Fleet management: Man vs machine
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Fleet management: Man vs machine

13 May 2021 (Last Updated May 13th, 2021 12:17)

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Fleet management: Man vs machine

Sounds like the stuff of movies, right?

Well, not anymore. Machine learning and artificial intelligence (AI) are becoming more widely adopted across industries of all sorts. Contrary to popular belief, its use is not to replace workers and jobs but rather to complement company operations.

One area where machine learning can play a big role is fleet management. Yet, surprisingly only 25% of transport operators in Australia are using Big Data analytics provided by telematics to help in making more informed decisions when it comes to managing their fleet operations. That means a massive 75% of transport operators have yet to tap into the benefits that machine learning and AI can provide in not only augmenting human decision-making with new insights and recommendations, but also in automating the decision-making process by removing the error-prone human component.

Augmenting decision-making

Today, people make decisions with a lot of assistance from data when it comes to managing fleets, Examples of this type of data include driving behaviour data, Hours-of-Service data, environmental conditions, and smart city data such as traffic and micro-weather forecasting.

Many fleets are using AI-based data mining tools to identify outliers, or to discover the relationships between different types of data to gain a better understanding of certain behaviours or outcomes. Data mining enables the analysis of data sets that are too large for people to analyse on their own. Equipped with this data, people can better consider business context and make informed, future-proof decisions.

An example of this might be an AI model that determines driver performance based on a series of events that occur over a time period, and then creates a driver scorecard that rates the driver’s safety. This type of AI model may provide scorecard details such as, “This driver tends to brake harshly in poor weather conditions.” A manager might then receive a recommendation that the driver undergo remedial training on how to drive safely in poor weather.

Another example where AI can help augment fleet managers’ decision-making is being able to understand the fuel economy of your fleet. Fuel is the second biggest operational cost for a fleet, after drivers. AI can help fleets see relationships and make correlations between a specific driver in a specific vehicle on a specific terrain. Using AI, you can see whether the driver is the problem, or if it’s the wrong vehicle for the terrain, or both. Once you know the correlation, you can change the variables to get a better outcome – in this case, better fuel economy.

The bottom line is that by augmenting decision-making with AI technology, managers can make better decisions without putting in a lot of extra effort and time.

Automating decision-making

AI can either assist with human decision-making or help to automate decision-making entirely to eliminate mundane tasks or simply save time.

To build on the example above, AI can not only help create driver scorecards, but also evaluate those scorecards to see where drivers may need help and then automatically provide it. Instead of waiting for a manager to intervene, a system could automatically send the driver a link to a remedial training course on driving in poor weather conditions. The system could send periodic reminders until the driver registers for and completes the course. If the driver does not comply, the system could even take the driver off the schedule and re-assign their jobs to other drivers.

AI-driven automated decision-making is also becoming more common for fleet maintenance issues. Many fleets today practice preventive maintenance – for instance, sending a notification or alert every 10,000 kilometres to change the vehicle oil to prevent engine problems. By analysing engine data, fleets can get more precise and more predictive than ever before. Not every vehicle needs its oil changed at 10,000 kilometres – where, how and how frequently a vehicle is driven all factor into the condition of the oil. Telematics data pulled from the engine and analysed can give a more precise picture and enable fleets to predict exactly when maintenance is needed to suit a particular circumstance.

Fleets can build AI models and workflows that automatically analyse engine data periodically, assess whether action is needed, schedule maintenance as needed, and take vehicles out of service for that maintenance. The workflow could even include automatically ordering parts and supplies.

You can see how such a process would lead to better efficiency, greater vehicle uptime and increased productivity. Preventive fleet maintenance is where AI really shines.

Another feature of AI that could help automate operations is facial recognition. In-cab cameras are a big growth area in telematics. According to Frost & Sullivan, the market for video telematics will grow by 22.2% between 2020 and 2025, to 3.2 million subscribers. Video telematics can reduce collisions by 60% and the related costs by 75%. Visual evidence collected by in-cab cameras is a powerful tool for insurance claims and driver safety training.

These in-cab cameras are not just for looking back on incidents, but can also provide important proactive safety benefits – models that can detect a driver falling asleep while driving or an imminent accident can trigger audible in-cab warnings that can help prevent a crash. Machine vision technology – powered by AI – can also be used to streamline driver ID and security processes. For instance, you could use facial recognition to unlock or start vehicles, or enable auto-login to your telematics system.

Getting started with AI

Most fleets don’t have the resources to find and hire data scientists to build their models. Fortunately, vendors are starting to incorporate AI features into their platforms, providing a data scientist in a box. This approach masks the complexity of AI, enabling fleets to take advantage of the technology without needing advanced knowledge of how it works.

Integrations between telematics vendors and vehicle OEMs are key. These integrations give telematics systems access to rich new data streams for fleets to capture and analyse, for enhanced insight and understanding that, again, will lead to better performance and lower costs. The growing use of 5G networks will be an enabler, allowing fleets to capture and convey large data sets over the air.

The more data you have, the smarter you can be about making decisions that improve performance, efficiency and most of all safety. Generating actionable insights will be the key to success in this brave new world of AI-enabled fleet management.

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