In the race for world-class integrated OEE and CMMS results, many manufacturers believe that a PLC connection is the "gold standard" for data accuracy.
While PLCs are excellent for timing, they are notoriously blind to the manual inefficiencies and subtle process drifts that define the Hidden Factory.
To achieve 100% visibility in 2026, you must move beyond signal-based tracking and implement a unified System of Action that adds visual intelligence to your production heartbeat.
Want OEE captured straight from your machines, no manual logs?
See it livePLCs provide the "When"; Computer Vision provides the "Why." Signal data identifies a stop, but visual proof identifies the root cause.
"Ghost Losses" are invisible to sensors. Micro-stops lasting under 30 seconds are often filtered out by PLCs but accumulate into massive revenue leaks.
Hybrid connectivity is the new ROI standard. Combining machine signals with AI-powered video is the only way to eliminate the "Blame Game" on the shop floor.
Computer Vision OEE is an advanced production monitoring technology that uses AI-powered cameras to visually identify, categorize, and record production events, such as micro-stops, jams, and operator delays, that traditional PLC sensors often miss or mislabel.
For Mike (the Tactical Manager), relying only on PLC signals is like listening to a heartbeat without being able to see the patient.
A sensor can tell him that Line 4 stopped, but it cannot tell him that an operator was struggling with a specific material batch or that a guide rail was vibrating.
Fabrico’s Inefficiencies Zoom-In module bridges this gap, providing the visual truth needed to trigger a permanent maintenance cure.
High-speed Food & Beverage and Plastics lines operate at such high frequencies that mechanical failures often happen in the blink of an eye.
Traditional sensors are programmed with "debounce" timers to prevent noise, which means they often ignore micro-stops that are shorter than two seconds.
These "Ghost Losses" represent the 10-15% of capacity that Paula (the Strategic Leader) needs to reclaim to hit her revenue targets.
Without the visual layer of the Visibility Trifecta, these losses are categorized as "Unknown" or "Minor Stoppages," leaving your Continuous Improvement team with zero actionable evidence.
The true power of a System of Action is the speed at which it moves from identifying a visual fault to executing a mechanical fix.
When Fabrico’s Computer Vision module detects a recurring jam, it doesn't just log a performance loss.
Detection: The AI camera identifies a bottle tip that the PLC categorized as a simple "Stop."
Verification: The system flags a 10-second video clip, allowing Mike to see the exact mechanical friction.
Action: The integrated OEE and CMMS triggers a prioritized Work Order for Tom (the Technician) with the visual proof attached.
This ensures your maintenance team focuses on the Value Fulcrum, fixing the root causes that actually drive Availability.
| Capability | PLC-Only OEE | Computer Vision OEE | Fabrico (Hybrid System of Action) |
| Accuracy (Timing) | Absolute / High | Moderate | Absolute (PLC Baseline) |
| Root Cause Depth | Data-Only / Guesswork | Visual Only | Advanced (Visual + Signal) |
| Micro-stop Detection | Filtered / Low | High | Absolute (100% Visibility) |
| Manual Line Support | None | High | High (Hybrid Support) |
| Maintenance Link | Manual / Siled | None | Native Integrated CMMS |
| Decision Latency | Moderate | High (Human Review) | Zero (Automated Triggers) |
For Paula, the business case for a Hybrid System of Action is built on "Capacity Reclamation."
By capturing the losses that sensors miss, you effectively find "free revenue" in your existing equipment.
Moving to Condition-Directed Tasks based on visual evidence reduces your Maintenance Cost per Unit because you stop fixing parts that aren't the problem.
As you gather 12 months of clean visual and signal data, you are preparing the facility for the Fabrico Agent (AI Roadmap) to automate these diagnostic cycles.
Stop guessing why your machines are stopping. Start seeing the truth with a System of Action.
Whichever source you start with, both ultimately feed the same AI OEE software layer, so this is a sequencing decision rather than a permanent one.
Turn downtime into a number your team can actually act on.
Get a demoNeither is universally better, they measure different things. A PLC reports what the controller believes; a camera reports what physically happened. On a fully automated line the controller is usually enough. Where product moves by hand, or the controller only knows running and stopped, vision sees more.
When the equipment already exposes the signals you need, when cycle counts are reliable at the controller, and when the environment makes a stable camera view difficult. It is also the lower ongoing maintenance option once wired.
On manual and mixed stations, on older equipment with no usable data out, and when you need to know why a stop happened rather than only that it did. It is also faster to trial, because it does not require touching controls.
Yes, and it is the common end state. The controller drives availability where it is reliable, and vision fills the unwired or manual sections so the OEE figure covers the whole line instead of the automated part of it.
Vision, in most cases, because mounting a camera does not require a controls change window or a PLC programmer. That speed is the main reason it works well as a first proof before a wider rollout.
An automated optical inspection system and an OEE vision setup get grouped together because both use cameras, but they answer different questions and are usually bought by different people.
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