Solving the Last Mile Problem with Route Optimizations

The Last Mile Problem has been around for a while now and has been a thorn in the side of any number of industries, from telecommunications to transport and logistics.

In essence the transport version of the problem is this,

Last mile deliveries can incur up to 30% of all transport costs.

Essentially this means that, no matter where the product was manufactured or warehoused, and how far it has traveled, getting it to the customer's door from the nearest depot will cost almost a third of that entire trip. This issue is only becoming more and more important to solve as cities and towns become ever more congested and eCommerce drives ever more demand for deliveries (and in particular, free deliveries) placing ever more pressure on companies to lower costs associated with the last mile.

As you'd expect, with an important business problem to solve, entrepreneurs and inventors have been hard at work coming up with creative ways to try and solve this issue. this includes crowd-sourced delivery apps in which people from the general public can pick up something and drop it off (because they happen to be travelling in that direction already) for a very small fee. The trade off is that your reliability might suffer for deliveries people simply don't want to make.

Other companies have downsized their city delivery vehicles to nimble bikes or scooters. This also enables those deliveries to avoid the worst delays associated with traffic congestion, and, of course, these vehicles are much lighter on fuel and less expensive to maintain. The trade-off is that they have less capacity and so have to return to the depot far more often to pick up new deliveries.

But no matter what methods you try to reduce costs, there is something you can do that will knock a significant portion of the last mile costs down sustainable, and in a way that improves your efficiency and makes operations, management and logistics a lot simpler.

This is where route optimizations and route planning comes in.