Implementation of the Statistical Process Control with Autocorrelated Data in an Automotive Manufacturer

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.
Original languageUnknown
Pages (from-to)325-344
JournalInternational Journal Of Industrial And Systems Engineering
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Jan 2013

Cite this

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title = "Implementation of the Statistical Process Control with Autocorrelated Data in an Automotive Manufacturer",
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.",
keywords = "automotive manufacturing, continuous improvement, SPC, Shewhart control charts, autocorrelated data, quality assurance, EWMAST chart, ARIMA models, MCEWMA chart, automobile industry, statistical process control, vehicle painting",
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AB - 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.

KW - automotive manufacturing

KW - continuous improvement

KW - SPC

KW - Shewhart control charts

KW - autocorrelated data

KW - quality assurance

KW - EWMAST chart

KW - ARIMA models

KW - MCEWMA chart

KW - automobile industry

KW - statistical process control

KW - vehicle painting

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