Incident early detection
Detect critical process states early and intervene.
Incident early detection analyses process, safety and environmental data to identify unusual states, so risks can be mitigated before they escalate.
- More safety
- Lower risk
- Cost reduction
incidents
response time
payback
early-warning lead time
Calculated from avoided downtime, environmental and personnel costs.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Incident early detection.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Incident early detection model on historical data.
- 04
Pilot
Pilot Incident early detection in one area and collect feedback.
- 05
Rollout
Scale the solution and integrate it into operational processes.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Sensor data
Real-time IoT and process sensors.
Historian data
Time series from the process control level.
Environmental data
Emissions, wastewater and permits.
Maintenance data
Faults, maintenance and spare-parts consumption.
HR data
Shift plans, qualifications and absences.
ERP data
Master and transaction data from ERP.
Production manager
Uses insights directly in daily operations.
Sustainability lead
Captures and communicates sustainability metrics.
Maintenance
Plans maintenance and spare parts precisely.
Management
Receives reliable metrics for strategic decisions.
More safety
Lower risk
Cost reduction
Easier compliance
More transparency
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central data platform
Real-time data integration
Scalable analytics pipelines
Reusable data products
Predictive maintenance
Sensor data provides wear indicators.
Emissions monitoring
Environmental and process data enable reliable emissions balances.
Predictive quality
Same data reveals quality deviations.