Optimal reliability acceptance sampling plan based on accelerated life test data

Ramyamol, P. C.; Kumar, M.

doi:10.1504/ijpqm.2020.108377

Cited by 2 publications

(2 citation statements)

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“…Guha and Bose (2020) presented two modifications: one for inventory computations and the other for non-destructive testing. The similar argument of truncated mean lifetime test are studied for exponential, Pareto and Weibull distributions in the studies of Ramyamol and Kumar (2020) and Wang et al (2020), respectively. Yen et al (2020) provided a repetitive rectifying SP and process capability index to minimize the total quality cost of sentencing one lot, including inspection cost, internal failure cost and external failure cost.…”

Section: Literature Reviewmentioning

confidence: 99%

A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry

Yüksel

Kazançoğlu

Sarma

2021

IJQRM

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PurposeThis paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.Design/methodology/approachBased on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.FindingsA three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.Practical implicationsThe current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.Originality/valueAcceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

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Section: Literature Reviewmentioning

confidence: 99%

A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry

Yüksel

Kazançoğlu

Sarma

2021

IJQRM

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“…There is tough competition in the industry by rapidly increasing the needs of statistical and analytical techniques towards the improvement of product quality [1][2][3]. This study is related to Bayesian group chain sampling plan (BGChSP) by using a novel approach called quality region.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Group Chain Sampling Plan for Poisson Distribution with Gamma Prior

Hafeez¹,

Aziz²,

Zain³

et al. 2022

Computers, Materials &Amp; Continua

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Acceptance sampling is a statistical quality control technique that consists of procedures for sentencing one or more incoming lots of finished products. Acceptance or rejection is based on the inspection of sampled products drawn randomly from the lot. The theory of previous acceptance sampling was built upon the assumption that the process from which the lots are produced is stable and the process fraction nonconforming is a constant. Process variability is inevitable due to random fluctuations, which may inadvertently lead to quality variation. As an alternative to traditional sampling plans, Bayesian approach can be used by considering prior information of the process. Using different combinations of design parameters, this study introduces a Bayesian group chain sampling plan (BGChSP). For the first time in group chain sampling plan, the probability of lot acceptance is derived by using Poisson distribution to estimate an average number of defectives. Gamma distribution is used as a prior distribution with Poisson distribution. Taking into account both consumer's and producer's risks, this research considers two quality regions namely, probabilistic quality region (PQR) and indifference quality region (IQR). By minimizing consumer's and producer's risks, BGChSP can be used to minimize the average number of defective products in industry.

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Optimal reliability acceptance sampling plan based on accelerated life test data

Cited by 2 publications

References 0 publications

A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry

A multiphase acceptance sampling model by attributes to investigate the production interruptions in batch production within tobacco industry

Bayesian Group Chain Sampling Plan for Poisson Distribution with Gamma Prior

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