Predictive Maintenance
Motability Operations wanted to use connected vehicle data to predict breakdowns before they happened so it could reduce disruption for disabled customers, improve service quality, and prove out a scalable predictive-maintenance architecture.
Reported impact
In a 6,500-vehicle pilot, Motability Operations predicted 49 percent of breakdowns, corrected seven major faults, saw less than 0.5 percent customer opt-out, and improved customer experience by intervening proactively before failures occurred.
Motability Operations ran a predictive-maintenance pilot on AWS to use connected vehicle data more proactively across the Motability Scheme fleet. Working with AWS Professional Services, the team spent six months collecting and training data for foundational ML models, then used Amazon SageMaker to build models that predict whether a vehicle is likely to break down in the near future. The AWS-based approach relied on serverless and managed services so the team could focus on business value instead of infrastructure and security complexity. In practice, vehicle telemetry and maintenance context flow into the ML pipeline, predictions identify likely failures ahead of time, and operations teams intervene before breakdowns happen. This created an early proof point for AI-driven fleet support that can improve customer experience at national scale.
Connected vehicle data is collected into Motability Operations' predictive-maintenance workflow, used to train Amazon SageMaker models, and converted into predictions that help teams intervene before vehicles break down.
Less than 0.5 percent of people chose to opt out.
Motability Operations spent 6 months collecting and training data for its solution’s foundational ML models.
The pilot consisted of 6,500 vehicles. In total, 49 percent of breakdowns were predicted, and seven major vehicle faults were corrected.
To create these models, Motability Operations used Amazon SageMaker.
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