Continued process verification is evolving from a periodic, retrospective review of historical data to a more connected, always on approach for maintaining process understanding and state of control.
Regulators increasingly expect firms to use process knowledge, contextualized data, and timely monitoring to detect drift early, focus attention where risk is emerging, and drive meaningful continuous improvement.
This presentation will explore how an interconnected analytics foundation spanning process, batch, and quality data can help pharmaceutical manufacturers move beyond static CPV reporting toward proactive quality management by exception. By connecting critical process parameters, batch context, and quality results in a single analytical environment, applying statistical process control and advanced AI-augmented analytics, organizations can monitor leading indicators of process drift in near real time, identify emerging risks earlier, and use critical quality attributes as confirmation rather than the first signal that something has already gone wrong.
We will show how this modern CPV model enables a shift from manual data compilation and lagging KPI reviews to scalable, exception based monitoring, automated reporting, and faster, more consistent decision making across products, lines, and sites.
Attendees will leave with a practical vision on operationalizing CPV as an always on capability that:
- Strengthens compliance
- Improves process performance
- Delivers clearer outcomes—lower product quality risk, fewer deviations, and reduced potential for recalls
