عنوان مقاله [English]
Nowadays, statistical methods play a significant role in reducing waste and improving product quality at various stages of the product life cycle. Statistical process control (SPC), as the application of statistical methods, has been applied most frequently to monitor and control a process to ensure that it operates at its full potential to produce the conforming product. Moreover, control charts, known as Shewhart charts, are considered the most important tool in SPC. However, it has been shown that memory control charts, such as the Exponentially Weighted Moving Average (EWMA) control chart, can detect small shifts in a process faster than the Shewhart control chart. In other words, due to the added adjustment parameter, the memory control chart performs much better than the Shewhart control chart in monitoring small shifts of the process mean. The weighted moving average (GWMA) control chart is a generalization of the EWMA control chart. In recent years, the GWMA and the Double Generally Weighted Moving Average (DGWMA) control charts have demonstrated their higher efficiency compared to the EWMA control chart and the Double Exponentially Weighted Moving Average (DEWMA) control chart. Moreover, these charts are often designed for monitoring variable quality characteristics under normal distribution assumption. Attribute quality characteristics, which are based on counting the numbers of nonconformity with binomial distribution, or the numbers of nonconformity with Poisson distribution, have many applications in statistical process control. In this paper, we design the GWMA control chart and the DGWMA control chart for attribute quality characteristics and consider the effect of changing the parameters of charts on their false alarm rates. In addition, the performances of these charts are compared to each other (the EWMA control chart and the DEWMA control chart) using Monte Carlo simulation. Based on the results, it is seen that these new control charts also perform well in detecting small shifts when the attribute quality characteristics are monitored.