Manual PID tuning remains common—and Control Station even teaches a repeatable, calculator-based approach for estimating models and tuning coefficients. But relying on trend estimates and slope calculations can be imprecise, time-consuming, and disruptive. Without visibility into the controller’s predicted response, tuning often requires multiple iterations that slow down operations and impact production.

Innovations in process modeling effectively eliminate these challenges by generating accurate models from real operating data, even when noisy or oscillatory, and by validating PID performance before parameters are put into service.

Intended for individuals responsible for PID loop tuning, this session will equip you to:

1) Understand why manual tuning frequently leads to inaccurate results, repeated adjustments, and prolonged performance disruptions.
2) Learn how an innovative modeling approach develops reliable models and optimal tuning parameters—even from imperfect data.
3) See how modeling eliminates issues by simulating the PID response prior to implementation, ensuring first-time-right optimization.

Discover the hidden costs of manual tuning and how NSS accelerates plant-wide control improvement. Register now to streamline your tuning workflow and improve process performance.

For decades, discrete manufacturers have relied on Overall Equipment Effectiveness (OEE) to identify underperforming machines. As a normalized metric, OEE can be applied consistently across individual assets, production lines, or entire cells. When a line’s OEE drops, the math makes it easy to isolate the specific machine or asset dragging performance down.

Process manufacturers can apply this same powerful concept to PID controller performance—enabling objective benchmarking, rapid root cause analysis, and smarter optimization decisions.

Intended for corporate and plant managers, this session will equip participants to:

1) Understand how the OEE framework can be applied to assessing controller performance at the loop, unit, and plant levels.
2) Use this method pinpoints underperforming controllers and identifies root causes, including tuning issues, equipment problems, or process changes.
3) Leverage new KPI insights to prioritize improvements and drive plant-wide performance gains using a data-driven, OEE-style approach.

See how applying proven OEE concepts to process control can simplify optimization and improve operational effectiveness. Register now to learn why leading manufacturers are embracing a new method.