• International Journal of Quality Innovation
• /
• v.7 no.3
• /
• pp.107-115
• /
• 2006

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of $\bar{x}-control$ charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it dose not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic-statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model's working and its sensitivity analysis is also provided.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.4
• /
• pp.939-950
• /
• 2003

Control charts are very useful tool for monitoring of process characteristics. This paper discusses the problem of design of control limits when the subgroups are composed by cluster sampling type. As an alternative method of design of control limits XbBar chart is proposed, which uses the control limits based on the variation between subgroups instead of using classical variation within subgroups. Two examples are presented for reasonable design of control limits and conditions of subgroups based on the cluster sampling. Through examples the guidelines for making proper control limits are proposed.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.10a
• /
• pp.97-103
• /
• 2003

Shewhart-type control charts have historically been used for attribute data, though they have ARL biased property and even are unable to detect the improvement of a process with some process parameters. So far most efforts have been made to improve the performance of attribute control charts in terms of faster detection of special causes without increasing the rates of false alarm. In this paper, control limits are proposed that yield an ARL (nearly) unbiased chart for attributes. Optimal design is also proposed for attribute control charts under a natural sense of criterion.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.116-122
• /
• 2016

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too. ${\bar{x}}$, s estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, ${\bar{x_{\alpha}}}$, $s_{\alpha}$ were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

• The Korean Journal of Applied Statistics
• /
• v.19 no.1
• /
• pp.81-96
• /
• 2006

This paper proposes a simple control chart method which can be practically used for asymmetric process data where the distribution is unknown. If we use the Shewhart type control charts which are based on normality assumption for the asymmetric process data, the type I error could increase as the asymmetry increases and the effectiveness of control chart to control variation decreases. To solve such problems, this paper suggests to calculate the control limits based on the quartiles. If we obtain the control limits by such quartile method, the type I error could decrease and it looks much more practical for asymmetric distributed process data.

• Korean Management Science Review
• /
• v.31 no.3
• /
• pp.27-39
• /
• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.1
• /
• pp.94-101
• /
• 2009

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. S.M. Na et al.(2003) proposed the Demerit-CUSUM for the improvement of the demerit control chart performance and Nembhard, D. A. et al.(2001) and G.Y Cho et al.(2004) developed a Demerit control chart using the EWMA technique and evaluated the performance of the control chart. In this paper, we present an effective method for process control using the Demerit-CUSUM with fast initial response. Moreover, we evaluate exact performance of the Demerit-CUSUM control chart with fast initial response, Demerit-CUSUM and Demerit-EWMA according to changing sample size or parameters.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.2
• /
• pp.9-17
• /
• 2010

Complex products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. So, demerit systems are very effective systems to monitor the different types of defects. Recently, Kang et al.(2009) proposed the revised Demerit-CUSUM for the evaluation of the Demerit-CUSUM control chart performance exactly. In this paper, we present an advanced Demerit control chart using the double EWMA technique. The double EWMA technique is very efficient and strong method for process control where defects and nonconformities occur with various defect types. Moreover, we compare exact performance of Demerit-CUSUM, Demerit-EWMA and Demerit-DEWMA control chart according to changing sample size or mean shifts magnitude. By the result, we confirm that the performance of Demerit-DEWMA control chart is more than the performance of the Demerit-CUSUM and Demerit-EWMA control chart.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 1998.11a
• /
• pp.198-207
• /
• 1998

The p chart approximate to the normal distribution has a difficulty to analyze the process condition precisely when the negative LCL is occurred. Furthermore, the probability of Type I error increases compared with using its original binomial distribution. For a long time the p chart has been used as approximated to the normal distribution because of its easy use. However, it becomes rapid and convenient to calculate the binomial distribution through the development of computer and software, so it is strongly suggested to use the binomial distribution determining control limits to reduce the probability of Type I error. In this study, I suggest that the control limits can be designed in use of binomial distribution and they can be utilized without special software by illustrating the certain work for establishing p-chart with the commercial one(EXCEL).

• Journal of the Korean Professional Engineers Association
• /
• v.21 no.4
• /
• pp.19-32
• /
• 1988

Whereas is non-symmetrical distribution manufacturing process the traditional X-chart by Shewhart is not plotted relatively on the central line but plotted on the skew of upper-hand side or lower-hand side. That is to say, for the purpose of producing either upper-specification-oriented items or lower-specification-oriented items, and when we carry out tighter control so as to have them pass only its specifications, the distribution shape naturally has a non-normal distribution. In the Shewhart X-chart, which is the most widely used one in Korea, such skewed distributions make tile plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such short comings is non-normally distributed processes, a distribution-free type of confidence interval can be used, which should be haled on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for non-normal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, and Truncated-normal distributions, may be easily analyzed. To enhance this improvement, I proved the property of practical applications of control chart method by comparing and analyzing the case studies of practical application of special purpose control chart method, and also by introducing the new designed median control chart.