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INSIGHTS

4 fleet management scenarios that can especially benefit from AIoT

4 fleet management scenarios that can especially benefit from AIoT
One of the chief benefits of AIoT in fleet management is the ability to provide real-time fleet visibility and insights, helping managers optimize planning and fleet utilization.
In the previous article, we looked at how fleet management can benefit from AIoT, leveraging connected devices and the data they generate to make fleet managers’ job easier. In this article we look at specific fleet management scenarios that can especially benefit from AIoT.
 

Real-time fleet visibility and insights

 
One of the chief benefits of AIoT in fleet management is the ability to provide real-time fleet visibility and insights, helping managers optimize planning and fleet utilization.
 
“AI-powered telematics platforms can deliver up-to-the-minute insights so that decisions can be made with confidence. AI-based fleet management platforms deliver accessibility, accuracy, and speed to the transportation, logistics, and mobility industry, making the transport of goods and people safer, faster, and more economical,” said Guang Sun, Lead Data Intelligence and BI Architect at Cetaris.
 

Predictive maintenance

 
While predictive maintenance is often associated with smart manufacturing and Industry 4.0, it also has applications in fleet management where fleet managers can be informed of impending vehicle failure or other issues. In this regard, AIoT can also help.
 
“We can tell which vehicles – once they’ve reached a certain mileage – will need maintenance or might have failures. For example, with EVs, we’re using AI to predict when battery failures will happen, when they will die and need to be replaced. This can also be applied to other parts of the vehicle,” said Mayank Sharma, Head of Global Product Management and UX at Teletrac Navman.
 
“AI can help with predictive maintenance in fleet management by forecasting fleet maintenance based on available fleet management data and experience. AI can reveal both explicit and implicit dependencies between events as well as identify recurring patterns and trends. AI-based internet of things (IoT), data analytics and predictive maintenance are transforming fleet vehicle repair by anticipating an engine problem and reporting it before the driver notices that anything is amiss,” Sun said.
 
“AI can also help reduce vehicle downtime by utilizing advanced technologies to improve and offer accurate self-diagnostics and solutions to faults,” he added. “The evolution of data analytics, IoT and predictive maintenance are revolutionizing vehicle repairs by forecasting potential defects before they even happen. This helps detect faults long before they eventually occur.”
 

Route planning

 
AI and IoT can take into account road conditions, weather patterns and other factors to achieve route optimization, which can lead to positive outcomes for fleet managers.
 
“AI can help plan better routes in fleet management by easily planning and adjusting routes on the fly. A fleet management system that takes real-time traffic data, road closures, and other unexpected events into account can find the best routes for drivers. This means fewer delays, happier customers, and increased profits. AI can also help fleet managers monitor their fleet closely and provide route recommendations to drivers in case of road risk,” Sun said.
 

Safety

 
Finally, AIoT can also help with safety in fleet management. “Safety is another big use case. Specifically, knowing overall behavior and which drivers are prone to risky driving and could potentially get into an accident. Using IoT and AI together, we can identify patterns in driving behavior, such as what types of roads they use, how they drive on those roads (freeway or inside roads), whether they accelerate, break, speed, or corner,” Sharma said.
 
“AI can help with driver safety in fleet management by detecting risky driving behavior and common compliance violations, such as speeding, distracted driving, driver fatigue, and more. This data gives fleet managers insight into drivers’ performance and track where a driver needs to improve or be rewarded. AI can also help fleet managers monitor their fleet closely and provide route recommendations to drivers in case of road risk,” Sun said.
 

Edge or cloud

 
AIoT in fleet management is all about processing vehicle-related or other relevant data. The next question, then, becomes where is the data processed – in vehicle (edge) or in the cloud. This actually depends on the type and nature of the data.
 
“Most of the data is processed in the cloud and on the edge. Some of the data which is critical is processed at the edge, such as vehicle data. Other data, such as sensor data and telematics data, gets processed in the cloud. Anything that is mission critical, like driving data, is on the edge. Data that needs to be combined with other data sets gets processed in the cloud,” Sharma said.
 
A hybrid solution may also be a viable option. “A hybrid architecture can be more suitable for fleet management. For example, some fleet management software has re-architected their fleet control software to be highly available, geo-redundant, and vendor-neutral, and to run in a hybrid cloud context. This architecture provides geo-redundancy with zero-downtime failover for 99.9 percent availability and disaster recovery,” Sun said.


Product Adopted:
Transportation
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