In high-speed production, the greatest threat to profitability isn't the catastrophic eight-hour breakdown; it is the "Micro-Stop." These minor interruptions,lasting less than two minutes,are frequently excluded from manual logs but represent a massive, unrecovered loss in integrated OEE and total output.
See our roundup of the analytics layer this technique depends on.
Micro-stops are the "Silent Killers" of OEE, often responsible for a 10-15% drop in performance that goes unrecorded by traditional systems.
Turn downtime into a number your team can actually act on.
Get a demoPLCs identify when, but not why. Without visual context, technicians are left guessing at the root cause of recurring jams.
Computer Vision (Zoom-In) transforms ambiguous downtime into actionable improvement tasks by providing "Replay" intelligence.
Why do traditional OEE systems fail to reduce micro-stops?
Traditional OEE systems rely on PLC signals that only register "Running" or "Stopped." They lack the contextual intelligence to explain if a stop was caused by a material defect, an operator adjustment, or a mechanical misalignment, leading to a perpetual cycle of "firefighting" without resolution.
In a packaging or bottling plant, a micro-stop occurs so frequently that operators often view it as part of the machine's "personality." This is a dangerous trap. For Mike, the Maintenance Manager, these stops are symptoms of underlying "Bad Actor" components.
Without Integrated OEE and CMMS, these symptoms never escalate into a permanent fix. They remain "Performance Losses" that frustrate the team and bleed the bottom line.
Fabrico bridges the "Context Gap" by utilizing Computer Vision (CV). While a sensor can count a bottle on a conveyor, it cannot tell you why the bottle tipped over.
Visual Evidence over Text Logs: When a machine stops, Fabrico automatically flags the timestamp and clips the video footage from the overhead camera. This allows Mike to "Zoom-In" and see exactly what happened, no interviews required.
AI-Suggested Tagging: By learning from historical data, the system suggests likely causes (e.g., "Infeed Jam" or "No Material"). This eliminates "Pencil Whipping" where operators simply select "Unknown" to save time.
The "Value Fulcrum" for Maintenance: Once the micro-stop is identified as a recurring mechanical issue, Fabrico’s System of Action can trigger a follow-up task, and the team opens a prioritized Work Order from the same machine record. We prioritize the fix based on its impact on Effective Runtime, not just the order in which it broke.
| Feature | Basic OEE (Scoreboard) | PLC-Only Tracking | Fabrico (OEE + CV + CMMS) |
| Micro-stop Detection | Manual/None | Automated (When) | Automated (When & Why) |
| Root Cause Analysis | Guesswork | Log-based | Visual "Replay" Evidence |
| Operator Input | High/Manual | Low | Low (AI-Supported) |
| Maintenance Link | Disconnected | Manual Trigger | Native (Auto-Work Order) |
| Ideal Industry | Low-Volume | Standard Automation | High-Speed (FMCG/Food) |
For Paula (the Strategic Leader), micro-stops are a direct drain on Maintenance Cost per Unit. If your machines are stopping 40 times a shift for two minutes each, you are losing over an hour of production daily, roughly 12.5% of your capacity.
By implementing a Field-Ready CMMS that is natively tied to these micro-stops, you reclaim this capacity without buying a single new machine.
Reduce Scrap Rate: Many micro-stops result in "Start-Stop" quality defects. Stabilizing the flow reduces the volume of bad parts.
Optimize SMED (Single-Minute Exchange of Die): Use Computer Vision to analyze changeovers and identify "Waste Motion" to get the line back to full speed faster.
Standardize Tribal Knowledge: When the Assistant (Roadmap) sees a recurring fault, it provides the technician with the exact SOP to fix it, ensuring the "Night Shift" fix is as good as the "Day Shift" fix.
Stop guessing why your machines are stopping. See the truth, fix the cause, and reclaim your OEE.
See how Fabrico unifies OEE and maintenance in one platform.
Book a demoThey are individually trivial, so they never trigger a corrective action, and each one is cleared by a person rather than logged. Without automatic measurement there is no data to build a case on, so the loss stays invisible while everyone stays busy.
Usually material presentation, minor misalignment, sensor sensitivity and changeover leftovers rather than mechanical failure. They are tuning problems, which is why they respond well to small fixes once you know where they cluster.
Group by station and by reason, then rank by total time lost rather than by count. A frequent, quick stop can matter less than a rarer one that takes six minutes to recover from.
If the controller already reports true run state at the resolution you need, use it. Vision earns its place where the stop happens in a manual area or where the PLC reports only running and stopped. See the comparison.
That depends entirely on how much of your performance gap is short stops, which is why the honest first step is measurement rather than a target. Book a demo to see how the detection works on your line.
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