A well-designed and robust continued process verification (CPV) program delivers more than compliance — it enables real-time visibility into product quality, strengthens your control strategy, and continuously surfaces opportunities for improvement. Yet many organizations still struggle to put CPV into practice at scale. In this blog post, we unpack why adoption lags and walk through a stepwise approach to set up a digital CPV plan that’s practical, efficient, and inspection-ready.
What's Getting in the Way of Digital CPV?
The concept of CPV was first introduced in 2011 by the "FDA Guidance for Industry: Process Validation: General Principles and Practices" (FDA, 2011), which states that a new commercial manufacturing process should go through three distinct stages: process design, process qualification, and continued process verification (CPV). CPV was described as the lifecycle stage that makes validation continuous in practice, using real production data to prove the process remains in its validated state of control.
This guidance was followed by the European Medicines Agency (EMA) "Guideline on Process Validation for Finished Products" (EMA, 2016), which encourages CPV as an alternative to — or alongside — traditional validation; and by EU GMP Annex 15 (European Commission, 2015), which embeds a lifecycle approach through ongoing process verification (i.e., routine monitoring to ensure the process remains in control).
Despite regulatory expectations for CPV adoption in commercial manufacturing, it is still not widely or consistently implemented across products and plants. Here are some possible reasons why CPV adoption continues to lag:
- Weak data governance: Fragmented data systems and reliance on manual tools lead to inconsistencies and data integrity risks. Outsourced development and manufacturing can add to the challenge, as maturity levels, data integration, governance, and CPV practices vary across partners.
- Limited skilled resources: Many organizations lack dedicated resources for data trending and analysis, leading to inefficient application of statistical process control in pharma operations. The lack of advanced training on multivariate methods also hinders efficient and continued analysis of critical process parameters (CPPs), critical quality attributes (CQAs), and critical material attributes (CMAs) within the CPV plan.
- Poor lifecycle integration: Overreliance on static documents and manual recordkeeping hinders end-to-end data flow from development to commercialization. Integrating disparate systems is time-consuming, but shifting from retrospective to real-time data collection reduces manual effort and errors, keeps teams aligned, and improves process visibility.
Given these challenges, the digitalization of CPV, from data collection through report generation, offers a practical solution. Yet many companies delay technology adoption due to upfront costs, internal resistance to change, the complexity of securing cross-functional buy-in, and concerns about disrupting ongoing operations.
Encouragingly, the industry’s move toward platform unification, lifecycle traceability, and knowledge reuse, combined with advances in artificial intelligence (AI), machine learning (ML), and predictive analytics, is enabling the transition from manual to digital CPV.
How To Set Up a Digital CPV Plan?
Setting up a CPV plan can be challenging, especially for a portfolio of multiple products. We propose adopting a stepwise approach to ensure reproducibility, scalability, and a science-based justification behind each decision.
Below are the necessary steps for configuring a digital CPV plan:
Step 1. Variable Selection
In this step, you'll select the variables to be monitored in the CPV program based on the criticality assessment, which evaluates each variable’s risk and impact on the product's efficacy and safety. Best practices recommend including all CQAs and CPPs in the CPV plan, but noncritical variables may also be included, depending on their variability and the degree of certainty around their criticality.
Step 2. Reference Batch Selection
Equally important is the selection of reference batches, i.e., completed batches that are representative of the validated state of control of the process. A practical way to establish this is to apply multivariate methods that capture system variability across CQAs, CPPs, and CMAs. This provides a statistically sound reference space against which routine production can be monitored and maintained within a predefined state of control.
Step 3. Variable Configuration
When setting up a CPV plan, evaluate and configure each variable so you can define clear criteria for demonstrating state of control during routine manufacturing.
Configure the following:
- Statistical control strategy (e.g., control chart type, control limit rules, target sigma level)
- Distribution assumptions and normalization (e.g., normality tests, variable transformations)
- Process capability and performance indices (e.g., Cpk, Ppk) and their acceptance thresholds
In addition, because each variable has different behaviors, warning limits, and specification ranges, it's important to define variable-level alarms that will detect statistical signals in routine manufacturing.
How Does Digital CPV Turn Compliance Into Performance?
Running a continued process verification program is more than a regulatory mandate. It’s a performance lever that reduces the cost of poor quality by catching drifts early and preventing deviations, rework, and scrap. By converting routine data into actionable signals, CPV deepens process understanding and extends the control strategy beyond process performance qualification (PPQ). With predefined statistical triggers and readily available evidence, investigations and change control move faster, while automated and traceable documentation makes audit readiness a natural byproduct of efficient operations.
A digital CPV strategy combines all these gains. At a productivity level, organizations typically see a 30% to 50% reduction in manual effort for planning and execution, freeing specialists for higher-value work. Data integrity is built in (ALCOA+), with complete traceability across data sources, analyses, and reports. Real-time process oversight surfaces issues immediately through automated monitoring and alerts, and periodic reports and rolling KPIs are generated automatically to drive continuous improvement at scale.
We Can Help You With Your Digital CPV Implementation
Transform CPV from a compliance requirement into a strategic advantage with ValGenesis iCPV™. Our experts will help you design and implement a digital CPV strategy tailored to your products, processes, and sites, improving oversight, accelerating investigations, and reducing manual workloads.
References
EMA. (2016). Process validation for finished products – information and data to be provided in regulatory submissions - Scientific guideline. Retrieved from https://www.ema.europa.eu/en/process-validation-finished-products-information-data-be-provided-regulatory-submissions-scientific-guideline
European Commission. (2015). EudraLex - Volume 4 - Good Manufacturing Practice (GMP) guidelines. Retrieved from https://health.ec.europa.eu/medicinal-products/eudralex/eudralex-volume-4_en
FDA. (2011, January). Process Validation: General Principles and Practices. Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/process-validation-general-principles-and-practices
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