Measuring Six Sigma Project Effectiveness using Fuzzy Approach

Six Sigma methodology for process improvement is being used by industries to improve customer satisfaction, business results or both. The success of Six Sigma implementation can be measured by evaluating the effectiveness of the completed projects. The other objective of project effectiveness measurement scheme is to keep the team focused and motivated. A good measurement system should be able to measure and compare projects of various types and need, including benefits from the projects. The project effectiveness measurement scheme should include success factors like project selection, involvement of management, results achieved, conduct of the project and monitoring and review of the project. A Six Sigma project effectiveness evaluation system is generally based on the perception of people that can result in unreliable measurement. To overcome this deficiency, we used a fuzzy approach based on linguistic variables and fuzzy numbers for measuring the project effectiveness in this study by using the perception of management. Two methods for measuring effectiveness of selected sample projects are suggested. The outcome from this research would be helpful for the practicing industries to use this methodology for an unbiased evaluation of completed projects. Copyright © 2012 John Wiley & Sons, Ltd.     

Designing New Housing at the University of Miami: A “Six Sigma” DMADV/DFSS Case Study

The “Six Sigma” management DMADV model is used in this paper to design a new dormitory concept at the University of Miami. It is intended to provide a roadmap for conducting a Design for Six Sigma (DFSS) project.     

An Application of Six Sigma to Reduce Waste

Six Sigma has been considered a powerful business strategy that employs a well‐structured continuous improvement methodology to reduce process variability and drive out waste within the business processes using effective application of statistical tools and techniques. Although there is a wider acceptance of Six Sigma in many organizations today, there appears to be virtually no in‐depth case study of Six Sigma in the existing literature. This involves how the Six Sigma methodology has been used, how Six Sigma tools and techniques have been applied and how the benefits have been generated. This paper presents a case study illustrating the effective use of Six Sigma to reduce waste in a coating process. It describes in detail how the project was selected and how the Six Sigma methodology was applied. It also shows how various tools and techniques within the Six Sigma methodology have been employed to achieve substantial financial benefits. Copyright © 2005 John Wiley & Sons, Ltd.     

Six Sigma in industry: some observations after twenty‐five years

Six Sigma has enjoyed considerable popularity in the industry for about a quarter of a century. While the standard contents of Six Sigma have been described and discussed widely, some little articulated aspects of Six Sigma implementation deserve the attention of serious practitioners. In this paper, a ‘5W+1H’ (What, Why, When Where, Who, How) format is used to elucidate the nature of Six Sigma in a non‐mathematical discussion, followed by observations peculiar to the usual mode of development of Six Sigma professionals. It is pointed out that Six Sigma has Statistical Thinking as its foundation; for Six Sigma and its associated frameworks such as Design for Six Sigma and Lean Six Sigma to continue to be effective, it is important that users have a clear understanding of the nature of Six Sigma and be able to address the related challenges in practice. Copyright © 2010 John Wiley & Sons, Ltd.     

Paper Organizers International: A Fictitious Six Sigma Green Belt Case Study. I

“Six Sigma” management is in vogue in many of the world's largest and most successful corporations. However, for all of its popularity, there is much confusion as to the exact structure of a Six Sigma project. The purpose of this article is to present the first part of a detailed, step-by-step case study of a simple Six Sigma Green Belt project. This part of the case study presents the Define and Measure phases of the define-measure-analyze-improve-control (DMAIC) method for improving a process.     

A strategic assessment of six sigma

Six Sigma as a quality improvement framework has been gaining considerable attention in recent years. The hyperbole that often accompanied the presentation and adoption of Six Sigma in industry could lead to unrealistic expectations as to what Six Sigma is truly capable of achieving. In this paper, some strategic perspectives on the subject are presented, highlighting the potential and possible limitations of Six Sigma applications particularly in a knowledge‐based environment. Without delving into the mechanics of the subject in detail, the points raised could be useful to those deliberating on the appropriateness of Six Sigma to their respective organizations. Copyright © 2002 John Wiley & Sons, Ltd.     

The Role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner

Six Sigma has now been well recognized as an effective means of attaining excellence in the quality of products and services. It entails the use of statistical thinking as well as management and operational tools to bring about fundamental improvements. This article explains, in a nonmathematical language, the rationale and mechanics of design of experiments as seen in its deployment in Six Sigma. It also outlines the way in which the design of experiments has been utilized in the past for quality improvement, culminating in its important role in Six Sigma. An appreciation of the changing scope of experimental design applications over the years and in the future would provide useful perspectives on the significance of Six Sigma in an organization's quest for quality excellence.     

Lean,Six Sigma and Lean Six Sigma: an analysis based on operations strategy

There is a growing need for operations management models that contribute to the continuous improvement of company processes, among them we highlight lean manufacturing, Six Sigma and, more recently, Lean Six Sigma (LSS). This article aims (1) to identify and analyse the differences and complementarities in the production decision areas for each one of the three models; (2) to identify the competitive priorities that lead to the best performance as a result of policies followed in the decision areas as a result of the adopted model. First, a theoretical conceptual model was developed based on a review of the literature, followed by a exploratory research questions applied to manufacturing companies that use the lean, Six Sigma or LSS manufacturing models in southern Brazil. The main results show that there are differences in the models in relation to the importance of the decision areas and the performance achieved in the competitive priorities. Individually, lean manufacturing, Six Sigma and LSS have varying degrees of importance in the Facilities, Vertical Integration and Production Planning and Control decision areas. The performance dimensions with the best performance are speed, quality, reliability and cost.     

Future‐Proofing Six Sigma

Six Sigma started some three decades ago as a problem solving framework for quality improvement. It has since evolved, while being implemented in industry, into a management approach to performance excellence. Whether Six Sigma will continue to enjoy the attention it has been getting and keep embraced by practitioners depends on whether its implementation continues to be in line with new organizational needs in the twenty‐first century. This paper considers what it would take for Six Sigma to face the future world and analyzes from both strategic and operational directions a number of maneuvers necessary to sustain its relevance. Various inclusive and proactive features are explained for remaking of a Six Sigma that is to remain in demand for many years to come. Copyright © 2013 John Wiley & Sons, Ltd.     

基于六西格玛设计理念的农村医疗药箱设计

基于对六西格玛设计理念学习研究的基础上,将六西格玛设计的方法运用到农村医疗药箱的设计中。按照DFSS中DMADV模式的设计步骤,在了解人群的使用要求之后,确定药箱的关键质量特性,通过KANO模型分析确定优先等级,在设计阶段提出概念方案逐个优化设计方案。经过小批量设计检验和完善设计方案后,批量生产完成最终设计。整个设计旨在通过科学合理的六西格玛设计理念对药箱进行设计,提高药箱使用的舒适性,实现其低成本美观实用的优良特点。     

Winning customer loyalty in an automotive company through Six Sigma: a case study

Six Sigma is a disciplined approach to improving product, process and service quality. Since its inception at Motorola in the mid 1980s Six Sigma has evolved significantly and continues to expand to improve process performance, enhance business profitability and increase customer satisfaction. This paper presents an extensive literature review based on the experiences of both academics and practitioners on Six Sigma, followed by the application of the Define, Measure, Analyse, Improve, Control (DMAIC) problem‐solving methodology to identify the parameters causing casting defects and to control these parameters. The results of the study are based on the application of tools and techniques in the DMAIC methodology, i.e. Pareto Analysis, Measurement System Analysis, Regression Analysis and Design of Experiment. The results of the study show that the application of the Six Sigma methodology reduced casting defects and increased the process capability of the process from 0.49 to 1.28. The application of DMAIC has resulted in a significant financial impact (over U.S. $110 000 per annum) on the bottom‐line of the company. Copyright © 2006 John Wiley & Sons, Ltd.     

Six Sigma experience as a stochastic process

This study empirically investigates the states of Six Sigma from a stochastic point of view. By the means of an advanced survey, 97 respondents are asked to rate the effect of Six Sigma on different performance categories, the cost of implementing Six Sigma, the level of enthusiasm and expectations from Six Sigma over 20 years. The autocorrelation and cross-correlation functions of these processes are analyzed to investigate the stages of Six Sigma. Consequently, new concepts namely steady state of Six Sigma and Six Sigma experience functions are introduced which shed light on the life cycle of Six Sigma within the companies.     

Application of six sigma methodology to reduce defects of a grinding process

Six Sigma is a data‐driven leadership approach using specific tools and methodologies that lead to fact‐based decision making. This paper deals with the application of the Six Sigma methodology in reducing defects in a fine grinding process of an automotive company in India. The DMAIC (Define–Measure–Analyse–Improve–Control) approach has been followed here to solve the underlying problem of reducing process variation and improving the process yield. This paper explores how a manufacturing process can use a systematic methodology to move towards world‐class quality level. The application of the Six Sigma methodology resulted in reduction of defects in the fine grinding process from 16.6 to 1.19%. The DMAIC methodology has had a significant financial impact on the profitability of the company in terms of reduction in scrap cost, man‐hour saving on rework and increased output. A saving of approximately US$2.4 million per annum was reported from this project. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating Six Sigma failure rate for inverse Gaussian cycle times

Six Sigma is a quality philosophy and methodology that aims to achieve operational excellence and delighted customers. The cost of poor quality depends on the sigma quality level and its corresponding failure rate. Six Sigma provides a well-defined target of 3.4 defects per million. This failure rate is commonly evaluated under the assumption that the process is normally distributed and its specifications are two-sided. However, these assumptions may lead to implementation of quality-improvement strategies that are based on inaccurate evaluations of quality costs and profits. This paper defines the relationship between failure rate and sigma quality level for inverse Gaussian processes. The inverse Gaussian distribution has considerable applications in describing cycle times, product life, employee service times, and so on. We show that for these processes attaining Six Sigma target failure rate requires higher quality efforts than for normal processes. A generic model is presented to characterise cycle times in manufacturing systems. In this model, the asymptotic production is described by a drifted Brownian motion, and the cycle time is evaluated by using the first passage time theory of a Wiener process to a boundary. The proposed method estimates the right efforts required to reach Six Sigma goals.     

Statistical Control of a Six Sigma Process

Six Sigma as a methodology for quality improvement is often presented and deployed in terms of the dpmo metric, i.e., defects per million opportunities. As the sigma level of a process improves beyond three, practical interpretation problems could arise when conventional Shewhart control charts are applied during the Control phase of the define-measure-analyze-improve-control framework. In this article, some alternative techniques are described for the monitoring and control of a process that has been successfully improved; the techniques are particularly useful to Six Sigma Black Belts in dealing with high-quality processes. The approach used would thus ensure a smooth transition from a low-sigma process management to maintenance of a high-sigma performance in the closing phase of a Six Sigma project.     

Management of Parametric Response in Design of Experiments

Routine applications of design of experiments (DOE) by non‐mathematicians assume that each response value is static in nature, i.e. with an expected value that is constant for a given set of input factor settings. When this assumption is not valid, it is important to capture the dynamic characteristics of the response for effective process or system characterization, monitoring, and control. With the purpose of recognizing the self‐changing nature of the response owing to factors other than those built into the DOE, thereby gaining a better ability to shape the behavior of the response, this paper describes the reasoning and procedure needed for such ‘parametric responses’, via common techniques of mathematical modeling and optimization. The procedure is intuitive but essential and useful in DOE studies as these become increasingly popular by practitioners in the context of improvement projects such as those related to Six Sigma or stand‐alone performance optimization initiatives. Copyright © 2013 John Wiley & Sons, Ltd.     

A new model to implement Six Sigma in small- and medium-sized enterprises

The Six Sigma approach improves the quality of products in order to ensure customers’ satisfaction. This approach has yielded to interesting results for large enterprises. However, its implementation remains difficult for small- and medium-sized enterprises (SME). In fact, the use of the same tools is insufficient to achieve the objectives when considering financial constraints and the lack of data. The regular tools are complex for SMEs which require an adapted model to implement the approach successfully. In this paper, we propose a new model having the objective to facilitate the integration of Six Sigma in SMEs by avoiding the use of Black Belts, optimising the implementation costs and period, simplifying the Six Sigma structure and enhancing the communication between staff and managers. The model includes two imbricated loops: the first offers immediate improvement actions by estimating the capability and the stability of the process, while the second provides profound improvement actions using the fuzzy logic system and the analytic hierarchical process (AHP) method. An example illustrates the application of the proposed model in an SME.     

Paper Organizers International: A Fictitious Six Sigma Green Belt Case Study. II

The purpose of this paper is to present the second part of a detailed, step-by-step case study of a simple Six Sigma Green Belt project. In the last edition of Quality Engineering, the first part of a Six Sigma Green Belt case study was presented. That paper showed the Define and Measure phases of the DMAIC method. This paper presents the rest of the case study, or the Analyze, Improve, and Control phases of the DMAIC method.     

Reducing Patient Waiting Time in Outpatient Department Using Lean Six Sigma Methodology

This article addresses the issue of longer patient waiting time in the outpatient department (OPD) of a super specialty hospital attached to a manufacturing company in India. Due to longer waiting times at OPD, employees need to be away from the workplace for a longer duration. This problem was addressed through the Lean Six Sigma (LSS) methodology. The process, starting from registration of a patient to dispensing of medicine, was included in the project. The non‐value added steps in the process were identified, and actions were initiated. A cause and effect diagram was prepared for high patient waiting time, and causes were validated with the help of data collected from the process. Statistical tools such as Kruskal–Wallis test, Box – Cox transformation, Control charts, normality test, etc., were used within the LSS methodology not only to identify the causes but also to sustain the improvements. As a result of this project, the average waiting time reduced from 57 min to 24.5 min and the standard deviation was reduced to 9.27 from 31.15 min. This will help the hospital to serve patients better and faster, which, in turn, will lead to a reduction in delay of treatment and a faster recovery of patients. The productivity loss due to absenteeism of employees from the workplace could be reduced. Generally, in an Indian health care scenario, most of the activities were dependent on individual doctors rather than processes. This project has helped the clinicians and the hospital management to identify the weak areas in the process for improvement. Because of the implemented solutions, understanding the history of past treatments and medications of the patients was easy for the doctors. Also, the practical validity of deploying LSS in a healthcare scenario was justified with this study. Copyright © 2013 John Wiley & Sons, Ltd.