By the time a weekly production report shows a work center running behind, the bottleneck that caused it has usually already moved somewhere else. Real-time bottleneck detection watches machine state, cycle time and queue length continuously, flagging the constraint as it forms rather than after a week of output is already lost. An hour lost at a true bottleneck is an hour lost for the entire plant — there is no making it up downstream — which is exactly why catching it live matters more than catching it in a report. You can see your current bottleneck live on your own line with our team.
The Bottleneck Moves. Your Monitoring Should Move With It.
iFactory, an on-premise AI platform for production monitoring, tracks machine state, cycle time and queue depth continuously across every work center, flagging the active constraint the moment it forms.
What Does Real-Time Bottleneck Detection Actually Measure?
Real-time bottleneck detection identifies which work center is currently limiting total line output by watching live signals — cycle time against standard, queue length building upstream, and whether downstream stations are starved waiting on it — rather than inferring the constraint from a report compiled after the shift ends.
Four Live Signals That Reveal the Active Constraint
Why the Bottleneck Rarely Stays in One Place
| Cause of movement | What changes |
|---|---|
| Product mix shift | Different SKUs load different stations unevenly |
| Maintenance event | A fixed station's capacity drops, shifting the limit elsewhere |
| Staffing change | A station loses an operator and its effective rate falls |
| Upstream fix applied | Solving yesterday's constraint reveals the next one |
Find Out Where Your Bottleneck Is Right Now
Share live or recent machine-state data from one line and see which station is actually limiting output today.
Book a Bottleneck Detection Review Ask About Sensor RequirementsWhat Catching a Bottleneck Live Is Worth
A Swiss precision parts manufacturer facing growing delivery delays found, through a constraint analysis, that a single manual quality inspection station was the actual limit on the whole line — not the modernized machining centers everyone suspected. Once the team protected that station with a buffer and stopped over-optimizing stations around it, production throughput increased 25% within three months (industry case analysis, 2026).
What a Production Engineer Would Check First
What Production Teams Ask About Real-Time Bottleneck Detection
Catch the Bottleneck While It's Still Costing You Output
Book a session with iFactory AI to set up real-time bottleneck detection on your line and see the constraint as it forms.
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