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Automotive · components · Catalonia
Predictive maintenance on stamping line
Model trained on 2 years of vibration and consumption data. Predicts failure 48-72 h in advance. Maintenance strategy change.
−30 % unplanned stops
Predictive maintenance, automated quality control, real planning.
We apply AI in Spanish industry for predictive maintenance, in-line defect detection, production planning and energy optimisation. With data from your SCADA/MES and no need to replace systems.
Every hour of stopped line has direct cost (lost production) and indirect cost (client commitments). Reactive maintenance is very expensive.
Operators visually reviewing parts. Fatigue, subjectivity, defects reaching client. Impossible to scale with growing volume.
Line scheduling depends on plant manager's criteria. Last-minute changes break everything. No global optimisation visible.
Peak consumption spikes, inefficiencies in idle times, impossible to identify which line/machine is the real problem.
Not a list of technologies. Concrete processes where AI delivers measurable euros or hours from the first month.
Models that detect drift patterns in vibration, temperature and consumption. Alert to maintenance team before machine fails.
In-line defect detection with cameras + AI. Traceability per part. Integration with line stop or automatic diversion.
Algorithms that assign orders to lines maximising OEE, minimising tooling changes and respecting delivery commitments.
Identification of anomalous consumption, parameter adjustment proposal and line balancing to reduce electricity bill.
Consolidation of SCADA + MES + ERP data for client reporting, ISO audits and internal analysis without manual work.
Chatbot with access to manuals, procedures and machine data. Responds in real time to operator queries on line tablet.
Ingestion from SCADA/MES, OEE dashboards, root cause analysis.
Info → 02Predictive, quality, planning and operator assistant.
Info → 03OT network protection and industrial systems, anomaly detection.
Info → 04SCADA-MES-ERP integration, automatic reports, cross-system alerts.
Info →Invented cases with metrics consistent with our real ranges, until a client authorises publishing their own.
Model trained on 2 years of vibration and consumption data. Predicts failure 48-72 h in advance. Maintenance strategy change.
Industrial cameras + vision model detect defective sealing, foreign bodies and labelling errors. Automatic diversion without stopping line.
Model that suggests parameter adjustments and furnace balancing. Integration with existing energy management system.
Our pilots incorporate from design the regulatory requirements that apply to the industry sector in Spain:
No. We work on your current SCADA and MES. We extract data via standard protocols (OPC UA, MQTT, Modbus, API integration or periodic exports). We don't touch your control system or its automata.
The AI layer deploys on top as a separate analytical layer, with read-only connection to SCADA in most cases. If the use case requires action (line stop, parameter adjustment), it's done via explicit integration with human validation or hard rules.
This is critical for OT safety: the AI layer can't bring down production if it fails.
Depends on the case, but rule of thumb: 6-12 months of historical data with at least 3-5 recorded failure/incident events per critical machine. With less history you can start with simpler anomaly detection models (without failure-type prediction) and refine as data accumulates.
If starting from scratch, the first quarter is instrumentation and clean data capture. In parallel you can deploy descriptive analytics (OEE dashboards) for immediate value.
The industrial camera installs parallel to the current process (mounted on structure, without touching line). First phase is capture and calibration with real product, without acting on line yet. Lasts 2-4 weeks.
Once the model reaches target accuracy (usually >97% on defined defect detection), action activates: visual/audible alert to operator, or automatic diversion of defective parts if applicable.
Full line stop by AI is reserved for critical defects and always with human review before resuming. Availability rules.
Predictive maintenance on a critical machine: 8-12 weeks, €20-40k. Typical ROI: first avoided stop usually covers pilot cost.
Vision for quality control on a line: 10-14 weeks including hardware, €30-60k. Typical ROI: 4-8 months in industry with high quality requirements.
Energy optimisation: 6-10 weeks, €15-30k. Typical ROI: first year electricity bill reduction covers cost with margin.
Free 30-min diagnosis. We come out with 3-5 use cases prioritised by ROI for your specific industrial plant.
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