Designing and Selection of Bayesian Skip Lot Sampling Plan Type BSkSP-2 with Single Sampling Plan Under Zero-Inflated Poisson Distribution

Abstract

Sampling plans are effective statistical technique for testing and deciding whether to accept or not a lot based on quality inspection studied under highway construction and materials. This article presents a designing methodology for the selection of Bayesian Skip lot Sampling Plan type 2 (BSkSP-2) based on Gamma-Zero Inflated Poisson (GZIP) distribution. A Gamma distribution is a baseline distribution that is to be considered as a conjugate prior for Zero-Inflated Poisson (ZIP) distribution. Generally, ZIP distribution is used for count data with an excessive number of zeros. In this article, ZIP distribution is mainly focused upon to study the occurrence of defective units in a well-designed manufacturing environment. In such case, one can develop an attribute sampling plan under the SkSP-2 through a G-ZIP model, to protect both the producers and consumers with the minimum inspection efforts rather than the conventional Poisson distribution. Necessary tables are developed for the proposed sampling plan and numerical illustrations are made for the applications on highway construction and materials are given for the execution of the plan.