Design of Multiple Dependent State Sampling Plan Application for COVID-19 Data Using Exponentiated Weibull Distribution

The proposed sampling plan in this article is referred to as multiple dependent state (MDS) sampling plans, for rejecting a lot based on properties of the current and preceding lot sampled. The median life of the product for the proposed sampling plan is assured based on a time-truncated life test, when a lifetime of the product follows exponentiated Weibull distribution (EWD). For the proposed plan, optimal parameters such as the number of preceding lots required for deciding whether to accept or reject the current lot, sample size, and rejection and acceptance numbers are obtained by the approach of two points on the operating characteristic curve (OC curve). Tables are constructed for various combinations of consumer and producer’s risks for various shape parameters. The proposed MDS sampling plan for EWD is demonstrated using the coronavirus (COVID-19) outbreak in China. The performance of the proposed sampling plan is compared with the existing single-sampling plan (SSP) when the quality of the product follows EWD.

Implication of Production and Monitoring Techniques in Bayesian Single Sampling Plan using Gamma-Zero Inflated Poisson Distribution

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.

Modified chain sampling plan for assuring percentile life under Weibull distribution and generalized exponential distribution

Purpose The purpose of this paper is to design a modified chain sampling plan for assuring the product percentile life where the lifetime follows Weibull or generalized exponential distributions (GEDs). In order to reduce the cost of inspection when implementing the proposed modified chain sampling plan, it is also considered the economic aspect of designing of proposed plan in this paper. Design/methodology/approach The authors have designed the proposed plan on the basis of two points on the operating characteristic (OC) curve approach. The optimization problem is used to determine the plan parameters of the proposed plan so that the specified values of producer’s risk and consumer’s risk are satisfied simultaneously. Findings The results we have obtained, confirm that the proposed plan will be very effective in reducing the sample size rather than other existing sampling plans. The OC curves of proposed plan, chain sampling plan and zero acceptance number single sampling plan show that the performance of proposed plan in discriminating the good and poor quality lots is better than other two plans. In this paper, it is proved that the value of number of preceding lots required for current lot disposition plays an important role. Originality/value The proposed modified chain sampling plan for assuring the percentile lifetime of the products under Weibull or GEDs is not available in the literature. The proposed plan can be used in all the manufacturing industries to assure the product percentile lifetime with minimum sample size as well as minimum cost.