Industrial Engineering
Digital Twin Platform.
Engineering a real-time digital twin and predictive maintenance system that saved an automotive manufacturer $12M annually in unplanned downtime.
client
IndustrialCore Ltd.
industry
Manufacturing
duration
18 Months
team Size
22 Specialists
investment
$2.4M+
Empirical Data
Engineering ROI at Factory Scale.
$12M
Annual Savings
Downtime costs eliminated annually across 6 automotive manufacturing plants.
450%
ROI in Year 1
Return on investment achieved within the first 12 months post-deployment.
94%
Fault Prediction
Accuracy rate for predicting equipment failures 72 hours before occurrence.
67%
Less Downtime
Unplanned production downtime reduced by 67% plant-wide.
Engineering Challenges
Legacy OT Integration
Connecting 40-year-old PLC systems and SCADA infrastructure to a modern cloud data pipeline without halting production lines.
Real-Time Sensor Fusion
Synchronizing 180,000 sensor data streams per plant across vibration, temperature, pressure, and acoustic domains with sub-second latency.
Safety-Critical Accuracy
Achieving 94%+ fault prediction accuracy with a near-zero false positive rate to prevent unnecessary maintenance shutdowns.
Engineering Solutions
We built a physics-informed digital twin using Azure IoT Edge, TimescaleDB, and custom transformer-based anomaly detection models.
- OPC-UA + MQTT bridge for legacy PLC integration
- TimescaleDB for 180k sensor streams per plant
- Transformer ML models for predictive fault detection
- 3D digital twin visualization in real-time
Technical Stack
Built for factory reality.
Every technology decision was driven by the harsh realities of industrial environments — intermittent connectivity, legacy hardware, and zero-tolerance for false positives.
Az
Azure IoT
Ts
TimescaleDB
Py
Python
Tf
TensorFlow
Gr
Grafana
K8
K8s
Rk
Redis
Nx
Next.js