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The traditional car maintenance model is based on a break-and-fix or scheduled maintenance approach, which is largely reactive. Car owners and fleet managers take action only after a problem occurs, leading to elevated service costs, longer vehicle downtime and added frustration with the post-purchase experience.
For original equipment manufacturers (OEMs), a reactive maintenance model that addresses quality and safety issues only after they occur can result in supply chain constraints, frustration in the customer-dealer relationship and large-volume recalls.
Transitioning to a predictive maintenance model—enabled by the proliferation of sensor technology, rich vehicle data and powerful machine learning models—helps car owners and fleet managers avoid vehicle issues before they occur.
The global market for automotive predictive maintenance is expected to grow at a CAGR of 26.2% from 2023-2029 *1.
Predictive maintenance as a digital service is highly valued among car owners, who cite car maintenance and upkeep as a top concern *2.
There’s an opportunity to provide comprehensive predictive maintenance solutions via an ecosystem of partners that support both the vehicle and adjacent products and services.
“Now’s the perfect time for OEMs to invest in predictive maintenance, working together with fleet managers to unlock value by using AI to move beyond dashboards and leverage the right insights to optimize and grow their business.”
Amadeus Tunis , Associate Managing Director, Transportation & Mobility
Save on maintenance costs, optimize fuel and/or battery efficiency, avoid unexpected breakdowns, extend vehicle lifespan, Improve vehicle safety, Improve resale value & overall brand loyalty
Save on maintenance costs, increase technician efficiency, Increase operation uptime, optimize parts inventory, reduce roadside breakdowns, Improve labor effectiveness
Faster recall & quality issue detection, increased process efficiency, decreased warranty & repair costs, enhanced product design, improved customer service & customer loyalty, improved sustainability
Publicis Sapient has developed a proven machine learning (ML) model—which can be enhanced with OEM and third-party data—to provide actionable recommendations for car owners and fleet managers. In the early stages, we were able to predict vehicle failures with an accuracy of around 80 percent.
Fleet Manager is notified there is an issue with the vehicle and can schedule a repair appointment. When sensors determine an issue, registered vehicles are scheduled for repair during a preferred time. The appointment syncs with the driver’s phone calendar and a reminder is sent prior to the appointment. Navigation directs the customer to the dealership.
OEM/dealer is sent the parts needed, the repair instructions and the customer’s arrival time. The appointment is immediately synced with the dealership’s calendar. A check is made to see if parts are in stock, and if not, the parts are ordered and sent to the dealership prior to the appointment. Prior to the appointment, the dealer is sent issue details and repair instructions.
OEM runs analytics on aggregated vehicle sensor data to identify patterns. Analytics are run on all vehicle sensor data to identify patterns. Insights can be used to identify issues, provide vehicle health and driver reports, reduce fleet downtime, improve product design and enhance the customer experience.
See how Publicis Sapient helped a Global OEM leverage connected car data and machine learning to deploy a predictive maintenance solution.
The Predictive Maintenance Solution is part of Publicis Sapient’s Connected Commerce Toolbox—a suite of solutions designed to help Transportation & Mobility clients elevate their digital ecosystems.
Find out more by reaching out to our experts:
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