Fleet Management and Big Data
Big data. Predictive modeling. Data analytics. Once the joint domain of IT and statisticians, these tools are rapidly spreading and will ultimately impact every aspect of our work and our lives, including fleet management. Especially fleet management. Fleets generate mounds of data daily, from telematics info to road conditions to vehicle health status. Data that savvy fleet managers use not just to pore over past events, but to manage present operations, and predict future events and trends as well.
While predictive analytics is now gaining greater prominence and being more broadly applied in the fleet industry, it has played a significant role for years. Predictive modeling is the magic behind GreenRoad’s driver behavior change solution: monitoring driving maneuvers, predicting their level of risk, and prompting drivers to alter their behavior when necessary.
The Fleet Productivity Revolution
In a recent article in Automotive Fleet, industry expert Mike Antich explores the likely effects of big data and predictive analytics on fleet productivity. According to Antich, “the fleet industry is on the verge of a productivity revolution”. “With the use of fleet analytics and accessibility to greater amounts of data, fleets will become ultra-efficient and gain the capability to make quantifiable increases in driver productivity,” writes Antich.
These are not pie-in-the-sky speculations. As early as 2004, UPS turned to fleet data to improve efficiency, and discovered that making left turns against oncoming traffic wasted time and fuel, and led to a disproportionate number of accidents. The ‘minimize left turns policy’ ,which was a direct result, saves UPS over a million gallons of fuel every year. Ten years later, valuable data-driven insights and optimization are available to virtually all fleets, not just the industry leaders.
Today, SaaS fleet analytics providers like GreenRoad empower hundreds of fleets of all sizes, across a wide variety of industries, to develop and implement benchmarks and best practices for increasing efficiency, reducing costs and emissions, and keeping drivers, vehicles and payloads safe.
The Future is Now
In his article, Antich envisions utilizing data to optimize preventive vehicle maintenance and selection, and to maximize return on investment by scheduling replacements on a vehicle-by-vehicle basis, rather than choosing a general replacement date based on best guesses.
The prospects for improving driver safety are even more tantalizing. By identifying previously unrecognized patterns that frequently precede accidents, driver behavior and fleet performance systems will be able to alert drivers and managers and prevent dangerous incidents before they happen.
The real challenge is here: It is to go beyond “business as usual” for fleet management and to begin leveraging the valuable analytics tools that are already available today to maintain profitability in the face of ever-shrinking margins. Endless opportunities, in the form of productivity-boosting applications of big data analyses, are soon to follow.