<p>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 due to the lack of standardized, data-driven frameworks to support decision-making. 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. Historical QC data, defined as routine batch-release testing results collected across multiple production lots, were obtained from a local pharmaceutical manufacturer. A total of 11 parameters across five raw materials, including aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium, were analyzed. 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 proposed 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.</p>