Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs.

Statistical Process Control and Process Adjustment theory is gaining recognition in the process industries where the process frequently experiences a shift mean. This paper aims to study, the theory difference between Statistical Process Control and Process Adjustment in simple terms and presents a case study that demonstrates successful integration of Statistical Process Control and Process Adjustment theory for a product in drifting industry.

We developed the basic SPC software named as 100 ？ Quality Renovation Software in 1998 as a government project supported by the Small and Medium Industry Administration and the Korean Standards Association. In 2002, Ministry of Industry and Energy conducted a survey research on the current status of quality management in the domestic industries including manufacturing and service industries. Among about 1000 answered industries, 40.6% has been using this 100？ S/W for their quality improvement activities. In this paper, we present our ways to help small and medium sized industries improve quality by our user-friendly SPC software.

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have developed independently. In general, both techniques have reduction of variability as their objective, although they seek to accomplish these objective in different ways. Integration of SPC and EPC is finding wide recognition and is successfully used in continuous process industries. A simple case of an integral controller is presented here to show the application of MMSE controller to stochastic process. In order to keep the system on target, the actual adjustment in terms of MMSE using integral control can be implemented. This paper demonstrates that SPC, already used for process monitoring in the parts industries, and EPC, now used in process industries, may be integrated in various type industry for more effective control of processes.

상수관은 급격한 기온 변화나 지진에 의한 지반의 변이 및 노후화에 의해 파단이 일어나게 된다. 관의 파단은 상수관망에서 발생할 수 있는 가장 흔한 파괴 상황 중 하나로 시스템으로 공급되는 전체 유량과 관망 내 수두 손실을 증가시킨다. 이러한 변화는 시스템 내에 설치된 압력 및 유량계의 측정값에 영향을 주며 사용성을 저하시킨다. 관 파괴 탐지가 지연되면 주위 지반의 액상화에 의해 싱크홀이 발생하게 되어 복구 시간 및 비용 (사회적 비용 포함)의 증가를 야기하기도 한다. 따라서, 관 파단의 정확하고 신속한 탐지가 필요하다. 본 연구에서는 공정관리 및 품질공학에서 적용되어온 통계적 공정관리 방법을 관 파단 탐지에 적용하였다. 시스템 내에 설치된 압력 및 유량계로부터 측정된 시계열 값을 과거 통계 값과 비교하여 관 파단 여부를 결정하였다. 실제 네트워크에서 수요량의 변동성을 고려해 발생시킨 압력 및 유량 자료를 이용하여 단변량과 다변량 방법을 포함, 총 여섯 가지 통계적 공정관리 방법의 성능을 비교하였다.