Automated CAPA trending and deviation detection across 12 manufacturing lines
An FDA inspection found 'systemic weaknesses' in their CAPA process. The 483 observations were embarrassing. The quality team knew the problems existed — they just couldn't see them across 12 manufacturing lines generating 50,000 data points per day. The countdown to a potential Warning Letter had started.
This surgical device manufacturer in RSM ran 12 production lines making 40+ device variants. Quality data lived in 6 different systems. CAPAs were tracked in spreadsheets. Trending was done quarterly — which meant problems that developed in January weren't visible until April's review. The quality team of 22 was buried in documentation and couldn't see the forest for the trees.
We built a unified quality intelligence platform that ingests data from all 6 quality systems in real time. AI models detect trending deviations before they become systemic — identifying patterns across production lines, shifts, materials lots, and operators. Automated CAPA recommendations include root cause suggestions based on historical corrective actions. Our overnight team monitors production data from the night shift and flags emerging quality signals.
The AI caught a sterilization temperature drift that was affecting one product line on third shift only. It had been happening for weeks. In the old system, we wouldn't have seen it until the quarterly review — by then, 8,000 units would have been affected.— VP of Quality Assurance
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