Predictive Maintenance Transformation

TechManufacturing Inc., a mid-sized manufacturer of precision components, was struggling with unexpected equipment failures that disrupted production and drove up costs.

The Challenge

Before AI implementation, TechManufacturing faced:

• Reactive maintenance: Fixing equipment only after it broke
• Production delays: Unplanned downtime averaging 12 hours per week
• High costs: Emergency repairs cost 3-5x more than planned maintenance
• Quality issues: Degrading equipment produced inconsistent parts

The Solution

Working with Pelles fractional AI engineers, TechManufacturing implemented a comprehensive predictive maintenance system.

Phase 1: Data Foundation

We started by establishing the data infrastructure:

• Installed IoT sensors on critical equipment
• Built a data pipeline for real-time collection
• Created a historical database from maintenance records
• Cleaned and normalized sensor data

Phase 2: Model Development

Our team developed predictive models for equipment failure:

• Analyzed patterns in historical failure data
• Trained models on sensor readings and outcomes
• Validated predictions against known failure modes
• Achieved 95% accuracy in failure prediction

Phase 3: Integration

The predictive system was integrated into operations:

• Dashboard for maintenance teams
• Automated alerts for predicted failures
• Integration with work order system
• Mobile app for technicians

Results

Within 8 months of deployment:

Metric	Before	After	Improvement
Unplanned downtime	12 hrs/week	4.8 hrs/week	60% reduction
Maintenance costs	$850K/year	$510K/year	40% savings
Prediction accuracy	N/A	95%	-
Mean time to repair	6 hours	2 hours	67% faster

Key Success Factors

1. Executive buy-in: Plant manager championed the initiative
2. Cross-functional team: Maintenance, IT, and operations collaborated
3. Iterative approach: Started with one production line, then scaled
4. Change management: Technicians were trained and involved early

Lessons Learned

• Start with high-value, high-frequency failure modes
• Invest in data quality before model complexity
• Build trust with the maintenance team gradually
• Plan for model updates as equipment ages

Client Testimonial

"The Pelles team didn't just build us a model—they helped us transform how we think about maintenance. The fractional approach meant we got enterprise expertise without the enterprise cost."

— Operations Director, TechManufacturing Inc.