OEMs and Fleet Owners lack trusted and actionable insights about their fleet despite collecting large amounts of data.
Today’s Connected Vehicle platforms have limited ability to accurately translate data into timely actionable intelligence.
The solution lies in leveraging an AI/ML based approach to predict the maintenance needs of the vehicle or asset for maximum yield.
Such a solution helps improve asset uptime, reduces warranty cost and improves customer service.
OEMs and Fleet Owners collect large amounts of data, however, struggle to get meaningful insights that can drive business decisions to improve customer service and profitability. The challenge lies in usage of manual approaches to analyze data and reliance on historical knowledge and methods to extract insights.
An AI/ML based Remaining Useful Life methodology and solution which offers predictive model readiness across vehicle components can alleviate this situation. Fusion of predictive analytics and decision-making algorithms can help OEMs improve efficiency of their fleets and optimize the cost of operation.
Such a solution can improve vehicle uptime, reduce warranty and recall cost, improve quality, improve customer service and bring dynamic control on supply chain variables.