Strategic Optimization of Sampling Points in API Tablet Coating

Abstract

This project evaluates a strategic adjustment in the tablet coating process by modifying in-process sampling points to monitor coating progression in pharmaceutical manufacturing. The current method, relying on late-stage sampling (60%, 70%, 80%), contributes to extended equipment downtime due to laboratory (HPLC) analysis delays. This study explores the feasibility of shifting sampling to earlier stages (40%, 50%, 60%) to reduce hold times without compromising product quality or regulatory compliance. Using real production data and rigorous statistical methods, including paired t-tests, Pearson correlation analysis, Fisher’s Z-tests, and variance comparisons (F-tests), this study statistically validated the accuracy and robustness of endpoint predictions. Results demonstrated that earlier sampling points provide statistically equivalent predictive accuracy with significantly improved operational efficiency, including reduced downtime (up to 2.64 hours per batch), increased production capacity (~13 million additional tablets annually), and substantial economic savings related to reduced overtime and deviation investigations. This optimized approach aligns strongly with lean manufacturing principles, offering a practical, scalable, and regulatory-compliant solution for maximizing throughput and efficiency in pharmaceutical contract manufacturing environments. Keywords - API Coating Process, Downtime Reduction, Endpoint Prediction, Operational Efficiency.