Medical equipment maintenance
Predict failures of critical devices.
Predictive maintenance for medical devices evaluates operating, fault and maintenance data to plan maintenance on need, minimising downtime.
- Higher availability
- More safety
- Cost reduction
device failures
maintenance costs
payback
early-warning lead time
Calculated from avoided treatment disruptions and fewer emergency repairs.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Medical equipment maintenance.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Medical equipment maintenance model on historical data.
- 04
Pilot
Pilot Medical equipment 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.
Machine data
Condition, parameters and production counters.
Maintenance data
Faults, maintenance and spare-parts consumption.
ERP data
Master and transaction data from ERP.
Patient data
Treatment data, appointments and resource use.
Quality data
Inspection results, lab values and complaints.
HR data
Shift plans, qualifications and absences.
Maintenance
Plans maintenance and spare parts precisely.
Patient management
Improves processes and resource utilisation.
Controlling
Quantifies effects and supports budgeting.
IT
Builds on a scalable and secure data infrastructure.
Higher availability
More safety
Cost reduction
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.
Predictive quality
Same data reveals quality deviations.
Automated reporting
Metrics are provided without manual effort.