Case Study: CloudNC achieves real-time factory efficiency gains with Quix

Optimizing manufacturing efficiency with streaming data and ML

CloudNC, a company focused on autonomous manufacturing, needed a better way to use live machine data to improve factory scheduling, predictive maintenance, real-time alerting, and machine learning for optimizing part production. Its existing batch-based tools were too delayed and incomplete to support fast decisions on the factory floor, so it looked for a way to ingest and analyze high-frequency time-series data more effectively with Quix.

Using Quix Streams and Quix’s real-time and batch data processing capabilities, CloudNC built a streaming data architecture that keeps machine data flowing from the factory floor into immediate monitoring, alerting, storage, and ML workflows. The solution was developed by two engineers in just 4 weeks, ran 4x faster than before, cut development effort by 16 weeks, and delivered an estimated 5% increase in factory efficiency, while also helping CloudNC reduce downtime and improve maintenance planning.

CloudNC

Chris Angell

Lead Systems Engineer