AI Case Study
UPS aims to avoid trouble spots in its global network by rerouting packages away from snow with machine learning
UPS has developed an app, the Network Planning Tools (NPT), based on machine learning and advanced analytics. With it engineers can analyse data on facilities and packages, such as capacity, volume, and delivery deadlines to optimise its network. The system is thought to be very useful for eliminating bottlenecks especially during the winter and holidays season by diverting packages away from troubling situations such as snowstorms. The company believes that it will be able to save $100 million to $200 million through the program.
Freight And Logistics
"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. 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% 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.
In the Denver snowstorm scenario, engineers in Portland, Oregon, could log on to NPT, see that a number of Massachusetts-bound packages were heading for trouble, and divert some of them through UPS’s Chicago hub, sending the rest through the one in Meadowlands, New Jersey, to avoid swamping Chicago. Both of those facilities would be able to sort the packages and send them on for local distribution in time to fulfill UPS’s contracts. The app would not list UPS’s hub in Greensboro, North Carolina, as an option because that facility doesn’t have as much space for loading tractor-trailers bound for the Northeast and thus would not be a good detour.
Another NPT feature helps engineers group all their outbound packages into the smallest possible number of trailers or cargo planes. NPT can also schedule truck and plane trips so those drivers and pilots always pick up parcels on their return trips and never return home with empty vehicles. Like the rest of the app, these features enable UPS employees to look at a number of alternatives, assess them, and put them into action. Papetti describes the app as a big-data platform with a simulation engine that tells users the ramifications of every decision they make.
UPS has caught flak in the past for developing software that appears to disregard employees’ knowledge and preferences in the name of efficiency. That’s how some people think of ORION, which the company began deploying in 2012. But the NPT app lets the company’s engineers ignore the algorithms and create their own plans, at least for now. While it gives suggestions and prepopulates forecast data into certain fields, the engineers are the ones who ultimately route the packages. Papetti says he will ask engineers to explain why they followed their own ideas and supply supporting evidence so his group can refine the NPT software. “We want to give the [engineers] the opportunity to say, ‘Hey, that’s wrong’ and make this a learning platform for our algorithms and AI,” he says."
"UPS expects the program to save it $100 million to $200 million a year."
Machine learning and advanced analytics
"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 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."
Historical data and data on UPS facilities, route shipments, packages in transit, including their weight, volume, and delivery deadlines