The delivery giant’s new machine-learning app aims to reroute packages away from snow and other trouble spots in its global network.
If a snowstorm hits Denver, it can delay thousands of packages that travel through the city before reaching their final destinations on the other side of the country. But if UPS knows a storm is coming, what is the most efficient way to divert all those online orders and holiday gifts around the bad weather?
“UPS built an online platform that combines machine learning and advanced analytics.”
UPS grapples with this question every winter. Identifying the facility best equipped to handle a large, unplanned shipment and the most efficient way to transport those packages is a tough call for even experienced UPS employees.
The variables – among them the types of packages, their destinations and the deadlines by which they need to be delivered – add complexity that could slow down UPS engineers and make it harder to nimbly shift resources.
Help is on the way
To help, UPS recently built an online platform that combines machine learning and advanced analytics. The app – called Network Planning Tools, or NPT for short – lets the company’s engineers view activity at UPS facilities around the world and route shipments to the ones with the most capacity.
This screen in the Network Planning Tools app shows what types of packages customers are sending through UPS.
They can also see details about the packages in transit, including their weight, volume and delivery deadlines. While UPS already has a system called ORION that maps out last-mile delivery routes, and a technology program called EDGE focused on upgrading UPS’s internal processes, NPT gives its engineers a bird’s-eye view of package volume and distribution across its pickup and delivery network.
The app gets some of its smarts from AI, which it uses to create forecasts about package volume and weight based on analysis of historical data.
Rob Papetti, who leads NPT development for UPS, says the machine-learning algorithms also analyze decisions the company’s engineers made and assess how they affected customer satisfaction and internal costs.
“[The app] starts to learn from itself and suggest this option versus that option, based on what enabled us to give our customers better service,” he says.
That kind of insight is crucial during the frenetic holiday season. This year, UPS expects to deliver more parcels during that period than ever before – nearly 800 million, up 5 percent from 2017.
In preparation, the company has used the NPT app to identify and eliminate bottlenecks such as an Illinois facility that was struggling to process packages quickly.
“Within a few minutes, we were able to determine how to get around and relieve [the backlog in] that building and still make our service commitments to customers,” says Papetti. “Before NPT, that would have taken at least a week.”
UPS expects the program to save it $100 million to $200 million a year.
This NPT app feature shows how many packages need to be sorted across UPS’s network on a given day.
Chris Caplice, who heads MIT’s Center for Transportation & Logistics, says companies like UPS need programs like NPT because they have sprawling road, rail and air operations and need to be able to switch between the modes quickly.
“They have a portfolio of options, of ways they can flow packages [to their destinations], and they’re picking which ones make the most sense,” he says. “This should give them a ton of flexibility during the peak [holiday] season or whenever there’s a disruption.”
This excerpt first appeared in MIT Technology Review and was republished with permission. You can read the full article here.
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