Summary
Joannes Vermorel’s Introduction to Supply Chain begins by asking what most textbooks dodge: what is supply chain, and what is it for? He rejects catalogues of formulas and KPIs as trivia, arguing that supply chain is the mastery of options under uncertainty about physical flows. That pulls pricing, assortment, and merchandising into its scope, while leaving branding and legal plumbing out. Variability is treated not as a nuisance to be averaged away but as a source of profit. The real scandal, Vermorel argues, is not practice—but the theory that has misled it.
Extended Summary
Joannes Vermorel’s Introduction to Supply Chain is not another “how-to” manual filled with formulas pretending to be science. It is, in his own words, closer to a very long internal memo: the distillation of a decade of painful trial and error at Lokad. The first chapter sets out to answer a simple but neglected question: what exactly is this thing we call supply chain? Vermorel insists the book was written first for Lokad’s own supply chain scientists, then reworked so that any practitioner, student, or executive with limited time could read it. The aim is not to hand out yet another bag of tricks, but to give people instruments to think their supply chains: why they exist, what they are trying to achieve, and how time, information, and computers actually matter. Against this, he contrasts the existing literature: catalogues of time-series algorithms and hundreds of KPIs, such as the SCOR model’s 250 metrics. That, he argues, is trivia masquerading as rigor. Starting from there is like beginning accounting by memorizing every legally recognized invoice status. It is “massively simplistic,” not genuinely simple. His definition of supply chain is deliberately sharp: “the mastery of optionality under variability in managing the flow of physical goods.” Anything that changes what moves, where, when, or how much is supply chain. That pulls pricing, assortment, and merchandising into its remit—and exposes as organizational nonsense the common split between pricing and replenishment. By contrast, branding, long-horizon image-building, and payment plumbing sit outside supply chain: they affect demand in the very long run but do not participate in the high-frequency allocation of stock. Supply chain, for Vermorel, is not trucks and warehouses but an intent—a web of expectations about future exchanges. Focusing on the mechanics of engines or network protocols is as misplaced as expecting a lawyer to specialize in the chemistry of printer ink. The practitioner’s scarce attention must be directed at the decisions that actually move goods and money. Crucially, variability is not treated as an enemy to be “smoothed away” but as a condition of reality that can be turned into profit. Examples range from airlines buying surplus parts when a teardown floods the market, to fashion brands paying more for nearshore capacity when a product unexpectedly explodes in demand, or deliberately lowering service levels to slash inventory before a crash. This, he says, is not an exotic theory but entrepreneurship 101—what successful companies already do while apologizing for “deviating from the plan.” Where Vermorel becomes most uncompromising is in his indictment of mainstream quantitative methods: millions of papers on “optimal” inventory policies that nobody uses, and that fail when tried. Real planners rely instead on tribal lore embedded in messy spreadsheets and informal pattern recognition. That this works at all is, for him, precisely the evidence that the dominant theory is bankrupt. The book, then, is offered as the reference he wishes he had in 2008—an attempt to rebuild foundations so that what already works in practice can finally be made explicit, automated, and improved, instead of being undermined by bad theory.
Full Transcript
Conor Doherty: Hello Joannes, welcome to the Black Lodge. It’s good to see you. It’s winter now, so we have to stay inside where it’s warm. Today, by popular demand, we’ll have a discussion on your book, specifically chapter one, but it will touch on themes that actually kind of permeate the book. And I’m going to push you a little bit. You know, you’ve requested me to go back, read the book, as you put it: “Imagine you didn’t know me, more or less, that’s how you put it. If you didn’t know me, Joannes, you didn’t know Lokad, you just walk into a store, you pick it off the shelf and you start reading, what questions would you have?” So that is the frame with which, tabula rasa, that is the frame with which I come to this. And I think on that note, actually, the very first question, and it will frame the discussion, is: who exactly did you write this for?
Joannes Vermorel: The primary audience that I had in mind when writing the book was the supply chain scientists at Lokad, okay. You know, this is first and foremost a document that gathers all the insights that Lokad uncovered through a decade and a half, and to provide them in a way that is consolidated. So it’s, in essence, you can think of it as a document, as an internal memo, but like the very long version, like the 500-pages version of the memo. But then I made also the book non-technical, so that, I mean, when I say non-technical, it’s not littered in equations or algorithms. And going again at the manuscript I try to make it quite accessible to any supply chain practitioner or any supply chain executive, including people who would be short on their time. So it started with an internal audience, and then as I went again over the manuscript I tried to make it, you know, digestible and to avoid using too much of, I don’t know, Lokad jargon. You don’t have Envision code or anything. It’s very light.
Conor Doherty: But just to be clear, because again, even just on the book jacket itself, you do explicitly state: for specialists, for students, for professors. So you do stand by that it is at least, at least, there’s maybe a dual audience, but let’s just say at least 50% of that audience is the general practitioner. That’s fair?
Joannes Vermorel: Yeah, exactly. And when you have to think, when I say students, you have to think that the supply chain scientists that Lokad hires are typically fresh out of university, so there is kind of an overlap here.
Conor Doherty: Okay, well, so for the sake of discussion, and I got these figures from ChatGPT, so full disclosure, but I used the 5.1 Thinking model for this, and I basically asked it to estimate roughly, for the sake of this discussion, how many white-collar practitioners there are in supply chain in the world today, not including logistics, just like demand planners, category managers, anyone who you, the average person, would agree, “Yep, that’s supply chain.” And it gave me the number of approximately 10 million. So again, maybe it’s a bit more, maybe it’s a bit less, but for the sake of discussion we’ll just say 10 million. There are LinkedIn groups with over a million members for supply chain management. So assuming that those LinkedIn groups have like 10% of worldwide, you know, interest, I mean, it feels at least the ballpark feels correct. But even if it were off, for the sake of this discussion it wouldn’t even matter. It’s a lot of people. So in your own estimation, if one of those 10 million people picked up the book, a book titled Introduction to Supply Chain—again, Introduction. It’s not Rethinking Supply Chain. It’s not like a Boethius-style metaphysical meditation. It’s Introduction to Supply Chain. One of those 10 million people picks it up. What practical tools do you think they’ll come away with?
Joannes Vermorel: They will come away with the tools, the instruments, to think the supply chain. That’s really what this book is about: to think your supply chain in the very foundations. You know, why do you even have a supply chain, why does it matter, what value is there for the world at large and your company specifically of thinking of its operations through the lenses of supply chain. That’s the sort of things that I detail: what are you exactly trying to achieve and why. How should you even think basic elements such as time, information, intelligence. What is the role of computers in a modern approach of supply chains. I mean, those are very fundamental questions, and that’s what I’m trying to address in this book, so that people can really think somewhat correctly about their supply chain. Because again, my beef with the literature is that the literature typically goes on technical tangents such as, “Here are 57 time series forecasting algorithms.” And it’s not helping. It’s not useful. And I would say people end up overloaded with tons of irrelevant trivia. An example would be the Association for Supply Chain Management, ASCM, has a SCOR document and here they outline, top of my head, something like 250 metrics. This is nuts. This is just nuts. Two hundred and fifty metrics, that’s a huge amount of trivia, and it’s not helping to actually understand anything about the supply chain. It’s just an extensive list. It would be like approaching accounting like, “Here are the 250 invoice statuses recognized by the French law.” I mean, you do not start with that. Those are very much technicalities, and that’s what I’m trying, through this book, to do: to approach through the things that would matter for every single supply chain on earth, every single practitioner, because we are dealing with really the very foundations, the fundamentals, the things that do not change if your company is manufacturing airplanes or shipping, you know, sport shoes. Those concepts matter.
Conor Doherty: Okay. I do want to come back, actually—it’s later, I will come back to the idea of the state of the literature. We will touch on that, you’ll have time to comment. But staying with the literal term “introduction,” right at the start you open with a quote from—is it pronounced “Bastad”? I’ve never read it out loud.
Joannes Vermorel: It’s Bastiat. Bastiat. Yeah, Bastiat.
Conor Doherty: Bastiat. Then you say that, and again, as you’ll know, I’m largely quoting, that approaching supply chain is an arduous task in that it is both, quote, “very abstract and very concrete.” You argue supply chain cannot be touched and that factories, warehouses, vessels and trucks are not supply chain. Supply chain is in fact an intent, not a thing. It is a web of expectations. Yes, you just talked about the practical, the average practitioner. That all sounds lovely and, you know, I’m a student and teacher of philosophy, but again, what practical differences flow from accepting that vision of supply chain?
Joannes Vermorel: Because it lets you direct your attention, you know. For example, if you think that supply chain is really about the trucks and factories, should you become an expert in the way factories are actually built? Should you become an expert in how trucks are operated and their mechanical characteristics? And what I argue is: no, absolutely not. I mean, because fundamentally this is the intent that supports those things. That’s what you will need to understand. Again, this is why, you see, we are introducing through supply chain an abstraction. You will end up in a company being part of, let’s say, supply chain departments. The supply chain department is not building the factories. They are not, I would say, owning or even operating the trucks there. So you see, you have to think, “Where is my focus?” And my focus, what I say, is that this is the intent that connects all those things together, and that’s really where it starts to matter. That means that, for example, those expectations: what is really the customer expecting from you That’s a very difficult question, and that’s what I argue in this book. This is a question for supply chain. That’s what supply chain, among many other answers, will have to deliver: what are your customers expecting from you? And you could ask this question from, “What are your suppliers expecting from you?” I mean, you would say payments, yes, obviously, but there are probably a million other things that also matter for your suppliers, etc., etc. And again, the reason is that we are dealing with something that is fairly abstract. A supply chain is not the only domain that is fairly abstract. Accounting is fairly abstract. Legal is fairly abstract, or even worse, marketing. So that’s why those things—there are no physical objects like trucks involved in that, though. I mean, you don’t have trucks to deliver legal documents or legal judgments. There is a physical domain that you are in, but it needs to be approached from a very specific perspective. What I say is that, again, if you learn the fine print about the combustion engines of your trucks, it doesn’t help. It doesn’t help for supply chain. It will help for a lot of other problems, including possibly repairing the trucks, but not for the purpose of supply chains. You see, that’s the thing. That’s why I state it as an introduction. We need to be able to direct the attention of a practitioner to the things that are most relevant to his field.
Conor Doherty: Do you think that the average, let’s say, warehouse manager is sitting around contemplating how combustion engines work? So the average warehouse manager is—I mean, first, he’s probably not really even part of this audience, you know. Well, you can adapt any question you like there. But do you think people using computers for demand planning are sitting around understanding how Wi-Fi works, or trying to accomplish goals?
Joannes Vermorel: Actually, that’s the thing, is that the perversions vary, but yes, there are many planners who are, again, spending a lot of time focusing on the wrong things. Obviously I was taking, you know, an example that was ludicrous, such as combustion engines, but the reality is that many demand planners are going to focus, for example, on those 250 KPIs of SCOR, and I say it’s not even remotely relevant. And they are going to focus on, potentially, trying to learn more about time series forecasting algorithms, which is not even remotely relevant, especially if you want to go through the catalog of the hundreds of algorithms that are known in the literature. All of this knowledge is not very useful. And that’s what I’m trying to clarify: what is actually the intent, so that we can sort out what is actually useful or relevant.
Conor Doherty: So when you say useful or relevant, before I respond, unpack what you mean there, because you’re saying those are big claims, a lot of information.
Joannes Vermorel: We have to go back to what is the intent and what is, you know, the purpose and value of supply chain. You see, so in the book I detail what is the intent and the purpose of supply chain, which is to essentially increase the long-term profitability of the company through better allocation of resources that pertains to the flow of physical goods. You know, that’s really the—
Conor Doherty: Are we in the richest state in the history of the world right now? Aren’t supply chains basically generating, like, point to a time in history where supply chains generated more money than they do today?
Joannes Vermorel: None. But you see
Conor Doherty: So how is it useless or not useful or not relevant?
Joannes Vermorel: I’m saying that what is actually used in companies to operate the supply chain has nothing to do with what you will find in most textbooks. And that’s also the reason why I made this book, is that I see a lot of companies where the default attitude is, “Yes, we are going to improve. We know that we’re not using the optimal inventory formula. Yes, yes, we know, we know. We tried, it didn’t work, so we are doing something completely different. Yes. “And yes, we know that there are 250 metrics, and yes, we don’t follow them. Yes, we have them in the BI report, we don’t follow them. But we will improve, because right now we are busy doing other things, and we will improve.” You see, that’s the kind of attitude that I see among practitioners and even executives nowadays. So there is this grand disconnect where the company is working kind of, you know, the supply chain operations manage to deliver stuff, kind of. It’s not very automated. And what is on the roadmap are items that have been on the roadmap for the last 20 years. And people try those optimal methods and then they pull back, it doesn’t work. They try to add more metrics, but it doesn’t work, so they just ignore them, etc., etc. But when you discuss with them, they would say, “No, next year, next year we will do the right thing. We will start doing the optimal thing next year. Yes, we will start following the KPI.” And meanwhile they do something completely different. And I believe that’s the crux of the problem. It’s not what they’re doing presently, it’s what they have in their roadmap. You see, what they are doing, in fact—and that would be the point—is that actually what they do is kind of intuitively very aligned with what I present in this book. You see, it’s not formalized as such, but in fact it is much more aligned with the content of this book than the classical supply chain textbooks. And what I’m trying to push forward is that the sort of mainstream, classical, obsolete ideas about supply chains are just bogus. They don’t work. They have been tried for the last four decades. They have been implemented through dozens of enterprise software solutions. They work poorly. People revert to their spreadsheets for good reasons. And so what I’m saying is that the reason why it fails is that the sort of paradigms and ideas that have been pushed by supply chain authors, academics, consultants is essentially a mismatch. It doesn’t work operationally. And I try to produce an alternative vision that starts with the right foundations and that actually reflects what people are already doing, with a much stronger emphasis on making the most of what computers can deliver.
Conor Doherty: Okay. Well, you mentioned foundations and I think a foundation of not only Lokad but the book, your overall philosophy, is your own definition of supply chain, and that is—and I quote, and we’re going to investigate this a little bit—“Supply chain is the mastery of optionality under variability in managing the flow of physical goods.” Okay. You also expand that a little bit more and you say that the boundary of supply chain is crisp and it’s precise. You’re able to separate what is supply chain from what isn’t supply chain. You append this with the idea that, and I quote, “Anything that changes what moves where, when, or how much is a supply chain decision.” You further include things like pricing, merchandising and assortment. You say that those are squarely within the remit of supply chain. A skeptical person seeing that for the first time—so I don’t know you, imagine I don’t know you, I haven’t worked here—a skeptical person or executive listening to that might think, “Okay, what isn’t supply chain under that definition?” So if you look at an org chart, what isn’t supply chain?
Joannes Vermorel: Well, there are tons of things. Product research, it’s not. Branding, it’s not.
Conor Doherty: Why not?
Joannes Vermorel: The problem of brand influences what people want to buy. Branding is a multi-decade effort to create kind of a persona where your brand exists in the mind of customers. It will lift all your products, but fundamentally it is not really attached to any single product. You know, just think of Louis Vuitton. Louis Vuitton is a massive brand that has been built over half a century. It has been steadily rising through the last three decades, and thus every single product sold by Louis Vuitton has been rising in volume and price for the last probably two decades. The brand Louis Vuitton is not really attached to any product reference. You know, it’s like the tide, it raises all the ships. And so it’s abstract. It’s not really connected No, I say it is not. It is very much something that is, if you look at the flow, they don’t really operate on the same time horizon. We are talking, for branding, of a multi-decade effort. And when you do your operations in, let’s say, branding, you’re not thinking of, “I need to promote this specific unit or this specific unit.” You are again pushing the image of your company. That would be the same—think of Nike giving a sponsorship to an athlete. You know, they are not pushing exactly this specific type of shoes. It is much more diffused. So there are plenty of elements in the company that are not exactly related to, I would say, your flow of goods. Another one would be the general contractual terms when you negotiate with your suppliers.
Conor Doherty: Well, that’s not a department. You don’t point to “general terms” on an org chart. You make the claim that like—
Joannes Vermorel: Yeah, but for example purchasing, for example, purchasing will negotiate literally frameworks to buy stuff with your suppliers. For example, you will negotiate the financial terms and, for example, let’s point the Incoterms. This is not exactly within negotiating the exact sort of legal terms and who is buying the insurance, because you see, for example, there is a shipment. Your supplier can buy the insurance for the shipment, or you, the buyer, can buy the insurance. At some point somebody has to buy an insurance. It can be either way. Those sort of considerations are not exactly supply chain. They are important, plenty of detail. It’s not supply chain. Same thing, for example, somebody has to manage—again, that would be part of procurement—that you keep up to date the correct bank numbers of your suppliers to pay them. So there is staff where sometimes suppliers, they would change bank, so you need to send the money to the new bank account, etc., a lot of plumbing here. It’s not supply chain again. That’s not—no, that’s not. If we look at the allocation of resources, this is not an allocation of resources. This is merely a technicality in the way you execute your payments. This is very important. That will keep a lot of people busy. But again, this is not supply chain. So you see, there is
Conor Doherty: I wasn’t arguing about, I wasn’t asking you about the allocation of resources. I was asking you about, from a pure organizational perspective, supply chain subsumes how many different departments, in your view, of enterprise. So I’m not talking about the allocation. I’m saying, how many departments answer to supply chain?
Joannes Vermorel: Not that many, not that many. You know, again, it’s just when you think about it, I’m just pointing out that there are things that are just completely insane that are even attempted by the market. For example, pricing. If to organize a big promotion for a product, the quantity that you will sell will go up. So obviously those two things go hand in hand. Trying to do one without the other is insane. And by the way, this is why in practice I’ve seen many companies where they treat, as absolute silos, pricing and replenishment. And guess what? Those people, even if they are, I would say, the organization is a complete misfit, those people will spend a lot of time through informal coordination to precisely try to remedy this insanity. So there will be tons of emails going back and forth to remedy this insanity. I’m just saying that the org chart is wrong on this front. You should just have this as a single team. And again, pricing, it’s not like a team with a thousand people. It’s just going to be a few people. And what I say is that it belongs to supply chain rather than to marketing.
Conor Doherty: And then what about marketing? Because you mentioned marketing, because frequently pricing is under the umbrella of marketing. Not always.
Joannes Vermorel: Some companies are already putting pricing under the umbrella of supply chain. So overall, and then, for example, assortment. Again, it’s the sort of thing where, if you do not put it under the umbrella of supply chain, then whoever is dealing with assortment will need to cooperate super tightly with supply chain. Why? I mean, just take a retail network. You have 200 stores. If you decide to put a product on display in every single store, then you at least need 200 units to cover that, you know. So obviously, from a replenishment perspective, it’s very different if you say this product will only be visible in, like, the five flagship stores that we have, or if it’s always present in every single store of the 200. Do you see? There is like basic implications, and if you try to have those teams separated, you will just create a lot of friction. And thus there will be a massive email trail with plenty of Excel spreadsheets going back and forth, which is frequently the case. Which is frequently the case. So as a corollary of this point, you’re suggesting what, in terms of organization—
Conor Doherty: I’m just saying that, again, if we look at, for example, if we go back to the example of branding versus supply chain as you define it, do we need tight coordination, for example, branding and the people managing replenishment?
Joannes Vermorel: No. At Louis Vuitton—again, let’s pick this example—does the people who are managing store replenishments need to have a tight collaboration with the people who are picking the next imagery for the brand? The answer is no. Yes, if the people who pick the next manage to do a fantastic job, ten years down the road the company will have grown substantially. But you see, the connection is very loose, very diffuse. It will take plenty of time. In contrast, if we think about replenishment, do the replenishment team need to be in touch with the people who are managing the merchandising, so which products are going to be put on display in the shop windows of the stores? Yes, obviously. Because otherwise, if you’re not tightly coordinated, you won’t even have the stuff that you want to put on display. So you see, what I say is that there is a practical element of the frequency of the decisions, and how many decisions are we talking about. Again, if we are talking of a decision that is like once a year, we don’t need to have close collaboration.If we are talking of thousands, tens of thousands of decisions daily, that’s a completely different game. That’s where I say, “Okay, we need to make it tied together, otherwise it’s just going to take forever.”
Conor Doherty: Thank you. I’m going to push on to another little part of the definition, which is actually one that I really like personally, and I think, again, I think even a skeptical reader—or not skeptical, someone who’s never read any work—would look at the idea of “variability as opportunity” and think, “Interesting.” And again, for the record, I’m limiting—I’ve read the rest of it—but I realize you expand a lot, yes. But just in case people make a comment, in case people think it’s not really fair to take, you know, the first chapter and grill you on it, but actually the framing of this conversation is: if the average person picked it up, they’re only going to read the first. You can’t say, “Oh, but if you read to page 500…” So that’s the perspective I’m taking here. So if you only read the first 15 pages, what would your impression be? And I think “variability is opportunity” is a great concept. That said, the example that you give—you probably recall, and again I’m going to paraphrase here—you give the example of a bottled water producer who invests in additional capacity so that they can be strategically placed to capitalize during a heat wave. And that’s a real example, by the way. It’s—I did not name the company, but that’s an actual European company. Exactly. And you call that a textbook illustration of variability turned into profit. Now, again, perfectly fine, I don’t think anyone would dispute that. If you’ve got money to burn and you can just build another production plant, sure, that’s fantastic, you’ve got insurance, you’ve underwritten your own risks. A skeptical reader might also look at that and say, “That’s kind of a cherry-picked example,” because let’s say there are 364 other days of the year where there aren’t heat waves. So then I’ve just tied up millions in a warehouse or in a production plant that’s doing nothing. So do you have concrete day-to-day examples of how the variability that you describe can in fact be capitalized on, because I think people would be very interested to hear that.
Joannes Vermorel: It, again, your mileage may vary enormously depending on your verticals. So retail—let’s take, for example, let’s start with aviation. Aircraft get dismantled all the time. When an aircraft gets dismantled, the market is flooded immediately with like something like half a million parts, you know. That’s because when you dismantle an aircraft you have so many things that can be reused. So it creates like mini shocks on the market and the price of those parts varies enormously, which means that if you are a fleet, should you buy when you need or should you buy when there is an opportunity? And I would say it’s kind of both. If you see a part that you don’t need immediately, you know, maybe not in the next year, but you do consume these types of parts, and here it is at like one-third of the usual price, maybe you should buy it. Maybe. I know the exact—so that’s where I say the mentality is, okay, this variability is not just my enemy. It is also something where it’s an opportunity. Do I take it or not? In fashion—that would be, if we take another example—in fashion, normally, let’s say a brand does not produce in Europe, it’s too expensive. But one of their products just exploded and they have the opportunity to produce more at a much higher price, let’s say in Italy or Spain. And the thing is that they have so much demand that they are kind of confident that even if they have to raise their price, they will still be selling a lot. Should they actually just do something that they don’t do usually, which is go for a super close supplier that is charging twice what your supplier in Asia is charging, but then you can have that within a week for the emergency replenishment? So you see, here there is an element of risk. If you do not manage to sell it, then you will have purchased at a very high per-unit price something where the demand may collapse much faster than you expected. But again, is it really like a bad situation? No. It’s an opportunity. If you really see like a massive, massive spike, maybe it is the opportunity to just do a massive surge on your procurement, procuring those units at a much higher price point much faster, and expand the reach of your brand. And we can go on. You see, those are cash-intensive options though, that you’re describing, or capital-intensive, excuse me. You’re talking about buying an engine that comes on the market 30% below value. Like, you have to have money to burn. And you see, you can think also of the other way around. Sometimes, if you have a market slowdown, should you very aggressively, a little bit, crash your quality of service to diminish the amount of stock that you’re carrying, to make it much, much lower, just because you think that not only the demand is getting down, but it might crash altogether and you want to reduce at least temporarily your financial exposure. And if you play your cards right, maybe—and I’ve seen that happening—like, you lower your stocks, your inventory levels, the demand indeed, it was a low-probability event, but it does really collapse for a month or two. And then all your competitors are filing for bankruptcy and you’re the one survival. Sometimes they—and then when the market finally recovers, well, with much fewer competitors, you can renegotiate your prices higher and business is good again. You see, variability, what I’m saying is that variability is just a thing of the universe, and it’s fundamentally something that is, it’s not for you to decide. You see, that’s the thing with variability in supply chain, is that it exists. It’s outside your control. And what I’m saying is that you should stop thinking of it as something that is bad. It just is. And now, as we accept that it exists, the game becomes, “How do we try to make something profitable out of it?” And that means to have this sort of opportunistic mindset, where we see this variability as something that can be exploited to increase the profitability of the company.
Conor Doherty: Do you see this worldview being exportable across all supply chains, across all companies, or do you see that there’s like a minimal threshold at which you would need to have X technological journey, capital—
Joannes Vermorel: It is already what people are doing. I’m not—and I just described it later in the book—it is, this view is just entrepreneurship 101. So in fact it is exactly what companies have been thinking for ages. And that’s the sort of things where, and you see, for us at Lokad, that was again schizophrenia, where there was like the classical theory, the organization of the supply chain, and people were doing things that were completely disconnected, because they say, “Ah yes, yeah, the plan, we didn’t plan that. We didn’t plan the market to be flooded with parts, but now that it is, ah, it would be stupid not to do it. So let’s do it.“Oh, the plan is going to be so bad. We are going to deviate so much from the plan, it’s going to be bad. So maybe we are going to reduce a little bit the advantage that we are taking by taking advantage of this opportunity, because we would be deviating too much from the plan. But let’s try to still make a little bit of profit.” You see, that’s complete schizophrenia, where people would generally do the right thing but find excuses to deviate from the plan. And what I’m saying is that you are doing the right thing. The plan is wrong. Forget the plan. A plan that sends you on a path of lowered profitability is not a good plan. You see, that’s the set of things that I’m arguing about supply chain, is that variability is irreducible, variability needs to be exploited, and whatever successfully exploits this variability is good, you know, and screws the plan. It doesn’t matter. It’s more important to be profitable than to have a good plan.
Conor Doherty: One of the things, both listening to you now and then rereading with my best attempt to do it with a clean mind, tabula rasa, one of the phrases—and I actually wrote it down in my notes—was “Plato’s supply chain.” I want to bounce this off you. Listening to your description and reading, certainly the “mastery” section, the idea—you talk about mastery, not improvement, mastery—of the optionality inherent to the variability flow, that nice definition. In that section you describe supply chain as fundamentally intangible. It’s a set of expectations about future exchanges. You give a nice example about a vendor with milk and then the customer. You have competing bets: I bet that I’ll sell it, you bet that I’ll have it in stock, etc. As a student of philosophy and a teacher of philosophy, I like all of this, because I look at it and I go, “Ah, that’s like, if I were to build from the ground up my vision of supply chain, that’s what it would look like. It would just be this perfect understanding of the interplay of all of these forces.” Now again, a maximally skeptical but fair pushback would be: how realistic is that vision in companies that already exist, with all of the political problems and, let’s be honest, politics, interpersonal relationships, dynamics, incentives, all of these things exist. And I know you talk about those later in the book, but I’m simply pointing out, if you were reading those first few pages, do you understand how someone might get the impression that that’s kind of very abstract and a little bit fanciful, or it might be too fanciful? Excuse me. How do you respond to that?
Joannes Vermorel: I would say maybe yes, there is a degree of abstraction. That is true. It’s higher than most supply chain textbooks, I would say. That is true. But another beef
Conor Doherty: It’s not a bad thing necessarily.
Joannes Vermorel: Yeah. Another beef that I have with the supply chain literature is that it is not simple, it is massively simplistic.
Conor Doherty: And what do you mean by that? I mean, I know what you mean, but imagine I didn’t know what you mean.
Joannes Vermorel: For example, it makes assumptions that demand pre-exists, you know, as if demand was a thing that was already there, and you just have to have your statistical estimator with a time series forecast to just capture this demand. This is nonsense. Demand is engineered by the company itself. There is no pre-existing demand. You have to generate the demand for your own product So what I say is that there are plenty of things that are easy, such as time series forecasting. It’s very easy. I can show you a spreadsheet. I can show you how to build a moving average. I can show you how to put a seasonality. And all of that is technically easy. And same thing, I could come up with so many quality metrics, again SCOR, 250 of them. Each one of them is easy. I just need to pick a definition, this is what I measure, etc., etc. But what I’m saying is that those easy things are not fundamental. You know, they are a distraction. They are technicalities. It is, I would say, a lazy approach to supply chain which prevents you from actually mastering anything. That’s what I’m saying: those things that are just catalogues of stuff, disconnected, are not building anything toward mastery. And that’s, generally speaking, a problem that I have with most of this literature: that it tends to catalogue everything endlessly, and those catalogues are absolutely not any kind of essential categorization. There, time series forecasting algorithm, I can give you 50 more. Give me 250 metrics like SCOR does, I can give you 250 more metrics.
Conor Doherty: Well, people can put them into practice, is the point. That’s the challenge being made: can they?
Joannes Vermorel: You know, I really challenge that. Can they?
Conor Doherty: You have 250 metrics, but they can choose the ones they want.
Joannes Vermorel: But no, no, no. I mean, okay, if we say you can choose based on—
Conor Doherty: Not based on which criteria?
Joannes Vermorel: Based on other metrics? You’re going to pick metrics based on other metrics. You see the element of choice here. You have a problem. You have 250 metrics and say, “They can pick the ones they want.” But are they? We have to reason about that. You can’t just say, “You roll a dice and you pick some.” Obviously that doesn’t sound like a very sound approach to supply chain. You just let your intuition
Conor Doherty: Is that what you think people are doing?
Joannes Vermorel: No. Okay. And that’s precisely where there is this disconnect. That’s why I say there is a massive disconnect. People are not doing that. People are doing—and that’s the reason why supply chains actually work nowadays—they are doing something that is much smarter, much more elaborate, much more fundamental. And it just happens that they usually do that feeling guilty that they are betraying the practice, the best practice. You see, it’s like, “Yes, there is this best practice, but when I do it, it doesn’t work. So I just, you know, please don’t tell my boss. I don’t do it the way I should, but it just works. “And when I try to do it, you know, by the book, it doesn’t work, so I’m a little bit in a pinch. So don’t tell the hierarchy, but I will keep with my stuff.” You know, that’s a sort of, again, complete schizophrenia. And again, you end up with those discourses that I’ve heard many, many times, like, “Yeah, we have this optimal inventory policy. There was this university professor, they showed us, yes, it’s absolutely profitable, I mean it’s absolutely proven that if we were to use it, we will earn so much money. “But we have tried a dozen times. It was a catastrophe every single time. So, I mean, we are keeping that for the roadmap, but next year. This year we are just going to do other things, and yes, they’re kind of wrong, but because they generate money we are kind of okay with that.” You see, this is the sort of discourse where I say, if you’re doing something and it creates money for the company, then it’s not wrong, you know. Then you are on the right path. And if you have something that is supposedly optimal, and when you try it it’s a catastrophe, then it’s not optimal. Again, we have a problem of terms. And that’s what I’m trying to address in this book, is that the classical views from academia and from consultants are quite bogus. And the litmus test for that is—and that’s what I argue—that by and large supply chains have not gotten automated. And the reason is that the logic that we have at our disposal, the sort of instruments, were incorrect. And thus, when you were trying to put that in a computer, it didn’t work, because the thinking was incorrect, and what people did on their spreadsheets, which was correct, but very different from what the mainstream theory was proposing.
Conor Doherty: Well, you’ve taken the very next question I wanted to ask you about, and we can finally unpack this: your perspective on the mainstream approach. And that is something that, again, if someone just picks up the book and starts reading, immediately—I’m going to need to set the table on this one—what they’ll be presented with is a quite strong perspective against mainstream practices. I would call it almost hostile, of a supply chain landscape, like the current supply chain landscape, which is fine. But I would also add that, again, if I were approaching this, if, let’s say, I’m one of those 10 million practitioners and I just pick it up, there’s also, I would say, a reasonable case to be made that there are some critical remarks about the training of the average practitioner. And again, I’m reading quotes. Feel free to correct me, but I am reading quotes. You make the claim that the quantitative supply chain literature right now fails to harness modern computing, fine. Actually, I really feel this is worth just reading in full, because people might think I’m cherry-picking. So the actual quote—
Joannes Vermorel: You are cherry-picking, but that’s okay.
Conor Doherty: Cherry-picking—well, I think it’s kind of a striking symptom. This is in the parting thoughts: “A striking symptom of this vacuum is the prevalence of self-taught practitioners in large companies. Society rightfully tolerates no self-taught surgeons; the gap between amateurs and trained experts is too vast to risk human life. Yet, when billions in inventory are at stake, enterprises routinely rely on planners who have pieced together their craft from tribal lore and blog posts. “The comparison is stark, and it underscores how underdeveloped the discipline remains. Mainstream methods have not merely under-delivered. In many cases, they have actively misled.” How do you square that—all of that, everything, all of that—how do you square that with the fact that basically right now we’ve never been in a more profitable global network of commerce and exchange? If it’s so crap, how do we explain reality as it currently is?
Joannes Vermorel: So again, what I’m saying is just that people are self-taught. You can go quite far, you know, but fundamentally what you have is, I would say, what you have in large companies is a lot of haphazard institutional knowledge. And this haphazard institutional knowledge is, I would say, an asset for the company. It is what makes the company, you know, tick. But fundamentally it is very much unformalized. And that’s why I say there is no, I would say, massive performance gap between someone who is self-taught and someone who has a certification, because the bottom line is, if you join a large company and their supply chain team, you will be in contact with colleagues, and within six months, you know, this institutional knowledge will permeate your thinking, and that’s how you’re going to operate. And you see, that’s okay. You know, that’s okay. But that means that, where I say it’s again, in my view it demonstrates the fact that we don’t have, I would say, solid foundations. Because if you take other areas where foundations, solid foundations, do exist, a person that joins the company with this extra knowledge is just delivering miracles. If I were to take an example, let’s take software engineering. Yes, I have seen startups, I’ve audited over 100 startups during the last decade, and I have seen small teams where people that were, I would say, very weak teams, you know, again, startups, so it was people who just were venturing into the software business. And they had people that were cheap but not very good. The founders were trying to, you know, put together a little bit of software, but they were typically—for example, an example would be typically former consultants, so they were not really software engineers. And at some point they get traction, they raise some money, and they hire their first actual software engineer, someone who has formal training in software engineering and who has experience in an actual software company with a reasonable process. And this one person just revolutionizes the way they operate. You know, it’s like day and night. It’s an enormous gap from people who are doing, like essentially, monkey programming, to actual modern engineering with reasonable practices. You see, that’s what you get when you go from haphazard, self-taught stuff to people who have formal education plus formal experience. The gap is huge. And that’s the exact same thing if you were to, again, just think of the gap I gave the example of a surgeon—but again, just think of the gap between someone who has been formally trained as an accountant and a secretary who has been filling a little bit the accounting software, you know, helping with accounting by copying invoices in the accounting software After a while, someone who has been, you know, just doing that will kind of get a feeling about what accounting is about, because they have been doing so much data entry. But the gap, in terms of skills and of quality of thinking, between someone who has just been entering spreadsheets versus someone who is formally trained as an accountant is going to be absolutely enormous. And it’s not something that you can really—and the reason why you have such a gap is that, for example, accounting has a very high-quality formal training. And so if you’re being trained in accounting, it makes a huge difference compared to someone who is not. You see, that’s—and you can be self-taught, but then you will be self-taught with an accounting book, and you will follow the same process.
Conor Doherty: I don’t disagree. I agree with you. What I’m trying to—what’s slightly confusing me here is the claim that if you say people are, for example, and I mean this with all respect, you’re self-taught in—you didn’t go to school for economics. You’re a mathematician, correct?
Joannes Vermorel: So, yes, but for economics I went through a series of books that are considered as absolute classics.
Conor Doherty: Yeah. Okay. You’re self-taught.
Joannes Vermorel: For me—
Conor Doherty: Okay, that’s absolutely fine. I see no contradiction. So am I. I see no contradiction here. What I’m saying is, when you say self-taught—sorry, the current state of literature is crap, more or less, my words, my summary of your position, that it’s lacking, it has misled, actively misled. The reality is, though, most people who work in, let’s say, demand planning offices, they come in and they are provided these materials. These are not random people who are pulled off the street and just thrown in front of a computer and said, “Here, pick numbers.” They’re using formulas. They’re using inherited wisdom. And that’s what they’re teaching themselves with, and they’re making lots and lots and lots and lots of money. So I’m just—I don’t really understand.
Joannes Vermorel: It’s really informal. That’s the sort of thing—people are not handed over, “Here are the formulas that you should use.” No. They have like a messy spreadsheet left by a former colleague, and then initially they will try to go through the spreadsheet, and then they will have a colleague that says, “Ah, this number, I kind of feel it should be higher, because, well, I really feel it like it,” you know. “It’s, I believe, in the past we kind of did it lower, it didn’t turn out so well, and here that would be, you know, pattern recognition. I think this is the way you should push.” And after a while, you know, the newcomer will have the same sort of informal pattern recognition, and they will do the job. But it’s very—that’s where it’s very informal. It is absolutely not like, “Here is a formula that you should use.” If it was shared formulas, that would be so easy. You would just, at some point, have a consultant who says, “I collect all your formulas. I consolidate a master document, and then we update the software and we robotize everything.” It never happens, because there are no such formulas. And it is critical institutional knowledge, but as fuzzy pattern recognition techniques, you know. And that’s the problem: it doesn’t lend itself to a formalization.
Conor Doherty: I think listening to that explanation, which, to be fair, is quite different to what’s here, and again, we’re only talking about the first 15 pages, would you agree that you’re drawing a distinction between competence and credentials?
Joannes Vermorel: Okay.
Conor Doherty: Okay, well, that’s slightly different now. That’s quite different to claiming that people who are self-taught are basically—self-taught, you’ll kill yourself if you actually try to do surgery on yourself.
Joannes Vermorel: Yes. No, you have a practical problem. But you see, what I’m saying is that the domains on which you have, I would say, real fundamental materials, when you’re using them to progress, using those materials to progress, you’re progressing orders of magnitude faster, and you end up in a much better point compared to the people who did not. For example, when I studied algorithms, I read a book called Introduction to Algorithms. It’s a masterpiece. That’s your formal training, right? Yes. It’s a masterpiece. It is like a thousand years of brain power of some of the very best mathematicians of the 20th century put together in one book of a thousand pages. There is nowhere that, in terms of algorithmic skills, that a person who knows how to program but has not read a book like Introduction to Algorithms or an equivalent—because there are like a thousand equivalents nowadays, formulas—that someone, there is no way that this person at algorithms is going to be anywhere near as good as me. I could have Albert Einstein, who is just, you know, an incredibly smart person, who is trying, self, to progress from, “I know programming, I want to discover algorithms,” and me, who is nowhere near this level, with this book, I will end up way higher, just because the foundational materials are so vast and so good and so crisp that not going through this path is insanity. You see, that’s the sort of things where the fact that someone self-taught can rival someone with credentials really means that the credentials are not worth much. You see, again, if you think of all the areas where credentials really, really matter because they are effective, the effectiveness of people with or without is just orders of magnitude. And even if you are a genius—and when I say self-taught, I mean it in a specific sense: I mean someone who did not go through the reference materials. Because obviously if you say, “Oh, I am self-taught in accounting. I just read the accounting book.” Oh, yeah, you are not—I mean, yes, you are self-taught in the sense that you didn’t have the teacher to do that, but you went through the exact same mental pathway as a student studying in a classroom. You see, that’s not the distinction. The distinction that I’m using is the one that Feynman was using. He was very frequently saying, “Oh, I don’t want to, when a fellow physicist gives me an idea, I want to—I don’t want to read his paper. I just want to redo and reinvent the math myself, eventually redo myself the experiment, and then I am convinced.” So it has this, you know, sort of vibe, like, “You just give me a direction, and then I discard everything that you have to say, and I will do all the math and all the experiments, and then we will re-discuss.” And he was, for example, when he had to review papers, he would very frequently just look at the conclusion and say, “Can I end up with the same conclusion, just discarding everything in between?” But that’s, you know, that’s a sort of journey where you are not using what your peers are progressing, or very, very slightly. Obviously Feynman was an absolute genius, and also a funny guy actually from what I’ve read. But you see, again, when I say self versus credentials, I mean: did you go through what is considered the reference material of the field? And if doing that doesn’t really make a difference, then the field is garbage. That’s pretty much my statement.
Conor Doherty: Yeah, the thing is, but again—and I personally, I’m on board with that—but then there’s that last comment there, “It doesn’t really make a difference.” It’s like, but again, the skeptic would say to that, “But it does. Look at—I work at X company. We make billions of dollars per year. We’re making money with the practices that you decry. “We’re managing the global flow.”
Joannes Vermorel: No, no, no. The practices that I decry are not this institutional knowledge that exists in those companies. That’s not what I’m decrying. I’m decrying the million-plus papers describing inventory optimization techniques.
Conor Doherty: So are you suggesting then that in those companies you have a bunch of maverick lone wolves who are disregarding all the guidance and structure of that company?
Joannes Vermorel: Yes.
Conor Doherty: That was a deliberately exaggerated position. That’s your formal position on this.
Joannes Vermorel: Yes. The average guy who is doing inventory replenishment, he may have looked at papers giving optimal inventory replenishment techniques like twice in his life, and he’s ignoring all of that, you know. So there is a million-plus papers on this front that are completely ignored, and people are just doing, for example, inventory replenishment based on what I say, tribal lore, that has nothing to do with those mathematical theories. Same thing for production scheduling. People do that with things that have nothing to do with what is formally known and published in operational research. None. That’s the statement. I’m not saying that this tribal lore is without merit. I’m just saying that the papers are without merit.
Conor Doherty: So you say that you’re not claiming that the tribal lore or the blog post information is without merit.
Joannes Vermorel: Yeah. I mean, the blog post would be just, when I say “blog post,” it’s informal sources of information. Yes. This is what I’m saying: those things are essentially irrelevant. The tribal lore is relevant, but as a rule of thumb, formalized, it is not even written. It is, again, think of it as pattern recognition. You sit next to another inventory guy, and then you say, “Okay, I would pick that,” and the other guy says, “Yes.” And then you would say, “I pick 10,” and he says, “I think you should pick 15. I think you should take 15. Just, I feel it, 15. Look at how it’s set up.” And they say, “Okay, 15.” You see, that’s the sort of thing where it’s okay, it’s super fuzzy, and yet that’s the way it’s done. And what I say is that this tribal lore, it works, yes, and the problem is that the reason why it never gets formalized is because the mainstream paradigms are broken. So it doesn’t fit. That’s why.
Conor Doherty: Broken or sub-optimal? They are not—again, they’re not sub-optimal.
Joannes Vermorel: Because it’s not about an algorithm. “Broken”—just don’t work.
Conor Doherty: Just for the record, at least that’s what I, as a casual listener—you say something’s broken. Yes. The car is broken, it does not work.
Joannes Vermorel: Yes. Again, when I say it’s broken, you have a million-plus papers that claim that they have an optimal inventory optimization technique. “Optimal” means it can never be improved upon, you know. That’s what “optimal” means. So if you tell me that you have an optimal solution—for example, again, in algorithms, if you tell me that you have an optimal sorting algorithm, and there are a variety of proofs, it means that indeed it is not possible to even conceive an algorithm that, in the general case, will be faster than your optimal algorithm. And, by the way, it exists, and there are limits, etc., etc. So when people in computer science speak about optimal solutions, they really mean it. So if you had like a crappy solution and then you see in a paper that they published a new solution where they say, “It works and it’s optimal,” it gets used. So you see, that’s the interesting thing. Again, if we compare the world of software with, let’s say, optimal solutions in computer science, literally one month after the publication they are used by tons of people. So you see, there is like a computer science optimal solution that is published, and then a month later tons of people are using it. Why? Because it’s really meaningful, and when people have something that is genuinely optimal, it works and gets used. Why do we have, in supply chain, a million-plus papers that claim optimality, and none of those papers are used? And even worse, when people use them or try to use them, it gives them crap results. And here we get down to the problem that this optimality is not what you think it means. And that’s the problem, is that the perspective is wrong, the optimization perspective is wrong, and so you end up with a mathematical proof that is worthless. That’s, you know, and that’s why I say it’s broken. It’s because you have something that is mathematically correct and business-wise irrelevant.
Conor Doherty: All right. We’ve been talking now for more than an hour, and my last question is actually like a full circle to the start. Again, you’ve written—we’ve been talking about Introduction to Supply Chain, and the last thing we just discussed was, again, the idea of being self-taught, an autodidact. My closing question—and you don’t have to limit yourself to chapter one, you can go off-piste, it’s absolutely fine, you have my permission—a challenge that I, again, if I were looking at this for the first time and I listened to everything you just said, a question that I would have is, and this is with respect: What makes you so confident that you—and you admit, self-taught in supply chain, self-taught in economics, no one questions your mathematics and engineering credentials, no one does that—but what makes you so confident that, with all your self-taught expertise, that you know how to fix all this? What makes you so confident?
Joannes Vermorel: So, I mean, first we have to separate—in economics I am not self-taught in the same sense as in supply chain. Economics, I went through absolute masterpieces, you know. Just read Human Action from Ludwig von Mises. So again, the fact that there was a professor in the room is kind of irrelevant. I went through the very, I would say, the classics. So I would consider myself as classically educated on, contrary to supply chain. Economics, that would be classic education, formal university degree on that, but that’s not an insult. It’s just classic education. Same thing for algorithms, classic education. For math, classic education. And so, for supply chain, the reality was that—so where did I start to realize there was a problem? It’s because we never managed at Lokad, for like five years, to operationalize the mainstream theory. You know, at Lokad, as a software vendor, we implemented the known algorithms. We implemented those optimal inventory optimization techniques. We implemented those time series forecasting algorithms. And it didn’t work, again and again. And we tried hundreds of known techniques and they all failed dismally. So it was like, I would say, an endless series of painful failures, from essentially 2008 to 2012. And at some point, you know, the reality was, enough is enough. It’s just not working, and we are not one formula away from getting this stuff to work. So that’s why we had to discard pretty much all of the supply chain literature and restart from scratch, because it was not working. It was not a valid foundation at all. I mean, that’s—so we, that’s why I say, the sort of mainstream supply chain theory, it just does not work. You cannot operationalize that into any kind of automation. And the reason is because the theory is wrong. That’s why automation remains elusive. That’s why whenever you implement those supposedly optimal formulas, you still need to override half of the numbers that they produce. It’s because they are fundamentally wrong. And though that’s—so yes, I would say, why should you go to the book: it’s because this journey cost us like over a decade of efforts. And frankly, this is literally the book that I would have loved to have had in 2008. It would have saved me like a decade of pain, and we would have been trail-blazing straight on stuff that works in production, generating unattended decisions, instead of muddling through years of stuff that didn’t work, and then, after 2012, very painfully and very slowly uncovering piece by piece the stuff that was actually working.
Conor Doherty: You see, that nice narrative that you just described there is a very understandable way to pitch this, in my opinion. If you say, “Here, learn from my mistakes.” No, but I’m being totally serious. Like, if I had had the information—some people say that’s arrogant. I don’t think so. It’s reflecting on one’s own development. But the idea that I may—I, Joannes, internal monologue—I learned from my mistakes and I have catalogued as best I can so other people can avoid those errors. I don’t see anything wrong with that.
Joannes Vermorel: Yes. And by the way, there was an early version of the draft many years ago, because I wanted to write this book even many years ago. And in this super, super early draft, I listed all the things that were kind of wrong with the mainstream, all the things that we had tested and didn’t work. And it was so long that actually the first guy that I had look at this—actually, I had like a 150-page manuscript, and the person that just had a look at this actually told me, “You need to start with what the hell are you even proposing? Because right now you’re just giving me an endless criticism of all the stuff that is broken. “But once you flesh it out, you will have like a thousand pages of a complete rebuttal, the most extensive rebuttal of the mainstream supply chain literature ever written, and people will have no alternative.” So, you see, the point was I should not—that’s why I say it’s an introduction. I did not position this book as “all the stuff that other people got wrong through the ages,” because that would be endlessly tedious. So I’m going to kind of skip that part entirely and let’s go straight to the stuff that actually works. And so I need to be very terse on all the stuff that does not work, because the reality is that the stuff that does not work is like 100 times longer than this book. It’s kind of irrelevant. And also what is sort of interesting is that once you start to tackle supply chain from the right perspective, all this literature becomes just irrelevant and you can just move on. And there is nothing you will have no fear of missing out, you know, no FOMO. You just realize, okay, it’s irrelevant. Just like when you start to understand chemistry, you realize that you’re not missing much by not studying alchemy. You know, alchemy, they had tons of books written on it. It doesn’t matter. It’s irrelevant. Chemistry is the good stuff, and that’s where you should start from, not from You see, that’s why when you have, like, an introduction to chemistry, it doesn’t start with a 500-page criticism of alchemy, because that would be nuts. At some point you just say, “Okay, the past is the past. Move on. Let’s try to go on. Let’s spend time on stuff that works.” As an introduction, that would not be a service to my readers to just spend so many pages on stuff that doesn’t work. That’s why, actually, the stuff that doesn’t work is addressed in the very last chapter, at the very, very end, where I describe stagnation. But that comes really as the last element of the book, because it’s probably the least actively useful element of the book.
Conor Doherty: Well, I’m sure we’ll have future conversations on other sections of the book. I know people are interested to hear more about—I think it’s chapter four is economics and chapter eight, decisions. I think those are the two really big ones that people would like to probably hear another Black Lodge episode analysis on. So I’ll bring you back for that. But as we stand right now, or sit, I don’t have any more questions. Joannes, as always, thank you very much. And to everyone else, thank you for watching. If you want to continue the conversation with Joannes and me, reach out to us on LinkedIn. We’d love to talk. And on that note, we’ll see you next week. Get back to work.
