• 품질경영학회지
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• 제31권2호
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• pp.183-193
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• 2003

A special cause producing temporary deviation in the underlying process can influence on process adjustment in responsive feedback control system. In this paper, the impact of special causes on the EWMA(Exponentially Weighted Moving Average) forecasts and the process adjustment that is based on the EWMA forecasts are derived. For some special causes with patterned type of contamination, the influence of the causes on the output process are explicitly investigated. A data set, contaminated by a special cause of level shift, is analyzed to evaluate the impact numerically.

• Journal of the Korean Statistical Society
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• 제33권2호
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• pp.219-231
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• 2004

Process adjustment is a complimentary tool to process monitoring in process control. Although original intention of process adjustment is not identifying a special cause, detection and elimination of special causes may lead to significant process improvement. In this paper, we examine the impact of special causes on process adjustment. The bias in the adjusted output process is derived for each type of special causes, and average run length (ARL) of the Shewhart chart applied to the adjusted output is computed for each special cause types. Numerical results are illustrated for the ARL of the Shewhart chart, thereupon seriousness of special causes on process adjustment is evaluated for each type of special causes.

• 대한안전경영과학회지
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• 제9권5호
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• pp.49-55
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• 2007

A special cause producing temporary deviation in the underlying process can influence on process adjustment in First-Order System feedback control system. In this paper, the impact of special causes on the forecasts and the process adjustment that is based on the EWMA forecasts are derived for a first-order system. For some special causes with patterned type of contamination, the influence of the causes on the output process are explicitly investigated. A data set, contaminated by a special cause of level shift, is analyzed to confirm the impact numerically.

• 산업경영시스템학회지
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• 제18권36호
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• pp.243-251
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• 1995

본 연구에서는 자동상관인 공정의 변화를 빠르게 탐지할 수 있는 Special-Cause CUSUM 관리도를 사용하여 다섯가지 시계열 모델에 대해 다음과 같은 연구를 수행한다. 첫째 ACF와 PACF로 파라미터에 따른 ARL의 변화를 쉽게 해석할 수 있는 방법과 둘째로 독립인 관측값에 적용하는 Hawkins(1992)의 ARL 간략계산법을 자동상관인 공정에서도 사용할 수 있는 기법을 제시하여 기존의 시뮬레이션을 이용한 ARL 계산법에 비해 빠르고도 정확한 값을 구한다. 끝으로 두가지 유형의 평균이동에 대한 ARL 변화를 각각 계산해 보아 그 효과를 비교분석 한다.

• International Journal of Quality Innovation
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• 제2권2호
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• pp.136-144
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• 2001

The Markov Chain approach is used to develop an economic adjustment model of a process whose quality can be affected by a single special cause, resulting in changes of the process mean by incorrect adjustment of the process when it is operating according to its capability. The $\bar{X}$ control chart is thus used to signal the special cause. It is demonstrated that the expressions for the expected cycle time and the expected cycle cost are easier to obtain by the proposed approach than by adopting that in Collani, Saniga and Weigang (1994). Furthermore, this approach would be easily extended to derive the expected cycle cost and the expected cycle time for the case of multiple special causes or multiple control charts. A numerical example illustrates the proposed method and its application.

• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.319-336
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• 2000

A mixed model with a white noise process and an IMA(0,1,1) process is considered as a process model. It is assumed that the process is a white noise in the absence of a special cause and the process changes to an IMA(0,1,1) due to a special cause. One useful scheme in measuring the process level is to use the variable measurement interval (VMI) between measurement times according to the value of the previous chart statistic. The advantage of the VMI scheme is to measure the process level infrequently when in control to save the measurement cost and to measure frequently when out of control to save the off-target cost. This paper considers the VMI scheme in order to detect changes in the process model from a white noise to an IMA(0,1,1). The VMI scheme is shown to be effective compared to the standard fixed measurement interval (FMI) scheme in both statistical and economic contexts.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.963-972
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• 2007

When a control chart signals that a special cause is present, process engineers must initiate a search for and an identification of the special cause. Knowing the time of the process change could lead to identify the special cause more quickly, and to take the appropriate actions immediately to improve quality. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the parameters of a process in which the observations can be modeled as a first-order autoregressive(AR(1)) process plus an additional random error.

• 경영과정보연구
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• 제22권
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• pp.211-229
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• 2007

The ISO/TS16949 APQP goal of defect prevention and decrease of spread waste, is the customer satisfaction which leads a continuous improvement and profit creation. The quality expense where the most is caused by but with increase of production initial quality problem occurrence is increasing to is actuality. Like this confirmation amendment. with the problem which is forecast in the place development at the initial stage which it does completeness it does not confront not to be able, production phase to be imminent, the problem accumulates and it talks the development shedding of which occurs. In opposition, prediction confrontation. is forecast in development early stage to and it is a structure which does not occur a problem to production early stage. Like this development is a possibility of accomplishing competitive company from production phase. Which attains an goal of, chance cause it leads a APQP activity (common cause) with special cause prevention & detection the connection characteristic of the focus technique against a interaction is important. And the customer requirement satisfaction and must convert a APQP goal of attainment at the key characteristics action step. (1) The Prevention - with Design FMEA application prevention of the present design management/detection, (2) the Detection (prevention/detection) - with Process FMEA application prevention of the present process control/detection, (3) Special Cause - statistical process control (SPC) 4M cause spread removal, (4) Common Cause - statistical process control (SPC) the nothing zero defect which leads the continuous improvement back of spread with application it will be able to attain with application.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.429-436
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• 2009

통합공정관리의 기본절차는 잡음이 내재하는 공정에 수정조치를 취하여 공정 편차를 백색잡음으로 전환하도록 하여 공정제곱편차를 최소화하게 된다. 이러한 수정활동 중 공정에 이상원인이 발생하면 관리도를 통하여 이를 탐지하고 제거하게 된다. 수정된 공정은 이상원인 발생 전에는 백색잡음이 되지만, 이상원인 발생 후에는 다양한 형태의 시계열 모형으로 변환하게 된다. 만일 수정된 공정을 탐지하여 이상원인의 신호가 있을 경우 교정활동을 통하여 이를 제거해야 하지만, 교정활동의 비용이 많이 발생하거나 또는 구조적으로 이를 제거할 수 없는 경우에는 이상원인의 효과를 감안하여 수정활동을 재조정해야 할 것이다. 이 논문에서는 공정모형으로 IMA(1,1) 모형을 가정하고 통합공정관리 절차를 수행하는 경우, 이상신호 발생 후 재수정 절차를 제안한다.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.395-403
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• 2012

Process adjustment, also called engineering process control(EPC), is applied to maintain process output close to a target value by manipulating controllable variables, but special causes may still make the process deviate from the target and result in significant costs. Thus, it is important to detect and mediate deviations as early as possible. We propose a one-step detection method, the moving search block(MSB), with which the time and type of a special cause can be identified in short periods. A modified control rule that can entertain the effects of the special cause is proposed. A numerical example is presented to evaluate the performance of the proposed scheme.