Just think about it. UPS (and FedEx and USPS etc.) drivers are employees—benefits, job security, the works!—that pilot around company-owned vehicles which are maintained and fueled on the company's dime. Clearly, this scenario can be improved with an app and a mostly pre-existing "sharing economy" workforce. This is the argument made by Ryan Petersen, the CEO of Flexport, the would-be "Uber of the oceans," in a post at Techcrunch's "Crunch Network" crowdblog. It's all just a matter of sufficient driver saturation or route density.
As explained by Petersen, route density is what keeps the big three "last mile" shippers mentioned above in business: a driver on every block on any given day. And with a driver on any given block on any given day, the cost of delivering packages plummets to as low as $1.50 per parcel and, for additional parcels being delivered to the same address, the cost to the shipping company drops to near zero. His argument is that services like Lyft and Uber can offer drivers on any given block not just on any given day, but drivers on any given black on any given hour.
This amounts to a higher route density, an offering that Petersen thinks will be enough to unseat dedicated shippers. "While both FedEx and UPS do offer scheduled pickups, only Uber (and perhaps someday Lyft) has the density needed to offer instant pickups and on-demand deliveries," he writes. "This is a game-changer, as it enables a whole new generation of real-time e-commerce experiences." Uber Cargo in Hong Kong and Uber Rush in New York City are sort of the idea, but they utilize dedicated vans and couriers; Petersen imagines Uber drivers just accumulating packages in their trunck as the day goes on and dumping them at a distro center at the end of a non-shift shift.
There isn't much more to Petersen's argument, and it would seem that he's missing a crucial feature of what makes UPS and FedEx, in particular, so successful. It's not just route density, at least as defined by Petersen, it's intense top-down micromanagement and route optimization. Route density is achieved here by as few drivers as possible thanks to exceedingly clever logistics schemes—algorithms, actually.
The Uber delivery system would be deoptimized by definition, I think. It banks on presumed inefficiencies in the Uber system elsewhere, for one (idle drivers), but mostly it requires a whole lot more drivers per package than UPS or FedEx, probably by entire orders of magnitude. You can say that it will still work out because those drivers are not being paid continuously (despite the packages in the trunk?) and are operating only on an on-demand basis, but, even given that, mileage is mileage and if a driver is accumulating deliveries and pickups on a more or less random basis (because as an Uber driver their day is more or less random), they're traversing a whole lot more miles than a route-based UPS driver for that reason alone.
That's just part of it though.
UPS continues to succeed in some part due to data. For the past several years, UPS has been developing a program called ORION, or On-Road Integrated Optimization and Navigation.
A 2013 piece in Forbes explains:
Every parcel that UPS delivers contains extractable data on time of shipment and how it matched delivery commitments. But ORION also scans map data and historical GPS tracking of similar routes to come up with a solution.
The software has 250 million address data points to access and runs on an algorithm Perez says is the equivalent of 1,000 pages of code. Each individual route has an average of 200,000 possible ways to go.
UPS saved 3 million gallons of fuel during its testing of the program from 2010-2012 and says it'll reduce its consumption by another 1.5 million gallons this year. Once the program is rolled out to every driver by 2017, the company says it can save $50 million by taking just one mile off each of its driver's daily routes.
It's expected that ORION will be fully implemented in 2016.
It doesn't take much to make transportation problems incredibly complex, especially when it comes to optimizing what's known as transshipment, which is the problem of routes that involve intermediate points. This is most transportation really, from USPS Priority to Southwest Airlines: origin to warehouse/distribution center to destination. This is actually an exponential time problem in the worst-case, which basically means that it has no efficient solution for arbitrary inputs, but it can be solved reasonably given more restrictive conditions.
My general point is that delivery systems are more likely to succeed with top-down optimization, no matter how badly a sharing economy corporation tries to screw its non-employee employees, and top-down optimization doesn't mesh well with the randomized patterns of app-connected curb sharks.