IoT Analytics for Manufacturing

Business Challenges

Many manufacturers are still finding industrial IoT adoption a challenge, not knowing where to start or which automated processes will prove to be most advantageous. But there’s tremendous potential for enhancing production levels and driving a variety of other innovations. And with expectations for there to be 50 billion connected devices in the world by 2020, manufacturers can’t afford to leave such a massive network untapped for achieving higher levels of efficiency and proactive rather than reactive interventions.

How SAS Can Help

Manage and analyze your industrial IoT (IIoT) data where, when and how it works best for your business. Understand which data is relevant so you’ll know what to store and what to ignore. SAS delivers trusted, automated IoT analytics solutions that can help you:

• Measure customer perception of quality. 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 lessen their impact. 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 while lowering maintenance costs. Mine and analyze IIoT data at rest, in stream and at all points in between. Use predictive modeling to avoid issues – like unplanned maintenance or efficiency loss – before they occur.

Why SAS?

• 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 explorative 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.