Abstract
The continuous improvement on quality of products and processes is a constant concern at organisations, as a response to growing competition and demands of the market. The implementation of statistical techniques adjusted to different situations is one way to achieve this goal. The application of traditional control charts requires that collected data are independent and identically distributed. However, this is not always assured, reflecting a drastic increase of false alarms. This paper presents a methodology for the traditional univariate control charts application, when data exhibit significant autocorrelation. To obtain the residuals and predictive errors, the suggestion is to use the ARIMA methodology of Box and Jenkins. Implementation took place in the painting process from an automotive company, providing continuous adjustment of the same and statistically grounded, enabling the organisation to produce vehicles with greater quality assurance, lower costs and an advantageous position against their competitors.
Published Version
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 callMore From: International Journal of Industrial and Systems Engineering
Disclaimer: 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.
