Manufacturing · 2025
Predictive maintenance that keeps production lines running
-31%
unplanned downtime
- Client
- Industrial components manufacturer (demo)
- Industry
- Manufacturing
- Services
- AI & Generative AI, Cloud Architecture
- Year
- 2025
01 Project overview
A manufacturer with six plants wanted to anticipate equipment failures and give leadership real-time visibility of operations.
02 Business challenge
- Sensor data stayed on plant-floor historians and was never analyzed at scale.
- Maintenance was reactive or calendar-based, wasting parts and labour.
- Plants had limited connectivity and strict OT security requirements.
03 Solution architecture
How it was built.
Edge
Secure gateways buffer and forward telemetry from PLCs and historians.
Streaming
Event streaming into a lakehouse with time-series optimization.
Intelligence
Anomaly detection and remaining-useful-life models per asset class.
Action
Work orders raised automatically in the maintenance system.
Technologies used
- Azure IoT
- Kafka
- Databricks
- Python
- MLflow
- Power BI
- React
04 Implementation approach
Delivered in clear phases.
- 01 · Pilot
One line, three asset classes and a 12-week value proof.
- 02 · Industrialize
MLOps, monitoring and integration with maintenance workflows.
- 03 · Scale
Rollout to six plants with a reusable deployment blueprint.
05 Results & impact
- Unplanned downtime
- -31%
- Overall equipment effectiveness
- +11%
- Spare-parts inventory
- -18%
- Plants connected
- 6
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Want results like these?
Curious what your sensor data could predict? Let’s scope a 12-week pilot on a single line.