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How AI Changes Continued Process Verification in Pharma Manufacturing. Traditional tools can connect manufacturing data, but they often leave quality teams manually building SPC charts, tracking trends, and interpreting results across disconnected systems.
AI transforms this into an Intelligent Continued Process Verification (CPV) workflow. It automatically contextualises data from MES, LIMS, ERP, and other sources to generate and continuously maintain SPC charts for critical process and quality parameters.
Instead of manually reviewing hundreds of charts, AI monitors them in real time, evaluates them against established SPC rules like Nelson and WECO, and notifies teams as soon as meaningful process drift is detected.
This helps identify potential deviations before they become quality events. QA professionals can instantly explore historical data, perform trend analysis across batches, products, or equipment, and ask natural language questions without building reports or spreadsheets.
Generative AI also summarises complex charts, highlights significant patterns, and surfaces the insights that matter most, making investigations faster and more efficient. If you want to leverage AI in your CPV processes, you can use the Mareana platform…
…where every chart remains inspection-ready with native version history, electronic approvals, and an immutable audit trail, allowing auditors to reproduce any historical chart with complete confidence.
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Frequently Asked Questions
Mareana has helped numerous firms in the Pharmaceutical, Chemical, Medical Device, and Industrial Manufacturing industries.
Continued Process Verification (CPV) is the ongoing monitoring of manufacturing processes to ensure they consistently produce products that meet predefined quality standards throughout the product lifecycle.
CPV uses process and quality data to detect variability, verify process performance, and support regulatory compliance. It is a key requirement under modern pharmaceutical quality systems.
AI automates data analysis, trend monitoring, and anomaly detection in CPV.
Instead of relying on manual reviews of statistical process control (SPC) charts, AI continuously monitors process data, identifies meaningful trends, applies SPC rules automatically, and alerts quality teams when potential process drift is detected.
Common sources include:
- Manufacturing Execution Systems (MES)
- Laboratory Information Management Systems (LIMS)
- Enterprise Resource Planning (ERP)
- Process historians
- Quality management systems
- Equipment and sensor data
This provides a complete view of process performance without manual data consolidation.
SPC (Statistical Process Control) charts monitor process stability and detect variations over time.
They help manufacturers identify trends, shifts, and unusual process behavior before product quality is affected. AI can automatically generate, maintain, and interpret these charts in real time.
AI continuously evaluates SPC charts using established statistical rules to identify process drift early.
By applying rules such as Nelson and WECO, AI can detect abnormal patterns, notify quality teams, and help prevent deviations before they become quality events.
AI significantly reduces manual SPC chart reviews but keeps quality experts in control.
Rather than reviewing hundreds of charts individually, quality teams receive alerts only when statistically significant events occur, allowing them to focus on investigation and decision-making.
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