Fleet Management Analytics: Benefits and Trends

Alissa Machin |

Fleet management analytics benefits and trends is an important topic within the area of fleet management. As a set of computational analysis tools available to fleet management systems, fleet analytics supports fleet managers’ monitoring and tracking efforts.

The key benefits of fleet management analytics are that drivers can use analytics tools to improve vehicle performance, prevent vehicle breakdown, and minimise vehicles’ environmental and economic footprint. Using data from machine learning-based algorithms and data analytics software, fleet management analytics provide a data-driven interface which creates a feedback loop for the optimisation of fleet operations. 

Key Aspects of Fleet Management Analytics

Data analysis tools form the backbone of fleet management systems, using software to provide analytics feedback in real time. These data-driven tracking systems monitor vehicle location, speed, and status, streamlining operations through resource management. Monitoring solutions capture vehicle health at a glance, whilst tracking driver behaviour and fuel consumption to support proactive maintenance and cost optimisation. 

Central to fleet management analytics is the collection and analysis of data from various sources, including GPS trackers, onboard sensors, maintenance records, and fuel consumption logs. Vehicle tracking systems, such as Electronic Logging Devices (ELDs)1, predict driving conditions based on real-time vehicle performance and driver behaviour. These systems display important data on dashboards, providing stakeholders with insights into key performance metrics and trends.

Data Analysis Tools and their Applications within Fleet Management Systems

Data analysis tools and their applications within fleet management systems are an area of evolution for the automotive industry. In this regard, data analysis tools extract diagnostic data, from data points and engine functioning to vehicle location. An important development in the area of data analysis tools is the use of AI dashboard cameras to monitor driver behaviour and minimise traffic accidents. AI dashboard cameras generate fleet management analytics, offerig an accurate and precise way of tracking and improving driver safety.

When Strategy Analytics demonstrated the importance of comparing dashboard camera models, there were significant differences in the capacity of AI dashboard cameras to detect unsafe driver behaviour. In 342 separate tests at various times of day, some AI dashboard cameras could detect unsafe driving events with nearly six times the accuracy of other models.2

AI in Business: Applications, Use Cases, Benefits, Companies, and Solutions

Benefits of Fleet Management Analytics

Benefits of fleet management analytics in today’s ever-evolving landscape of fleet management include leveraging analytics as a cornerstone for unlocking the potential of AI. In this section, we will see how predictive analytics are reshaping fleet management. From enhancing safety protocols to optimising resource allocation and fostering environmental sustainability, the utilisation of analytics is revolutionising fleet management practices.

If you want to read more about the benefits of fleet management, you can also explore our other article Fleet Management Best Practices, which offers further insights into maximising efficiency in fleet operations. Let’s now delve deeper into the transformative potential of these advancements.

Improving Fleet Safety with AI and Predictive Analytics

Improving fleet safety with AI and predictive analytics mitigates future safety hazards within the context of fleet management. Moreover, tracking driver behaviour with in-vehicle monitoring devices reveals data analysis tools as the mainstay of predictive analytics.

A key advantage of fleet management analytics is their reliability, in contrast to the manual checking of vehicles. Not only do predictive analytics use tracking tools to provide an accurate history of vehicle usage and driver behaviour; they also use past data to predict challenges while clamping down on mistakes made due to human error. 3

AI Software Optimises Fleet Management Systems

AI software optimises fleet management systems by making several layers of potential human error insignificant, whether in unsafe driving practices on the part of the drivers themselves or in the manual checking of vehicles on the part of vehicle specialists.

Fleet managers can then benefit from timely predictions from AI-vehicle monitoring, improving the overall safety, efficiency, and cost-effectiveness of their fleets. Moreover, fleet management analytics allows for resource optimisation, enabling fleet managers to prioritise compliance with the latest standards for the reduction of carbon emissions, a third benefit this article will now consider. 

Monitoring Fuel Efficiency and Emissions Reduction

Monitoring fuel efficiency and emissions reduction are two aspects of fleet management which the AI revolution will impact, in the transition to low-emission infrastructures. While training large machine learning models to support the rollout of big tech infrastructure can itself become a significant carbon emitter 4, in the context of predictive analytics for fleet management systems, this is not the case.

Indeed, Expert Market’s survey of analytics tracking systems includes three products for tracking carbon emissions and outlines the benefits of their predictive software for monitoring fuel efficiency. For example, Teletrac Navman’s software offered live tracking features for mileage and was an overall top performer in driver management functions, including driver scorecards in real time.5

Meanwhile, Verizon Connect’s fleet visibility software was useful for tracking in the context of construction industry fleets, achieving a net positive impact on fuel efficiency and emissions reduction. Monitoring driver safety and assessing vehicle performance, through the visualisation of a vehicle’s carbon footprint, enables vehicles to optimise their routes. As a result of this process, fleet managers can minimise safety-associated costs and carbon emissions.

EU Policy Developments Influence Existing Fleet Management Systems 

EU policy developments influence existing fleet management systems, since they have consequences for environmental, social, and governance (ESG) ratings, affecting industry innovation and investor confidence alike. 6 Following the EU Commission’s final vote in March 2023 that from 2035, the only new cars and vans registered in Europe will be zero-emission vehicles7, the owners of existing fleet management systems will require tracking solutions to mitigate the effect of petrol or diesel-fuelled cars throughout the transition to the new paradigm.

In parallel, fleet managers will continue to contend with stricter regulations for carbon emissions from vehicles which fall outside of the category of cars and vans, such as commercial buses. 8 

Fuel Monitoring and Route Optimisation

Fuel monitoring and route optimisation is a second area of emerging trends in fleet management analytics. The common denominator which unites all vehicles, regardless of their registration category, is the routes they take to their destinations. In the automotive sector, utilising software for tracking fuel consumption and automatically rerouting vehicles is catching on.

According to MICHELIN Connected Fleet, more than half of companies have embraced data-enhanced route optimisation as the choice strategy for reducing carbon emissions. 9 In addition to in-vehicle analytics tools for safety, fleet managers can also deploy AI-based predictive monitoring tools to gather data from satellite information and traffic conditions.

By tracking journeys in real-time, fleet management analytics can use GPS tracking to reroute drivers and take the most carbon-smart route. As Expert Market shows, the capacity of such software to adjust the journey in real-time, based on advanced tracking functions, elevates analytics to the next level.

In this way, their route planning feedback networks factor in missed stops and scheduled arrival times into their predictions. When it comes to acquiring a zero-emissions certification and compliance with ESG goals, fleet management analytics will therefore have a powerful role in putting more fleet management firms on the map. 

Predictive Analytics within a Path-breaking Paradigm

Predictive analytics within a path-breaking paradigm of machine learning is a gateway to the net-zero model of vehicle and transport systems. With international carbon emissions milestones on the horizon, the rapid development of tracking software to offset negative environmental impact is inevitable in the context of fleet management analytics.

Monitoring and tracking carbon emissions is therefore the cornerstone of analytics products arriving on the market. Another component of this new paradigm will be self-driving vehicles, whose reliance on machine learning and autocorrection will further enhance the benefits of data analytics for route optimisation in fleet management systems.


Using fleet management analytics to enhance a data-informed approach to fleet management systems is the bedrock of monitoring and tracking efforts. In this way, detecting issues before they become serious problems has a downstream effect on the whole paradigm of fleet management. Automating functions vulnerable to human error, such as manually checking vehicles or engine monitoring, also gives fleet managers more freedom to optimise their fleets.

By relying on the accuracy and reliability of dashboard cameras to monitor driver behaviour and tracking software to automatically reroute vehicles, fleet management systems have a greater chance of reducing carbon emissions. Moreover, the intersection of AI-powered fleet management analytics and EU environmental policy is an area for further investigation. If fleet management analytics can regulate fleet functions downstream via automation, then they may play an important role in shaping tech-aware climate policy, in turn. 10