 So good morning, everyone. Well, it's great to have you here. Welcome to the MIT Center for Transportation and Logistics. My name is Chris Mejia. I'm a research scientist here. And I'm the director of the MIT Graduate Certificate for Logistics and Supply Chain Management, what we call the G-Select Program. And today, well, hopefully, you will enjoy this very nice interview that Professor Jose Sheffi and Susan Laysfield are going to have in order to describe what are the future opportunities in all of this AI in the future of work, right? And having said that, I'm going to hand over this to my colleague, Benji Kantor, the director of the Marketing and Communications team. So to you. Yep. Thank you. Welcome, everyone, to the magic conveyor belt, the Supply Chain AI and the future of work and interview with Dr. Jose Sheffi. I am here to welcome not just you in the crowd, but also the close to 1,000 people that are viewing us online. We're very excited to have you here. One quick reminder, and I will do this here myself. If you have not silenced your cell phone, we are recording today, so I appreciate if you would do that. It's my pleasure to introduce to you our host for the day, Susan Laysfield. As the executive editor of CSCMP's Supply Chain Quarterly, Susan serves as the primary curator of the publication's content. And Susan was one of the founding members of the publication back in 2007, having worked prior at Supply Chain Management Review. We're so grateful, Susan, thank you for being here today. I invite you to join me in welcoming her to CTL, Susan Laysfield, and Dr. Jose Sheffi. Thank you. So I really wanted to thank Benji and Chris and all the folks at CTL for inviting me here today as a Supply Chain Editor and professional fan of the Supply Chain. I feel like it's a real privilege to have a chance to sit down with Dr. Sheffi and talk about his new book, The Magic Conveyor Belt. Just as to give you an idea of how I see this going forward, Yossi and I will be having a conversation for like 35, 40 minutes. And then we really want to hear the questions that the audience has. So I'm going to rely on Benji and Chris and you all to make sure I stay on track so we can get to your questions, because that's the part I'm really excited about. And at this point, usually when I'm doing an interview like this, I would introduce the person I'm interviewing, but that feels really silly here because this is basically Yossi's house as the director of CTL. So I thought we'd just kind of dive right into the questions, Yossi. And maybe a good place to start is with the title of the book. Can you explain the analogy you make between the Supply Chain and The Magic Conveyor Belt? And what makes it magical? Well, so let's start with why I wrote this book. Yes. And after the pandemic, a lot of people were getting to my wife and asking her, we understand your husband is in supply chain. What is this? Right. Amazing, even after the pandemic, people heard a lot of supply chain, didn't know what it is. So rather than having one-on-one interview with one of the several hundred friends that my wife has, I decided to write a book. And so the first part of the book is explaining what supply chains are, why they are complex, and in some sense, why people should not be pissed off when something is not on the shelf or not available on Amazon, but should be amazed and awe-inspired when it's there. Once they understand what it takes to get something from the mines in China or somewhere to a finished product on a shelf, how many processes it has to go to, how many people are involved, how many different tax regimes, customs regime it has to go through before we get the final product. So this was the rationale, and the magic conveyor belt is because once you understand what it takes, you think it's magic. It's very true. That's the title. That's just true. So it was to get away from people asking you why they're cat food. Yeah, absolutely, absolutely. So as you mentioned, the first part of the book really talks about the growing complexity of the supply chain over the past few decades. And I was wondering, do you think we're gonna reach a point where companies are gonna push back and say things are getting too complex and we need to maybe take a step back and look at simplifying or is complexity here to stay? I'm not sure. I think complexity is here to stay, complexity is here to grow because of unexpected event that's happening. Because, and furthermore, I'm not sure there's a pressure to do it because a lot of the technology that is being available help company deal with the complexity, help deal with the unexpected event. So I'm not sure there's a pressure to do it, especially among large sophisticated companies. So the answer is no. No, it is here to stay. Here to stay. And one of the things that I liked in the book that you said is... One of the things? One of the many, many, many things. One of the many things. I said one of. I know, okay. Not just one. Just two. As you talked about one of the most mind-blowing facts about any product that we touch is the thousands of organizations that have been involved in creating it and that they have done that without any central control. And I was wondering if decentralization is, do you feel that's crucial to supply chain efficiency and operating in this complex world? The answer is categorically yes. Okay. The idea that somebody can control, control supply chain is controlling the economy. We tried it once or twice. Didn't work very well. So we're talking about modern markets. Supply chain is actually a whole set of a buyer-seller, buyer-seller, buyer-seller, negotiation, transaction, operation. It works because everybody's trying to do the right thing to minimize cost and maximize the level of service, by and large. Now there are other things people are worried about like sustainability and resilience, but everybody's worried about it. So everybody's trying to get the best outcome. I don't see how central planning can work. Even in China, we don't see, it's not central planning. It's the central control of certain aspects, but not of the transaction. In fact, the Chinese seem to be leery of very large corporations who control more of the larger part of the economy as happened to several tech companies in China. They actually seem to encourage competition between companies. So I think it works. The market works. But as you introduce decentralization, there's an element of risk that kind of enters the equation. I was wondering, how do we balance that risk with all the benefits? No, it's a contrary. The risk goes down because the risk to a particular company maybe goes up. They are out there on the front line, but the risk to the economy goes down. Look, you can always find good restaurant in New York. Always. You walk to a random restaurant, the chances are it's a very good one. Why? Because restaurants in New York, if you open a restaurant in New York, the chances are within a year you'll have to close it. The competition is murderous. There are so many good restaurants. So you can say the chances for an individual restaurant to succeed is not very high. But going to New York and having a good restaurant, the environment is great. It works. There's no risk. You don't risk going to New York and not finding a good place to eat. I'm not saying there's a place to eat, a good place to eat because it is decentralized. Okay. But there is, when you outsource to a supplier and they're outsourcing to other suppliers, there is that added risk of a quality defect that you can't control or a sustainability issue popping up. Is that a concern with this decentralization? How do you control for that service? I don't see it as a decentralization issue. Okay. I see it as the depth of the supply chain, the lack of visibility, it exists. It gets slowly better with new technology, but there are limits here. The limits are that the suppliers, for suppliers to tell their customer who their supplier is, not every supplier is willing to do it. It's a competitive advantage to know who the suppliers are. And they're always the fear that the customer will go around them, will go directly to the supplier. So there's kind of built in opaqueness to the supply chain which we're trying to get through to visibility and good relationship and all of this. And some people are more successful than others, but this issue is not a technology issue and it's not, it will be very hard to solve completely. And it's not decentralization issue. Okay. It's the depth of the supply chain. Okay. So it's a different. Different issue. Different issue. So in the second half of the book, you spend a lot of time talking about artificial intelligence and the effects that AI is having on the supply chain. And I was wondering, when chat GPT hit the scene in November, suddenly generative AI became a very hot topic. And I was wondering if you could talk about some of the applications for generative AI that you are seeing in the supply chain. First of all, let me just explain that we have been using even AI for a long time. Yes, I should have promised that. You're absolutely correct. It's not as new as starting November last year. We see, in fact, if you read today's Wall Street Journal, there was an article about the chatbots using that all the restaurants, all the drive-through restaurants are using chatbot, but it's not on a drive-through. Every time you call, now a day customer service function, you are talking to a chatbot to interpret the results and try to give you answer. And if sometimes it gets stuck or you get stuck and start screaming agent, agent, agent or something to this effect, a human comes on. Just like when you go to the drive-through and you start ordering champagne, and McDonald doesn't have it, a human comes on board and say, well, I'm sorry, we don't yet serve champagne. So it's happening all the time. An interesting application is in risk management on a supply chain. Trying to look at suppliers and finding out how risky they are. Turns out that when you look at metrics like then at Bread Street and Financial Metrics, they're backward looking by about two quarters. You want to see what's going on now. We knew for a long time that one of the warning signs is having coverage about executives living, about having failing some projects, failing some M&A project in particular, having bank governance that are a little problematic. All of those are in the media, actually. So now we have several companies are using large language model particularly to look at tens of thousands of suppliers at the same time and analyzing all of them, analyzing all the mentioned the medias of redundancy, of executive living, whatever, in order to generate an alert. And have somebody visit there and finding out what's going on if we need to start looking for another supplier or what. So this is something that they could not have imagined before we had this thing looking at, if you look at a company like, I don't know, I'm working with Flex a lot and they have 18,000 suppliers, that's just first tier suppliers. Just finding out what's going on with them is an issue. But having a much better alert when something goes wrong is something that we were not able to do before this type of technology was available. We could check tens of suppliers at the time, checking tens of thousands was impossible. Now it's being done. So the AI is going to actually enable even more complexity in the supply chain in the future as we're... Yes, it can enable more possibilities. More possibilities create complexity. So of course, when people get into, when there's pressure, economic pressure, whatever pressure, we know that during recessions, company reduced the number of SKUs. So they're trying to simplify. They're also trying to reduce costs to improve service, but they're trying to simplify. But there's the accordion theory of management that when recession happened, the number of SKU goes down and then marketing comes up with all the good reasons why we need more and more and more SKU to serve more and more territories, more and more cause, all kind of option. So that's the accordion theory of management and it works actually. So it's like the pendulum swing that kind of balances. Yeah, between recessionary period, expansionary period. So we've talked a little bit about AI. Are there, and the benefits and possibilities of AI, is there anything about the application of AI to the supply chain that gives you pause or areas of concern? Not about the supply chain. Not about the supply chain. Not about the supply chain. The areas that give me cause are the areas that give other people cause. The area of fake news can be done very, very convincingly. The area of giving instruction how to build, improvise roadside explosives, the area. But while I'm saying this is a concern, a concern in the media, I'm not that concerned about it because just give you an idea. Unlike the early days of the internet when we all, everybody thought this is the greatest thing since sliced bread, right? Because we can communicate with everybody, families can see each other, all the distance is dead to quote Tom Friedman. Nobody thought about identity theft. And stealing customer data and terrorists communicating to each other on the internet. Now it's different. With the generative AI, the companies, the media, the politicians are all aware of the dangers. So there's a lot of work is going on already. Already the companies themselves are putting guardrails on this. So if you talk, if you get chat GBT or any one of the others and ask how to prepare a molotov cocktails, it's not gonna answer. So this is not, I'll give you an answer. So they already started to put guardrail and there'll be more of this. Are you seeing that also with use of analytics and companies where you might have an algorithm, there's an example in the book about two competing bookstores and they're both using a pricing algorithm on Amazon. And as a result that drove up the price of the book very high. Are you seeing companies that already have those human interventions in place to make sure that the algorithms don't go out of control? Let me give you a more even general answer. One of the most important type of work in the future will be monitoring. Letting the automation in AI infuser otherwise, but having a human monitoring, that's a tough job. It's a tough job because you have to monitor something you don't do every day and actually you lose expertise. It's hard to keep sharp. And we have cases when things did go all right. So this would be, it's important. How do we train people to do it? For example, today modern aircraft can basically fly by itself gate to gate. Now, talking about autonomous vehicles, not too many people will go on aluminum cylinders that fly 35,000 feet over the ocean without a pilot. But why is that good? I lost my turn. That's okay, but we're talking about training. We're talking about training. So the pilots in the aircraft actually don't need to do anything. They can just sit there and nap. But what we do, we let them do the communication basically. It's the number one job. So they always have to communicate and change frequency. So they keep alert. It's one way to do it because flying the airplane, it flies itself. So you really don't, once you put the crew, it flies, it changes routes, it goes automatically. But you give some jobs to the human, they're not gonna fall asleep. That's part of the challenge of the future. There are several models how people and machine can work together. Now, one such model is what we talk about, the chatbots. The chatbot has a monitor because you talk to McDonald, whatever in the drive-through, you actually talk to a chatbot in most places and they respond. And then when they don't understand something, a human comes on and you talk to him. So there's a monitoring of what's going on and the minute that the chatbot doesn't understand what's going on or gives the wrong answer, whatever a human comes in. So that's actually a monitoring function that we don't even think about, but happens every day with most customer service function. It used to be that press one for this, press two for this, press three for this. That's rare now. Now you just talk to the computer and it turns into text that appear on somebody's screen and then they report and try to find an answer. That's AI. Do you have any good examples of companies that are doing good thinking around what should be given to humans to do in the supply chain and what should be outsourced to AI? Okay, that's to me, that's the question of the future. The question of how the integration of humans and AI-infused automation is a question of the future. So we talk about one model. The monitoring is one model. You can think about when the human is in the loop. The human is in the loop, for example, think about an Amazon warehouse. When a person, when the picker stands in one place and there's a, you know, the aisle comes to the picker, that's something, then another aisle comes to the picker. So the human is in the flow of the work. So that's another example. A third example is the human operates. When you go to several automotive plants, for example, you see workers standing with iPad-like devices and basically running the robots. So that's another way of working with AI. So that's, as I said, the question of the future, how to organize the work. Exactly. And how to, in some sense, how to get the best out of the machine and out of the human, because they have complementary skills. Machines work all the time, don't get breaks, don't go to the bathroom, they just work. And they don't get sick. They usually, so they usually very accurate. They do repeated work over and over time. What machines don't have is context, understanding when something does not belong, has to change. When we think about something change in the economy and suddenly people order things differently. So many standard automated ordering system use the point of sale data and order based on this, put it into some forecast. This forecast is based on, at best, say on machine learning, which is basically looking at past data. All forecasts are based on past data. When something is changing structurally, suddenly there's a pandemic, suddenly there's something else happening and people change the buying habits, then humans have to intervene again because the machine does not have context. As the machine is causing nothing change. I mean, it gets, you know, point of sale data, but something has changed and people understand the context. Now, there's other things about empathy and bias and things in general that the human can make sure that they happen or don't happen. It's harder for machines. Do you think we're gonna get to that point where machines are gonna be better at that or mimicking that empathy piece? Cause it feels like the people who are working on AI are trying to get there, you know, see AI used in mental health these days. Yes, there are some actually automated psychologists that try to help people. Who knows? Who knows? Yeah, yeah, yeah. You say, can you, I'm not sure about this because that's exactly a question of context. Yes. Two people coming and saying, you know, I hate my children. Or I hate my supplier. I hate my supplier. Well, you hate your supplier, you don't go to the psychologist, but you know, you hate your children, you go to the psychologist. And the, but the context may be entirely different. You know, I hate my child because he's a thief and a liar or I hate my child just because I don't like tall kids. I don't know. Who knows? I mean, the context is matters. It gets freaking my neck when I talk to him. It's hard to imagine some of these things moving to AI completely. And they talk about supplier. Again, it is hard to imagine. Let me put it strongly. I don't think in the next 10 years, when it's five, 10 years, we will be able to have an algorithm setting up a contract with a supplier in China or Vietnam, let's say, to set up a contract and relationship for a long while. It requires somebody to fly to Vietnam, to negotiate like hell for two days and then see it and have dinners or two dinners and talk about the kids and talk about the family and create relationship. It's, I don't see it changing in the near future. I mean, AI will have to be so much better and have to have another quantum job in capability to be able to do it, which right now, not clear it's possible. It's interesting though, because there's been a movement with technology of making decisions more fact-based, as opposed to, you know, I like Joe over at, so it's in such trucking a company, so we're gonna use him. But it seems like that human relationship is, you're saying is still gonna be an integral part of supply chain management in the future. Yes, it is still integral because, for example, if something goes wrong and there's some disruptions, how do I make sure that this supplier knows my situation, knows me? And if I'm calling and say, really, look, I really need it and everybody else called and say, I need it. I really need it, yeah. But I know this guy and I know that he really needs it. So the knowledge is, I think, still very important, the personal and personal relationship. Now, one has to be real there. There may be critical suppliers and maybe suppliers that are not so critical. Right. And if they maybe supplier, if I have some part, some commodity that they have dozens of suppliers and if that supplier goes down or I have some shortage, there are many others, maybe that I don't need to be close to them. But for most important suppliers, I don't see any other way. Sometimes it's hard to know what your critical suppliers, you might need that little screw and then suddenly that screw goes down. It's called in the automotive business, they call it the golden screw. One part that's meeting and you cannot make a car. Right. The example in your book about the Ford not being able to ship out because they didn't have the little Ford logo that sticks on the truck. This was last year. Ford has the blue little oval that they put in the front of the truck. They didn't have them during the shortages. They couldn't make trucks. I mean, the trucks were standing in the yard and they couldn't sell them for a month, actually. So can AI be helpful of identifying who it is that you need to spend your time developing that human relationship with? It might not be who you think it is. You also have to... The question, you can take AI, I think it's simpler. But as an aside, let's me say that AI became the buzzword of the time. We used to think about blockchain or FID or became and people who are doing blockchain projects they're actually just fixing up their systems and to get funding from management they call it that's a blockchain project. Now they call it that's an AI project for doing some optimization. So that's the learning to go away. When you go back to your company make sure your project is AI. Use AI. Okay, what exactly are you using? And it is appropriate. I mean, the idea that the... Can that tell you how many companies I was there? The tail is wagging the dog. I used to go to boards and people would ask the CEO what's your China strategy? Or what's your blockchain strategy? Now they ask what's your AI strategy? And I always say stop it. What's your problem? Start with the problem. Maybe the solution is AI maybe just hiring another person. I mean, it's not, you don't start with the solution but it's amazing how many people still do it. Because I don't know in part because Wall Street pays premium for having an AI strategy or something of this effect. It's not clear to me, it makes no sense. Can AI is figuring out the problem an AI issue or essentially human issue? Is that something that's gonna... AI issue, operation research issue, statistics issue, people issue, process issue can be anything. So that's why I don't like having an AI strategy or blockchain strategy or whatever is the current Fed. Right, right. I should say AI is not Fed. It's been growing for many, many years and we got to the point that it could make substantial changes in how people work. The relationship between people and machine. Right. We were seeing, just like a year earlier, I think the buzzword was all robotics. So it's kind of co-bots and so it's the same sort of thing. Robotics are also now fused by AI. Right. I mean, so... Right. It's not the actual hardware of the robot. It's the software, of course. Yeah. So kind of taking a step back to your point about contacts and the pilots and training. You sometimes you have to do all the low level jobs to get that contacts to know when there's what to do next. So... I do talk about it. Yes. So what can we do with our supply chain pilots, so to speak, to make sure that they have the background, the knowledge to be able to take over those unusual events? Again, I take the problem a little further from your question. Okay. So I was interviewing a shop, basically a software provider, asking them about ShedGBT taking the job because it can now program. They say, the senior program, the senior computer scientists are not worried about it, but it may take the job of the junior computer scientists. Now saying, guess what? Senior computer scientists don't come with senior computer scientists. They start as junior computer scientists. You don't have a junior computer scientist. You don't have work for them. You are not gonna have senior computer scientists. So there's a, even for monitoring, you need people with experience. In the book, I talk a lot about how to do it and how to upgrade skills. But there has to be a recognition that you need to hire people at the lowest level. One of the suggestions that I made is one of the thought is maybe pivot in the United States for more of the German system of people spending half time in a company and half time in a university. And they come up, it's called the dual education system. That's about 52% of the German high schoolers going to the system, which is government control. The government defines Germany. So the government defined 365 professions where this can be done. And the university, you apply actually to the company and they work with a local college or university. You spend half the time studying the theory basically and half the time doing the work. 70% of these people get hired by the company that they do the internship with, but they come with experience, knowing the culture, knowing the company. It's much higher to move them, much easier to move them along. The United States, we suffer another problem, is every mother wants to say that their child goes to college. My child goes to so-and-so college and your child just goes to trade school. I always say that people should meet my plumber. My plumber is driving her all sorts. Just saying. It's not a Bentley actually. My plumber drives a Bentley. We don't have enough plumbers and they can set the price. And they do set it high. So we say that too many people who go to college in the United States and unfortunately in many cases come back with after-college debt for a long, long period rather than go to trade schools and community colleges or combination. Actually there's a university here that does it. Northeastern. Northeastern, yes. The combination of work and it's not as organized as in Germany but it's the same idea. You work one semester, you study one semester and you flip between them. And it's interesting. I feel like more and more people Northeastern is becoming a school that more and more people want to go to nowadays. I know. I came to the United States about the dark ages. Many, many years ago in 1975 you could walk into Northeastern and get in. They were just glad to have, you needed to have a pulse in order to go to Northeastern. They now became a very, very selective school. They are about 14, 12, 14% acceptance rate. And almost everybody who gets accepted comes in. So it's become really selective school because of this. People start to realize that it may be a good idea. Yes, and a lot more students from all across the country are looking to go to Northeastern. It used to be a local school. It used to be a local school. Yes. So that comes back to your main job of training students. How have what you feel are the necessary skills for a supply chain manager changed recently? Okay. And how do you train that sort of? Look, if I go over the history, we started, this program here started a very analytical program. Right. And then we realized that our graduates who are very analytically savvy end up working for Harvard MBAs who are half as smart and get paid twice as much. We said, this is... Probably good at talking. This is not working. Yeah. So furthermore, furthermore companies came to us and say, your graduates don't go up the ladder in the company because they need the soft skills. They need to be able to communicate. They need to be able to sell. They need to be able to explain a position. They need to be able to work in a team. So the programs changed. They started doing a lot more of this. I think that as AI and it's getting more and automation is getting more and more into the workplace is the soft skills that will become even more important. Become, and how do you work in team? How do you make sure that your people can work with AI? How you can make sure that the promise of AI is that it will do the job that nobody wants to do and people will do the more interesting and fulfilling job. How do you make sure that this actually happens? Yeah. So all of this is part of the challenge of the future. We don't have all the answer yet. We don't even have some of the answer yet but we're thinking about it. So people will need to understand. We're not training computer scientists but people need to understand the capabilities and where it can go wrong. So people need to be sophisticated users. It's like my colleague Chris Kaplis always talks about driving. There are mechanics who actually can fix the car and know what's inside and there are drivers. You don't have to know what's going on. You can just operate it. We like to train drivers. People who understand what the system cannot do but they don't need to be builders of AI, of a generative AI system but they need to do the promise, the limitation and how to best use them. Yes. People always ask me if I allow, in classes, if we allow people to use LGBT. That's a big debate in universities. Some universities absolutely disallow it. It's ridiculous. It's like this allowing, let me go back. When I was your age and actually younger, they used to teach me how to take square root by hand. None of you studied it because they are calculators. None of you are studying how to do a financial statement by hand because there's no Excel and spreadsheets. So the question is, why do you need to do to replicate what LGBT can do by hand? What you need to do is when it goes all right, when it starts making, you need to test it. You need to make sure that the results are not what they call hallucinations. Because LGBT can hallucinate and invent stuff, invent sometimes reference, they don't exist. So you need to be sure of this because one thing, you can submit to me a paper written by ChetGBT as long as you realize that if something is wrong, open AI is not getting an F, you're getting an F. Just so we understand each other. So in short, the responsibility is still on the user. But not using a tool that's available for me, it's a losing proposition. You cannot, it's very hard to work it. Another example, when you of how automation is this killing jobs but having other benefits. So if you go to London and you go to a black cab, to drive a black cab in London, you have to study for three or four years and pass an exam which is considered the toughest exam in the world. Because you need to know every point of interest in London and how to go from everywhere to everywhere and you sit in an exam that you have to show that you can drive from everywhere to everywhere in the shortest route. And you have to understand congestion and you have to understand people who are doing this and spending four years of their life doing this. And then came Google Maps and Uber. Everybody can do it. Now there's still the number of black taxis in London went from $25,000 to about $8,000. But the number of Uber's available is now about $60,000. Lots more of them are available. So win some, lose some. I wonder if the black cabs in London, if now they are serving a very different customer base, are they serving a lot of the tourists who kind of want the experience of riding in a black cab. And if that's what we're gonna have to be thinking about too. Go on a red bus and go on a black cab. Yeah, usually take whoever's car. Right, exactly. Whereas the guy who needs to get across the city just wants. These have all been great. So another thing you talk about in the book is how technology has had an impact on enabling supply chain strategy. Like we wouldn't have been able to do all the outsourcing and offshoring if we didn't have advanced communication technology. And do you see some radical changes on how companies will be structuring their supply chain or organizing it because of the AI or other emerging tech like robotics? Look, it's already happening in the sense that the number one use of robotics is in warehouse automation. I mean, warehouse and putting robots like there's no tomorrow. Autonomous vehicles, autonomous vehicles are robots. So there's a lot of work on autonomous tracking. Let me just say, however, that I talk to a lot of people, a lot of interviewing people are worried about the jobs. There's a number one fear job. And again, people should chill at least for the short term because it doesn't happen fast. Give you one example, in 1892, AT&T invented the automatic telephone exchange. Until then, there were women putting plugs. Where's Mrs. Smith today? She went to the supermarket, they'll connect you later. Very personal service. That's what my grandma did. Yeah, okay. By 1950, there were still 350,000 operators like this in the United States. Only by the 1980s, it started to go really close to zero. Nine decades from the invention until it really, all the jobs went away, or most of the jobs went away. So it takes time and it takes time because there are many hurdles. You see already hurdles, you see the, what are the writers and actors worried about? They're worried about using AI. Yeah. And they are stopping the industry, putting the industry down. And the industry will have to come to some kind of agreement. My guess will be part of the agreement will be somehow slowing down or putting guardrails on the use of AI. Kind of like dock workers with, I was about to say dock workers also fight automation. Yeah. LA Long Beach is nothing like Rotterdam or Singapore or Dubai because of the afraid for the job, afraid for the immediate job and not taking into account that you can increase the throughput and get even more jobs for this. Or there'll be more jobs elsewhere in this. In general, that's the most difficult thing in this area, in this, when people are worried about jobs and understand it is anxiety because you know the people are gonna lose their job. You see it in the supermarket when you get to, when you can check out yourself. People are gonna lose their jobs. So these are people that you know. What you don't know is all the new industry and the new jobs will come. So one quick example of this, that is, all the examples. So Ford when came up with the assembly line system, change manufacturing of course, but it used to be the specialty team used to build one car at a time and for the several thousand workers. During the height of the model T using the assembly line for the about 150,000 workers. But this is not the big impact. The big impact was that automobiles became less expensive. Highway develop. Hotels, motels, restaurant, the whole hospitality industry created millions of jobs. This was not what Henry Ford had in mind. I mean, but it was a side effect of what happened. That is why it is so hard to imagine all the new jobs that will come. Many of the jobs that exist today did not exist, you know, few decades ago. Who thought a few decades ago about people who will optimize ads on Google? Or people, there are so many jobs that are totally new because of new industries that came up. So this will, it's hard to predict what will be, you know, all the new jobs. The one thing about supply chain coming back because that's what you ask about is it still involve physical movement. Product have to move. So there are some things that are, that will be still grounded for a long time until we start having 3D printing at scale. This can change supply chain, but it will be a long time because 3D printing is still very slow technology. It cannot replace mass production, not even close to replace mass production. So I don't see fundamental changes. The changes that may come will come because of geopolitical, you know, consideration, resilience consideration, sustainability consideration because, but this, to get this done, we'll need to have some more system thinking, which is in very short supply. Among the political class, the media class, people are talking about, you know, give you an example. Where are the minerals? I use in every sophisticated product now, China controlled 80, 90% of the world supply. Aluminum, China controlled most of the world supply and most of the smelters are in China. You know which country has more earth minerals in the ground than China, the United States? But we don't want to mine it because it's environmentally problematic. Even though one should say, if it will done in the United States, it'll be probably done in a lot more responsible way than it's done in China, but still. So we have to decide. We have to stop saying green is, and we just go green. Right. We go security. We go standard of living. We have to think more holistically. And this is system thinking that, as I said, in short supply because there are pressure groups where the green parties in Europe or environmental lobbies in the United States, there are the security hawks that want everything to be from here. But again, from supply chain point of view, moving the assembly or the last stage of manufacturing to United States is meaningless or to Europe is meaningless because there's a whole supply chain that was built after investment of billions of dollars and decades that they see in China. Very hard to get out of this. It will take billions of dollars and decades to get out of there. So we need to stop talking about totally separating the Chinese and the Western economies and starting to work better together. It's just not realistic. So what are they, two-pronged or... Two-pronged supply chain. It's a nice thought. It's just not realistic, I think, because people don't realize how much is there already. That is very hard to move. And by the way, even if you move some manufacturing, how much of the resources are coming, are mined, not in the West. So still need that. And as long as you depend on something, you're not really independent. Well, I see Benji standing up. So that's my cue, that it's time for the audience to ask their questions. I would be really interested to hear what reflections you have for Dr. Shelby. Somebody here, somebody there. Hello, Victor Silva from Chile. People are having a hard time getting used to new technologies due to the speed at which they appear. Not even leaving young people or young adults time to adapt. How can we solve this from the perspective of a company knowing that adapting to new technologies is even more hard for them? Good question. So for those who didn't hear it, the question was adaptation to new technology among young and old alike. So two answers. First of all, good new technology make it simple to use. And a lot of what companies are investing in is making the technology simpler to use. You guys don't remember when you had only green screen and you had to type stuff. You now move a mouse on there. It's a huge advance by the technology is to make it simpler to use. So a lot of these are getting simpler to use. So that's one drive. Another drive, the problem is not that it gets simpler to use. The problem is that you have to understand the output and make sure that it's correct. And that's very hard. That requires education and training and learning. There's just no way around it. The real challenge, I think, is not so much large companies who are actually investing in this. Many companies are investing in upgrading employees' capabilities. I think the big problem is all the gig workers, the independent workers, the small companies, that don't have the resources to take time off and invest in upgrading skills. And as it pains me to say that I think this is a good role for government, that the government has to be able to have the ability to pay for people to upgrade skills. And I'm not sure how to do it. I'm not sure how to fund it. But that's a role for government. Because otherwise, the fear is inequality will grow substantially. And that's not good for democracy. It's not good for the country. It's not good for anything. Sure. Toby Gulley from CTL, a related question. Given the growing, inevitably growing importance of AI in supply chain management, how should that be incorporated into supply chain professional and academic education? Is that something that a must have for every program? What do you see happening there? What would you recommend? OK. Again, this is something that I think I touch slightly. The question is, to what level you want to train people? You want to train people to be able to write algorithm? That's not supply chain function. You want to train people to understand how to use and how to know the promise and limitation of algorithm? Yes. That is something that you do. And you do it with just like we teach everything else, with exercise and case studies and lectures and examples and bringing people from companies to show how they do it. But that's the point. To what level you want to get people? I would say in supply chain, we want to get people to be good users and understanding the technology, but we don't have to strive to have people who can develop the technology. Now, look, at the same time, let me just say, there will be a few people. I'm not sure there will be supply chain people, but there will be a few people in every company who will be technologists, will be basically data scientists, be able. The company who now, when you go to the drive-through and you talk to a chatbot, you talk to a several retailers, several restaurants, you talk to a system that sits on top of open AI developed by the company itself. So it recognizes things on the menu and it's recognized if it can answer what is gluten-free and what is not, so it can do certain related things. But if you ask about champagne for breakfast, it gets dumb. It doesn't know how to deal with it. So this was developed inside the company. So there are people inside the company who understand how to build on top of open AI. They do a lot of other applications. I'm not sure these people will be in supply chain specifically, but there will be some kernel of expertise in the companies to do things that are applicable to this company. Hi, this is Sri Devi from CTL. I think AIs tend to think the way they are trained to think or taught to think. So from that perspective, do you think AIs are also subject to psychological biases that humans have and hence make erroneous decisions? OK, for those of you who are here, the question was, is my book available on Amazon? Let's talk about the question. That has to do with biases in the algorithm. Sure, the problem is not so much built-in biases. Those exist, of course, but there's something specifically built in. But biases in the training data that the people who use it don't even realize they are there in the training data. And this has to be found out by people, by running the algorithm, by trying it. You find out that a bank that uses AI to decide on the risk for a loan certainly doesn't approve loans for certain type of people. They want to go in and find out what's going on. But it can be unintended because it's just the training that is collected from lots of data. For example, if the bank in the past had gave loans only to certain kind of people, not to others, it will be in the training data of that bank. So one has to correct for it. But it requires human intervention. Again, context, a quite human intervention. Good morning, Jorge Haldín from Bolivia. Hello. Glad to be here. My question is, what potential do you see in artificial intelligence technology like convolutional networks to interpret image and video? How can this impact the supply chain management works or jobs? Because interpreting images and videos could make things more easier. Yes, absolutely. We'll be able to, just like chatbot, are able to interpret speaking words. It's actually easier to interpret what's in an email, what's in a message, what's in an EDI, and take action based on this. In fact, in many cases, you are already interacting with it. You send messages to some companies, and several things can happen. Either a chatbot comes out and say, I'm Linda, and I want to talk to you. It's always a woman for some reason. Start chatting with them. You are chatting with an AI system. You're chatting with a chatbot. If you talk to, if you try to look for a car, you look at the Honda dealership, Linda will come out and try to talk to you. And what you do with this, you actually write down stuff. You write it, the chatbot picks it up, and writes back. So it's already happening. Whether it will start happening with emails? Yes, the question is only when? And people develop the system. There's so much development going on that it's hard to say when, but clearly it will happen. I'm really looking forward to that application because I need something to help me with my out-of-control email. Out-of-control email, yes. I think we have time for one more question. Oh, put the... There's a question there. There's one over here. I'm dueling. Go to David. Thank you, I'll be quick. Thank you so much for this. You mentioned two particular incidents that caught my ears, the striking port workers and the striking actors. And I was thinking about how those are both industries that were particularly struck by COVID-19 disruptions. Port workers, there was the big story about all the ships that were off the port of Los Angeles, Long Beach. And in Hollywood, they couldn't produce for a long time. And I guess I just wonder if you see a connection between industries that were struck, especially hard by COVID-19 and the acceleration of AI that might lead to these strikes. Okay, that's actually a question. Again, I'm going met on your question because when you will start having more and more email interaction with coworkers, with your boss, when you start to have more interaction between worker, between project member, between team, as you'll be automated, this will be easier because more people work at home because of COVID and they are, you have anyway less face-to-face interaction. And maybe over time, people will start value less face-to-face interaction. So you have all these things working together. Whether you're talking about this industry or in terms of industry that were hurt, many industries were hurt. I don't see universities, schools going on strikes. They were certainly hurt, close by this. How about school teachers? Yeah, public school teachers, but they perennially go on strikes. It's not, I'm not sure. I think they're under a little more stress now because of COVID. So they're making them more likely to go on strike. I'm sorry, that's a totally different subject now. It's okay. So I, do you see a connection? I don't, I'm not sure. I see, I see a connection between the, say, we talk about strikes. The labor stand on yellow truck line that they are willing to let yellow go bankrupt because there is such shortage of truck drivers that the membership is not gonna be hurt by this. The membership is gonna find other jobs. And maybe with the pandemic with the shortage of truck drivers and all this. So you see, because it's amazing that the union is willing to let 33,000 workers just lose their job. But that's the stance. I mean, they're ready to strike. Yellow was not paying for the pension already. So it, and actually customers are living yellow. So they're really in dire strait and they're gonna go under unless the union gives them some ability to operate differently and it doesn't look it. So again, it has to do with the situation of labor about the, I'm, if I would be a screenwriter or an actor, I would be worried. This job can be replaced to a large extent by AI. Where they, look, screenplay from Hollywood are so, you know, you can predict what they say. You can predict the ending in 99 out of 100 movies. It's not a surprise. So as long as this is standard stuff, just a bit, they can do it better. Once in a while, of course, you'll have indie movies that break them all, then do something different. Sure, this will require, but for the bulk of the work, especially from Hollywood, give me a break. It is so predictable. And so you can see how things go if you watch enough movies. So yeah, about this particular strike, I would say they are not in a winning position. And the problem mainly is what we forget. We look at the stars who make millions of dollars, but the most actors make hardly living ways. They also work in other areas, workers, writers, everybody in the business working other areas, like 90% of them are the salary from the movies below the poverty line. So those people are gonna break. It will be very hard for them to continue. We'll see. One last question. Hi, professor. Better be a good one. No pressure. No pressure, better be a good one. I'm from Brazil. I wanted to know a little bit more about decentralized supply chain and its complexity. And given the recent events that we are seeing in the world, like the war in Ukraine, China moving towards Taiwan, I want to get your view if this is going to affect supply chain, world supply chain somehow, and what companies need to do if this affects their supply chain. Okay. My guess, if China is gonna take Taiwan, the last thing you should worry about is supply chain. This will be, if the United States will start a war with China, that's Armageddon. That's, you know, I'm not clear how this will happen. Our two superpower with nuclear, you know, nuclear arsenal going against each other, that's unthinkable. So I'm not sure supply chain are gonna be affected, of course, but that's not gonna be the issue. It's also, look, the United States, by and large, can grow its own food. Because we started talking about the basics, about food and medicines, and the United States is not independent in terms of many medical supplies. But neither is China. See, that's the issue. China is also not independent. And the reason that China invests in the, in all the infrastructure all over the world is basically to create, to get a lot of material out of these places. Well, all these sea lanes will be closed instead of a war between the United States. You cannot be able to get stuff from Africa to China. So it's not clear what will happen. It's very hard. But by and large, of course, geopolitical tension, whether they were in Ukraine or even just tension in terms of tariffs, do impact supply chain, of course, and make it more difficult to operate, more complex to operate, but not impossible. Anyway, let me stop here and thank you all. Thank you, Yossi. This has been a great conversation. I've enjoyed it. Thank you. Thank you all. Thank you.