Production Line Optimization

Challenge‍

A manufacturing facility was experiencing frequent unplanned downtime (8% of capacity) due to equipment failures, causing production delays and increased maintenance costs. Their reactive approach to maintenance was inefficient and costly.

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Solution

An AI predictive maintenance system with early warning alerts using Obviously AI and n8n that:

• Monitored equipment sensors and operational data in real-time
• Identified patterns preceding failures based on historical maintenance records
• Predicted potential failures days or weeks before they would occur
• Generated prioritized maintenance tasks with specific component recommendations
• Optimized maintenance scheduling to minimize production impact

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Implementation

‍The implementation required:

• Sensor data integration from multiple equipment types
• Historical failure analysis and pattern identification
• Predictive model development for different failure modes
• Alert system design with appropriate urgency levels
• Maintenance workflow integration

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Results

• Unplanned downtime reduced from 8% to 1.2%
• €230,000 annual savings in emergency maintenance costs
• 1.0 FTE saved in maintenance management
• 23% increase in overall equipment effectiveness (OEE)

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Knowledge Management

The system included a learning component that documented the relationship between symptoms and actual failures, creating a valuable knowledge base for the maintenance team.

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