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Industry Technology Applications

IoT Analytics for Manufacturing

Boost your quality performance.

Business Challenges

Six Sigma, line-level reporting, MES systems and other methods are no longer sufficient for gaining insights from IoT data to improve decision making. Industrial IoT (IIoT) data operationalized through machine learning, streaming analytics and artificial intelligence accelerates innovation in all elements of manufacturing – from supply chain, to delivery, to service – for a cohesive view of production, process and product data. With IoT analytics from edge to cloud, manufacturers are better able to scale human observation and decisioning, explore new dimensions of digital transformation, and bring order to disorder.

How SAS® IoT Analytics Can Help

Manage and analyze your IIoT data where, when and how it works best for your business – from edge to cloud. Understand which data is relevant so you'll know what to store, what to ignore and what to act on now. SAS delivers trusted, automated IoT analytics solutions that can help you:

  • Get higher manufacturing quality at a lower cost. Access and analyze all types of data – from call center systems, traditional news sites, social media forums or written records of service calls. Then integrate the data with your issue detection process for earlier warnings and corrective action guidance.
  • Reduce warranty costs and risk. Consolidate warranty data from multiple sources and quickly decode its meaning. Automated quality control measurement combined with monitoring, tracking and reporting saves time and money by helping you focus on mission-critical issues in a timely manner.
  • Improve production yield and throughput while lowering maintenance costs. Mine and analyze IIoT data at rest, in stream and at all points in between. Use predictive modeling to uncover insights and avoid issues – like unplanned maintenance or efficiency loss – before they occur.

Why choose SAS for IoT analytics?

  • Enterprise-quality data management. Integrate structured and unstructured quality-related data from all sources to get an enterprise view of quality performance and drive improved quality outcomes.
  • Superior root-cause analysis. Take advantage of a complete spectrum of analytical tools – from exploratory analysis, to design of experiments with optimizers, to cause-and-effect tools like Ishikawa diagrams.
  • Advanced early-warning analytics. Identify potential issues early, even before they occur, so you can proactively take corrective action to improve outcomes.
  • SAS is a leader in Streaming Analytics. According to The Forrester Wave: Streaming Analytics Q2 2021, “SAS packs the most analytics punch. SAS Event Stream Processing stands out as the platform with the most built-in analytics for machine learning and other advanced analytics. It also has a mature edge analytics capability for IoT applications.”