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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 distribution against the exact convolute compound hypergeometric distribution when both types of inspection errors are present.

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.

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.

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.

The purpose of this paper is to report the first practical applications of FCAUP (Framework for Conformity Assessment inspired by the Uncertainty Principle) carried out using a pilot implementation in a boat trailer manufacturer company in Maryville, Tennessee, USA and a practical application in a metrology company in São José dos Campos, São Paulo, Brazil.

Field studies have been conducted in two different companies: a pilot implementation during the preparation activities of the acceptance inspection of finished products and a practical application during the internal audit related to the quality management system.

(1) partial double inspection is advantageous in conformity assessment; (2) a structured procedure tends to reduce the risks arising from the use of partial double inspection; (3) the recommended amount of double inspections is directly proportional to the uncertainty involved in the process and it is inversely proportional to the degree of trust in a presumed conformity status.

This novel framework is for practitioners in manufacturing industries and test laboratories.

The results of these practical applications suggest that FCAUP is a consistent approach to be used in several categories of conformity assessment in manufacturing industries and test laboratories due to the balance between result and time (cost).

This new framework, named FCAUP, for planning and executing conformity assessment activities has been introduced in a theoretical study published by Moraes and Silva (2018) and it is based on an analogy with the Uncertainty Principle of Quantum Mechanics.

Sampling plans for inspection by attributes contain rules for passing from normal to tightened inspection. Usually only the operating characteristic curve (O.C.) will apply but in tightened inspection, lots are unlikely to remain in tightened inspection and inspection will be terminated. This means that the normal and tightened O.C. curves are boundary curves and the true O.C. curve is a composite of both. This article presents a method for calculating the composite O.C. curve and proposes three criteria to compare switching rules.

Seeks to determine whether hybrid inspection performance is superior to manual performance in a generic manufacturing setup. Explains the design of an experiment to achieve this comparison. Results include the fact that the hybrid method took substantially less time and caused fewer inspection errors. Notes that cost factors would need to be carefully considered before selection of a preferred method but that ultimately the hybrid method should be the logical choice.

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.

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.

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.

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.

Due to the increase in the use and importance of co‐ordinate measuring machines (CMM) in manufacturing systems, it is necessary to develop reliable and accurate performance tests for their verification to assure the quality of manufactured components. Presents general requirements and a classification of test methods. Surveys and compares some performance methods to evaluate CMM and concludes that is necessary to find a multi‐purpose method that is quick and easy to use and which could be widely accepted by manufacturers and users.

Blood cold chain (BCC) represents a system for preserving the blood during its journey from the donor to the ultimate transfusion site. Existing BCCs have many drawbacks related to information transparency and information security. Secured and real-time information sharing in BCC can bring several benefits. The purpose of this paper is to summarise the issues in typical BCCs and to explore the scope of blockchain in the management of BCCs.

Issues in the existing BCCs are identified through a narrative review. To explain the potential of blockchain in mitigating these issues, a blockchain-based traceability solution is demonstrated with respect to a particular BCC scenario. The BCC management system discussed in this study makes use of the Ethereum blockchain’s smart contract feature and internet of things (IoT) technology. The smart contract is written in the solidity programming language and tested and validated using the Remix integrated development environment.

BCCs are concerned with several issues both from technical and non-technical perspectives. Blockchain technology is capable of troubleshooting the issues in the existing BCCs. Combining blockchain and IoT technology enables real-time information sharing among the entities. The demonstration presented in this work depicts how the blockchain-based smart contract can support operations in a typical BCC.

This paper explores the scope of blockchain in BCCs through a demonstration. To get insights into its technical and economical feasibilities, further investigations are needed.

Blockchain-based traceability system presented in this work can be adopted in BCCs to ensure the quality of blood or blood products. Blockchain-based smart contracts can aid the BCCs to achieve a proper balance between blood shortage and outdating.

Investigates whether institutional and industrial organizations buying capital items exhibit different patterns of structural configuration across the purchasing cycle. Measures the structure of the purchasing function by three composite parameters especially developed for this research. These parameters express aspects of articulation, depth of analysis and decentralization respectively, and are measured for each of four purchasing cycle phases. Using regression analysis on data from certain Greek organizations, aims to investigate the differences in the purchasing structure arising from capital items purchases of institutions and industrial organizations respectively. This can help industrial marketeers to adapt their strategies to institutions′ purchasing patterns.

The reduction of the time and resources spent inspecting product is critical to the success of Company L's continued resourcing efforts. The use of Mil‐Std and other sampling plans with acceptance numbers greater than zero usually results in increased inspection sizes and potential for controversy in inspection results between inspectors. The time and resources used to complete these outgoing inspections are directly related to the amount of product currently required to be inspected in order to determine the acceptance or rejection of a lot of finished goods. This paper proposes a new sampling policy that will allow Company L to reduce the size of outgoing inspections. The data used in the paper are from 2006 to 2007. It is a combination of Overseas Inspection reports from all suppliers as well as sales volumes for products sold to Company L's partner companies. There are currently over 80 suppliers that manufacture products for Company L. The major finding of this paper is that it is possible to reduce inspection size while still maintaining, or in most cases reducing, the risks associated with sample inspections. This will be accomplished by switching from the current Mil‐Std plan to a Zero acceptance number sampling plan.

The purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased understanding of how the areas of research have evolved through the years. An additional purpose is to show how text mining methodology can be used as a tool for exploration and description of research publications.

The study applies text mining methodologies to explore and describe the digital library of IJQRM from 1984 up to 2014. To structure and condense the data, k-means clustering and probabilistic topic modeling with latent Dirichlet allocation is applied. The data set consists of research paper abstracts.

The results support the suggestion of the occurrence of trends, fads and fashion in research publications. Research on quality function deployment (QFD) and reliability management are noted to be on the downturn whereas research on Six Sigma with a focus on lean, innovation, performance and improvement on the rise. Furthermore, the study confirms IJQRM as a scientific journal with quality and reliability management as primary areas of coverage, accompanied by specific topics such as total quality management, service quality, process management, ISO, QFD and Six Sigma. The study also gives an insight into how text mining can be used as a way to efficiently explore and describe large quantities of research paper abstracts.

The study focuses on abstracts of research papers, thus topics and categories that could be identified via other journal publications, such as book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); introductions or summaries from conferences; notes from the publisher; and articles without an abstract, are excluded.

There do not seem to be any prior text mining studies that apply cluster modeling and probabilistic topic modeling to research article abstracts in the IJQRM. This study therefore offers a unique perspective on the journal’s content.