Abstract
Two of the main functions of statistical process control (SPC) are process monitoring and process variation reduction. Traditional SPC techniques, such as control charts, have been based on the assumption of independent data and mostly univariate process characteristics because of the difficulty of data collection and data analysis. However, with the development of advanced sensors and computere, a number of characteristics can be measured on every product, resulting in 100% measurements. These 100% measurements are usually serially correlated and also cross-correlated. This paper explores the effects and benefits of both autocorrelation and cross-correlation in controlling manufacturing processes using case examples from automotive manufacturing processes. The author shows that making use of the correlation structure in the measurements can greatly reduce variability in the process.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
