. I have to tweak the problem this way and so on despite the fact that it's sort of an insurmountable challenge if you tried to do so. And concretely, it is like, just give me enough data, and I'll solve it. And if you don't have enough data, you know what? Go back and get more data. [chuckles] That's what I myself literally said. But people don't have the luxury of going back to get more data. They have to go to market in six months, and so on. TROND: Right. And so manufacturing...and I can think of many use cases where obviously failure, for example, is not something...you don't really want to go looking for more failure than you have or artificially create failure in order to stress test something unless that's a very safe way of doing so. So predictive maintenance then seems like a, I guess, a little bit of a safer space. But what is it about that particular problem that then lends itself to this other approach to automating labeling? Or what exactly is it that you are advocating one should do to bridge to digital and the physical AIs? CHRISTOPHER: I actually disagree that it is a safer space. TROND: Oh, it's not a safer space to you. CHRISTOPHER: That itself there's a story in that, so let's break that down. TROND: Let's do it. CHRISTOPHER: So, again, when I say Silicon Valley, it is a symbol for a larger ecosystem that is primarily software and digital. And when I say we, because I've worn many hats, I have multiple wes, including academia; I've been a professor as well. When we approach the predictive maintenance problem, if you approach it as machine learning, you got to say, "Do this with machine learning," the first thing you ask for...let's say I'm a data scientist; I'm an AI engineer. You have this physical problem. It doesn't matter what it is; just give me the dataset. And the data set must have rows and columns, and the rows are all the input variables. And then there should be some kind of column label. And in this case, it'll be a history of failures of compressors failing, you know, if the variables are such, then it must be a compressor. If the variables are such, it must be the air filter, and so on. And it turns out when you ask for that kind of data, you get ten rows. [laughs] That's not enough to do machine learning on. So then people, you know, machine learning folks who say they've done predictive maintenance, they actually have not done predictive maintenance. That's the twist. What they have done is anomaly detection, which machine learning can do because, with anomaly detection, I do not need that failure label. It just gives me all the sensor data. What anomaly detection really does is it learns the normal patterns. If you give it a year's worth of data, it'll say, okay, now I've seen a year's worth of data. If something comes along that is different from the past patterns; I will tell you that it's different. That's only halfway to predictive maintenance. That is detecting that something is different today. That is very different from, and it isn't predicting, hey, that compressor is likely to fail about a month from now. And that when we were part of Panasonic, it turns out the first way...and we solved it exactly the way I've described. We did it with the anomaly detection. And then we threw it over the wall to the engineer experts and said, "Well, now that you have this alert, go figure out what may be wrong." And half of the time, they came back and said, "Oh, come on, it was just a maintenance event. Why are you bothering me with this?" TROND: But, Christopher, leveraging human domain expertise sounds like a great idea. But it can't possibly be as scalable as just leveraging software. So how do you work with that? And what are the gains that you're making? CHRISTOPHER: I can show you the messenger exchange I had with another machine-learning friend of mine who said exactly the same thing yesterday, less than 24 hours ago. TROND: [laughs] CHRISTOPHER: He said, "That's too labor-intensive." And I can show you the screen. TROND: And how do you disprove this? CHRISTOPHER: Well, [chuckles] it's not so much disproving, but the assumption that involving humans is labor-intensive is only true if you can't automate it. So the key is to figure out a way, and 10-20 years ago, there was limited technology to automate or extract human knowledge, expert systems, and so on. But today, technologies...the understanding of natural language and so on, machine learning itself has enabled that. That turns out to be the easier problem to solve. So you take that new tool, and you apply it to this harder physical problem. TROND: So let's go to a hard, physical problem. You and I talked about this earlier, and let's share it with people. So I was out fishing in Norway this summer. And I, unfortunately, didn't get very much fish, which obviously was disappointing on many levels. And I was a little surprised, I guess, of the lack of fish, perhaps. But I was using sonar to at least identify different areas where people had claimed that there were various types of fish. But I wasn't, I guess, using it in a very advanced way, and we weren't trained there in the boat. So we sort of had some sensors, but we were not approaching it the right way. So that helped me...and I know you work with Furuno, and Garmin is the other obviously player in this. So fish identification and detection through sonar technology is now the game, I guess, in fishery and, as it turns out, even for individuals trying to fish these days. What is that all about? And how can that be automated, and what are the processes that you've been able to put in place there? CHRISTOPHER: By the way, that's a perfect segue into it. I can give a plug perhaps for this conference that I'm on the organizing committee called Knowledge-First World. And Furuno is going to be presenting their work exactly, talking a lot about what you're talking about. This is kind of coming up in November. It is the first conference of its kind because this is AI Silicon Valley meets the physical world. I think you're talking about the fish-finding technology from companies like Furuno, and they're the world's largest market share in marine navigation and so on. And the human experts in this are actually not even the engineers that build these instruments; it's the fishermen, right? The fishermen who have been using this for a very long time combine it with their local knowledge, you know, warm water, cold water, time of day, and so on. And then, after a while, they recognize patterns that come back in this echogram that match mackerel, or tuna, or sardines, and so on. And Furuno wants to capture that knowledge somehow and then put that model into the fish-finding machine that you and I would hold. And then, instead of seeing this jumbled mess of the echogram data, we would actually see a video of fish, for example. It's been transformed by this algorithm. TROND: So, I mean, I do wish that we lived in a world where there was so much fish that we didn't have to do this. But I'm going to join your experiment here. And so what you're telling me is by working with these experts who are indeed fishermen, they're not experts in sonar, or they're not experts in any kind of engineering technology, those are obviously the labelers, but they are themselves giving the first solutions for how they are thinking about the ocean using these technologies. And then somehow, you are turning that into an automatable, an augmented solution, essentially, that then can find fish in the future without those fishermen somehow being involved the next time around because you're building a model around it. CHRISTOPHER: I'll give you a concrete explanation, a simplified version of how it works, without talking about the more advanced techniques that are proprietary to Furuno. The conceptual approach is very, very easy to understand, and I'll talk about it from the machine learning perspective. Let's say if I did have a million echograms, and each echogram, each of these things, even 100,000, is well-labeled. Somebody has painstakingly gone through the task of saying, okay, I'm going to circle this, and that is fish. And that is algae, and that's sand, and that's marble. And by the way, this is a fish, and this is mackerel, and so on. If somebody has gone through the trouble of doing that, then I can, from a human point of view, just run an algorithm and train it. And then it'll work for that particular region, for that particular time. Okay, well, we need to go collect more data, one for Japan, the North Coast, and one for Southwestern. So that's kind of a lot of work to collect essentially what this pixel data is, this raw data. When you present it to an experienced fisherman, he or she would say, "Well, you see these bubbles here, these circles here with a squiggly line..." So they're describing it in terms of human concepts. And then, if you sit with them for a day or two, you begin to pick up these things. You don't need 100,000-pixel images. You need these conceptual descriptions. TROND: So you're using the most advanced AI there is, which is the human being, and you're using them working with these sonar-type technologies. And you're able to extract very, very advanced models from it. CHRISTOPHER: The key technology punch line here is if you have a model that understands the word circle and squiggly line, which we didn't before, but more recently, we begin to have models, you know, there are these advances called large language models. You may have heard of GPT-3 and DALL-E and so on, you know, some amazing demonstrations coming out of OpenAI and Google. In a very simplified way, we have models that understand the world now. They don't need raw pixels. These base models are trained from raw pixels, but then these larger models understand concepts. So then, we can give directions at this conceptual level so that they can train other models. That's sort of the magic trick. TROND: So it's a magic trick, but it is still a difficult world, the world of manufacturing, because it is physical. Give me some other examples. So you worked with Panasonic. You're working with Furuno in marine navigation there and fishermen's knowledge. How does this work in other fields like robotics, or with car manufacturing, or indeed with Panasonic with kind of, I don't know, battery production or anything that they do with electronics? CHRISTOPHER: So, to give you an example, you mentioned a few things that we worked on, you know, robotics in manufacturing, robotics arm, sort of the manufacturing side, and the consistency of battery sheets coming off the Panasonic manufacturing line in Sparks, Nevada as well as energy optimization at Westinghouse. They supply into data centers, and buildings, and so on. And so again, in every one of these examples, you've got human expertise. And, of course, this is much more prevalent in Asia because Asia is still building things, but some of that is coming back to the U.S. There are usually a few experts. And by the way, this is not about thousands of manufacturing line personnel. This is about three or four experts that are available in the entire company. And they would be able to give heuristics. –They will be able to describe at the conceptual level how they make their decisions. And if you have the technology to capture that in a very efficient way, again, coming back to the idea that if you make them do the work or if you automate their work, but in a very painstaking way like thousands of different rules, that's not a good proposition. But if you have some way to automate the automation, automate the capturing of that knowledge, you've got something that can bridge this physical, digital divide. 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: How stable is that kind of model knowledge? Because I'm just thinking about it in the long run here, are these physical domain experts that are giving up a little bit of their superpower are they still needed then in a future scenario when you do have such a model? Or will it never be as advanced as they are? Or is it actually going to be still kind of an interface that's going to jump between machines and human knowledge kind of in a continuous loop here? CHRISTOPHER: Yeah, in the near term, it turns out we're not working on replacing experts as much as scaling experts. Almost every case we've worked on, companies are in trouble largely because the experts are very, very few and far between, and they're retiring. They're leaving. And that needs to be scaled somehow. In the case of, for example, the cold chain industry all of Japan servicing the supermarkets, you know, there's 7-ELEVEN, there's FamilyMart, and so on, there are three experts who can read the sensor data and infer what's likely to fail in the next month. So in the near term, it's really we need these humans, and we need more of them. TROND: I'm glad to hear that even that is a bit of a contrarian message. So you're saying physical infrastructure and the physical world matters. You're saying humans matter. [laughs] It's interesting. Yeah, that's contrarian in Silicon Valley, I'll tell you that. CHRISTOPHER: It is. And, in fact, related to that problem, Hussmann, which is a refrigeration company, commercial refrigeration supplies to supermarkets. It was a subsidiary of Panasonic. It has a really hard time getting enough service personnel, and they have to set up their own universities, if you will, to train them. And these are jobs that pay very well. But everybody wants to be in software these days. Coming back to the human element, I think that long-term I'm an optimist, not a blind optimist but a rational one. I think we're still going to need humans to direct machines. The machine learning stuff is data that reflects the past, so patterns of the past, and you try to project that in the future. But we're always trying to effect some change to the status quo. Tomorrow should be a better day than today. So is that human intent that is still, at least at present, lacking in machines? And so we need humans to direct that. TROND: So what is the tomorrow of manufacturing then? How fast are we going to get there? Because you're saying, well, Silicon Valley has a bit of a learning journey. But there is language model technology or progress in language models that now can be implemented in software and, through humans, can be useful in manufacturing already today. And they're scattered examples, and you're putting on an event to show this. What is the path forward here, and how long is this process? And will it be an exponential kind of situation here where you can truly integrate amazing levels of human insight into these machine models? Or will it take a while of tinkering before you're going to make any breakthroughs? Because one thing is the breakthrough in understanding human language, but what you're saying here is even if you're working only with a few experts, you have to take domain by domain, I'm assuming, and build these models, like you said, painstakingly with each expert in each domain. And then, yes, you can put that picture together. But the question is, how complex of a picture is it that you need to put together? Is it like mapping the DNA, or is it bigger? Or what kind of a process are we looking at here? CHRISTOPHER: If we look at it from the dimension of, say, knowledge-based automation, in a sense, it is a continuation. I believe everything is like an s-curve. So there's acceleration, and then there's maturity, and so on. But if you look back in the past, which is sort of instructive for the future, we've always had human knowledge-based automation. I remember the first SMT, the Surface Mount Technology, SMT wave soldering machine back in the early '90s. That was a company that I helped co-found. It was about programming the positioning of these chips that would just come down onto the solder wave. And that was human knowledge for saying, move it up half a millimeter here and half a millimeter there. But of course, the instructions there are very micro and very specific. What machine learning is doing...I don't mean to sort of bash machine learning too much. I'm just saying culturally, there's this new tool really that has come along, and we just need to apply the tool the right way. Machine learning itself is contributing to what I described earlier, that is, now, finally, machines can understand us at the conceptual level that they don't have to be so, so dumb as to say, move a millimeter here, and if you give them the wrong instruction, they'll do exactly that. But we can communicate with them in terms of circles and lines, and so on. So the way I see it is that it's still a continuous line. But what we are able to automate, what we're able to ask our machines to do, is accelerating in terms of their understanding of these instructions. So if you can imagine what would happen when this becomes, let's say, ubiquitous, the ability to do this, and I see this happening over the next...Certainly, the base technology is already there, and the application always takes about a decade. TROND: Well, the application takes a decade. But you told me earlier that humans should at least have this key role in this knowledge-first application approach until 2100, you said, just to throw out a number out there. That's, to some people, really far away. But the question is, what are you saying comes after that? I know you throw that number out. But if you are going to make a distinction between a laborious process of painful progress that does progress, you know, in each individual context that you have applied to human and labeled it, and understood a little case, what are we looking at, whether it is 2100, 2075, or 2025? What will happen at that moment? And is it really a moment that you're talking about when machines suddenly will grasp something very, very generic, sort of the good old moment of singularity, or are you talking about something different? CHRISTOPHER: Yeah, I certainly don't think it's a moment. And, again, the HP-11C has always calculated Pi far faster and with more digits than I have. So in that sense, in that particular narrow sense, it's always been more intelligent than I am. TROND: Yeah. Well, no one was questioning whether a calculator could do better calculations than a human. For a long time -- CHRISTOPHER: Hang on. There's something more profound to think about because we keep saying, well, the minute we do something, it's okay; that's not intelligence. But what I'm getting to is the word that I would refer to is hyper-evolution. So there's not a replacement of humans by machines. There's always been augmentation, and intelligence is not going to be different. It is a little disturbing to think about for some of us, for a lot of us, but it's not any different from wearing my glasses. Or I was taking a walk earlier this morning listening to your podcast, and I was thinking how a pair of shoes as an augmented device would seem very, very strange to humans living, say, 500 years ago, the pair of shoes that I was walking with. So I think in terms of augmenting human intelligence, there are companies that are working on plugging in to the degree that that seems natural or disturbing. It is inevitable. TROND: Well, I mean, if you just think about the internet, which nowadays, it has become a trope to think about the internet. I mean, not enough people think about the internet as a revolutionary technology which it, of course, is and has been, but it is changing. But whether you're thinking about shoes, or the steam engine, or nuclear power, or whatever it is, the moment it's introduced, and people think they understand it, which most people don't, and few of us do, it seems trivial because it's there. CHRISTOPHER: That's right. TROND: But your point is until it's there, it's not trivial at all. And so the process that you've been describing might sound trivial, or it might sound complex, but the moment it's solved or is apparently solved to people, we all assume that was easy. So there's something unfair about how knowledge progresses, I guess. CHRISTOPHER: That's right. That's right. We always think, yeah, this thing that you describe or I describe is very, very strange. And then it happens, and you say, "Of course, that's not that interesting. Tell me about the future." TROND: Well, I guess the same thing has happened to cell phones. They were kind of a strange thing that some people were using. It was like, okay, well, how useful is it to talk to people without sitting by your desk or in the corner of your house? CHRISTOPHER: I totally remember when we were saying, "Why the hell would I want to be disturbed every moment of the day?" [laughs] I don't want the phone with me, and now I -- TROND: Right. But then we went through the last decade or so where we were saying, "I can't believe my life before the phone." And then maybe now the last two, three years, I would say a lot of people I talk to or even my kids, they're like, "What's the big deal here? It's just a smartphone," because they live with a smartphone. And they've always had it. CHRISTOPHER: They say, "How did you get around without Google Maps?" And then somebody says, "We used maps." And I said, "Before Google Maps." [laughter] TROND: Yeah. So I guess the future here is an elusive concept. But I just want to challenge you one more time then on manufacturing because manufacturing, for now, is a highly physical exercise. And, of course, there's virtual manufacturing as well, and it builds on a lot of these techniques and machine learning and other things. How do you see manufacturing as an industry evolve? Is it, like you said, for 75 years, it's going to be largely very recognizable? Is it going to look the same? Is it going to feel the same? Is the management structure the way engineers are approaching it, and the way workers are working? Are we going to recognize all these things? Or is it going to be a little bit like the cell phone, and we're like, well, of course, it's different. But it's not that different, and it's not really a big deal to most people. CHRISTOPHER: Did you say five years or 50 years? TROND: Well, I mean, you give me the timeframe. CHRISTOPHER: Well, in 5 years, we will definitely recognize it, but in 50 years, we will not TROND: In 50 years, it's going to be completely different, look different, feel different; factories are all going to be different. CHRISTOPHER: Right, right. I mean, the cliché is that we always overestimate what happens in 5 and underestimate what happens in 50. But the trend, though, is there's this recurring bundling and unbundling of industries; it's a cycle. Some people think it's just, you know, they live ten years, and they say it's a trend, but it actually goes back and forth. But they're sort of increasing specialization of expertise. So, for example, the supply chain over the last 30 years, we got in trouble because of that because it has become so discrete if you want to use one friendly word, but you can also say fragmented in another word. Like, everybody has been focused on just one specialization, and then something like COVID happens and then oh my God, that was all built very precisely for a particular way of living. And nobody's in the office anymore, and we live at home, and that disrupts the supply chain. I think if you project 50 years out, we will learn to essentially matrix the whole industry. You talked about the management of these things. The whole supply chain, from branding all the way down to raw materials, is it better to be completely vertically integrated to be part of this whole mesh network? I think the future is going to be far more distributed. But there'll be fits and starts. TROND: So then my last question is, let's say I buy into that. Okay, let's talk about that for a second; the future is distributed or decentralized, whatever that means. Does that lessen or make globalization even more important and global standardization, I guess, across all geographical territories? I'm just trying to bring us back to where you started with, which was in the U.S., Silicon Valley optimized for software and started thinking that software was eating the world. But then, by outsourcing all of the manufacturing to Asia, it forgot some essential learning, which is that when manufacturing evolves, the next wave looks slightly different. And in order to learn that, you actually need to do it. So does that lesson tell you anything about how the next wave of matrix or decentralization is going to occur? Is it going to be...so one thought would be that it is physically distributed, but a lot of the insights are still shared. So, in other words, you still need global insight sharing, and all of that is happening. If you don't have that, you're going to have pockets that are...they might be very decentralized and could even be super advanced, but they're not going to be the same. They're going to be different, and they're going to be different paths and trajectories in different parts of the world. How do you see this? Do you think that our technology paradigms are necessarily converging along the path of some sort of global master technology and manufacturing? Or are we looking at scattered different pictures that are all decentralized, but yet, I don't know, from a bird's eye view, it kind of looks like a matrix? CHRISTOPHER: I think your question is broader than just manufacturing, although manufacturing is a significant example of that, right? TROND: It's maybe a key example and certainly under-communicated. And on this podcast, we want to emphasize manufacturing, but you're right, yes. CHRISTOPHER: The word globalization is very loaded. There's the supposedly positive effect in the long run. But who is it that said...is it Keynes that said, "In the long run, we're all dead?" [laughs] In the short run, the dislocations are very real. A skill set of a single human being can't just shift from hardware to software, from manufacturing to AI, within a few months. But I think your question is, let's take it seriously on a scale of, say, decades. I think about it in terms of value creation. There will always be some kind of disparity. Nature does not like uniformity. Uniformity is coldness; it is death. There have to be some gradients. You're very good at something; I'm very good at something else. And that happens at the scale of cities and nations as well. TROND: And that's what triggers trade, too, right? CHRISTOPHER: Exactly. TROND: Because if we weren't different, then there would be no incentive to trade. CHRISTOPHER: So when we think about manufacturing coming back to the U.S., and we can use the word...it is correct in one sense, but it's incorrect in another sense. We're not going back to manufacturing that I did. We're not going back to surface mount technology. In other words, the value creation...if we follow the trajectory of manufacturing alone and try to learn that history, what happens is that manufacturing has gotten better and better. Before, we were outsourcing the cheap stuff. We don't want to do that. But then that cheap stuff, you know, people over there build automation and skills, and so on. And so that becomes actually advanced technology. So in a sense, what we're really doing is we're saying, hey, let's go advanced at this layer. I think it's going to be that give and take of where value creation takes place, of course, layered with geopolitical issues and so on. TROND: I guess I'm just throwing in there the wedge that you don't really know beforehand. And it was Keynes, the economist, that said that the only thing that matters is the short term because, in the end, we are all dead eventually. But the point is you don't really know. Ultimately, what China learned from manufacturing pretty pedestrian stuff turned out to be really fundamental in the second wave. So I'm just wondering, is it possible to preempt that because you say, oh, well, the U.S. is just going to manufacture advanced things, and then you pick a few things, and you start manufacturing them. But if you're missing part of the production process, what if that was the real advancement? I guess that is what happened. CHRISTOPHER: Okay. So when I say that, I think about the example of my friend who spent, you know, again, we were a Ph.D. group at Stanford together. And whereas I went off to academia and did startups and so on, he stayed at Intel for like 32 years. He's one of the world's foremost experts in semiconductor process optimization. So that's another example where human expertise, even though semiconductor manufacturing is highly automated, you still need these experts to actually optimize these things. He's gone off to TSMC after three decades of being very happy at one place. So what I'm getting to is it is actually knowable what are the secret recipes, where the choke points are, what matters, and so on. And interestingly, it does reside in the human brain. But when I say manufacturing coming back to the U.S. and advanced manufacturing, we are picking and choosing. We're doing battery manufacturing. We're doing semiconductor, and we're not doing wave soldering. So I think it is possible to also see this trend that anybody who's done something and going through four or five iterations of that for a long time will become the world's expert at it. I think that is inevitable. You talk of construction, for example; interestingly, this company in Malaysia that is called Renong that is going throughout Southeast Asia; they are the construction company of the region because they've been doing it for so long. I think that is very, very predictable, but it does require the express investment in that direction. And that's something that Asia has done pretty well. TROND: Well, these are fascinating things. We're not going to solve them all on this podcast. But definitely, becoming an expert in something is important, whether you're an individual, or a company, or a country for sure. What that means keeps changing. So just stay alert, and stay in touch with both AI and humans and manufacturing to boot. It's a mix of those three, I guess. In our conversation, that's the secret to unlocking parts of the future. Thank you, Christopher, for enlightening us on these matters. I appreciate it. CHRISTOPHER: It's my pleasure. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Human-First AI. Our guest was Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talked about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame. My takeaway is that physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure. 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 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat as our guest. 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 the people, machines, devices, and systems used in a production and 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. Please share this show with colleagues who care about where industry and especially about how industrial tech is going. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and on YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Christopher Nguyen.

Today's guest is Santiago Burridge, Co-Founder and Executive Chairman at Lumiant. Lumiant is a behavioral finance and client experience platform built to support financial professionals with the tools they need to deliver scalable, client-centric advice. There are many well-meaning businesses in this industry, but some are not serving their clients as well as they could be. Santiago shares the difference between approaching clients with products to sell and understanding clients with the goal of customizing services/products to their individual needs. Listen to find out how Lumiant can help anchor your business in experience, instead of sales, while helping you scale and grow. Join us today to learn about these things: • Here we discuss the realization that became a turning point for Santiago in his career. (1:01) • How purpose aligns with balance. (7:14) • Seasons of imbalance and how to avoid making them a long-term lifestyle. (14:49) RESOURCES Learn more about our guest: https://www.lumiant.io/ Email: travis@travisparry.com Get Travis' newest book!

My guest on the show today is Bryan Freeman, Executive Chairman of The Real Good Food Company (NASDAQ: RGF). Real Good Foods is a leading health and wellness frozen foods company, providing a better way to enjoy your favorite foods. The Company's mission is to provide “Real Food You Feel Good About Eating”, making delicious, nutritious foods that are low in sugar, low in carbohydrates and high in protein. The Real Good Foods family of products includes breakfast, lunch, dinner, and snacks – available in over 16,000 stores nationwide with additional direct-to-consumer options. I have interviewed companies in the frozen foods space over the years, and I wanted to chat with Bryan today about the industry as it exists today, especially since COVID, as well as: How RGF positions their frozen food products and the Frozen Foods total addressable market Real Good Foods product differentiation Digital marketing strategy by working with social media influencers; and, Growth strategy and path to profitability With that, please enjoy my conversation with Bryan Freeman, Executive Chairman of The Real Good Food Company. For more information about The Real Good Food Company, please visit: https://realgoodfoods.com/ Today's episode is sponsored by: Socialsuite takes the complexity out of Environmental, Social, and Governance or ESG reporting. Socialsuite helps organizations to measure, monitor and report on their progress to create value through ESG in order to raise capital, improve brand and reputation, as well as mitigate risk. Socialsuite's software platform makes ESG reporting fast, simple and affordable. Companies can start building a baseline report in under 60 minutes and start reporting publicly within 30 days. Start your ESG journey - today. Visit https://www.socialsuitehq.com/ to learn more. This podcast was recorded and is being made available by SNN, Inc. (together with its affiliates and its and their employees, “SNN”) solely for informational purposes. SNN is not providing or undertaking to provide any financial, economic, legal, accounting, tax, or other advice in or by virtue of this podcast. The information, statements, comments, views, and opinions provided in this podcast are general in nature, and such information, statements, comments, views, and opinions, and the viewing of/listening to this podcast are not intended to be and should not be construed as the provision of investment advice by SNN. The information, statements, comments, views, and opinions expressed in this podcast do not constitute and should not be construed as an offer to buy or sell any securities or to make or consider any investment or other course of action. The information, statements, comments, views, and opinions expressed in this podcast (including by guest speakers who are not officers, employees, or agents of SNN) are not necessarily those of SNN and may not be current. Reference to any specific third-party entity, product, service, materials, or content does not constitute an endorsement or recommendation by the SNN. SNN assumes no responsibility or liability for the accuracy or completeness of the content contained in third party materials or on third party sites referenced in this podcast or the compliance with applicable laws of such materials and/or links referenced herein. The views expressed by guest speakers are their own and their appearance on this podcast does not imply an endorsement of them or any entity they represent. SNN does not make any representation or warranty as to the accuracy or completeness of any of the information, statements, comments, views, or opinions contained in this podcast, which may include forward-looking statements where actual results may differ materially. SNN does not undertake any obligation whatsoever to provide any form of update, amendment, change, or correction to any of the information, statements, comments, views or opinions set forth in this podcast. SNN EXPRESSLY DISCLAIMS ANY AND ALL LIABILITY OR RESPONSIBILITY FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL OR OTHER DAMAGES ARISING OUT OF ANY INDIVIDUAL'S USE OF, REFERENCE TO, RELIANCE ON, OR INABILITY TO USE, THIS PODCAST OR THE INFORMATION PRESENTED IN THIS PODCAST. By accessing this podcast, the listener acknowledges that the entire contents and design of this podcast, are the property of SNN, or used by SNN with permission, and are protected under U.S. and international copyright and trademark laws. Except as otherwise provided herein, users of this podcast may save and use information contained in the podcast only for personal or other non-commercial educational purposes. No other use, including without limitation, reproduction, retransmission, or editing of this podcast may be made without the prior written consent of SNN.

Autos are back on the agenda as Chris and Scotty welcome Rob Wildeboer, Executive Chairman and Co-Founder of Martinrea International Inc., for a private sector perspective on supply chain near-shoring, electric vehicles, and North American opportunity in the wake of the Inflation Reduction Act and the Russian Invasion of Ukraine.

Join Mike and Mark as they go through Bob Iger's career at Disney, and discover what we can decode about being our best versions.Bob Iger, who retired last year after 15 years as chief executive, has agreed to return to the post for two more years, Disney said in a statement late on Sunday local time.Robert A. Iger was Executive Chairman of The Walt Disney Company and Chairman of the Board of Directors. During his tenure as CEO, Mr. Iger expanded on Disney's rich history of unforgettable storytelling with the acquisitions of Pixar (2006), Marvel (2009), Lucasfilm (2012) and 21st Century Fox (2019), as well as the landmark 2016 opening of Disney's first theme park and resort in Mainland China, Shanghai Disney Resort. Over his 15 years leading the Company, Mr. Iger built Disney into one of the world's largest and most admired media and entertainment companies ★ Support this podcast on Patreon ★

Ahead of her concert next week with the LSO, Tom Service speaks to the pianist Alice Sara Ott who is also preparing to embark on a tour which features lighting and images alongside performances of Chopin's Op 28 preludes, and other contemporary works from her recent Echoes of Life album, to create a multi-media experience that extends the boundaries of what's possible in concert halls. As the 2022 United Nations Climate Change Conference concludes, Music Matters hears from Managing Director of the London Symphony Orchestra, Kathryn McDowell, Chief Executive of the Royal Liverpool Philharmonic Orchestra, Michael Eakin, Executive Chairman of Harrison Parrott, Jasper Parrott, and Professor of Geosystem Science and leader of the group responsible for climate modelling at the University of Oxford, Myles Allen, about the degree to which the classical music industry is delivering its own promises to reduce its impact on the environment. With all eyes on Qatar for the opening of the FIFA World Cup, Tom hears from the BBC's Series producer for Arabic Digital Investigative Documentaries, Rosie Garthwaite, about the construction she witnessed of Doha's opera house in the Katara Cultural Centre. He learns how the country has nurtured both Western art forms and cultural institutions, and the potential projection of soft power. Tom joins the soprano Danielle de Niese and tenor Frederick Ballentine during rehearsals for a new production, by English National Opera, of Jake Heggie's It's a Wonderful Life. The composer shares how he adapted the story behind Frank Capra's classic movie, and Tom speaks to the journalist, broadcaster, and author, Matthew Sweet, about the phenomenon of setting operas from film, as well as different roles music plays on both the screen and the stage.

Anu Shukla, Co founder and Executive Chairman of Botco.ai, joins the show to talk about:- The keys to finding product market fit as an entrepreneur, and her experience finding product market fit, multiple times, in various market conditions.- Anu's experiences as a pioneer in the marketing automation space, as well as the ups and downs of building on someone else's platform. - Anu's evolution as an entrepreneur from a specialist to a portfolio aSaaSin.

In the second episode of the GPCA Value Navigation series, our guests unpack a variety of topics, but with the central key theme of “value-based outcomes” for mandatory accountability; defining the action space and pushing minimum requirements. The statement “it's not enough to” sets the stage for individuals and companies to raise the bar and set new competitive advantages. When something is “enough” it means that it is at a satisfactory level or quantity. The phrase “it's not enough” is obviously a negative form of that – and it shows that things are not satisfactory. “It's not enough” means less than needed/expected. “It's not enough” means not meeting expectations or requirements for the minimum of what is necessary to fulfill any specific desire/request/or need. It's easier to explain quantifiably than qualitatively. In this episode, we host a range of global experts that intellectually joust on a range of multi facet business decisions, choices, and trade-offs petrochemical industry leaders face today. Furthermore, the series, will push to identify levers, (inputs, and outputs) for measurement consideration, and realization of value creation. Guest: Maryam Telmesani is the Chair of Global Compact Network Saudi Arabia CSO - MBL Dr Bo Bai is the Executive Chairman and Co-founder of MetaVerse Green Exchange (MVGX) Bandar A. Al Blehed is the Head of Client Relations Development at Saudi Stock Exchange (Tadawul) Bhairav Raja is the Director at DAI's Sustainable Business Group James Brimm is the Advisor to the CEO of SIPCHEM Kirk-Dale McDowall-Rose is the Co-founder and Managing Director of Boonio  Keith Hutton is the Legal and Compliance Advisor to SIPCHEM For more podcasts and the latest i nsights on the regional chemical markets, visit www.gpca.org.ae/thought-leadership

Lay of The Land's conversation today is with Ed Weinfurtner, Executive Chairman of Great Day Improvements & Co-Founder at Blue Olive PartnersEd is an entrepreneur at heart and has been an investor, operator, and board member in a variety of privately held businesses over the past 40 years developing specific expertise in DTC home improvement, distribution, and manufacturing businesses. Most recently at the helm of Great Day Improvements, he's grown the company by an order of a magnitude from $44M to $922M in sales in just 7 years — in the last year alone, the company grew its remodeling jobs from 9000 the year prior to nearly 68,000!As one of the largest Northeast Ohio companies, employing more than 2,100 people, Great Day Improvements is a vertically integrated, direct-to-consumer provider of home remodeling and premium building products: including Patio Enclosures brand sunrooms and screen rooms; Stanek brand windows and doors; Apex Energy Solutions energy efficient windows and doors; and Hartshorn Custom Contracting pool enclosures.Since Ed acquired the business in 2014, it has quadrupled in size and is now ranked as the 11th largest residential remodeling company in the US by Qualified Remodeler. It was recently named among the fastest-growing private companies in America by INC. Magazine and by Crain's Cleveland Business as the fastest-growing privately held business in Northeast Ohio.I really enjoyed Ed's reflection on his career and learning how he navigated the business through the pandemic and the deluge of downstream headwinds to his business from labor shortages to inflated material costs, to double down on the company's core competency and find success through it. Please enjoy my conversation with Ed Weinfurtner.--Learn more about Great Day ImprovementsLearn more about Blue Olive PartnersConnect with Ed Weinfurtner on LinkedIn--Connect with Jeffrey Stern on LinkedInFollow Jeffrey Stern on Twitter @sternJefeFollow Lay of The Land on Twitter @podlayofthelandhttps://www.jeffreys.page/

On this week's Industrial Talk we're onsite at IoT Solutions World Congress and talking to Michel Morvan, Executive Chairman and Co-Founder of Cosmo Tech about "Digital Twin Simulations to Optimize Operational Efficiency". Get the answers to your "Simulation" questions along with Daniel's unique insight on the “How” on this Industrial Talk interview! Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2022. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! MICHEL MORVAN'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/michelmorvan/ Company LinkedIn: https://www.linkedin.com/company/cosmotechweb/ Company Website: https://cosmotech.com/ PODCAST VIDEO: https://youtu.be/mOkpc2p8J0c THE STRATEGIC REASON "WHY YOU NEED TO PODCAST": OTHER GREAT INDUSTRIAL RESOURCES: NEOM: https://www.neom.com/en-us Hitachi Vantara: https://www.hitachivantara.com/en-us/home.html Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ Industrial Academy: https://industrialtalk.com/industrial-academy/ Industrial Dojo: https://industrialtalk.com/industrial_dojo/ We the 15: https://www.wethe15.org/ YOUR INDUSTRIAL DIGITAL TOOLBOX: LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ Active Campaign: Active Campaign Link Social Jukebox: https://www.socialjukebox.com/ Industrial Academy (One Month Free Access And One Free License For Future Industrial Leader): Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? Live your business the way you want to live with the BUSINESS BEATITUDES...The Bridge connecting sacrifice to success. YOU NEED THE BUSINESS...

Doing the work as a leader is not enough!! You must market your leadership. You must tell the story of you, your team, and your accomplishments. If you don't, nobody will truly know what you do. In this episode, best-selling author and leadership coach Nils Vinje speaks with Anu Shukla, Co-Founder and Executive Chairman at Botco.ai. We discussed how leaders need to market themselves to be heard, seen, and recognized. Anu has been recognized as one of the top B2B Marketers in the world. Buckle up and get your notepad ready for this episode! Podcast highlights: 0:24 - Anu's background - Anu explains her role at Botco.ai. 1:14 - The speed of marketing - How does this idea play into Botco.ai's automation? 7:18 - First leadership position - Anu got her start in leadership after graduating with an MBA in Ohio then immediately heading over to California. 11:49 - Navigating gray areas - How Anu learned skills that weren't a part of her background. 14:33 - Making yourself available - Anu tells a story about when she went back to Ohio. 19:14 - Anu's startup advice - Anu shares the most impactful stories and advice for individuals who are early in their journey. 26:30 - Sharing your story and results - Anu shares advice for how leaders can share their stories without feeling like they're bragging. 30:24 - Anu's advice to herself - What advice would Anu give her younger self? Learn more about Botco.ai at https://botco.ai/ Learn more about your own leadership style at: https://www.B2BLeadersAcademy.com/ This episode is brought to you by the B2B Leaders Academy The cost of not consistently developing your leadership skills is enormous. At the B2B Leaders Academy you can gain access to monthly leadership training and live coaching. Being a great leader isn't hard, you just need a guide and the right set of tools. Head on over to b2bleadersacademy.com and become the leader you have always wanted to be. #Leadership #B2bleadership #BusinessLeader

Michael Saylor is an American entrepreneur, executive, inventor, author, and philanthropist. He is co-founder and Executive Chairman and CEO of MicroStrategy (MSTR), a publicly traded business intelligence firm he founded in 1989. Michael is an advocate for the Bitcoin Standard (www.hope.com) and is the first CEO of a publicly listed company to make a long-term investment in Bitcoin. He founded and serves as the trustee for the Saylor Academy (www.saylor.org), a non-profit organization that provides free education to people around the world. Record date: 11/11/2022 Coin Stories is powered by @Swan Bitcoin the best way to build your Bitcoin stack with automated Bitcoin savings plans and instant purchases. Swan serves clients of any size, from $10 to $10M+. Visit https://www.swanbitcoin.com/nataliebr... for $10 in Bitcoin when you sign up. If you are planning to buy more than $100,000 of Bitcoin over the next year, the Swan Private team can help.  BITCOIN 2023 by @Bitcoin Magazine will be the biggest Bitcoin event in history May 18-20 in Miami Beach. Speakers include Michael Saylor, Lyn Alden and Michelle Phan, plus a Day 3 music festival. Nearly 30,000 people attended Bitcoin 2022. Get an early bird pass at a steep discount at https://b.tc/conference code HODL for 10% off your pass. With iTrustCapital you can invest in crypto without worrying about taxes or fees, through an individual retirement account. IRAs are tax-sheltered accounts, which means all your crypto trading is tax-free and can even grow tax-free over time. The best part is it's totally free to open an account, and there are no hidden fees, monthly subscriptions or membership fees. Get a $100 funding bonus if you open and fund an account. Go to https://itrust.capital/nataliebrunell to learn more and open a free account. Fold is the best Bitcoin rewards debit card and shopping app in the world! Earn Bitcoin on everything you purchase with Fold's Bitcoin cash back debit card, and spin the Daily Wheel to earn free Bitcoin. Head to https://www.foldapp.com/natalie for 5,000 in free sats! Health insurance needs an overhaul. The government and insurance companies have jacked the price, increased complexity, and made insurance almost unusable. You send your money to the health insurance black hole and never see it again. Then, when you get hurt you have to send them more money. The great news is now you have an alternative: CrowdHealth. It's totally different from insurance. Instead of sending your hard earned money to an insurance company, you hold your money in an account CrowdHealth helps you set up when you join. You can even convert dollars in that account into Bitcoin. When someone in the community has a health need, you help them out directly and if there is Bitcoin or $ left over in your account when you leave, you take it with you. https://www.joincrowdhealth.com/natalie OTHER RESOURCES - Natalie's website https://talkingbitcoin.com/ - Swan Bitcoin: www.swanbitcoin.com/nataliebrunell VALUE FOR VALUE — SUPPORT NATALIE'S SHOWS Strike ID https://strike.me/coinstoriesnat/ Cash App $CoinStories BTC wallet bc1ql8dqjp46s4eq9k3lxt0lxzh6f2wcu35cl6944d FOLLOW NATALIE ON SOCIAL MEDIA Twitter https://twitter.com/natbrunell  Instagram https://www.instagram.com/nataliebrunell   Linkedin https://www.linkedin.com/in/nataliebrunell DISCLAIMER This show is for entertainment purposes only and does not give financial advice. Before making any decisions consult a professional.

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 Manufacturing. Our guest is Michel Baudin (https://www.linkedin.com/in/michelbaudin/), author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich (https://www.augmentedpodcast.co/84). 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: Lean manufacturing might mean many things, but industrial work has largely been a consistent practice over several hundred years, which is not necessarily a bad thing. Having said that, if we want to go about improving it, we might want to stay pretty close to the workforce and not sit in statistics labs far removed from it. Efficiency is tied to work practices, and they cannot be optimized beyond what the workforce can handle or want to deal with. As we attempt to be lean, whatever we mean by that, we need to remember that work is a thoroughly human endeavor. 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. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Lean Manufacturing. Our guest is Michel Baudin, author, and owner of Takt Times Group. In this conversation, we talk about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work and a future where manufacturing is not going away. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Michel, welcome. How are you? MICHEL: Fine, thank you. How about yourself? TROND: Things are good. Things are looking up. I'm excited to talk about lean manufacturing with you, having had such a rich, professional background. Michel, you're French. You originally, I think, were thinking of becoming a probability researcher, or you were actually, and then you went to Japan and studied Toyota. You have had this career in English, German, Japanese sort of consulting all the way back from 1987 onwards on exciting topics, lean manufacturing, and especially implementing it, right? The real deal. You've authored at least four technical books that I know about. And I think you listed probably a while back, having written 900 blog posts. You've been very busy. You are the owner of the Takt Times Group, which is a consulting firm on lean manufacturing. And you love math, but you have this very interesting attitude, which we'll talk about, which is math is great, but it's not always the best communication tool. Tell me a little about that to start off. You're a probability researcher that doesn't use math; I think that's fascinating. MICHEL: I use it, but I don't brag about it with people that it turns off. So I have to be in the closet for this because people who work in manufacturing usually focus on concrete things, things that they can see and touch, and abstraction is not something that they respond well to. So whenever you explain a principle, my approach is to state this principle and then dig into some very specific examples right away; otherwise, I'm losing the people I'm talking to. But anyway, that's what I've had to do. TROND: So, did I capture your background okay? I mean, you've had a very international life so far. I hope it's been enjoyable and not just professional because you've spent your time in Germany, and Japan, and in the U.S., So you're really enjoying the different kinds of manufacturing environments. Or is it that you just want to be close to where it's all happening? MICHEL: I've enjoyed living in many different countries. And so you mentioned I'm French. I was born and raised in France, but I'm an American citizen, and I spent most of my life in the U.S. I think of myself as being part French, part American, part German, part Japanese. Because when I'm in a country, I tend to immerse myself in the culture; I don't stay aloof from it. TROND: Well, I'm curious about that because in the abstract... so if we are in the world of math, then you could maybe say that efficiency techniques are global; that was the idea. Some people have that idea, let's say, that efficiency is a global thing, and there's one thing called efficiency, and everybody should just learn it because then it's all better. It seems to me that because you spent a lot of time in three different places, it shows up differently. MICHEL: I don't use the word efficiency so much because it's limited. There are techniques to improve manufacturing performance in every aspect of it, efficiency only being one of them, and these techniques are pretty universal. Now, when you're trying to help people in different countries, it's a postulate. You have to postulate what works in one place will work in another. So far, I haven't found any reason to believe otherwise. I have encountered many people who are saying things like, "This is country X, and these techniques don't work because our people are from country X." It's one of the most common techniques to refuse to implement anything new. The fact is the Toyota Production System wasn't supposed to be applicable to American workers until Toyota applied it with American workers in its joint venture with GM in the early 1980s at NUMMI specifically. It became a showcase. Later, Toyota opened its own factory in the U.S. in Georgetown, Kentucky, and applied the system there. And then, a few years later, it opened its own factory in France, and it worked with French workers. So it's really the idea that this only works in certain cultures or this only works in Japan. It's just the reality is different. It works pretty much everywhere. TROND: Well, that's fascinating, though, because, like you said, you have immersed yourself in these different factory and industrial cultures, if you may, and you are implementing lean in all of them or advising on lean methods. Why don't we start with that, then, perhaps? Tell me a little bit, what is lean to you? MICHEL: Lean to me...and I use the term less and less because I think over the past 30 years, it's lost a lot of its meaning. When it first came out, it was the latest in a number of labels that have been applied to the same thing. In the early 1980s, you talked about just-in-time then there was world-class manufacturing. A number of different terms were used and never really caught on. This one caught on. And the way I took it, I took it to mean generic versions of the Toyota Production System. There are very good reasons why you can't call what you're proposing to a company that makes frozen foods a Toyota Production System. There are also very strong reasons why you can't even go to a car company and do this. It's very awkward for a car company to openly admit to be using a competitor's system. So you have to have a label that refers to the content but doesn't refer to where it's coming from. TROND: So for you, at the basic level, if you strip away everything, it still is essentially the Toyota Production System, and lean is just to you, I'm just paraphrasing, it's a convenient wrapping for a way to explain it in a way that's non-threatening. But it is essentially the lessons from the Toyota Production System from a while back. MICHEL: That's the way I took it. That's why I adopted this label in the early 1990s, but a lot of time has elapsed since then. Because it became popular, very many people started using that label. And the content they were putting under it was pretty much...they were attaching this label to whatever they were doing. It has lost a great deal of its meaning which is why at this point, I rarely refer to it. TROND: So you're saying a lot of people are attaching lean to whatever they're doing, I mean, understandably so, Michel, right? Because it's become a very successful term. It sells books. It sells consulting. It does refer back to something that you think is real. So can you understand why people would do this if you are in consulting, or even in teaching, or you work in an industry, and you're managing something, why people would resort to this label? MICHEL: First of all, consultants have to have a brand name for what they're selling. It was useful. As a brand name, you have to call what you're offering by a given name, and clients look for this. It's a keyword they look for, and that's how they find you. So it's really necessary. I'm not criticizing consultants for using that. TROND: No, no, I understand it. And, I mean, you're also a little bit in a glass box in the sense that you are within the general tent of lean yourself. So I understand that. I fully understand it. MICHEL: What happens when it's successful is that more and more people jump on this bandwagon and say, okay, I'm going to offer a lean. When you look at what they're saying, it does not reflect the original content. By about 2000s, it had evolved into...what most consultants were offering was drawing value stream maps and organizing Kaizen events. Those two keywords are absent from the Toyota Production System. TROND: Can you explain...so this is interesting. Because I was going to ask you exactly this, what are the types of elements that you react to the most that you feel is really...because one thing is to say it diverged from the original content, but if it is kind of a valuable extension of something...but you're saying value streams and the Kaizens, the Kaizen practices they have very little to do with the Toyota Production System in your reading. MICHEL: That's right. The value stream mapping is a new name for a technique that they call; I mean the translation of the original name is, Materials and Information Flow Analysis (MIFA), Mono to Joho no Nagare in Japanese, flow of materials and information. So that's one idea. And there is a particular graphic convention that has actually evolved from Toyota that became the value stream mapping graphic convention, but it never was in the Toyota context. Mike Rother's own admission (He wrote Learning to See, which promoted this technique.) said it was not an important topic at Toyota. It has some uses, but if you go on factory tours in Japan, you don't see a lot of value stream maps. And so it's been taken...it was a specific tool for a specific purpose like figuring out how to work with a particular supplier. And then it was made into this supposedly all-powerful analytical tool that is the first thing that you have to do when you go into a factory is map its value streams, so that's taking a very small part of what Toyota does and make it into this big thing. As for Kaizen Events, it's actually an American invention. It's something that came out of...in the early 1990s; there were a number of executives who were frustrated with the slow pace of lean implementation with other methods. So they came up with this format they called the Kaizen Blitz, that became the Kaizen events. It's also traced back to some Japanese consulting firms, which found this particular format as a convenient way to make good use of a trip from Japan to the U.S. They would organize one-week events at their clients because it was a good way to justify essentially the cost and the trouble of flying over. TROND: I'm going to go with your story here. So let's say these two are kind of examples for you of things diverting from the original content. Why don't we speak about what the original content then is for a minute? What is the core of the Toyota production method or of lean in its original form for you? MICHEL: Well, the Toyota Production System is something I'm very interested in and still studying. And it's not a static thing. It's something that, for example, the first publication about it was from the early 1970s, an internal document from Toyota with its suppliers. And then there have been many, many other publications about it through the decades. And it's changed in nature, and the concepts of manufacturing have evolved. By definition, the Toyota Production System is what Toyota does. They're very good at making cars. And so it's always important to try to keep up with what it is they're doing, knowing that there is a 5 to 10-year gap between the time they come up with new concepts and the time that the rest of the world gets to know about them. And so, in the early 1990s, there were essentially concepts of how to organize production lines, how to lay out production lines, how to design operator workstations. And there were concepts on how to regulate and manage the flow of materials and the flow of information between stations and lines and between suppliers and customers. And there was also an approach to the management of people and the whole human resource management aspect of hiring people for careers, having career plans for everybody, including shop floor operators, managing to improve the operations based on this infrastructure. So it's a very rich concept, and it encompasses every aspect of manufacturing, logistics, and production control, all the way to accountability. So it's compared with other things like the Theory of Constraints or TPM that are much more limited in scope. There is an approach to quality that Toyota has. The quality improvement is not all of the Toyota Production System. It's a complete system for making a product covering all the bases. TROND: Let me just pick up on one thing, so you're saying it's a complete system. So one thing you pointed out was the HR aspect, and hiring people for careers is one thing, but you also said the career plans for shop floor operators. So I took two things from that, and I was going to ask about this because this has been used as one example of why you cannot implement the Toyota Production System in the same way in different countries, namely because that is one aspect of society that a company doesn't fully control because it is regulated. So, for example, in Europe and in France, which you know, really well, and Germany, you know, employment is regulated in a different way. If a company was going to have the same HR policy in three different factories in three different countries, they would have to have, first of all, obviously, follow the national regulation. But then they would have to add things on top of that that would, you know, specific employee protections that are perhaps not part, for example, of U.S. work culture. So that's one thing I wanted to kind of point to. But the other thing is interesting. So you said career plans for shop floor operators meaning Toyota has a plan for even the basic level worker meaning the operators, the people who are on the floor. And that seems to me a little bit distinct. Because in the modern workplace, it is at least commonly thought that you spend more time both training and caring about people who are making career progression. And you don't always start at the bottom. You sort of hope that the smart people or whatever, the people who are doing the best job, are starting to advance, and then you invest in those people. But you're saying...is there something here in the Toyota Production System that cares about everybody? MICHEL: Yes. But let me be clear about something. The way Toyota manages HR is not something that there are a lot of publications about. There's probably a good reason for this is because they probably consider it to be their crown jewel, and they're not that keen to everybody knowing about it. A lot of the publications about it are quite old. But there's nothing in the regulations and labor laws of any country that prevent you from doing more for your employees than you're required to. TROND: That's a great point. That's a great point. MICHEL: So there are laws that forbid you from doing less than certain things, but they're not laws that prevent you from doing more. There is no rule that you have to offer career plans for production operators because there's nothing preventing you from doing it. In a completely different situation, a large company making personal products ranging from soap to frozen foods...I won't name what the company is, but they have a policy of not being committed to their workers. Essentially, if business is good, you hire people. If there's a downturn, you lay people off. They wanted to migrate from the situation where you have a lot of low-skilled employees that are essentially temps to a situation where they have higher level of qualification and fewer people. So the question is, how do you manage the transition? The way this company eventually did it in this particular plant was to define a new category of employee like, say, technical operator. And a technical operator will be recruited at higher a level of education than the general population of operators. They will be given more training in both hard skills and soft skills and the specific processes they're going to be running, and some additional training on how to manage the quality of these processes, that sort of thing. But at the level of a production operator, they will be put in charge of these processes. And this small group would be separate job categories than the others. And gradually, this evolves to a situation where you only hire into this group. You don't hire any more of the traditional operators. And then, you provide a transition path for the other operators to become members of that group so that over a period of time, gradually, the general population of less skilled, less stable operator shrinks. And you end up over a number of years with a situation where all of the operators that you have are these highly trained operators who are there for the duration. So that's one kind of pattern on how you can manage this kind of transition. TROND: Super interesting. Can I ask you a basic question? So you've been in this consulting part of this venture, you know, of this world for a long time. Where do you typically start? When do you get called, or when do you sign up to help a company, at what stage? What sort of challenge do they have? Do you visit them and tell them they do have a challenge? What is the typical problem a company might have that you can help with or that you choose to help with? MICHEL: There are a lot of different situations. One particular case was a company in defense electronics in the U.S. had a facility in Indiana, and they were migrating all this work to a new facility in Florida. What they told me...they called me in, and they told me that they wanted to take the opportunity of this move to change the way they were doing production. Generally, my answer to that would be, well, it's really difficult to combine a geographical change of facility with an improvement in the way you do the work. Normally, you improve first where you are. You don't try to combine transformation and migration. TROND: It's a funny thing, I would say. It seems like the opposite of what you should be doing to try to make one change at a time. MICHEL: But there were several circumstances that made it work. You can have general principles, and when you're in a real situation, it doesn't always apply. One is the circumstances under which they were doing this migration was such that the people in the old plant were in an environment where there was a labor shortage, so none of them had any problem finding jobs elsewhere if they didn't want to move to Florida. If they wanted to move to Florida, they could, if they didn't want to move to Florida, they had to leave the company, but there were plenty of other companies hiring around. And so there was not this kind of tension due to people losing their jobs and not having an alternative. And then, the transition was announced way ahead of time, so they had something like a 15-month period to plan for their transfer. And to my great surprise, the operators in the old plan were perfectly...were very helpful in figuring out the design for the new lines and contributed ideas. And there was no resentment of that situation. TROND: In this particular example and in other examples, to what extent is production, you know, process redesign a technology challenge, and to what extent is it a human workforce challenge? Or do you not separate the two? MICHEL: I try not to separate the two because you really have to consider them jointly. A technical solution that nobody wants to apply is not going to be helpful. And something everybody wants to apply but that doesn't work, is not going to be helpful either. So you have to consider both. And in this transition, by the way, between these two plants, most of the labor difficulties were in the new plant, not in the old one, because this plant became a section of the new plant. And none of the other lines in that new plant did anything similar, so it stood out as being very different from what all the other lines did. What all the other lines did is you had a structure that is common in electronics assembly where you have rows of benches at which people sat and did one operation, and then the parts were moved in batches between these rows of benches. And instead of that, we put cells where the parts moved one at a time between different operations. And it was also organized so that it could be expanded from the current volume of work to higher volume of work. And so a lot more went into the design. I was a consultant there, but I don't claim credit for the final design. It was the design of the people from the company. They actually got a prize within the company for having done something that was exceptionally good. And when I spoke with them a few years later, they had gone from having something like 20% of the space used for production in the new facility to having it completely full because they were able to expand this concept. 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: Michel, I know that you have a consulting life and a consulting hat, but you also have a teaching hat and a teaching passion. Why did you write this recent textbook which is coming out on Routledge this fall, I believe, with Torbjø Netland from ETH? It's an Introduction to Manufacturing but with a very specific kind of industrial engineering perspective. You told me when we talked earlier that there's a really specific reason why you wrote this textbook, and you have some very, I guess, strong views or worries about how manufacturing education, but perhaps the way it's taught really needs to change. And you feel like some schools are drifting away from the core. What's happening there? MICHEL: Well, industrial engineering as a discipline is about 100 years old, take or leave a decade or two. It started out as...the way I describe it is the engineering of human work in the manufacturing environment. And it expanded to fields other than manufacturing, even at the time of pioneers like Frank and Lillian Gilbreth. For example, we know the way operating rooms in hospitals work with the surgeon being assisted by nurses who hand all the tools to the surgeon; that particular form of organization is due to Frank and Lillian Gilbreth, industrial engineers who looked at the way operating rooms worked and figured that you really don't want to leave a patient with his belly open on the table while the surgeon goes to fetch the tool. You got to have some people giving the tools to the surgeon so that the surgeon can keep operating on the patient. It sounds obvious now, but it wasn't obvious in 1910. And so they were immediately some applications outside of manufacturing, but the bulk of the work was on manufacturing. And the way it's evolved, especially in the past few decades, is that it's gotten away from that focus on human work. And when you look at the research interests of the academics in this field, you find that it's completely dominated by operations research and math. TROND: So we're back to the math. [chuckles] So I find it fascinating that...well, you obviously have a deep insight into it, so you are sensitized to the challenges of overfocusing on one technical discipline as kind of the mantra and the fodder, I guess, the research data for all kinds of processes. I mean, why is math such a big problem, and what do you mean by human work in industrial manufacturing? Because to many people, the advanced work right now is about digitization, digitalization, and it has to do with machines and computers, and one would assume with big data or at least with data. Are you arguing against that trend? MICHEL: No. I mean, if you ask the question of what is human work? The classical answer that I would give is what happens when the guy picks up the wrench. That's one answer. But what happens when the operator sees an alarm message on the control screen of a machine, that's a different answer, a more modern answer. So you had people with the torque wrench applying the right torque to a bolt manually, and then the torque wrench would tell him when the torque was achieved. That's one form of human work. But monitoring and looking after multiple machines that are connected and have a central control system is also human work. You also have people doing it. And they have to feed these machines. They have to make sure that the machines have the right kinds of tools and dyes available to them. They have to maintain these machines. They have to program these machines, and they have to monitor them during production. And one particular problem with automatic systems is micro stoppages. Are you familiar with that term? TROND: Well, explain it to all of us, micro stoppages. I mean stoppages, obviously, anything that stops the production line, whether it's a minor, major, I mean, that would be what I think you are saying. MICHEL: Well, if it's a big problem, the operator doesn't solve it. The operator calls maintenance, and maintenance sends somebody to solve it. Micro stoppage is a problem that's small enough for the operator to deal with. And so, in daily life or in any office life, one very common micro stoppage problem is the copier, right? You tell the copier to print 20 collated copies of a document, and you walk away expecting to find these 20 copies ready when you come back. It doesn't happen because there are some paper jams and so you have to clear the paper jam and restart. You have a lot of things like that in production where parts jam and shoots and stop coming down in automatic system. You have all sorts of issues like this which cause production lines to stop in a way that the operator can resolve in half a minute or a minute and restart. What these things cause is that you have to have an operator there. And so if you really want to have an automatic system that are fire and forget...when you press a button, you move away to do something else while the machine goes through an automatic cycle. When that automatic cycle is finished, you come back. Micro stoppages prevent you from doing that. And they're very difficult to avoid, but they're a major problem, even today. TROND: Michel, I wanted to keep talking about the educational part. But before that, I just wanted to benefit from your experience here and ask you a much more basic question which is so you're writing this textbook about the future or introducing prospective students to industrial engineering and manufacturing. My question is, historically, factories were a very, very big part of manufacturing. Nowadays, meaning in the last few years after the pandemic and other things, a lot of us start to spend a lot more time on an issue, which I'm assuming you have spent a lifetime working on as well, which is supply chain which goes far beyond the factory because it's not located in any one factory, if anything, it's a system of many factories, and it's obviously the supplies of material flows into the factory. And the reason I'm asking you about this is in thinking about the future, which I'll ask you about in a second, a lot of people are sort of factory of the future, this and that. And there are visions about how this is going to change. But it strikes me that manufacturing is and has always been so much more than the factory. What are the components that you really worry about? So, humans, you worry about humans. And you worry about materials. And then you obviously have to worry about the physical infrastructures that are regulating these things. What else goes into it on the macro level? What is this book about, I guess? MICHEL: We're talking about supply chains as well because, as you mentioned, they're a very important part of manufacturing. And when you design a manufacturing system to make a product, you have to make decisions about your products, about components of your product, and what you make in-house, and what you buy from the outside. And there's a major difference between supply chain issues relating to customers, on one hand, the suppliers on the other. It's not just suppliers; it's both sides, incoming supply chain and the outgoing as well. One major difference with what happens in the factory is that you don't control what other people decide, what other organizations decide. So when you manage a supply chain, you have to manage a network of organizations that are independent businesses. How do you get this network of independent businesses to work with you, to cooperate with you, to make your manufacturing successful? That is a big challenge in supply chain management. Inside a factory, that's an environment you control. It's your organization. What management says is supposed to go; it doesn't always, but it's supposed to go. And you have a lot more control over what happens inside than over what happens in the supply chain. And how much control you have over what happens in the supply chain depends greatly on your size. For example, if you're a small customer of a special kind of alloy that only has one manufacturer in the world, you're a very small customer to a very large manufacturer, a metals company. You're not in a position of strength to get that supplier to work with you. If you're a car company making 10 million cars a year and you're dealing with a company that is making forgings for engine parts, you have a lot of control. You have a lot of influence. You represent a large part of their business. They can't afford to lose you. You can't afford to lose them. You can replace them if they don't perform. They can't afford to lose you. They might go out of business if they did. So it's a very different kind of position to be in. And so when you deal with that sort of thing, you have to think through, what is my position with respect to suppliers and customers? Where is it? Where's the driving influence? And it's not always...power in a supply chain is not always resident with the company that does the final assembly of consumer products. In electronics, for example, semiconductor manufacturers are much more key than people who assemble computers. TROND: I wanted to ask you a little bit about the trends and how these things are evolving in the next decade and beyond that. And one example you gave me earlier when we talked was pilots and jetliners because manufacturing in...well, the aviation industry is an example of an industry that, yes, it has an enormous amount of high tech. It's a very advanced science-based development that has produced air travel. But yet these pilots...and I experienced it this summer, a pilot strike stops everything. So the role of people changes as we move into more advanced manufacturing. But people don't always disappear. What do you see as the biggest challenge of manufacturing and the role of manufacturing in the emerging society? What is going to happen here? MICHEL: What I think is going to happen is that in many countries, the manufacturing sector will remain a large part of the economy, but as economies advance, it will have a shrinking share of the labor market. So it's a distant future, maybe like that of agriculture, where 2% of the population does the work necessary to feed everybody else. And manufacturing is now about 10% of GDP in the U.S., 20% in Germany and Japan, about 10% in England, France, Italy. In China, we don't really know because they don't separate manufacturing from industry. And industry is a broader category that includes mining, and it includes road construction, et cetera. They don't separate out manufacturing, but really, it's a big sector of the economy. And so it can remain a big sector, that's not a problem. But you have to think through a transition where the number of people that you employ doing this kind of work goes down, their level of qualifications go up, and the nature of the work they do evolves towards telling machines what to do and maintaining machines. So telling machines what to do can be programming machines when you develop processes, or it can be scheduling what work the machines do. TROND: Is that incidentally why you have gone into teaching in a kind of an academic setting or at least influencing curriculum in an academic setting so much that you see a role here in the future? Beyond what's happening in factories today, you're quite concerned about what might happen in factories ten years from now, 20 years from now when these students become, I guess, managers, right? Because that's what happens if you get education in management at a good school, reading your hopefully great textbook. It takes a little time because you trickle down and become a manager and a leader in industry. So I guess my question then is, what is it that you want these people to know ten years from now when they become leaders? What sort of manufacturing processes should they foster? It is something where humans still matter for sure, and machines will have a bigger part of it. But there's things we need to do differently, you think? MICHEL: The airline pilot metaphor, you know, you have this $300 million piece of equipment. And how much money you make from operating it depends on these two people who are in the pilot's cabin. You have to pay attention to the work of people. And in most factories, the work of people today is an afterthought. So you put in machines. You put in production lines without thinking how will people get from the entrance of the building to where they actually work? TROND: I was going to say it's a fascinating example you had with the airline industry in the sense that, I mean, honestly, even in the old industrial revolution, these machines were expensive, but I guess even more so. I don't know if you've done any research on this, but the amount of dollars invested per worker presumably has to go up in this future you are talking about here where we're increasingly monitoring machines, even these perhaps in the past viewed as low-skilled jobs or operator jobs. I mean, you are operating, maybe not airplanes, but you're operating industrial 3D printers that cost hundreds of thousands of dollars with presuming error rates that could be catastrophic, either for you, for the production line, or for the product you're making. MICHEL: Or photolithography machines that cost millions. TROND: Right. But then that begs the question for me, Michel, how on earth is it possible? If you are right about this that education has been somewhat neglected and skills has been neglected, how's that even explainable? If you are a responsible factory manager or executive of a large manufacturing firm, how could it have gotten...and I'm obviously paraphrasing here. I don't know if you think it's that bad. But how could it get this bad that you actually had to come out and say it's a massive problem? MICHEL: What happens is that you hear a lot about systems thinking, which, to me, it's pretty obvious there's more to a factory or more to a manufacturing system, to supply chain than the collection of its components; it's pretty obvious. And when you change the way a supplier delivers parts, it has an impact over what happens at the assembly workstations where these components are being used, for example. You have to think of the whole as a system. And you have to think about whenever you make any changes to it; you have to think through how these changes affect the whole. What's happening is that there has been a great deal of specialization of skills; I'm not talking about factory workers here. I'm talking about engineers and managers that have been put into silos where they run production control. They become production control manager in the factory. Their next career move is to become production control manager in the factory of a different company. TROND: So here's my open-ended question to you; you're sort of saying that industrial engineering, in one sense, needs to go back to its roots where it was. But the other side of the coin here is you're also talking about a world that's changing drastically. So my question is, the industrial engineer of the future, what kind of a person is this ideally, and what sort of skill sets and what sort of awareness does this person have? MICHEL: The skill sets that this person should have are both technical and managerial. It's management and technology considered together. So they may not be able to write code, or they may not be able to design how to cut a piece of metal, or how to tweak the electrical properties of a circuit, but they know the importance of these things. They've been exposed to them through their education and career. And they have an appreciation for what they are. So, for example, one particular task that has to be done in every manufacturing organization is technical data management. You have to manage the problem definition, the process definitions, which machines you use to do what, down to the process program that these machines run. All of this is data, technical data that has to be managed, put under revision control. And you'd expect someone with training in industrial engineering to understand the importance of revision control on this. If you change something to the cutting program of a milling machine, you may affect what happens elsewhere. You may affect the mechanical properties of the product and make it difficult to do a subsequent operation later. And that's why before you implement this change in production, you have to have a vetting process that results in revision management. So I would expect an industrial engineer to understand that. TROND: Well, you would expect an industrial engineer to understand that, but, I mean, some of the challenges that come from these observations that you're making here they impact all operators, not just engineers. And they certainly impact managers because they are about this whole system that you are explaining. So it sounds to me that you're mounting a pretty significant challenge to the future manufacturers, not just in skills development but in evolving the entire industrial system. Because if we're going to make this wonderful spacecraft, and solve the environmental crisis, and build these new, wonderful machines that everybody expects that are going to come churning out every decade, we certainly need an upskilled workforce, but we need a whole system that works differently, don't we? MICHEL: Yes. Can I give you a couple of examples? TROND: Yeah. MICHEL: One company outsourced the production of a particular component to a supplier then there were technical problems with actually producing this component with the supplier. So the customer company sent a couple of engineers to the supplier, and they found some problems with the drawing that had been provided to the supplier. And they made manual corrections to the drawings, the copies of the drawing in possession of the supplier. And it worked. It solved the immediate problem. But then, at the customer company, they didn't have the exact drawing. The only place with the exact drawings was at the suppliers. And a few years later, they wanted to terminate this supplier. TROND: Aha. MICHEL: You can see the situation. You want people to be able to understand that you just don't do that sort of thing. TROND: Right. So there are so many kinds of multiple dependencies that start to develop in a manufacturing production line, yeah. MICHEL: And then you find a company that's a subcontractor to the aircraft industry. And you find out they route parts through a process that has about 15 different operations. And the way they route these parts is they print a traveler that is 50 pages long, and it's on paper. And the measurements they make on the parts that they're required to make by their customer they actually record by hand on this paper. What's wrong with this picture? TROND: So yeah, multiple challenges here. MICHEL: Yes. TROND: Are you sensing that these things are fixable? Are you optimistic in terms of this awareness of all aspects of the systems changing both among managers and next-generation industrial engineers, and perhaps even among the operators themselves to realize they're getting a more and more central role in the production system? MICHEL: I won't try to prophesy what will happen to industry as a whole but what I'm confident about is that the companies that know how to address these problems will be dominant. Those are the sort of basic mistakes that really hurt you and hurt your competitive position. So there will be a selection over time that will eliminate people who do these kinds of mistakes. TROND: Michel, I don't want to put you on the spot here. And you have spent your career researching and tracking Toyota as an excellent, excellent manufacturer that has graciously taught other manufacturers a lot. And also, people have copied and tried to teach them Toyota methods, even if Toyota wasn't trying to teach everyone. Are there any other either individual companies or things that you would point to for the eager learner who is trying to stay on top of these things? I mean, so lean, obviously, and the Toyota Production System is still a reference point. But are there any other sources that in your career or as you're looking at the future where there is something to learn here? MICHEL: Oh yes. Toyota is a great source of information, but it's by far...it's not the only one. One of the key parts of Toyota's management system is Hoshin Planning. Hoshin Planning didn't come from Toyota; it came from Bridgestone tires. And so that's one case where a different company came up with a particular method. Honda is a remarkable company as well, so there are things to learn from Honda. HP was, under the leadership of its founders, a remarkable company. And they had their own way of doing things which they called The HP Way. Companies have recruited a lot of people...electronic companies have recruited a lot of people out of HP. And you feel when you meet the old timers who have experienced The HP Way, they feel nostalgia for it. And there were a lot of good things in The HP Way. They're worth learning about. So I also believe that it's worth learning about historical examples because history is still with us in a lot of ways. The Ford Model T plant of 100 years ago was a model for a lot of things at the time. It also had some pretty serious flaws, namely, its flexibility. And you still see people putting up the modern-day equivalent of a Model T plant with new products and new technology but without thinking about the need. That particular plant may have to be converted in the not-too-distant future into making a different product. So it's always worth looking at examples from 100 years ago, even today, not for the sake of history but because, in a lot of ways, history is still with us. TROND: Well, on that note, history is still with us; I thank you for this, Michel. And I shall remember to forget the right things, right? So history is still with us, but [laughs] you got to know what to remember and what to forget. Thank you so much. MICHEL: Culture is what remains once you've forgotten everything. TROND: [laughs] On that note, Michel, thank you so much for your time here and for sharing from your remarkable journey. Thank you. MICHEL: You're welcome. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Lean Manufacturing. Our guest was Michel Baudin, author, and owner of The Takt Times Group. In this conversation, we talked about how industrial engineering equals the engineering of human work and why manufacturing and industrial engineering education needs to change because it has drifted away from industrial work. And indeed, we are looking at a future where manufacturing is not going away. My takeaway is that lean manufacturing might mean many things, but industrial work has largely been a consistent practice over several hundred years, which is not necessarily a bad thing. Having said that, if we want to go about improving it, we might want to stay pretty close to the workforce and not sit in statistics labs far removed from it. Efficiency is tied to work practices, and they cannot be optimized beyond what the workforce can handle or want to deal with. As we attempt to be lean, whatever we mean by that, we need to remember that work is a thoroughly human endeavor. 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 84 on The Evolution of Lean with Professor Torbjørn Netland from ETH Zürich. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us because we would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform connecting people, machines, devices, and 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, and you can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially where industrial tech is heading. 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: Michel Baudin.

Welcome to another episode of The Action and Ambition Podcast! Joining us today is Ryan Williams, the Founder, Executive Chairman, and Co-Chairman of the Global Investment Committee at Cadre, a groundbreaking commercial real estate investment platform that offers institutional and individual investors the opportunity to access expertly curated real estate opportunities. Cadre offers lower minimums, lower fees, and unprecedented potential for liquidity to its investors, institutions, and individuals. Cadre's data-driven and transparent investment platform opens participation in a historically opaque and illiquid asset class. Tune in to learn more!

David Gurle is a serial entrepreneur whose career has been a catalyst to positive change in secure collaboration and communication, from everywhere to Thomson Reuters, Skype, Microsoft, Perzo, and Symphony Communications. Today, he's the Founder and Executive Chairman of Hive, a distributed cloud computing and storage platform that disrupts the expensive, error-prone, and insecure centralized compute and storage paradigms. He shares personal stories of growing up in Beruit, Lebanon, amid a civil war, where he quickly learned the value of empathy and diversity.

Segment 1: Ilyce Glink, owner of Think Glink Media and Best Money Moves, joins Anna Davlantes in for John Williams to talk about the latest news involving cryptocurrency exchange FTX and what’s next for those who used the exchange and Illyce and Anna also chat about Executive Chairman of Amazon Jeffrey Bezos giving away millions of his own dollars […]

In Part 4 of “The Stage Is Being Set!” I continue to address some of the many factors that are giving credence to the reality that everything needed for the fulfilment of God's Prophetic Word of Truth to be fulfilled is being readied, giving certainty that we are in the final prophetic moments before the Rapture of God's True Church and the Tribulation Period which will follow that glorious event.Not only are we in the throes of the revisited “Days of Noah” Lord Jesus said would be one of the major signs of His Return, but the rebuilding of the “Tower of Babel” is almost complete, as the stage is being set for the entrance of the “Man of Lawlessness” to appear as the counterfeited “Savior” of the world and to rule the people of earth from the pinnacle of this global “Tower of Power.”Through the many technological innovations that have surpassed man's imagination, along with the spirit of deception which is stronger and more beguiling in this age than it has ever been, the people of earth are being led into the enemy's web of destruction like sheep being led to the slaughter, as they succumb to the lies of the agents of darkness, who like the Pied Piper, is seductively drawing them into the Antichrist's persuasions with their captivating sorceries.We are going to look in particular at two of these servants of Lucifer who are playing a vital role in the final preparation which is setting of the stage for the Antichrist who will be Lucifer's “Golden Boy” so to speak and his primary conduit, along with the False Prophet, to gain the worship of the world that he has always longed for.The first is Klaus Schwab, founder and Executive Chairman of the World Economic Forum, and the second is Yuval Noah Harari, who is his advisor and a major player in the WEF's Great Reset and Transhumanism goals.Support the showVisit our website: https://agapelightministries.com/

Brought To You by MrCreditRepair.biz Guests: Host of Ron Edwards American Experience - Ron Howard talks Midterm Election Results, Dr. Jennifer London - coauthor of "Leadership and Legacy, by the Executive Chairman of CACI" talks business and entrepreneurship

Millions of people across the country cast their ballots in the 2022 mid-term elections. While some votes are still being counted and certain races remain too close to call on the day after Election Day. On today's podcast, Lisa Kidder Hrobsky, AHA's senior vice president of federal relations, advocacy and political affairs, leads a discussion with two veteran political consultants – Dave Bockorny, Executive Chairman and CEO of the Bockorny Group, and Dave Leiter, President of Plurus Strategies. They examine key results of the House and Senate races (1:08), thoughts on the future 2024 election (16:57), and the mid-term election's impact on health care in America (24:14).

One thing the pandemic has taught is that we don't need to be in front of each other to get the job done . Well the same is true about getting sales for your business. Joining me now to discuss how to get sales over the phone better known as telemarketing we have Executive Chairman of TGL Group, Duane Lue- Fung https://kalilahreynolds.com/programmes/moneymoves Visit EXIM Bank's Business Advisory Service at: https://eximbankja.com IG: @eximbankja Giving you the tools to grow your business!#MoneyMovesJa- How to Get Sales Over the Phone --- Support this podcast: https://anchor.fm/kalilahrey/support

Find out how to build a learning culture with Ulrik Christensen, the Executive Chairman for the Area9 Group. Having spent the last 20 years pioneering advancements in adaptive learning, blended learning and computer simulation, Area9 Group was founded to develop the next generation of infrastructure in education.Discussions in the episode:The balance between personal development and business needsHow to spot a mindset of learning when hiring What does an effective board look like?The interconnectivity between a learning culture and retentionClick here to reach out to Peter Rabey direct Like this show? Please leave us a review. Every review helps. 

Dan and FirstMark Capital's Rick Heitzmann discuss how the results of the midterm elections may impact the markets and regulation (1:00), intensifying layoffs in the tech industry (3:45), Binance's shock move to buy FTX sparking new fears and volatility in crypto (11:00), why Rick is bullish on “pick-and-shovel” startups (17:15), Elon Musk's chaotic handling of Twitter so far (20:45), Tesla shareholders feeling the pinch from Musk's Twitter mess (23:15), and if it's the beginning of the end-game for the market slump (32:45).   Later, Dan talks with Jake Wood, Groundswell's founder and CEO & Team Rubicon's co-founder and Executive Chairman, along with Joe Marchese, Build Partner at Human Ventures, about what drove Jake to enlist in the Marine Corps (41:00), how 2010's devastating earthquake in Haiti led to the creation of Team Rubicon (45:30), the importance of giving veterans purpose after military service (48:30), starting Groundswell and the growth of Philanthropy-as-a-Service (57:30), the “founder's journey” during the COVID-era (1:09:00), navigating privacy protections in philanthropy (1:11:30), and how inflation and recession fears are impacting philanthropy and charitable giving (1:16:00). ---- Check out Groundswell's work Make a donation to Team Rubicon ---- View our show notes and transcript here ---- Email us at contact@riskreversal.com with any feedback, suggestions, or questions for us to answer on the pod and follow us @OkayComputerPod. We're on social: Follow Dan Nathan @RiskReversal on Twitter Follow @GuyAdami on Twitter Follow us on Instagram @RiskReversalMedia Subscribe to our YouTube page

Dan and FirstMark Capital's Rick Heitzmann discuss how the results of the midterm elections may impact the markets and regulation (1:00), intensifying layoffs in the tech industry (3:45), Binance's shock move to buy FTX sparking new fears and volatility in crypto (11:00), why Rick is bullish on “pick-and-shovel” startups (17:15), Elon Musk's chaotic handling of Twitter so far (20:45), Tesla shareholders feeling the pinch from Musk's Twitter mess (23:15), and if it's the beginning of the end-game for the market slump (32:45).   Later, Dan talks with Jake Wood, Groundswell's founder and CEO & Team Rubicon's co-founder and Executive Chairman, along with Joe Marchese, Build Partner at Human Ventures, about what drove Jake to enlist in the Marine Corps (41:00), how 2010's devastating earthquake in Haiti led to the creation of Team Rubicon (45:30), the importance of giving veterans purpose after military service (48:30), starting Groundswell and the growth of Philanthropy-as-a-Service (57:30), the “founder's journey” during the COVID-era (1:09:00), navigating privacy protections in philanthropy (1:11:30), and how inflation and recession fears are impacting philanthropy and charitable giving (1:16:00). ---- Check out Groundswell's work Make a donation to Team Rubicon ---- View our show notes and transcript here ---- Email us at contact@riskreversal.com with any feedback, suggestions, or questions for us to answer on the pod and follow us @OkayComputerPod. We're on social: Follow Dan Nathan @RiskReversal on Twitter Follow @GuyAdami on Twitter Follow us on Instagram @RiskReversalMedia Subscribe to our YouTube page

Andy Anderson, DVM, MBA, Executive Chairman, CityVet, Inc. attended Texas A&M University earning a Bachelor of Science Degree in Biomedical Science and a Doctor of Veterinary Medicine. He attended Harvard University and received a Master of Business Administration Degree. Anderson worked in the corporate finance industry and private equity for eight years at firms including Donaldson, Lufkin & Jenrette and Goldman Sachs. Dr. Anderson served on the Board and Executive Committee of the American College of Veterinary Surgeons and was the organization's Treasurer from 2009-2015. Anderson co-founded multiple veterinary related entities. These entities were merged into BluePearl Veterinary Partners which now operates a network over 80 hospitals nationally. Anderson leads the controlling investment group in Goodside Health, a leading provider of clinic based and telehealth to pediatric patients. The Company's SchoolMed Division provides telehealth through school nurse's offices to districts representing over 1,000,000 Texas and Florida K-12 school children. Anderson is the Vice Chairman of Texas Biomedical Research Institute, one of the leading infectious disease focused research campuses in the nation. Dr. Anderson resides in San Antonio, Texas, with his wife Kim and two dogs, Gunny and Tillie. The Andersons attend Christ Episcopal Church.

Chris Lynch is the Executive Chairman, and CEO at AtScale, the industry leader in data federation and cloud transformation. Sometimes known as the Punk Rock VC, Chris is a VC investor, operator, advisor, and mentor to dozens of entrepreneurs and startups. He has been in the founding team of Arrow Point, Acopia Networks and Vertica.We talk about the influence of punk rock and the Sex Pistols on his approach to work, why connection is the key to success in any industry. Chris is also pushing for more transparency and disclosure in equity compensation in privately owned companies, especially in high tech and private equity/venture capital environments. So we talked about the changes needed, and most importantly, about what questions people should ask before accepting a job in a privately owned company where equity is a significant part of the compensation.KEY TAKEAWAYS[02:40] - Chris shares how he arrived at where he is today.[05:01] - How work, love, and punk rock influenced Chris' business career. [08:23] - From dreams of playing bass in a punk rock band to inventing a persona that helped him 'fake it until he made it.'[11:04] - Why connection and authenticity are the keys to success no matter what you are 'selling.'[13:10] - How accepting failure as part of the process ultimately leads to success.[16:21] - "Celebrating success is fine. But it doesn't make you more successful. Overcoming failure makes you more successful."[19:41] - Chris shares how his definition of success has evolved over his career. [24:43] - What are the traits of a good Venture Capitalist (VC)?[26:25] - Chris shares his answer to the 'trick question' that opened the door to him working with the Godfather of VC (and a top 5 IPO).[28:50] - Chris shares the secret to his success and his message for those he mentors.[30:18] - How to join Chris in his mission to create more transparency in the private equity market.[34:57] - The most important questions to ask before joining a startup or a privately owned venture that features equity compensation. [37:51] - Why employees need to negotiate equity compensation the same way they would for cash. [39:47] - How to find more information about Chris and the types of investments he is [41:28] - Crowdsourcing the power of music and the tech entrepreneur ecosystem to do good through Tech Tackles X.[44:13] - Why perfection is the enemy of progress.[46:05] - Chris shares a question for listeners to answer for a special opportunity to meet with him via ZOOM. Jean dot O'Neill at escale.com.[47:16] - Chis shares the VC jargon that leaves him empty.[50:34] - Why we all need to stop fighting amongst ourselves to connect with what's real.[53:04] - Chis shares his favorite meal and drink.[53:50] - Why Chris believes the Sex Pistols changed the world with just one record.Contact Dino at: dino@al4ep.com Websites:al4ep.com AtScale.comreverbadvisors.comTech Tackles Cancer / Tech Tackles X: techtacklesx.orgOther Christopher Lynch links:LinkedIn:

Guest Scott Ernst is an entrepreneur who's been at the intersection of market research, commerce and technology for three decades. He was building startups on Newbury Street in Boston long before this recent wave of VCs and startups began moving into the Back Bay area. After stints at multiple startups in the 1990s, Ernst was a founding management team member of Compete in the 2000s and helped the company grow to a $100 million revenue business resulting in the 2008 acquisition by MIllward Brown Digital/WPP's Kantar. In June 2022, he made headlines for his appointment as CEO of Boston-based Drift, a marketing technology trailblazer best known for single-handedly introducing conversational marketing to the market. Ernst joined his friend, company co-founder and long-time CEO, David Cancel, who stepped into the role of Executive Chairman. Ernst most recently served as CEO of Tubular Labs, a social video analytics company headquartered in San Francisco. Prior to that, he was the CEO of Macromill, a Tokyo-based global marketing research business, which he took through an IPO with an enterprise value of over $1 billion. In this episode, Ernst discusses the future of Drift, OG Boston tech, his time in Japan, taking a company from early stage to IPO, how San Francisco and Boston compare, the future of marketing and much more.

The Salvation Army of Metro Detroit recently received a $50,000 donation from Schostak Brothers & Company. The donation from the Michigan-based real estate company will help support the nonprofit's Bed & Bread Program which supplies the metro Detroit area with food and shelter every day of the year.   The Salvation Army's Bed & Bread trucks make 57 delivery stops throughout the city every day. In addition to serving meals, Bed & Bread trucks supply Detroiters with coats, blankets, gloves, and other personal items. And each year, the program provides shelter to more than 72,000 people.   Mark Schostak, Executive Chairman, said: “We're inspired by The Salvation Army's unwavering dedication to serving our community for more than 125 years. Their tireless efforts change and save lives. It is a privilege to support them in their mission to provide meals and shelter through their incredible Bed & Bread Program.”   For more information on The Salvation Army's Bed & Bread Program, please visit www.SAmetrodetroit.org.  

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 "How Academia Shapes Manufacturing". Our guest is John Hart (https://www.linkedin.com/in/ajhart/), Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT. In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 92 on 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: There has never been a more interesting time to be in manufacturing or to watch manufacturing. The tremendous breakthroughs that we are about to witness have been made possible by a confluence of emerging technologies and startup innovations, as well as a growing awareness of the importance of building human-centric technologies. We are indeed at a crossroads with profound challenges in the growing talent shortage, the need for workforce training, an aging industrial base, and the demands for manufacturing competency from the wider innovation ecosystem. We have to make progress fast, and innovations are just maturing to be able to do so at the scale and pace required. It will, again, be amazing to watch the manufacturing industry. Parts of it will perhaps, again, become the industry of industries. 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. Technology is changing rapidly. What's next in the digital factory, and who is leading the change? And what are the skills to learn and how to stay up to date on manufacturing and industry 4.0. In this episode of the podcast, the topic is How Academia Shapes Manufacturing. Our guest is John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT. In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history. 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. John, how are you? Welcome. JOHN: I'm well, Trond. Great to see you. Thank you for having me. TROND: Well, I'm excited to have you talking about...well, hopefully, a lot of different things, but how academia gets to shape manufacturing, this fascinating venture that is manufacturing. But you yourself, John, you grew up in Michigan, is that right? You were close to this from an early age. JOHN: I was close to it. Yeah, I grew up in Royal Oak, Michigan, a suburb north of Detroit. If you know the Detroit Metro area, there are the mile roads, and the Detroit River is sort of Zero Mile. And I grew up between 14 and 15 Mile Roads, so in the hotbed of the good, old U.S. auto industry. TROND: Well, exactly. Because looking a little bit at your background here, you spent quite a few years as a summer intern at General Motors before you got yourself to...or actually perhaps in the beginning, in your undergrad years from UMichigan, is that right? JOHN: I did. After my first year at UofM, I worked as a summer intern at GM and went back a few years in a row in different roles in different areas. And honestly, when I decided to pursue a graduate degree and ended up at MIT, I thought I might just get my master's and go back and work in the auto industry, but things changed, and here we are today. TROND: Well, here we are today. You got yourself an undergrad from UMichigan. And you worked there for a little while, I believe, but then came to MIT with a master's, Ph.D. This is way back. But you won the prize for the best doctoral thesis in micro and nanotechnology. So that set you off on the path to rediscover nanomaterials, I guess. JOHN: Yeah, well, it's a really maybe exotic combination of topics. My master's thesis was on precision machine design, the design of these large mechanical couplings for industrial robots. And then, for my Ph.D., with the same advisor, I worked on carbon nanotube synthesis. But there you have the dipoles of manufacturing research, materials, processing, and mechanical design that have shaped how I've taken things forward since then. TROND: Well, but it is in these unique combinations that innovation starts to occur, right? JOHN: Yeah, exactly, combining different topics. And that's one reason I love manufacturing is that it is the union of materials processing, and automation, and software, and now also getting more interested in the organizational workforce aspects. It's a very rich, multidisciplinary layered topic. TROND: Yeah. And we'll explore this both from the organizational angle, and, indeed, I'm super interested in this material angle on things because it seems to me like you're exploring the very, very small nanostructures, but then you're then printing them on the very large canvas. So you're exploring materials from one extreme to the other. JOHN: Yeah. Well, it depends on your objective and what topic you're working on. There are cases in our research where we need to understand the formation of materials, not quite from the atom up but from the nanoscale or microscale up. And there are cases where we more or less abstract or coarse grain those link scales and focus on macroscale properties. TROND: Well, and then you also focus quite a bit on teaching. I noticed that you actually launched the first massive online course on manufacturing processes, and hopefully, we'll get to this a little bit as well. JOHN: Sure. TROND: But teaching and basically working on the next generation of manufacturers, whether they be the engineers or really anybody else, has certainly been one of the big challenges in manufacturing really forever. What is it that fascinates you so much about teaching this to a grander audience than the usual university audience? JOHN: Well, first, I'll say I believe that the top priority of universities, including in the area of manufacturing, is to educate future leaders and engineers. That said, the number of people we educate on our campus is a small fraction of those who could really benefit from what we teach and the way we teach. And that's not just geographically, but it's also in terms of their role in the workforce. So I believe manufacturing education should address all levels of the workforce. And to get at your question more directly, when I came to MIT, I was asked to take over our core undergraduate manufacturing class in the Department of Mechanical Engineering. And as I learned to teach the class for myself, I was intrigued by this emerging trend of digital learning, and this was 2015, 2016. And I was able to get some funding from MIT internally to create an online version of the course that would be offered free to the world, and probably 100,000 People have taken it so far. And it's been a great experience and evidence of how there is very broad interest in manufacturing really across the world. TROND: 100,000 people have taken this course. JOHN: Yeah. Well, I'll say 100,000 people have signed up for the course. This is the classic trade-off with online courses. It doesn't mean 100,000 people complete the course. It means that number signs up and hopefully took something away from it. It also speaks to the flexibility. You can sign up for a course and maybe just listen to one lecture, but if you take something valuable away from it, that's great. TROND: So I wanted to talk a little bit about how academia shapes manufacturing. And I know that there are, you know, you and I work at MIT, and you've had experiences obviously at University of Michigan. But there are other manufacturing centers and institutes all around the world. Could you lay out this landscape a little bit for us so that we get a sense of where the excellent centers of manufacturing are located? I mean, one structure, just to pick that, is manufacturing institutes, and I know that's sort of dear to your heart for a couple of different reasons that we'll get into. But what are some of the centers beyond MIT where there is activity that is organized in a way that really is something to focus on? JOHN: First, I think of in the U.S., Carnegie Mellon, Georgia Tech, Purdue, Michigan, Stanford, places that have defined manufacturing centers or have a body of work that relates to manufacturing that I would say there's a critical mass of faculty, and students, and affiliation with industry. Also, Penn State in the area of additive manufacturing and product design. It's hard to be comprehensive. I don't want to forget anyone big, but that's a sample of some of the notable ones. Internationally, a lot of activity in Europe; I admire the University of Cambridge, the Institute for Manufacturing there, where manufacturing is more or less a department, or it's within the Department of Engineering, which is analogous to what we would say is a school or college of engineering here in the U.S. And they have a broad set of activities that have been there for decades focused on manufacturing at the IFM. TROND: And if you think about the best schools to get educated in this topic, is it necessarily only the top brands? I mean, certainly, they have different roles. So when it comes to undergrads or even shorter, or I guess even community colleges have a really fundamental role in the formation of this sector, can you talk a little bit about that? JOHN: Oh, for sure. When you think of manufacturing education, we must think of the full stack of institutions that educate the workforce, from vocational institutions to community colleges where the student's goal may just be to complete a vocational program or complete a two-year degree and then exit the workforce, all the way to the four-year degrees, advanced degrees, and executive education. And given how manufacturing is paramount in the workforce and the economy, we need to educate folks at all those levels. But by far, the largest number of people are at those vocational community college levels and then to the bachelor's level. So I have a Ph.D. I love to mentor Ph.D. students. But that's a small fraction of the manufacturing workforce. TROND: What about in the U.S. setting? There's something called the Manufacturing USA, and there are these institutes that have sponsorship from various government agencies, most of them through the Department of Defense. But there's also a bunch at the Department of Energy and one, I guess, from the Department of Commerce. What is the role of basically government-sponsored sort of research and innovation activities in this field? It would strike me, I guess, that historically, it's quite important. JOHN: Certainly. You're alluding to the manufacturing innovation institutes, the MIIs that were started during President Obama's administration. Actually, MIT's work, the Production in the Innovation Economy study, and the Advanced Manufacturing Partnership, which emerged from that, was key in scoping the MIIs, and now there are 16 or so around the country. It's one example of public-private partnership. Public-private partnership is key to cultivating interest in manufacturing and also providing resources for technology translation and commercialization. I think the MIIs have had a great impact on awareness of manufacturing, on R&D, and really applied research in some critical technology areas. But it's only a small part of what we need to do to regrow and expand our industrial base in the U.S. TROND: So I want to move us shortly to MIT to discuss both your own research activity and how extensively you are now aiming to take a more organizing role to kind of get more out of all of the exciting work that's happening at MIT. But before that, I just spotted perhaps an older project of yours that I thought was extremely cool. You were once called a nanoartist, and you had this NanoArt Nanobliss gallery with visualizations. You previously mentioned Obama. I believe you made a NanoArt structure called Nanobama or something of that sort. How did this come about? And, again, I mean, I'm guessing this just sort of testifies to your interest in science communication as much as in the depths of science, which we'll get into in a moment. JOHN: You got it. The inspiration was how do we communicate what we're doing in the lab to broader audiences just to make them aware of what's happening in new technology, new materials? In that case, it was nanotechnology. If you don't mind, I'll tell you a bit more of the story. When I was an assistant professor at Michigan, we were doing a lot of work on carbon nanotube manufacturing, which was a follow-on from my graduate work at MIT. And I admired President Obama, or he was a presidential candidate at that time. And without implying a political inclination, I somehow put together the words nano and Obama in my mind. TROND: [laughs] JOHN: And I said, wow, it would be cool to have a Nanobama. So one thing led to another, and I actually worked with some students in my group to fabricate these little portraits out of carbon nanotubes representing Shepard Fairey's portrait of Obama that was used widely during that first presidential campaign. And I just posted it online, I think one day after the election, and it took off. It went viral, so to say, and was featured as Nature's Image of the Year. It was printed on the newspapers you used to get as you walk onto the subway in the morning around the world. There was a company that would syndicate this stuff, and they just sent it around. So it got a lot of attention. And it showed me the power of an image in communicating something. And, of course, President Obama, that was a historic election. The play on words was exciting, and also the fact that it was a little bit intriguing science and technology that was nano was interesting. And one more thing, a colleague of mine at Michigan then was working in the White House, and he said, "Hey, can you send us a Nanobama?" So I made this frame with a little piece of the real material, and a picture of it from the microscope sent it to Washington. I didn't hear anything about it until I got a call from the White House asking me to declare the value for the President's tax return because he decided to keep it; I kid you not. And then, after Obama left office, I was with my family at a bookstore in Wellesley, and I saw the book, the retrospective book of Pete Souza, the White House photographer. And I opened up the book, and I see a picture of Obama and John Boehner in the Oval Office in the middle of this book. And right on the doorframe is the Nanobama. So it actually made it to the White House, which was a pretty awesome feeling. TROND: It must be an awesome feeling, and, again, I think that, especially in this field of manufacturing which is so challenged at times, right? And people are talking about how these factories are greedy, or is this a great job, or whatnot. And there have been all of these historical moments. But then there is also this fascination around the topic of certainly of technologies and the excitement around it. Why don't we continue a little bit on this strand before we get into sort of the overall role of MIT? I'm really curious about how your research has evolved. So generally, I get that you're combining these nanostructures with manufacturing and materials research, and certainly, you have applied it to additive manufacturing. How would you say that your research has evolved over these years? What are the things that you have been doing? I've picked up on a few things that I definitely wanted to cover. I mean, certainly, you've been working on this industrialization of 3D printing, both as a research area and as a commercial area. Carbon nanotubes must have been kind of where you started. I'm curious where that work is going. And then I saw that very recently, with a student, you've been doing some work that I'm personally very enthused about, which is a plant-derived composite that might replace, hopefully, plastics with sort of a hardness and stiffness that is somewhere at the boundary between conventional plastics and metals. I mean, for me, I don't quite see how all of these things are intimately connected. Where do you go for, you know, where's my next proposal here, and where's my next patent? JOHN: They aren't necessarily closely connected. But I like to say that the themes are typically one or more of materials, manufacturing, and mechanical systems or automation. And what I love about manufacturing, especially in the materials domain, is to control a process, to understand a process, and then to do something new, you need to investigate its fundamentals. And sometimes, you need to design a new instrument or machine to get the job done. So our work is often problem-inspired or opportunity-inspired. Like, the cellulose work that you mentioned recently was actually sponsored by a large consumer products company interested in a more sustainable composite material that could be used in packaging. And we looked at potential routes to formulating different materials, and we landed on cellulose. And then, we developed a formulation, a mixture of cellulose nanocrystals and polymers that ended up having exciting mechanical properties, particularly very high hardness, and toughness, more so than existing polymers. And another unifying theme is scalability. It's important not to worry too much about scalability in the early stage of research, and there's lots of amazing research that's just for science. But we like to do things that we hope will be scalable one day, so choosing ingredients that would be cost-effective or using techniques that could be industrialized, even if the techniques look very different in the lab. And maybe I've lacked to give a precise definition or focus, but I think it's also indicative of the broad span of manufacturing. And manufacturing has many, many dimensions beyond the ones that we work on in my lab at MIT. TROND: Well, you kind of answered a question that I was going to ask, too, which is it doesn't seem like you start in a linear fashion, you know, in other words, you start with some sort of basic problem that everybody in their literature has established and then you move to this, that, or the other. Sometimes it comes from a company. The challenge comes from a company, but you formulate the solution completely. It seems to me that students also have lots of ideas and kind of formulate projects. Talk to me a little bit about this process of where the problem comes from versus where the solution and impact comes from because you seem to...sometimes the output truly is just, you know, like, in this case, art or a physical prototype, and you're sort of happy with that outcome. Other times, you're actually delivering something into, presumably, eventually, an assembly line. JOHN: Yeah. And we work as hard as we can on technology translation, both in terms of the knowledge that we publish but also in terms of the steps that we take to spin technology out. You're right; the early stage is very important. And I like to often see the early stage as a collaboration between myself and the researchers. And in many cases, the core idea we end up pursuing comes largely from the research or the research team. In many cases, it might be seeded by the interest of a sponsor or an idea I have, and then we work together on actually figuring out what's the approach, what are the outcomes, and what's the path to success. 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 have commercialized at least two ventures together with others at MIT and external people as well that I know about for sure. I wanted to just briefly mention both Desktop Metal and VulcanForms. Let's perhaps cover Desktop Metal first, so that's a 3D printing company. Tell me how that got started and what your role was there. JOHN: So I was very fortunate to be a member of the founding team of Desktop Metal. So there were seven co-founders, and we launched the company in early fall of 2015. And Ric Fulop, who's the lead founder and CEO, approached me at that time, and he heard that I was interested in working on 3D printing and, of course, knew a bit about my background in manufacturing and machine design and asked me to jump on board. And funny story, how just connections persist over the years; I actually knew Ric when I was a grad student because I was doing my carbon nanotube work using the space of now my colleague, Yet-Ming Chiang. And at that time, Yet and Ric were launching A123 Systems, a successful battery company. So that was a reason why I think Ric knew to get in touch with me when he heard about me. And serendipity was a great experience. TROND: Serendipity when you are in the right places, right? If you're hanging around Yet-Ming Chang, yeah, that's right, very special serendipity. Tell me a little bit about VulcanForms. Until very recently, you couldn't talk so much about it. Nowadays, you did go out in New York Times. I've read that piece. So there is a little bit more detail around it. Let me ask a very basic and perhaps dumb question, large-scale metal 3D printing, what's the big deal there? I thought didn't Desktop Metal do 3D printing? So it's kind of a dumb question. Why is there a second company? Is there really such a variety? I think that the regular person just thinks 3D printing is 3D printing. JOHN: 3D printing is a broad and deep subject. Like, first of all, 3D printing processes exist for polymers, for metals, for many other materials. And there are even several 3D printing technologies for metals. I'll tell the origin story for VulcanForms quickly if that's okay, and then get back to the question. So when I came to MIT as faculty in 2013, I had been a professor at Michigan for a few years. And I landed, and one of the topics I thought of looking into was 3D printing. I was actually asked by a colleague to teach a class not on 3D printing, but I was able to propose the topic. And in that class, there were many incredible students. One of them, named Martin, stuck around at MIT after finishing his master's in manufacturing, and we ended up comparing notes and launching VulcanForms in 2015, a little bit before Desktop Metal came to be, but not that long before. And we stayed quiet for seven years. We raised our seed round a couple of years ago. And the focus of the company is number one, laser-based metal additive manufacturing. And second, while we've built our own additive technology, we're a manufacturing company. So we produce parts at scale, and that is a real need and has been a barrier to growth of the additive industry. There's so much interest and uptake in additive. But the ability to achieve high-quality production using additive as the formative step in the process at scale has largely been untouched. So from the early days, we thought that we could approach the market with that plan to become a manufacturing company. TROND: Staying quiet for seven years that can't have been [laughs] particularly easy. JOHN: Yeah, it's not easy, but it's very, very worth it because we got to focus. And also, there are different boundary conditions that allow you to keep your head down and get work done, and one of them is having great and patient investors who believe in your approach and who see the progress behind the curtain. And as a result, we felt we would hold off launch. And we were fortunate to get picked up by the New York Times earlier this summer. And now we're excited to talk about what we do. TROND: Yeah, that article did hint a little bit at what your printers can print that others cannot and kind of at what scale. Can you give some examples of the kinds of things that you are now contracted to print or are perhaps already printing? JOHN: So the company is focused on a variety of industries, generally industries where high-value metal parts are difficult to manufacture and where there is a real pent-up need for more agile, high-value manufacturing medical devices such as medical implants, semiconductor components, not microchips but cooling devices for various computer systems. We have a lot of business in the aerospace and defense area, working with several of the defense primes, both on additive parts and on machining, honestly. The company, as described in the New York Times article, we acquired a machine shop in Newburyport, Massachusetts, earlier this year. And that was twofold, one because in order to deliver finished parts, you need to often integrate additive with machining. So it's not just 3D printing; it's building a stack of software and physical processes to create a finished part. Second, advanced machining is also a digital manufacturing technology, and as a company, we're very interested in applying our capabilities as a digital manufacturing organization to the area of CNC machining as well. TROND: So, taking that experience then from these two companies and your vast interest and research area plus your interest in communication, what is it that you're now focused on at MIT more largely? That's another kind of secret that's slowly being let out. But you have had this notion and have shared this with me and others, obviously. There was a seminar open to whoever was invited, I think, but not a full public launch. Manufacturing at MIT has historically been quite important, but you think that there's even more, to be done. You lined up a couple of the projects, but there are many more things that MIT has done. Could you maybe just briefly address the role of MIT historically in influencing manufacturing? And what else is it that you now want to accomplish? JOHN: Yeah, for sure. And since I came to MIT nine years ago, I've learned of the incredibly rich history that the institute has in manufacturing, both on the technology side, you know, in the mid-1950s, building among the first CNC machines, ultimately transformed commercial aviation in 1980 building one of the first 3D printers in the world, and so on. But not only that, but also, historic accomplishments in the social sciences, understanding the globalization of manufacturing, you know, what delineated the U.S. versus the Japanese auto industry in the 1980s. What is the intrinsic role of manufacturing in innovation, the production, and innovation economy led by my colleague Suzanne Berger in around 2010. And then broader than manufacturing, though, the work of the future study just a couple of years ago looking at the connection between technology and work. So looking at all those accomplishments and understanding the present moment that we're in, which I can also reflect on later, I've been exploring how to create a new presence for manufacturing at MIT. And the term manufacturing at MIT is more or less a placeholder representing the community of faculty and students across disciplines, both technology and social sciences, that touch on all the dimensions of manufacturing. So as we've returned from Zoom life to more in-person life, I've been making my way around campus and building a team of folks, faculty advisors, external advisors, industry partners, and so on to hopefully put forward a new center at MIT that has a focus on manufacturing across the disciplines. And this is not to replace existing activities but just to augment those activities and bring industry together with us to support research, to lean deeply into workforce training programs, to collaborate with public organizations at the state and federal level and internationally, and also hope to cultivate more entrepreneurship. Because my experience, fortunate experience as an entrepreneur over the past several years tells me that there's opportunity for more new companies that contribute to the future of manufacturing, whether they're manufacturing companies actually making stuff, whether they be software and services companies. Or perhaps the biggest need is hardware companies for whom manufacturing is a route to success. So you may not be manufacturing something yourself, or you may not be manufacturing goods for others, but understanding manufacturing and scaling a process is really key. And that intellectual DNA of manufacturing is more cross-disciplinary than ever. And I've observed over my nine years at MIT how there's just more engagement in manufacturing as a discipline, as this cross-disciplinary theme. And that's an area where I feel such a center can really play a role by adding something to the intellectual community across the institute. TROND: There are so many things that come to mind when you produce this narrative because, I guess, on the one hand, manufacturing is a little bit of everything. On the other hand, it is clearly very delineated because it's all about making things and making them at scale. And there's a whole industry, but, of course, every industry almost has a manufacturing arm. How do you delineate the subject of manufacturing? And I'm sort of curious, you know, at MIT, if you use a broad church definition, almost everybody there contributes to manufacturing. So that would be both a challenge and an opportunity, I guess. JOHN: Yeah, you're exactly right. So, first, within MIT, we have many collaborations with different departments and other research centers. And the nature of the collaboration depends on what the focus is. Second, when it comes to interfacing with industry, I've come to look at industry as kind of a grid where you could say the columns are the end users, say, aviation and space or consumer or construction. And then, the horizontal lines in the grid are technologies, robotics and automation, 3D printing, software and IT, et cetera. And getting a little bit in the weeds of the organization here, so first, we're working on launching a flagship industry consortium, or we're recruiting flagship industry partners for a new center. And those will be companies, world-leading manufacturing companies across the grid. Second, we will operate consortia in different technology in industry areas that may be located within our center that may be in collaboration with others around MIT to really drive focus. And when industry comes and interacts with us, I want them to understand how their business fits into the broader spectrum. And we find particularly in the work related to 3D printing that companies appreciate being connected with peers across the value chain. They say 3D printing is materials at the frontend and finished parts at the backend, and there are some machines and software, and so on. When you bring companies together across their value chain, across their supply chain, under the umbrella of an academic organization with this sort of problem-solving mindset, we find that that can be valuable to the companies that we partner with. TROND: And, John, there's obviously a scale at MIT that's hard to replicate for any university or school just because there are so many people involved in technical innovation. But on the other hand, I would say there has been a sense that other sectors if you could call them that, have always been moving much faster than manufacturing. And, you know, okay, fine, there are industrial revolutions, but the ones we talk about now as industrial revolutions are more, you know, they are maybe on the software side and stuff, but that the core of manufacturing it may be because of its inherent nature. It's complex; it's about physical infrastructure, at least a lot of it still. So it's hard to innovate in that sector. Would you say that one of the ambitions you have with this manufacturing at MIT initiative is to speed up that innovation? And if so, what are the mechanisms that would bring manufacturing as a whole, I guess, on an even faster sort of clip? JOHN: First, if I look within MIT, we see the opportunity to combine the physical side, the mechanical engineering, the material science, with the digital side, with software, and controls, and computation. And that's an area where it's clear that new technologies can be de-risked, can be scaled more quickly. And it really requires this symbiosis of the physical processes and the digital intelligence. Second, I think we can do better research. I can do better research by understanding where the big problems and opportunities are. And by connecting closely with industry, forming networks with various stakeholders, we can define better problems that we can ask our students to solve. And third, I've noticed, especially over the past year with all the geopolitical discussions and the imperative for sustainability, that we're at a time where there's this alignment between industry and government and the investment community and manufacturing, physical manufacturing, physical industry is vital. We can't do enough there to catch up, to grow. And I think that's a real opportune moment to recognize that while I think the pendulum has swung to the digital world and software over the past 10, 20 years, life has changed for the better in so many ways. We have to focus on the physical world now, especially to address the climate crisis, and also think of how we can improve economic equality across our communities, how we can provide better job opportunities, how we can deliver education to individuals who don't have the opportunity to go to university or don't have the resources to travel, all those things. So that's another reason why, one, I see manufacturing as this rich, cross-disciplinary topic that I can file a patent and write some exciting papers and graduate with a Ph.D., but it means so much more to feel technology at scale. And second, you need the intersection of these disciplines to understand not just technology but organizations and human dynamics to create change and create positive impact. TROND: So I realized that we're going to have to cover... there are so many other questions I have for you is what I'm trying to say here. But my last question in this round, I think, is going to be one on...we briefly mentioned, or you briefly talked about augmentation. And you know that I have a special interest; obviously, the topic of the podcast and the title is augmentation. So there is something here about the tension, perhaps between augmentation and automation. How do you see that tension or the relationship between working from the human-centric perspective that technologies are in service to perhaps augment people and processes versus this automation perspective which maybe takes, and I'm paraphrasing here, a little bit more of an efficiency approach and tries to go for machine scale first and then just adjust everything later? How do you see those two things now, as perhaps, you know, manufacturing is coming into another kind of growth moment? JOHN: If I understood you correctly, I don't think they're mutually exclusive, right? Certainly -- TROND: No. Not necessarily. Not necessarily. JOHN: Certainly, manufacturing will become more automated in places where automation makes sense. Certainly, automation is challenging to implement to scale, to get right. But in some cases, the driver to more efficient technology-first manufacturing is automation. In other cases, and hand in hand with that, human workers and businesses, organizations can only become more effective and efficient, working in synergy with data and automation. I'll use the example of someone overseeing a 3D printer, a state-of-the-art 3D printer, and watching the screens to make sure everything is going well and doing a better job by being presented with information that shows, hey, this might be a problem, or there are no problems here, but being empowered to make that data-driven decision. And also, from my work outside of MIT, we find that folks who do best operating that advanced equipment with digital data might have a machining background. They might also have a passion for gaming on the side. So they might be used to sensing and responding to dynamic digital events. And that's another comment on skills evolving in the workforce too. TROND: Well, I mean, one thing that is for certain is that if MIT gets its act together on manufacturing, things will happen. I trust that we're going to have to come back and talk about a lot of emerging projects here in the coming years if you get people lined up. So very exciting. Thank you for speaking to me. Is there sort of a challenge that you want out there to the community when it comes to how, you know, not just academics can contribute to shaping manufacturing but how we all should think of these manufacturing challenges? Is it something that we should leave to experts right now because it's so complicated? Or are there ways that the broader interested public can get engaged in this problem? Is it possible to engage, and where should one engage? JOHN: That's a great question. First, to the general public, I'd say stop and think about what manufacturing means to you, or find one of your favorite things and look up how it's manufactured. Imagine the life, the journey of the product as it comes to your door. And second, I'd say the area where most of us can make an impact is in education and learning and contributing to our communities. Perhaps if you're an engineer working somewhere, you might want to teach at a community college one night a week if you have time in a future semester or explore ways that you can bring new knowledge, new technology to your organization if it makes sense. TROND: Exciting challenges. Thank you so much for sharing a little bit of what you're up to with us, John. JOHN: Thank you, Trond. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was How Academia Shapes Manufacturing. Our guest was John Hart, Professor of Mechanical Engineering and Director at the Center for Advanced Production Technologies at MIT. In this conversation, we talk about John's research on micro and nanotechnology and material science, which universities and colleges that teach manufacturing, the role of MIT in this ecosystem, and why now is a key moment in manufacturing history. My takeaway is that there has never been a more interesting time to be in manufacturing or to watch manufacturing. The tremendous breakthroughs that we are about to witness have been made possible by a confluence of emerging technologies and startup innovations, as well as a growing awareness of the importance of building human-centric technologies. We are indeed at a crossroads with profound challenges in the growing talent shortage, the need for workforce training, an aging industrial base, and the demands for manufacturing competency from the wider innovation ecosystem. We have to make progress fast, and innovations are just maturing to be able to do so at the scale and pace required. It will, again, be amazing to watch the manufacturing industry. Parts of it will perhaps, again, become the industry of industries. 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 Epi