Open AccessOptimization of Bayesian repetitive group sampling plan for quality determination in Pharmaceutical products and related materials
Author(s) -
V. Kaviyarasu,
Palanisamy Sivakumar
Publication year - 2022
Publication title -
international journal of industrial engineering computations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.564
H-Index - 26
eISSN - 1923-2926
pISSN - 1923-2934
DOI - 10.5267/j.ijiec.2021.9.001
Subject(s) - acceptance sampling , poisson distribution , sampling (signal processing) , bayesian probability , quality assurance , quality (philosophy) , computer science , plan (archaeology) , operations research , statistics , sample size determination , mathematics , engineering , operations management , philosophy , external quality assessment , archaeology , filter (signal processing) , epistemology , computer vision , history
Sampling plans are extensively used in pharmaceutical industries to test drugs or other related materials to ensure that they are safe and consistent. A sampling plan can help to determine the quality of products, to monitor the goodness of materials and to validate the yields whether it is free from defects or not. If the manufacturing process is precisely aligned, the occurrence of defects will be an unusual occasion and will result in an excess number of zeros (no defects) during the sampling inspection. The Zero Inflated Poisson (ZIP) distribution is studied for the given scenario, which helps the management to take a precise decision about the lot and it can certainly reduce the error rate than the regular Poisson model. The Bayesian methodology is a more appropriate statistical procedure for reaching a good decision if the previous knowledge is available concerning the production process. This article proposed a new design of the Bayesian Repetitive Group Sampling plan based on Zero Inflated Poisson distribution for the quality assurance in pharmaceutical products and related materials. This plan is studied through the Gamma-Zero Inflated Poisson (G-ZIP) model to safeguard both the producer and consumer by minimizing the Average Sample Number. Necessary tables and figures are constructed for the selection of optimal plan parameters and suitable illustrations are provided that are applicable for pharmaceutical industries.