The methodical collection and analysis of operational data forms the central foundation for data-driven production optimization. A structured data architecture enables precise analysis and continuous improvement of all manufacturing processes.
Data Collection Systems
The systematic implementation of data collection is based on defined recording levels:
- Machine data through integrated sensor networks
- Process parameters from control systems
- Quality data from test protocols
- Productivity indicators from MES systems
Data Structuring
Methodical organization of operational data through:
- Hierarchical data models according to ISA-95 standard
- Standardized data formats for system integration
- Defined attribute structures for process parameters
- Systematic metadata management
Performance Analytics
Integration of quantitative performance analyses:
- OEE calculation according to standardized methodology
- Productivity analysis through defined KPIs
- Process capability studies (Cpk)
- Utilization analyses of production resources
Quality Data Management
Systematic recording of quality-relevant parameters:
- Statistical Process Control (SPC) data
- Test results from quality controls
- Traceability data for batch management
- Scrap and rework statistics
Predictive Analytics
Implementation of predictive analysis models:
- Trend analyses for process parameters
- Anomaly detection through pattern recognition
- Predictive maintenance algorithms
- Capacity forecasts for production planning
Reporting Systems
Structured reporting systems for:
- Real-time performance dashboards
- Standardized production reports
- Management Information Systems
- Quality reporting according to industry standards