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SAS® for Improved Manufacturing Quality and Yield

de SAS Institute Inc.

Reduce costs, improve productivity and minimize risk

SAS Institute Inc.- Global

SAS is the leader in analytics and artificial intelligence and is used by 2800+ manufacturing customers in more than 52 countries. Commercially active since 1976, we’re the founder, and future, of analytics. With connected quality from SAS, gain a consolidated view of quality across people, places and processes and improve production operations within the agile factory. By combining the power of data integration, automation and analytics, SAS enables you to fully understand operational processes so you can make sustainable improvements and lower associated costs. Manufacturing operations leaders need to reduce cost, improve productivity and minimize risk. Industry 4.0 initiatives can help you transform from a reactive to a proactive approach through scalable data integration and advanced analytics for extracting deeper insights. With SAS you can:
Improve manufacturing quality: Provide a holistic view of quality across the enterprise and throughout the product life cycle
Achieve higher manufacturing quality at a lower cost: Manufacturing operations leaders need to reduce cost, improve productivity, and minimize risk. Industry 4.0 initiatives help organizations transform from a reactive to a proactive approach through scalable data integration and advanced analytics to extract deeper insights
Achieve improved yields at a lower cost: Access, integrate and enrich data from multiple sources. Simplify your analytics culture. Embed insights from the production floor to the executive suite.

SAS® for Improving Manufacturing Quality and Yield is:

Powerful

  • Support digital transformation initiatives
  • Advanced data models built to support sensor and industrial IoT data streams
  • The world’s most advanced analytics, including artificial intelligence and machine learning

Productive

  • Everyone from the shop floor to the top floor has the right tools at the right time
  • Quality explorations allow users to view high volumes of sensor data in context
  • Visually identify areas needing investigation for a deeper process understanding

Proactive

  • Use machine learning algorithms to analyze your process
  • Determine optimal setpoints
  • Run models in real time and get predictive alerts on quality issues before they happen.

D'una ullada

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