_Trucking company uses data to survive a bumpy economy
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"Bernard Johnson is a trucker and a math problem. Johnson pulls trailers filled with everything from TVs to toilet paper on as many as 25 trips a month for stretches of up to 500 miles. Schneider National has to design the most efficient routes for Johnson and 13,000 other drivers. With diesel at $4 a gallon, this is not an equation it can afford to get wrong. “It’s like a big jigsaw puzzle,” says Ted Gifford, an operations research scientist at Schneider. At any given time the company has 10,000 trucks on the road, with another 33,000 trailers available and waiting to be picked up. Drivers are on the road between four days and three weeks at a time—alone and in pairs—and Schneider must get them back to their homes by a certain date. Drivers have to take breaks according to government regulations. Their customers are only open during certain hours. “We want to avoid the situation where a driver may live in Alabama, and it’s time for him to go home, and he’s in Minnesota,” says Gifford. “And we don’t have any freight for him to get home, so he has to drive empty.”
Previously the company tried to solve these issues by doing small scale surveys and pilot projects that cost a lot of money, and didn't scale from 20 people in the pilot to a fleet of 200.
Now however, thanks to a sophisticated data and scenario modeling software, this has changed to allow a complete picture of the trucking system at any one time (relevant to the from sampling to census category?)
Gifford estimates that the simulator has helped Schneider save tens of millions of dollars. The simulator has, for example, allowed Schneider to justify price hikes to customers. In 2008 a customer wanted to restrict the number of hours that Schneider could drop off goods. Schneider ran the simulator. “We showed that we could limit the hours but that doing so would cost us $600,000 more,” says Gifford. “We went back to them and said, ‘This is the impact of restricting your hours.’” The customer ultimately decided not to limit its hours.
One of Schneider’s biggest challenges is maintaining its fleet size, since drivers often burn out and leave the company. Schneider regularly uses the simulator to determine how many jobs to offer and where it’s best to hire drivers. The model can determine the marginal value of hiring ten new drivers who live in central Illinois, say, based on the number of times that freight departs from the Midwest.
In the future Schneider wants to use the simulator to decide which new business to pursue. The company currently employs three different fleets of drivers: long-haul truckers who live in one city but can travel all over the country, regional drivers who drive within a 500-mile radius of their homes, and dedicated fleets for specific customers. A decision to increase one of those fleets can affect the others: When Schneider creates a regional business, for example, it cannibalizes some work that the long-haul fleet is doing.
From Sampling to Census?