Quality, as one of the principal factors in manufacturing success, must be controlled in a manner appropriate to the technology of manufacture. In a flexible, automated production…
Abstract
Quality, as one of the principal factors in manufacturing success, must be controlled in a manner appropriate to the technology of manufacture. In a flexible, automated production environment, inspection and quality control systems must be effectively designed for automation and integration. Some of the considerations of quality system integration are addressed, and an application of automated inspection in assembly is described in its context as part of an integrated system of quality control. In this application, an analysis is used of the force signature of a high‐speed automated assembly operation to detect error conditions and report quality information.
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Barbara M. Savage and James D.T. Tannock
The development of a quality database is central to effective automation of the operational activities of quality control. The importance of automation to quality data management…
Abstract
The development of a quality database is central to effective automation of the operational activities of quality control. The importance of automation to quality data management is stated and a quality database structure outlined. The analysis and specification phase for a prototype system is discussed, with the functional requirements identified, and the choices of software, hardware and communications strategy described. Integration requirements with other computer systems are considered.
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Wimalin Sukthomya and James D.T. Tannock
The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.
Abstract
Purpose
The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.
Design/methodology/approach
The objectives are achieved with two separate techniques: the Retrospective Taguchi approach selects the designed experiment's data from a historical database, whilst in the Neural Network (NN) – Taguchi approach, this data is used to train a NN to estimate process response for the experimental settings. A case study illustrates both approaches, using real production data from an aerospace application.
Findings
Detailed results are presented. Both techniques identified the important factor settings to ensure the process was improved. The case study shows that these techniques can be used to gain process understanding and identify significant factors.
Research limitations/implications
The most significant limitation of these techniques relates to process data availability and quality. Current databases were not designed for process improvement, resulting in potential difficulties for the Taguchi experimentation; where available data does not explain all the variability in process outcomes.
Practical implications
Manufacturers may use these techniques to optimise processes, without expensive and time‐consuming experimentation.
Originality/value
The paper describes novel approaches to data acquisition associated with Taguchi experimentation.
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J.D.T. Tannock and L. Krasachol
Reports on the Thai Foundation Quality System Standard (TFQSS) project which aimed to develop a basic quality management system standard for small Thai manufacturing businesses to…
Abstract
Reports on the Thai Foundation Quality System Standard (TFQSS) project which aimed to develop a basic quality management system standard for small Thai manufacturing businesses to assist them in increasing awareness of quality and develop towards the ISO 9000 series standards, without introducing unnecessary complexity or cost. The project involved five small Thai companies who were assisted by a project facilitator in the development of quality systems to meet the requirements of the TFQSS. The choice of companies was representative of typical Thai small manufacturing businesses, and covered a number of significant industry types. The five companies, all made considerable progress in quality management and have achieved the requirements of the standard at audit. The standard was initially seen by most collaborating companies as a significant challenge, and yet was achieved by all in about one half of the average implementation period for ISO 9002. Most of the companies, after having achieved TFQSS were confidently planning to move further in quality management, towards either ISO 9000 or TQM. Describes the TFQSS project, outlines the contents of the standard, and compares it with another basic national quality standard, Q‐Base Code from New Zealand, and with ISO 9001.
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Control chart pattern recognition is a critical issue in statistical process control, as unnatural patterns on control charts are often associated with specific assignable causes…
Abstract
Control chart pattern recognition is a critical issue in statistical process control, as unnatural patterns on control charts are often associated with specific assignable causes adversely affecting the process. Several researchers have recently applied neural networks to pattern recognition for control charts. However, nearly all studies in this area assume that the in‐control process data in the control charts follow a normal distribution. This assumption contradicts the facts of practical manufacturing situations. This paper investigates how non‐normality affects the performance of neural network based control chart pattern recognition models. Extensive performance evaluation was carried out using simulated data with various non‐normalities. The non‐normality was measured in skewness and kurtosis. Numerical results indicate that the neural network based control chart pattern recognition models still perform well in a non‐normal distribution environment in terms of recognition accuracy and speed.
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Krishna Teja Perannagari and Shaphali Gupta
Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical…
Abstract
Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.
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Yun Qui and James D.T. Tannock
This paper aims to achieve a better understanding of the dissemination and adoption of quality management in China, in the context of theory on management trends and fashions…
Abstract
Purpose
This paper aims to achieve a better understanding of the dissemination and adoption of quality management in China, in the context of theory on management trends and fashions, dissemination and adoption.
Design/methodology/approach
Following a literature review, the research adopts a qualitative, multiple case‐study approach, based on the study of six Shanghai manufacturers.
Findings
A dissemination and adoption model is presented, which contains nine observed dissemination and adoption factors. These factors and their relationships are identified, analysed and discussed.
Research limitations/implications
The selection of case study companies was constrained by practical considerations of location and access. Further constraints of time and resource meant that only 14 interviews with managers from case‐study companies and three interviews with quality experts and consultants were conducted.
Practical implications
The findings will be of interest to those involved in developing QM within China, or working with Chinese manufacturing partners. They suggest that Chinese businesses do not blindly adopt QM initiatives simply because they are the current trend or fashion – instead, companies make decisions based on several rational adoption factors.
Originality/value
The research contributes to a richer understanding of the dissemination and adoption of QM in China, and extends understanding of QM dissemination in the context of management fashion and dissemination theories, using a qualitative approach.
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Natcha Thawesaengskulthai and James D.T. Tannock
The variety of possible quality management (QM) and continuous improvement (CI) initiatives and their various possible permutations can make it difficult for a company to choose…
Abstract
Purpose
The variety of possible quality management (QM) and continuous improvement (CI) initiatives and their various possible permutations can make it difficult for a company to choose the best approach for their requirements. This paper aims to address the selection issue by presenting a method to compare popular QM and CI initiatives from the perspective of the pay‐offs, or expected benefits, to an organisation which successfully adopts the approach.
Design/methodology/approach
The relevant QM and CI literature was analysed, examining key initiatives and their reported pay‐offs to the organisation. A matrix diagram approach is introduced which presents the extent and credibility of arguments advanced for these initiatives, in seven categories of pay‐off. A system of assessment is proposed, which quantifies the extent and weight of empirical evidence and estimates the strength of the claim for each pay‐off.
Findings
The pay‐off matrix summarises the claims in each of the pay‐off categories, assesses their credibility, and displays the similarities and differences for six key initiatives: total quality management, six sigma, ISO 9000, business process reengineering, lean and business excellence. Graphical pay‐off profiles are presented. Significant differences between the claimed pay‐offs for these initiatives are identified, analysed and discussed.
Research limitations/implications
The proposed matrix and assessment system attempts to support a comprehensive and rational approach to assess the pay‐offs of QM and CI initiatives. As with any analysis of literature, there is inevitably an element of selection, but this approach consciously attempts to avoid omission and promote objectivity. The analysis is based on articles published between 1990 and 2005. Hence, new research and additional evidence may change the weight and credibility of claims.
Originality/value
This paper suggests a way in which evidence from the literature might be most effectively used by managers for decision support in the choice of quality and improvement initiatives. A similar approach might also be used for other areas, where businesses face choices and a considerable body of evidence exists to assist the decision‐making process.
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A COQ model plays an important role in the total quality cost survey. Based on the methodology of continuous quality improvement, a dynamic COQ model for different quality level…
Abstract
A COQ model plays an important role in the total quality cost survey. Based on the methodology of continuous quality improvement, a dynamic COQ model for different quality level is developed in this paper. A quality level is defined by Six Sigma level that can be measured by two indicators. The relationships among the four major quality costs are analyzed. Finally, the curves of total quality costs for different quality level are presented.
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James D.T. Tannock, Oluwatuminu Balogun and Hisham Hawisa
The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM…
Abstract
Purpose
The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM) and six‐sigma approaches.
Design/methodology/approach
The research methodology was experimental prototyping conducted in collaboration with industry partners. A prototype IT system was developed and tested to implement the approach. A quality cost‐based system was used to assess variation at each operation stage, for every product characteristic.
Findings
A comprehensive approach to the management of manufacturing variation is introduced, based on a new process risk matrix which can be used to specify an individual variation risk for every manufactured characteristic, throughout a manufacturing process chain. The approach has been implemented in a prototype software system and is aimed at the complex products such as those manufactured by the aerospace industry.
Research limitations/implications
The IT approach described was developed during the research and is not commercially available.
Practical implications
Manufacturing industry should be able to use this approach, in particular the process risk matrix concept, to develop more effective management of product variation and resultant cost, in complex process chains.
Originality/value
The paper describes a novel approach to combine VRM and six‐sigma concepts, and introduces the process risk matrix as a structure to understand process variation.