Omnitracs says its new ELD Driver Retention Model can analyze the data provided by electronic logs to help predict which truck drivers are most likely to quit, helping fleets pinpoint retention efforts.
The tool uses data that companies are already collecting through electronic hour-of-service applications to detect subtle changes in driver habits, which can be key indicators of the desire to voluntarily leave their jobs. These individual driver logs produce over a thousand pieces of data related to hours worked, customer site delays, lack of hours and amount of activity on the clock. Omnitracs ELD Driver Retention Model can also leverage HOS data generated by competitive solutions.
“The Omnitracs ELD Driver Retention Model allows fleets of all sizes to reap the benefits of predictive modeling to prevent driver turnover and address the root causes of voluntary termination,” said Brad Taylor, Omnitracs vice president of data and IoT solutions. “In a 500-driver fleet, preventing just half of the voluntarily turnover in the most at-risk group of drivers saves nearly $29,000 per month in direct hiring costs.”
It also helps identify fatigue, giving fleets the knowledge and techniques to manage fatigue at the driver and organizational level. Users can engage drivers through training and intervention programs to increase driver safety and satisfaction, improve driver-manager relationships and prevent driver turnover.
Drivers who attend sleep classes and participate in the intervention programs showed improved productivity and safety outcomes, according to Omnitracs. The company says that those same drivers were 30% less likely to voluntarily leave. The drivers also reduced loss-of-control accidents by 50% and had five times fewer run-off-road accidents compared to drivers without sleep education.
Omnitracs ELD Driver Retention Model was designed for any size fleet using electronic HOS management applications and is available now.