A global manufacturer had deployed predictive maintenance AI across multiple
facilities. While the model performed well initially, there was no operational layer to
monitor performance across plants, equipment types, and data pipelines.
AI Smartsource introduced continuous monitoring across the full AI estate — tracking
model accuracy, data quality, and anomalies in real time. When sensor data quality
dropped at one facility, the issue was identified and resolved before it impacted
predictions or operations.
The outcome:
●Downtime reduced through early detection of data and model issues
●Centralised visibility across all facilities from a single dashboard
●Pipeline disruptions identified and resolved before impacting production
●MLOps standardised across the full manufacturing AI estate