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MarketScreener Homepage  >  Equities  >  Xetra  >  SAP SE    SAP   DE0007164600


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SAP : Uber Freight Helps Deliver Greater Profits and a Cleaner Planet

10/19/2020 | 07:25am EST

Truck drivers have a phrase of pride that says, 'If you bought it, we brought it,' meaning the products you purchase at your local store were delivered by trucks. About 70 percent of America's freight travels that way, according to the American Trucking Association. But the system the industry relies on to find and book cargo is manual, time-consuming, and inefficient.

Uber created Uber Freight to bring the same ease to trucking that it did to ride-sharing. A better system means less greenhouse gas for all of us and more pay for truckers.

Drivers only make money when they carry freight, but about 20 percent of their miles are with empty trucks, referred to as 'deadhead' miles. Reducing that number by half would cut emissions by 40 million tons per year and reduce diesel fuel consumption by more than US$8 billion dollars a year.

But it turns out that driving with a full trailer is a complicated process.

'With the old system, drivers spent 30 to 40 percent of their time just waiting around,' says Bill Driegert, general manager at Uber Freight. 'On average, it takes four hours to find their next load.' If they find one at all.

Here's what the old-school process looks like. A driver based in Atlanta, Georgia, hauls cargo to Dallas, Texas. To find a return load for the trip home, the trucker calls a broker, who then makes five or 10 calls to try to find a company that needs freight moved from Dallas to Atlanta.

Matchmaker, Make Me a Match

Clearly, a trucker never wants to drive empty. With Uber Freight, they can almost always do better than a one-way trip.

Uber Freight provides an intelligent marketplace that matches trucking companies and their drivers with cargo. It is not unusual for a driver to book a job in 30 seconds. They can do it resting at a truck stop, or a dispatcher can do it from a shipping office. It is as simple as opening the app and seeing the array of loads and where they need to go. They can find a load and then another one back home without wasting time or money.

'We tackled this challenge because we can,' said Bar Ifrach, director and head of Marketplace at Uber Freight. 'Uber is well known for having tremendous data, algorithms, and machine learning. It's in our blood.'

The new system benefits all parties involved.

Companies that need to ship goods can reach a large population of available drivers. And trucking companies and independent drivers - also called owner-operators - can easily find and choose loads that fit their business and schedule.

It has made paperwork easier, too. With the old approach, documents that confirmed a load was dropped off had to be faxed. With Uber Freight, documents are photographed with a smartphone and uploaded.

More Than One Way to Get Home

Using its massive datasets, Uber Freight developed load bundles to help drivers pair multiple loads and create optimized custom routes, all while cutting down on empty miles.

Say the driver in Dallas can't find a load for the trip back home to Atlanta. There just isn't anything currently available for a direct path. Instead of going home with an empty trailer, the driver can use Uber Freight to bundle multiple loads along the way.

Thanks to this load bundling, Uber Freight is already seeing a 23 percent reduction in those dreaded empty miles.

'We show them they can book two stops or more to get where they want to go,' says Ifrach. 'They can haul one job from Dallas down to Houston and then a second job from Houston over to Atlanta, or at least to a location closer to Atlanta.'

This means that in addition to cutting emissions, saving gas, and earning more, the driver can make it home in time to watch their child's ballet recital or soccer game. And that is truly using technology to help the world run better.

Learn more about SAP's partnership with Uber Freight.

SAP and our partners. Improving lives. That is our purpose.


SAP SE published this content on 19 October 2020 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 19 October 2020 11:24:00 UTC

© Publicnow 2020
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