Data-Driven Contamination Control: Leveraging Inferential Statistics to Confirm State of Control Through Objective...

Current contamination control strategy (CCS) performance reviews in pharmaceutical manufacturing often rely on descriptive statistical trending of key performance indicators (KPIs), focusing narrowly on compliance within defined periods rather than evaluating long-term process behavior and control. This approach limits the ability to detect systemic risks, verify the effectiveness of corrective and preventive actions (CAPAs), or assess performance across operational changes. To address these gaps, this article advocates for a shift from basic descriptive metrics to an inferential statistics and data science-driven framework. A structured four-step methodology is proposed: (1) anchoring KPIs to identified controls related to the risks, (2) classifying data to guide appropriate statistical test selection, (3) applying inferential statistics with predefined decision thresholds, and (4) integrating univariate analysis into multivariate monitoring where applicable. This approach enables objective confirmation of the state of control, evidence-based assessment of CAPA effectiveness, and data-driven resource prioritization where required. By adopting this rigorous, risk-based analytical foundation, sterility assurance and senior leadership can strengthen decision-making, enhance regulatory readiness, and progress from static monitoring toward predictive contamination control where applicable.