A global automotive manufacturer struggled with unplanned downtime across critical production assets, including robots, conveyors, weld cells, stamping presses, and paint systems. Real-time operational data was scattered across PLCs, SCADA systems, historians, and maintenance logs, forcing engineers to manually compile information to diagnose issues. This lack of timely insight led to delayed interventions, higher maintenance costs, and reduced line throughput.
We implemented the predictive maintenance platform materially improved reliability, productivity, and maintenance efficiency across production lines:
22% reduction in downtime across targeted production assets
Earlier detection of failures, reducing emergency maintenance and production delays
Improved runtime performance through data-driven tuning of equipment
Reduced manual engineering effort, freeing teams for higher-value work
Real-time visibility into performance drift, anomalies, and operational bottlenecks
THE SITUATION: Production reliability was a major constraint on the company’s ability to meet demand and control operating costs. High asset complexity, aging equipment, and inconsistent maintenance processes made it difficult to identify early signs of degradation or performance drift. Without a unified view of machine behavior, maintenance teams remained reactive. As the workforce aged and knowledge gaps grew, the organization needed a more scalable and predictive approach to ensure continuous production.
THE PROBLEM: The manufacturer faced significant data, visibility, and reliability challenges that limited its ability to proactively manage equipment health, including:
Fragmented operational and sensor data across PLCs, SCADA, historians, and CMMS systems
No unified view of equipment performance or early anomaly detection
Manual diagnostic workflows slowing decision-making and delaying repairs
Limited predictive insight into equipment degradation and failure patterns
Lack of scalable architecture to support real-time reliability analytics
High variability in troubleshooting effectiveness across shifts and technicians
The Frame Solution
Deployed a modern predictive maintenance solution built on cloud, lakehouse, and machine learning capabilities to provide real-time visibility into asset performance and reduce downtime. Our end-to-end approach spanned data engineering, modeling, monitoring, and frontline enablement.
Unified SCADA, historian, and CMMS data into a Lakehouse architecture
Implemented real-time equipment health dashboards with anomaly detection
Developed predictive models to identify machinery at risk of failure before impact
Created automated alerts for early-warning indicators and process deviations
Implemented automated alerts for emerging quality risks
Delivered GenAI-enabled insights that summarized root causes for technicians and supervisors
