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More Intelligent Industries

The Cost and Complexity of Last Mile Delivery

MIT_Matthias-Winkenbach_1024x1024 1638931051717
Dr. Matthias Winkenbach, Ben Taylor
S2 E17 · June 1, 2022

If the pandemic has taught us anything, it’s the value of having goods delivered right to our front door. From fast food ordered on an app, to next day shipping from an online store, the distance from click to delivery is getting shorter all the time. What most of us don’t realize is that the hardest part of delivering an order is the last mile.

More Intelligent Tomorrow host Ben Taylor asked researcher Dr. Matthias Winkenbach to explain what he means by “the last mile” and why it’s so difficult to cover.

The term “the last mile” refers to the home stretch of the supply chain where goods are moved from the final distribution location to the customer. It can represent as much as 40% of the overall supply chain costs despite almost always being the shortest segment. This is why there’s a lot of effort going into optimizing it.

The cost and complexity of last mile delivery very much comes from the fragmentation of shipments.”

Customer expectations for faster, more flexible deliveries, with shorter lead times are running up the cost of covering the last mile. But financial costs aren’t the only concern. Our increase in demand might be having a negative impact on the environment as well. Increased demand means fewer opportunities for suppliers to consolidate, and that means more trips are needed to deliver the additional load of packages. Those extra trips use more fuel and put more traffic on the road.

Companies have had to adjust their network structure to accommodate. They’ve moved from having a few large, centrally located warehouses to a collection of smaller, satellite fulfillment centers. This newer approach means there are more options in how a package can be delivered, such as using ecologically friendly vehicles for the last mile.

The challenge is that you’re adding complexity to the system. Now, you’re managing inventory across many more locations while trying to predict demand in any one location. With this move to hyper localized inventory, the traditional methods of optimization must be updated.

There’s a theoretical optimal solution to routing a package. It’s sometimes referred to as the Traveling Salesman Problem. But variables like traffic, customer availability, parking, and even the mood of the driver can impact the delivery in ways the optimal solution can’t predict. And all these things can change once the delivery is underway, meaning the optimal solution may no longer be valid. AI/ML can complement traditional planning methods to create better routes.

Ben wonders if aerial delivery drones are the answer. They can fly directly to a location and don’t have to deal with things like traffic and parking.

Matthias thinks the answer seems to be no, at least not right away. There are regulatory, technological, and societal issues we need to overcome first. The density of deliveries in an urban area could lead to large swarms of drones buzzing overhead. The sky would fill up quickly.

From an economic point of view, it’s hard to beat an effectively designed ground delivery route. Drones are still expensive, and you’d need up to 300 of them to match the capacity of a single truck. But one day, there might be a hybrid solution where a drone and delivery truck could collaborate to deliver your package.

Matthias speculates on a shopping model where we use VR technology to select something, such as a sofa, and order it online, cutting out a trip to a store and the inconvenience of getting large items home. By ordering this way, your delivery could be consolidated with other deliveries in the area and cut down the cost and carbon footprint of the entire process.

Ben asked what the technological challenges are to covering the last mile.

Matthias answered that we’re not pushing the boundaries on finding new ways to optimize routes. The biggest challenge comes from not having very many AI/ML models to solve the problem. The other challenge is developing automated logistics that can cover the last mile.

A lot of people don’t realize how difficult it is to get that package to the doorstep every morning.”

Listen to this episode of More Intelligent Tomorrow to learn:

  • What the last mile means
  • How it increases the cost of goods
  • The impact the last mile has on the environment
  • If drones might be the answer to the problem
  • What’s needed so AI/ML can help solve the problem
  • If delayed gratification is the answer
Speakers
MIT_Matthias-Winkenbach_1024x1024
Dr. Matthias Winkenbach
Director, MIT Megacity Logistics Lab. Research Scientist.
1638931051717
Ben Taylor
Chief AI Evangelist
Better World/Better Business
From R&D to ROI: Five Reasons ML Doesn’t Go Into Production – and How to Solve Them
Read More
Tags: data science drone last mile logistics machine learning More Intelligent Industries More Intelligent Tomorrow Season 2 supply chain

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