SAS Production Quality Analytics enhances manufacturing quality by integrating data from various sources, enabling real-time monitoring, predictive modeling, and process optimization. This leads to improved production efficiency, reduced costs, and higher product quality.
Vendor
SAS
Company Website
SAS Production Quality Analytics
Drive deeper production process understanding with predictive analytics. Maximize throughput and quality with prescriptive analytics.
Get support for the Industrial Internet of Things (IIoT)
With real-time monitoring throughout the enterprise, it's easier to identify patterns while processes are ongoing. Take advantage of the large volumes of diverse data generated by the IIoT – at the edge or in the cloud – through our common code base and data model for key quality areas, including asset performance and field quality.
Gain a holistic view of the enterprise
Integrate any type of data relevant to quality, productivity and utilization using our analytics-based enterprise quality solution. Proactively monitor the health of your processes, and generate sustainable quality and yield improvements while containing costs.
Improve quality & minimize production costs
Support multiple data domains, including material movement tracking, genealogy data, process data and asset condition data using our advanced analysis workspace. Get a rich set of interactive root-cause analysis and quality improvement tools that can predict quality issues and operational performance degradations before they become serious problems.
Understand changes quickly
Gain true process understanding across your entire manufacturing operations with world-class data mining capabilities. Document findings and problem-resolution measures, while promoting collaboration and knowledge sharing with best-practice workflows and case management.
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.