The year is 2015 and a news headline caught the attention of car aficionados around the globe: “Mr Bean crashes McLaren F1 twice, sells for $12.2 million”. The car here is not the lime green British Leyland Mini that makes Rowan Atkinson immortal for us, but the McLaren F1 Supercar, which the veteran British comedian had owned almost for two decades. Interestingly, it was reported by Auto Evolution that after the second crash, it took four weeks to calculate the repair costs of the car, which was a whopping USD1.4 million!
Spin forward to today, this four-weeks-worth of time for repair cost calculation sounds absurd. Digital technologies are gaining momentum and the automotive industry is undergoing a rapid change. For instance, in 2023, ShipCars.com, an auto logistics technology provider, launched the AI-powered SmartHaul TMS transportation management system, reducing vehicle inspection time by up to 60%, with full condition reports generated within 30 seconds. Moreover, soon after its launch, users of this platform were completing around 50,000 such inspections per month.
This blog explores how solutions enabled by AI and IoT are being deployed to support the service, maintenance and repair of vehicles at service centres and other locations. The observations made here are drawn from the “Enhanced Vehicle Assessment” domain of change discussed in Transforma Insights’ recent report Digital Transformation in the Automotive Industry, which includes an in-depth analysis of Enhanced Vehicle Assessment and eight other domains of change in the automotive industry.
There are two key ways in which disruptive digital technologies are enhancing the process of vehicle assessment and so improving the overall experience for drivers: smart vehicle condition inspection and smart service support.
Traditionally, vehicles have been manually checked for dents and physical damage and missing, worn or faulty parts at service or repair shops, which is both time-consuming and prone to human errors, resulting in undetected defects. Today, AI-enabled video analytic solutions are being increasingly deployed to support vehicle inspections. These systems use computer vision-enabled cameras to detect vehicle issues, thereby eliminating human error. This both reduces the requirement for manual work and increases the accuracy of fault detection and diagnosis. For instance, Findlay Cadillac partnered with UVeye and deployed scanners to capture high-resolution vehicle images, achieving an increase of 16% and 17% in the average revenue and the average gross profit per repair order respectively.
Key vendors for vehicle condition inspection include Clearquote, ProovStation, and Ravin. The vehicle inspection offerings of these companies is summarised below.
Drivers often face issues like flat tyres, dead batteries, electrical issues, or mechanical failures, and require roadside assistance. These days connected roadside assistance systems can be used, which provide near-real-time diagnostic information (such as engine status, error codes, tyre pressures, and fuel levels). Moreover, vehicle tracking helps to identify the exact location of any breakdown. Together these data enable technicians to arrive at the exact location equipped with the necessary tools and parts. Some automakers, dealers, and service centres are also using AR and VR to remotely support vehicle breakdowns, reducing the need for specialist technicians to be physically present, saving time and enhancing the customer experience. For example, in November 2021, MHS Truck & Bus equipped its technicians with RealWear’s HMT-1 ruggedised wearables, which connect remote expert technicians with a wearable device. This helped the dealer to reduce diagnosis and travel time by 75% and 50% respectively.
New technologies like artificial intelligence have transformed vehicle assessment by enabling faster and more accurate diagnostics. These innovations enhance efficiency, reduce costs, and improve overall vehicle safety. As technology advances, digital assessments will continue to drive smarter maintenance and result in a more transparent, data-driven automotive ecosystem.
