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Why Machine Learning Is The Future Of Fleet Management

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When you think about artificial intelligence (AI) and machine learning (ML), you might jump straight to images of advanced robotics and Hollywood films. In reality, these technologies aren’t designed to supersede workers, but rather, enhance the overall working experience.

In recent years, AI and ML have slowly crept into industries across Australia and the world, helping businesses accelerate their digital transformations and realise benefits previously unseen. However, the technology has only just started gathering strength across the transport sector.

Designed to tell you what you don’t know, AI and ML streamline manual processes, deliver valuable insights and help businesses of all sizes be more productive.

Valuable insights at your fingertips

Many transport operators track their fleet activities via telematics and fleet management solutions.  Yet according to last year’s Telematics Benchmark Report, only a quarter of Australian transport operators are using big data analytics provided by telematics to guide business decision making. Due to the volume of data being collected from on-board sensors, satellite tracking and IoT devices means operators of larger fleets may struggle to find the data that’s most relevant to them.

Businesses can use ML-enabled telematics systems to guide their employees in real-time. For example, data such as road and traffic conditions, weather and environmental hazards can be leveraged to predict and anticipate incoming risks, allowing back office workers to guide drivers through dangerous conditions as they unfold.

By instantly sifting through big data to find the most relevant nuggets of information, fleet management solutions that leverage machine learning technologies also help drivers prepare for any unexpected events, while helping businesses improve customer service. Likewise, ML-enabled telematics can analyse mass amounts of data to predict trends around fuel use and speeding to help your business stay safe and save money in the long run.

Automatic anomaly detection

ML is all about how a system refines its interpretation of big data. As you use an ML-enabled fleet management system, the solution itself begins growing with you and ‘learns’ your key habits, what data you view the most, how long it takes drivers to complete certain tasks and much more.

Before you know it, ML-enabled telematics solutions will be able to automatically sift out anomalies within driver or vehicle behaviour based on your previous trends, alerting the business of an unusual change, such as a sudden spike in speeding violations or idle time.

ML-enabled systems feature advanced dashboards that offer a visual display of the data collected, allowing you to easily identify anomalies and drill into data. You can also further explore these insights by adding more parameters into the dataset to see find out more about what is happening and why these changes are occurring and to view possible solutions.

Improved maintenance decisions

As machine learning continues to transform various sectors and industries around the world, the technology is already being harnessed by businesses such as Tesla to generate sophisticated neural networks within vehicles that automatically detect and alert drivers when there’s a technical error in the car, or something requires a check-up.

AI and ML-based fleet management solutions work like this too, alerting fleet operators of any potential issues with vehicles. This gives operators and their mechanics plenty of time to diagnose the issue and fix the fault before it becomes a serious liability and puts a driver in danger.

Engine management and vehicle performance data is integrated in real-time and sent straight to the monitoring dashboard, allowing those in the back office to pull up fault codes and create an all-around view of each vehicle, its performance and condition without even needing to physically see the asset.

Most importantly, smart fleet management systems save businesses time diagnosing issues and give operators a clear picture of their fleet, while also offering efficient and cost-effective solutions to potential vehicle faults. According to a report from McKinsey, predictive maintenance technology helps to reduce overall costs by up to 40 per cent.

Moreover, businesses can achieve a competitive advantage and stay ahead of the curve by adapting AI. The substantial uses of AI are dedicated on feature additions that allow businesses to grow quicker than their competitors and in turn, become more favourable for procurement and contracts.

The future of Australia’s transport industry is a machine learning-enabled one, helping fleets proactively avoid issues as they occur, providing valuable insights and reducing mechanical faults.

Transport operators that adopt these innovative technologies now will see increased productivity, reduced downtime and slashed administrative costs, all while identifying opportunities for improvement and helping you stay ahead of the competition.


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