Thinking about the future, the first things that come to mind are artificial intelligence and machine learning. Could predictive big data models aid automation in the automotive industry? Or even more, could artificial intelligence control autonomous vehicles without the babysitting of an actual human being? The latter is rather far and autonomous driving would most likely not steal the job of our drivers this decade. Big data analytics and integrating complex algorithms can decrease maintenance costs, operation costs and optimise all fleet operations.
At the current rate of new technologies advancement AI systems in the automotive industry aim to improve driver safety, moderate driver behaviour, implement learning systems and use predictive models to give optimal suggestions for a fleet business.
How Will Artificial Intelligence Affect Fleet Management Systems?
According to a recent study, only a quarter of the Australian businesses that operate fleet businesses use big data analytics and machine learning algorithms to form predictive models and assess the situation of their fleet in real-time. The majority of fleet managers use telematics in combination with the traditional fleet management systems to track the different data points of their fleets.
Business decision-making can sometimes be hard and takes huge amounts of accurate data. Integrating a machine learning algorithm that picks out the relevant information might be crucial to the success of your business. Smart fleet management takes big data collection to the next level as all sensors across all your vehicles get stored in a large database from where you can compare all vehicles with each other and with the rest at once. A well-developed algorithm can help you automate that process and do that instead of you. Moreover, it can determine which business vehicles are lagging and in which regards. Predictive analytics based on deep learning can foresee where possible driver behaviour might need changing for the optimisation of your fleet.
Many businesses in North America such as Amazon have already made a large capital investment in data analytics and artificial intelligence. Amazon already uses GPS in combination with machine learning-based telematics that provides traffic insights to the drivers in real-time. Moreover, they are able to predict some future risks, such as weather conditions, traffic volume depending on the time of the year and suggest better routing.
Machine learning algorithms can allow for a fleet management system to deal with certain data sets faster and predict trends across fuel usage in your fleet. Predictive maintenance is also something that comes along with predictive big data algorithms.
How Does Machine Learning Improve Fleet Management Systems?
Machine learning improves how data analytics systems interpret big data. In case you are using a machine learning-enhanced fleet management system, the system would start learning which data is the most frequently checked on your day-to-day use and adjust itself in real-time according to your needs. For example, if you check fuel expenses the most, this is the first thing it will put on your desktop when you open the application.
Such machine learning-based algorithms allow the development of advanced dashboards with many functionalities that facilitate the display of different data points like the downtime of each vehicle and certain driver behavior that needs to be changed.
Companies like Tesla, Amazon and IBM, that base their progress on new technologies, have already made great capital investments in creating sophisticated neural networks that would alert drivers if the vehicle is in need of maintenance or is about to experience technical difficulties.
Fortunately, fleet management systems that imply artificial intelligence and machine learning in their alerting algorithms operate in a similar way. Aside from alerting the driver, any potential issues with your vehicles would be shared with you as a notification, via email or an SMS, depending on your choice.
Smart fleet management systems do a lot more in terms of diagnostics than traditional ones. With the ability to get the big picture of your entire fleet at one place is essential for business decision making. A report by McKinsey named “Unlocking the potential of the Internet of things“, states that predictive maintenance owed to new technologies, machine learning and AI systems can lead to the decrease of overall costs up to 40 per cent.
Get a Competitive Advantage with an Improved Fleet Management System
Although we are far from allowing self-driving cars on the roads, machine learning and artificial intelligence allow automation in the automotive industry that can give us the edge over our competitors. A well developed fleet management system with adaptive tracking of data points can give us the exact information on how to optimise our business and achieve a higher level of decision making.
The early adopters of new technologies lead the way toward improving until all their competitors are forced to either adapt by doing the same or get pushed out of the market. Currently, the ones who have already applied these methods to their fleet operation have seen increased productivity, greatly reduced downtime, and have driven administrative costs down. And although it might seem like a big capital investment at first, one-time payments pay for themselves over time with a large return as an improved fleet management system would drive down your overall costs by a wide margin.
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