Designing a yield-based skip-lot sampling plan for lot acceptance determination

Wu, Chien‐Wei; Chen, Jr-Tzung; Liu, Shih‐Wen

doi:10.1080/01605682.2020.1854630

Cited by 9 publications

(1 citation statement)

References 36 publications

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“…Process capability indices (PCIs) are used to measure and evaluate the quality of processes and products. In recent years, many scholars have developed numerous variables sampling plans based on various PCIs for different scenarios, such as Wu, 5 Wu et al, 6 Seifi and Nezhad, 7 Wu et al, 8 Lee et al, 9 Wu and Liu, 10 Wang and Wu, 11 Arizono et al, 12 Wu et al 13 and Wang et al. 14 Many popular PCIs, such as 𝐶 𝑝 , 𝐶 𝑝𝑘 , 𝐶 𝑝𝑚 , and 𝐶 𝑝𝑚𝑘 , are based on the following assumptions: (i) the process must be stable and not easily altered due to any factors.…”

Section: Introductionmentioning

confidence: 99%

A repetitive group sampling plan based on the lifetime performance index under gamma distribution

Lee

Liu

2022

Quality & Reliability Eng

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As technology and living standards improve, people are becoming more concerned with the importance of quality. Quality, thus, becomes the key to company competitiveness, and process capability indices (PCIs) are effective means of measuring process performance. Most PCIs have been developed under the assumption that the quality characteristics are normally distributed; however, the lifetime of products generally possesses a non‐normal distribution. A lifetime performance index (LPI) CL has been proposed and used for measuring product lifetime performance. In this high‐quality era, the use of traditional testing methods incurs very high costs, so acceptance sampling plans have been widely used by industry to perform the inspection. This study considers the product lifetime with a gamma distribution and uses the uniformly minimum‐variance unbiased estimator (UMVUE) of the LPI to develop a repetitive group sampling plan (RGSP). For the practical purpose, this study presents a step‐by‐step procedure and tabulates the plan parameters for various combinations of acceptable and rejectable quality levels, producer's and consumer's risks, and shape parameter. Thus, practitioners can easily know the sample size and the corresponding critical values for decision making. This study also uses the results of the proposed plan to compare with the single sampling plan. Lastly, a practical example is used to illustrate how to apply this acceptance sampling plan.

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