Predictive Quality

Using ML to predict and prevent quality issues. This term is essential for understanding industry 4 in industrial automation and PLC programming. It represents a key concept that professionals encounter regularly in their work with control systems and automation projects.

As factories move from isolated control systems to data-rich ecosystems, Predictive Quality emerges as one of the building blocks that makes the transition tractable. Using ML to predict and prevent quality issues.

Predictive Quality typically introduces new concerns to traditional automation projects: cybersecurity, cloud integration, cross-functional teams that include both OT and IT engineers, and longer planning horizons because data infrastructure outlasts individual machine deployments.

Cybersecurity in the context of Predictive Quality is non-negotiable. Anything that connects plant data to external networks must be designed against attacks from day one, not bolted on after deployment.