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Industrial — Warranty transformation

Context​

An industrial OEM faced rising warranty costs, slow investigations, and limited visibility across design variants, supplier batches, and real-world operating conditions. Data existed, but it was fragmented.

Our approach​

We established a navigable digital representation connecting design (BOM, variants), manufacturing (batches, stations), operating conditions (loads, environments), and service (symptoms, actions, outcomes). This enabled analytics and decisions to flow through the same context engineers use.

What we built​

  • Integrated graph view across design, manufacturing, usage, and service
  • Quality metadata (recency, validity, consistency) on entities and relationships
  • Failure clustering and claim risk scoring pipelines
  • Outlier detection on supplier lots and process steps
  • Dashboards and workflows for engineering and service

Outcomes​

  • Faster root cause isolation and targeted corrective actions
  • Lower "no fault found" rates and reduced unnecessary replacements
  • Negotiation leverage with suppliers via evidence-backed insights
  • Warranty policy tuning that balances cost and customer experience

Why it works​

  • Digital representations preserve context and causality across domains
  • Fit-for-purpose quality thresholds and human-in-the-loop keep decisions safe
  • Iterative improvements compound as signals and feedback loops strengthen