Abstract
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.
Highlights
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
The presented work is limited to BGChSP and estimate probabilistic quality region (PQR) and indifference quality region (IQR) for the specified producer’s and consumer’s risks
Summary
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. Bayesian approach is based on the combination of current and prior information for the selection of distribution. Bayesian approach is required to specify a distribution of defectives from lot to lot [7]. Prior distribution is formulated before taking the sample and empirical knowledge is based on the sample under study called the distribution of sample or data. The combination of prior and empirical information leads to a decision about the current lot either it will be accepted or rejected
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