Boost your quality performance with IoT analytics.
There’s a lot of buzz around Internet of Things (IoT), but how can you effectively leverage this data? With the Analytics for IoT solution, manufacturers can boost quality performance and bring value to your machine and sensor data. With Analytics for IoT, you can make your IoT data work for you. Watch our half-minute video on how manufacturers can leverage IoT analytics to address challenges like variation and errors in production processes and product waste.
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
How SAS Can Help
Manage and analyze your industrial IoT (IIoT) data wher, 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
- 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.
- 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.
SAS IoT Analytics Solutions for Manufacturing
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