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Automakers and retailers explore bots and lockers to automate delivery

Illustration: Rebecca Zisser/Axios

In an eye-catching video, Ford and Agility Robotics recently touted their partnership and vision for autonomous delivery, illustrating the need for a system that can navigate from a parked vehicle up a set of stairs to reach the front door, for example.

The big picture: Autonomous vehicles are expected to decrease delivery costs by eventually removing drivers from the equation entirely, but the logistics of last meter delivery in particular remain intractable.

Details: Industry leaders in the autonomous delivery space are generally pursuing two solutions to get deliveries into the hands of recipients: parcel lockers and “motherships."

The parcel locker model involves storage units in highly trafficked areas that separate and secure packages for numerous recipients. Amazon already has lockers at Whole Foods locations.

  • Parcel lockers decrease delivery costs, mainly by reducing driving distances.
  • However, if parcel lockers proliferate, they will likely demand some infrastructure investment in city centers, where real estate costs are high.
  • Not to mention, parcel lockers put a burden on consumers to ‘commute’ to retrieve parcels and to get them home.

In the mothership model, a large autonomous vehicle would not only hold the parcels, but also fleet of drones or bots that can navigate stairs and apartment buildings for example.

  • This model has a high potential for cost reduction as no human driver is needed in the vehicle.
  • However use of drones at scale will require a federal regulatory framework, and the drones themselves will need HD maps for every potential delivery location.
  • This model could run into additional complications in dense cities as buildings and high foot traffic could make navigation more challenging.

The bottom line: The last meter of package delivery may prove to be the biggest challenge.

Philipp Kampshoff is a partner at McKinsey & Company in the automotive & assembly practice.