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And I think as a big part of it, as a company, setting these big goals, if we want to be number one, if we want to be at the top in these areas, if we want to continue to produce, it forces us to say. Results, how do we achieve them using technology? And that forces us to guess that because you can’t follow a person, if you want to be number one, you can’t follow a person to be number one. And we understand that the way to get there is really through technology and software and enablement and investment, but really focusing on the goal. If we look at these examples of how we can create the infrastructure through technology to support these great goals, we ourselves should be enthusiastic because if we come up with a solution that I am, it will be a copycat, that will not happen. We don’t have diversity, that doesn’t motivate us to be, for example, a top 10 supply chain. It just doesn’t go away.
So I think it starts at the highest level with business ambition. And from there, we can organize ourselves at the intersection of business needs and technology trends, those very rich conversations and the glue of how we bring together the many moving pieces, constantly scanning the technology landscape for new technologies that can come in and be part of achieving that mission. And so how do we prepare through the process. For example, one of the things that I think, and it’s innovative, but it’s not talked about much, but I think it’s going to be very important to the community out there, is how do we stay. Questions of data sovereignty and data localization? There’s a lot of work to be done to rethink what your cloud, private, public, edge, front-end looks like so we can stay decisive and competitive in each of our markets while meeting the increasing directives about data localization and data sovereignty from countries and regulatory agencies.
And in our case, as a global company listed in Hong Kong and operating around the world, we had to think deeply about our solution architecture and apply innovation on how to engineer long-term growth. , but in an increasingly uncertain world. So I think there are a lot of drivers in some sense, which is our corporate ambitions, our work environment, there’s a lot of uncertainty going on and that’s really forcing us to take a very sharp lens on what the cutting edge looks like. And it’s not always bright and shiny technology. Cutting the edge might mean going to the executive committee and saying, hey, we’re going to have a challenge about compliance. It’s the innovation that we’re bringing in so that we can handle not just the next country or regulatory system, but the next 10, the next 50.
Laurel: Well, and to follow up on a few more examples, how does R&D help improve productivity in the software supply chain and improve technologies like artificial intelligence and industrial automation?
Art: Oh, I love this because it’s a perfect example of a lot happening in the technology industry and there’s so much to the early applied curiosity and how we can test this. So especially around artificial intelligence and industrial automation, I think those go hand in hand with Lenovo’s natural strengths. Our heritage is as a leading global manufacturer, and now we are looking to transition into services-leading, but also applying AI and technologies like Metavas to our factories. I think it’s too easy to talk about Laurel in reverse. Because we… because and I clearly remember that we developed this map, there is no place in the supply chain and in manufacturing that is untouched in these areas. . If I can think of an example, actually, we’re having this conversation very timely. Lenovo was recognized a few weeks ago by the World Economic Forum as a leading manufacturer as part of the global lighting network.
And that depends on implementing AI and metaverse technologies and embedding them in every aspect of what we do about our own supply chain and manufacturing network. And if I take a couple of examples on the quality side of the factory, how we can integrate digital twin technology into cost, we’ve implemented design faster and more efficiently than ever before. In a digital world where it’s fast and cheap, prototyping and fixing bugs is more up-to-date and up-to-date. So we can quickly iterate on our products. We were able to have better quality. We have adopted advanced computer vision so that we can detect quality defects early. We’ve been able to apply technologies around the industrial metaverse so that we can train our factory workers more effectively and using the features of AR and VR.
We’re also able to say that one of the most important parts of running an efficient manufacturing operation is actually production planning, because there are so many thousands of parts coming in, and I think everybody who knows how much uncertainty and uncertainty there is. were in the supply chain. So how do you take a multi-thousand scale planning problem like this and optimize it? These are the things we implement smart production planning models to keep our factories fully operational to meet our customers’ delivery deadlines. So I don’t want to throw it at you, but I think it was a literal answer: there was no place, if you think about logistics, planning, production, scheduling, shipping, we didn’t find AI and metaverse use cases, there was no place. We can significantly improve the way we do our work. And again, we’re doing this internally and that’s why we’re so proud to be recognized by the World Economic Forum as a manufacturer member of the Global Lighthouse Network.
Laurel: It’s certainly important, especially as we bring together computing and IT environments in this increasingly complex environment. So as businesses continue to transform and accelerate change, how do you build resilience at Lenovo? Because this is definitely another basic feature that is important.
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