Cutting-edge technologies and real-time data insights are transforming the way fleet managers operate and improve fleet performance, reduce operating costs, and enhance overall productivity.
These transformative technologies are helping them meet their core objectives, namely:
- Defining fleet management strategies
- Enhanced productivity with real-time insights into fleet activities for faster and informed decision-making
- Seamless operations to ensure on-road availability with real-time alerts for maintenance issues, driver behavior, and route deviations
- Improved driver safety, optimized routes, and reduced fuel costs with real-time monitoring
Today, managers are replacing obsolete systems and manual processes by adopting technology-based real-time fleet management solutions to stay ahead of the competition. Let me share some insights about how technologies and innovations can help them meet their key objectives as mentioned above.
Disruptions ahead: 4 key challenges in fleet management
Let’s understand some of the challenges in day-to-day fleet operations.
- Difficulty in monitoring vehicles and their drivers in real time to know where the vehicle is at any point of time
- Need for assessing and monitoring driver behavior to improve their own safety and wellbeing, and reduce accidents
- Clamping down on the carbon footprint of the fleet
- Overall, unoptimized fleet performance
In the rapidly evolving digital ecosystem, how can technologies such as GPS, AI and ML address the above challenges?
Revolutionizing fleets on the road
In turn, these provide a holistic view of fleet performance for better informed decisions, more productive operations and cost saving.
Now, let’s explore how Vehicle Digital Twin and GenAI-based Driver co-pilot can help.
Vehicle Digital Twin
The digital twin of a vehicle is a replica of a physical vehicle in the digital world. This is created by collecting and integrating real-time data from various embedded sensors, diagnostics and systems installed in the vehicle. Now, this nifty solution can redefine proactive maintenance scheduling, thereby preventing breakdowns and reducing downtime.
By continuously monitoring and analyzing data from the sensors, vehicle digital twins can provide insights into its current operating conditions. Using artificial intelligence (AI) and machine learning (ML) algorithms, the digital twin can also analyze data from the vehicle’s systems, such as engine performance, fuel consumption, tire pressure, and battery health. This makes it easier to detect patterns and trends that may identify potential issues before they occur. Additionally, this facilitates monitoring, analysis, and simulation of the vehicle’s behavior and performance in a virtual environment and supports decision-making related to vehicle operations, optimization and improvements.
GenAI-based Driver copilot
Adopting a GenAI-based driver assistant allows managers to monitor vehicle health in real time and can assist the driver to operate it optimally.
This emerging technology can bridge data gaps between systems and its users. It even enables interactive and real-time dialogue with drivers to warn and advise drivers of vehicles issues before they become operationally disruptive.
The driver assistant can be developed by combining the capability of GenAI in the areas of summarization, data production, and natural language communication with real-time vehicle data that brings game-changing insights into systems. It provides the current live state of vehicle and helps drivers navigate and operate their vehicle.
Using vehicle data and OEM manuals provides an assistant with the ability to monitor the vehicle’s health, alert the driver, and even explain potential issues and remediations to help the driver take a decision to optimally operate its vehicle.
On the road and in action
Let’s talk about an example, where predictive maintenance supports real-time fleet management.
In this example, a truck manufacturing company provides telematics platform services to their clients to manage their fleet of trucks in real time. By using telematics platform services, client expects to accomplish the following:
- Reduce unplanned vehicle downtime
- Empower their drivers and fleet owners to predict issues
- Reduce prolonged service timelines
- Minimize maintenance costs
The telematics platform receives diagnostic data from the vehicle sensors through on-board telematic units. This data is analyzed by leveraging AI and ML algorithms along with complex events processing to predict when a component is likely to fail and its severity level. Based on the severity level (i.e. service required immediately, later or just for information), an alert can be raised to fleet owners, drivers, and nearby dealers / car shops through push notifications for faster service.
In case of immediate service requirements, the platform uses GPS to guide the driver to the nearest car shop.
In addition, the driver assistant uses the vehicle data and truck OEM manuals to monitor the vehicle health in near real time, and even explain potential issues and remediations and guide drivers to navigate and operate vehicle.
With failure analytics data and vehicle telematics information, vehicle digital twin can provide insights to the manufacturer’s engineering department to improve vehicle component quality. The component quality check can then be done in a vehicle digital twin virtual environment before going into production.
The journey ahead
With the above example we can see how real-time fleet management systems with cutting-edge technologies help truck/fleet owners in improving availability of their fleet and reducing maintenance costs. It also helps the OEM in improving the quality of the vehicle components, enhancee service efficiency, optimize warranty costs, and boost overall customer satisfaction.
If you would like to discuss how IoT is changing the game in the automobiles industry, let’s connect.
Learn more about Eviden’s transformative solutions for the business of tomorrow: Internet of Things (IOT) Solutions for Enterprises.
