Max-X^̅S^tn Control Chart for Monitoring Mean and Variability Process

Control charts are the most often technique used in the industry to continuously monitor a process for quality improvement. This paper proposes a variable control chart based on attribute inspection, denoted as Max-X^̅S^tn, to evaluate the stability of mean and variability processes using a single chart. The main advantage of using the attribute inspection is its ease of use and lower costs required compared to the variable-type inspection that using the actual value. Quality characteristics are monitored using a go/no go gauge with five categories. In practice, a sample with the size of n is taken periodically and each item is allocated to one of five categories with adjusted go/no go boundaries, then a value is generated randomly for each item based on a truncated normal distribution with an upper and a lower limit truncated according to the dimensions of go/no go gauge. The performance evaluation is carried out using the Monte Carlo simulation and shows that Max-X^̅S^tn chart with the addition of sample size of three items has a better performance in detecting various mean and/or variability processes shifts than W^tn chart, another simultaneous control chart based on attribute inspection. Therefore, the proposed Max-X^̅S^tn chart can be considered as an alternative to control charts with the variable-type inspection. An example with the real case using piston ring data is presented to illustrate the application of Max-X^̅S^tn chart.