Automotive OEM Accelerates Root Cause Analysis with Structured Plant and Field Data
“By connecting plant and field data with explainable AI, a leading automotive OEM moved from reactive troubleshooting to proactive, data-driven reliability engineering, turning post-launch data into strategic advantage“
Sr. Development Engineer
Faster RCA
Reduced time to identify and resolve issues.
About the study
Once products enter production and the field, vast amounts of data are generated across manufacturing systems, test benches, and in-service operations.
However, this data is isolated within plant and field systems, unstructured, inconsistent in format, and difficult to connect to real-world performance issues, leading to challenges. Linking manufacturing variations to field failures was slow and complex. Root cause analysis (RCA), a process used to identify the causes of failures, required significant manual effort. High-impact failure drivers remained hidden in large datasets. Warranty costs increased due to delayed issue identification.
A solution was needed that would unify production and field data efficiently, enabling its engineering teams to extract meaningful insights that would improve reliability and reduce costs
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