Manufacturing
Predictive Maintenance
for Industrial Equipment
Key Results
$3.2M → $1.9M
Repair Costs
Emergency Repair Spend
2–4 wks
Early Warning
Failure Prediction Lead Time
200+
Assets
IoT-Connected Equipment
The Challenge
A manufacturing company with $500M+ equipment portfolio experienced unexpected failures causing production delays and safety concerns.
Pain Points
- Frequent unplanned downtime disrupting production
- $3.2M annual emergency repairs
- Safety incidents from equipment failures
- Reactive maintenance culture
The Solution
We implemented a predictive maintenance platform:
1
Sensor integration
IoT connectivity across 200+ critical assets
2
Failure prediction
ML models detecting anomalies 2-4 weeks ahead
3
Work order automation
Automatic scheduling based on predictions
Timeline: 16 weeks including sensor deployment
The Results
Before
- Frequent unplanned downtime
- $3.2M emergency repairs
- Reactive maintenance
- Safety concerns
After
- Rare unplanned downtime
- $1.9M emergency repairs
- Predictive approach
- Zero incidents
"We went from fighting fires to preventing them. The safety improvements alone justified the investment."