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The exponentially weighted moving average (EWMA) control charts with variable sampling intervals (VSIs) have been shown to be substantially quicker than the fixed sampling intervals (FSI) EWMA control charts 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 the present paper, the performances of the EWMA and combined X-EWMA control charts with VSIs are evaluated under non-normality. It is shown that adding the VSI feature to the EWMA control charts results in very substantial decreases in the expected time to detect shifts in process mean under both normality and non-normality. However, the combined X-EWMA chart has its false alarm rate and its detection ability is affected if the process data are not normally distributed. [ABSTRACT FROM AUTHOR]

Recent studies demonstrated that the variable sample size (VSS) &Xmacr; chart is quicker than the standard Shewhart (SS) &Xmacr; chart in detecting small 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 tenable in some production processes. The Burr distribution has been used in the literature to represent various non normal distributions. In this article, the Burr distribution will be employed to evaluate the control charts for non normal populations. We first show the VSS and Shewhart &Xmacr; charts are sensitive to non normality. Then we propose a method of varying the sample size and the control limits simultaneously. The variable sample size and control limit (VSSCL) &Xmacr; chart is shown to be quicker than the VSS &Xmacr; chart in detecting small and moderate shifts in the process. Most importantly, the risk of false alarm for the VSSCL &Xmacr; chart can be substantially decreased. In addition, with proper selection of chart parameters, the VSSCL &Xmacr; chart is more robust to non normality than the VSS and Shewhart &Xmacr; charts. [ABSTRACT FROM AUTHOR]

The signal-to-noise ratio is an indicator, introduced by Taguchi, for evaluating the experimental data in robust design. Estimating the confidence interval of the signal-to-noise ratio is an important topic in data analysis of robust design. Calculating the confidence interval for a parameter usually needs the assumption about the underlying distributions. Bootstrapping is a nonparametric, but computer-intensive estimation method. In this article, we present the results of a simulation study on the behavior of three 95% bootstrap confidence intervals (i.e., SB, PB, and BCPB) for estimating the smaller-the-better signal-to-noise ratio when the data are from either a normal distribution or one of the Burr distributions. A detailed discussion of the simulation results is presented and some recommendations are given. [ABSTRACT FROM AUTHOR]

Control charts are widely applied to monitor manufacturing processes. In 1962, Page presented a modified chart with warning limit, which includes an upper and lower warning band. In 1975, Gordon and Weindling presented a cost model for determining the five parameters of a warning limit chart: the sample size, the sampling interval between successive subgroups, the control limit coefficient, the warning limit coefficient, and the significant run length. When conducting the design of control charts, one usually assumes the measurements are normally distributed. However, this assumption may not be tenable in some specific production processes. Burr developed a general probability distribution to represent a wide range of density functions, including normal and nonnormal ones. In this article, we study the effect of nonnormality on the design of warning limit charts by combining Gordon and Weindling's cost model with the Burr distribution. In this study, it is observed that negative skewness leads to wider control and warning limits. In addition, larger kurtosis results in a longer significant run length and wider control and warning limits. [ABSTRACT FROM AUTHOR]

Chen and Chou presented the economic design of type I continuous sampling plan (CSP-1 plan) under the linear inspection cost. However, it can be argued that the production process is seldom independent. In this paper, we further propose the problem concerning the economic design of CSP-1 plan under the dependent production process and linear inspection cost. A solution procedure is developed to find the unique combination (i*, f*) that will meet the average outgoing quality limit (AOQL) requirement while also minimizing the total expected cost per unit produced during one inspection cycle. [ABSTRACT FROM AUTHOR]

The fast-food industry has experienced prosperous growth over the last three decades. In fast-food operations, the term “fast” refers to “quickservice.” The customers coming to a fast-food restaurant do not expect to wait for a long time to receive the food service. The waiting time for customers to receive the food service becomes one of the key quality characteristics in a fast-food restaurant. However, waiting lines always exist in a fast-food restaurant during peak hours. In this article, a simulation model is built to study the queuing system in a fast-food restaurant, which is located in the downtown area of the city of Touliu, Taiwan. From the results of the study, the manager of the restaurant should recruit another server during peak hours such that the customers' waiting time can be reduced. Meanwhile, the validation procedure for a simulation model is also briefly reviewed in this article. [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]