Understanding how data can benefit a company which operates a commercial fleet is vital for businesses in today’s highly competitive industry. For example, construction companies, where businesses rely on their commercial fleets, could see a financial benefit from understanding the data their vehicle’s produce.
One of the key areas where fleet managers can save money through data analytics relates to the maintenance needs of a fleet. By installing fleet telematics systems across all company vehicles, to create a fleet of connected vehicles, fleet managers can gain a far greater understanding of the maintenance needs of each vehicle. The data produced from each vehicle every time they are used can be collected and viewed over time.
By doing this, managers can see exactly how many miles specific vehicles have completed. This allows maintenance work to be scheduled before any issues develop. Unexpected maintenance for commercial vehicles can cause serious problems for both the business and its customers. Furthermore, leaving a vehicle unchecked, particularly one which travels what can be very long distances every day, could lead to a serious issue developing. If a fleet vehicle suddenly becomes unavailable because of a mechanical problem, it can lower the productivity of the fleet overall, and its impact can be significant if it’s only a small fleet. Obviously, it’s not possible to prevent sudden and unexpected mechanical failures, but having vehicles undergo routine checks based on the distance they have travelled can go some way to minimising the chances of a severe issue happening without warning.
Another key area any fleet manager will be keenly aware of is the amount of fuel their vehicles are using when out on the road. Data analytics can make a significant improvement in this area for several reasons. The first is identifying driving habits which contribute to excessive fuel use. These habits include letting the vehicle’s engine idle when stationary, speeding and harsh acceleration. Fleet managers can use data analytics to identify which drivers continually exhibit these habits and then training can be designed for them to rectify the issue. This can have a significant impact on the amount of fuel used by a commercial fleet and can save the business money in the long-term.
The second is using data analytics to identify vehicles which, despite fuel-efficient driving, are continuing to use high amounts of fuel. There can be many reasons for this. A particularly common reason is that the vehicles are older and therefore not as efficient as modern alternatives. While some companies may not have the finances available to replace such vehicles immediately, the data that connected vehicles produce can be incorporated into a company eco plan. These plans can detail the long-term environmental goals of the company, and they can also include when older less-efficient vehicles should be replaced based on the financial benefits of doing so.
The final key area where data analytics can prove extremely useful for fleet managers is calculating the exact number of hours their drivers have worked. Older systems may require drivers to calculate their own working hours and then submit them to a fleet manager. With vehicle tracking systems, the exact amount of time drivers have spent at work can be quickly determined. The benefit of this to the business and employee is that drivers will always be paid the correct salary for the number of hours they have worked. From a safety point of view, it’s also incredibly useful as it can alert managers when a driver is approaching the number of hours they can work without a break, reducing the risk of tired drivers causing serious accidents.
Overall, it’s clear that data analytics can improve the performance of a commercial fleet and save it money. For large commercial fleets, with hundreds of vehicles, the amount of money which could be saved per year on fuel alone could be very significant. For reasons like this, it’s crucial fleet managers are aware of the benefits and understand how to interpret fleet data.