Loss-based control charts for monitoring non-normal process data

Abstract

Quality and loss of products are crucial factors in competitive companies, and firms widely adopt a loss function to measure the loss caused by a deviation in the quality variable from the target value. From Taguchi`s view point, it is important to monitor any deviation from the process target value. While most existing studies assume the quality variable follows a normal distribution, the distribution can in fact be skewed or deviate from normal in practice. This paper thus proposes loss-based control charts for monitoring the quality loss location or equivalently the deviation of the quality variable from the target value under a skew-normal distribution. We consider the exponentially weighted moving average (EWMA) average loss control chart, which illustrates the best performance in detecting an out-of-control loss location for a process with a left-skewed distribution. Numerical analysis demonstrates that the proposed EWMA average loss chart always performs better than the existing median loss chart for both left-skewed and right-skewed distributions. A numerical example illustrates the application of the proposed EWMA average loss control chart.