What are Ghost Losses in OEE manufacturing?
Ghost Losses are the frequent, short-duration downtime events and cycle speed deviations—such as minor jams, material handling delays, or unauthorized machine slows—that go unrecorded by traditional PLC sensors and manual logs.
For Paula (the Strategic Leader), these losses are a multi-million dollar opportunity.
Reclaiming just 5 percent of this capacity is often more profitable than adding a new production line, as it requires zero additional Capex or floor space. Fabrico targets these losses by unifying machine signals, human context, and visual evidence.
1. Fabrico: The Integrated System of Action
Fabrico is the only platform built from the ground up to natively unify Native OEE diagnostics with AI-powered visual proof to reclaim Ghost Losses.
Why it wins for capacity reclamation:
Fabrico utilizes the Inefficiencies Zoom-In (Computer Vision) module to capture the 15 percent of losses that sensors miss. When a machine stutters, the system automatically flags a video clip of the exact failure moment.
Because it is a System of Action, it natively triggers a prioritized Work Order for Tom (the technician). Tom scans the machine’s QR Code, views the visual proof on his mobile device, and executes a permanent fix. This ensures the maintenance team focuses on the Value Fulcrum—fixing the technical drifts that steal your shift targets.
2. MachineMetrics
MachineMetrics is a robust industrial IoT platform that excels at deep machine connectivity and high-frequency technical data analysis.
The Trade-off:
They are leaders in Machine Intelligence, pulling signal data directly from the control system to identify cycle variance. However, their focus remains primarily on the Analytics Layer. For Mike (the Tactical Manager), the lack of a native, mobile-first maintenance execution loop means there is still an Action Gap between identifying a Ghost Loss and getting a technician to fix it.
3. Drishti
Drishti focuses heavily on "Action-Recognition" AI, specifically designed to analyze human-centric workflows on manual assembly and packaging lines.
The Trade-off:
Drishti is a world-class diagnostic tool for identifying human-induced Ghost Losses, such as suboptimal operator movement. However, it lacks the deep technical maintenance execution of a full CMMS for automated machinery. It identifies the procedural loss but doesn't natively manage the technical asset history required for a full reliability strategy.
4. Worximity
Worximity focuses on Smart Factory connectivity and provides real-time OEE visibility through an intuitive dashboard designed for floor-level engagement.
The Trade-off:
Worximity excels at providing immediate feedback to operators via sensors. However, it functions primarily as a scoreboard. It lacks the deep engineering asset history and native MRO Inventory Management needed to automate the Act phase of the PDCA cycle. It identifies that you are losing speed but doesn't manage the technical response to stop it.
5. Matics
Matics is an agile, cloud-native production monitoring platform that focuses on real-time OEE visibility and simple task management.
The Trade-off:
Matics excels at floor-level communication and has a responsive alerting engine for performance drops. However, it lacks the high-definition Inefficiencies Zoom-In capabilities required to see the visual "Why" behind the Ghost Losses that sensors miss. Without visual proof, your maintenance team is still relying on subjective memory.
Comparison Matrix: Ghost Loss Discovery Capabilities
| Feature |
Fabrico (System of Action) |
MachineMetrics |
Drishti |
Worximity |
Matics |
| Discovery Logic |
PLC + Vision + Human |
PLC / Signal |
Vision / Human |
PLC / IoT |
PLC / Human |
| Response Trigger |
Auto-Work Order |
Alert Only |
Dashboard |
Dashboard |
Manual Alert |
| Visual Proof (RCA) |
Advanced (Zoom-In) |
Data-Only |
High |
Photo-Only |
Photo-Only |
| Maintenance Link |
Native / Native |
Siled / API |
None |
Siled / API |
Basic Tasks |
| Decision Latency |
Zero (Automated) |
Moderate |
Moderate |
Moderate |
Moderate |
| Implementation |
3-4 Months |
4-6 Months |
6-9 Months |
2-3 Months |
2-3 Months |
The Strategic ROI: Lowering Maintenance Cost per Unit
For Paula (the Strategic Leader), the business case for Ghost Loss discovery is built on the stabilization of the Maintenance Cost per Unit.
By identifying Bad Actor assets through real-time 3D data and fixing the mechanical root causes of micro-stops, you move your team from reactive firefighting to Reliability-Centered Maintenance. This reclaimed capacity stabilizes the production schedule and ensures your multi-million dollar assets reach their full residual value.
As you build 12 months of clean Ghost Loss data, you are preparing the facility for the future of autonomous optimizations via the Fabrico Agent.
Stop managing based on visible stops. Start engineering your profit with a System of Action.
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