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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Improving wooden parts' quality by adopting DMAIC procedure

This paper utilizes the six‐sigma define‐measure‐analyze‐improve‐control (DMAIC) procedure to improve quality through enhancing the measurement system capability (MSC) of a wood industry. In the first stage, the major reported defects at the customer site are identified, and then the performance of the measurement system is evaluated by conducting a gage repeatability and reproducibility (GR&R) experiment on a drilling process, i.e. the outputs of a drilling machine. The obtained results show that the measurement system is inadequate and results in wrong classification of the produced wooden parts. Thus, to improve MSC, corrective actions, including operators' training, proper selection of measuring instruments, and improved measuring methods, were implemented. To determine the achieved improvement in MSC, a second GR&R study was carried out under the same conditions. In conclusion, adopting the DMAIC procedure including GR&R study turns out to be an effective method in improving the quality system involving measurements. Copyright © 2008 John Wiley & Sons, Ltd.     

On the evaluation of Six Sigma projects

Most of the research on the success in implementing Six Sigma agrees upon the fact that one of the key success factors is the selection of profitable projects. This seems to be especially important for high‐risk, large‐scope and long‐term projects, as is mostly the case in the design for Six Sigma projects. The purpose of this paper is to outline Six Sigma project characteristics and to present a new model for evaluating Six Sigma projects. To design a Six Sigma project evaluation model, we utilized mathematical optimization modeling techniques and real options theory. The model allows for the quantification of not only the project's value prior to its start but also its progress and the consideration of possible decisions based on this progress. Copyright © 2009 John Wiley & Sons, Ltd.     

A fuzzy AHP-based performance evaluation model for implementing SPC in the Taiwanese LCD industry

Statistical process control (SPC) is one of the most practical and widely used tools to enhance product quality and reduce costs. However, the implementation of SPC has often resulted in unsatisfactory performance; moreover, there is no well-established standard for evaluation of the results of introducing the system. The present study addresses this problem by proposing an effective and convenient performance-evaluation model for implementing SPC. The proposed model draws on the DMAIC methodology of Six Sigma, the performance-evaluation model of Lin et al. (Lin, W.T., Liu, C.H., Hsu, I.C., and Lai, C.T., 2004. An empirical study of QS 9000 in the automobile and related industries in Taiwan. Total Quality Management, 15 (3), 335–378), and the fuzzy mathematical programming of Kaufmann and Gupta (Kaufmann, A. and Gupta, M.M., 1991. Introduction to fuzzy arithmetic: theory and application. New York: Van Nostrand Reinhold) to define the fuzzy indices and control values of importance, action, and performance in developing the proposed performance-evaluation model. The model is then applied in a case study of a Taiwanese liquid crystal display manufacturer. A questionnaire is designed to establish fuzzy indices of importance, action, and performance values for assessment by analytic hierarchy process methodology. Various critical factors and feasible improvement strategies are then compared (using computed weights) to determine the priority of the improvement strategies. To verify improvement, the same model is used to re-evaluate system control performance after implementing the improvement strategies for some time. The study demonstrates that the proposed model is an effective and convenient tool that can be used to analyse and improve the performance of an existing SPC system or to enhance success in implementing a new SPC system while working within constraints of time and costs.     

Six sigma-based approach to optimize deep drawing operation variables

The six sigma approach has been increasingly adopted worldwide in the manufacturing sector in order to enhance the productivity and quality performance and to make the process robust to quality variations. This paper deals with one such application of six sigma methodology to improve the yield of deep drawing operations. The deep drawing operation has found extensive application in producing automotive components and many household items. The main issue of concern of the deep drawn products involves different critical process parameters and governing responses, which influences the yield of the operation. The effects of these parameters are analysed by the DMAIC (Define, Measurement, Analyse, Improve, Control)-based six sigma approach. A multiple response optimization model is formulated using the fuzzy-rule-based system. The functional relationship between the process variables and the responses is established, and thereafter their optimum setting is explored with the aid of response surface methodology (RSM). Rigorous experimentations have been carried out, and it is observed that the process capability of processes is enhanced significantly, after the successful deployment of the six sigma methodology.     

TRIZ在管理流程优化中的应用

 TRIZ理论是基于专利分析提出的用于解决发明创造问题的方法论,已经在工程技术领域取得丰硕成果.随着TRIZ研究的不断深入,其基本思想和分析工具已开始在管理领域使用.由于管理问题非结构化的特点,TRIZ工具和解决问题的流程很难直接应用到管理问题中,缺乏TRIZ在管理领域应用的系统化方法.在分析大量文献的基础上,将TRIZ解决问题的一般流程和六西格玛的DMAIC流程作为基础,提出一种TRIZ在管理流程优化中的应用流程——DTSIC （define-translate-solve-improve-control,定义—转化—解决—改进—控制）.并详细分析了每一阶段可以使用的TRIZ方法和工具以及可以结合的管理流程优化工具,给出了一些选择工具的原则.该流程将管理方法与TRIZ原理和工具包有效结合,在不断循环过程中使流程得到改善.     

Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach

Nd: YLF laser beam machining (LBM) process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. Continuous improvement (CI) effort for LBM process is usually realised by response surface methodology, which is an important tool in Design of Six Sigma. However, when determining the optimal machining parameters in CI for LBM process, model parameter uncertainty is typically neglected. Performing worst case analysis in CI, this paper presents a new loss function method that takes model parameter uncertainty into account via Bayesian credible region. Unlike existing CI methods in LBM process, the proposed Bayesian probabilistic approach is based on seemingly unrelated regression which can produce more precise estimations of the model parameters than ordinary least squares in correlated multiple responses problems. An Nd: YLF laser beam micro-drilling process is used to demonstrate the effectiveness of the proposed approach. The comparison results show that micro-holes produced by the proposed approach have better quality than those of existing approaches in terms of robustness and process capability.     

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.     

六西格玛在广州市规划局IT运维服务质量管理中的应用

论文总结了目前广州市规划局IT运维服务工作中存在的一些问题,介绍了使用六西格玛管理方法测量IT运维服务流程、发现服务流程缺陷、改善服务质量,指出六西格玛管理方法与IT服务管理密切结合是提高用户满意度的有效方法。     

精益六西格玛理论研究与应用综述

精益六西格玛(Lean Six Sigma, LSS)作为持续改进与创新的有力工具,在业界(制造业、建筑业、服务业甚至非盈利性组织)得到广泛的应用,但学术界和产业界对LSS的认识和理解仍存在一些差异。为了构建对LSS更为全面的认识,基于Web of Science 数据库中题目含有“六西格玛”或“精益六西格玛”的2701篇文献分析,简要回顾LSS的起源,系统梳理LSS在理论研究和产业界应用的现状,给出6个主要的研究内容。结合LSS在中国的应用实践,并从战略、系统和集成3个角度展望LSS的未来发展趋势,期望对精益六西格玛的学术研究和产业应用提供指导和参考。     

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.     

Implementing Evaluation of the Measurement Process in an Automotive Manufacturer: a Case Study

Reducing process variability is presently an area of much interest in manufacturing organizations. Programmes such as Six Sigma robustly link the financial performance of the organization to the degree of variability present in the processes and products of the organization. Data, and hence measurement processes, play an important part in driving such programmes and in making key manufacturing decisions. In many organizations, however, little thought is given to the quality of the data generated by such measurement processes. By using potentially flawed data in making fundamental manufacturing decisions, the quality of the decision‐making process is undermined and, potentially, significant costs are incurred. Research in this area is sparse and has concentrated on the technicalities of the methodologies available to assess measurement process capability. Little work has been done on how to operationalize such activities to give maximum benefit. From the perspective of one automotive company, this paper briefly reviews the approaches presently available to assess the quality of data and develops a practical approach, which is based on an existing technical methodology and incorporates simple continuous improvement tools within a framework which facilitates appropriate improvement actions for each process assessed. A case study demonstrates the framework and shows it to be sound, generalizable and highly supportive of continuous improvement goals. Copyright © 2003 John Wiley & Sons, Ltd.