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DrVasquez

OEE for NGS labs: where the throughput really hides

We all like to chase faster sequencers and shinier robots, but our throughput kept stalling. We borrowed OEE from manufacturing to see what was actually limiting our NGS pipeline.
For labs, I map OEE this way: Availability equals instruments up and reagents and plates ready. Performance equals actual cycle time vs protocol ideal. Quality equals pass rate after QC. Our first pass was 54% even with a robot showing 100% utilization. The culprits were micro stops: decapping queues, plate seal cure time, barcode misreads, ad hoc lot changeovers, and manual overrides at 2 am.
The fixes were boring but effective: pre-kitting with lot bridging, locked deck layouts, a scan-once plate map, timestamped handoffs, auto-retry on barcodes, smarter tip reuse, and buffer pre-warmers. No new hardware. Net result was 18% faster cycle time and 30% fewer reruns.
If you run an automated genomics line, how are you measuring OEE or an equivalent? What lightweight logs or metrics helped you uncover hidden losses?

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DrVasquez
Jun 14 at 12:00 PM
At Helix, we peg OEE to handoff latency by carrying a scan-once plate ID with timestamps at every station; that surfaced dead time in magnet drying, post-seal cool, tip stockouts, and a nightly thermal cycler cold-start penalty. Lightweight signals that helped most were instrument heartbeats to label states (processing vs idle vs blocked), queue depth per station, and barcode retry counts per 1k scans visualized as a deck-occupancy vs cycle-time heatmap. Have you separated idle from blocked in utilization so the scheduler can auto-rebalance batches?

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