Why is the last-mile of delivery so costly and complex? Matthias Winkenbach

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E-commerce, supply chain and urbanisation are just the beginning. We found out that what people want, will change cities. We challenged if data is enough to solve the complex logistics puzzle of last-mile delivery. And investigated what makes Gen Z tick as consumers.

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Matthias Winkenbach – Director of the MIT Megacity Logistics Lab and the MIT CAVE Lab looks at how businesses can use data and design to solve the challenges of urban distribution.

"Where you have your distribution facilities, what kind of facilities you have and how you use them, is basically the most strategic question that you have to answer if you want to master the urban last-mile.

My name is Matthias Winkenbach and I am the direct of the MIT Megacity Logistics Lab and the MIT Cave Lab.

The last-mile is typically what we refer to as the last leg of transportation to the final recipient. That is what we define as the urban last-mile. While the last-mile is just the shortest, final leg of the global supply chain, it's actually the most complex and also the most costly part. The final mile actually accounts to 40% of the overall supply chain cost, so that tells you how important it is to get that last-mile right. It's not about applying off-the-shelf solutions, it's about having tailor-made solutions to the specific urban environments that you care about.

A very common problem in last-mile logistics is a driver deviating from the optimal route that was planned for him or for her. We were experimenting lately with a couple of machine learning tools, for instance, to use something simple like GPS data or just log files from the vehicle, like when which customer was being served and feeding that into these rather complicated models and then basically having a way to identify which customer was actually causing the disruption along the route. Which was otherwise only possible by talking to the driver directly and actually asking, "Why did you deviate?" which you obviously can't do at scale. So understanding both the behaviour of the driver and also extracting the local knowledge that this driver has about the urban environment that he or she operates in.

Now we have a tool that just uses data to extract human knowledge from seemingly cold data. There's not a one-size-fits-all solution to any given city in the world. So a solution for last-mile logistics in New York City might look very different from the best solution for last-mile logistics in Sao Paolo or in Paris.

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