Connect with Renan Devillieres
Lisa Ryan: Hey, it's Lisa Ryan. Welcome to the Manufacturer's Network Podcast. Our guest today is Renan Devillieres. Renan is the visionary Founder and CEO of OSS Ventures, who is dedicated to shaping the future of the manufacturing sector through startups and innovative solutions. With substantial operations, SaaS, and venture capital background, Renan is an inspiring leader committed to driving sustainability and fostering adaptability in the ever-evolving industrial landscape. Renan, welcome to the show.
Renan Devillieres: Thank you for having me.
Lisa Ryan: Please share your background and what led you to do what you're doing with OSS Ventures.
Renan Devillieres: Sure. I'm 36, and I started my career as an operations guy. I was working in factories. I was a factory director and supply chain director. Then, I became the assistant director of Richmond Co., a luxury company. And I got to see the incredible world that produces physical things, and I loved it. And then, as I was always a geek, I started coding something in my bedroom and ended up creating a good tech company, leaving my job and ending up in San Francisco, setting my startup to Google, and having a lot of traction there.
I discovered the world of tech. And when I sold my company and my shares in the company, I told myself the World of Tech is just incredible. I love operations. Let's bring those two worlds together. And as I had a bit of money, I chose the investor slash venture builder path.
What we do at O US Ventures has been three years and a half. We either create or invest in startups technological startups that are exclusively helping the physical world, operations, retail, and those kinds of things. So we've been doing that for three years and a half. We are based in Paris, but we are all around the world. We created 15 companies. We live in a little over 1000 factories worldwide, and 350 people are working for all those awesome tech solutions for the manufacturing world.
Lisa Ryan: You started by saying that you were 36 years old, and I know that in manufacturing, changing the conversation to attract younger people to it. What originally made you consider manufacturing an option and something you became passionate about?
Renan Devillieres: It's a funny story. I come from a background where people need to learn what even a company is. And I was good at math. So I did the math, then opened Google, and I put a company most well-known for being good at doing business, and the number one result was McKinsey Company. So I applied to McKinsey Company, not knowing what it was, and I was recruited there. I'm very grateful because they taught me a lot about business. On my first day, they told us to choose orientations, and I ended up selecting operations because it sounded cool, and I wanted to do things with the physical world.
I did not know what a factory was. I was just a fresh graduate knowing nothing, and I learned my job there. So the first time I went to a factory, I saw that incredibly complex web of people working and incredible speed-changing matter, and I said, okay, this is the dream. This is where I want to work for a lot of time because when you're a geek like me, and you like systems, and you're interested in those kinds of things, It's paradise to me.
The two most interesting things you can think of, and work on are either a tech company or a factory because those two systems are incredibly complex, beautiful, and interesting.
Lisa Ryan: It's nice to see with such an emphasis on tech, and I know we're going to be talking about AI and all of the new things that are coming into manufacturing, but just to be able to marry those two worlds. Because, again, it's changing that conversation to find people passionate about the physical world and the intricacies that go on with that. In your life, how do you see the future of manufacturing evolving? Particularly when looking at integrating new technologies and increasing automation in factories.
Renan Devillieres: It's very interesting to see. I think manufacturing is just the result of two basic passes. First, what do people want, and what does society want? And the second is which technologies are available. And to those two questions, I answer very simply. People want a different social model. People do not want their products to be made by slaves in Bangladesh or China anymore. That has changed, and people would like to leave. Unbelievable planet. People want local things that are more respectful of the environment. Energy prices are high, everything. That's the forces.
What is new in technology is that the steam engine and the electrical power automated everything manually and moving beats around. Now, we will automate everything repetitive and intellectual with computers and AI. And what I think is going to happen is that our production model, our factories will be different. They're going to be more local, more green, and more respect for the workers and more automation. So that's a big part of what operations are going to be. And there will be all new infrastructures, giga factories such as massive infrastructure models with billions and billions of capital.
It's going to be taking all over manufacturing. And let me take two examples. When I was in California, I went to visit Tesla Freeman. In Tesla Freeman, you have nerds younger than me running around wearing t-shirts and programming machines with their Macintosh laptops. That is the future.
Another glimpse of the future of manufacturing operations is a company that stopped sourcing in China. What they do is, as a customer, you pay every month, and you get clothes for your baby. And when your baby has grown, you give back the clothes, and they give you clothes of the right size. What they do is they repair all kinds of clothes, and it is very automated. It is very different. And they don't use materials anymore. They just repaired it. And it's very different as a system, but it's very green and profitable.
Lisa Ryan: Wow, I never even heard of that. And we're going to talk a lot about startups and helping them scale, but if you have a legacy manufacturing plant doing things and all of this, you're speaking a foreign language to them; what do you mean? Mail ordering clothes, repairing them, and walking around in t-shirts and programming machines. So is there hope for legacy manufacturers, or is it best to start with a clean slate and have a startup?
Renan Devillieres: Yes, there is hope, and I like to take an example, a very simple one: 30, 40 years ago, a little Japanese car company named Toyota invented a new way of organizing factories. They invented Lin, which was ridiculously different from everything everyone did then. Radically different, but it was 20 to 30 points better. And everybody started doing Lin. When you look at a high-level picture of what happens, it took 20 years. 25% of all the factories died because they were not competitive and could not transform themselves. But 75% made it on the other end. And with varying degrees of success, you can only go to a factory with a continuous improvement arm.
And they do things, and they do daily meetings. It's going to be the same with tech. Tesla F is the new Toyota. That's the new blueprint, but there are 20 years in the future, and there is to, there is going to be a catch-up movement. It's going to take time. Some companies will die, but some will make it, and it will take time, not because of the tech but because of the people in the future.
Lisa Ryan: Such a good point of just realizing that everything is changing and looking at asking questions that you may have never had to ask yourself before. Because business is changing, and as you said, there's going to be a certain percentage of those businesses that, if they don't change, will die.
If they want to keep up that, then to explore when you're talking about your approach to identifying and supporting innovative manufacturing startups, you have the small company. So how do you help them scale?
Renan Devillieres: We have a very particular method of doing that. First, you need to address a widely available pain point to scale. So I have a 20% team each week. My team visits three and four factories all over Europe, and they ask people like, what are your pain points? What needs to be sold? How much money do you have to solve it? Is it in your top three? And we only work on what we call a burning platform.
Like burning hair issues, nice-to-haves are not candidates. That's the first part of the answer. The second part of the answer is that we don't invest in startups or create startups where implementation time is more than two weeks per factory. This rules out 90% of the startups that we are seeing.
Of course, you can create a company with six-month or 12 months implementation cycles. Yes, can you scale That? It seems too hard for me. And we only invest in startups that are doing that. So the third thing that we do to help them scale is very simple. We created a platform, and that platform shares technical components to be able to connect to the top 15 ERPs in factories instantly.
And that platform shares the contacts of all the innovation-friendly European factories and us. And when you enter the USS community as a startup, you get access to roughly 250 companies—accounting for 1000 factories already deployed on the SS network. And by combining those three factors, our startups achieve consistent 300-person growth year, the average in Repository.
Lisa Ryan: I think the lesson that anybody listening to this show can take from you is going around and asking employees about their pain points and acting. On the most critical, those burning issues because there are many times that we think we know what the employees are doing, where they know their job better than we know their job.
And when employees see that you're listening to them, there's a much better chance that they will be engaged and committed and help in the process. So true. You mentioned this several times regarding the importance of caring for the planet. What are some of the key strategies that companies can implement to become more environmentally responsible?
Renan Devillieres: I can tell you what I've seen starting to get a lot of traction. It's an ongoing issue. Everybody's speaking about it. We'll see. It's a continuous development, but we've seen first improving processes and improving your footprints on the planet. A consistent thing we have seen is that leaders in being positive for the planet are leaders in performance. There is no anti-correlation. That's the first. The second thing that we have seen is that focusing on the value that you can bring to your customer and then trying to perform that value in a very lean way in terms of the effort and the materials, and the footprint that you have on the planet can lead to incredible results.
Let me take an example. One of our customers is Michelin, the tire maker; I love it. I love them. I love all my clients, Michelin. They have a factory producing 40% fewer tires and making 40% more money. And they do that by putting a chip in the tire and selling kilometers or miles for you, us people, to their clients.
And they say, I, this is a price per mile. You only pay for the miles that you do. By doing that, they can start improving the lifetime of the tire. They're incentivized. To do less and fewer tires, and they'll get more and more money by producing fewer and fewer tires. And their engineers started working not on improving processes to do more tires per hour, but more kilometers per euro, which is different because you're not optimizing for the same thing.
It can help you optimize for the right thing, which is the value, not the object. And what we see a lot of shifts from optimizing for the objects you're producing, which is sub-efficient. You stop doing that, and you optimize the value you create for your clients. And you can reduce the items, increase the value, and take more money.
Lisa Ryan: Yeah, that gets into that quality versus quantity conversation. And it's also; you're making fewer tires, you're going to have less waste because you're getting more kilometers/miles for each product sold. So what an interesting way of looking at the things we know are the right thing to do, but when you put dollars behind it, it makes a little bit easier conversation.
Renan Devillieres: Of course. Let me take another example. We are working with some of the suppliers of Apple. Apple created the machine. The machine's name is Julia, and what is the machine doing? You can take any iPhone and put it in the machine, and the machine can take any iPhone apart, change components, and put them in little boxes. You can reuse those components for the next generation of iPhones. Their goal for the next generation of iPhones is to have at least 50% of all the value reused in the following phones. When you do that, you reuse the stress on the planets per iPhone and increase the dollars per iPhone.
Lisa Ryan: Wow. And people feel better about buying iPhones because they know they are getting products made of 50% recycled material and have less of a negative impact on the planet.
Renan Devillieres: Precisely. And for the listeners, if you want to put on YouTube, you put on YouTube Apple Machine, iPhones, and you'll get a video of the machine. It's incredible.
Lisa Ryan: We spoke about a little bit, but what do you think about artificial intelligence and machine learning regarding the future of manufacturing? And if you still need to get there, how can companies prepare for these advancements in both?
Renan Devillieres: artificial intelligence is both over-hyped and under-hyped. We are overhyping what it will do in the short term and under hyping what it'll do five to 10 years from now. Five to 10 years from now, I'm a former artificial intelligence researcher, and 30 to 40% of all intellectual jobs we're doing will be automated.
By AI, if it's intellectual and it's repetitive, it can be automated that will happen. And for example, in manufacturing, and designing new parts, there is already a lot of generative AI and putting a new program in a robot. It's very repetitive. And there is already there is a Stanford startup that is coming from you. Just type a sentence, take the red thing, do a quarter rotation, and put it in the box, and the robot does it.
This is going to happen for sure. And it'll take time. And you ask the question, as a manufacturer, what can I do for it? In the artificial intelligence age, your edge is your data and ability to implement fast. With the new technologies on those two fronts, manufacturers are historically bad. 70% of all the data in ERPs and systems are not usable for artificial intelligence because of the data structure, bad data, and because they need more data. As a manufacturer, having good data hygiene and having some nodes and data scientists is an excellent first step. And then the second, when you look, Tesla or SpaceX, or B Y D be your dream is the number one car, an electric car manufacturer in the world. It's a Chinese company. They have data scientists who change the production process every three or six months. Having agility helps you as a manufacturer use the new technologies because if you change your process every six years, then at best, you'll be three years outdated. When technology paces of change go from five to 10 years to one to two years, your frequency of changing your processes has to go down. You have to be more agile, and this is something that manufacturers have historically been bad ads. Let me take basics as an example.
SpaceX is launching a new rocket every three to four months. And each rocket is a new version. Building upon the learnings of the last rocket today, no space company could have the path. The best space company's path was one to two years between each launch; they're bringing that down to freaking four months. The versioning of the cars is once every six months. And increasing the pace of change to embed those learnings and those new technologies is essential to surviving that next normal.
Lisa Ryan: That is undoubtedly one of the significant challenges that are facing manufacturing today. What would you say would be other significant challenges that manufacturers are dealing with, and how do you think startups can help address these issues?
Renan Devillieres: Number one, talent for sure, like number one. If you look at another vertical, another market, retail. Retail. When Amazon came, they were bad at the internet. They were bad at marketplaces. They were terrible at all—that new stuff. And there was a first wave where they said, ah, it's not going to happen. Denial manufacturers a lot of manufacturers are in denial right now. Still, in retail, after that, Amazon became dominant, and when Amazon became dominant, they morphed, and they had to hire talents from startups from the tech world.
From those new companies to increase the pace of change, change the culture, and modify the systems. And in manufacturing today, for example, decathlon, an excellent sportswear brand, they are new. C e o comes from Google. Wow, it's a freaking manufacturing textile company. Steel is from that new layer of talent, bringing a new set of crypto technology processes; the pace will change the manufacturing world. But the challenge is getting them on board, retaining them, and giving them the keys to change the crypto. But that's going to happen out of all of this.
Lisa Ryan: As we end our time together, what would be your best tip to a manufacturer listening today? And let's say that they are not necessarily a startup but want to make some of these changes. What would you advise?
Renan Devillieres: A very simple thing. Get your hands dirty in one big issue that you have at your factory, and be open and say, okay, what if I could solve this with tech and start small, have big ambitions and scale fast for that problem, and then do one another, and then do another, and then do another and exponential change comes from rapid scaling of good local solutions, gone global.
Lisa Ryan: And how do you work with your clients? How would somebody listening today know they should schedule a conversation with you? How can you help?
Renan Devillieres: Two things that we do. The first thing is if any manufacturer in the world has an issue or something they want to talk about, they can book a call with someone from my team, and they will say either, look, we have our database, 100 manufacturers like you. They all had the same issue, and here is how they dealt with it.
That's the first type, the second type of answer; we have more than thousands of...