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Robust design is a well‐known quality improvement method that focuses on building quality into the design of products and services. Yet, most well established robust design models only consider a single performance measure and their prioritization schemes do not always address the inherent goal of robust design. This paper aims to propose a new robust design method for multiple quality characteristics where the goal is to first reduce the variability of the system under investigation and then attempt to locate the mean at the desired target value.

The paper investigates the use of a response surface approach and a sequential optimization strategy to create a flexible and structured method for modeling multiresponse problems in the context of robust design. Nonlinear programming is used as an optimization tool.

The proposed methodology is demonstrated through a numerical example. The results obtained from this example are compared to that of the traditional robust design method. For comparison purposes, the traditional robust design optimization models are reformulated within the nonlinear programming framework developed here. The proposed methodology provides enhanced optimal robust design solutions consistently.

This paper is perhaps the first study on the prioritized response robust design with the consideration of multiple quality characteristics. The findings and key observations of this paper will be of significant value to the quality and reliability engineering/management community.

The selection of the optimal process target for a manufacturing process is critically important as it directly affects the defect rate, rejection and rework costs, and the loss to customers. A recent review of process target literature suggests that future work should incorporate models using multiple quality characteristics. Thus, the purpose of this paper is to create a more flexible and realistic approach to solving the multi‐response process target problem.

A design of experiments methodology is proposed to provide estimates of process parameters and a nonlinear constrained optimization scheme is employed to identify optimal settings.

The approximation of cost savings undoubtedly has a higher degree of accuracy than in the case where the engineer assumes values for the process parameters. Furthermore, greater flexibility is obtained in finding solutions that support both the manufacturer and the customer.

This methodology relies on controlled experimentation and the replication of observations made on multiple nominal‐the‐best quality characteristics. Future research may include examining the effects of using smaller‐the‐better or larger‐the‐better type characteristics.

Unlike traditional attempts at solving the process target problem where the process mean, variance, and covariance between characteristics are assumed known in advance, this paper uses an approach that removes these assumptions, thereby providing a more practical depiction of the overall system. Furthermore, this methodology broadens the scope of process target problem research by seeking the simultaneous optimization of process parameters and considering a loss in quality attributed to deviation from a target value.

The purpose of this paper is to focus on the importance of the project selection process and its role in the successful deployment of Six Sigma within organizations.

A review of the literature is presented, highlighting the importance of project selection in Six Sigma deployment, which is an area of extreme importance that has been less researched in the past. The paper, through a real‐life case study, proposes a hybrid methodology, which combines the analytical hierarchy process and the project desirability matrix to select a project for Six Sigma deployment.

The paper demonstrates the efficacy of proposed methodology by its application in a small and medium‐sized enterprise (SME) manufacturing die‐casting product. The example provided is a real‐life case study conducted by the authors in an organization embracing the Six Sigma business strategy within their day‐to‐day functioning.

The proposed methodology is tested only in a case study SME, which is the limitation of the paper. The robustness of the methodology can be tested by conducting several case studies in organizations and comparing the results with other existing methodologies for project selection such as project prioritisation matrix or the failure mode and effect analysis.

The paper accentuates the importance of the project selection process for Six Sigma deployment, which can have a tremendous effect on the business profitability of an organization. The paper is relevant to both industry practitioners and researchers.

The paper presents a methodology linking the project selection process to successful deployment of Six Sigma within organizations, an important topic that has been neglected in the past. The paper will enable managers and practitioners to emphasize the importance of project selection and to identify and focus on the critical success factors in successful deployment of Six Sigma projects.

Six sigma has received considerable attention over the last four years in the UK service sector. The purpose of this paper is to present a review of the literature on six sigma as applied to the service industry, followed by a presentation of the key findings obtained from a pilot survey carried out in UK service organisations.

This paper presents some of the most common challenges, difficulties, common myths, and implementation issues in the application of six sigma in service industry settings. It also discusses the benefits of six sigma in service organisations, tools and techniques of six sigma for service performance improvement, key criteria for the selection of winning projects, followed by the results of a six sigma pilot survey in UK service organisations.

The results of the study show that the majority of service organisations in the UK have been engaged in a six sigma initiative for just over three years. The average sigma quality level of the companies was around 2.8 (approximately 98,000 DPMO). Management commitment and involvement, customer focus, linking six sigma to business strategy, organisational infrastructure, project management skills, and understanding of the six sigma methodology are the most critical factors for the successful introduction, development and deployment of six sigma.

This paper reports the first study on the status of six sigma implementation in UK service organisations. The findings and key observations of this paper will be of immense value to the six sigma academic and research community.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.