For fleets looking for a more holistic safety program, a wide variety of safety-enhancing technologies offer added insight into the day-to-day lives of truck drivers, allowing fleet owners to anticipate potentially risky events and react accordingly. With the help of recent advances in technology, fleets can look past one-size-fits-all safety programs and turn to a tool that delivers tangible results by analyzing thousands of data points.
Today, predictive modeling plays an indispensable role in almost every industry, from sport to finance to healthcare, and particularly in transportation. Even the best driver can place themselves at risk should they experience trouble at home, financial problems, or grow frustrated with their management team. With predictive analytics tools, fleets can gain instant visibility into what is happening right now by studying past events. Examining data patterns and trends from past events, predictive analytics identifies changes in data that point to behavior patterns to predict the outcomes of certain situations, arming managers with the knowledge needed to proactively intervene and avoid potentially risky situations.
Leading providers of predictive modeling solutions within the transportation industry, like FleetRisk Advisors, equip fleet managers with added insight, allowing them to reduce risk in the following areas:
- Safety: By identifying at-risk drivers prior to an event occurring, fleet managers can intervene and have a remediation conversation with the driver, where the issue often gets resolved and the driver is reassured that the manager is there to support them through any challenges that they might be facing, making the road safer for everyone.
- Fatigue: using a data feed from electronic log data and predicting the most likely sleep scenario during off-duty and sleeper berth periods, predictive models can accurately measure how tired a driver is on any given schedule.
- Retention: Leveraging the information provided by predictive analytics solutions, fleets can prevent the loss of their most valuable asset – their drivers – by narrowing their retention efforts to a smaller pool of at-risk drivers, and as a result, creating meaningful, longer-lasting relationships.
- Recruiting: understanding why drivers quit voluntarily can lead to recruiting solutions which predict which drivers will be safe, tenured and productive. By studying application data sets at the hiring stage and a predictive model, recruiters can rank applicants minute by minute on the drivers likelihood to meet the companies hiring criteria.
- Workers’ Comp: Predictive modeling also helps fleets avoid another costly human resources issue – workers’ compensation claims. Rather than accepting that workers’ compensation claims are a part of doing business, predictive modeling brings a new approach to the table by allowing fleet managers to identify the driver behaviors that lead to claims and eliminate them.
