SAS Production Quality Analytics
Drive deeper production process understanding with predictive analytics. Maximize throughput and quality with prescriptive analytics.
Key features
Fully understand operational processes so you can make sustainable improvements and lower associated costs with the combined power of data integration, automation and analytics.
Cloud native
Compatible with cloud technologies – including Docker and Kubernetes – for large-scale, elastic, multitenant, distributed services. Ready-made to take advantage of the large volumes of data generated by the IIoT.
Enterprise quality-centric data model
Captures large volumes of data regardless of format or source – from legacy to modern MES, ERP and other systems.
Automated monitoring & alerting
Continuously monitors the health of all processes to ensure quality throughout manufacturing and operations with a large-scale, automatic monitoring engine.
Predictive modeling
Provides an array of analytical tools – including explorative analysis, design of experiments with optimizers, and cause-and-effect tools such as Ishikawa diagrams – to optimize process and equipment setups.
Advanced analysis workspace
Lets users analyze quality issues and explore areas of improvement in a highly interactive and visual environment. Serves a broad variety of users, from the casual user to the high-end statistician.
Reporting & KPI dashboards
Delivers customizable reports and graphs enabling information sharing among all who need it. Includes standard and ad hoc reports, KPI scorecards, drillable views, snapshots and trend analysis from across the manufacturing operation.
Quality explorations
View high volumes of process sensor data in context of production events like batch and product changeovers. Visually identify areas needing investigation for a faster, deeper understanding of process.
Predictive quality
Mathematically model your production quality measures, such as yield. Run these models in real time and get predictive alerts on quality issues before they happen.
Process optimization
Use machine learning algorithms to analyze your process and determine the optimal setpoints. Maximize yield throughput while minimizing cost.