Implication of Production and Monitoring Techniques in Bayesian Single Sampling Plan using Gamma-Zero Inflated Poisson Distribution
V.Kaviyarasu1, P.Sivakumar2
1V.Kaviyarasu, Department of Statistics, Bharathiar University, Coimbatore, Tamilnadu, India.
2P.Sivakumar, Department of Statistics, Bharathiar University, Coimbatore Tamilnadu, India. 

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 10110-10119 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5136098319/2019©BEIESP | DOI: 10.35940/ijrte.C5136.118419

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Abstract: This article explores the problem of investigate Single Sampling Plan (SSP) by attributes under Bayesian theory and illuminate its importance methodology in manufacturing industries. The modern technological advancements and well monitoring of the production process are facilitate to enhance the standard of product. In such situation products are not meeting the specified quality standards is a rare phenomenon. However, random fluctuations in producing processes might lead some merchandise to an imperfect quality. It has been assumed that the number of defects per unit of product follows a Zero Inflated Poisson distribution (ZIP) and the Gamma distribution is the conjugate prior to the average number of non-conformities per item. This article proposed a new sampling procedure as Bayesian Single Sampling plan (BSSP) using Gamma-Zero Inflated Poisson (G-ZIP) distribution. Necessary tables for the selection of optimal plan parameters and numerical illustrations were made for this sampling plan. Furthermore, the applicability and usefulness of the proposed Bayesian sampling plan under the G-ZIP model have been demonstrated by a few examples and comparisons were made with other sampling plans.
Keywords: Single Sampling Plan By Attribute, Bayesian Methodology, Gamma Prior, Zero Inflated Poisson Distribution (ZIP), Producer and Consumer Risk.
Scope of the Article: Emulation and Simulation Methodologies for IoT.