Leverage Your Data to Increase Efficiency and Optimize Profitability
Presented by Seeq – DRIP – Data Rich, Information Poor – defines the reality of many process manufacturing companies that lack the time or solutions to find insights in their data to improve asset availability, yields and quality metrics, and production KPIs. This session will present use cases demonstrating how self-service data analytics can accelerate the work of engineers and analysts to deal with DRIP, saving time and money for their organization.
Attendees will learn:
- How engineers can leverage their expertise and experience to rapidly find root causes for process anomalies, monitor process and asset performance, and predict future performance and outcomes
- How machine learning and big data technologies enable engineers to quickly build models of production processes with Google-like search, advanced visualization, and data cleansing features.
- How to rapidly connect to enterprise data historians—such as OSIsoft PI, Honeywell PHD, and GE Proficy—and contextual production data with other data sources
- How integrated knowledge capture, collaboration and publishing capabilities enable all levels of an organization to benefit from improved access to insights and underlying data.
Who should attend:
Specialists and engineers responsible for analysis, reliability, and optimization of control systems, assets, and processes; engineering leads, managers, and directors; and analysts driving continuous improvement initiatives and projects.