Prevention Over Reaction: Why Modern CPV Requires Automated Process Monitoring

Continued Process Verification (CPV) was never meant to be a “check-in-a-box" exercise. At its best, CPV is a living confirmation that a process remains in its validated state of control, through every production batch and inevitable changes. But many manufacturers still operate in reactive mode, performing periodic and manual trending that highlights issues only after the process has already drifted, deviations have been detected, or supply is at risk. 

Modern manufacturing generates more data than ever, across equipment, materials, and analytical methods. The challenge is more than data availability—it is whether teams can interpret process signals quickly enough to act. Automated process monitoring addresses that gap by evaluating data as it is generated, identifying early signs of drift, and documenting decisions as part of routine operations. This allows teams to respond before deviations escalate, while maintaining inspection-ready evidence without adding manual effort.

The Shift From Periodic Review to Continuous Confidence

For years, CPV has often been interpreted as a scheduled activity (monthly, quarterly, or per batch) where teams compile data, run a handful of trend charts, and confirm the process is running within expectations. That approach is sustainable when processes are simpler, product portfolios are smaller, and data volumes are easily managed. However, it is far less robust in an environment defined by frequent change, complex supply chains, and demanding regulatory expectations.

Today, CPV programs are expected to provide continuous regulatory confidence instead of retrospective assurance. Teams need to have continued visibility over process performance and product quality to spot emerging drifts early and act before events escalate. In practical terms, this shifts CPV from a retrospective and periodic review to an ongoing program that supports process control, understanding, and better decision-making across the lifecycle.

What Being Preventive Looks Like in Practice

Prevention in CPV does not mean preventing every deviation and batch failure. It means building the capacity to detect risks earlier, investigate and mitigate faster, and to reduce the likelihood that small drifts become major events. In practice, it is about identifying patterns in data: how a gradual drift in a CPP may affect a CQA, the impact of a subtle shift associated to a raw material lot change, or a site-to-site performance difference that would otherwise be missed.

When CPV is preventive, teams spend less time reconstructing events and more time improving process performance. A centralized record of historical trends, prior investigations, and parameter behavior allows quality and process owners to assess risk faster, initiate root cause investigations, and implement corrective actions before issues repeat or expand in scope. Over time, process monitoring stops being a compliance requirement and becomes a feedback loop that improves robustness, supports tech transfer, and accelerates continuous improvement across the lifecycle.

Why Automated Process Monitoring Is the Backbone of Modern CPV

Traditional and manual CPV can deliver the expected outcomes, but it is difficult to sustain in practice. Data is typically scattered across multiple spreadsheets, shared drives, and local files, which makes CPV heavily dependent on recurring data collection, reconciliation, and spreadsheet-based analysis and trending. That work is time-consuming, inconsistent across teams or sites, and often yields insights only after deviations are detected, investigations are underway, or supply is already at risk.

Automated process monitoring improves CPV by moving it from a periodic, retrospective review to continued science- and data-driven process oversight. Instead of waiting for reporting cycles, automated monitoring evaluates process performance and product quality as data is generated and collected from external systems. It flags parameter drift, near misses, and out-of-trend values using predefined statistical and multivariate rules and thresholds. This creates a repeatable control approach, with consistent trending logic and capability assessment applied across products and sites.

Operationally, automated monitoring significantly reduces the effort required to assemble CPV programs and reports, while also lowering the likelihood and scope of preventable quality events by identifying risks earlier. At the same time, it strengthens inspection readiness because data evidence is captured continuously, not retrospectively. The result is a CPV program that is more scalable, defensible, and efficient.

Getting Started: A Phased Path to Preventive CPV

Moving from reactive CPV to preventive, automated monitoring involves more than developing dashboards to enhance visibility, but it does not require a disruptive shift either. A phased approach allows teams to establish control incrementally, deliver value faster, and reduce organizational friction.

ValGenesis iCPV™ is an intelligent digital solution that supports this approach through continued monitoring, analytics, and automated reporting. It allows life sciences organizations to ensure automated process monitoring, detect deviations earlier, and maintain documented oversight covering the entire process and product history. ValGenesis implementation services can also support planning and execution, helping organizations scale automation and digitalization of CPV activities.

Most organizations start small with a limited pilot that focuses on a select number of products or parameters. Once the foundation is in place, monitoring can expand systematically to include additional parameters, products, sites, and deeper integration into quality workflows. Over time, CPV shifts from a periodic effort to a continuous capability, reporting time decreases to 80% and process understanding improves, supporting more consistent control and operational resilience.

A Smarter CPV: Continuous, Governed, and Proactive

CPV delivers the most value when it helps teams prevent issues rather than document them after they occur. In a modern environment where processes are data-rich and change is constant, reactive and manual monitoring introduces delays, inconsistency, and avoidable risk. Automated process monitoring shortens the time between detection and response and provides clearer evidence that processes remain in control as conditions change. In practice, this means moving CPV from a scheduled activity to an ongoing, governed capability that protects supply, strengthens quality oversight, and drives better outcomes across the lifecycle.

See what automated and preventive CPV looks like in practice in this webinar.