Name: Brainstorm AI 2023: Supplying Solutions for Industry
Uploaded: 2023-12-13
Duration: 19 min 7 s
Description: Jon Chorley, Chief Sustainability Officer and Senior Vice President, Supply Chain Management, Oracle Rudina Seseri, Founder and Managing Partner, Glasswing Ventures Rebecca Yeung, Corporate Vice President, Operations Science and Advanced Technology, FedEx Moderator: Verne Kopytoff, FORTUNE

Jon Chorley, Chief Sustainability Officer and Senior Vice President, Supply Chain Management, Oracle Rudina Seseri, Founder and Managing Partner, Glasswing Ventures Rebecca Yeung, Corporate Vice President, Operations Science and Advanced Technology, FedEx Moderator: Verne Kopytoff, FORTUNE

Brainstorm AI 2023: Supplying Solutions for Industry

Conferences

on Chorley, Chief Sustainability Officer and Senior Vice President, Supply Chain Management, Oracle Rudina Seseri, Founder and Managing Partner, Glasswing Ventures Rebecca Yeung, Corporate Vice President, Operations Science and Advanced Technology, FedEx Moderator: Verne Kopytoff, FORTUNE

Jon Chorley, Supply Chain Management, Oracle Rudina Seseri, Glasswing Ventures, Rebecca Yeung, Operations Science and Advanced Technology, FedEx, Verne Kopytoff, FORTUNE

Welcome back to the second day of fourteen's brainstorm A I conference. We hope you enjoy dinner. We hope you enjoy breakfast. And now let's get down to business today. So before we get started, I want to extend a really big thanks to our partners for their huge contribution to this event, our founding partner, Accenture and then our partners who are a ARP Amgen chat point hand show sales force and work day. And then we have our supporting partner, Felicia. Thank you so much to all of our partners. So a quick reminder before we kick off about our reporting and social media rules, anything that said here on stage or in the strategy session throughout the day is on the record and that includes the Q and A s but what's said over the table during a meal or on a break in a corridor that is private unless agreed otherwise by all of the parties involved. OK. So with that out of the way, let's get started with today's first session since its inception A I has been a helpful and powerful tool in making supply chain predictions with the help of A I organizations are now focused on optimizing the supply chain and ensuring it can be tailored to the needs of clients and customers. With the holiday season, fastly approaching supply chains, technologies and cost management abilities are as ever being put to the test. So here to discuss how A I is tackling the challenges of the supply chain. Please welcome John Chorley, who is the chief sustainability officer and senior Vice president of supply chain Management at Oracle, Regina Sri, who is the founder and managing partner at Glass Wing Ventures. Rebecca Young Corporate Vice President for Operation Science and Advanced Technology at fedex where she also oversees the company's innovations in robotics and they will be interviewed by my colleague, Fortune, senior editor, Vern K. Yep. Have a seat. Hello, everybody. I hope you had a fantastic dinner last night and thank you panelists for joining us. Let's first off, let's set the stage for uh the discussion and, and just give us a maybe start with you, John, uh an example of your favorite A I for the supply chain. Well, that's that's a tough question long continuum of A I being applied to supply chain, obviously the latest. So as it were is Ja I and you know, there's that, but the history of A I and supply chain is really looked at a lot of optimization problems and trying to help solve those. One of the things we've worked out on most recently is predicting your estimated time of arrival, more accurately using A I technology and in including in that other causal factors, weather, seasonality and so on. So that one, I think it is a great example of how we can continue to evolve the use of A I in supply chain. Well, I should go to Regina. I think I know what you're going to say. We'll get to that. Thank you and good morning everyone. So selfishly helping from one of my portfolio companies because we're very active investors, early stage investors in the application of A I for supply chain. And one of those companies being Verison based out of Atlanta, where they're focused on inventory optimization and particularly in direct parts. So not the parts that actually get embedded in the production of a finished good, but the parts that go into the machinery um there is an average of 60 to $80 billion waste pure, poor tracking um over stocking under stocking emergency ordering. And what this team has done is Jena is the wrapper, not the differentiator. They're actually leveraging recurrent neural nets with deep learning and some other architectures and technologies to fundamentally both harmonize the data. So you know exactly what you have in stock, harmonize it even if the data is inputted in perfectly, even if it's off off prem rather than just in the cloud. And then also give you visibility into optimization for when you order because they see the entire supply chain so super excited about that one. And Rebecca. Yeah, it's really hard question for me to answer because uh we our operations cover both the physical and digital side. And so putting the perspective of fedex operation, we just celebrated our 50th anniversary. Today, we move on average 14.5 million shipments on daily basis to 220 countries and territories. So as John said, for supply chain, it takes a lot on the digital side, actually optimize the movement of goods. And also we have think about we have 700 planes, we have 210,000 vehicles and 5000 locations. And so in order to um move the shipments in the most optimized manner, we use a lot of the A I tools, whether it's the JA I or like predictive analytics, etcetera. But what I most excited, I think we that's my answer is on the physical side. I mean we talk about Ja I always like joke with my colleagues like this is the language part of the A I. There's also a math part of A I, right on the robotics and auto technology side. And um we really see the convergence of underlining technologies from a sensor perspective. The the sensor gets a lot better. So robots actually see better today and also the computation gets faster. With GP U, the robot can think faster. And then the ultimate A I algorithms enable robots today to have the touch have the force control, have ability to actually move in efficiently in tight space. So in the past, robots are very much doing precision work and the secret is the imprecision, like more dynamic like movement is more difficult. So for our operations, you think about 14.5 million shipments on daily basis, it all vary in size, shape, weight, packaging material. And it's also randomized because they come down from sort and also have very high like high throughput needed because you all want your Christmas gifts to be on time. You don't want us to hold inventory. And so if you combine the randomized shipment, um real time decision making and also high throughput, we require very high advanced A I. So that's what I'm most excited about. And let's be specific. You have a robot that packs trucks with boxes, random sizes, random weights and what and what are the limitations of a robot like that? It's just an experiment, experimentation phase. They're not quite as good as humans yet, right? Yeah, we're still developing that because like, uh if we look at the history of robots like uh um like in the past, it's more like a very precise decision work and now we move towards making real time decisions. So the it's a lot of decisions to make because in the inside the trailer, you can have hundreds to thousands of packages and for robots to not only actually sense how heavy is the package. But also um you have to be cognitively playing the Tetris game where to place the package. So that uh a few factors you have to think about is put the heavier boxes at the bottom because you don't want the heavy boxes to be on the top, which to make the world less steady and also easy to cause damage because uh in our operations reliability, highest customer service are all important factors. So the robot basically is doing both the heavy lifting of moving the boxes as well as playing the 3D te at the same time. So that's the complication. And John um we had the uh the, you know, the COVID pandemic which upended the supply chain. What did we learn from that? And are we prepared for the next major disruption? Are we better prepared this time around? Yeah, I I think we learned that the tremendous amount we learned that you can become too lean, you can become too just in time. And that essentially injects risk risk into your supply chain. We learned a lot of breakdowns in communication between different parts of the supply chain. That information flow wasn't as smooth as it could and should be. Uh we learned that flexibility is important. And I think now that we've sort of come through the acute phase, the fever phase. If you like of the of the pandemic, we're now into the hangover phase. Maybe we've oversupplied inventory in certain cases and it's in the wrong place and we're trying to move that through the supply chain. But I think the longer term lesson is going to be around resiliency, how to build resiliency into your supply chain. And that I think means looking at flexible sources of supply, looking at different ways of hedging your inventory, multi enterprise inventory optimization techniques and those kind of things where mathematics can really and A I can really help, help give you an optimal solution. Um And I think we're also learning about planning that, that you know, a single answer plan isn't, isn't correct. And that you need to look at um a scenario planning where different factors possibly those more likely to change you per those and understand how that might affect your plan and get a range of outcomes with probability uh helping direct uh how you make your decisions. So these things are all evolving. Obviously A I has an enormous part to play in those processes. Computational power lets you do these things now. And I think as we emerge from this, that core aspect of resiliency is going to be is going to be central and Regina, you had a little midi announcement to make that not only deals with supply chain but all A I just go ahead. Sure. So basically this morning, we announced what we call the A IA I pallet. So my firm were an A I Originalist Fund and started investing in the application of A I in 2010. And through the years have developed proprietary model where one can start with the use case and try to figure out what are the best techniques, architectures ways to train the algorithms or you can start with, you know, I have this algorithm at hand, what kind of data do we need? So we provide a framework which has been proprietary to date and we have open sourced it as of this morning for whether it's a software founder that is trying to recruit, you know, a co founder, that's an ML expert. And he is trying to figure out what technology or what capabilities should they have or whether you're an executive and you're getting the three letter acronyms from all your techies and are trying to make head or tails and everyone in between. And we think this is important because even in our chat right now, two things have happened. We use A I as if it's a, you know, monolithic term. It is not, it's like saying technology behind it, there are literally hundreds of architectures and within those architectures, hundreds of techniques. And then we combine them in ensemble models and it's basically thousands of possibilities. So how do you and us as executives and investors know that we are making the right decisions in what we are building. Bless you. Furthermore, far more though even the sort of beyond that point, the second point is gen A I and A I are not the same thing. May I repeat that? Gen A I and A I are not the same thing. Gen A I is incredibly transformative. We have crossed the chasm to mass consumerization. Therefore, we all latch on that is what drives the, the optimization is we to mention it or we won't get any traction. But it's really because grandma now can hug the computer because it's cute in the responses. And my 10 year old why just get some help for a book report? And I'll tell her mom. So when it touches all sides of the spectrum, you know, that is mass consumer that is based on a very fundamental underlying architecture called transformers. Happy to talk to you about that that knows them. And I repeat it is very important because we will never interact with software in the same manner. Having said that there are many, many, many other architectures and you know, machine learning techniques that will drive a lot more value in the enterprise, whether it's on the central loan or on a combined basis. So I just want to make sure that in all these conversations, we don't miss the forest for the trees. OK, John um to sort of put on your sustainability hat does A I or generative A I also have a role in terms of improving sustainability in the supply chain. Absolutely. I mean the enemy of sustainability is waste. And clearly, if you can run an efficient supply chain where you're just making what you need, you're not wasting either fuel because you're having to expedite things because you made a mistake on your planning or you're putting moving material into the wrong place at the wrong time. If you can eliminate all of those wasteful activities, you know, better routing. For example, where A I has an enormous part to play. If you can use those techniques, you can significantly drive down the waste in your supply chain, the fuel, the carbon intensity in your supply chain. And we have many customers doing exactly that. So you know, you can obviously move that upstream and start thinking about network design, which can help you improve the structure of your network to be more suited to how your business is moving and changing. Beyond that, you can look at transform transformations in your business where you're moving from being a product company into a service company. How can A I give you insight into how your products are being used? Therefore, you can fold that back into the way you operate your business and design and, and support your products. So, you know, at the end of the day, we are going to solve this existential sustainability, environmental crisis through the clever use of technology. And A I is clearly a key component of that Rebecca at fedex, not only are you doing robots you're doing? Um autonomous trucks. Uh could you tell us a bit about that? Yeah, I think this is another form. I totally agree. Like for A I, it comes in different format and the key is to use the right A I technology to solve the strategic problem. So it's more like lead with the business values and they match the best technology. So in the A B space, we are actively testing with Aurora here in the Valley. Um um we actually have been testing since 2021. So over the last five years, it's actually correction. Last two years, we have been working together with Aurora and the pack up because we believe that for A V technology, the OEM redundancy is really important to lead with safety testing, the lanes and really focusing on the like safety and um the reliability of the technology. So, so far, we have been had a sound load and 250,000 miles. And uh uh literally like we move about half a million pounds autonomously every month. So it very much help us balance the business needs, which is imbalance the length as well as like length. We have driver shortage and Regina, what's the future of autonomy in the factory and on apparently on the roads as well? Big question. Um So I think I would articulate it as a two part, perhaps, perhaps process one, this notion of the co ploting dynamic that we will have with A I, whether we have it with the agents in our everyday lives as private citizens. But also in our professional setting, you can think of it as you know, it's ambient A I, it's everywhere and we have so much data that therefore our performance is, you know, intricately tied with what we do around A I and we may or may not need neuralink, we'll figure that out, but fundamentally, it will drive productivity and then there will be certain areas where we can have complete autonomy or at least one of those five stages of autonomy. But there will be other areas where I think the human given our creativity and inherent ability to reason will dominate but will be enhanced by our co ploting and ambient relationship with A I and uh to, to wrap things up, what, um, how do you choose which projects to pursue they're out of so so many, you mean as, as, as me as a developer or considering there's dozens of options you could, how do you choose? Yeah, I mean, I think I'm a believer in crawl walk run. I suppose that, you know, clearly solving supply chain problems is a journey. It's requires continuous focus. So, you know, begin, begin with a project that is tractable and which addresses a specific need and then gradually grow based on your experience with that. I think that, you know, obviously A I has changed enormously, uh with the advent of chat GP T um it's sort of, as you said, it's the sort of the, the, the liberal arts side, liberal arts side of A I, as opposed to the more mathematical algorithmic side that we've been used to up until now, actually, it is a mathematical just manifests as a Exactly. So, so look, I think, you know, you need to look at your own business, understand where some of the challenges are there, Look to see if there's A I techniques that can be applied to that that are approachable. Our, our approach within oracle is to deliver solutions, not, not just technology, we want them to be easily adopted. There's obviously a learning curve associated with getting data scientists on board, the extent to which you can find problems where you can minimize that you can move forward quicker. Uh So you know, get some experience, get some results and then iterate and move forward. Well, thank you to the panelists. You've had a fascinating discussion and uh well, the next panel coming up next. Thank you.