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.
- 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.
- White Paper Modern manufacturing’s triple play: Digital twins, analytics and the Internet of Things To imagine the power of digital twins, think in terms of your own health care
- Interview IoT in manufacturing: Voices from the field
- White Paper Understanding Data Streams in IoT
- E-Book Quality 4.0 Impact and Strategy Handbook Getting Digitally Connected to Transform Quality Management
- Article Internet of things applications across multiple industries From manufacturing to retail, where are the IoT opportunities?
- Article Modern manufacturing's triple play: Digital twins, analytics, IoT To imagine the power of digital twins, think in terms of your own healthcare
- White Paper IoT Analytics in Practice Blue Hill Research - Analyst Insight
- E-Book Making Sense of AI
- E-Book Internet of Things: Understanding the Journey
SAS® IoT Analytics Solutions for Manufacturing
- Data Management SoftwareGo beyond managing your data to unleashing its full potential.
- SAS® Analytics for IoTDrive innovation, efficiencies and results by putting powerful IoT analytics with embedded AI and industry-leading streaming capabilities in users' hands.
- SAS® Asset Performance AnalyticsHarness M2M and sensor data to boost uptime, performance and productivity while lowering maintenance costs and reducing your risk of revenue loss.
- SAS® Customer Intelligence 360Infuse your marketing decisions with unprecedented customer insights, and create relevant, satisfying, valued customer experiences.
- SAS® Event Stream ProcessingGet immediate analytic insights from real-time big data streaming into your organization.
- SAS® Field Quality AnalyticsDetect emerging issues and perform root-cause analysis to improve product quality and brand reputation.
- SAS® Production Quality AnalyticsGain a holistic view of quality across the enterprise and throughout the entire supply chain.