Predictive maintenance
Predict machine failures and optimise maintenance.
Predictive maintenance in mechanical engineering evaluates sensor, control and maintenance data to detect wear early, so maintenance happens at exactly the right time.
- Higher availability
- Cost reduction
- Better planning
unplanned downtime
maintenance costs
payback
early-warning lead time
Calculated from historical downtime costs and reduced emergency repairs.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Predictive maintenance.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Predictive maintenance model on historical data.
- 04
Pilot
Pilot Predictive maintenance 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.
Machine data
Condition, parameters and production counters.
Maintenance data
Faults, maintenance and spare-parts consumption.
ERP data
Master and transaction data from ERP.
MES data
Production orders, feedback and machine data.
Quality data
Inspection results, lab values and complaints.
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
Better planning
More safety
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.
Predictive quality
Same data reveals quality deviations.
Digital twin
Models can be reused in simulations.