On designing robust and efficient CUSUM chart for mean monitoring: an application in chemical engineering for polymerization reactors

Early detection of shifts in process mean is crucial for maintaining product quality and operational integrity in chemical industries. This paper proposes a new cumulative sum control chart named the CMD chart, that leverages an auxiliary variable for robust and efficient monitoring. The CMD chart is designed through various parameters, with control limits calibrated to ensure a desired average run length when in control. Its performance is assessed using multiple run-length metrics, including average run length, standard deviation, expected average run length, extra quadratic loss, relative average run length, and performance comparison index. An R Shiny app is also developed to enhance usability, simplify calibration and evaluation for different parameter combinations. Through extensive simulation across a broad range of shifts, the CMD chart consistently outperformed existing charts in quickly detecting shifts while minimizing false alarms. A practical case study in a polymerization reactor further highlighted effectiveness of CMD chart, demonstrating earlier, more accurate, and frequent detections of subtle shifts compared to competing methods. Overall, the CMD chart proves to be a robust and high-performing tool for process monitoring, making it highly relevant for modern chemical-engineering applications.