AI-Driven Failure Analysis SystemCash Positioning & Forecasting – Multi-Entity Medical Device Group
The Challenge:
A particle-counting equipment manufacturer analyzed 200+ test failures monthly, with engineers manually reviewing logs, images, and sensor data. Root cause identification took 6-8 hours per failure, delaying corrective actions and extending development cycles.
The Solution:
Deployed Azure AI Vision and Language models via Copilot Studio to analyze failure data, identify patterns, and suggest root causes based on historical failure database. Power BI dashboards tracked failure trends and recurring issues across product lines.
Result:
Failure analysis time reduced from 7 hours to 45 minutes, recurring failure patterns identified 3x faster, and mean time to resolution decreased by 55%, accelerating product release schedules by 4 weeks on average.