 Hi everyone. Welcome to another life event of the MIT MicroMasters in Supply Chain Management. I'm Miguel Rodríguez García, a researcher at MIT Center for Transportation and Logistics, and I'm the course lead for SU1X Supply Chain Fundamentals. First, I just want to say thank you to everyone for joining us today. This is the second and final life event of the full series, a series of course course life events for SU1X Supply Chain Fundamentals and SU3X Supply Chain Dynamics, and that's why I'm really happy to be co-hosting this life event with Michael Lee, Paulo Sosa Jr., course lead of SC3X. Hi Paulo, how are you? Hey Miguel, how are you? Thank you for the introduction. Hi everyone. It's great to be here with you all. We are excited to share some great insights about supply chain and this live event today, and our agenda for today is the following. First, our guest speaker will give us a presentation that will last around 25 minutes, then we will have some time at the end when he will answer questions from the audience. So we encourage you to participate and use the Q&A feature in Zoom, not the chat box but the Q&A feature, and then Miguel and I will take those questions and channel as many as we can to our speaker. But before we introduce our guest speaker, we want to share something with you all. Right Miguel? Yeah, that's right Paulo. So we just want to remind everyone that this event is part of the MITx MicroMasters program in supply chain management, a program that we develop here at the Center for Transportation and Logistics at MIT, and as well as supply chain fundamentals and supply chain dynamics, the MicroMasters program includes three other courses. So five courses in total and some of them are currently open for enrollment. So don't hesitate to check them out. We'll be posting the link in the chat group in case you guys are interested in completing the program, which of course we encourage you to do. So without further ado, let's introduce our guest speaker Paulo. All right, so today we are honored to have Yashar Ahmadov as our guest speaker. Yashar is an industrial engineer with more than eight years of work experience in supply chain management, and he is currently a senior simulation data scientist at Amazon. He uses simulation, mathematical optimization and data science tools to solve complex supply chain problems. This includes network design, facility location, resource planning, inventory optimization and scheduling, among others. Yashar holds a master's degree in supply chain management from MIT. He was part of the 2021 blended cohort. He is also a MicroMasters alum, which means he passed all courses from the MicroMasters program, like many of you are doing right now. We always like to remind the audience that one, among many other benefits from earning the MicroMasters program credential is that you become eligible to apply to the supply chain management blended master's program at MIT, just like Yashar did and to other universities around the globe. So welcome Yashar. Hello. Thank you. Thank you, Paulo and Miguel. I'm happy to be here and I greet all the people who are watching this live video event. I'm going to start sharing my screen. So today we're going to dive deep into simulations and what they are used for, what are they good and in which situations should we use simulations. So mainly I will focus on inventory transportation and system dynamics aspects of simulations. So here we go. The overview is what is simulation? And then I will talk about applications in inventory management, transportation, system dynamics. And the most exciting part will be a live demo. And my target is to show you that within a very short timeframe, let's say five minutes, you can create a very visual simulation of your supply chain. And that's going to be the last part. So first of all, what are simulations? We hear this word a lot in different contexts, but it's a collection of methods and applications to mimic the behavior of real systems. It can be any system. A supply chain is one of them, fulfillment networks, warehouses. And in this picture you see the truck simulator. It is also a simulator because we are trying to mimic the behavior of some real systems. So a lot of things go under simulation. However, in this context, in supply chain, we're talking about industrial simulation. And why we do so? We want to understand, predict the system's behavior and evaluate various alternatives. So the word digital twin emerged in the last years, which means we have physical factories, we have ports, we have vessels, trucks, warehouses, retailers, and so on. We want to create their visual representation and digital representation so that we can experiment on top of it. And the world is changing fast, the situation is changing fast. And we want some kind of tool that would let us to do what if analysis. So there are many different types of simulations within industrial simulations also. At some point at the beginning when we did not have very strong computers, the simulations were mainly deterministic, meaning that there is no random variables inside that simulation. And whatever input you give, you always take, you always get a unique output. On the other hand, we have stochastic simulations, which is the widely used in this domain. And here you can have random variables. And the beauty of having stochastic simulations is we know that in real life, nothing is deterministic, right? One day we receive orders maybe for a thousand units, the next day thousand one hundred, the next day nine hundred. It changes all the time between days, seasons, weeks. The lead times are also stochastic. That's also one of the major things taught at MITX MicroMasters in supply chain management. We have some kinds of probabilistic distributions. Most of the time we can approach it as normal distribution. And we want to optimize our policies, our resources under this probabilistic environment. So the lead time could be one week, eight days, five days, but it's never a stable number. Sometimes there are static simulations that have no time dimension. For example, Monte Carlo simulations. This is also taught, you open an Excel file, generates some random variables and experiment on top of it. And we mainly focus the ones that are stochastic dynamic. That is the system behavior changed over time. We have, let's say, thinking of ocean transportation. We have vessels with a number of containers moving the state of the system changes every minute, every second. And the third dimension is continuous versus discrete. So continuous is system state changes on a continuous basis. And discrete is basically you have discrete points in time. And then that's it. There are some defined points that you jump from one state to another. An example of continuous system is an altitude of an airplane. And as long as it's flying, there is a number that's evolving. It goes up when we take off and landing, it goes down. And it can be zero when it's on ground for some time, but that's okay. It's also continuous thing. Or discrete events like customers visiting a supermarket. The customers, if you try to take notes when they enter to the supermarket, it's like some points in time. This is discrete. So mainly the most advanced methods focus on stochastic dynamic and continuous types of simulations. Okay, this brings us to the next slide. And in the world of industrial simulation, we have three paradigms, three different worlds. And this started with system dynamics and discrete event simulations. And this type of discrete event simulations became very popular in the 90s and 2000 years. And now we have evolved a new stage where we have agent based simulations and a big fan of agent based simulation, because it lets you model all kinds of complexities. So system dynamics, if you have a look at this chart, the y axis, it goes from low abstraction to high abstraction, meaning that how detailed you want to model your system. When you're doing a modeling system dynamics, that's a high level, a macro economic policies, the overall behavior of the system, you're on the top right corner, it's mostly continuous system, and you have a very high abstraction. And you have very less details. And discrete event simulations are located around this region. They lower you modeling average number of details like you can model your warehouse operations, you can model your port and trucks arriving and so on. And so the discrete event allows you within, let's say, a little bit above micro level until to mid level. And this was dominating for two decades. Now we have agent based simulations, which led us model from a very low abstraction to a very high abstraction. It gives you basically the whole bunch of opportunities. You can model the active objects, individuals, behavior rules, interactions between the different agents and models. This is mostly what I prefer because of the flexibility that it gives. The discrete event simulations were working like the model was scheduling discrete events, like the truck arrives, the time is equal to zero, it gets loaded at time is equal to one hour. So that's why it was called discrete event simulations. What are the advantages of simulation? There are plenty, increased realism, existing or non existing systems can be started. Let's say you have a certain supply chain, but you want to transform it. You're going to buy from suppliers that maybe does not exist in your supply chain right now. Maybe you're going to get new customers that are not existing now. You can model both of them. Hazardous systems can be started without risks. Bottleneck analysis, usually if you have read the book called The Goal, where the theory of constraints are explained, their main idea is to find the bottleneck in your system, understand it better, make it lighter, because bottleneck is the problematic part where which defines the throughput of our system. You can do this in a digital environment. What if questions can be answered? For scenario analysis, you can say, what if I change this? What if I add a new warehouse? What if I get a new retail customer? Results are reproducible as long as you keep, although it does a stochastic or probabilistic simulation, but as long as you keep the random seed the same, every time with the same inputs you will get the same outputs. And one of the things that I love about simulations is their explainability. Nowadays we have AI solutions, ML solutions, mathematical optimization solutions and so on. Everybody is now most of the people are using, for example, chat GPT. When you ask what is supply chain management, it generates an answer, but why does it use certain words but not the others? Nobody can answer that because it is how it is trained based on the data it has been trained. There is a complex neural network behind and it's really difficult to explain complex models, why they make certain decisions but not the other. So this always comes as a question when using other types of solutions. But with simulations you can say, hey, here is the truck, here is the customer and at that point in time this was the cheapest option. That's why I chose this route, for example. It is of communication with the management, especially with the help of animation and this has helped me a lot when talking to customers to the leadership management. Instead of some theoretical tables or data, you just open and show what's going on. There are also some cases where you should not use simulation. If you can do a common sense analysis, you don't need. There are some simple queuing systems in the literature. If you can use it for driving restaurants and so on, there is no need to spend a lot of time and energy to build the simulation models. When you don't have resources, if you cannot validate or verify the behavior of this system, you can't meet the expectations of the project. You should not overpromise or the system behavior is ill-structured. So basically nobody knows what to expect from the system or how it behaves. So this is as a side note. And within this agent-based simulations, there are multiple providers. The one that I use is software called Nelogic. It's written in Java and it offers GIS maps, space markup tools, 2D, 3D simulations, many industry-specific libraries for process modeling, material handling, pedestrian, rail, and road traffic libraries, fluid library for chemicals manufacturing, for example. And they also have a section called system dynamics where you can take it and use. So the conveyor system, for example, the transportation system, this is not unique to every company. Some things are generic and already these packages are created for you where you can just drag and drop and use them and spend your energy to fine-tune the model towards the details of your system, the things that are not general or applies to everybody. For inventory management, typical tasks, demand for recasting, safety stock optimization, order points, lead time, ABC analysis. Often, when you're managing inventory, you use some kind of, again, probabilistic model. You can use, for example, economic order quantities. You can use computations for order up to points or the minimum stocks. Again, you can compute things, but will it work in reality under the probabilistic behavior of the system? So this is a good place to test what is going on and what is happening, and you can do scenario analysis. So one of the things that the COVID period taught us is that instead of forecasting the future, the other way is to do scenario planning and prepare accordingly. What if World War III starts tomorrow? The worst case, and what if everything goes perfectly fine and the interest rates again go down and the shipping rates are affordable? You can define a certain number of scenarios and prepare accordingly. This is where the simulation comes in handy. Again, for example, for the warehouse simulations, and I do have some simple examples on how to model this or visual simulations. So as I said, this is the sample warehouse simulations here. You have racks, you have employees, you have forklifts, you have trucks coming in. They bring goods for you, and some of them, these blue ones they take and take it to your customers. And here you can do a lot of different type of experimentation. And you can change, for example, let's say you want to know how many forklifts you need. You can change some figures from eight to nine and see what is their utilization factors and different number of employees. So it is helping you to make decisions on how many resources you need. This is usually the case when we need to make a decision on the resources. We don't want to overshoot and also underestimate. So we're trying to find the golden mean. And one of the examples that I like, the system dynamics, that's like a very high level formulations, they use this agent-based modeling to predict the COVID infections. And this is one example of that. And there were many proposals and there was not enough data to validate which systems give you the best projection to the future. And there were many different methodologies proposed. And the agent-based simulations outperformed others in terms of how many people are susceptible, they exposed, what is the infection rate, and if they get infected, how many of them get recovered, how many of them lose their lives. And based on these system dynamic simulations, in the hint site now we see that this type of agent-based simulations yielded one of the most accurate projections on what's going on. In transportation, traffic flow modeling, airport port operations, public transportation traffic safety, many things are possible by using simulations. Again, most of the simulation packages come with GIS maps, which means that already contains the information about the railways, highways, and you don't need to guess the transit times. It already comes in a package. You just tell the origin and destination and then it is going to tell you what's going on the system. And for the system dynamics against supply chain dynamics, policy modeling, environmental systems, healthcare systems, and also the COVID analysis that I showed you are some examples of system dynamics. Right now I will stop these and jump on to this simulation software just to show you that in a few minutes, in some minutes, you can create a simulation that is very visual and it is basically once you master the basics, it will take you less and less time. Here is the question. I have one manufacturing site in Albany, New York. I have two distribution centers in Springfield, Massachusetts, and Hartford, Connecticut, and I have two retailers in Boston and Providence. So the aim is to create the simulation of these small supply chains. So I picked these as an example. I will show you here on this software. I don't expect you to follow all the steps. I'm going to go fast just to show that it works. And if you later want to follow, you can watch the recording or in a slow mode, or I can share some examples of this step by step. So the first thing here is I'm going to create a new model. I'm going to call it supply chain simulator. And then I'm going to set the model time to hours. And it creates a blank model for me. The first thing I'm going to do is to drag and drop the GIS map. So on the left side, there are different libraries that you can use. And one of them is Space Markup. And there is GIS map. And this map, as I said, contains all the information about, you know, basically Google Maps, but it's coming from an open street map provider. So it's a different provider, but it already contains all the routes and highways and you don't need to tell what is the exact truth. So in our example, we have one manufacturing site in Albany. So I'm going to just double click and zoom around Boston to show it easily. And if I search Albany, it's popping up here. I'm going to convert it to GIS map and then remove all other elements. And so this red point here, it's going to be our manufacturing facility. And then I will also locate to the others, the Springfield Massachusetts. I'm going to type Springfield Massachusetts. And it's going to give me multiple options. So I'm going to convert this also, which is located here, and then remove all other elements. And here I will zoom it a bit to see better in this region. So here we go. And then the other one is in Hartford. I'm going to search for Hartford. And then the others, I'm going to remove all the elements. The next step is two retailers, one, let's say in Bolton, Massachusetts. And then I add it here and remove the others. Then the last one is in Providence. It gives me this option. I add it here and remove all the elements. Now I have all the nodes located here. This is going to be my manufacturing facility. And these two are going to be my distribution centers. And these two places are going to be my retailers. And once I do this, I can create some kind of collection. Again, on the left hand side, you can create different collections. And I will use one for manufacturing site location. And it's going to include the GIS point. And once I add it into this collection, it is giving me the option to iterate over this set. And then you can easily create other collections for the, let's say, distribution centers. And then you can also select here. It's going to be other type. It's going to be a GIS point. And here I will add the distribution centers, which was in, one was in Hartford and the other one was in Springfield. And then I will create another collection for the retailer locations. And I will add here the other two points, which is Boston. And I will put plus sign here. I will add Boston and Providence. Now, this map contains most of the information I need. If I run this simulation, it's just going to stay there. And no movement or anything per se. But now I need to tell what this is going to look like. It's just plotted in the locations. And that's it for now. And then now I need to create the actual agents for different types. And in this case, I will have one manufacturing site location, manufacturing site. It's going to be a single. And let's select an animation for this. Let's call it warehouse. And then finish. Now, once we do this, here we need to tell the model where it's located. It is located in a node. And it's called, it's located in Albany. Now, if I rerun the simulation, it's going to pop up in the right place with the right animation. You see the factory sign here, which means everything is fine. I need to do the thing for the other two. I need to create the respective agents for the population agent, male agent. And then this is going to be the distribution center. And then it's going to have also 2D animation. I'm going to use this warehouse and then finish. And then these are also going to be located in the node. And that node is defined by the collection here, which is distribution center location.get index. So this is going to put in the right place, initial number of agents. And this is going to be this menu, the size. And when I run it now, so we got these two also located here. And last one is the retailer part. I need to do that also. I put here and I'm going to collect population of agents. These are going to pop retailer. And then next, it's going to be a retail store sign and then finish. I will do the same thing here. It contains this retailer. We have two of them right now, retailer location, that size, which means it will take it from there. And they are located in the node. And this is going to be retailer location.get index. Right now, if I do this, it's going to also plot the last piece. I have only one thing to create the tracks. And then I'm going to finalize just to show you how it moves. Now on the map, we see all our nodes. Right now, I need to also create the tracks. And for that, I will go to the main palette and then bring agent here. And it's going to be population of agents. It's going to be used in flowcharts. And this is called track. I will select a sign from here, which is this one next. And it will have a client, which is of type. Manufacturing centers will send to distribution centers and then finish. And right now, we can create the initial number of agents, let's say 100. And it's going to be the tracks. If I run this over, we see the track also located here, but it's huge. So we need to make it smaller. I will go to the track section and then reduce the scale. I will put maybe 0.5, 0.5. Then at the end, this is how it's going to look like, the simulation. And if I run this, we will see that all the tracks are moving in the right direction. So when you create the truck agents, select them. Here is our manufacturing facility. These are the two distribution centers and these are the retailers. Now, with just a few commands, I was able to create the simulation. And I don't care about the roads and so on. The tracks are already following the actual routes between the cities. And why is this beautiful? Because it's easier to communicate. It is visual. And there are tons of things that you can add. This was the thing that I did in just five or six minutes. But you can add tons of other things on top of this. Different types of KPIs, visualizations, and any type of thing, like time stack charts, plots, bar charts, histograms. You can use entire library for system dynamics for cart library. There is an entire thing designed for you here. And for example, for warehouses, conveyor, you don't need to define it. It's already here. You drag and drop this conveyor object and tell what is the size, what is the speed, and so on. Here, I finish my part. Now it's the Q&A session. That's correct. Thank you so much, Yashar, for walking us through so many examples of applications, simulations, and supply chain management. And also for sharing this live demo, which is great. I'm pretty sure the audience appreciate this as well. By the way, we have a great audience today. We have many questions. And we will share some of those right now. I want to encourage you. If you have a question, please use the Q&A feature. And we will channel it to Yashar. So let me start with two questions. The first one, I can take myself. Are there any MITX classes that focus on supply chain simulation and optimization? The answer to that is yes, we do have. So you have content on SC0X, supply chain analytics. You have content on SC2X, supply chain design, and also SC3X, supply chain dynamics. We cover optimization and simulation content there. So feel free to enroll in one of the links that Emma is sharing right now in the chat. And the question that is addressed to Yashar, so Darryl Fernandez is asking, what skill sets do you recommend we concentrate to learn in order to have a career in supply chain simulation field? And the learner is also asking about tools that we should be well versed to be relevant in this field. Yes. So the simulation tools that I use as of now, Java, they are based on Java programming language. You don't need to be an expert, just understand how it works, the object oriented programming, how you create classes, and basic syntax. And there is a software that I use today is called AnyLogic. But you can also look at the market if there are other agent-based simulation providers, you can speak to any one of them, but the ones that I prefer is AnyLogic. And the thing is, I've tested this in very complex environments, right? Today I had just one manufacturing to distribution center and two retailers. What if I had hundreds of suppliers, thousands of delivery stations, and millions of customers? So this methodology would work in that case from my experience. But the other types of approaches don't work, because when it's too complex, it takes you like 40 hours to run the whole simulation, which nobody is willing to wait for. So my answer for this, I needed the basic Java and this specific software called AnyLogic. And you should understand how object oriented programming works. All right. Thank you so much for your answer, Gershard. I believe that your answer actually is related to one of our learner's questions. Mario Lavarello was asking about the agent step. And I think this is kind of related to what you just mentioned. So maybe if you can explain a little bit better, that step when you relate the agents to nodes, and also, for example, to the trucks, to the different elements in the simulation, because some of our learners are still wondering what that means. Yeah. Okay. So very basic thing, but you're probably, if you're familiar with programming, you know the difference between functional programming and object oriented programming. If you're not familiar in very basic words in Java, for example, you create objects. And over these objects here, you see the distribution center is an object. It has certain parameters and certain behaviors. And manufacturing site is another agent, and it has its own behavior. In other simulation paradigms like discrete events or functional programming, you don't have this concept of objects. You create a function, for example, a truck movement function, and you define there. But here, at high level, you create a truck agent. And inside it, you define what's going to happen with this. So object oriented programming takes this idea and applies it to here. Let's say I have a manufacturing site. Right now, I have not modeled anything inside this. But let's say you have a thousand manufacturing sites, and they have certain production process going in. So the good part of this is when you double click inside the manufacturing site, you can define what is going to happen with this agent. The same with the trucks, lorries, distribution centers. Let's say inside the manufacturing, you have certain, let's say, orders arriving, then you put a source block here. Right now, it generated random demand, so just random numbers. But if you have a certain demand pattern, and you have certain processing times, and inside this manufacturing site agent, you can define what is going to happen with it. Again, you can have thousand of them, and their processing times can be different. That's totally fine. You can define this inside your manufacturing site agent. And then, for example, you have some resource pools. You can drag and drop and say, I have here associates, and there, for example, the capacity, which means the number of associates I have in warehouse. It's 100. They have certain schedule of working. You can define inside these agents what is happening. Some of these come with a pre-built behavior, like the truck, it has origin and destination. It moves in between these two. When I create the truck agent, it has this idea that it needs to move from origin and destination. I put their origin as our manufacturing site, and destination is randomly selected between our distribution centers, and then they start moving in between. This is the strength of the object-oriented programming. Where you define the high-level agent, and then inside of the agent, you can define what they are going to do, how are they going to behave. Thank you so much, Yes, I think that clarifies a lot of our learners' questions. I appreciate it. Paulo, do you want to take the next? Yes, we have one more here. Many can then ask, what are the common pitfalls that we need to avoid while making the simulation? I know that you already told us in what situations we should not apply the simulation, but assuming that we start the simulation, what would be the common pitfalls to avoid? Yeah, so new practitioners usually, when they start working on a project, they think that I can model the whole complexity from the first shot. If you have a very complex supply chain, my suggestion is to start simple, build a very small prototype that it works, and then you can add complexity as you move forward. At each step, you need to test whether the system behaves as it should do. For example, here it's visual. If the trucks are going in the correct direction, it means they are behaving correctly. Sometimes when you do this, you can have logic errors. None of us are perfectly making mistakes. Here, you need to be able to debug what's going on wrong. But you need to do it incrementally instead of doing everything at once and then getting maybe hundreds of errors here. If I put something illogical here, it's going to throw an error. When you do this with a complex system, you get a list of, let's say, 50 errors and welcome, how are you going to debug that? This is one thing. Then try to communicate with the stakeholders. People want to know how they don't want you to treat it, the system as black box. You need to give them visibility on how your system is working. Somebody will be consuming your results, your model runs, and so on. Stay in close touch with them. Communicate and get approvals, sign-offs that this is what they're expecting. These are the two main things that I would suggest. Awesome. Great recommendations. Thank you so much. Miguel, do we have time for one more? Yeah, maybe one or two. Let's see. I can do the next one and then we can decide because we have a lot of questions. Thank you so much to all our learners and the audience for bringing all those super nice questions, but we are not going to have time to answer them all. I think one that is really interesting is, because we've talked a lot about simulation, but we all know, and you mentioned it, that a lot of the times simulation is done together or in parallel with optimization, or do you simulate and then you optimize or whatever. When you have high variants, I don't know what tools do you use or what's the process to actually merge and put together simulation plus optimization? Yeah, so these are the set of tools. AI ML is a set of tools of mathematical optimization, and it has also sub-branches like mixed integer linear programming, pure linear programming, non-linear programming, dynamic programming, which they also offer a lot of tools for you. This simulation is another type of tool. Now, it might be that if somebody comes to you with one terabyte of data and they are asking for insights, they are purely hinting at a data science-based solution. If somebody brings you some fixed demand figures and transit times and let's say the supply capacities and is asking you what is the cheapest way of fulfilling this demand, this is clearly a mathematical optimization problem. Simulation is not, it has its own domain, so if you have limited number of options to set, to test, simulation is the way to go, but if you have billions of different options, simulation can't really tell you which one is the best. So depending on the ask, you need to find out which is the right tool to use, but they are used in conjunction with each other. Let's say you build a supply chain, you optimize your very complex supply chain, and then you want to validate it with simulation. Usually complex optimization problems, they use mainly deterministic ones like mixed integer linear programming, where they say my demand is 1,000 tons and supply is 2,000 tons. There is usually no variability because it would take ages to optimize a stochastic system in that way. So you can take that optimal network, create different scenarios, and test if it is really answering your needs. Might be that if the demand goes up by 10%, your supply chain is going to explode. Nowadays, we also want to model the weather disruptions like strikes and lots of things going on in our supply chain. It's not a flat and many things can happen. You can test different scenarios with simulation. Some people use machine learning to feed the parameters of the simulation. Inside the simulation, you need to make a decision, for example, which supplier to choose. They have a machine learning model, they connect it to each other, and every time the simulation needs to make a decision, it calls that API, and it responds like in this situation, this is the best way to go. So yeah, you can use in conjunction with each other these different methodologies. Well, thank you so much for the answer, and of course for being here. We are going to wrap it up now because it's been 50 minutes, it's been a super insightful session, but we want to be really respectful with everybody's time. Again, thank you so much to everybody who decided to join us today to learn more about simulation and how it can solve really complex problems in supply chain. In particular, I think the overview of the types of simulations at the beginning, the discussion of when to use and when not to use simulation, and also bringing that real example. It was great. I think everybody got a really nice understanding of this simulation in supply chain management. So I would say that before saying goodbye, this was the last life event of the fall series that Paolo and I co-host, so it's been a pleasure to share this experience with you guys. And second, as we mentioned before, several SCX courses are still open for those completing SC1s and SC3s soon. It's going to be important to know that SC2X and SC4X are going to open right after the Christmas holidays, so you guys can take a break during that special time, recharge your batteries, and then continue your path to complete the MicroMasters in supply chain management. It's going to be January 3rd when the both courses open, if I remember correctly, so we encourage you to check them out. Again, thank you so much to everyone. Of course, thank you Paolo for co-hosting this with me. Thank you, Jashar, so much for joining. If you want to share any final words, the floor is you guys. Yeah, thanks a lot for the invitation. It's my pleasure to be here. Thank you both. Thank you so much. It was awesome. Thanks to our audience. I just want to remind that this session will be uploaded to CTL's YouTube channel. Have a great week. Have a great time. Thank you so much, everyone.