Predictive analytics can be the right hand of fleet managers to help them proactively manage company fleets in the highly competitive automobile fleet industry. Big data, definitely one of the most popular buzzwords in the business world, is the technology coordinated activity beperation of all areas of the business to get insights applicable in fleet management. Using predictive analytics and machine learning in fleet management to improve the decision making process can benefit every area – from safety to productivity – and save your company a lot of money.
What Is Fleet Management?
In plain English, fleet management is the management of commercial motor vehicles, specialist vehicles, and trailers aimed at the optimization of costs, risks and efficiency in fleet operations. According to Wikipedia, “fleet management can include a range of functions, such as vehicle financing, vehicle maintenance, vehicle telematics (tracking and diagnostics), driver management, speed management, fuel management and health and safety management.”
In more detail, fleet management touches these aspects of fleet operations:
· Fleet strategy including fleet wellness, fleet goals and priorities, fleet administration, and fleet KPIs
· Fleet acquisition including selection of vehicles (new or used ones), branding, fleet incentives, etc
· Fleet services, such as fleet registration, fleet analysis, and fleet rebalancing
· Maintenance management including fleet maintenance program, preventative maintenance, roadside assistance, reporting, and accident management
· Energy management, such as fuel management, energy planning and infrastructure
· Risk management, which deal with fleet driver policy, driver education and testing
· Tools and technology management, which includes GPS telematics systems (read more here), routing, speeding, location tracking in real time, driver training, and geofencing
In many organizations fleet managers control costs, maximize profitability, and mitigate risks of their fleet vehicles using various tools to get the right information to make informed decisions.
Why You Should Use Predictive Analytics?
Business decisions in today’s world are based on data. Big data technology allows organizations to harness the value of their data and enables informed business decisions. Predictive analytics in fleet management based on that data helps fleet managers solve a wide variety of problems before they even materialize.
Companies that have their own fleets use software to constantly monitor live data, such as data from vehicle sensors, transportation management systems and other sources. A prime example of this kind of software is the GreenRoad driver safety platform, a part of which – the Driving Behavior Monitoring and Safety Management Solution - was developed by Archer Software.
While mining this data and analyzing it, fleet managers can predict problems, trends and behavior patterns. Telematics and other sources of data provide information to identify problems and save fleet owners money.
Fleet management predictive analytics is an ideal tool for:
· Driving risk analytics
· Equipment usage analysis
· Fleet productivity schedules
· Future maintenance schedules
Let’s take a look at the areas where predictive analytics in enterprise fleet management can be the most rewarding in some detail.
How predictive analytics can impact fleet operations?
Taking action based on predictive data is a new and better alternative to avoid financial losses, reduce fleet downtime, and literally save lives.
One of the most important areas of fleet management data analytics meaningful use is accident prevention and driver safety. According to Automotive Fleet, the primary cause of the uptick in preventable fleet accidents is driver distraction, which is the cause of 25-30% of all fleet-related accidents. The total accident rate in the US for commercial fleets averages 20%, and sometimes even higher. And the share of accidents in the fleet’s total expenses represents in average of 14%.
Fleet management data analytics is based on a variety of data types, such as telematics data, data from various cloud or edge devices, GPS, vehicle cameras, traffic cameras, and driver monitoring applications. The collected data can reveal the most common causes of accidents and give insight into how to avoid them. For example, if the major cause of accidents is risky driving behavior, the problem can be address with a suitable training program. Or, if accidents are being caused by the vehicles themselves, fleet managers can predict accidents before they occur and prevent them from happening through predictive maintenance.
Fleet predictive maintenance can have significant positive effects on fleet management. Fleet managers can use fleet management data analytics insights to keep fleets up and running all the time by avoiding downtime associated with emergency repairs. Thanks to predictive analysis, maintenance issues can be resolved before they become breakdowns.
By detecting imminent failures through the data gathered by dozens of sensors installed on trucks and other fleet vehicles, it is possible to improve safety drastically. The sensors provide fleet managers with data showing possible malfunctions when machines or vehicles are in use.
This means decreased downtime and cost savings as planned maintenance is usually much cheaper than emergency maintenance. According to Fleet News, “fleet managers can use predictive maintenance on an asset-by-asset basis to utilize traditional maintenance methods when needed”.
How to use predictive analytics in fleet management
How does connected car data analytics work? Let’s take a look at the process. There are a variety of forms of predictive models and they depend on the behavior or event to be predicted. In his interview with Fleet Owner, Richard Holada, VP-BI/AA for IBM’s Software Group said, “Predictive analytics is all about taking a business problem and addressing it. We pick a point in a process flow where, if we could be predictive, it would make a lot of difference. If predictive analytics works, you don’t even know it is there. It exists in the background”.
The techniques used in the analysis of current of historical facts to make predictions about future include:
· machine learning
· data mining
The key component of any successful predictive model is gathering and analyzing actual fleet data. To be effective, all departments (fleet operations, safety department, HR and IT) must be involved in the process, their efforts must be aligned, and the process must be adopted by the whole company.
To solve problems, you have to identify the areas where the challenges are and choose the necessary data for analysis. To avoid data overload and focus on the areas with the biggest impact, choose only the areas of greatest needs (for example, driving safety, fleet maintenance, or downtime) and ensure you are collecting the data related to those areas from multiple sources of data.
When selecting the vendor of predictive analysis IT solutions or a software developing company, keep in mind that their role in making the information impactful is quite significant. Anyway, the final product you use must process and deliver the information in a clear and comprehensive format which can be readily used by fleet managers to take informed action based on the data obtained.
Archer Software is a professional development agency with many years of experience in creating IT solutions for the automotive industry, including car manufactures such as Renault and Volvo. We will gladly help you create a prediction analysis tool tailored to the needs of your fleet. Contact us at email@example.com to learn more.