Developing a family of Bayesian group chain sampling plans for quality regions

Acceptance sampling is used to decide about the lot under inspection, either to accept or to reject. Various acceptance sampling plans under group chain consider only consumer’s risk to develop the plan, but this study focuses on both consumer’s and producer’s risks. If past information about the pr...

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Main Author:	Hafeez, Waqar
Format:	Thesis
Language:	eng eng eng
Published:	2022
Subjects:	TS155-194 Production management Operations management HD56-57.5 Industrial Productivity HD61 Risk Management
Online Access:	https://etd.uum.edu.my/9809/1/permission%20to%20deposit-allow-902911.pdf https://etd.uum.edu.my/9809/2/s902911_01.pdf https://etd.uum.edu.my/9809/3/s902911_02.pdf
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id	my-uum-etd.9809
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spelling	my-uum-etd.98092022-09-07T07:25:47Z Developing a family of Bayesian group chain sampling plans for quality regions 2022 Hafeez, Waqar Aziz, Nazrina Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences TS155-194 Production management. Operations management HD56-57.5 Industrial Productivity HD61 Risk Management Acceptance sampling is used to decide about the lot under inspection, either to accept or to reject. Various acceptance sampling plans under group chain consider only consumer’s risk to develop the plan, but this study focuses on both consumer’s and producer’s risks. If past information about the product is available, then Bayesian approach is the best approach to make a decision. This research aims to develop a family of Bayesian group chain sampling plans. The following plans are developed in this study: Bayesian group chain sampling plan (BGChSP), Bayesian new group chain sampling plan (BNGChSP), Bayesian modified group chain sampling plan (BMGChSP), Bayesian two sided group chain sampling plan (BTSGChSP), Bayesian new two sided group chain sampling plan (BNTSGChSP) and Bayesian two sided complete group chain sampling plan (BTSCGChSP). These plans consider multiple product inspections and use the past information of the product as prior distribution. To estimate the average proportion of defectives, binomial distribution is used with beta distribution as prior distribution. Meanwhile, to estimate the average number of defectives, Poisson distribution is used with gamma distribution as prior distribution. Four quality regions are estimated, namely, probabilistic quality region (PQR), quality decision region (QDR), limiting quality region (LQR) and indifference quality region (IQR). For all quality regions, acceptable quality level (AQL) associated with producer’s risk and limiting quality level (LQL) associated with consumer’s risk, are assessed. Simulated work is done by using R language computer-based programs and operating characteristic (OC) curves are used to monitor the effect of design parameters and for measuring performance between the proposed plans. Findings indicate that all the proposed plans provide a smaller number of defectives compared to the existing non-Bayesian plans. This would be very beneficial to practitioners, especially those involved with destructive testing of high-quality products. 2022 Thesis https://etd.uum.edu.my/9809/ https://etd.uum.edu.my/9809/1/permission%20to%20deposit-allow-902911.pdf text eng 2025-05-10 staffonly https://etd.uum.edu.my/9809/2/s902911_01.pdf text eng 2025-05-10 staffonly https://etd.uum.edu.my/9809/3/s902911_02.pdf text eng 2025-05-10 staffonly other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Aziz, Nazrina
topic	TS155-194 Production management Operations management HD56-57.5 Industrial Productivity HD61 Risk Management
spellingShingle	TS155-194 Production management Operations management HD56-57.5 Industrial Productivity HD61 Risk Management Hafeez, Waqar Developing a family of Bayesian group chain sampling plans for quality regions
description	Acceptance sampling is used to decide about the lot under inspection, either to accept or to reject. Various acceptance sampling plans under group chain consider only consumer’s risk to develop the plan, but this study focuses on both consumer’s and producer’s risks. If past information about the product is available, then Bayesian approach is the best approach to make a decision. This research aims to develop a family of Bayesian group chain sampling plans. The following plans are developed in this study: Bayesian group chain sampling plan (BGChSP), Bayesian new group chain sampling plan (BNGChSP), Bayesian modified group chain sampling plan (BMGChSP), Bayesian two sided group chain sampling plan (BTSGChSP), Bayesian new two sided group chain sampling plan (BNTSGChSP) and Bayesian two sided complete group chain sampling plan (BTSCGChSP). These plans consider multiple product inspections and use the past information of the product as prior distribution. To estimate the average proportion of defectives, binomial distribution is used with beta distribution as prior distribution. Meanwhile, to estimate the average number of defectives, Poisson distribution is used with gamma distribution as prior distribution. Four quality regions are estimated, namely, probabilistic quality region (PQR), quality decision region (QDR), limiting quality region (LQR) and indifference quality region (IQR). For all quality regions, acceptable quality level (AQL) associated with producer’s risk and limiting quality level (LQL) associated with consumer’s risk, are assessed. Simulated work is done by using R language computer-based programs and operating characteristic (OC) curves are used to monitor the effect of design parameters and for measuring performance between the proposed plans. Findings indicate that all the proposed plans provide a smaller number of defectives compared to the existing non-Bayesian plans. This would be very beneficial to practitioners, especially those involved with destructive testing of high-quality products.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Hafeez, Waqar
author_facet	Hafeez, Waqar
author_sort	Hafeez, Waqar
title	Developing a family of Bayesian group chain sampling plans for quality regions
title_short	Developing a family of Bayesian group chain sampling plans for quality regions
title_full	Developing a family of Bayesian group chain sampling plans for quality regions
title_fullStr	Developing a family of Bayesian group chain sampling plans for quality regions
title_full_unstemmed	Developing a family of Bayesian group chain sampling plans for quality regions
title_sort	developing a family of bayesian group chain sampling plans for quality regions
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/9809/1/permission%20to%20deposit-allow-902911.pdf https://etd.uum.edu.my/9809/2/s902911_01.pdf https://etd.uum.edu.my/9809/3/s902911_02.pdf
_version_	1747828675647111168

Developing a family of Bayesian group chain sampling plans for quality regions

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