The Performance of Empirical Bayes Based on Weighted Squared Error Loss and K-Loss Functions in Skip Lot Sampling Plan with Resampling

Skip lot sampling plans can be applied to manufacturing process to reduce sample size and inspection costs in the lot. In this paper, the objective is to propose the Empirical Bayes approach based on weighted squared error loss (WSEL) and K-loss (KL) functions in skip lot sampling plan with resampling (SkSP-R) on variables sampling plan for lot inspection with normally distributed data, assuming unknown mean and unknown variance. The proposed plans are also compared to traditional plans including skip lot sampling plan 2 (SkSP-2) and SkSP-R with single sampling plan (SSP) as a reference plan. The probability of acceptance (P-a), average sample number (ASN), and average total inspection (ATI) are considered as criteria for comparison. Afterwards, the proposed plan is applied to real data, amplified pressure sensor process. The results indicated that the proposed method yielded the smallest ASN and ATI but the highest P-a.