The old school approach to managing fleet data

Before the advent of predictive analytics, information such as dispatch driver, route mapping, or maintenance alerts were the key considerations driving fleet management optimization. And while it’s true that these insights provided a snapshot of fleet costs and inefficiencies, legacy fleet management systems lacked the predictive capabilities. The resulting data was retroactive and singular in use, limiting the ability of the fleet manager to view the totality of fleet management issues holistically and proactively. Moreover, many legacy solutions were often not flexible enough to account for the data outputs from modern vehicles and telematics devices.

Particularly with regards to analytics, legacy systems have traditionally relied on ABCS (Acceleration, Braking, Cornering, and Speeding) models which fail to paint the full picture of each trip. They do not factor in an individual’s driving style and the trip context, such as road topology, environment, or weather. Too simplistic, they don’t account for specific conditions and factors that determine each driver’s decision to accelerate, brake, or take a sharp turn, for example. They also fail at producing predictions that empower more accurate decision-making related to fleet maintenance, budgets, and safety.

Predictive analytics in the fleet manager’s driver’s seat

And here comes the moment when fleet predictive analytics systems step in. These data-driven platforms have changed the rules in the fleet management industry by not only collecting and displaying trip and vehicle data, but also augmenting them with contextual information from maps and weather services and accident reports. As a result, they have become an enabler of an overarching fleet management approach that embraces previously unconsidered data that drives today’s proactive business decisions.

Predictive fleet analytics uses telematics to incorporate hundreds of information elements to create a unique profile on every vehicle, every driver, and trip, replacing human guesses with data-driven scheduling and planning. Based on data such as general trip information, road environment, traffic, weather, points of interest, and multiple others, these systems provide multi-dimensional insights. Using them, fleet managers can understand how their vehicles, equipment, and employees operate and predict the outcomes of certain behaviors.

Imagine, for example, that one of your drivers has a tendency for hard braking or abrupt speeding that not only uses more fuel but also wears down tires and the engine. By analyzing their driving patterns, predictive telematics systems will identify weather, location, and road conditions when these behaviors happen, allowing managers to coach the driver in efficient and safe driving “in context” to accurately minimize the risk and optimize vehicle use.

A 360° approach to answering the call for fleet management

A holistic approach to fleet management addresses the key issues of operating a fleet, which include mobility cost reductions, predictive maintenance, driver safety, residual vehicle value, emissions control and route optimization. Analytics systems leveraging mobility data stand to improve every area of fleet management to boost operators’ profitability and win market share from competitors with increased efficiency.

Reduce costs

The key issue to driving profitability in fleets is understanding the overall costs of transportation and how they relate to the business and costs of ownership. By overlaying GPS data, driver data, and advanced traffic pattern analysis, fleet optimization analytics informs the Total Cost of Ownership (TCO) decision while also providing data-based insights to reduce the Total Cost of Mobility (TCM). This more sophisticated approach incorporates many more factors than just the cost of purchasing and operating a vehicle.

Focusing on some car and route metrics, traditional fleet management systems have supported operators in reducing the total cost of ownership. But it is only part of the total mobility expenses. In today’s fleet management environment, the real issues are how costs of ownership, route selection, and human behavior can be harnessed to complete the operating chain of mobility events as efficiently as possible.

By looking at metrics like driver’s safety profile, trip eco-efficiency, or fleet composition, predictive analytics takes fleet decision-making one step further. It uses in-vehicle telematics data, augmented with various topology and weather information, to provide valuable insights for fleet managers. As a result, they can make far better use of their vehicles while reducing carbon emissions and fuel consumption, improving customer satisfaction, and coaching drivers on how to drive more efficiently. Ultimately, all these outcomes translate into higher profit and competitive advantage.

Enhance vehicle residual value

The end of the road is where the vehicle is disposed of in the final sale, and the issue of residual vehicle value can be that one sore spot that separates the stellar performers from the also-rans. The use of predictive maintenance systems combined with intelligent route planning offers the latest approach to enhancing residual vehicle value. Thanks to modern optimization models, operators gain a profound understanding of fleet performance and driving habits and their impact on vehicle wear and use. With a more holistic perception of all factors determining car mileage and damage, they can proactively implement steps to extend the vehicle life cycle and maximize car residual value. Saying goodbye no longer has to have a sad ending.

Implement predictive maintenance

Having the oil changed, tyres rotated, and brakes checked periodically to prevent a future failure on the road or loss in value is no longer enough to ensure optimal fleet performance. Basic planned maintenance systems do not account for the routes drivers have driven, the traffic they encounter, or how the vehicle is operated on the roadways. These issues combine to create significant deviations that could lead to fleet units being down for unscheduled maintenance problems (i.e., roadside breakdowns) that cost money in terms of unplanned emergency repairs and costs to revenues.

Predictive analytics addresses these concerns, allowing fleet operators to act before even a breakdown happens. That’s because they also consider the influence of individual driving behavior on car component wear, offering comprehensive insights into the fleet’s performance. Additionally, these insights can be used to coach drivers to reduce wear further.

Improve safety

Safety is a crucial concern for all fleet managers, especially those dealing with truck fleets. Roughly 4,000 deaths are caused by truck accidents each year, with about 130,000 people getting injured. And these statistics apply to the USA only. As a result, improving safety is high on the fleet managers’ priority list, not only because of the impact on human lives. Accidents that involve fatalities or severe injuries can also blemish a company’s reputation and put businesses at risk of drawn-out lawsuits. By analyzing the effect of driver behavior on safety, AND enriching this assessment with contextual data, predictive fleet analytics reduces the possibility of mishaps, supplying essential information for driver coaching and training and encouraging safe behavior.

Optimize route planning

Route planning is an area of fleet management that demonstrates the true power and evolution of fleet predictive analytics. In the 20th century, the process involved measuring distances on a paper map, estimating average travel distances and fuel costs to get the shipment to the destination point. Information that would otherwise support the consideration of issues such as route traffic accident rates, construction areas, traffic density at a given time or day, or their potential to cause vehicle wear was just not available.

Predictive analytics systems suck in reams of data on traffic patterns for given points on the roadway, accident reports, and construction reports, then overlay them onto the delivery vehicle’s profile and the driver’s behavior. The result is a list of possible routes ranked not only by the time needed to travel the total distance but also by factors such as the possibility of traffic or heavy road conditions. The so-delivered route planning alternatives increase fleet efficiency and can lead to lower vehicle wear at the same time.

The road forward

The evolution of predictive analytics will continue to impact fleet management practices over the foreseeable future. However, suppose we allow predictive analytics to drive the bus from the solitary viewpoint of the operating cost/efficiency analysis. In that case, we are missing the larger opportunity that predictive analytics holds for modern fleet management. It starts with understanding the impact of human behavior on future business operating outcomes and how these behaviors may either be modified or turned into a predictable advantage by which all parties profit. That’s a prediction we can all call a win-win.