Predictive maintenance technology helps maintain and monitor the health of the vehicle. IoT (Internet of Things) and connected vehicle solutions help predict and respond to the maintenance needs of the vehicle by analyzing the real-time and stored data as captured by the vehicle sensors. The predictive maintenance technology transmits the vehicle health information to the OEM, which alerts the driver about the action required.

The auto industry is evolving from preventive to predictive maintenance in order to reduce the hassle of unanticipated breakdown and reduce the failure cost. The cost-saving nature of predictive maintenance is driving the market. Predictive maintenance can estimate the downtime of the vehicle by collecting and analyzing data from various vehicle sensors, such as tire pressure monitor, throttle control and battery system and thus, alert the driver to attend to the maintenance need. An increasing number of commercial vehicle fleets are adopting predictive maintenance in order to avert unexpected delays due to vehicle breakdown. Fleet operators have to incur operational loss due to unplanned vehicle breakdown, which in turn is driving the demand of the automotive maintenance technology.

The predictive maintenance technology is expensive and requires the vehicle to be connected over the internet. Increased adoption and development of predictive maintenance technology is expected to lower the cost.

The global automotive predictive maintenance market can be segmented based on deployment, vehicle type, end-use, and region. In terms of deployment, the automotive predictive maintenance market can be segregated into embedded and smartphone app. The embedded segment is expected to account for a major share of the market by 2027, as the new vehicles sold are fitted with embedded connectivity systems. The embedded type predictive maintenance system is more expensive than the smartphone app type system; however, it supports more functions and hence, the embedded segment is expected to hold a major share of the market during the forecast period. Smartphones have been widely adopted by consumers, the smartphone enables vehicle connectivity and also supports predictive maintenance technology. The smartphone app type predictive maintenance system is robust and is being increasingly preferred by consumers as an aftermarket and cost-effective solution.

Based on vehicle type, the automotive predictive maintenance market can be bifurcated into passenger vehicle and commercial vehicle. The commercial vehicle segment can be further sub-segmented into light commercial vehicle and heavy commercial vehicle. Most new passenger vehicles sold have connected systems that are essential to support the predictive maintenance technology. Therefore, the passenger vehicle segment is expected to dominate the automotive predictive maintenance market.

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In terms of end-use, the automotive predictive maintenance market can be divided into personal use and commercial use. Commercial fleet operators, such as truckers and cab services, are adopting predictive maintenance technology to effectively map vehicle health and prevent downtime. Therefore, the commercial use segment is estimated to expand at a high growth rate during the forecast period.

Based on region, the global automotive predictive maintenance market can be classified into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Europe and North America are estimated to lead the global automotive predictive maintenance market due to high penetration of vehicle connectivity in these region

Key players operating in the global automotive predictive maintenance market include Robert Bosch GmbH, Rockwell Automation, Harman International Industries, Inc., and Delphi Tech.