Acceptance Sampling Plans Research Articles

Integrating order statistics in reliability acceptance sampling plans for a compound distribution: a case study

Integrating order statistics in reliability acceptance sampling plans for a compound distribution: a case study

Optimizing Group Size using Percentile Based Group Acceptance Sampling Plans with Application

The present paper focuses on optimizing the sample size and the acceptance number which are commonly known as design parameters for the group acceptance sampling plan (GASP). Design parameters are analyzed under the assumption that the characteristic of interest for the product follows Another Generalized Transmuted-Exponential (AGTransmuted-Exponential) distribution. Using different values of the quality levels (median lifetime), the values of the Operating Characteristic function is determined. The proposed plan satisfies two types of risks based on producer's point of view and consumer’s point of view at varied stipulated quality levels. Optimization of the sample size, group size and acceptance numbers is obtained through Monte Carlo simulation, for which relevant R codes were developed. Specific R codes are appended for future usage by academia and practitioners from various fields of life. The simulated results of the study are exhibited in the form of tables and explained with relevant examples. Results of the proposed GASP are compared with plan parameters obtained using MLE estimates of AGTransmuted-Exponential distribution and also by design parameters obtained using mean as quality level. Results of the study exhibited that Median as a quality parameter resulted in the decrease of group size and acceptance number simultaneously at all quality levels. Easy to follow the methodology of the current paper will open new vistas for applying the proposed GASP to a family of transmuted probability distributions. For illustration purposes, a real data set for fatigue fracture stress is analyzed using MLE estimates of AGTransmuted-Exponential distribution to demonstrate the implementation of the proposed sampling plan.

Performance evaluation study of reliability acceptance sampling plans based on compound exponential-geometric distribution

Performance evaluation study of reliability acceptance sampling plans based on compound exponential-geometric distribution

Group acceptance sampling plan based on truncated life tests for Type-I heavy-tailed Rayleigh distribution

This study on the Type-I heavy-tailed Rayleigh (TI-HTR) distribution is a special case of Type-I heavy-tailed (TI-HT) family of distributions was studied. The characteristics were derived, including the moment and its measures, quantile function, reliability measures, and other statistical properties as well as parameter estimation using the maximum likelihood method and penalized likelihood estimation. The behavior of its various functions were shown graphically. Analytically, we showed that model linearly grows near the origin and exhibits rapid exponential decay. However, the tail behavior cannot equal the traditional heavy-tail in the power law sense, hence it is called the type-I heavy-tail. Interestingly, we designed a group acceptance plan (GASP) and demonstrated usefulness with both assumed and maximum likelihood estimates. The GASP under the TI-HTR distribution is preferable when the parameter values are small. The distribution was used to model real-life data sets to justify its usefulness. The results of the application of the model to both COVID-19 and Cancer data showed that the model fits the two data better than the competing models and also suggest that inference from the model is better than those of the competitors. In estimating the parameters, the penalized likelihood procedure perform considerably better with minimum standard error of the estimates. From the Cramér-von Mises test results which guides against the heavy-tail sensitivity, the TI-HTR distribution offers a better model for fitting fast decaying exponential data since it has the least statistics in both datasets.

Enhancing quality in Lot reception: A comparative analysis of innovative attribute acceptance sampling plans

Purpose: This study aims to enhance production quality by applying Quality Control (QC) principles through acceptance sampling, specifically analyzing the efficacy of attribute acceptance sampling plans in final lot receptions.Design/methodology/approach: Through a comprehensive review and critical evaluation of various sampling methodologies found in literature, this paper assesses their efficiency under distinct administrative and operational conditions. It emphasizes the comparison of different attribute acceptance sampling plans by examining variations in parameters and key performance indicators, such as Average Outgoing Quality (AOQ) and inspection time allocation percentage. Furthermore, it proposes a model for Continuous Sampling Plans (CSP) to evaluate these plans' performance in response to operational characteristic variations.Findings: The analysis reveals that the selected methodologies significantly aid in decision-making processes for lot acceptance, utilizing non-conforming rates depicted by the Average Quality Level (AQL). This provides a robust framework for improving Quality Control strategies, demonstrating the potential of these methodologies to optimize production quality through strategic lot acceptance.Practical implications: This paper outlines a practical approach for industry practitioners to enhance decision-making in lot acceptance, offering a method to balance quality control with operational efficiency effectively.Originality/value: By comparing a wide range of attribute acceptance sampling plans and introducing a novel CSP model, this research contributes valuable insights into the optimization of QC strategies. It offers a unique perspective on enhancing production quality, marking a significant advancement in the field of Quality Control and management.

Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type-I hybrid censoring

Acceptance sampling plans with censoring schemes are crucial for improving quality control by efficiently managing incomplete information. This approach improves cost and time effectiveness compared to traditional methods, providing a more accurate assessment of product quality. In this study, a variable acceptance sampling plan under Type-I hybrid censoring is designed for a lot of independent and identical units with exponential lifetimes using Bayesian estimation of the mean life. This novel approach diverges from conventional methods in acceptance sampling plans, which rely on maximum likelihood estimation and the minimization of Bayes risk. Bayesian estimation is obtained using both squared error loss and Linex loss functions. Under each method, a nonlinear optimization problem is solved to minimize the testing cost, and the optimal values of the plan parameters are determined. The proposed plans are illustrated using various numerical examples, with each plan presented in tables. The acceptance sampling plan using the squared error loss function proves to be more cost-effective than the plan using the Linex loss function. A comparative analysis of the proposed plans with existing work in the literature demonstrates that our cost is much lower than the cost of existing plans using maximum likelihood estimation. Additionally, a real-life case study is conducted to validate the approach.

Two-stage group reliability acceptance sampling plan for Lindley distribution

Two-stage group reliability acceptance sampling plan for Lindley distribution

Acceptance sampling plans based on percentiles for extended generalized exponential distribution with real data application

In this paper, when the life test is truncated at a pre-determined time, new acceptance sampling plans (ASP) are developed based on percentile lifetime as a quality parameter for the extended generalized exponential distribution (EGED). An inspection of a representative sample of goods or materials from a big lot or batch is used in quality control testing under acceptance sampling plans to assess whether the lot satisfies predetermined quality requirements. For the suggested new acceptance sampling plans, the minimum sample sizes needed to emphasize the determined lifetime percentile are obtained for a given customer's risk. The sample plans' operating characteristic function values and the producer's risk are built at various quality levels for some distribution parameters. Some numerical examples are presented to show how the suggested ASP can be used. A genuine industrial data set from a top Korean automobile is fitted to the EGED relative to other competitors and the applicability of the suggested ASP is illustrated. It is found that the suggested ASP for the EGED can be applied by the researchers in production engineering fields.

Developing variables two-plan sampling scheme with consideration of process loss for lot sentencing

This paper considers a well-known two-plan sampling system, Tightened-Normal-Tightened (TNT) sampling scheme, which includes two single sampling plans (SSPs) with regulations for transitioning between tightened and normal inspections. Tightened inspection is conducted when there is a noticeable decline in quality, and normal inspection is employed when the quality is satisfactory. Most acceptance sampling plans evaluate product quality in terms of process yield, which does not differentiate between products that fall within specification limits. To address this issue, a new performance measure called the process loss index Le was developed, which accounts for the quality loss function. This index is determined by dividing the “expected quadratic loss” by the “square of half the specification width.” Using this index, two primary types of variables TNT sampling scheme were constructed and evaluated in this study. Their efficacy and features were investigated, discussed, and compared. The findings reveal that the TNT sampling strategy delivers the required quality assurance with a reduced sample size compared to the conventional SSP.

Statistical Inferences about Parameters of the Pseudo Lindley Distribution with Acceptance Sampling Plans

Different non-Bayesian and Bayesian techniques were used to estimate the pseudo-Lindley (PsL) distribution’s parameters in this study. To derive Bayesian estimators, one must assume appropriate priors on the parameters and use loss functions such as squared error (SE), general entropy (GE), and linear-exponential (LINEX). Since no closed-form solutions are accessible for Bayes estimates under these loss functions, the Markov Chain Monte Carlo (MCMC) approach was used. Simulation studies were conducted to evaluate the estimators’ performance under the given loss functions. Furthermore, we exhibited the adaptability and practicality of the PsL distribution through real-world data applications, which is essential for evaluating the various estimation techniques. Also, the acceptance sampling plans were developed in this work for items whose lifespans approximate the PsL distribution.

Time Truncated Attribute Median Control Charts for Logistic-Exponential Process Distribution

Abstract In this article, time truncated attribute control chart is proposed for logistic-exponential distribution based on four acceptance sampling plans. The performance of the proposed control chart is evaluated in terms of average run length. Furthermore, the efficiency of the proposed attribute control chart is compared for all given acceptance sampling plans, viz., single sampling plan, repetitive sampling plan, multiple dependent state sampling plan and multiple dependent state repetitive sampling plan. Also, the application of the proposed control chart is illustrated by using real life example.

Designing acceptance sampling plans for polynomial profiles using EWMA statistics based on process yield index

Inspection of raw materials, semi-finished products or final products is an important part of a quality assurance system. Acceptance sampling is a procedure used to decide either to accept or reject suppliers’ submitted lots in accordance with a specific standard. In this manuscript, designing acceptance sampling plans for lots with polynomial profile as quality characteristic is considered. To use previous lot information on deciding for accept or reject the available lot, exponentially weighted moving average (EWMA) statistic based on process yield index (Spk ) of polynomial profiles with two-sided specification limits is applied. For this purpose, three acceptance sampling plans of repetitive group sampling (RGS), resubmitted and also combined RGS-resubmitted plans are developed in this paper. The Parameters of the proposed plans are determined using non-linear optimization model. Performance of the proposed acceptance sampling plans are compared using numerical methods and Monte-Carlo simulations. Simulation results emphasize that the proposed combined sampling plan mostly requires less average sampling number (ASN) and consequently leads to decreasing inspection time and costs. The application of the proposed combined plan is also demonstrated using a real case.

Constructing Optimal Quick Switching System with Hurdle Poisson Distribution

Objectives: Optimizing the sum of risks involved in the selection of acceptance sampling plans playing a vital role. This paper uses the Hurdle Poisson distribution to design an optimal quick switching system attribute plan for a given acceptable quality level (AQL) and limiting quality level (LQL) involving a minimum sum of risks. Methods: The sum of producer's and consumer's risks has been met for the specified AQL and LQL. The sum of these risks, as well as the acceptance and rejection numbers have been calculated using the Hurdle Poisson distribution. The operating characteristic function for the quick switching system attribute plan has also been derived using the Hurdle Poisson distribution. Findings: The producer and the consumer both represent the same party in the final inspection. As a result, the sum of these two risks should be minimized. In this paper, the sum of risks for various operating ratios are tabulated using the Hurdle Poisson distribution. These tabulated values are less than the sum of risks calculated using the Weighted Poisson distribution. Novelty: Reducing the sum of risks is the ultimate aim of the work. In this proposed paper, to attain the minimum sum of risks, the authors make an attempt to approach the Quick Switching System Sampling Plan, when the number of defectives in the submitted lots are very less. In other words, the probability of getting defective is very less. This indicates the quality of the lot selected for the inspection to ensure the protection for the consumer. And the plan is also designed in the way that the producer is also not get affected by rejecting a good lot by the consumer. This is the requirement of minimizing the risks. Keywords: Acceptable Quality Level (AQL), Limiting Quality Level (LQL), Minimum Risk Plan, Quick Switching System Sampling Attribute Plan, Operating Characteristic (OC) Function, Hurdle Poisson Distribution

Two-Stage Life Test Acceptance Sampling Plans for the Weibull Distribution using Trinomial Distribution

In materials engineering applications, lifetime of product is one of the quality characteristics of interest. Lifetimes of products are inspected in life tests upto a predetermined time t0, which may observe early failure of some of the products; incorporation of this fact may not only reduce the cost of inspection, but also provide some more insight into the failure mechanism. In this paper, we consider a two-stage time censoring scheme by dividing test truncation time t0 into two parts [.. formula..] and observe the number of failures in each interval, considering the remainder items as censored at t0. The trinomial distribution has been used to develop life test acceptance sampling plans for establishing quantile life of items having Weibull lifetimes. The proposed life test sampling plan gives added advantage to producers and extra safeguard to consumers, as compared to the life test sampling plans available in the literature for the Weibull distribution. Tables of plan parameters are constructed through code written in R 4.1.2, and illustrated for some real-life industrial cases.. KEYWORDS :Quantile, Two-stage censoring, Trinomial distribution, Weibull distribution.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk -based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners.

A cost-effective skip-lot sampling scheme using loss-based capability index for product acceptance determination

Acceptance sampling plans are essential for quality control and assurance, empowering businesses to make well-informed decisions regarding lot disposition. Among these plans, the skip-lot sampling plan (SkSP) stands out as a cost-effective strategy for suppliers with consistently excellent product quality. In this article, we present an enhanced version of the SkSP, termed Cpm-based SkSP-RGSP, which incorporates a repetitive group sampling plan (RGSP) using the Taguchi capability index Cpm as its reference plan. We develop an optimization model to ensure that the proposed plan meets a two-point condition in its operating characteristic function and effectively minimizes the average sample number (ASN). Through investigations of two critical performance measures, ASN and discriminatory power, we demonstrate the superiority of our plan over conventional sampling approaches. Detailed plan parameters, including required sample size and lot sentencing criteria, are provided to assist examiners in practical implementation. Additionally, we showcase the plan's applicability and practicality through a case study. This research contributes to the field of quality control by offering an efficient and cost-effective sampling scheme for lot disposition.

Optimal double acceptance sampling plans based on truncated life tests for Tsallis q-exponential distributions

Optimal double sampling plans for lot acceptance based on truncated life tests are derived by minimizing the expected sample size when the lifetime variable follows a Tsallis q-exponential distribution. The proposed test plans significantly reduce the inspection effort with respect to the best single sampling schemes. Some tables and figures are presented to analyze the behavior of the suggested test plans. The results show that the proposed lot inspection scheme clearly outperforms the standard single sampling plan. A justification is provided using a comparative study for existing double sampling plans. Finally, several applications are provided for illustrative purposes.

Optimal time truncated double acceptance sampling plan for generalized half normal distribution

Optimal time truncated double acceptance sampling plan for generalized half normal distribution

Bayesian reliability acceptance sampling plans under interval censoring

. This work considers Bayesian reliability acceptance sampling plan (RASP) under interval censoring. It is considered that the consumer and manufacturer agree on a common lifetime distribution of the product. However, they differ in their assessment of the prior distributions and utility functions because of the adversarial nature of the consumer and manufacturer, but the manufacturer knows the consumer’s utility and prior. The consumer accepts or rejects a lot of products based on his/her own priors about the quality of the product and a utility function. If the consumer rejects the lot, the manufacturer gives additional information on lifetime based on a life test to update the consumer’s belief. The consumer decides on accepting or rejecting the lot based on updated information. The life test is conducted under an interval censoring scheme, where the decision on acceptance or rejection of the lot can be taken sequentially at pre-specified inspection times. The manufacturer’s task is to determine the optimal interval-censoring life testing plan which maximizes his/her utility. Last, we discuss a methodology where the manufacturer does not require knowledge of the consumer’s utility function and prior distribution for planning the life test.