 Welcome, everyone, to our MicroMasters in Supply Chain Management Life event. My name is Seba Ponce, I'm the executive director of this program and also the director of the OmniChannel Research Lab at MIT, the Center for Transportation and Logistics. The MITx MicroMaster is a global, massive, online and open program. It's truly a massive program. We have more than one million enrollments in this program and learners from all over the world, more than 190 countries are represented in this program. So our mission here at MIT is to educate the world for free in supply chain management. But here we are also trying to provide you the latest trends and cutting-edge technologies in supply chain management. Today we have the privilege to learn about the AI role in supply chain management. For that, we have a great guest speaker, Dr. Edgar Blanco. Edgar is the VP of supply chain strategy at Walmart in the Centroid team. He has an impressive background in supply chain management, working for a wide range of organizations like MIT and companies like Amazon and Walmart. He's, I would say, one of the most knowledgeable people I know in this field and has been working in many different topics and areas like e-commerce, network distribution, carbon footprint, and last mile deliveries for more than 25 years. It's truly a privilege to have you as our guest speaker today, Edgar. Thank you so much for joining us for this live event. Eva, thanks a lot for your kind words. Thanks for having me and thanks everyone for attending this webinar and making time on their busy days to spend the next hour with us. And I really love this topic and I'm looking forward to our conversation. Excellent. Thank you so much. So just a quick theme for our audience. Please use the Q&A feature in order to bring your questions for our guest speaker. Okay. So Edgar, let's start by laying down some concepts and establishing, I would say, a common language for our webinar. Okay. So the first thing I want you to do is start by your definition of supply chain management. Great. Well, Eva, that's a great place to start. And since this is an AI flavor webinar, what I decided to do when you and I were talking a little bit earlier about starting with this question is that, you know what, let me do this. Let me put it to chat GPT. He tells us what supply chain management is all just bear with me. I'll just take a couple of minutes to read what, verbatim what chat GPT told us supply chain management is the process of planning, optimization and overseeing the flow of goods, services, information and finances as they move from supplier to manufacturer to wholesaler to retailer to consumer. The goal of supply chain management is to efficiently and cost effectively coordinate all these activities to ensure that products are available to customers when and where they are needed while minimizing ways and costs. It involves various components such as procurement, production, distribution, logistics and demand forecasting. And it plays a critical role in the success of businesses across industries. That's kind of the end of chat GPT. I think it's pretty good. I'll say I'll stick with that. Definition is pretty close to how the way I will do it and maybe next time we should add GPT to our panel. I think it will help us. I truly believe that humans matter now more than ever. So I think I am still going to invite you the next time. But I also agree that AI solutions definitely will enhance and augment our skills. So, but I was really surprised was really a comprehensive definition, isn't it? I agree. It's getting better. Okay, so I know you have been working in supply chain management for more than 25 years. And as I mentioned before, touching many different key areas in supply chain management like e-commerce and network design and automation, many different relevant areas. Could you tell us a little bit more about your passion for supply chain management and your experience, of course? Great. Thanks. Yes, I'm a total supply chain geek. I think I started my career in science. I did my PhD, applied for and when I started from the very beginning, since I was a kid, I always loved math and tech at that time, I guess. And when I was starting kind of my journey in my advanced studies, I was actually looking for a problem to solve. And I just found logistics. And at that time, I thought, well, it's a pretty straightforward problem. It seems like fun. I can do a lot of cool things there with the math ML. And a little, I knew that this would have become my career and actually has become my passion. And I'm looking to already 25 plus years and many more to come. I've been able to marry this passion with science, tech, then product. I have deep, deep respect towards people, operations that make things happen, teams that just coordinate and translate all these ideas that maybe science and math can point us to. And now technology increasingly part of it. And I think I've had a place to work with people that are brighter than me, at my side, in my team, alongside them. And just, I'm always at all on what we're able to achieve in this field is like magic. If you think of your day-to-day experience, you want something and you find it. And behind the scenes, there's all this network of information flow, et cetera, that makes it happen. And I'm just at all that I kind of made this my career. And I would love to say that I had a vision. They said, well, I want to end up alive, but it was not. But definitely I was very lucky to find all these things in one place. That's awesome. And I'm also happy to hear, glad to hear, that you love math. This is something that I'm trying to communicate to my teenagers, to my sons. So math is so important. Okay, so 25 years working on supply chain, contributing in many ways. And you have also observed the evolution of supply chain and how technology has been impacting many areas in this field. And now AI, artificial intelligence. So when we refer to AI in supply chain management, what are we referring to? Yeah, so let's kind of start level setting some of the definitions. And by the way, I would prefer to charge EPT. Probably we'll do a more structural answer, but I'll do it this time on my own words. The way I like to think about AI and I'm sure there's similar definitions out there is just a simulation of human intelligence. And I emphasize two words here, simulation and human intelligence. And so what we're, but this is all about is algorithm software, hardware that make machines kind of do things that we will usually associate to human intelligence. And since the word intelligence is so broad, then that also makes AI pretty broad. On one extreme of AI, we have these very specialized decisions. They're two pretty hard, very difficult and very impactful in our organizations, but they are very well defined, narrow. And on the other extreme of AI, we have these very ambiguous and, you know, decisions that encompass multiple dimensions, that tasks are kind of hard to define sometimes, but we all kind of know what I want to get out of it. And AI is trying to cover in some ways both extremes, which is makes it something hard to follow what people are talking about. But the first AI, the one extreme that is specialized is what, at least when I was starting, it was commonly referred to as machine learning, where you have a specific output that you're trying to do, you train a machine to know what is right and wrong. And then you use computer behind the scenes to make that decision more accurate with more data and better over time. And it's very powerful and complex. Now, the second time is what we're kind of talking about really here is that it's what lies ahead, is how can we just move out of those narrow, well-defined machine learning kind of models that mimic intelligence into really the more ambiguous, long-term, very complex decisions that supply chain management professionals and people face every day across the supply chain. So I think that that's, in my words, AI. So then what is AI in supply chain management then? So supply chain management is all about coordination, planning, dealing with a mismatch of supply and demand, making decisions all the time across all levels. And basically, AI is just bringing more of that ambiguous extreme capabilities into the supply chain management field. So this simulated judgment is just going to get more and more encompassing, which allow us to be used in different parts of the business. I really like how you frame it and how you relate AI with supply chain management using this data and human judgment at the same time and thinking in AI as a tool to simulate human judgment. I really like that part. Now I would like to dive more in that connection, this connection between AI and supply chain management. So since supply chain management is comprehensive and needs this coordination and also brings this complexity, that is kind of a common factor that we always have in supply chain management, what do you think are the top supply chain management areas and processes that will be more impacted by AI? This is the next question I know is a broad question, but we can see how we can structure that. Before answering that question, Edgar, I would like to connect with the audience and launch our first poll, because definitely we would love to hear from our audience about what they think about that. What do you think are the top supply chain management areas that will be impacted by AI? The poll is open now, so please take some seconds to answer the poll. While our audience is answering the poll, Edgar, let's start with your pick number one topic. Sure, good. Let me start before we look what the audience have to say about the areas that are more relevant. But again, I think the poll touched on those key areas. Again, you can slice and dice supply chain management in different ways. On one end, you have operations, on the other, you have demand forecasting, inventory, supplier, and customer experience. But let me start with automation, because this is one area that I think has been really advancing very fast. From the very beginning, just with automation, naturally moves into ML and increasingly into more advanced AI, more on the narrow kind, but increasingly into the more ambiguous kind. So again, robotics really, really continues to unlock capabilities in all those, I'll use the technical way, like nodes and arcs of the supply chain. Those could be warehouses, those could be stores, those could be fulfillment centers, those could be ports, or in the links between those nodes, planes, trucks, vehicles, drones, and so robotics and automation naturally is one of those areas where this activity is increasingly adopted in a huge, huge, huge value. This is true for Walmart. We have kind of launched tremendous progress in this area. I also made your comments around this automation. Again, allows for precision, also allows, and most importantly, at least from my point of view, has allowed the operators or associates, in the case of Walmart, that are in those nodes to spend more time in areas first are more fulfilling for them, but also that are more value added to the organization. So that's kind of one part in operations of AI. Again, robots are better at finding corners, identifying objects, needing less help to solve the task, and then the operators that run those buildings can spend more time on solving these complex supply chain trade-offs. But also an area that I'm super excited with in operations. And it's a think of these smart assistants that help those humans that are working in those nodes or those stores to figure out complex tasks. Again, have the privilege to walk into our newest facilities that are very large. They have lots of operations that we can do. And sometimes you can be surrendered and knowing what you do next or even asking for advice or training in a new task. Imagine these AI assistants helping our associates and the humans in those nodes and links to just be better. It's like having, you know, I remember every time I trained in the organization, I always get like an onboarding buddy, a person that I can be around and that I can ask all the questions that I have about everything. That kind of AI is very, very important and very powerful. And I think that's one of the kind of a little bit of a twist on operation AI improvement that I will expect in the future. Yeah, I can imagine to have a body trained based on previous experiences and helping the associates. This sounds really, really awesome. I'm very powerful to transfer knowledge because this is one of the big issues we have in big organizations to transfer this knowledge from one employee to another. Okay, so before you move into the second topic, let's try to have a look to the results. So thank you so much for filling out the poll. Okay, so 43% of our audience, Edgar, pick the month forecasting as one of the top SEM areas impacted by AI. I think we are fully aligned with that. What do you think? I love it. You have a pretty savvy audience. That's for sure. Again, the foundation, if you think of what we do in supply chain management, this coordination and this mismatch between supply and demand starts with demand. And usually we associate demand and all the process around management, demand creation, and demand management with forecast. So absolutely. I also agree, if I always think of the first area that will have any impact across the board and can propagate in an organization, especially in supply chain management, it's forecasting. Poor forecasts, as we all know in this group, adds friction. You have to deal with the uncertainty that you have your missed forecast. In efficiencies, you may need to carry more inventory, do more rebalancing, just because the forecast is not what it needs to be. And all those gains that you do in forecast quickly propagate and improve across the company. So I totally agree with the audience that that's one of the areas where I will think AI will be very impactful. And for that reason, ML has been extensively used, always to bring more data to help improve forecast. So it's a natural progression. But even though we've done tremendous progress and there's continuous innovations in ML, what I call this narrow AI, and I'm sure it's going to be more to come, everyone that hopefully in this webinar has had experience with demand forecasting, there's so much more we can do. I think the way AI will is going to permeate first. But it will be just kind of the same way, like more features to the ML model, more layers to a neural network, more powerful computers, all those are going to come for sure. But is this judgment piece that comes into forecasting that we have been trying to capture through this narrow AI view? But even anyone that has been in a meeting with an experienced demand planner, that planner, you know, you sits on a room, looks at some forecast data and quickly says something's off you. Why is this off? Not because that person has all the features, you have the experience, you know, he knows where to look, he knows what to do. And also is able to ask the top questions and highlight those anecdotes, you know, that I say make humans great at destroying all this narrow ML. For example, you know, we all know by human nature that if there was a rainy season or it's sunny, or if the holiday was on a Monday or a Friday, all those things we kind of know. And when a forecast doesn't match that into issue, we at least challenge the forecast. And most of these narrow ML models have a very hard time to bring these multiple dimensions. Sometimes I'm not featured. It's like these expertise. And I think that that is as AI gets more powerful, where I would think, imagine this demand planning meeting where when the forecast is presented, already comes with questions to say, hey, is this right? Are we seeing the same system, the same weather, not just taken for granted and have a prediction, but really have a conversation with an AI agent that helps you get narrow and better. So I think that's definitely most impactful and one that I'm also super excited about. Yes. And adding these judgment components into forecasting seems like a revolution, especially for those data-driven organizations. And we can talk later more about the role of humans here. You also mentioned those of you who are already working in demand forecasting, you should be, you are aware about this impact of AI in this area. For those of you that want to learn more about demand forecasting, I truly encourage you to join our supply chain fundamental course. We cover demand forecasting and the next topic we are going to be discussing now. So the second topic that our audience picked was inventory management. Edgar, what do you think about that area? Well, again, this audience is pretty aligned with some of my own thoughts. Again, I spent a lot of time in demand and forecasting earlier in my career, but lately I've been personally spending a lot more time on inventory management. Again, inventory management, one of the key inputs, of course, is forecasting. So I'm always keen to innovate in that area. But inventory management is once you have a forecast, good, right, mediocre, excellent, you still need to make decisions about flow. Where is the inventory? How do you move it? How do you make the trade-offs with limited capacity? And how do you position it? And this is so rich and complex that here's what I also think that an AI engine is going to help guide action and I think will be one of the game changers as well in this revolution. Again, on one end, we have the sophisticated models that can predict safety stocks that can find optimized trucks in terms of order sizes, etc. But in this field of inventory management, of determining where to put inventory, how much to carry, how to plow it, and this is like, I was the other day doing some math and I was looking for how many options you have most of the time to explore and look around and these are like quadrillion of options. And machines are great at estimating many of them as a quadrillion of options to monitor, track, improve. And in that quadrillion, you need to find similar to what you're talking about forecasting. Are we having a risk in one item, in one FC, in one item, in one store, in one item, in one input center? And if we are low and there's risk, why is the risk we should worry about? It's something we should take action. And if you think of how we work today with technology on inventory management, we have models that predict and give us recommendations and then we have people that look at those recommendations and kind of inspect them and similar to demand forecasting. But in here it's exponentially more complex because you have a multiple geographical difference, but also time difference where you need to take action. And I think that's where AI is going to transform this function. And people that work in inventory management today, it's also spent a lot of time on auditing the narrow AI recommendations. And they don't have a lot of time to actually take action as strategically as they want to, just because of the amount of time they have. Imagine you have this AI engine that will help you pass parts of the data, point you to directions, maybe make recommendations to look for some other things, get more information, and then learn from the loop on a regular basis. Then it will just make inventory planners across the organization to truly be more focused about the customer, the strategies that will drag customer experience and all the auditing and risk management will start to be powered by these AI machines. So I think that that's definitely, in my mind as well, the second layer of impact in supply chain. Yes, I fully agree on that. It's not only to know the inventory management models that you need to know and the inventory policies. It's also to see how you can translate this context into the code and into your models that is sometimes the toughest part of this. Okay, okay, perfect. So looking to this comprehensive way of supply chain, we also need to look at suppliers. You also mentioned a little bit of customer, but what about supplier management? How do you think is going to impact upstream the supply chain? Now it's kind of maybe not on these two other that I also think are very impactful where AI is going to continue to improve. One is supplier management, the other one absolutely customer experience. I kind of left it last a little bit, but we all know the customer conference and everything we do is just for that customer. But I'll explain maybe why I put customer experience last in at least for this webinar. But supplier management, as I said, when we talk about operations, we talked about demand forecasting and then we talked about inventory. Those three tend to focus within our kind of what we think of within our control in some way. But supplier management and customers outside of it is where AI is going to help us be better partners with our stakeholders outside. And I think that those are again very, very, very important processes in our supply chain management domain. And let's start with suppliers. Suppliers regularly observing their performance is what we do. Finding opportunities to find joint value, changing the way we receive products, the way packaging is being designed, potentially new product development cycles. And if I look into what ML, our narrow AI has come, it's kind of pretty narrow. The impact that we have been able to see. Again, there's always great risk management forecasting engines out there. But supplier management is a lot more about these relationships. And the relationships are very, very ambiguous. This is where exactly one metric is not great. We have performance cards, but these performance cards are augmented with the contents of the supplier. And I think that's where machine learning has been probably helping on a few of those or identifying disruptions potentially, but it's not even close of what we need to transform this field. So I think this is a big opportunity, but I don't, again, since we have not done the first wave of narrow AI really influence here, we have maybe a little bit longer way to go on how we're going to incorporate the supplier in supplier management. But again, this is that ambiguity is exactly where AI could help. How do you bring the context of a supplier relationship with you as you think of how to improve your inventory flow, your purchasing, etc., etc. Again, people who have a big part still on this, but how can we bring AI into this would be a really something I'm looking forward to see. And in that case, it's not only the context of your organization, it's the context of your stakeholders, which make the problem even more complex. Okay. And last but not least, customers. Yeah, I left a last because probably a lot of people in the audience hear a lot about customer experience is constantly on the news. Again, we're all customers, we all get impacted by technology. We've seen the examples on how this already changing, you know, we talked about child GPT on how you look for information. We all have so have familiarity with assistance in our homes. And again, those same assistance have been kind of naturally projected into why you work in a store. And you have an assistant that help you, hey, help me find the items that I want to make my lasagna, right? And you can walk around or if you are on a website, easily find you ways to get reviews that matter to you, summarize the reviews. And all those parts of customer experience are super critical. And again, continue to invest. I use them last, not because they're least important, mostly because we all have maybe seen them already materialized, because that's also a very broad use. We have return management. Every time you have returned a product, and you need to get into a customer service agent, we all know how frustrated it is to have to wait for 10 minutes, you know, and go through 25 prompts and get to a person. And then that person has the information you need about you. And you have to give it again. So if you think about that, that's definitely moving very fast. I've been interfacing quite successfully with chatbots that help my queries. Initially, I wasn't really skeptical, but now I am increasingly relying on them because they do have the information. I don't have to wait online, and I can get 85% of my queries resolved pretty quickly. And the more conversations comfortably though, the experience will elevate. And I think this is true for Walmart, it's true for every organization that has a customer facing. And this is the same if you have to be a B2B as well. At the end of the day, where all people will want to help organizations and having a good customer experience is paramount. So again, this is an area that is, for obvious reasons, very advanced. It's widely used, especially in OVNI and e-commerce. And I will just continue to expect innovations. I put it last because we're kind of on the supply chain inside organizations. Yeah, I truly believe that these type of AI tools are going to impact customers' omnichannel experience, but not only online, but also in person. For example, with the virtual dressing rooms, offering items that are customized to customer preferences while they are trying garments in the store. It's another big thing that I think we are going to see a lot of progress in that area. And as you mentioned, impacting omnichannel and e-commerce. Okay, so that's great because I think we covered the key areas in supply chain and how AI, do you believe, is going to be impacting these top areas and also with some vision to the future. Let's try to move now to the key considerations because there are key considerations we need to take into account before implementing any of these AI tools. So what do you think are these keys for a successful AI implementation? So absolutely. I think that's a great, great kind of follow-up of our previous discussion. We can dream the future, but the most important thing, first and foremost, is really people. And we, again, when we talk about technology, we always get carried away with what it could do, the power of codifying this thing with intelligence. And of course, that's wonderful because Ghani has very, very far. But as we move from this narrow AI to more general AI, we are moving from tasks that are well-defined to organizations and people. And what we do is all about people. For example, at Walmart, we're very proud of being clear that we're people-led and tech-powered. Similar with AI. AI should be people-led. And people will come first on technology next. If you kind of go back and see the way I think about these examples of AI, I always think of AI as enhancing the decision-making capabilities. That means that the first thing that we need to think about is the people, as we think of these technologies, and how they're going to be impacted. And at least from my point of view, not in the same way of narrow AI, we're training a machine. It's just more that they'll always be like a human oversight. We all welcome automation. We all welcome efficiency. Everyone that I always was kind of reflecting the other day, we tell my colleagues, you know, of course, I'm used to look at the physical maps and passing pages, but I enjoy more the scenery. And having a map that tells me where to turn left, turn right is much more fun than just looking at a piece of paper. So again, this makes our experience better in whatever we do. So humans remain the loop and are the center of our efforts. That's kind of, for me, the first key of success. Without that, I think we would be one of those dystopian futures that we're going to get there, but it's just avoid that journey. The second one, again, once we talk about people first, you're always going to have to talk about ethics. And that's in the news all the time. And it's super complex, because we're going to bring simulated intelligence into our team. That simulated intelligence needs to somehow encode our values in our organization, whatever the organization stands for, the mission, that makes you make different judgment decisions, very different based on where you are and what your field is and your area of business is. And the best way I always like when I think through this, and you heard me in Matthew's example, think of a new person you hire into your team. You're getting great technical training, you spend a lot of time with them through their induction to share the values of your organization, because you believe that that person needs to understand and for them to do their role right. As we go into this complex AI, we need to somehow bring that into those AI assistance. It's no different than having a new associate or a new person joining your team. You will also, that means you will need some sort of AI ethical office. I mean, we still have them. It's not that we don't trust the people that work with us, but we just want to make sure that whenever they have a certainty, they know where to go. And then we have a debate and what is ambiguous, we just make it clear for everyone. And then we just continue to progress and stay in the right path. That same rigor needs to be in AI. And I think the other one, it's related to transparency. One reason we love to work with people is that we can talk to them, we know who we're talking to, we can give them feedback and they can take it and get better. And it's not, again, it's not different with AI. Simulated intelligence, simulated judgment. You need to get people first, clear, I think of guidelines and just be, have those clear ways to get feedback and have transparency. It's not about kind of telling you how I build the engine. It's not about giving trade secrets of within the AI. It's about the respect we should have with everyone that interfaces with this technology. Our employees, our customers, our partners, our suppliers, they, if we are going to bring this technology, we should bring them along in the sense of telling them what we're doing, asking for their feedback so it gets better. That way we build the trust and we can then train these AI systems to get better and do our job better. And then last one, this is not like a deploy and leave kind of situation. If you get into this journey and everyone that has done even the narrow AI implementation, these AI systems should be monitored continuously. But you need to make sure there's no biases that you can usually get in without paying attention. Talk about the compliance and the overall behavior of the system. Those systems should have the ability to be expected and audited. Like we do when we go regularly through our organization in any function that has impact. We just make sure that we are able to ask questions and the system should be able to tell us. So I think that those are for me four key considerations. And again, there's always a list of things you expect in any technology problem. Make sure you have a clear objective, make sure you have the right security, you have the risk assessments that you do continuously planning. I think those are common in technology and big deployments of any change in organization. But the four ones that I mentioned above, I believe are key specifically for AI. People, ethics, transparency, and just continuous monitoring. You need to maybe this journey we're in. Yeah, it's interesting because last week I attended a conference and we were discussing about the leadership style now. And definitely people first, it was one of the key takeaways, but also living based on values. And this is now more important than before because, of course, we need a KPI and performance metrics and still needs to be in place. But the leadership style needs to focus more on values than on the performance metrics by itself. So very good points, Edgar. Let's try to move now more into the existing challenges and common pitfalls when implementing AI because it also exists. And there are many. So let's try to cover that area now. Yes, I think that if you think of the opportunities and the key things you have to consider, those are usually your risks, but I think summarizing a couple of different bullets. One is people first. Never forget that. Get your ethical compass straight. Once you have an AI system in your organization, it will make you verbalize that. And I think that is the other one. And in this journey, we have to be very humble of what we know and what we do not know. Bring the right talent to help you in the journey. I think that in every organization, having part of being humble of what we know and don't know about this technology is important. Otherwise, you will just get the tidbits you get in the news and bias your own journey based on what you see everywhere that gets the headlines. And that's not always what matters the most. So be humble. And by bringing the right people and the right talent, not because you don't know what you're doing in your business, because these questions that I brought up in terms of ethics and people and inspection and transparency are both technically challenging, but also require the right orientation to be able to deploy that skill. And complexity will always be a case. Again, none of this, even the narrow ML everyone that has gone through a journey of bringing a new system of forecasting or inventory knows it. You can make it as complex as you want. The more complex, fantastic, harder to inspect, takes more time. So things are pretty complex the way they are. So you don't want to make it even more complex by taking all these kind of areas at once. Be humble, experiment, don't get carried away, but at the same time, we all need to bring value. So that those kind of four things for me always in the forefront, people first, never forget that. You're going to get the corporate strength. Be humble. Ask for help, bring people that know what they're doing and bring them and make them part of your journey. And, you know, as you go through this journey, keep complexity at bay. Definitely, you want to show progress. You want to get them excited. You want to deliver value, but this is pretty complex, even the way it is today. Imagine all these visions that we were going to discuss. Yeah, and connecting with the complexity part that you mentioned, certain tasks such as routine and repetitive tasks are the most likely to become automated. However, these complex tasks that you are bringing to us today are the ones that I think will be augmented by AI. And supply chain problems are great examples of how AI can enhance those tasks and augment it rather than replace those tasks. So, yes, and we also need to look at how companies navigate this technology world. And I like the way you frame people first and values. Values matters more than ever. So now, Edgar, looking at the future, how do you see AI augmenting human skills? Because you have been discussing a lot about the importance of human in all of this new environment. How do you think it's going to augment these human skills? Yeah, it's a fantastic question. AI is about augmentation, augmentation of our potential, our potential of humans, you know, so we can do more. And it's always hard to verbalize this in terms of technology. So what I prefer to do is is kind of to verbalize it. This is kind of the dream I have as we single is come in and help us transport, envision that everyone in your company is able to hire these fantastic associates out of college, super bright, fast learner, great potential, and that is eager to get guidance from you. And this new associate, you know, has, by the way, immediate access to all the data that is relevant to you and your function. And then you can bring this associate to every meeting. And in that meeting, you can ask the AI associate, the AI technology, to pull data for you, show me this, bring me this, and even better, it will come with some kind of things that it will ask for you to weigh in and to help you understand in that meeting what you need to decide. And if for whatever reason, it brings you something that is not relevant or points in the wrong direction, you can put the agency back. And it will quickly adjust, forget, recalibrate, no hard feelings. In mind, you have like this sidekick by your side everywhere you go. How efficient will your meetings be? How much more time you can spend in the more even more complex and the more you teach that AI what matters to you, the more efficient you can make it. At the same time, you can then rotate that AI and send it to other parts of your organization, spend some time in human process, spend some time in capacity planning, spend some time in inventory. And the more you're able to cross train this AI, the more things you can do together, same language, same, same decisions. That is the vision. That's the technology we need to aspire towards. But you need to be a good mentor to this AI as well. You need to be better at your job than you were before because you're mentoring this new system along your journey. You have to, if you're mentoring someone for doing more and help you around, think again, think someone in your team, someone that joins, that person is looking up to you. So that AI is looking to us to guide you. You need to be then growing all the time. Because as you teach and the AI knows how to do things that you probably need to spend more time. Now you have time to think around the corner. And then point the AI, and this is the journey of business, the journey that I look forward to be part of. I think this is the mental model we should take. It's not about the techniques. It's not about the neural network. It's not about how many layers you have. It's how do you establish this journey of communication? And maybe you're associated, maybe wrong. And it's okay. You are there to supervise them and help them and then grow and grow and grow. And this is the journey, I think, of augmentation that I envision. Hopefully we'll get in this in my lifetime. I would say it's the journey and the mindset. We need to start the mindset. So this is truly a changing and very dynamic environment. And we need to prepare for that. You mentioned that we need to mentor on that. We also need to prepare our students for this future. And we need to prepare the future supply chain professionals to be competitive in this context. So we are almost wrapping up. Edgar, what are the key skills you want to see in the future supply chain professionals? In addition to knowing what you need to do, and I think that that's always the starting point, you need to be AI savvy. And this is, you know, you are going to be a key part. Anyone that comes to your attention now is going to be a key part of adapting, evolving, and embracing this Azure in the right speed with the right mindset. AI savvy in one. Critical thinking is always going to be even more powerful. The more powerful you have systems that help you and amend your capabilities, the more critical thinking is needed from you to help disambiguate even more complex and more impactful decisions. Again, people first. I see people, people solving problems. So you always have to communicate. Communication is critical. Supply chain is all about coordination. And even with the most powerful technology helping us out of our skills and our AI assistance, we're still going to be in control. And we need to communicate and convince people and persuasion. And I think that's going to continue to be paramount. So AI savvy, critical thinking, communication. And as I have learned in my own life, lifelong learner, I'm obsolete every month pretty much, you know, so you need to have that attitude of learning. How do you organize yourself to stay current? And to continue to help the organization, you learn within the job for sure, you learn around the organization, but you need to have that lifelong learning attitude. It was hard, at least maybe my I'm glad to hear you mentioned that lifelong learning is key. And I want to launch now the second poll and see how our audience is trying to keep up to date with all of this technological revolution. So this is a trend we are also observing at MIT, the Center for Transportation and Logistics. More and more companies are coming to us and they are asking to help about how they can train, how they can upskill their associates. And I truly believe that online education is an effective and very powerful way to help the industry and society in this endeavor. Thank you so much, Edgar. Thank you for sharing your experience with our MicroMasters in Supply Chain Management community. It has been wonderful to have your brilliant mind sharing your insights about this relevant topic. I'm going to share the last poll because I'm very happy to see that most of our audience are planning to take the MicroMasters Program in Supply Chain Management to upskill their knowledge in supply chain. That's great. And remember lifelong learning is key. Stay curious and don't forget about the importance of upskilling in supply chain management to remain competitive. Thank you so much, Edgar again, and thank you everyone for joining us today. Thanks to the team behind the scenes. Thank you. Thank you, everyone.