The volume of fleet safety data is continually expanding. The data alone, even when normalized and aggregated, cannot identify the difference between good and risky drivers. The adage “You can’t manage what you can’t measure” explains the need for tools to use actionable data in driver safety.
Benchmarking
The adage “You can’t manage what you can’t measure” explains the need for benchmarking in driver safety. The volume of data is continually expanding. The data alone, even when normalized and aggregated, cannot identify the difference between good and risky drivers. To assess risk, the data needs to be compared to appropriate benchmarks. Benchmarks can be set both at the fleet and driver level to compare data against the organizational goals and industry standards. Benchmarks established at the driver level should be dynamic and consider the type of driver, experience level, and the kind of work performed. These factors enable an intelligent peer standard for which the driver can be measured against that also accounts for seasonality and industry trends.
Driver Score Carding
Driver score carding combines data aggregation and benchmarking to measure safety focused KPIs to positively impact driver safety and efficiency. Data is pulled in from numerous sources, normalized, aggregated, and then utilized to assign a score to each driver. The driver manager is then able to look at the results and easily compare a driver to the organization’s benchmarks as well as their peers. Drivers are then flagged for remediation and training based on carrier defined scoring thresholds per event type or wholistically based on the aggregate score. The driver manager can take immediate actions to improve the behavior of those that show elevated risk scores or to reward those that are performing well.
Predictive Analytics
Predictive analytics is the process of analyzing current and historical facts to make predictions about future or otherwise unknown events. Regarding driver safety, predictive analytics evaluates the driver score card and tells the driver manager what the most likely outcome for a specific driver is in the future. The driver manager now has an opportunity to think proactively rather than reactively.
Learn more about creating and using actionable data in “Using Data to Improve Driver Safety”