Graduate students in the MIT Supply Chain Management master’s program are using statistical modeling and artificial intelligence to study:
- Company data, including GPS stats for more than 7,500 tractors
- Loaded and unloaded data for nearly 15,000 trailers
- Driver hours of service
- Shipper rates
- Appointment times
- Arrival and departure trends
MIT plans to publish research findings in Summer 2021.
“This partnership with MIT is another example of U.S. Xpress tapping the brightest minds in technology to help drive company innovation,” said Eric Fuller, president and CEO of U.S. Xpress. “Improving driver efficiency can ultimately enhance the driver experience and generate more value for our shipping partners.”
Per federal hours of service regulations, truck drivers are limited to a 14-hour shift, but just 11 of those can be spent driving. This capstone research will outline opportunities to safely maximize efficiency within that 11-hour driving window to bring more value to shippers, drivers and the company.
U.S. Xpress has been working to improve efficiency by developing parking locators which reduce wasted miles finding open parking spaces; telematics, geo-fencing, and GPS tracking to route tractors to service facilities; and predictive analytics that bring tractors in for service before service failures occur over-the-road. In 2019, U.S. Xpress introduced Variant, a new carrier brand that utilizes machine learning and algorithms to automate load planning and scheduling, generating more revenue for drivers and the company.