Data sharing is the way to profitable last-mile delivery

Fractured last mile without collaboration remains expensive proposition for brands

One of the positives to have grown out of the e-commerce boom is the opportunity small and midsize businesses have to garner sales. The small retailer is no longer limited to just its geographic area, now able to reach an audience of millions.

But that does come with a cost — and that is delivery. Shipping goods around the country — and even the world — gets expensive. And while there are hundreds of delivery options beyond the big three of FedEx, UPS and the U.S. Postal Service, there are trade-offs that must be made for the smaller retailer.

“The smaller guys, there are some compromises if you want to participate in this shared economy if you want to … keep up with the [larger guys for delivery],” said Jorge Lopera, vice president of global strategy at delivery management company FarEye. “Data is becoming more of a community topic. When you start to think about it, from a retail perspective, you are releasing who is buying and where they are buying but not necessarily what they are buying.”

Lopera told Modern Shipper the smaller brands have plenty of options, but they need to be diligent about who they partner with. 

Many larger brands, including American Eagle Outfitters (AEO), Walmart and Amazon have begun offering delivery services for outside brands. AEO has been building out its logistics operation, while Walmart has been expanding its GoLocal service to include non-Walmart clients. Amazon recently started a pilot allowing delivery of goods from non-Amazon stores.

Lopera said these types of options are ultimately beneficial for smaller brands, but they have to understand there is a trade-off.

“You have to compete [with the Amazons and Walmarts], and you have to give like services,” he said. “I think we’ve seen that this year. Even eight years ago, a lot of carriers banded together to compete with the Postal Service.”

Data sharing is the new form of currency to facilitate lower-cost delivery options, especially for the smaller brands. It doesn’t mean they have to relinquish their data, though. It does mean they have to be more diligent about using that data and demand planning.

“Some of that data piece is just the cost of doing business in this environment,” Lopera said. “You have to give up something to get something [or you won’t be in business].”

Lopera said FarEye is working to create options for businesses to use the data for their benefit.

“We want to create as much of a variable model as possible because if we don’t do that … [the cost of the last mile can doom business],” he said. “The cost of not doing something is too great.”

Whether the brand is using its own delivery service or an outside firm, visibility remains the buzzword of the day. It is, though, more than just jargon.

“Any solution should allow you to have visibility all the way up to the manufacturing line to your customer’s doorstep,” Lopera said. “You can do that through a shared network or a connected network.”

FarEye’s global customer base gives it expertise and unique use cases that can be shared with other customers, Lopera said. The key, he said, is to collect that data and make it “digestible and actionable.” While each use case may be unique, there are commonalities that FarEye and its customers can learn from.

“At the end of the day, it’s getting product from point A to point B and you want to do that effectively,” he said.

Even though it may seem that delivery fleets are multiplying, securing capacity remains a concern for FarEye customers. Using the data brands collect to more effectively forecast capacity need is part of the push to create a more effective last-mile operation.

“How do we start to translate demand into capacity?” Lopera asked. “Because there is enough data, how do we take that data and translate it into a fleet strategy? How do we push ourselves to do more and how do we optimize it?”

Machine learning is solving some of this complexity, allowing FarEye to identify situations where, for instance, four vehicles may be enough to manage the delivery operation instead of the five the brand was using. The company also is able to better understand the commodities being transported and the environment the delivery will take place inside of.

All of which is only possible with data sharing — bringing the smaller brands right back to where they started.

“We can’t do it alone,” Lopera said. “Demand is what it is. Consumer expectations are what they are. Unless we come up with shared answers, [delivery efficiency is not possible].”

Originally published here