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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.

Neutral format translators such as STEP and IGES have been used to exchange CAD model between the various CAD systems in the product development process. But imperfect interoperability imposes costs on the industry due to higher costs of design and production and slower implementation of design changes. In this paper, we propose a procedural approach to inspect the CAD model's errors that occur in a neutral format. We employed two separate inspection processes for checking CAD model errors without wasting excessive resources: the topological data structure and the geometrical data structure. We developed the 3D CAD model inspection system to check the topological and geometrical errors and applied it to case study. An inspection record can be edited in HTML, and thus can be shared by any designer at any time.

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.

Inspection is an activity that controls the outgoing product quality and involves search, detection and measurement or diagnosis. Traditionally, inspection tasks have been allocated to humans. Attempts to automate industrial inspection in order to eliminate errors and alleviate monotony have faced difficulties due to technological limitations and/or prohibitive implementation costs. An occasional compromise is partial automation (hybrid inspection). Reviews published research in manual, hybrid and automated inspection to understand the current research status.

Signal Detection Theory (SDT) has recently been used to evaluate the performance of imperfect inspectors. SDT model is based on a priori probabilities and perceived payoffs and penalties to study inspectors′ behaviour. In this article, Bayes′ theorem is used to compute posterior probabilities of the two types of inspection error. These posterior probabilities give rise to the definition of Receiver Analysis Curves (RAC), which depict the “after the facts” consequences of inspection error. A cost model is also developed that reflects the true benefits and costs of inspection accuracy to the organisation.

The aim of the paper is to present the impact of human errors in maintenance as found in the literature in order for practitioners to be aware of their impact and develop actions to mitigate their effect.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

Human error in maintenance is a pressing problem.

A maintenance person plays an important role in the reliability of equipment. It is also a well‐known fact that a significantly large proportion of total human errors occur during the maintenance phase. Human error in maintenance is a subject which in the past has not been given the amount of attention that it deserves. This paper will be useful to people working in the area of maintenance engineering, as it presents a general review of literature published on maintenance errors in various sectors of industry.

The paper contains a comprehensive listing of publications on the field in question and their classification according to industry. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of human error in maintenance.

The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

This paper proposes a new inventory model with inspection policy because in practice the received orders may contain non- conforming (NC) items. So, a buyer who receive an order from a supplier should use an inspection policy.

The inspection policy is assumed to be zero-defect single sampling. Under this policy a lot is accepted only if no defect has been identified in the inspected sample. The fraction of NC is assumed to be a random variable following a Binomial distribution and the number of NC items detected by inspection assumed to be a random variable, which follows a hypergeometric distribution. Order quantity and sample size are the two decision variables. A solution procedure is presented for the proposed model. The proposed procedure presents the optimal solution.

Numerical examples presented to illustrate the procedure outlined for the proposed model and its applicability. The results of numerical examples and comparing them with traditional EOQ model reveal that by the proposed model, the buyer could reduce total cost that shows the efficiency and validity of the proposed model.

The novelty of this paper is the new proposed model that considers inspection policy in inventory management. The proposed model determines sample size as well as order quantity to consider both subject of inventory management and quality control, simultaneously.

The objective of this paper is to demonstrate basic economic principles underlying the use of inspection systems and to derive a basis for comparing inspection costs with the alternative costs of quality improvement.

For this purpose, the impact of final, rectifying and sequential inspection on unit production costs is assessed using a production process model with uniform defect propensity in the single process steps. Based on this model, an objective function with the objective of minimizing unit production costs including inspection costs is formulated and a genetic algorithm method used to optimize it.

Two distinct patterns of optimal inspection allocation could be detected for changing defect rate, processed volume, fixed inspection costs and variable input costs. These allocation patterns highlight the basic economic relations of an inspection approach and verify the assumption that sequential inspection schemes are cost‐optimal. However, the benefits of quality improvement are found to be superior to any inspection scheme for a majority of cases.

The findings are derived from a theoretical examination. Further limitations stem from the cost‐centred approach which only covers the internal component of an economic assessment of quality. However, the economic perspective advanced in this paper can in future be subjected to empirical testing and be elaborated by subsequent research.

Although built on simplifying assumptions, the process model can provide important insights into basic economic relations and demonstrate that inspection is an inferior way of dealing with quality problems. It can thereby help to promote a better economic understanding of quality.

This paper provides an economic assessment of an important aspect of quality management, which has so far not been advanced.

A building deteriorates over time due to aging, wear and tear, and inadequate maintenance. Building diagnosis requires a sound knowledge of engineering, building defects, and detection tools to assess the condition of a building. The physical deterioration of a building reduces its ability to perform its intended function, while environmental deterioration influences the comfort and health of building occupants. This study presents a multi-tiered framework for the inspection of building elements and the environmental conditions of a building.

A three-tiered building inspection framework is proposed in this study, which consists of the following: Tier-I—a preliminary inspection, Tier-II—a detailed inspection, and Tier-III—an expert investigation. Each tier of inspection assesses the severity of building defects using different technologies for different levels of inspection.

Proposed multi-tier inspection framework is tested and implemented on a case study. Results were promising, with organized data management on a common platform for both physical and environmental condition inspection having the potential to save time.

The application program developed for the implementation of structured multi-tiered building inspection provides better documentation and data management for building inspection data that can save time involved in manual data operations in traditional paper-based processes.