Visual downtime verification means the OEE platform watches the line with a camera, sees what happened during the stop, and lets the supervisor or maintainer replay 15 seconds of video around the loss event. No more arguments about whether it was operator behaviour, machine failure or material starvation. The video is the source of truth.
Quick answer: The best OEE software for visual downtime verification uses computer vision (CV) to record the machine's state when a stoppage is reported, so operators no longer have to type a reason. Fabrico's Computer-Vision OEE replays the 30 seconds before and after every downtime event and auto-classifies the cause. Result: downtime data accuracy jumps from ~40% (manual reason codes) to ~95% (CV-verified), and Pareto charts finally tell you the truth.
Related deep-dives: Computer Vision OEE field guide · OEE monitoring without PLC · automated downtime categorization · closing the OEE-CMMS loop.
Visual downtime verification means the OEE platform watches the line with a camera, sees what happened during the stop, and lets the supervisor or maintainer replay 15 seconds of video around the loss event. No more arguments about whether it was operator behaviour, machine failure or material starvation. The video is the source of truth.
This review compares the five platforms European mid-market plants shortlist in 2026 for real visual verification (not just dashboards over PLC data).
The honest bar: every stop event has a 15-second clip attached, watchable from the dashboard in under 30 seconds, ideally without operator confirmation. That clip resolves the categorization argument before it starts. Maintainers stop saying "the operator did it"; operators stop saying "the machine did it". The video says what happened.
Best for mid-market European plants that want visual verification from day one without a separate vision platform. Fabrico computer vision is the OEE engine, not an overlay, so every stop already has the video frames tied to it. Clip is one tap from the dashboard.
Realistic 2026 numbers: Year-1 TCO EUR 22k-70k for a 6-line plant fully loaded. Deployment 30 days. No PLC required. Read the OEE software pricing breakdown.
Best for plants that already run Tulip for operator workflow and want the camera as an add-on. Vision is a module, not the core, so video lookup is per-asset configuration. Strong app-building flexibility.
Realistic 2026 numbers: Year-1 TCO EUR 60k-180k for a multi-line plant including vision. Deployment 45 days.
Best for process industry plants where TrakSYS is already the OEE platform and vision is added on top for specific assets. Strong PLC depth, vision is bolted on. Read the full TrakSYS review.
Realistic 2026 numbers: Year-1 TCO EUR 150k-500k including vision overlay. Deployment 6-9 months.
Best for very large discrete plants that buy vision as a strategic capability, not a feature. Drishti specializes in assembly-line action recognition. Heavier deployment, deeper analytics.
Realistic 2026 numbers: Year-1 TCO EUR 250k-600k+. Deployment 60-90 days for pilot.
Best for plants where vision plus machine acoustic monitoring matter equally. Augury extends visual verification with sound-based anomaly detection. Niche but powerful when the use case fits.
Realistic 2026 numbers: Year-1 TCO EUR 80k-250k. Deployment 90 days.
Real concern. Fabrico, Tulip and Drishti all support privacy-preserving modes: pose detection without facial recognition, region-of-interest masking, GDPR-compliant retention. Works council buy-in is part of the 4-week pilot.
Sometimes. Fabrico and Drishti can ingest existing IP camera streams if resolution is sufficient. Tulip and TrakSYS typically prefer their own cameras.
Categorization tags the stop with a reason code. Visual verification produces the video clip that proves the reason. Best paired together.
Visual downtime verification ends the categorization argument. The video is the source of truth. Match the platform to plant size and complexity. Run the 4-week pilot. Drive resolved-without-dispute above 85% before you ship.
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