First-pass yield is among the most significant quality measurements that manufacturers track in their operations. It is a key metric for measuring the effectiveness of a given process, encompassing vital manufacturing benchmarks like scrap and rework.
As a result, first-pass yield is recognized as an important indicator of continuous improvement performance. In fact, recent winners of the IndustryWeek’s Best Plants competition, an annual contest for North America’s top manufacturing facilities, had an exceptional median first-pass yield rate of 98.2%.
Though first-pass yield is important and correlated to top performance, manufacturers often struggle to fully understand their first-pass yield performance across the entire enterprise as this requires pulling data from hundreds of machines and systems.
Lack of access to quality results. Manufacturing teams often struggle to obtain current test stand results, which would enable them to proactively respond to issues before failures occur. For many manufacturers, it can take days or even weeks to process data from disparate data streams.
Manual data collection. Many manufacturers rely on manual collection and curation to pull disparate data sources together, including OPC UA servers, analytical equipment, and SQL databases—trapping valuable insights in unprocessed data and inhibiting process improvements.
High first-run failure. Production lines that have a high first-run failure rate typically exhibit low productivity, high scrap rates, and late deliveries to customers due to frequent rework.
Collect test stand data. Using HighByte Intelligence Hub, manufacturers can automate the collection of quality data from CSV files after testing each product unit.
Merge test and controller data. After ingesting test data, the Intelligence Hub can merge test data with real-time process data from SCADA systems.
Standardize data payloads and add context to data. After connecting to data sources and target systems, the Intelligence Hub can act as a unified namespace that allows users to collect data from various sources, add context to this data and transform it into a format that any consuming system can understand.
Publish to the Cloud for visualization. The Intelligence Hub includes direct API integrations with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, where users can connect with business intelligence systems for further analysis or store the contextualized information in a data lake.
Real-time access to quality data. The Intelligence Hub enables real-time access to test stand and production metrics in a central system for the entire plant.
Predictive analytics. Engineers can see test stand results trend close to failure and respond prior to actual failure.
Contextualized data set. Using a standardized data framework, manufacturers can eliminate the time-consuming manual effort required to curate production data.
