The Evolution of Fleet Management Technology

The fleet industry has grown remarkably in recent years, improving fleet management processes with the integration of telematics, predictive analytics, and automation. Gone are the days when fleet managers had to put pen to paper to keep a fleet running smoothly. Now they can effectively monitor, optimize, and maintain their fleets, improving efficiency and reducing costs and downtimes.

Fleet management is a challenging and complex task but many tools and resources have been developed over the years to make it easier. In this blog, we’ll uncover the transformation from manual fleet management to the variety of cutting-edge, data-centered fleet management systems available to fleet managers today.

Early Fleet Management

A fleet manager’s day used to look very different then it does now. Before the introduction of technology and data-driven reports, all records were kept and updated manually, with little to no time left to make use of this data. Fleet managers would typically use paper logs or spreadsheets to track and record information on vehicle location, fuel consumption, maintenance schedules, and driver performances. The process was inefficient and oftentimes led to a loss of data or missed important alerts due to human error. Before real-time tracking, fleet managers communicated directly to drivers about issues with vehicles and routes, consuming a large portion of time from both the fleet manager’s and driver’s day. 

The fleet management system lacked efficiency and was incredibly time consuming. With hundreds of vehicles on a tight schedule to make deliveries and transports, fleet managers were in need of ways to streamline operations and reduce vehicle downtimes. Technological advancements made in the late 20th century began the transformations made to the fleet industry, remarkably improving efficiency and providing fleet managers with numerous options to collect and utilize vehicle data to stay ahead of the unpredictability that comes with managing fleets.

GPS Technology and Telematics

Introduction of GPS Technology

The invention of the Global Positioning System (GPS) was the first significant, technological improvement to managing fleets. The GPS, with its first commercial use in vehicles in the 1980s, allowed drivers and fleet managers to view information on estimated arrival times, taking into account route changes, traffic delays, etc., to plan ahead and improve efficiency on trips. The satellite-based navigation system allowed real-time tracking of vehicles so fleet managers can stay updated on their vehicles without the need for constant communication. 

The introduction of GPS technology also paved the way for the emergence of telematics and other  advanced features that would follow in later years. With the basis of the system in play, additional features such as weather forecast systems and security systems were later added to improve driver experience and safety.  

The Emergence of Telematics

Telematics, first coined in 1978, collects real-time data from an on-board device and provides information including driver behaviour, fuel consumption, and status of a vehicle. Telematics systems also integrate GPS tracking and on-board diagnostic reading capabilities to provide fleet managers with information on diagnostic trouble codes (DTCs) on vehicles. 

The benefits of using telematics and GPS technologies were greatly acknowledged, eventually leading to the ELD mandate, introduced in 2017 and officially enforced in Canada from January of 2023. The ELD mandate requires all commercial motor vehicles to use an electronic logging device to record driver and vehicle activity, further impacting the growth of data within the industry. 

With increased visibility of vehicles, maintenance insights, and safety, telematics has changed the game when it comes to fleet management. But as discussed earlier, the introduction of telematics and GPS technology paved the way for the emergence of new technologies and software to make managing fleet operations even easier. 

Data-Driven Technologies

Utilization of Big Data Analytics

The introduction of big data analytics revolutionized how fleet managers collect, process, and utilize information collected about their vehicles and drivers. With over 7 billion data points collected through telematics systems, applying advanced analytics techniques allows fleet managers to extract valuable insights and data-driven decisions to optimize their operations. 

One of the key features of big data analytics is enabling fleet managers to optimize routes and fuel consumption by analyzing data on traffic patterns, weather conditions, and vehicle performance. Fleet managers can identify efficient routes to minimize fuel consumption and reduce operational costs. 

Another important feature is analyzing driver behaviour on speeds and driving practices to enhance driver performance and safety, protecting both driver and vehicle health to reduce accidents and downtimes.

Big data provides fleet managers with billions of data points on driver performance, fuel consumption, etc., with the intent to help fleet managers make informed decisions. However, with the overload of information, there was the risk of decision making becoming even more complex and time consuming. Fleet managers were in need of a resource to accurately narrow down  billions of data points into feasible insights. 

Predictive Maintenance Technology

One of the most significant enhancements in fleet management is the integration of predictive maintenance software. Another data-driven technique, predictive maintenance technology takes billions of data points into analysis to anticipate equipment failures and proactively schedule maintenance activities. With the use of predictive maintenance analytics, diagnostic trouble codes (DTCs) can be remotely monitored and prioritized to notify fleet managers of major or critical fault codes while automatically clearing non-critical DTCs. 

By combining and analyzing data points into personalized, actionable insights, fleet managers are benefited with taking the guesswork out of fleet maintenance, and decreasing vehicle downtime by up to 25%. 

Predictive maintenance software usually pairs with a telematics device, such as Geotab or Samsara, using its real-time sensor data to analyze the current state of the vehicle and inform users of potential failures and upcoming maintenance recommendations with smart maintenance scheduling.

Where Fleet Management is Going

From lack of data to now perhaps, too much data, capitalizing on what is readily available to help translate data into actionable steps is already the current focus for many fleets. There is no doubt that the world of fleet management will continue to evolve as technology advances with new and innovative solutions to optimize operational efficiency.  

Autonomous and Electric Vehicles

The rise of autonomous vehicles presents significant opportunities for fleet management technologies. As self-driving technology and the transition towards electric vehicles progresses, fleet managers will need to adapt operations and analytical processes to take advantage of the benefits these vehicles offer to both fleet operations and environmental concerns. 

Existing telematics systems have already begun to expand their databases showing energy usage reports, real-time charge status, future charging needs, and more, exclusive to electric vehicles. 

Internet of Things (IoT) Integration

Internet of things (IoT) devices have already started to revolutionize the fleet management industry and are expected to double its users by 2025. By connecting vehicles, sensors, and other assets to offer comprehensive fleet monitoring and improved operational decision-making, IoT-powered systems can provide valuable insights on vehicle diagnostics and fuel efficiency that can lead to enhanced performance and cost savings. 

Machine Learning

Machine learning has also started to play a pivotal role in shaping how fleet managers extract insights and data. For example, Pitstop, a predictive maintenance software couples AI with Machine Learning to build predictive models for fleet managers to know when a breakdown may occur, 9 days in advance with 94%+ accuracy. 

Machine learning algorithms analyze complex data patterns and detect anomalies to predict equipment failures and identity potential issues before they occur. They can also optimize route planning, monitor driver behaviour and safety, and forecast demand to improve overall fleet operations. 

Further development made in Artificial Intelligence (AI) systems and machine learning will continue to improve fleet management practices. Challenges faced in the industry, such as unpredictable road conditions and driver retention problems, are expected to become obsolete with the continuous advancements in AI powered devices and software. 

Improve Your Fleet Maintenance Approach with Pitstop

In conclusion, over the course of 40+ years, the world of fleet management has shifted from manually recording information to getting data-driven, actionable insights provided right to you, and continues to evolve. 

Pitstop, a predictive maintenance software powered by AI, can help remove the complexities of data-driven fleet management with personalized insights and maintenance scheduling provided right to you. For more information on how you can remove friction between data, drivers, and fleet managers with automated maintenance, set up a call with us. 

About the Author

Tharinee Premkumar is the Marketing Co-op at Pitstop, a powerful predictive maintenance software for the transportation and automotive industry.

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