There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently.
Artificial intelligence is gradually becoming a constant presence in many technological applications. From apps and websites that show accurate user recommendations to gaming predictions, it is changing user experience in many fields.
What is AI Fleet Management?
AI fleet management is the use of artificial intelligence-based technology to manage fleet operations. In a constantly changing world, it streamlines the work of any fleet manager by gradually eliminating human error from the transport process.
AI-based recommendations ensure that fleet drivers, managers, and mechanics can make better decisions that improve the long-term performance of the fleet. It also serves as assistive technology, ensuring that drivers retain autonomy during each transport cycle. Here are some key aspects of fleet management that AI can optimize:
Real-time Fleet Analytics
Collecting data is a key element of any operational process because without analyzing past data, you cannot make informed decisions. With historical insights to inform millions of data points analyzed in real-time, the result is the prioritization opportunities and risks so that fleet managers and drivers can determine the best course of action to take in potentially problematic situations.
AI fleet management systems can be used to collect data for predictive analytics; data such as traffic and road conditions, environmental hazards, real-time weather, and mechanical faults can be used to predict incoming risk.
Better Repair and Maintenance Decisions
Predictive maintenance gives managers and their mechanics more than enough time for repairs which could potentially prevent accidents. More importantly, AI can recommend the most efficient and cost-effective solutions to mechanical faults. This has two major benefits:
- It saves mechanics’ time usually spent on diagnostics.
- It gives managers a clearer picture of the state of their fleets at all times. This could mean that service managers could save a lot of routine maintenance costs by carrying out repairs only when the AI systems show potential faults.
Fleet Integration
An AI system could simplify the process by seamlessly integrating every department on a single platform and feeding them information simultaneously. Service managers can save time and costs on planning, maintenance and monitoring operations since all data on those operations are fully accessible. This ensures that all personnel across the different departments have access to the data that helps them make informed decisions. It also leads to a more cohesive fleet, since every department automatically works in sync with the others.
Simpler Recruitment Process
As the older generation drivers and technicians retire, there is a need for younger tech-savvy replacements; however, this presents a problem with onboarding and training. AI can simplify the onboarding process by capturing the specialized skills of these workers before they retire.
It can also recommend the most qualified drivers that suit the needs of the company from a pool of thousands of applicants, reducing the strain on recruiters.
How is AI Integrated with Fleet Management?
AI-integrated software is usually a sophisticated system made up of several devices and applications such as Internet of Things, predictive data analysis and machine learning systems, HD cameras and sensors, communication and display systems, and WiFi.
Internet of Things (IoT)
The Internet of Things refers to a network of actuators and sensors continuously collecting data from their environment. In fleet management, IoT ensures that enough data is captured for analysis while promoting the seamless sharing of information between all stakeholders on the supply chain such as retailers and manufacturers.
Machine Learning
An AI system with all of the above components will be capable of performing the following tasks:
- Collecting accurate road data and transmitting it to other devices
- Passing information across every arm of the supply chain
- Analyzing data in real-time and advising the driver on the best course of action
- Detecting distracted or drowsy driving behaviors in drivers before they lead to accidents
- Running Self-diagnostics and recommending solutions through predictive maintenance
How AI Fleet Management Will Shape the Future of Transportation
Today, the automotive vehicle industry is faced with several problems that affect fleet activities and profitability. Risky road behaviors such as distracted and drowsy driving are often accompanied by signs that drivers are told to look out for. These signs include:
- Yawning
- Constant blinking
- Missing turns or exits
- Drifting out of their lane
- Slower reaction times
- Picking up a cellphone
It can also be trained to make smart predictions about the weather and detect environmental changes such as fog before a driver reaches that point.
Which is the Best Fleet Management Software?
Fortunately, AI-based fleet management software has gone from being dreamy concepts to reality. Several technology companies have created software that improves driver safety and fleet performance without compromising cost or efficiency.
In our research, we looked at the key components that made each one stand out.After analyzing their mapping capabilities, technological range, as well as sensor technologies, Driveri emerged as the best fleet management software due to the following features:
Final Thoughts
The future of transportation looks more promising than ever due to the exciting applications of AI in fleet management. Unpredictable road conditions, operational costs, and driver retention problems could easily become obsolete as fleets move to AI-based systems. Every stakeholder stands to benefit a lot from the efficiency and reliability of this technology because of a reduction in costs, accidents, driver turnover, and other problems which could reflect on the pricing of fleet services. It could also ensure that other road users remain safe.
This article originally appeared on netradyne.com.
