Overview
This use case utilises a FIWARE-based IoT tool to improve winery efficiency by monitoring Overall Equipment Efficiency (OEE). It evolves into an AI/ML-powered Manufacturing Execution System (MES), enabling predictive maintenance and optimising operations within the project’s cloud infrastructure.
Expected Outcomes
- Real-time monitoring of the bottling line and rapid anomaly detection.
- KPI: System latency - 30% reduction in downtime detection time (Labeler, Capsule Machine, etc.).
- KPI: Fault resolution time - 15 minutes from detection to corrective alert.
- KPI: OEE data capture accuracy - 98% accuracy
- Improved efficiency and reduced downtime, with more proactive, data-driven maintenance.
- KPI: Total machine downtime reduction.
- KPI: Predictive maintenance accuracy – 85% reliability in fault detection
- KPI: Production cycle efficiency – Maintain pre-production times
- IoT–ERP–MES integration for unified production visibility and data-driven decision-making.
- KPI: Data integration success rate - 99% ERP-MES order synchronization.
- KPI: Manual data entry reduction – 40%.
- Reduction of waste (bottles, cases, materials) and better energy use, aligned with sustainability and ESG goals.
- KPI: Downtime cost reduction.
- KPI: Decision-making agility – 20% reduction in production adjustment time
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Industrial optimisation and productivity
The Industrial optimisation and productivity Sphere targets industrial environments, improving efficiency, automation, and productivity through CEI technologies.
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