 Thank you so much for taking the time to be with us today. First, can you introduce yourself? Sure. So my name is Matthias Winkenbach. I'm, as you can tell from the name, obviously, German. And I'm the director of the Megacity Logistics Lab here at MIT CTL. So a lab that is mostly concerned about lost mile logistics and all the different challenges related to it. Perfect. So tell me a little bit more about the motivation behind the Megacity Logistics Lab, where did it come from? Where is it going? So the lab started in around 2012, actually. My predecessor, Edgar Blanco, started this lab. And we are working predominantly with companies on addressing the main challenges of how to serve large urban centers more efficiently and also with how to design innovative distribution models to these large centers of urban demand. Because what we see is that, well, first of all, cities are growing. We see a constant trend towards increasing urbanization. They are becoming more and more dense, more and more congested. At the same time, e-commerce volumes are still on the rise. So more and more individual small shipments need to go into those cities. And then at the same time, customer expectations are changing. So customers expect ever more fast, ever more flexible, and also more reliable delivery services. And those two don't really go together. So the environment gets more complex. Customer expectations are becoming more rigorous. So companies need to improve on that domain. And that's the main motivation for our research, helping companies to address these challenges in a mostly quantitative data-driven way. And would you say as the hunger for data and the amount of data available has increased, that that's changed the way that you look at things? A little bit. So a couple of years ago, the main challenge of a lot of companies that we talked to was probably how to get the right data, where to find it. Nowadays, the challenge is a little bit different. A lot of companies that we work with are sitting on huge piles of data and don't really know what to do with it. So now the challenge is more, how can we connect the dots? How can we use the data that is available to these companies and create value out of it? Tell me a little bit more about some of the current research initiatives within the lab. So for instance, for a couple of markets in Latin America, we are working with large retailers and manufacturers to redesign urban distribution networks for some of the major cities in those markets. So we try to answer very basic questions, first of all. Where do you locate a distribution center? Where do you, let's say, leverage transshipment and multi-tier network design to tackle the challenges that I mentioned before, like congestion and high density of urban centers. And also, how can we leverage new vehicle technologies? Where should we use which kind of vehicle technology to actually conduct our last mile delivery services? With one other partner, we are working on more the data analytics side. So how can we use their existing data on the performance of their delivery operations to basically develop a logic that helps them better understand the drivers behind route performance, the drivers behind route productivity. So we're working on building a tool for them to benchmark the productivity of their individual routes. So they are operating globally. So once we're done with this project, they will be able to basically tell for any given route in any kind of city what should be the target productivity level of that route, what's its current productivity level, and how can we close the gap between the two. Great. Obviously, this research is very interesting to a lot of our partners. What is the best way for a company that's interested to engage with you? So the way we typically work with our partners is we have more like longer-term engagements, like usually we work with them at least for a year in a row, and one specific problem that this partner is facing. We have a dedicated team on our end that works on this project. And we work very closely with the partner. So our objective always is not only to take a problem and create like a black-box solution to it, but we really want to engage our partners and also ensure a certain knowledge transfer. So once we're done with the project, we want the partners able and has the capability of tackling such a problem by himself in the future. And therefore, we like to interact closely with the project team on the partner's end. So basically, we're not tied to any specific industry or any specific geographic focus. We are generally interested in partners who face challenges in urban last-mile distribution and who have the necessary data and resources to support a research project with us. Great. Perfect. Thank you so much for your time and for sharing. It's been a pleasure. Thank you.