Intangles’ Co-founder and CTO, Neil Unadkat, writes for BW Disrupt. The article highlights the role of predictive analytics in making fleet operations more efficient and cost-effective. This knowledge of the mobility ecosystem is the fuel that drives us forward.
Using data collected from sensors or embedded devices on your fleet’s equipment, digital twins provide a visual representation of how the asset performs over time – essentially creating “digital models” for each vehicle/system component.
In a fast-paced and competitive automotive industry, fleet operators are constantly challenged with ensuring that their operations run efficiently and cost-effectively. However, maintenance costs account for up to 30% of operational lifecycle costs for many air, land, and sea fleets.
It is important to understand that a large fleet’s maintenance and repair costs can be considerable. For example, you can spend thousands of dollars on fuel for your fleet, but servicing these vehicles requires careful behind-the-scenes planning and operations to reduce long-term operational costs. The problem with traditional methods such as time-and-motion studies or visual inspections is that they give only part of the picture: what has been done vs. what needs doing next? What should we prioritize? And crucially, how much will it cost us if we don’t do these repairs now?
Modern digital technologies like AI (Artificial Intelligence) and ML (Machine Learning) are changing the fleet management landscape. These technologies can help identify and predict failures before they happen, optimizing service schedules and improving decision-making.
But why are AI and ML so important for fleet management?
First, AI and ML can help identify patterns and trends in data that would be difficult to see just by looking at raw data. By analyzing data from sensors on vehicles and equipment, weather, traffic, and other sources, AI can provide valuable insights on how best to operate a fleet.
Secondly, AI-powered decision-making can help reduce costs by predicting when maintenance is needed and allocating resources accordingly. A study by McKinsey states that predictive maintenance will reduce costs by 10-40%, downtime by 50%, and capital investment by 3-5%. For example, suppose a commercial vehicle needs a new part. In that case, an AI system may order it before the component fails – preventing an unscheduled outage that could cost the company thousands of dollars.
Finally, automation through AI can help in simpler recruitment processes. The U.S Bureau of Labor Statistics report shows that the need for automotive and diesel technicians is expected to grow up to 5% by 2028. However, this presents a deeper problem with onboarding and training. This is where AI comes into the picture. AI systems can develop a “skill profile” for each technician by analyzing past failures and repairs. This would allow companies to identify technicians with the right skill set for a particular job opening – even if the company does not currently employ them.
How does digital twin technology fit into the picture?
Digital twin technology plays a critical role in fleet maintenance. Digital twins are virtual representations of physical assets that improve both processes and products and monitor performance in real-time. Using data collected from sensors or embedded devices on your fleet’s equipment, digital twins provide a visual representation of how the asset performs over time – essentially creating “digital models” for each vehicle/system component.
Recent Global Market Insights suggest that the digital twin market-worth exceeded USD 5 billion in 2020 and is expected to grow at over 35% CAGR between 2021 and 2027. What accounts for this kind of growth? Digital twin technology is gaining momentum thanks to rapidly evolving simulation and modeling capabilities, better interoperability, and IoT sensors.
By monitoring components in real-time, digital twins help you know exactly when a potential failure will happen before it happens – helping reduce downtime across your whole operation. And by using AI systems to predict future failures based on historical data patterns, companies can also optimize their daily operations schedule more effectively. This will reduce the number of unnecessary road trips and prioritize urgent repairs.
Modeling the digital future
Fleet managers should adopt these technologies in order to stay ahead of the curve. As fleets become more complex, so too does the data that they generate. The challenge for fleet managers lies in making sense of this data and turning it into actionable insights. This is where advanced technologies like AI, ML, and digital twins can help you shine – allowing you to focus on your core business while they take care of monitoring your vehicles. This strategy is a win-win for both the company and its drivers, as it reduces the risk of breakdowns and improves driver morale by giving them more information about their vehicle’s health.