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Predictive Models Result in Reduction in Preventable Crashes, Driver Turnover Says Company

FleetRisk Advisors has announced trucking fleets who remediated with at least the top 10% of “at risk” drivers, achieved significant increase in driver safety and satisfaction.

by Staff
June 18, 2013
2 min to read


FleetRisk Advisors has announced trucking fleets who remediated with at least the top 10% of “at risk” drivers, achieved significant increase in driver safety and satisfaction.

That’s according to the results of its predictive models and remediation techniques across its customer base from June 2012 to May 2013. It says these drivers experienced an 87% reduction in accidents per million miles while fleets saw an 80% reduction in their driver turnover rate.

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Last year, out of the 62,986 drivers scored across its customers’ fleets, FleetRisk Advisors’ says its targeted approach uncovered 8,300 drivers that required remediation. It says by identifying the most “at risk” drivers, FleetRisk Advisors makes it more manageable for fleets to successfully implement “a people-centric approach” by narrowing managers’ focus on high-risk drivers before issues arise and providing recommended coaching plans to help improve driver safety and retention.

“The trucking industry is at a crossroads and we must re-evaluate our conventional thinking that events are random and that a driver’s behavior needs to be controlled to achieve great results, said, said Vikas Jain, vice president and general manager of FleetRisk Advisors. “Instead, we believe a fleet’s success hinges on a more holistic approach that combines effective driver training and collaboration, proactive remediation, business intelligence, and technology to improve driver safety, productivity and satisfaction.”

FleetRisk Advisors technology is currently deployed by companies including Averitt Express, Covenant Transportation, C.R. England, Dupre Logistics and Maverick USA. It says its system enables fleets to anticipate and manage risk by analyzing an organization’s historical data to identify patterns that have high correlation with preventable accidents or voluntary turnover in the next 28 days. It also pinpoints the reason for the increased risk along with the remediation plan to prevent it.

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