Optimal Design of Control Charts for Enhanced Statistical Process Control

This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the best configuration of chart parameters, such as sample size, sampling interval, and control limits, to minimize detection time for process shifts while controlling false alarm rates. It explores both traditional and advanced optimization techniques, including analytical models, simulation-based approaches, and data-driven algorithms. The research also considers practical constraints such as cost, process variability, and real-time monitoring requirements, with applications in manufacturing, healthcare, and service industries. The ultimate goal is to enhance process stability, reduce variability, and improve overall quality performance.