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The synthetic Tukey's control chart (denoted by Syn-TCC) applies a single observation in the process monitoring and is relatively robust to outliers. For a complex process, the probability distribution of quality characteristic must be carefully identified and the loss of quality characteristic, which is defined as the deviation from the target value, must be accurately estimated so that the quality state of the process may be more comprehensively described. However, the literature rarely studied the economic design of the Syn-TCC with Taguchi's loss function under non normality. In the present study, it is assumed that the quality characteristic follows a log-normal distribution and Taguchi's asymmetric linear and quadratic loss functions are involved to develop the economic-statistical design of the Syn-TCC for multiple assignable causes. A case study is presented to illustrate the application of this economic-statistical design of the Syn-TCC to an IC package industry. The sensitivity analysis of this case study reveals that a larger production lot size and larger two coefficients of loss functions generally lead to a higher total expected cost. [ABSTRACT FROM AUTHOR]

Coefficient of variation (CV) control charts are widely used to monitor processes in which the mean of process characteristic varies and its standard deviation changes with the mean. However, such charts are generally insensitive to small shifts. In the present paper, a double-sampling (DS) CV chart is proposed to effectively detect the CV shifts. An optimization model is developed to obtain the values of parameters in the DS CV chart. The performance of the DS CV chart is then compared with those of the variable parameter (VP) and the exponentially weighted moving average (EWMA) CV charts. Based on our study, the DS CV chart outperforms the VP CV chart in detecting all shift sizes. In addition, the EWMA CV chart outperforms the DS CV chart only for very small shifts, but the DS CV chart generally has better detection ability than the EWMA CV chart for small, medium and large shifts. A real example of integrated circuit lead electroplating is provided for illustration of applications and this example confirms that the DS CV chart has superior performance when compared with the other two charts. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.06.051 Byline: Yu-Chang Lin (a), Chao-Yu Chou (b) Keywords: Quality control; Control chart; Markov chain; Non-normality Abstract: Recent studies has demonstrated the adaptive (i.e., variable sample sizes, sampling intervals, and/or action limit coefficients) X charts are quicker than standard Shewhart (SS) X control chart in detecting process mean shifts. The usual assumption for designing a control chart is that the data or measurements are normally distributed. However, this assumption may not be true for some processes. In this paper, the performance of the adaptive X chart is evaluated under non-normality. Based on the study, it is shown that the variable parameters (VP) X chart is more effective than the other adaptive control charts in detecting small process mean shifts under non-normality. More importantly, the risk of false alarm of the VP X chart can be substantially decreased. Author Affiliation: (a) Department of Accounting, National Taichung Institute of Technology, Taichung 404, Taiwan, ROC (b) Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Touliu 640, Taiwan, ROC Article History: Received 15 October 2004; Accepted 17 June 2005

The performance of X control charts in a non-normality framework is assessed.