Suburban vs Urban Delivery Logistics: Not As Similar As You Might Think
Outsiders might think delivery logistics is easy. A customer places an order, and thanks to developed infrastructures, it’s delivered to them quickly, without a hitch. It’s not that simple.
Customers don’t live in homes that are carbon copies of one another. Some live in apartment buildings on the 27th floor, others live in standalone houses or condos, and some live in gated communities. They also don’t live in the same geographical areas. Some live in rural and sparsely populated regions while others live in dense, urban environments.
Over the last two years, and after executing hundreds of thousands of deliveries at our company, I’ve seen that suburban logistics is a two-dimensional problem and urban logistics is a three-dimensional problem.
Efficiency in Suburban Delivery
Efficient delivery is built around making the most deliveries in a set amount of time. In suburban areas, the primary challenge in logistics has to do with route optimization — i.e., ensuring drivers are quickly getting from Point A to Point B.
The general delivery life-cycle should be thought of as “pickup — transport — drop off”. In suburban areas, the “transport” segment is the overwhelming majority of this process. This is because areas are spread out over greater distances and traffic patterns can dramatically impact transit times.
Optimization of suburban logistics is a two-dimensional problem in the sense that you have to worry about the flow of goods in a flat plane: north/south and east/west. Therefore, focusing on data-driven route planning and transit impacts efficiency the most.
The Challenges of Urban Areas
In urban areas, logistics becomes a three-dimensional problem. In addition to the north/south and east/west grid (x and y axis), delivery efficiency has to account for the occupancy of tall buildings (z-axis), which is represented by the aforementioned customer sitting in his 27th-floor apartment.
Urban areas have considerably higher population densities than their suburban counterparts. For example, one tall building in a large city that occupies a footprint of 1/1000th of a square mile can hold 3,000 to 7,000 people. Alternatively, a suburban area that holds the equivalent amount of people can easily be 10–20 square miles. That is a distance ration of ~10,000 between these two geographies.
While efficiency in suburban logistics focuses on route optimization, efficiency in urban environments is much more impacted by terrain data such as building heights, building entrances and number/speed of elevator banks.
It might take two minutes to move from one building to the next, but how long will it take to get to the 50th floor and back? And what good is knowing the location of a building if you have no idea where the service entrance is located? The future of urban logistics involves moving from a 2D mindset to a 3D one as we solve these challenging optimization problems.
