When your fleet is your business’s backbone keeping every vehicle and driver on the road is your number one priority. And that’s where adopting predictive analytics for your fleet risk management can make all the difference.
From accurately budgeting for fleet repairs, to identifying high-risk behaviour in your drivers, predictive analytics gives you insight into your fleet’s operations at every level. Providing an invaluable 360-degree overview that becomes the basis of your fleet risk management strategy.
But if you think utilising predictive analytics sounds scientific, or indeed costly, think again. In today’s marketplace fleet managers have a wealth of efficient and easy ways to adopt predictive analytics that neither cost the earth nor require an advanced analytics degree.
In this article, bluedropservices.co.uk go into the mechanics of predictive analytics in a little more detail. As well as introducing some of the key benefits of utilising a form of predictive analytics to approach fleet risk management.
What is predictive analytics?
Broadly speaking, predictive analytics is the practice of gathering data and using the information to make informed predictions about future behaviours and likely outcomes.
It’s something consumer brands have been doing for years in the retail sector. And likewise it’s the reason you’re asked to accept cookies whenever you visit a new website for the first time. Every page impression you make is being logged against your unique IP address. Which collects data about your usage of the site and feeds that information back to the registered owner, who uses said data to improve the website’s performance for future site visitors.
In fact, predictive analytics is now an approach used effectively across a multitude of industry types. For the most part to minimise unbudgeted expenses, but similarly to deliver accurate forecasts for future growth and performance. For fleet managers and businesses, already using predictive analytics to assess fleet risk management, it’s helping to keep repair costs in line with expectations. As well as improve driver training and overall fleet performance.
Applying predictive analytics to fleet risk management
Effective predictive analytics hinges on accurate data capture. The more tangible and ‘trackable’ data you have available to analyse the closer your forecasts for the future will align with reality. So the first place to begin when implementing a predictive analysis approach to fleet risk management is by investing in the right data capture devices and technology.
The most relevant and common methods for tracking data in the automotive industry include those that monitor driver behaviour and vehicle lifecycle. Essential for building up an accurate picture of your fleet’s performance as a whole, as well as collecting data on individual drivers and vehicles. These types of telematics systems capture data in real time. Before transmitting it electronically to a central system to be logged and expertly analysed.
How predictive analytics can benefit your fleet
Just as Google, Amazon and Apple have been using predictive analytics to monitor consumer behaviour for years. Recent strides in telematics mean fleet managers can now use the same technology to run their operations day-to-day. Using informed insight, gleaned from data capture and analysis, fleet managers can influence decisions about vehicle assignment and driver capability.
Here are just three ways that predictive analytics can assist with fleet risk management:
- It ensures accurate lifecycle costing (LCC)
An integral part of any fleet manager’s role involves accurately monitoring each fleet vehicle’s performance. From breakdowns, and repairs, to accidents and age. Ensuring that no vehicle outlives its usefulness, or its value.
Predictive analytics assists with fleet risk management, gathering data about each individual vehicle’s performance across its lifespan, thereby providing an instant measure of which vehicles are most cost effective and efficient in the fleet.
This information can be used as a comparison versus those displaying a history of unreliability and maintenance. You wouldn’t, for example, wish to send out one of your most high-risk vehicles on an urgent job for a top-paying client. Predictive analytics (done well) ensures that no such eventuality will ever occur.
- It allows you to set a realistic repairs budget
It’s a fact of life that fleet vehicles depreciate in value far quicker than their consumer counterparts. Fleet vehicles by nature spend significantly more hours on the road, thereby increasing their chance of collision as well as advancing their age prematurely through increased wear and tear.
It’s inevitable then that fleet upkeep is going to cost a business a significant amount. After all, an organisation that relies on its fleet to function can’t afford for a single vehicle to be off the road. But how much to proportion to repairs, MOT and maintenance is a tough one to call if you have no measure of your fleet’s past performance.
That’s where predictive analytics can assist. By keeping track of past repair history, as well as performance at MOT, fleet managers and office accountants can more accurately forecast forthcoming repair costs. Managing expectations within the business and ensuring that when repairs are needed, the budget is there to get the job done.
- Predictive analytics minimises fleet collisions and improves driver safety
Among the advanced capabilities of today’s telematics data tracking is the ability to monitor and analyse driver behaviour behind the wheel. This can be recorded in a number of ways, through dash cam footage, which allows fleet managers to see first-hand how a driver responds on the road. Using this information together with mobile technology and applications that log behavioural patterns, such as instances of speeding and repeated heavy breaking, driving behaviour can be tracked.
The benefit of these technologies is threefold. One, they protect drivers in the event of of an accident, whereby the fleet driver is not at fault. Two, they diminish instances of a driver behaving irresponsibly behind the wheel (such as sending a text message while the vehicle is in motion). Thirdly, they help fleet managers identify individuals who repeatedly exhibit high-risk behaviours. All of which put the driver, passengers and other respective road users, at risk.
Once harnessed this knowledge is hard to dispute. It provides an accurate record of the driver’s responsiveness and accountability in real time. Such data helps fleet managers to assess the risk select drivers pose. If, when analysed, the data shows a small proportion of fleet drivers are repeatedly exhibiting risky behaviour, this paves the way for training, or other similar intervening action before an incident can result. Thereby saving the company reputational damage. Not to mention minimising the potential for loss of life.
The use of such Telematics data capture systems is especially significant in example 3. When you consider that with the right data, analysed in advance, the majority of road accidents are in fact preventable. So business’s that adopt predictive analytics as part of their fleet risk management should see a natural decline in the number of collisions involving their fleet.
Likewise if a singular driver is routinely involved in incidents of speeding or other similar traffic offenses, a business can use the data collected on that driver to justify a disciplinary or other HR intervention. Making predictive analytics effective not only in terms of fleet risk management, but also as a valuable HR tool to help manage your business’s work pool too.