So the idea of having seamless AI integration that is going to generate information that's going to help make decisions I think is going to make be a game-changer for manufacturing.
Where is the world going in the next 5 years? And we have to have a systematic way of really reskilling the American workforce.
There's something magical about being on a factory floor and watching something being made. And nothing can replace that.
We make things for other people and we make everything. We'll make everything from consumer products like vacuum cleaners to hair straighteners to very complicated things like autonomous car compute, you know, to healthcare devices to kind of, you know, data center products.
There's a technology revolution also happening. The growth is driving it, but the technology change is also driving why this makes sense at this point in time.
Good morning and welcome back. I'm David J. Lynch, global economics correspondent here at the Post. I'm delighted to be joined this morning by Revathi Advaithi, the CEO of Flex, for a conversation about American manufacturing. I want to start at the beginning of your career just very briefly. I remember from our earlier conversation several years ago that you began your career on the factory floor of an Eaton Corporation plant out in Shawnee, Oklahoma of all places. I think you were in a supervisor position at the time. If you took a snapshot of your daily work at that time and then went back to that plant today to see what your modern-day successor's day looks like, how different would the experience be, do you think?
Yeah, first, thanks for having me, David. And you and I first spoke in 2020, I think, just at the height of the COVID pandemic, and lots of different things since then. I would say, I remember my life then, you know, I would drive into shift change in the morning, leave my home at 4:30, get there by 5:30 for shift change at 6, and I had this notebook and pencil that I would kind of take the shift change notes from the previous supervisor. And then I'd translate all of that into this big whiteboard, you know, to make sure that everybody who came into the shift knew their task, because I had like 50 machinists, you know, big machine shop that we had there making hydraulic pumps and motors for the kind of construction industry. And it was all very manual. Every day we would manually exchange notes. We would manually give directions. I would take notes from the shop floor guys and then translate it back to the next shift. Nothing got stored. You know, we didn't know the issues that happened in the last shift the next day. And that was life then. That was in 1995. And I mean, you think about it now, right? And I'm sure our factories and those factories run similarly. A shop floor supervisor before they leave home probably knows how their day looks. What equipment is down? How many people are showing up? What setup changes? Utilization is going to run at 30% or 80%. Make all the tweaks probably in their bed in their house before they even come in, and probably driving an EV car or something like that. I was driving a Honda Civic. But the beautiful part I'm sure that's happening today in every factory is that all this information is getting stored, and next day when you have a machine down, you have all this intelligence that's making those decisions for you. You don't have to think about, oh, what happened 5 days ago and what did I do when that big machining equipment was down. So I'm sure it's a whole different world. At least I hope it's a whole different world. If it's not, Eaton's got a problem.
That's right. If it's not, we all have a problem. So, you know, as long as kind of the factory human side of the world is still the same, I think we've come a long ways. And, you know, I think of Flex as sort of one of the most interesting companies that the average person probably has never heard of.
You guys are behind the scenes. You're behind the scenes in a lot of markets. $28 billion in revenue, 150,000 employees spread all over the world. What do you want the company to be known for? How should people out in the audience think of Flex? What's the core competency?
Yeah, so I came into Flex 7 years ago and my whole career was with industrial companies like Eaton or Honeywell, working in aerospace, working in oil and gas, working in hydraulics, and then most of my career in the energy space. So when I came into Flex, I didn't know what it was all about until I found out more about it. It is one of the world's largest contract manufacturing companies, which means that customers give us their product and we make it for them. We could be making a very complex healthcare medical care device that some of you may be using, like continuous glucose monitors and things like that. We could be making very complex automotive equipment, or we could be making a vacuum cleaner that you use in your house. And today we make a lot of power infrastructure, compute cooling products for data centers. So we're the name behind the name, all the name brands you use. I always say you probably are using something that we have manufactured sometime during the course of your day. So for us, the complexity of we need to be able to make something that's going to go into the human body and the responsibility that comes with it, and we need to make that vacuum cleaner that you don't want it to break down and be kind of service-free for the life. So that complexity of manufacturing is significant and is always misunderstood that oh, it just shows up on your doorstep somehow. And that's what I want you to know about Flex, is that these 150,000 people across the world are showing up every day making extremely complex things that are hard to make that all of you get to use. And we have to do that with high quality and make sure that whether it's a pandemic or a supply chain crisis, our customers are still happy getting our products every single day. And I didn't appreciate that 7 years ago, David, when I first came into Flex, right? And today, 7 years later, I see the magnitude of what we're doing, particularly when this conversation of building manufacturing locally and close to the consumer and reshoring conversations are going across the world. I see the significance of what we do and what we provide every single day.
Yeah, we're going to get to reshoring in a bit. One thing I think, or one initiative that you undertook at Flex was to steer out of some of those consumer markets. Why did you do that and what have you prioritized instead?
Yeah. So, you know, having worked for 30 years, starting from Shawnee, Oklahoma to Hutchinson, Kansas, I have seen everything come in and out of where manufacturing gets done, where industrial companies put their base in the last 30 years, right? And I've seen the whole variety of things that has happened. I moved to China and lived and worked in China for Eaton, running their electrical business. Saw the magnitude of what's capable there. And when I came to Flex and I looked at the consumer side of our business, it was very clear that we would never be able to compete in that industry. One, being a western company that's publicly traded, right? We have to deliver shareholder value, which means you have to keep improving your financials every year. And so competing with Asian manufacturers where they have a low cost of capital and an unfair advantage was never going to work for us. And then the product life cycles were so short that every day you were making something and then tweaking it the next day, and there wasn't enough money to be made in that value chain end to end. Right? The end customer or the end manufacturer may be making a lot of money, but we weren't. And so very quickly came to a conclusion that when you're at 2-3% operating margin, you couldn't live in that product life cycle. It wasn't rocket science to figure that out. Every job I've taken, the first thing I do is look at portfolio and mix. And I said, we just needed to get out of that. We're never going to compete and be successful at it. So really deemphasize the entire consumer side of the business. We still do a lot of consumer stuff, but we only do high-end, difficult-to-do, complex products where people are going to pay a lot of money for it and our customers get to sell it at high value, then we do it. Otherwise, we exited all of those and really left that to a ton of Asian manufacturers, knowing that we could never compete in that space, and refocused the company on high-value, difficult-to-do things, charge a premium for it, vertically integrated. And that kind of philosophy of bringing manufacturing technology to the forefront is what drove our portfolio strategy.
Yeah. And let's talk about AI for a minute. It's already making itself felt on the factory floor. Where are you seeing the biggest effects from AI, and what sort of investments are you making to further develop that capability?
Yeah. So let's start with, first, manufacturing and industrial companies for the last 30 years. My hypothesis, and the data suggests this very clearly, is that outside of labor arbitrage, we truly haven't seen manufacturing productivity move. Right? Look at the last two decades. Look at manufacturing productivity. Take out labor arbitrage, it really hasn't moved the needle. And there are reasons why that is the case. One of the big reasons is that factories inherently are really built with many software systems that are put together that don't talk to each other very well. And it is a worse problem for midsize and small-size manufacturing. An average factory that is good will have 50 software systems. Most factories will have like 100 to 200 software systems. So we have been, you know, fallen for all these enterprise software systems that come our way. Put them all together. None of them talk to each other, don't work seamlessly, and we make suboptimal decisions all the time on every factory floor. And that world is going to change. And I'm super excited about that world changing because I've been pissed off about it for 20 years now. So why that world's going to change is because if you can get a data layer, forget your 50, 100, 200 software systems. If you can get a data layer that is clean, that is agile, then you can start making a lot of decisions really, really fast. And I'll give you some real examples to think about this. Average utilization in a factory, doesn't matter what people tell you. They'll tell you it's 80-90%. Those are all lies because they make it sound 80-90% by saying, 'Oh, I took out equipment downtime or I took out X, Y, and Z.' Usually 50% is a big number, a good number. Usually it's running at 30% utilization. The reason is because customers mix changes, then setup changes, or factory worker didn't show up, or machine downtime. The variables are many, and you put all those together and your equipment is sitting idle most of the time. So here comes AI. You can have planning systems that are taking all these complex variables and making smart decisions. If it moves your utilization up 10% or 20%, it's able to say, well, I'm going to predict that because Thanksgiving is coming along, I'm going to have 5% more absenteeism than I typically had, and your best use of equipment will be to run this set of products for this customer that can move your utilization up 10 points. That kind of intelligence is game-changing. Average factories don't do that. So I'm super excited about AI on the factory floor and what that's going to do for productivity and efficiency, because it's going to be a game-changer. We're not going to be falling prey to all these complex software systems anymore. And I know everyone's focused on automation and humanoids and all those cool things. All that's happening. Automation's happening, hardware automation is happening. All that is game-changing and it'll be great. But I'm super excited about AI's impact on software automation and what that does to the future of industrial companies. Very important for America, by the way, because we don't have the advantage of a low cost of capital. So we can take advantage of this game-changer for us.
That's really interesting. I haven't heard the productivity issue discussed in quite that way before.
Come to one of our factories, David. I'll show you how they work.
Be careful what you ask for.
The Nvidia CEO, Jensen Huang, the other day described AI as a once-in-a-generation opportunity to reindustrialize America and restore the nation's capacity to build. Where do you see the best, most achievable case for the use of AI to promote or be used for the reshoring to boost domestic production? How is AI going to play into that?
First, I'll say I agree 100% with what Jensen is saying, and I have real-life examples for this already happening. I've spent 30 years of my career watching factories move out of America to all parts of the world, and today I'm seeing a true rebuilding of factories in America, and it is being done in a smart way and in a thoughtful way because you're bringing back manufacturing in hard-to-do, complex integration, complex assembly and test. So for example, if you have to put together a compute integration rack for what goes into data centers, you can't put all that together and ship it from across the ocean. It's very complex, and there are lots of changes that happen all the time. So it's best done close to home. So setting up factories that put together those integrations, all the way from cutting the metal to putting it together, testing it, that takes a ton of power, and then being able to pop it into a data center where it's ready to go is something that's happening today in America, and that wasn't happening a few years ago because the volume wasn't significant enough, the complexity wasn't so big. So the things you're seeing coming back home are the difficult-to-do assembly and test integration. The very kind of high-power tests that are happening—those are the kinds of jobs that you're really seeing coming back into the U.S. Just a month ago, we signed another new lease close to Austin and Georgetown for a factory that's going to be up and running in 12 months. We hope to have 50 megawatts of power there, and that factory is already sold out. And that is the kind of jobs that are coming in because they will do very complicated things.
And what will be producing there?
Just what I talked about: compute integration. Things that go into a data center will be produced there. But it requires skilled technicians who know how to test this kind of equipment, who know how to build that kind of equipment. It'll require machining equipment to cut all the metal and put it together. So it'll be very high-skill jobs required to run that factory. Those are the kinds of reindustrialization we're seeing today in America, which is exciting for a person like me who's seen it go out and now come back all over again.
Right. And you mentioned labor arbitrage earlier, and obviously the wage differential between a lower-cost venue like China and the U.S. has narrowed over the last couple of decades, but it still remains the case that Chinese workers are fundamentally less well-paid than an American worker. So what kinds of work should we not be trying to bring back? There's still stuff that's going to be made in China or Malaysia or Mexico, other places. That's basic manufacturing that's not going to be our area of focus. So, is there a danger that we're going to try and overdo the reshoring and end up with stuff that we're just not cost-competitive in?
