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As opposed to general literature reviews, by narrowing down the context only around the resources related to Six Sigma tools, this study aims to offer a strong discussion about Six Sigma toolbox which has a vital role in the success of Six Sigma.

Based on a comprehensive literature research, the most used tools; classification of tools; flow of tools with respect to define, measure, analyze, improve and control (DMAIC) steps; tools as critical success factors and reasons of ineffective use of tools are reviewed. To stay focused and not to diverge from the research aim, 60 articles which are suitable to the context and flow of the discussion are selected during the construction of the study.

The study provides a detailed and integrated review of Six Sigma articles about tools. The most used tools are listed from different perspectives and resources, and the role of these tools has been discussed. After a broad review, a more practical and combined classification of Six Sigma tools is proposed. Next, the issue of using which tools during which steps of DMAIC is systematically addressed. Finally, emergence of tools as a critical success factor and the gaps in the literature related to tools of Six Sigma are pointed out.

Addressing important statistics and the facts related to the tools of Six Sigma helps new practitioners in particular to build a strategic filter to select the most proper tools throughout their projects.

This study is unique in investigating only Six Sigma toolbox and providing a literature review on this subject.

Statistical process control (SPC) is a powerful technique which organisations can use in their pursuit of continuous improvement of both product and service quality. Many organisations in the UK are still learning about the successful introduction, development and implementation of SPC, even though it has been widely and commonly used in many Japanese organisations with great success. Research in the UK academic institutions has clearly indicated that the only thing taught to engineers in relation to SPC is control charting and the mathematical aspects of the subject rather than the implementation aspects of the technique. It can be argued that it is not just control charts which makes SPC initiative successful in organisations, rather the emphasis should be on the critical factors which are essential for the success of SPC program and also issues such as “how to get started” and “where to get started”. This paper compares the existing frameworks for SPC implementation in terms of their strengths and weaknesses and then illustrates a conceptual framework for the successful introduction and application of SPC program in any organisation. The framework also shows a systematic approach to apply the SPC technique in an industrial setting.

Focuses on the Statistical Process Control (SPC) implementation phase in an effort to underline that SPC is not just control charts, and that many steps have to be accomplished before these charts are used. In addition, highlights the role of training and presents it as an ongoing process which involves everyone in the organization. These SPC implementation steps are not meant to be a checklist; they provide guidelines that can be modified in accordance with organizational‐specific requirements.

A process improvement sampling methodology, known as process variation diagnostic tool (PROVADT), was proposed by Cox et al. (2013). The method was designed to support the objectivity of Six Sigma projects performing the measure-analyse phases of the define-measure-analyse-improve-control cycle. An issue in PROVADT is that it is unable to distinguish between measurement and product variation in the presence of a poor Gage repeatability and reproducibility (R&R) result. The purpose of this paper is to improve and address PROVADT’s sampling structure by enabling a true Gage R&R as part of its design.

This paper derives an enhanced PROVADT method by examining the theoretical sampling constraints required to perform a Gage R&R study. The original PROVADT method is then extended to fulfil these requirements. To test this enhanced approach, it was applied first to a simulated manufacturing process and then in two industry case studies.

The results in this paper demonstrates that enhanced PROVADT was able to achieve a full Gage R&R result. This required 20 additional measurements when compared to the original method, but saved up to ten additional products and 20 additional measurements being taken in future experiments if the original method failed to obtain a valid Gage R&R. These benefits were highlighted in simulation and industry case studies.

The work into the PROVADT method aims to improve the objectivity of early Six Sigma analyses of quality issues, which has documented issues.

This article concerns the process of conducting a machine qualification (or process capability) study. It presents the results of a research project undertaken to investigate the shortcomings inherent in the typical team approach to machine qualification, and to design a comprehensive computer system to overcome these shortcomings. The article begins with a discussion of the steps involved in a qualification study, pointing out that inefficiencies and inaccuracies often arise due to the heavy time and statistical burden placed on the study team. The functionality and the features of the PC‐based computer system developed in the research are described. Numerous screens are provided to illustrate these features. Finally, a synopsis is given of the primary benefits of the system, as well as its limitations.

Devoted to a description and evaluation of a selected maintenance process (assembly) at the aero‐engines maintenance unit of a large aerospace company by implementation of TQM tools, this paper attempts to identify the causes behind the defect observed and form the scientific platform for initiatives in a TQM‐governed enterprise and to broaden the principles of TQM for the selected process, prior to moving to a more structured plan that will include the entire unit.

Process monitoring and evaluation are organised by an application of control charts in order to provide vital information regarding the level of control in the selected process. Quality control data are contrasted with component specifications by employing control charts to provide a metric for the level of the process capability index. As a result a Fishbone diagram is constructed to identify existing interrelations between the causes responsible for the defect observed.

The maintenance process selected was the assembly process of an aero‐engine module (exhaust nozzle unit) at the aero‐engines maintenance unit of a large aerospace company. Process evaluation by means of multivariate control charts and tolerance analysis exhibited poor results. It was observed that certain measurement stations were out of control, whilst low actual capability index values were exhibited in others..

Process monitoring and evaluation carried out for the purposes of the present study had the form of an off‐line tool. The paper shows that the aero‐engines maintenance unit had no infrastructure for an online process control and monitoring system. Consequently, performed analysis indicated that the implied assembly process was inadequately implemented. As a result, the maintained assembly units were out of stated specifications limits.

The study contributes to the literature on TQM in the aerospace maintenance business.

Continuous improvement of product, process and service quality is important for today’s organisations to remain competitive in global marketplaces. Statistical process control (SPC) is an important and powerful technique for the continuous improvement of product and process quality. The control chart is the familiar tool used within SPC for determining out‐of‐control situations and thereby eliminating or reducing variation in processes. However, a control chart is by no means the only ingredient – nor necessarily the most important – for the successful application of SPC. This paper identifies and discusses these key ingredients for the successful application of SPC in both manufacturing and service organisations.

The use of robots to control for quality in manufacturing raises the issue of choice and its effect on the probability of accepting defective parts or rejecting good ones. The application of robots to the quality gauges is described and robot repeatability and errors in production processes are examined.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.