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.
Fleet management is one of the areas that AI is disrupting. The growing need to put driver safety first without compromising cost or efficiency has led to the adoption of smart fleet management systems.
For the average driver, the presence of AI can be felt heavily in the use of smartphones and telematics devices that recommend the best routes to take in traffic. This used to be a herculean task marked by paper maps and listening to radio broadcasts of traffic routes; today, we have complex traffic apps that combine GPS and artificial intelligence to make drivers’ lives easier.
Fleets benefit from powerful AI-based applications that handle anything from route recommendation to road risk data analysis and even driver coaching. It provides the accuracy, efficiency, convenience, and ease that earlier technology failed to provide. As a result, it is becoming safer to transport goods and services.
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:
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. This allows fleet managers to make better routes, schedules, maintenance delivery, and dispatch arrangements that improve fleet outcomes and activities.
Finally, with AI-based analytics, drivers no longer need to go in blind and can stay prepared for any unexpected events.
In May 2019, autonomous driving car brand Tesla made headlines after debuting AI-based technology that allows Tesla vehicles to diagnose their faults accurately. Although this technology has existed for some time and has been seen in several modern cars, artificial intelligence is providing a more accurate self-diagnostics as well as solutions to faults.
AI ensures that potential faults can be predicted before they even happen. For example, a normal vehicle with a diagnostics system would most likely signal an engine problem when it has already occurred. On the other hand, AI-based Internet of Things (IoT), data analytics and predictive maintenance, can lead to fault detection long before it eventually happens. According to a study by McKinsey, predictive maintenance will reduce costs by 10-40%, downtime by 50% and capital investment by 3-5%.
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:
One major problem with fleet operations, especially in large fleets, is the number of moving parts within the system that need to be accessed. Several departments need a continuous inflow of information that needs to be in sync with all other departmental operations. Although a skilled workforce can make this happen, it is time and labor-intensive.
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.
According to a report by the U.S. Bureau of Labor Statistics. The need for automotive and diesel technicians is expected to grow by up to 5% by 2028. The American Trucking Association estimates that there will be a shortfall of up to 175,000 truck drivers by 2026.
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.
This is especially great for technicians with unique ways of carrying out their tasks. AI 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.
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.
For example, AI-based fleet management platform Driveri, currently deployed in fleets across the country is a combination of all of these components. There are also many other AI-integrated fleet management systems with one or more of these components.
Before understanding how each of these parts combines to create a fleet management powerhouse, it is important to know what each one does.
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.
IoT for fleet management works through the use of 3 main technologies:
Machine learning technology allows fleets to learn from data collected over time and make managed adjustments based on that data. The result is the creation of smart systems in which AI can learn decision making capabilities that enable more effective handling of practical situations.
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