Operational Data

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