TY - GEN
T1 - Review on some multivariate statistical process control methods for process monitoring
AU - Dhini, Arian
AU - Prajitno, Isti Surjandari
N1 - Publisher Copyright:
© IEOM Society International.
PY - 2016
Y1 - 2016
N2 - Quality control and improvement have become one of important aspects in assuring production sustainability. Quality has been considered as one of competitive advantages for a company. One of main techniques in conducting quality control is control chart. Early development of this control chart was univariate. However, product quality can not be justified only by one product characteristic. Hence, multivariate statistical process control (MSPC) is necessary to be applied in order to monitor many product quality variables simultaneously. It is important to explore more accurate methods for MSPC modelling. Moreover, rapid development in process automation and computer technology has shifted quality control process, from product quality monitoring into process monitoring. Real time process monitoring for many process characteristics has been conducted to ensure optimal process. Automatic data acquisition has provided massive data stream: large and complex data. These phenomena have supported the application of data mining techniques for process monitoring. This paper focuses on reviewing previous literature related to MSPC, from traditional to data mining based methods. This study aims to synthesize previous literature in multivariate control charts for process monitoring, hence the area for future research is identified.
AB - Quality control and improvement have become one of important aspects in assuring production sustainability. Quality has been considered as one of competitive advantages for a company. One of main techniques in conducting quality control is control chart. Early development of this control chart was univariate. However, product quality can not be justified only by one product characteristic. Hence, multivariate statistical process control (MSPC) is necessary to be applied in order to monitor many product quality variables simultaneously. It is important to explore more accurate methods for MSPC modelling. Moreover, rapid development in process automation and computer technology has shifted quality control process, from product quality monitoring into process monitoring. Real time process monitoring for many process characteristics has been conducted to ensure optimal process. Automatic data acquisition has provided massive data stream: large and complex data. These phenomena have supported the application of data mining techniques for process monitoring. This paper focuses on reviewing previous literature related to MSPC, from traditional to data mining based methods. This study aims to synthesize previous literature in multivariate control charts for process monitoring, hence the area for future research is identified.
KW - Assignable cause
KW - Data mining
KW - Fault detection
KW - Multivariate statistical process control
KW - Process monitoring
UR - http://www.scopus.com/inward/record.url?scp=85018451089&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85018451089
SN - 9780985549749
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 754
EP - 759
BT - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
PB - IEOM Society
T2 - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
Y2 - 8 March 2016 through 10 March 2016
ER -