Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing

Ensuring consistent raw material quality is essential in pharmaceutical manufacturing to maintain product safety and regulatory compliance. However, routine full-scope quality control (QC) testing is resource-intensive, and risk-based reduction strategies remain underutilized. This study proposes a data-driven benchmarking framework that integrates Relative Standard Deviation (RSD) filtering, distribution normalization, control chart analysis, and Process Performance Index (PPI) evaluation to assess material consistency. Using historical QC data from a local pharmaceutical manufacturer, we analyzed 11 parameters across five raw materials, including aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium. Results show that six parameters exceeded the company-defined PPI threshold (≥ 0.70) and were justified for reduced testing without compromising compliance or product safety. The framework demonstrates how statistical benchmarking of QC data can support risk-based decision making, optimize analytical resources, and align with GMP principles. This work highlights the potential for integrating structured data analytics into pharmaceutical quality systems to enable efficient, compliant, and scalable QC practices.