Autoregressed Controller, which have trend algorithm, seeks to minimize variability by transferring the output variable to the related process input variable, while multivariate process control techniques seek to reduce variability by detecting and eliminating assignable causes of variation. In the case of process control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We also investigate algorithm with relevant Shewhart chart, Theoretical control charts, precontrol and process capability. To help the people who want to make the theoretical system, we compare the main techniques in "a study on the relation between multivariate process control techniques and trend algorithms".

This paper seeks to Combination for Efficient Application of SPC/EPC minimize variability by transferring the output variable to a related process controllable variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with autoregressed disturbance. We compare three control systems; EPC, Cp, SPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC, Cp is more preferable than that of EPC.

EWMA관리도는 SPC 기술 중 가피원인을 탐지하는 기술이 최근품질특성에 가중치가 주어져 미래를 예측하는 능력이 우수하다. EWMA관리도와 EPC의 통합은 연속공정산업에서 성공적으로 사용되고 있다고 널리 인식되어 있다. 그러나, 이산적인 부품 제조공정의 탐지와 연속 생산공정 산업과 그중에서도 제조공정 산업에서의 이들 기법을 적용하기 위해서는 샘플링비율, 고장감지, 실시간 보정과 공정통제에 관한 연구가 필요하다. 본 연구에서는 SPC와 EPC의 차이를 비교분석하고, 제조공정 산업에서 EWMA와 EPC를 성공적으로 통합하여 제품품질특성에 적용하여 변동을 감소시키는 사례연구를 제시하고자 한다.

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.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems; EPC, EPC combined with EWMA. This paper shows through simulation that tlhe performance of the integrated model of EPC and EWMA is more preferable than that of EPC.

SPC와 EPC의 통합은 연속공정산업에서 성공적으로 사용된다. 그러나, 이산적인 부품 제조공정의 모니터링과 연속 생산공정 모니터링 양쪽 모두를 포함하는 산업에서의 통합기법을 적용하기 위해서는 실시간 보정과 공정조절 등에 관한 연구가 필요하다. 본 연구에서는 SPC와 EPC의 차이를 비교분석하고, 혼성 산업에서 SPC와 EPC를 성공적으로 통합하여 효율적인 공정관리의 적용 사례를 제시하고자 한다.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with first-order autoregressive disturbance.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. Tn the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressed disturbance. We compare three control systems; EPC, EPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

There are two approaches to process control. The one is engineering process control(EPC) which is one of the techniques very widely used in the process industry and based on control theory which aims at keeping the process on target using manipulating variable. The other is statistical process control(SPC) whose main purpose is to look for assignable causes(variability) in the process. To design an integrated or combined scheme of SPC and EPC is gaining recognition in the process experiences for hybrid industry. This paper aims to investigate recent study concerned on the integration of SPC and EPC. First, we consider the difference between SPC and EPC in simple terms and review various models of EPC for integration including evaluation of previous study. Finally, we suggest some prospective research area concerned on the integration of SPC and EPC.

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems； EPC, EPC combined with EWMA, and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

In this paper, we Investigate rationales for feedback adjustment using some techniques associated with automatic process control based on some nonstatioary disturbance models. Feedback-control schemes are often operated so that the nature of the disturbance that is being compensated is concealed and unusual deviations from the target cannot be taken account of. In this connection feedback control schemes is useful to extend the idea of common causes and special causes to such systems. Minimum-cost feedback schemes are discussed for some simple ,but practically interesting ,models.

Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.