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Statistical analysis of vehicle data has been seen to yield great value in delivering higher efficiencies in vehicle maintenance. With insights into vehicle health and indicators to help predict when a part might need replacement or a vehicle need service, predictive maintenance is a rapidly evolving concept that’s based around connected vehicle data.

Cloudera’s analysis of a McKinsey research claims that IoT can reduce automotive equipment downtime by as much as 50%, and reduce maintenance costs by up to 10%-40%. Backing the fact, Capgemini forecasts the predictive maintenance space to gain strong popularity and high adoption rate within the auto industry in the coming years.

Fundamental Working Process

Data generated by connected vehicles, through onboard sensors and OEM-clouds, constantly monitors different aspects of the vehicle. Everything from data related to vehicle movement, health indicators, passenger data and more is tracked. AI-powered solutions then analyse this vehicle data to identify potential problems with the vehicle components, schedule parts maintenance to avert malfunction or make usage recommendations to the driver.

What makes predictive maintenance a sought-after proposition?

The core philosophy behind predictive maintenance revolves around identifying when the vehicle is in need of maintenance and what activities need to be performed based on the actual condition, rather than on a fixed schedule. Factors that highlight the need for this concept include:

  • Performing maintenance activities at the very last moment costs valuable resources, time, and material, often resulting in part replacement.
  • Unplanned part failure and malfunctions result in big losses for both the customers and the OEMs, apart from low productivity.
  • Predictive maintenance offers a way out of reactive to proactive maintenance model, preventing redundant work and unnecessary efforts.

Top Benefits of Automotive Predictive Maintenance

When service and maintenance providers take a step towards enabling automotive predictive maintenance, a range of benefits get unlocked.

  • Reduced Asset Downtime

Predicting, and thereby preventing breakdowns improves the overall vehicle uptime for the customer that directly impacts their driving experience. Reduced interruptions are observed leading to accuracy in performance, safety of both the passengers as well as the vehicle components, and reduction in maintenance costs.

  • Optimized Maintenance

Maintenance efforts and time gets significantly reduced through accurate pre-scheduling of service appointments, expediting repairs and stocking up inventories proactively.

  • Extended Remaining Life

Predictive maintenance services make it easy for technicians to identify remaining life of assets, such as vehicle components, battery life, tire tread and more, while providing services to further extend their lifespan and performance.

  • Remote Diagnostics

Anticipating maintenance and repair services need by a vehicle allows service providers to remotely analyse historical data, troubleshoot repeat issues and convey precise information.

Some Use Cases and Applications

Data-powered predictive maintenance has a wide range of applications.

  • Real-time monitoring of on-road vehicles by maintenance service providers to prevent and assist with breakdowns.
  • Real-time access of diagnostic codes generated by vehicle engines, to maintenance providers.
  • Tire companies offering innovative solutions such as Tire-as-a-Service, with monthly fee for service cover and warranty against damages, instead of a high upfront premium.
  • Maintenance and warranty service providers access connected vehicle data to perform remote diagnostics.
  • Availing roadside assistance to customers based on data from malfunction indicators, such as fuel level, tire pressure, air temperature, oil temperature, engine temperature and more.

Unlock Data-Driven Maintenance Opportunities with CerebrumX

At CerebrumX, we are committed to assist you in developing future-ready predictive maintenance solutions that’ll help pave the path for sustainable driving solutions and safe experiences. We bring the power of industry’s first AI-driven Augmented Deep Learning Platform (ADLP) to ingest mobility and telematics data from millions of connected vehicles, enrich it using advanced AI signals and data standardization process, to generate predictive insights for you to act on.

We adhere to global privacy regulations to maintain driver confidentiality and offer a white label consent management platform, CerebrumX Secure Consent, that can be easily integrated within your brand’s existing web/app architecture.


For more information regarding connected vehicle data and the evolving ecosystem around it, Contact us or reach out to our experts.