An Automotive OEM Accelerates Root Cause Analysis with Structured Plant and Field Data

Why Root Cause Analysis Takes Too Long in Post-Launch Manufacturing

A leading global automotive manufacturer known for performance and engineering excellence generates vast amounts of data once products enter production and the field - across manufacturing systems, test benches, and in-service operations. In principle, this data contains the information needed to identify failure root causes quickly. In practice it is isolated: plant data, warranty records, and test bench data live in separate systems. Linking manufacturing variations to field failures requires connecting all three - a manual process that is slow, error-prone, and dependent on individual engineers.

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Root cause analysis required significant manual effort. High-impact failure drivers remained hidden in large datasets. Warranty costs increased due to delayed issue identification. The OEM needed a solution that would unify production and field data and enable engineering teams to extract meaningful insights systematically - not as a months-long investigation, but as a repeatable analytical capability.

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Explainable AI on Unified Plant and Field Data

Key Ward was used to connect, structure, and analyse plant and field data within a unified environment. Manufacturing and field data were merged into structured, queryable datasets. Machine learning models were applied to identify failure patterns and quantify the impact of key variables. Dimensionality reduction and advanced analytics revealed the production variables most strongly influencing field performance. Hidden patterns and anomalies were uncovered via unsupervised learning without requiring predefined labels.

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Explainable AI outputs - including feature impact analysis showing how much each variable affects the prediction - enabled engineers to understand, not just predict, failure drivers. Instead of manually searching for correlations, the team systematically identified root causes. The shift: from reactive troubleshooting to proactive, data-driven reliability engineering, turning post-launch data into strategic advantage.

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What the Full Case Study Documents

The full case study documents the data unification architecture, the explainable AI model structure, the identification methodology for high-impact failure parameters, and the measured outcomes: faster RCA, reduced warranty claims and associated costs, improved production quality, and increased product reliability. If your team is managing post-launch reliability with disconnected plant and field data, the approach documented here is directly applicable.