Byung Rae Cho

Six sigma in service organisations: Benefits, challenges and difficulties, common myths, empirical observations and success factors

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – 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.

Economic design of the specification region for multiple quality characteristics

Economic specification limits have typically been developed on the basis of a single quality characteristic. From the viewpoint of the customer, products are often evaluated based on multiple quality characteristics. The specification region for multiple quality characteristics must be determined on an economic basis where we minimize the total loss to both the producer and the customer and thus to the whole society. In this paper a multivariate normal distribution is considered for the quality characteristics. The specification region is given by truncating the multivariate normal distribution. We present the optimization model to develop the specification region for multiple quality characteristics based on the framework of multivariate quality loss function.

An integrated joint optimization procedure for robust and tolerance design

Many manufacturers have discovered that optimizing design parameters is a costeffective means of improving product quality and being competitive in the world market. In this regard, the issues of robust design (RD) and tolerance design (TD) are clearly important, but there is significant room for improvement. The primary objective of this paper is to propose a set of enhanced optimization strategies by combining RD and TD. To be more specific, first, we consider an alternative experimental scheme using response surface methodology, while avoiding the use of controversial tools for RD such as orthogonal arrays and signal-to-noise ratios. Secondly, we discuss an enhanced optimization model by simultaneously considering both the process mean and variance, and then show that this model provides a better (or at least equal) solution in terms of the control factor settings. Thirdly, we show how the response functions for the process mean and variance, which are estimated by using an RD principle, are transmitted into the TD stage. Fourthly, we propose an optimization model for TD and present closed-form solutions for optimum tolerance limits. Finally, we study the possible effects of major cost components, and observe the behaviour of the optimum control parameter settings and the tolerance limits by carrying out sensitivity analysis.

A MARKOVIAN APPROACH TO DETERMINING OPTIMUM PROCESS TARGET LEVELS FOR A MULTI-STAGE SERIAL PRODUCTION SYSTEM

Consider a production system where products are produced continuously and screened for conformance with their specification limits. When product performance falls below a lower specification limit or above an upper limit, a decision is made to rework or scrap the product. The majority of the process target models in the literature deal with a single-stage production system. In the real-world industrial settings, however, products are often processed through multi-stage production systems. If the probabilities associated with its recurrent, transient and absorbing states are known, we can better understand the nature of a production system and thus better capture the optimum target for a process. This paper first discusses the roles of a Markovian approach and then develops the general form of a Markovian model for optimum process target levels within the framework of a multi-stage serial production system. Numerical examples and sensitivity analysis are performed. 2003 Elsevier B.V. All rights reserved.

Project selection and its impact on the successful deployment of Six Sigma

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Practical implications – 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. Originality/value – 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.

Development of Priority-Based Robust Design

Among the engineering design methods currently studied in the engineering design community, researchers often identify robust design as one of the most important fields for the purpose of quality improvement. Although the notion of robust design is clearly important, the use of Taguchis signal-to-noise ratios and crossed arrays to capture variability has been a subject of controversy. As a natural alternative, a response surface approach to robust design has drawn much attention recently. This article first investigates the robust design models based on response surface approach and then proposes a priority-based robust design model to better reflect an engineers point of view at the early product design stage. Discussions are made and numerical optimizations are performed for the purpose of comparative studies.

Bias-specified robust design optimizaion and its analytical solutions

Robust design has received consistent attention from researchers and practitioners for years, and a number of methodologies for robust design optimization have been reported in the research community. However, the majority of these existing methodologies ignore the case where the customer may tolerate and specify an upper bound on process bias. This paper proposes a bias-specified robust design method and formulates a nonlinear program that minimizes process variability subject to customer-specified constraints on the process bias using the e-constrained method. This paper then derives the Karush-Khun-Tucker conditions and provides a solution procedure based on the Lagrangean method. A numerical example is provided for illustration.

Designing the optimal process target levels for multiple quality characteristics

The selection of the optimal process target has become an important research area in which the focus is to increase productivity and improve product quality. Although the quality engineering literature related to this issue contains a vast collection of work, some questions still remain unanswered. First, most previous studies have viewed this issue from a manufacturers perspective. When designing the optimal process target in the early stage, the customers perception of product quality needs to be incorporated. Secondly, many researchers have carried out their studies based on a single quality characteristic. From the customers viewpoint, however, products are often judged based on more than one characteristic. To address these questions, this paper first studies a multivariate quality loss function to capture customer dissatisfaction with product quality, and then proposes an optimization scheme to determine the most economical process target levels for multiple quality characteristics. The optimization procedures are demonstrated in a numerical example, and the effects of process parameters are examined by conducting a sensitivity analysis.

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.

Robust design modeling and optimization with unbalanced data

The usual assumption behind robust design is that the number of replicates at each design point during an experimental stage is equal. In practice, however, it is often the case that this assumption is not met due to physical limitations and/or cost constraints. In this situation, using the usual method of ordinary least squares (OLS) to obtain fitted response functions for the mean and variance of the quality characteristic of interest may not be an effective tool. In this paper, we first show simulation results, indicating that an alternative method, called the method of weighted least squares (WLS), outperforms the OLS method in terms of mean squared error. We then lay out the WLS-based robust design modeling and optimization. A case study is presented for numerical purposes.