Summary: Predictive Maintenance for Pharma Equipment in India
How Predictive Maintenance Works
Predictive maintenance for pharmaceutical equipment uses:
- Vibration sensors, temperature probes, and current monitors to continuously track equipment health.
- Machine learning models to analyse sensor data and detect early degradation, such as:
- Bearing wear in tablet presses
- Seal deterioration in fluid bed dryers
- Motor anomalies in mixers
This approach moves maintenance from reactive (after breakdown) to predictive (before failure), protecting both production schedules and product quality.
Boolean & Beyond’s Deployment Approach
Boolean & Beyond deploys predictive maintenance across pharmaceutical plants in Bangalore, Coimbatore, and across India in three phases:
- Phase 1 (Weeks 1–4): Sensor Installation
- Install IoT sensors on critical assets:
- Compression machines
- Coating pans
- HVAC systems
- Water purification units
- Phase 2 (Weeks 5–12): Data Collection & Model Training
- Collect baseline operating data
- Train ML models on site- and equipment-specific behaviour patterns
- Phase 3 (Weeks 13–16): Predictive Alerts & CMMS Integration
- Activate predictive alerts
- Integrate with your CMMS
- Automatically generate work orders when maintenance is required
Equipment Qualification & Compliance Integration
The platform natively integrates predictive maintenance with GxP compliance:
- Maintains complete digital records for IQ, OQ, and PQ alongside maintenance data.
- When maintenance triggers a need for requalification, the system:
- Automatically launches the correct IQ/OQ/PQ protocol
- Tracks execution and completion
- Ensures continuous compliance with:
- Schedule M requirements
- International GMP standards
- Centralises:
- Calibration records
- Maintenance logs
- Qualification certificates
- Provides a full audit trail for inspections and regulatory audits.
Measurable Results for Pharma Manufacturers
Pharmaceutical facilities using Boolean & Beyond’s predictive maintenance system typically achieve:
- 60–75% reduction in unplanned downtime within the first year
- 20–30% extension in equipment lifespan via condition-based, optimal-interval maintenance
- Near-zero batch loss due to equipment failure
- ROI in 10–14 months for manufacturers in Bengaluru and Coimbatore
- 15–25% annual savings on maintenance costs compared to traditional time-based maintenance programmes
In practice, this means more reliable production, fewer deviations, and a stronger compliance posture, all while lowering total maintenance spend.