A comprehensive survey of advanced control chart applications in data-driven pharmaceutical manufacturing

It is imperative to sustain processes, guarantee product quality, and enhancing patient safety in pharmaceutical and healthcare sector. With the increasing data-driven informed decisions in healthcare, statistical process control (SPC) emerged as a vital tool for real-time process monitoring, early detection of deviations and continuous quality improvement. This chapter reviews and classifies the application of control charts in pharmaceutical manufacturing and healthcare using data from 195 peer-reviewed studies. It categorizes control charts based on data type (discrete or continuous), monitored statistical parameters (mean or variability), structural design (memory-less or memory-type), and quality dimensionality (univariate or multivariate). While traditional tools such as P, X, and X-MR charts dominate, there is limited adoption of multivariate and memory-type charts like CUSUM and EWMA. Advanced approaches, such as regression-based and data-driven control charts using machine learning, are notably missing. Their integration offers great promise for improving process monitoring, predictive analytics, and real-time quality assurance. As the pharmaceutical sector becomes increasingly digitized and data-rich, the chapter argues for the need to move beyond conventional SPC tools and develop adaptive, intelligent monitoring systems. The insights provided offer a roadmap for advancing quality control in data-driven drug development and precision healthcare.