Enhancing Pharmaceutical Manufacturing through Statistical Process Control: An Industrial Engineering Approach to Quality Assurance

Abstract

In modern manufacturing, particularly within the process-driven healthcare industry, maintaining stringent control over critical quality attributes is paramount for ensuring product integrity and safety. This study demonstrates the application of Statistical Process Control (SPC), a core methodology in industrial and production engineering, to monitor and manage key process parameters: pH and the concentration levels of preservatives Methyl Paraben (MP) and Propyl Paraben (PP). Utilizing a comprehensive sixpack analysis, this research provides an engineering-focused framework for achieving optimal product quality. The study conducted a retrospective analysis of 184 measurements for each parameter from a healthcare product's routine quality control data. Due to the non-normal distribution of raw data, a Johnson SU transformation was applied to ensure the validity of the statistical analysis. The results revealed process instabilities despite some parameters showing acceptable capability indices (Pp​ and Ppk​). The pH process was marginally capable (Pp=1.32, Ppk=1.21) but out-of-control, with a high defect rate. The MP assay showed good potential (Pp​=1.70) and capability (Ppk​=1.44) but exhibited instability, while the highly capable PP assay (Pp=2.00, Ppk=1.61) also showed signs of a process mean shift. This investigation demonstrated that while all tested products did not show out-of-specifications results, they pinpointed to the hidden signs of processes that need re-examination for significant improvements.