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1 – 10 of over 126000Elart von Collani and Klaus Meder
The most frequently used attribute sampling plan in MIL‐STD 105 D. In cases, however, when the quality level of incoming lots is generally sufficiently good, MIL‐STD 105 D often…
Abstract
The most frequently used attribute sampling plan in MIL‐STD 105 D. In cases, however, when the quality level of incoming lots is generally sufficiently good, MIL‐STD 105 D often leads to unnecessarily high sampling cost. This can be avoided by using α‐optimal sampling plans. The authors outline the α‐optimal sampling scheme along with a simple procedure to determine α‐optimal sampling plans at workshop level. These plans depend on three parameters which have to be estimated from recorded data. In this article the effects of estimation errors in these parameters are investigated.
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Health care insurance companies often conduct sample surveys of health plan members. Survey purposes include: consumer satisfaction with the plan and members’ health status…
Abstract
Health care insurance companies often conduct sample surveys of health plan members. Survey purposes include: consumer satisfaction with the plan and members’ health status, functional status, health literacy and/or health services utilization outside of the plan. Vendors or contractors typically conduct these surveys for insurers. Survey results may be used for plans’ accreditation, evaluation, quality improvement and/or marketing. This article describes typical sampling plans and data analysis strategies used in these surveys, showing how these methods may result in biased estimators of population parameters (e.g. percentage of plan members who are satisfied). Practical suggestions are given to improve these surveys: alternate sampling plans, increasing the response rate, component calculation for the survey response rate, weighted analyses, and adjustments for unit non-response. Since policy, regulation, accreditation, management and marketing decisions are based, in part, on results from these member surveys, these important and numerous surveys need to be of higher quality.
Rafaela Aparecida Mendonça Marques, Aline Cristina Maciel, Antonio Fernando Branco Costa and Kleber Roberto da Silva Santos
This study investigates the repetitive mixed sampling (MRS) plan based on the Cpk index that was proposed by Aslam et al. (2013a). They were the first to study the MRS plan, but…
Abstract
Purpose
This study investigates the repetitive mixed sampling (MRS) plan based on the Cpk index that was proposed by Aslam et al. (2013a). They were the first to study the MRS plan, but they did not pay attention to the fact that submitting to the variable inspection a sample that was first submitted to the attribute inspection, truncates the X observations. In addition, they did not work with an accurate expression to calculate the probabilities of the Cpk statistic.
Design/methodology/approach
The authors presented the results based on their original sampling plan through Monte Carlo simulation and defined the theoretical results of their plan when the sample submitted to the variable inspection is no longer the same one submitted to the attribute inspection.
Findings
The β risks of the optimum sampling plans presented by Aslam et al. (2013a) are pretty high, exceeding 46%, on average – this same problem was also observed in Saminathan and Mahalingam (2018), Balamurali (2020) and Balamurali et al. (2020), where the β risks of their proposed sampling plans are yet higher.
Originality/value
In terms of originality, the authors can declare the following. It is not a big deal to propose new sampling plans, if one does not know how to obtain their properties. The miscalculations of the sampling plans risks are dangerous; imagine the situation where the acceptance of bad lots exceeds 50% just because the sampling plan was incorrectly designed. Yes, it is a big deal to warn that this type of problem is arising in a growing number of papers. The authors of this study are the pioneers to discover that many studies focusing on the sampling plans need to be urgently revised.
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Belmiro P.M. Duarte and Pedro M. Saraiva
This purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of…
Abstract
Purpose
This purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of non‐conforming items being modeled by a Poisson probability distribution function.
Design/methodology/approach
The paper approach stands upon the minimization of the error of the probability of acceptance equalities in the controlled points of the operating curve (OC) with respect to sample size and acceptance number. It was applied to simple and double sampling plans, including several combinations of quality levels required by the producer and the consumer. Formulation of the design of acceptance sampling plans as an optimization problem, having as a goal the minimization of the squared error at the controlled points of the OC curve, and its subsequent solution employing GAMS.
Findings
The results are in strong agreement with acceptance sampling plans available in the open literature. The papers approach in some scenarios outperforms classical sampling plans and allows one to identify the lack of feasible solutions.
Originality/value
An optimization‐based approach to support the design of acceptance sampling plans for attributes was conceived and tested. It allows for a general treatment of these problems, including the identification of a lack of feasible solutions, as well as making possible the determination of feasible alternatives by relaxing some model constraints.
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This research aims to investigate the differences in designing the zero acceptance number single sampling plans using the apparent fraction of nonconforming and the binomial…
Abstract
Purpose
This research aims to investigate the differences in designing the zero acceptance number single sampling plans using the apparent fraction of nonconforming and the binomial distribution against the exact convolute compound hypergeometric distribution when both types of inspection errors are present.
Design/methodology/approach
This research presents the derivation and uses the numerical study to compare the calculated probability of acceptance and the minimum sample size when using the present design concept of binomial distribution with true fraction of nonconforming replaced with the apparent one. Under the presences of inspection errors and zero acceptance number, the probability of acceptance is alternatively derived and presented in term of a function of the probability generating function. This research uses numerical method to determine the differences in the probability of acceptance. The computation of the minimum sample sizes are presented along with the numerical results and the comparison.
Findings
When the inspection errors are present, the probability of acceptance is extremely decreased even for 1 percent of inspection errors of Type I (rejecting good product) and Type II (accepting bad product). The binomial apparent nonconforming notions yields an over‐estimation of the probability of acceptance, comparing with the exact convolute compound hypergeometric notion under the zero acceptance single sampling plans especially at low fraction of nonconforming levels, the six sigma quality levels. The differences of the calculated probabilities of acceptance and the minimum sample sizes decrease as the inspection error of Type II increases given a fixed value of Type I error and consumer risk.
Originality/value
This research alternatively presents the mathematical derivation along with numerical study to assert the over‐estimation of the probability of acceptance and the minimum sample size if the existing methodology to design the zero acceptance number single sampling plans is used. This finding will help improve the sampling design strategy of the multistage production system.
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Selects one of Hamaker′s procedures for deriving a “σ” method (i.e. known process standard deviation) double sampling plan and exploits some of its properties to develop a system…
Abstract
Selects one of Hamaker′s procedures for deriving a “σ” method (i.e. known process standard deviation) double sampling plan and exploits some of its properties to develop a system of “s” method (i.e. unknown process standard deviation) double sampling plans by variables that match the system of single specification limit “s” method single sampling plans of the current edition of the international standard on sampling by variables. ISO 3951: 1989. The new system is presented in two forms, the second of which may also be used for combined double specification limits and multivariate acceptance sampling.
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Nataliya Chukhrova and Arne Johannssen
The purpose of this paper is to construct innovative exact and approximative sampling plans for acceptance sampling in statistical quality control. These sampling plans are…
Abstract
Purpose
The purpose of this paper is to construct innovative exact and approximative sampling plans for acceptance sampling in statistical quality control. These sampling plans are determined for crisp and fuzzy formulation of quality limits, various lot sizes and common α- and β-levels.
Design/methodology/approach
The authors use generalized fuzzy hypothesis testing to determine sampling plans with fuzzified quality limits. This test method allows a consideration of the indifference zone related to expert opinion or user priorities. In addition to the exact sampling plans calculated with the hypergeometric operating characteristic function, the authors consider approximative sampling plans using a little known, but excellent operating characteristic function. Further, a comprehensive sensitivity analysis of calculated sampling plans is performed, in order to examine how the inspection effort depends on crisp and fuzzy formulation of quality limits, the lot size and specifications of the producer’s and consumer’s risks.
Findings
The results related the parametric sensitivity analysis of the calculated sampling plans and the conclusions regarding the approximation quality provide the user a comprehensive basis for a direct implementation of the sampling plans in practice.
Originality/value
The constructed sampling plans ensure the simultaneous control of producer’s and consumer’s risks with the smallest possible inspection effort on the one hand and a consideration of expert opinion or user priorities on the other hand.
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Belmiro P.M. Duarte and Pedro M. Saraiva
This paper seeks to present an optimization‐based approach to design acceptance sampling plans by variables for controlling non‐conforming proportions in lots of items. Simple and…
Abstract
Purpose
This paper seeks to present an optimization‐based approach to design acceptance sampling plans by variables for controlling non‐conforming proportions in lots of items. Simple and double sampling plans with s known and unknown are addressed. Normal approximation distributions proposed by Wallis are employed to handle plans with s unknown. The approach stands on the minimization of the average sampling number (ASN) taking into account the constraints arising from the two point conditions on the operating characteristic (OC) curve. The resulting optimization problems fall under the class of mixed integer non‐linear programming (MINLP), and are solved employing GAMS. The results obtained strongly agree with classical acceptance sampling plans found in the literature, although outperforming them in some cases, and providing a general approach to address other cases.
Design/methodology/approach
The approach takes the form of formulation of the design of acceptance sampling plans by variables for non‐conforming proportions as optimization problems minimizing the ASN with the constraints being the acceptance probability at the controlled points of the OC curve, and subsequent solution of the mathematical programming problems arising with mathematical programming algorithms.
Findings
The results are in strong agreement with acceptance sampling plans available in the literature. The approach presented here outperforms the classical plans in some cases and its generality allows one to design other plans without the requirement of additional relations between the parameters and intensive enumerative algorithms.
Originality/value
The paper presents an optimization‐based approach to design robust acceptance sampling plans by variables for non‐conforming proportions that allows a general treatment and disregards the need for computational intensive enumerative‐based procedures.
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Balamurali Saminathan and Usha Mahalingam
The purpose of this paper is to propose a new mixed repetitive group sampling (RGS) plan based on the process capability index, Cpk, where the quality characteristics of interest…
Abstract
Purpose
The purpose of this paper is to propose a new mixed repetitive group sampling (RGS) plan based on the process capability index, Cpk, where the quality characteristics of interest follow the normal distribution with unknown mean and unknown variance. Tables are constructed to determine the optimal parameters for practical applications for both symmetric and asymmetric fraction non-conforming cases. The advantages of this proposed mixed sampling plan are also discussed. The proposed sampling plan is also compared with other existing sampling plans.
Design/methodology/approach
In order to determine the optimal parameters of the proposed mixed RGS plan based on Cpk, the authors constructed tables for various combinations of acceptable and limiting quality levels (LQLs). For constructing tables, the authors followed the approach of two points on the operating characteristic (OC) curve. The optimal problem is formulated as a non-linear programming where the objective function to be minimized is the average sample number (ASN) and the constraints are related to lot acceptance probabilities at acceptable quality level and LQL under the OC curve.
Findings
The proposed mixed RGS plan will be a new addition to the literature of acceptance sampling. It is shown that the proposed mixed plan involves minimum ASN with desired protection to both producers and consumers compared to other existing sampling plans. The practical application of the proposed mixed sampling plan is also explained with an illustrative real-time example.
Originality/value
In this paper, the authors propose a new mixed RGS plan based on the process capability index Cpk, where the quality characteristic of interest follows the normal distribution with unknown mean and unknown variance. Tables are constructed to determine the optimal parameters for practical applications. The proposed mixed sampling plan can be used in all production industries. This kind of mixed RGS plan is not available in the literature.
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Loganathan Appaia, Padmanaban Muthu Krishnan and Sankaran Kalaiselvi
– The purpose of this paper is the determination of reliability sampling plans in the Bayesian approach assuming that the lifetime distribution is exponential.
Abstract
Purpose
The purpose of this paper is the determination of reliability sampling plans in the Bayesian approach assuming that the lifetime distribution is exponential.
Design/methodology/approach
Sampling plans are used in manufacturing companies as a tool for carrying out sampling inspections, in order to make decisions about the disposition of many finished products. If the quality characteristic is considered as the lifetime of the products, the plan is known as a reliability sampling plan. In life testing, censoring schemes are adopted in order to save time and cost of life test. The inverted gamma distribution is employed as the natural conjugate prior to the average lifetime of the products. The sampling plans are developed assuming various probability distributions to the lifetime of the products.
Findings
The optimum plans n and c are obtained for some sets of values of (p1, a, p2, ß). The selection of sampling plans is illustrated through numerical examples.
Originality/value
Results obtained in this paper are original and the study has been done for the first time in this regard. Reliability sampling plans are essential for making decisions either to accept or reject based on the inspection of the sample.
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