New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs

Contemporarily, enterprises strive to continuously enhance quality which is a basis of customer satisfaction. Numerous advancements to the control charting scheme have been made to enhance process monitoring. In this thesis, the variable parameters chart with auxiliary information (abbreviated as...

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Main Author:	Chong, Nger Ling
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/55840/1/CHONG%20NGER%20LING24.pdf
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spelling	my-usm-ep.558402022-11-30T16:27:08Z New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs 2019-11 Chong, Nger Ling QA1 Mathematics (General) Contemporarily, enterprises strive to continuously enhance quality which is a basis of customer satisfaction. Numerous advancements to the control charting scheme have been made to enhance process monitoring. In this thesis, the variable parameters chart with auxiliary information (abbreviated as VP-AI) is proposed. The VP-AI chart is designed with a regression estimator that has an improved precision due to the use of auxiliary variable to estimate the population mean. By adopting the Markov chain method, the average time to signal (ATS) and expected ATS (EATS) formulae are derived for known and unknown shift sizes. The findings show that the VP-AI chart prevails over the basic VP chart and justifies the integration of auxiliary information to improve the sensitivity of the VP chart. A comparison of the VP-AI chart with its competing charts shows that, for all shifts, the performance of the VP-AI chart surpasses the Shewhart AI (SH-AI), synthetic AI (SYN-AI) and variable sample size and sampling interval AI (VSSI-AI) charts considerably. Additionally, for most shifts, the VP-AI chart has a superior performance in comparison with the exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The application of the VP-AI chart is shown using an illustrative example based on a real dataset. In many situations, the process is multivariate in nature, where more than one quality characteristic has to be monitored simultaneously. Furthermore, many companies have adopted the short production runs technique to be more flexible and specialized. 2019-11 Thesis http://eprints.usm.my/55840/ http://eprints.usm.my/55840/1/CHONG%20NGER%20LING24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia. Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Chong, Nger Ling New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
description	Contemporarily, enterprises strive to continuously enhance quality which is a basis of customer satisfaction. Numerous advancements to the control charting scheme have been made to enhance process monitoring. In this thesis, the variable parameters chart with auxiliary information (abbreviated as VP-AI) is proposed. The VP-AI chart is designed with a regression estimator that has an improved precision due to the use of auxiliary variable to estimate the population mean. By adopting the Markov chain method, the average time to signal (ATS) and expected ATS (EATS) formulae are derived for known and unknown shift sizes. The findings show that the VP-AI chart prevails over the basic VP chart and justifies the integration of auxiliary information to improve the sensitivity of the VP chart. A comparison of the VP-AI chart with its competing charts shows that, for all shifts, the performance of the VP-AI chart surpasses the Shewhart AI (SH-AI), synthetic AI (SYN-AI) and variable sample size and sampling interval AI (VSSI-AI) charts considerably. Additionally, for most shifts, the VP-AI chart has a superior performance in comparison with the exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The application of the VP-AI chart is shown using an illustrative example based on a real dataset. In many situations, the process is multivariate in nature, where more than one quality characteristic has to be monitored simultaneously. Furthermore, many companies have adopted the short production runs technique to be more flexible and specialized.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Chong, Nger Ling
author_facet	Chong, Nger Ling
author_sort	Chong, Nger Ling
title	New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
title_short	New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
title_full	New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
title_fullStr	New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
title_full_unstemmed	New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs
title_sort	new variable parameters chart based on auxiliary information and multivariate charts for short production runs
granting_institution	Universiti Sains Malaysia.
granting_department	Pusat Pengajian Sains Matematik
publishDate	2019
url	http://eprints.usm.my/55840/1/CHONG%20NGER%20LING24.pdf
_version_	1776101117376069632

New Variable Parameters Chart Based On Auxiliary Information And Multivariate Charts For Short Production Runs

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