ManufacturingPredictive Maintenance

Reducing Unplanned Downtime in Manufacturing

Most digital twins today are sophisticated dashboards. But intelligent digital twins go further—they predict, simulate, and recommend. Here's what makes the difference and why it matters for real operations.

35%

Reduction in unplanned downtime

45%

Decrease in emergency maintenance costs

2-4 Weeks

Advance warning of equipment degradationA

The Challenge

A global automotive manufacturer with over 15 production facilities was experiencing significant operational disruptions due to unplanned equipment failures. Their reactive maintenance approach was causing:

Unpredictable downtime events that disrupted production schedules and customer deliveries
High emergency maintenance costs including overtime labor and expedited parts procurement
Cascading production delays affecting multiple product lines and facilities
Difficulty planning maintenance resources due to lack of failure prediction capability

The company's existing condition monitoring systems generated alerts only when equipment was already failing, providing insufficient time for planned maintenance interventions.

Key Problem

Traditional threshold-based monitoring systems couldn't predict failures early enough to prevent unplanned downtime, leading to reactive "firefighting" mode across all facilities.

The Approach

Working with Duora, the manufacturer implemented intelligent digital twins across their critical rotating equipment fleet, including motors, pumps, compressors, and conveyor systems.

Implementation Strategy

Asset Prioritization & Data Integration

Identified 200+ critical assets across three pilot facilities and integrated existing SCADA, vibration sensors, and CMMS data into the Duora platform.

Digital Twin Development

Built physics-based digital twins that model normal vs. abnormal equipment behavior patterns, incorporating operational context like production schedules and environmental conditions.

Predictive Analytics Deployment

Implemented machine learning models that detect subtle degradation patterns 2-4 weeks before traditional threshold alerts would trigger.

Workflow Integration

Connected predictive insights to existing maintenance planning workflows, enabling proactive work order generation and resource allocation.

Technical Architecture

The solution integrated seamlessly with the manufacturer's existing infrastructure:

Data Sources

SCADA systems
Vibration sensors
Temperature monitoring
CMMS work orders
Production schedules

Duora Platform

Real-time data ingestion
Digital twin modeling
Predictive analytics engine
Risk prioritization algorithms

Outputs

Early degradation alerts
Failure probability forecasts
Maintenance recommendations
Resource planning insights

The Results

After 18 months of implementation across the pilot facilities, the manufacturer achieved significant operational improvements:

35% Reduction

in unplanned downtime events

From 120 hours/month to 78 hours/month average across pilot facilities

45% Decrease

in emergency maintenance costs

$2.1M annual savings in overtime labor and expedited parts

2-4 Week

advance warning capability

Enabling planned maintenance during scheduled downtime windows

85% Accuracy

in failure predictions

Significantly reducing false positives compared to traditional monitoring

Key Insights & Lessons Learned

Context Matters

Equipment behavior varies significantly based on production load, environmental conditions, and operational mode. Digital twins that incorporate this context provide much more accurate predictions than isolated sensor monitoring.

Change Management is Critical

Success required extensive training for maintenance teams to shift from reactive to predictive workflows. Clear dashboards and actionable alerts were essential for adoption.

Data Quality Drives Results

The most significant improvements came from assets with high-quality, consistent sensor data. Investing in sensor reliability upfront accelerated time-to-value.

Start Small, Scale Smart

Beginning with 200 critical assets across three facilities allowed for rapid learning and refinement before company-wide rollout to 2,000+ assets.

Scaling the Success

Following the pilot program success, the manufacturer is expanding the predictive maintenance program to:

12 additional facilities covering their global production network
2,000+ additional assets including static equipment and process systems
Integration with ERP systems for automated parts procurement and workforce planning
Mobile accessibility for field technicians and plant managers

The shift from reactive to predictive maintenance has fundamentally changed how we operate. We now have visibility into equipment health weeks before issues occur, allowing us to plan maintenance during scheduled downtime rather than fighting fires during production runs.

— Head of Manufacturing Operations