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Article
Publication date: 30 September 2021

Damla Yüksel, Yigit Kazancoglu and P.R.S Sarma

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any…

Abstract

Purpose

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.

Design/methodology/approach

Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.

Findings

A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.

Practical implications

The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.

Originality/value

Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 5 September 2008

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…

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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.

Details

International Journal of Quality & Reliability Management, vol. 25 no. 8
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 June 1996

Joanna R. Baker, Pamela K. Lattimore and Lance A. Matheson

The “at the source” emphasis of total quality management (TQM) has reduced the reliance on post‐production statistical quality control approaches such as acceptance

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1543

Abstract

The “at the source” emphasis of total quality management (TQM) has reduced the reliance on post‐production statistical quality control approaches such as acceptance sampling. In cases where it is appropriate more proactive approaches such as statistical process control have improved productivity in manufacturing environments. For social processes where the inputs are ill‐defined and the outputs are difficult to measure, traditional quality control approaches have rarely been applied. Addresses the problem of monitoring use of illegal drugs, a critical social problem. Because the inputs, the use of drugs, are not easy to document and the process which results in an individual’s decision to use drugs is too complex to model, one must rely on the detection of drugs as a measurement of drug abuse. The behaviour of interest is the detection of illegal drug use through urine testing. The technique for monitoring this behaviour in a population of interest is single‐attribute, Bayesian acceptance sampling. Applies a partial drug‐testing methodology based on single‐attribute acceptance sampling to a population of probationers in Madison County, Illinois, USA. The approach offers probation offices with a lower cost approach to monitoring drug use among populations of known drug users. The use of acceptance sampling allows Madison County to reduce the total cost of testing by reducing the total amount of testing that must be done to monitor use of drugs among their probation populations.

Details

Benchmarking for Quality Management & Technology, vol. 3 no. 2
Type: Research Article
ISSN: 1351-3036

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Article
Publication date: 3 August 2010

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…

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.

Details

International Journal of Quality & Reliability Management, vol. 27 no. 7
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 12 October 2021

Waqar Hafeez and Nazrina Aziz

This paper introduces a Bayesian two-sided group chain sampling plan (BT-SGChSP) by using binomial distribution to estimate the average proportion of defectives. In this…

Abstract

Purpose

This paper introduces a Bayesian two-sided group chain sampling plan (BT-SGChSP) by using binomial distribution to estimate the average proportion of defectives. In this Bayesian approach, beta distribution is used as a suitable prior of binomial distribution. The proposed plan considers both consumer's and producer's risks. Currently, group chain sampling plans only consider the consumer's risk and do not account for the producer's risk. All existing plans are used to estimate only a single point, but this plan gives a quality region for the pre-specified values of different design parameters. In other words, instead of point wise description for the designing of sampling plan based on a range of quality by involving a novel approach called quality region.

Design/methodology/approach

The methodology is based on five phases, which are (1) operating procedure, (2) derivation of the probability of lot acceptance, (3) constructing plans for given acceptable quality level (AQL) and limiting quality level (LQL), (4) construction of quality intervals for BT-SGChSP and (5) selection of the sampling plans.

Findings

The findings show that the operating characteristic (OC) curve of BT-SGChSP is more ideal than the existing Bayesian group chain sampling plan because the quality regions for BT-SGChSP give less proportion of defectives for same consumer's and producer's risks.

Research limitations/implications

There are four limitations in this study: first is the use of binomial distribution when deriving the probability of lot acceptance. Alternatively, it can be derived by using distributions such as Poisson, weighted Poisson and weighted binomial. The second is that beta distribution is used as prior distribution. Otherwise, different prior distributions can be used like: Rayleigh, exponential and generalized exponential. The third is that we adopt mean as a quality parameter, whereas median and other quintiles can be used. Forth, this paper considers probabilistic quality region (PQR) and indifference quality region (IQR).

Practical implications

The proposed plan is an alternative of traditional group chain sampling plans that are based on only current lot information. This plan considers current lot information with preceding and succeeding lot and also considers prior information of the product.

Originality/value

This paper first time uses a tight (three acceptance criteria) and introduces a BT-SGChSP to find quality regions for both producer's and consumer's risk.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 5 February 2018

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…

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.

Details

International Journal of Quality & Reliability Management, vol. 35 no. 2
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 May 1992

David H. Baillie

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…

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.

Details

International Journal of Quality & Reliability Management, vol. 9 no. 5
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 1 October 2018

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…

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.

Details

International Journal of Quality & Reliability Management, vol. 35 no. 9
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 5 August 2019

Amer Al-Omari, Amjad Al-Nasser and Enrico Ciavolino

Lifetime data are used in many different applied sciences, like biomedicine, engineering, insurance and finance and others. The purpose of this paper is to develop a new…

Abstract

Purpose

Lifetime data are used in many different applied sciences, like biomedicine, engineering, insurance and finance and others. The purpose of this paper is to develop a new acceptance sampling plans for Rama distribution when the mean lifetime test is truncated at a pre-determined time. The minimum sample sizes required to assert the specified life mean is obtained for a given customer’s risk. The operating characteristic function values of the sampling plans and producer’s risk are calculated.

Design/methodology/approach

The results are illustrated using numerical examples and a real data set is considered to illustrate the performance of the suggested acceptance sampling plans and how it can be used for the industry applications.

Findings

This paper shows a new acceptance sampling plans based on Rama distribution in the particular case when the mean life time test is truncated.

Originality/value

The results calculated in this paper demonstrate the differences between OC values for different distributions taken into account. In particular, OC values of Rama distribution are found to be less than the proposed distribution counterparts.

Details

International Journal of Quality & Reliability Management, vol. 36 no. 7
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 2 March 2015

Ching-Ho Yen, Heng Ma, Chi-Huang Yeh and Chia-Hao Chang

– The purpose of this paper is to develop an economic model, which could determine the acceptance sampling plan that minimizes the quality cost for batch manufacturing.

Abstract

Purpose

The purpose of this paper is to develop an economic model, which could determine the acceptance sampling plan that minimizes the quality cost for batch manufacturing.

Design/methodology/approach

The authors propose a variable sampling plan based on one-sided capability indices for dealing with the quality cost requirement.

Findings

The total quality cost is much more sensitive to process capability indices and inspected cost than internal and external failure costs.

Research limitations/implications

The experimental data were randomly generated instead of real world ones.

Practical implications

The proposed model is specifically designed for manufacturing industries with high sampling cost.

Originality/value

The one-sided capability indices were utilized for the first time to be suitable for the purpose.

Details

Kybernetes, vol. 44 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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