00:00:00 Introduction of the idea
00:02:20 Stock level challenges and min-max policy
00:06:09 Supply chain control and decision-making
00:10:07 Replenishment strategies and supplier constraints
00:14:51 Inventory tracking evolution and decision roots
00:22:34 Perishable stock issues and B2B dynamics
00:30:02 Traditional supply chain method limitations
00:36:35 Decision-making metrics and executive summary


In this LokadTV eposide, Conor Doherty interviewed Joannes Vermorel on inventory planning misconceptions. Vermorel dismantled the fallacy that stock levels are a direct lever for client satisfaction and profitability. He argued that companies should focus on serving clients profitably, not on the illusion of stock control. Vermorel criticized simplistic inventory policies like min-max, stressing that stock levels are influenced by myriad factors beyond direct control. He advocated for prioritizing quality supply chain decisions over stock level targets, which often fail to account for dynamic business realities. The conversation highlighted the need for a nuanced approach to supply chain management, emphasizing decision control over uncontrollable outcomes.

Extended Summary

In a recent dialogue on LokadTV, Conor Doherty, the Head of Communication at Lokad, engaged with Joannes Vermorel, the CEO and founder of Lokad. The conversation revolved around the complexities of inventory management and the fallacies that plague conventional wisdom in the field.

Doherty probed the assumption that stock levels are manageable through the right tools, prompting Vermorel to elucidate the primary objective of companies: to serve their clients profitably while ensuring a high quality of service. This, Vermorel argued, is the essence of maximizing profits and retaining customers.

The discussion then turned to the inherent issues with attempting to control stock levels. Vermorel highlighted the classic dilemma businesses face: the risk of overstocking, which ties up capital and space, versus understocking, which can lead to missed sales and dissatisfied customers. He challenged the notion that stock levels are a direct lever for ensuring client satisfaction and profitability.

When Doherty brought up simple inventory management policies like min-max, Vermorel was quick to clarify that stock levels are the result of a myriad of factors, both controllable and uncontrollable. These include customer purchases and supplier reliability, which are not directly governed by inventory policies.

As the conversation progressed, Vermorel emphasized that tangible supply chain decisions, such as purchase orders and pricing adjustments, are within a company’s control, not the stock levels themselves. He concurred with Doherty’s suggestion that market forces and competitors also play a role, adding that stock level targets often overlook other influential factors like delivery logistics, supplier incentives, and minimum order quantities.

Vermorel criticized the stock level framework for its lack of flexibility, a crucial element in supply chain decisions. He argued that stock levels are merely numerical artifacts that do not accurately reflect the dynamic realities of warehouses and stores.

Doherty and Vermorel discussed the differences between permanent and intermittent inventory systems, with Vermorel explaining that while modern systems track inventory changes, they can still fail to capture the true state of stock due to unpredictable customer behavior.

Summarizing the discussion, Vermorel noted that while stock isn’t directly controllable, the decisions over stock are. He compared stock control to market share control, emphasizing that neither is directly controllable and that the focus should instead be on supply chain decisions.

Vermorel criticized the traditional min-max approach and advocated for a shift in focus from stock levels to the quality of supply chain decisions. He used examples from various industries to illustrate how stock levels fail to capture critical business aspects, such as perishability, flight hours, collection schedules, and B2B client orders.

In the Maintenance, Repair, and Overhaul (MRO) sector, Vermorel highlighted the high costs associated with incorrect stock levels and the importance of nuanced decisions over stock level control. He argued that decisions affecting stock levels, such as repair turnaround times, are what can be managed.

Doherty and Vermorel discussed the popularity of simplistic theories in supply chain management, with Vermorel suggesting that these theories benefit supply chain professors and enterprise software vendors by providing easy-to-teach and easy-to-implement solutions. However, he acknowledged that while target stock levels computed by software are not entirely unreasonable, they often fail to align with what companies can execute due to various constraints.

Vermorel proposed assessing the quality of historical purchase order decisions as an alternative metric to stock levels. He emphasized the importance of attributing responsibility to decision-makers and criticized enterprise software vendors for creating opacity in supply chain management.

In conclusion, Vermorel argued that supply chain optimization should focus on controlling and measuring decisions, not outcomes that are influenced by factors beyond control. Doherty thanked Vermorel for the insightful discussion, and the interview concluded with both parties affirming the need for a more nuanced approach to inventory management.

Full Transcript

Conor Doherty: Many companies believe that they can, in fact, control stock levels if they just find the right tools. Now, this assumption is predicated on the very idea that stocks can, in fact, be controlled. But is this the case? Here to discuss is Lokad founder, Joannes Vermorel. So, Joannes, before we get into the methodology or anything more specific, let’s set the table. When clients make any stock or inventory-related decisions, what is the problem that they’re trying to solve?

Joannes Vermorel: Generally speaking, the problem is that they want to serve their clients profitably and they want to have a high quality of service so that their own customers remain customers. They want to do that in ways that maximize their profit. So, the bottom line problem is very simple.

Conor Doherty: Okay, well then, what is the problem with trying to control stock levels?

Joannes Vermorel: The problem is that on the surface, not being able to serve your clients due to a stockout is an obvious issue. If you’re in the business of operating supply chains where you have a flow of physical goods, it does make sense to think, “Okay, if I have zero stock, I can’t serve the client, so I need to have something.” And thus, you would like to have this “something” in control so that clients would be served.

The counterpoint is that you don’t have any stock, which is a problem, or, on the contrary, you have tons of stock and not enough clients to get rid of all this stock, which is also a problem. It seems like if you can have those stock levels in control, then everything will be just fine, meaning that clients will be happy and you will be profitable.

Conor Doherty: Clients are essentially in search of what is approximately the right level at all times.

Joannes Vermorel: Yes, and this perspective has also lent itself to fairly straightforward numerical recipes. So, if you think of your inventory in various ways, probably the easiest, simplest way is just the min-max. When inventory reaches a certain threshold, that’s going to be your min, you just reorder, and you reorder up to the max.

This is probably the simplest possible inventory management policy, and thus it gives the impression that controlling the inventory is mostly about setting those target levels, both the min and the max, plus potentially some nuances if you start considering safety stocks and whatnot, but fundamentally it gives the impression that inventory is controlled through a very short series of stock levels.

Conor Doherty: But again, to anyone listening, this is a very abstract philosophical point that you’re making. I don’t, and I’m going to be as a proxy for anyone listening, what is wrong with trying to leverage these tools and leverage them to try and again control or obtain the right level?

Joannes Vermorel: While it is true that having too little stock is a problem and too much stock is a problem, the fact is that stock is something that is indirectly obtained. It’s not something that you control directly. It is the result of things that you do control plus the things that you do not control. Let’s have a look: if you have 100 units of inventory left on hand in your inventory, it means that those units haven’t been purchased yesterday by clients. So, it’s only because the clients have not made those earlier purchases that there is anything left in the stock.

When you have something in stock, it’s always because your own clients have been polite enough not to swarm you and liquidate everything that you had, but it’s not entirely in your control. What is in your control is to decide that you want to bring more stuff into storage to service your clients, and even that is not entirely up to you because if you decide now that you want 100 extra units in stock, you have typically a varying lead time.

So, you can decide that you want 100 units, but that is not entirely in your control because the delay will vary, and sometimes your supplier might not be perfectly reliable as well. While you order 100, maybe they will only deliver 80 units. Thus, your intent was clear, but the end result comes with interferences.

Conor Doherty: So, the demand side, the clients that haven’t bought today, they might buy tomorrow, that’s the demand side of the correct stock equation, so that’s not within control. Everyone already knows that. So, delineate if you can, what is within control?

Joannes Vermorel: Literally, the only things that are in your control are the tangible, mundane supply chain decisions that you can make. You can decide to pass a purchase order, you can decide to have an order for inventory movements, relocating inventories that you already have across locations, for example.

You can decide to have a production order, so you will combine raw materials or some finished goods to produce something. You can decide to raise the price or lower the price of something that you’re already selling. You can decide to just dispose of something that you have. Why would you do that?

Well, if you have a problem of storage space, for example, there is not enough free space left to bring in more stuff that you need to serve your clients. Maybe at some point, you can just decide to liquidate that inventory to make room for some better inventory to flow in.

Fundamentally, what is in your control are those supply chain decisions that are typically mundane and repetitive. The stock level is not something that is in your power directly. It’s a reflection of the combination of the decisions that you’ve taken and the other semi-random events that just happen, such as your own customers making purchases or requests from you.

Conor Doherty: Not only your customers, presumably also other forces within the market. I mean, the actions of your competitors as well can influence what your customers have done.

Joannes Vermorel: Exactly. You take a decision, but then the outcome of this decision, good or bad, for example, you face a stockout, so clearly, your stock level is too low. That is true, but what you should have done is pass a bigger purchase order earlier. But fundamentally again, you don’t have direct control over the stock level. You have control over the decisions. This may seem like a subtle difference, but in practice, in real-world settings, it makes all the difference. Decisions about your inventory come as a package.

For example, if you want to replenish the store, you might say, “Here is my target stock level for every SKU that I have in the store, and I compute according to this ideal stock level exactly what I need to bring in.” Or, you can directly decide what to bring in. What is the difference between the two options? They seem like exactly the same. One way, you pick your stock levels, and that gives you a quantity.

The other one is, well, you just pick the quantity directly. It turns out that if you want to have a full truck delivery, if you are directly looking at quantities to be moved, then you have direct control on stretching those quantities so that they will match a full truck. But if you are operating based on target stock levels, then you will generate indirectly those quantities that need to be replenished, and then you will realize that the quantities that you want to replenish just do not match a full truck.

That’s the problem. If you tackle the problem of replenishment through stock levels, you are blind to all sorts of other elements that come into play in the final ordering decision. From afar, it seems like the stock level is a perfect way to control what is being ordered, but the reality is that an actual ordering decision has many more dimensions. We could enumerate them.

I was just mentioning the full truck case for a retail store, but if we add the warehouse level, the supplier might have price breaks such as if you order more, you get a discount. Again, if you pick a stock level, you do not see that. It means that you order exactly whatever brings your stock up to the level, irrespective of whatever benefits you may gain from ordering and reaching certain targets so that you hit your price breaks. Similarly, the supplier might have, and it’s very widespread, minimum order quantities (MOQ).

What happens when you say, “I want to target quantity at 100,” and that’s for your stock, but then the MOQ of your supplier is 200? What do you do? You have 50 units left in stock, so you’re well below your ideal stock level, which is at 100, but the MOQ of the supplier is 200 units. Do you order those 200 units now, and then that brings you to 250 units, more than twice your original target, or do you wait until you have almost completely exhausted your stock and then, at the last moment, you order, and then you bring in those 200 units that still puts you almost twice over your ideal stock target?

The problem that I have with the stock level perspective is that it is a simplification, and it is simplistic. It does not acknowledge the sort of subtle or not-so-subtle constraints and factors that come into the ordering mechanism.

What if your supplier can expedite the purchase order for a little fee? This might be of interest from time to time, to say, “I’m going to pay a premium to have this purchase order expedited.” But again, if you think in terms of stock levels the idea of expediting or slowing down an incoming batch of inventory just does not exist in the framework.

Conor Doherty: You’ve made a similar sort of atomized versus holistic analysis before when people talk about demand forecast and demand forecasting. That’s looking at only one dimension of this supply chain problem. So, you’re suggesting that thinking about stocks as stocks is also flawed? Yes, and that’s quite a deviation from orthodoxy.

Joannes Vermorel: Stock levels are numerical artifacts; they are not even real. People say, “My stock is obviously real; I can walk in a warehouse and see the stuff.” But what is happening in your software? When people say they have a certain quantity in stock, they don’t mean they have a matter-detecting device that counts the stuff on the shelf in real time. No, this is not how enterprise software works. This is not how inventory management system works.

Inventory management systems work by counting the increments and decrements. Whenever you’re picking something from the stock, you have to make sure that you create an electronic record that says, “I have just picked one unit.” And whenever you receive stock, you have to create an electronic record that says, “I just received this amount, this number of units,” and then that increments the stock.

The technical term for this is called permanent inventory. It was a big thing in the beginning of the 70s, as opposed to intermittent inventory where most of the time, you just don’t know your inventory levels. You have to count, you have to do this inventorying operation to know their inventory.

With modern software, you keep track of all the increments and decrements, and then you assume that your stock levels, in between, are correct. But again, this is a numerical artifact. Depending on your settings, that can be a very accurate numerical artifact. This is usually the case in warehouses just because everything is under control. This is a professional environment, so there is near-perfect tracking of what goes in and out.

But if you go into a retail store, the patrons, the customers, can be quite messy. They can misplace products, they can damage them, they can lose them, sometimes some people even steal them. So those introduce discrepancies in what you have in the electronic records and what you have in the stock.

So the bottom line is again that the stock is not really in your control. What is in your control are the decisions that you take over the stock. And yes, that’s a subtle nuance, but it’s like, again, it’s a little bit like, “Are you in control of your market shares?”

Clearly, if a company is successful and earns a large and increases its market shares, it is not a random accident. So the company has been doing a lot of things for this to happen. So the market shares are not just an act of God or just some random fluke.

There is obviously an intent, and there was some strategy, there were efforts to get there. But ultimately, no company can say, “I am in total control of my market share.” I mean, clearly, if you do something absolutely stupid, you’re in control in the sense that you can lose all your market share very quickly if you cease to be professional and diligent and whatnot. But ultimately, you can’t just decide that things will go well or go your way just because you want it that way. That’s the same thing with the stock levels.

When you focus on the stock level you are confusing the root cause with the effect. You’re focusing on the symptom as opposed to looking for the primary source of the performance, or lack of performance.

Conor Doherty: What would be the root cause? Where should we be redirecting our focus? Historically, companies apply a min-max formula. They think if the stock falls to a certain level, it triggers an automatic replenishment, and they have the right amount on the shelf. But that’s wrong. How then can companies know if their inventory decisions are right or approximately right or good or ideal, if not by recourse to a formula of some kind?

Joannes Vermorel: What I propose is to start paying attention to the supply chain decisions. Many companies are not paying any attention because all the attention is on proxies of the supply chain decision, such as the stock levels. They assume they assume that the stock level speaks the whole story about all the optimization that needs to happen, that if we pick the proper stock level then the proper inventory decision will happen.

What I propose is no, because the proper inventory decision is a higher-dimensional thing compared to the stock level. The stock level is a simplistic view, and there is no such thing as the ideal stock level because it cannot tell you all there is to know to get the best ordering decision.

Conor Doherty: So, it exists in a vacuum?

Joannes Vermorel: For example, if you have two suppliers at two distinct price points with different lead times, which is a simplest example of a multisourcing, then your stock level only tells you that you need more. It doesn’t tell you whether you should order from one supplier with a shorter lead time at a higher price point or the other supplier. It doesn’t matter which stock level you pick, it cannot tell you that.

No matter how you approach stock level-driven replenishment policies, there are plenty of ways you can inject safety stocks, you can have min-max, you can have plenty of things that are a lot more fancy, they all implicitly assume that all there is to be known for a good purchase order lies in picking the right level. But that’s not true. This one level is just one dimension, but the actual supply chain decisions involve other dimensions.

You cannot express in full the decision and multiple dimensions, so multiple multi-sourcing is out, dealing with MQS is also out, dealing with full tracks is also out. Even basic things such as dealing with the overall capacity of the store is out. What do you do? You decide that this SKU needs a higher stock level. You decide that and then you move to another SKU and you make the same decision, higher stock level.

I just analyzed that and I say I need more. Then at the end of the day, once you’ve reviewed, let’s say the 5,000 SKUs of the store, you realize that your total capacity exceeds the capacity of the store. What do you do?

That’s a problem of those stock levels. They do not tell the whole story. This is a very local, simplistic view that just misses mundane nuances. I’m not talking of missing advanced mathematical statistical considerations. It’s not about having a PhD in statistics to be able to spot the problem. The problem is literally super obvious. Pretty much every vertical has its problems that would just not fit the situation.

For example, perishable fresh food is super basic. What if I have 40 units in stocks and my ideal stock level is 40 units? Perfect. But this is a perishable product and I realize that 35 of those 40 units just expire by the end of today. So tomorrow morning, I’m left with five units, that’s the one that hasn’t expired. So that means that according to my ideal stock level, everything was just fine.

I was right on target, 40 units, 40 units, perfect. And today, I just realized that no, I am five units left, all the rest has evaporated. It’s super predictable and yet this ideal stock level just didn’t tell me anything. This simplistic perspective tends to ignore tons of things.

Conor Doherty: I want to come back to what you just said about anyone who sells perishable goods. It indicates that the misapprehension about control and ideal, perfect stock levels varies by vertical. If you sell hard luxury or FMCG goods, you’re not going to be as vulnerable because the dimensionality of expiration is absent there, or at least not as pronounced as in perishable goods.

Joannes Vermorel: The reality is that no matter which vertical you’re in, when you start paying close attention, you will realize that this simplistic, idealized stock level or control of your supply chain through stock level is harming you by being simplistic and not acknowledging tons of important aspects of your business. Which aspects get ignored? It depends on the vertical, but my proposition is that no matter which vertical, it’s still going to be substantial.

For fresh food, it’s going to be perishability. For aviation and aerospace, it’s going to be the flight hours and flight cycles that live in your stock of rotable parts. If you go for fashion, the problem will be the overall schedule of collections where you want consistency in your assortment: all colors, all sizes etc. The stock level just doesn’t tell you whether on the wall you have something consistent for the whole store; it just tells you whether this one article is properly stocked or not.

It doesn’t take into account any kind of substitution, cannibalization, and if you go into B2B businesses, then you don’t realize this stock level doesn’t, for example, let you correctly address basic problems such as one of your B2B clients has passed a purchase order in advance for a large quantity, giving you plenty of time to make sure that everything was ready to serve this client on a given day.

It’s something that really happens frequently in B2B. Let’s say you’re a wholesaler of electrical equipment. One of your clients is a large construction company and they will order 2,000 light switches for a building. And they say, we don’t expect you to have that available right now, so we pass the order three months in advance.

But in three months, we expect that on this date, for you to have all those light switches ready to be delivered where we want those things to be delivered. So that’s just an example, but if you think in terms of stock levels, those dimensions are just absent.

Conor Doherty: You’ve mentioned MRO. I really want to come back to that because it was a very interesting example. It opens up an immediate naysayer response, which is if you’re operating an MRO and you have rotables, expensive parts in and of themselves, but not having the correct level of those when you need them can result in hundreds of thousands of dollars in an AOG event.

So, what do you say to someone who says, “I agree with the philosophy, it’s fantastic, but the cost of me being wrong based on what you’re saying is immediately $300,000 for a 747 on the ground”?

Joannes Vermorel: The fact is that you do not control the stock level. Saying that the stock level is wrong is wishful thinking. You do not control the stock level; you only control decisions that ultimately govern, but incompletely, your stock level. If we go to the aerospace, for example, turnaround time (TAT) matters.

The stock might be low not because you don’t have enough equipment but just because they are getting delayed in being repaired and becoming serviceable again. The problem might not be that you don’t have enough parts in an absolute sense, but just because whoever or whatever is in charge of getting those parts repaired is just too slow. There are nuances in those decisions that are in your control that are just absent from the stock level.

You cannot govern the inventory through the lenses of these idealized stock levels. There are several ways to do that, but it’s incredibly simplistic. I believe that a lot of mainstream supply chain textbooks have made the problem worse by promoting tons of techniques that put this governance of the supply chain through the stock levels on a pedestal.

The reason is that if you make certain assumptions, such as stationary demand, stationary lead times, which are total nonsense, there is nothing in the real world that is even close to stationary anything. But if you make those assumptions, then under certain extra conditions you can get something where indeed the stock level is a strict logical equivalent to the purchasing decisions.

However, and that’s the big however, it is only if those big extravagant assumptions are true, like stationary demand. As soon as you say no, demand is not stationary, you will see that all those methods collapse because they are more like mathematical toys than reasonable approximations of the stuff that is happening in real-world supply chains.

Conor Doherty: I agree, and I want to underline one of the examples you gave. When you talk about not looking at stock levels in isolation and to build on the MRO example you gave, you have parts. Some of those parts might have the correct volume, but we can decompose parts further.

Some will be line repairable, some will be shop repairable, and the turnaround time for the shop repairable will be much longer than for the line repairable. This is not stock number related, but these are the peripheral variables that influence availability.

Joannes Vermorel: Exactly, but the counterpoint would be people saying that’s okay because we can collapse all of those numbers into this ideal stock level through analytics. My point is no, you can’t. That’s the big difference. What I’m saying is this is a dimensional problem.

If you have a black and white screen, it doesn’t matter if you can add more pixels; you are lacking the colors. Adding pixels does not solve the fact that you have no colors on the screen. Governing your supply chain through stock levels is like approaching images through black and white, and you’re lacking several dimensions. No amount of sophistication in picking the right stock level can fix these missing dimensions.

Why do you think these theories became so popular? Having a simplistic theory is of high interest for at least two crowds. First, supply chain professors, because it gives them something easy to talk about where you can have ridiculous assumptions such as stationary demand, which are very convenient when it comes to creating exercises that students can be expected to solve in 30 minutes.

If you realize those problems are so difficult that it will take weeks to get to the bottom of it, you have a problem with how to organize an exam. This is not really a valid point to teach those, but still, I can see why it became popular. That’s the beauty of having toy mathematical models that are very easy to teach and where it’s very easy to do a quick examination and have people going through multiple choice questions just to select the answers.

The second crowd that is also very happy to have those simplistic recipe is enterprise software vendors. Why? Because if you can get away with cheap, easy to implement numerical recipes and charge a lot for that, why not? It is certainly easier and some companies are still willing to pay you good money for that. Why say no? Why should you do something that is going to be more costly for you as an enterprise software vendor? For most enterprise software vendors, not Lokad but for most, the answer is that it’s good enough.

The net result is that the stock level computed by many enterprise software pieces is not entirely unreasonable. If we look at the target stock level, it’s kind of fine. It’s crude, and there are better ways to do it, but it’s kind of fine.

The problem is that supply chain practitioners realize that the quantities resulting from those target stock levels are completely incompatible with what they can actually execute. They have MOQs, full truck constraints, storage capacity constraints, and sometimes they have ordering schedules from the suppliers.

If your supplier tells you that you can only order from them twice a month, then your target stock level becomes problematic. What do you do if you diverge from your target but not on the exact day you’re supposed to place a replenishment order?

It’s a very mundane problem, and that’s why many companies end up with maybe halfway decent idealized stock levels. But when you look at their purchase order history or their production order history, there’s a complete disconnect because the idealized stock levels cannot acknowledge the basic constraints that define the supply chain.

Conor Doherty: If I were to condense the conversation so far, it would essentially be, and correct me if I’m wrong, that stock levels, much like your feelings—and it was a bit of a Freudian slip earlier when you said ‘service levels’ instead of ‘stock levels’—are a KPI, and it’s something upon which people tend to inappropriately focus attention.

If that is true, and you haven’t interrupted me, so I think it’s true, what metric should people then use in lieu of a KPI to determine if whatever their decision-making is, is making a positive difference?

Joannes Vermorel: That boils down to stepping back and going back to the assessment of the original decisions. It’s not very difficult. It’s just marginally more technical, but only so much. We are not in the 70s anymore; people don’t have to operate with computers that have one kilobyte of memory.

Accessing every single historical purchase order decision to assess whether it was a good or bad decision by looking at how the situation unfolded afterward is slightly more difficult than assessing the adequacy of the stock level, but it’s not vastly more difficult. It’s pretty much in the same ballpark of difficulty.

You have thousands of SKUs, and you have maybe 10,000 purchase order decisions for last year, so it’s not like on one hand you have something where it’s super trivial and on the other hand, it’s super complicated. Assessing stock levels is not that difficult, and assessing the quality of those purchase order decisions is only very marginally more difficult, but that much more.

My take is to stop looking at those stock levels and start looking at the decisions. Usually, the entire framework is geared in a way that makes those decisions entirely invisible to you. People complain usually way too late after the events that inventory is too high or too low, while in fact, the problems could be traced back to decisions that happened months earlier. Those decisions could have been challenged much earlier. Those decisions are in many ways much easier to challenge.

If I say this stock level is too high, there are many parties that could be blamed. For example, I could say the marketing team’s last campaign was really poor, and that’s why we didn’t get enough demand, so it’s their fault. But if we look at the decision, we could say, ‘This is a decision that you made, and it was really in your control.

It’s much easier to attribute responsibility to whoever is governing this decision, ultimately improving the process that governs those decisions, as opposed to improving this stock level that is a mix of a lot of stuff, some of which being in your control and some not.

Conor Doherty: That sounds like a cultural shift because, in many cases, there’s an aversion to any mechanism that would magnify the scope for allocating responsibility. There’s almost the opposite, a diffusionary mechanism in play.

Joannes Vermorel: Yes, I think that enterprise software vendors have been playing this card quite smartly over the last two decades. They make the situation very opaque. On paper, they would say their software gives you full transparency, but when you look at what they are promoting and the sort of numerical recipes they are promoting, those recipes, especially those looking at service level, are not fostering transparency. On the contrary, they are fostering a huge paradigmatic layer of opacity.

Conor Doherty: So let’s maybe start to wind up. But when we’re talking about culture, culture tends to come top down. So then we are talking about, let’s say, C-level execs and their ilk. How would you summarize your overall thesis today at a C-level?

Joannes Vermorel: At a C-level, you can optimize only what you measure and what you control. If you don’t control the stuff, then there is no optimization possible; it’s an outcome left to chance. You need to be in control of the thing you want to optimize, and obviously, you need to measure it; otherwise, you don’t know if what you’ve done is better or worse than what you used to do.

Apply this basic line of thinking to supply chain, and that means what you control are the supply chain decisions, not the outcome of those decisions that are mixing in tons of things that are not in your control.

Just say, “We optimize by focusing on what we control and what we can measure.” And by the way, the “what we can measure” is slightly dangerous because sometimes there are stuff that you can’t measure that are still very, very important.

But that, I would say, is a discussion for another day. When it comes to these stock problems, it’s okay. You can measure the relevant stuff. Measurement is not the biggest challenge. It’s a small challenge compared to other challenges.

Conor Doherty: Well, I’m convinced. Thank you very much for your time, a pleasure as always. And thank you very much for watching. We’ll see you next time.