Twelve manufacturing facilities used fixed maintenance schedules and human decision-making. Equipment failures became the largest source of production downtime. Meanwhile, data from thousands of PLCs and IoT devices was scattered among historians, hindering the use of predictive analytics.
All plant telemetry was consolidated into a single data store, after which equipment-specific failure prediction models were built in order to identify the signs of failure 72 hours in advance and give maintenance engineers enough time to schedule preventive maintenance during current maintenance windows rather than responding to equipment failure.
Stream telemetry was collected in Delta Lake (Bronze). The data was transformed by using Silver and Gold pipelines. MLflow managed model training. Predictions were provided in real-time as part of the CMMS integration process. Unity Catalog unified governance across all twelve plants—one reusable model factory, applied everywhere.
60% reduction in unplanned downtime across the manufacturing network. The shift from calendar-based to condition-based maintenance. The reusable AI model factory allowed the client to extend predictive maintenance capabilities to other types of equipment very quickly.
