Asset availability
Predict downtime and increase asset availability.
Predictive maintenance in process industry evaluates sensor, fault and maintenance data to detect critical equipment failures early, so maintenance is planned on actual need.
- Higher availability
- Cost reduction
- More safety
unplanned downtime
maintenance costs
payback
early-warning lead time
Calculated from historical downtime costs and expected reduction in unplanned failures.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Asset availability.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Asset availability model on historical data.
- 04
Pilot
Pilot Asset availability 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.
Maintenance data
Faults, maintenance and spare-parts consumption.
ERP data
Master and transaction data from ERP.
Environmental data
Emissions, wastewater and permits.
Machine data
Condition, parameters and production counters.
Maintenance
Plans maintenance and spare parts precisely.
Production manager
Uses insights directly in daily operations.
Controlling
Quantifies effects and supports budgeting.
IT
Builds on a scalable and secure data infrastructure.
Higher availability
Cost reduction
More safety
Better planning
Higher efficiency
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.
Energy optimisation
Process data enables demand-based energy control.
Predictive quality
Same data reveals quality deviations.