Stop Paying for Failures You Could Have Prevented
Unplanned downtime costs manufacturing plants an average of $260,000 per hour. Calendar-based PM catches only 18% of failures. Physics-based predictive maintenance detects degradation weeks before breakdown -- and auto-creates the work order.
The Real Cost of Reactive Maintenance
When equipment fails without warning, the cost extends far beyond the repair bill. Every hour of unplanned downtime triggers a cascade of hidden expenses.
Direct Costs
- Emergency labor (overtime, call-backs)
- Rush-shipped replacement parts
- Expedited vendor service contracts
- Damaged secondary equipment
Indirect Costs
- Lost production / throughput
- Missed delivery commitments
- Customer penalties and SLA breaches
- Regulatory non-compliance fines
Hidden Costs
- Shortened equipment lifespan
- Technician burnout and turnover
- Insurance premium increases
- Reputation and customer trust erosion
Why Calendar-Based PM Falls Short
Preventive maintenance was a step forward from reactive -- but it creates its own waste. The P-F curve shows why time-based intervals miss most failures.
The Over-Maintenance Problem
The Missed Failure Problem
How Predictive Changes the Math
Side-by-side comparison of maintenance strategy costs. Physics-based condition monitoring shifts the cost curve from firefighting to planned interventions.
| Metric | Reactive | Preventive | Predictive (TwinEdge) |
|---|---|---|---|
| Avg. downtime per incident | 72 hours | 24 hours | 4 hours |
| Annual maintenance cost / asset | $187,200 | $94,500 | $12,400 |
| Parts rush-order premium | 40-60% | 10-20% | 0% |
| Equipment lifespan | Baseline | +15% | +40% |
| Safety incidents / year | 3.2x baseline | 1.8x baseline | 0.3x baseline |
| Technician utilization | 35% reactive calls | 55% scheduled | 85% planned |
What You Are Monitoring
Every asset class has unique degradation signatures. Edge physics models detect them in real time. Cloud analytics finds cross-site patterns. Operations Intelligence closes the loop with automatic work orders.
| Asset | Edge (Real-time) | Cloud (Cross-Site) | OpsIntel (Action) |
|---|---|---|---|
| Centrifugal Pump | Vibration FFT, cavitation index, seal temp | Cross-site failure clustering, RUL curves | Auto work order on bearing wear |
| Screw Compressor | Discharge temp, oil quality, vibration | Cross-site efficiency benchmarks | Parts pre-ordering on degradation |
| Induction Motor | Current signature, winding temp, bearing vib | Motor population aging model | Rewind scheduling by condition |
| Battery Bank | Cell voltage drift, internal resistance | Capacity fade prediction | Cell replacement before capacity loss |
| Heat Exchanger | Fouling factor, approach temp, dP | Cleaning interval optimization | CIP triggered by fouling threshold |
Switching from another platform?
We'll migrate your data from ANY CMMS or analytics platform — free. Assets, work orders, maintenance history, sensor configurations — everything.
See the ROI for Your Operation
Tell us your asset count, failure rates, and downtime costs. We will show you exactly what predictive maintenance saves -- before you commit to anything.