Showing papers on "Design for Six Sigma published in 2003"

Design for Six Sigma: A Roadmap for Product Development

Abstract: Chapter 1. Quality Concepts Chapter 2. Six Sigma and Lean Fundamentals Chapter 3. Product Development Process and Design for Six Sigma Chapter 4. Design for Six Sigma Deployment Chapter 5. Design for Six Sigma Project Algorithm Chapter 6. DFSS Transfer Function and Scorecards Chapter 7. Quality Function Deployment(QFD) Chapter 8. Axiomatic Design Chapter 9. Theory of Inventive Problem Solving (TRIZ) Chapter 10. Design for X Chapter 11. Failure Mode--Effect Analysis Chapter 12. Fundamentals of Experimental Design Chapter 13. Taguchi's Orthogonal Array Experiment Chapter 14. Design Optimization: Taguchi's Robust Parameter Design Chapter 15. Design Optimization: Advanced Taguchi Robust Parameter Design Chapter 16. Tolerance Design Chapter 17. Response Surface Methodology Chapter 18. Design Validation Acronyms References Index

Lean Six Sigma for Service : How to Use Lean Speed and Six Sigma Quality to Improve Services and Transactions

Abstract: "How do I apply Lean Six Sigma in my service organization?" This is a question many executives and managers are asking. With all the emphasis on using Lean Six Sigma in manufacturing environments, the need for a clear methodology for implementing these major quality improvement initiatives in service functions has been mainly overlooked - until now. "Lean Six Sigma for Service" provides a service-based approach, explaining how companies of all types can cost-effectively translate manufacturing-oriented Lean Six Sigma tools into the service delivery process. Six Sigma expert Michael George reveals how easy it is to apply relatively simple statistical and Lean tools that will reduce costs and achieve greater speed in service processes. It's no secret that service functions have a harder time applying Lean and Six Sigma principles. The manufacturing roots of these initiatives have made it unclear how to apply these tools to services; this book effortlessly makes that translation.Here, for the first time, you'll read about how classic Lean tools such as "Pull systems" and "setup reduction" are being used in procurement, call centers, surgical suites, government offices, RD become a customer-centered organization; gain control over process complexity Improve response time on signature services; apply value-based management to project selection; clean up your workspace; and develop supplier relationships.For guidance in deploying Lean Six Sigma in service organizations, reducing lead times, streamlining processes, and holding down costs, "Lean Six Sigma for Services" is the most complete, authoritative guide you can own. "Lockheed Martin recognized that our business support processes have as much opportunity for improvement as our design and build areas. By applying Lean process speed and Six Sigma quality tools to marketing, legal, contract administration, procurement, etc. we have created a competitive advantage...The lessons learned and practical case studies contained in "Lean Six Sigma for Service" provide a road map which can create great value for customers, employees and shareholders." - Mike Joyce, Vice President, Lockheed Martin. For operational excellence deploy Lean Six Sigma in your service organization.Would you like to: reduce your company's service costs by 30 to 60 percent? Improve service delivery time by 50 percent? Expand capacity by 20 percent - without adding staff? If you answered yes - and who wouldn't - then this is the book for you. "Lean Six Sigma for Services" reveals how to bring the miracle of Lean Six Sigma improvement out of manufacturing and into service functions.Michael George describes the basic elements of successful deployment, including insights from corporate leaders who have already "walked the talk" to accelerate your own journey. Filled with case studies detailing dramatic service improvements in organizations from Lockheed Martin to Stanford University Hospital, this bottom-line book provides executives and managers with the knowledge necessary to blend Lean and Six Sigma to optimize services. You'll see how Lean Six Sigma can cut costs by reducing complexity; how to utilize its tools to provide better quality service; and how you can use shareholder value to drive project selection - without needing an MBA.

What is Lean Six Sigma

Abstract: This title offers a quick introduction on how to use Lean Six Sigma to improve your workplace, meet your goals, and better serve your customers. Lean Six Sigma combines the two most important improvement trends of our time: making work better (using Six Sigma) and making work faster (using Lean principles). In this plain-English guide, you'll discover how this remarkable quality improvement method can give you the tools to identify and eliminate waste and quality problems in your own work area. Packed with diagrams, cartoons, and real-life examples, "What is Lean Six Sigma?" reveals the four keys of Lean Six Sigma and how they apply to your own job: delight your customers with speed and quality; improve your processes; work together for maximum gain; and, base decisions on data and facts. You'll see the big picture of what your company hopes to gain with Lean Six Sigma, how it may affect your work area, and what it can mean to you personally.

Integrating Six Sigma with total quality management: a case example for measuring medication errors.

Abstract: Six Sigma is a new management philosophy that seeks a nonexistent error rate. It is ripe for healthcare because many healthcare processes require a near-zero tolerance for mistakes. For most organizations, establishing a Six Sigma program requires significant resources and produces considerable stress. However, in healthcare, management can piggyback Six Sigma onto current total quality management (TQM) efforts so that minimal disruption occurs in the organization. Six Sigma is an extension of the Failure Mode and Effects Analysis that is required by JCAHO; it can easily be integrated into existing quality management efforts. Integrating Six Sigma into the existing TQM program facilitates process improvement through detailed data analysis. A drilled-down approach to root-cause analysis greatly enhances the existing TQM approach. Using the Six Sigma metrics, internal project comparisons facilitate resource allocation while external project comparisons allow for benchmarking. Thus, the application of Six Sigma makes TQM efforts more successful. This article presents a framework for including Six Sigma in an organization's TQM plan while providing a concrete example using medication errors. Using the process defined in this article, healthcare executives can integrate Six Sigma into all of their TQM projects.

Going from six sigma to design for six sigma: an exploratory study using analytic hierarchy process

Abstract: Six sigma has been considered a business strategy that employs a well‐structured continuous improvement methodology to tackle process variability and drive out waste from the business processes using statistical tools and techniques. This paper first examines the differences and similarities of six‐sigma improvement methodology compared with the DFSS approach. This work illustrates the use of analytical hierarchy process (AHP), a multiple criteria decision‐making technique, for the evaluation of six‐sigma projects in order to determine when the six‐sigma approach becomes a priority over DFSS. The use of AHP to determine the transition from six sigma to design for six sigma represents a major challenge to many researchers today, as very little has been done on this subject.

Customer‐centric six sigma quality and reliability management

Abstract: This paper presents a customer‐centric six sigma quality management as an extension of the traditional six sigma way. It views product quality and process reliability as key to achieving six sigma and adopts a holistic view of quality. The aim is to offer practical guidelines to business leaders, practicing mangers and those interested in quality, the new directions in quality management. It views reliability management as an integral part of any six sigma strategy since the focus in reliability management is on process reliability and dependability. Thus, by bringing both product and process quality together, a customer‐centric six sigma can be achieved.

Variation Risk Management: Focusing Quality Improvements in Product Development and Production

Abstract: Preface. Figures. Tables. Text Boxes. Nomenclature. Acronyms. 1. Introduction. 1.1. The Competitive Advantage of VRM. 1.2. Guide to Readers. 2. Basics of Variation Risk Management. 2.1. Basic Principles of VRM. 2.1.1. VRM Must Be Holistic. 2.1.2. VRM Must Be Process Oriented. 2.1.3. VRM Must Be Data Driven. 2.2. Variation and Its Impact on Quality. 2.3. Summary. 3. Identification. 3.1. Definition of Key Characteristics and Variation Flowdown. 3.1.1. Key Characteristics. 3.1.2. Variation Flowdown. 3.2. Defining the Scope of the VRM Application. 3.3. Identifying Critical System Requirements. 3.3.1. Identify the Voice of the Customer. 3.3.2. Identify Specifications and Requirements. 3.3.3. Identify Critical System Requirements. 3.4. Identifying System Key Characteristics. 3.4.1. What Is a System Key Characteristic? 3.4.2. Examples of System Key Characteristics. 3.5. Creating the Variation Flowdown. 3.5.1. What Information to Gather. 3.5.2. How to Conduct the Top-Down Process. 3.5.3. How to Conduct the Bottom-Up Process. 3.5.4. How to Conduct and Document the Identification Procedure. 3.6. Summary. 4. Overview of Assessment. 4.1. Assessment during Product Development. 4.2. Assessment during Production. 5. Assessment of Defect Rates. 5.1. Predicting the Frequency of Defects. 5.1.1. Variation Models. 5.1.2. Prediction Tools. 5.2. Estimating the Contributions of Part and Process KCs. 5.2.1. Qualitative Analysis of Variation Contribution. 5.2.2. Quantitative Analysis of Variation Contribution. 5.3. Measuring the Frequency of Defects. 5.4. Measuring the Contributions of Part and Process KCs. 5.5. Summary. 6. Assessment of Cost and Risk. 6.1. Cost and Risk Assessment during Product Development. 6.1.1. Qualitative Assessments. 6.1.2. Step Cost Functions. 6.1.3. Continuous Cost Functions. 6.2. Total Cost of Variation Assessment during Production. 6.2.1. Cost Sources. 6.2.2. Representation of the Total Cost of Variation. 6.2.3. Cost Analysis and Aggregation. 6.3. Summary. 7. Assessment of the Quality Control System. 7.1. QC Plan Maturity. 7.1.1. Detection Capability and Effectiveness. 7.1.2. Diagnosis Capability. 7.1.3. Efficient Resource Utilization. 7.2. QC Location in the Manufacturing Process. 7.3. QC Effectiveness Matrix. 7.4. Summary. 8. Mitigation. 8.1. Mitigation during Product Development and Production. 8.1.1. Mitigation during Product Development. 8.1.2. Mitigation during Production. 8.2. Identifying Mitigation Strategies. 8.2.1. Design Changes. 8.2.2. Manufacturing Process Changes. 8.2.3. Manufacturing Process Improvements. 8.2.4. Monitoring and Controlling Manufacturing Processes. 8.2.5. Testing and Inspection. 8.3. Selecting a Mitigation Strategy. 8.4. Selecting a Project Portfolio. 8.5. Executing Mitigation Strategies. 8.6. Summary. 9. Integration of Variation Risk Management with Product Development. 9.1. Basics of Product Development. 9.1.1. Stage Gate Product Development Process. 9.1.2. VRM during Product Development. 9.1.3. Metrics. 9.2. Requirements Development. 9.3. Concept Development. 9.4. Product Architecture Design. 9.5. System Concept Design. 9.6. Detail Design. 9.7. Product Testing and Refinement. 9.8. Transition to Production. 9.8.1. Handling Customer Complaints. 9.8.2. Wrap-Up. 9.8.3. Documenting the Key Characteristic Plan. 9.9. Production. 9.9.1. Continually Monitor Total Cost of Variation. 9.9.2. Track Customer Complaint Data. 9.9.3. Review Quality Control Data. 9.9.4. Track Impact of Changes. 9.10. Summary. 10. Roles and Responsibilities in Variation Risk Management. 10.1. Product Development. 10.1.1. The Integrated Product Team Approach. 10.1.2. Expert Teams. 10.1.3. Coaches. 10.2. Production. 10.2.1. Production Teams. 10.2.2. Expert Teams. 10.3. Suppliers' Roles and Responsibilities. 10.3.1. Role of Suppliers during Product Development. 10.3.2. Role of Suppliers during Production. 10.3.3. What Does a KC Mean to a Supplier? 10.4. Summary. 11. Planning and Implementing a Variation Risk Management Program. 11.1. Planning a VRM Program. 11.1.1. Gathering Management Support. 11.1.2. Gathering Organizational Support. 11.1.3. Baselining the Existing VRM Processes. 11.1.4. Formalizing VRM. 11.1.5. Developing KC Tracking Methods. 11.1.6. Identifying Lead Users. 11.1.7. Developing Training Materials. 11.2. Implementing the VRM Program. 11.2.1. Identifying Initial Projects. 11.2.2. Training the Team. 11.2.3. Applying VRM. 11.2.4. Gathering Feedback. 11.3. Summary. 12. Summary. Appendix A: Maturity Models. Appendix B: Process Capability Databases. B.1. Background on Process Capability Data. B.1.1. Importance of Using Process Capability Data. B.1.2. Structure and Content of a Process Capability Database. B.1.3. Difficulties in Implementing Process Capability Databases. B.2. The Right Structure. B.2.1. Designing the Indexing Scheme. B.2.2. Choosing the Database Implementation Approach. B.2.3. Creating the User Interfaces and Data Analysis. B.3. The Right Data. B.4. The Right Management Support. B.5. The Right Usage. B.6. Implementation of a Process Capability Database. B.6.1. Who Should Be Involved. B.6.2. What Decisions Should Be Made. B.6.3. Implementation Steps. B.7. Summary. Appendix C: Other Initiatives. C.1. Six Sigma. C.2. Design for Six Sigma. C.3. Lean Manufacturing. C.4. Continual Improvement, TQM, and Kaizen. C.5. Dimensional Management. C.6. Design for Manufacturing. C.7. Quality Function Deployment (House of Quality). C.8. FMEA. C.9. Summary. Appendix D: Summary of Process Diagrams. Glossary. Bibliography. Index.

Lean Sigma [production and supply chain management]

Abstract: Lean strategy brings a set of proven tools and techniques to reduce times, inventories, set up times, equipment downtime, scrap, rework and other wastes of the hidden factory. The focus is on value from a customer perspective and flowing this through the entire supply chain. The statistically based problem solving methodology of Six Sigma delivers data to drive solutions, delivering dramatic bottom-line results. In the Six Sigma school of study, a problem is tackled by a black or green belt, depending on the nature of the problem and the degree of complexity involved in the determination of solutions. The authors compare and contrast the lean and Six Sigma approaches, and find that greater benefits can be reaped by blending the best of each.

Design for Six Sigma

Abstract: This book provides a systematic methodology for incorporating Six Sigma into the earliest stages of every project. Six Sigma is today's most honored and effective quality initiative. Yet to achieve optimal results from such an initiative, companies must first master the building blocks of Six Sigma. "Design for Six Sigma" shows decision makers at all levels how to implement Design for Six Sigma (DFSS) tools and techniques from earliest design through final production of virtually any product, service, or process. Look to this latest addition to McGraw-Hill's popular, reader-friendly "Briefcase Books" series to learn: specific functions and activities for each phase of the DFSS methodology; important tools - and their uses - for ensuring DFSS efficiency and efficacy; and vital guidelines and tips for seamless, successful DFSS implementation. Six Sigma is an integral methodology that, to ensure success, must be integrated at each step of production.Let "Design for Six Sigma" show you how to join today's quality leaders by incorporating DFSS into each step of your new product/service development program - for maximum impact and return on your Six Sigma investment. "Briefcase Books" are written specifically for today's busy manager.Each book features eye-catching icons, checklists, and sidebars to guide managers step-by-step through everyday workplace situations. Look for these innovative design features to help you navigate through each page: Key Terms icon - clear definitions of key DFSS terms and jargon; Smart Managing icon - how to use DFSS techniques to improve decision making; Tricks of the Trade icon - front-line hints and tips for integrating DFSS; Mistake proofing icon - advice for limiting costly Six Sigma miscues and mistakes; Caution icon - warning signs for potential problems or disasters; For Example icon - how others have used DFSS to optimize processes; and, Tools icon - specific methods for implementing DFSS at every level.

How six sigma improves R&D

Abstract: MANAGERS AT WORK Many people claim that Six Sigma is not useful in research. For instance, in a recent Quality Digest article, Dick Dusharme quoted author and quality consultant Thomas Pyzdek as stating that he would never apply Six Sigma to research because it would kill creativity (1). The experienced R&D leaders who participated in the Industrial Research Institute's "Six Sigma in R&D" Workshop last March would likely disagree with Pyzdek (2). They shared demonstrated results from use of Six Sigma and Design for Six Sigma (DFSS) in R&D. They told us how they taught Six Sigma concepts to R&D technical staff, guided R&D's participation in corporate initiatives, and implemented Six Sigma without spending a fortune on relatively little-used tools. One reason the workshop participants would disagree with Pyzdek: They understand, and shared with the rest of us, the importance of distinguishing between Six Sigma as management strategy and 6sigma as statistical terminology. As a metric, 6sigma performance simply represents a level of 3.4 defects per million opportunities. But in the broader context of management strategy, the 6sigma metric is an anchor. That anchor provides focused identity and stability to management strategy for eliminating defects and extracting value from many industrial activities, including R&D. Six Sigma is focused on measuring process capability and motivating improved performance to eliminate defects. This focus on "elimination of defects" is relevant to many, but not all, processes within R&D. Random, seasonal and biased processes that occur in R&D (and elsewhere) are difficult to reconcile with the concept of "statistical control." So in an R&D context, Six Sigma represents a mindset that is a consequence of adopting 6a as a business performance standard. In Six Sigma, the focus is on problem definition and problem solving. To apply Six Sigma, problems have to be stated formally-in the manner of solving y = f(x), where y is the dependent variable and x is the independent variable. In formulating a Six Sigma problem, this usually means that y is a symptom, output or effect and x is a cause, input-and-process or a problem. The Six Sigma mindset gives us a measurable, goal-- oriented context for working on quality improvement in R&D. At the "Six Sigma in R&D" workshop, experienced R&D leaders taught us that Six Sigma has broad applicability in an R&D context because R&D is fundamentally a series of problem-defining and problemsolving processes. Workshop Participants The two-day workshop was hosted by Lubrizol Corporation at its headquarters near Cleveland, Ohio. Attending the conference were 140 Six Sigma practitioners and R&D leaders from 49 companies representing $0.9 trillion gross sales and $34 billion in R&D spending. Topics included metrics, impact ("how do we know it's working?"), alignment of Six Sigma with other company initiatives, and implementation. A survey of the participants indicated that only 37 percent had a formal Six Sigma program in their R&D organization. Otherwise: * 70% have a formal Six Sigma program in their company. * 29% have a formal DFSS program in R&D. * 50% use Six Sigma methods to improve R&D. * 76% have 25% or less of their company's employees involved in Six Sigma. * 76% have 25% or less of their R&D projects using Six Sigma or DFSS. Key Insights Companies that have formally implemented Six Sigma have done so for a variety of reasons. However, the themes of decreasing cost, increasing speed to market, and improving both process and product quality emerged as dominant reasons for organizations to formalize Six Sigma as corporate culture or mindset. Because of the focus and sponsorship that these themes require, most companies use a similar approach to their Six Sigma programs. …

Ensuring optimal success with Six Sigma implementations

Abstract: To ensure the success of a Six Sigma process improvement effort, there are five critical steps to take: The company's top leaders must champion the undertaking. Six Sigma Knowledge and work practices must cascade down the organization. The active support and engagement of all business process owners in Six Sigma project implementation down the line must be enlisted. Black Belts must be selected on the basis of competencies necessary for the job. Black Belt training must address the needs of the individuals as well as the requirements of the job. © 2003 Wiley Periodicals, Inc.

Blending CMM and Six Sigma to meet business goals

Abstract: Tata Consultancy Services (TCS) blended Six Sigma concepts with the various SW CMM key process areas, thereby creating a quality management system This helped TCS improve its customer focus and sustain process improvement initiatives by explicitly linking them to business goals The TCS team implemented the QMS on the lines of Level 2 and 3 requirements of SW CMM, using Six Sigma concepts to reinforce quantitative process and product measurements and analysis, process improvements for defect prevention, and process optimization This article describes TCS's approach, highlighting the benefits gained by blending Six Sigma and CMM to provide quality deliverables to its customers

Statistical Control of a Six Sigma Process

Abstract: 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.

Six Sigma in software quality

Abstract: The success of Six Sigma in manufacturing in the past decade has encouraged moves to explore Six Sigma applications to other domains, such as the software industry, for performance improvement. Owing to the uniqueness of software processes, there have been disagreements as to whether Six Sigma should be adopted in software design processes. In this paper, we discuss the applicability of the Six Sigma framework to software. Some myths and facts about the Six Sigma Software Program (6SSP) are discussed. We also address some common misconceptions on the potential of Six Sigma in software, as well as some actual practical challenges. A framework is suggested for practitioners and managers interested in exploiting the benefits of statistical analysis in general, and 6SSP in particular. Some ideas are also raised on what remains to be done to make 6SSP work.

Design for Six Sigma in Technology and Product Development

Abstract: (2003). Design for Six Sigma in Technology and Product Development. Journal of Quality Technology: Vol. 35, No. 4, pp. 427-428.

Rath & Strong's Six Sigma Leadership Handbook

Abstract: Foreword. Preface. About the Company-Rath & Strong: A Distinguished History. Acknowledgments. Introduction. What Is Six Sigma? Why Six Sigma? Six Sigma and Its Application in Different Industries and Functions. Infrastructure and Roles. The Non-Delegable Role of Executives. Lean and Six Sigma. Work-Out and Six Sigma. Organization Culture and Six Sigma. The Customer Connection. Process Improvement: DMAIC. Design for Six Sigma. Process Management. Managing with Dashboards. Preparaing for Six Sigma. Launching Six Sigma. Cross-Cultural Aspects of Deploying Six Sigma. Stabilizing, Extending, and Integrating Six Sigma. Measuring the Effectiveness of Your Six Sigma Deployment. Change Management and Communication. Black Belt Selection and Development. Project Selection. project Reviews. Replicating Results and Managing Knowledge. Measuring and Auditing Results. Developing Change Leadership Capacity. Appendix A: Basic Six Sigma Concepts. Appendix B: Case Study: Six Sigma in Small and Medium Enterprises. Appendix C: DFSS Case Study. Notes. Index. About the Contributors.

Leaning Into Six Sigma: A Parable of the Journey to Six Sigma and a Lean Enterprise

Abstract: Reading this tale could result in effective change occurring in your organization! This is the book you will want everyone in your company to read before you start your Lean Six Sigma deployment. Written by professionals who have successfully rolled out Lean Six Sigma in numerous organizations, "Leaning Into Six Sigma" uses a fast-paced business novel format to explain how to combine today's two leading improvement methodologies - Lean Enterprise and Six Sigma - for dramatically improved quality and cycle times.The book tells the tale of a company in crisis that had to improve to stay in business - and how management and employees came to understand and implement a Lean Six Sigma initiative, facing challenges along the way. The story is designed to introduce everyone in an organization to problem-solving strategies that get rid of excess inventory, speed up processes, and improve quality at all levels. Using simple language, the authors help you grasp the basic quality concepts of Lean Six Sigma, including: the 5 S's - Sifting, Sorting, Sweeping and Washing, Spic and Span, and Self-Discipline MAIC (Measure, Analyze, Improve, Control); DOE (Design of Experiments); ANOVA (Analysis of Variance). Help your organization understand the need for change and begin to implement it by leaning into Six Sigma.

Quality improvement from the viewpoint of statistical method

Abstract: With the purpose of guiding professionals in conducting improvement projects in industry, several quality improvement strategies have been proposed which strongly rely on statistical methods. Examples are the Six Sigma programme, the Shainin System and Taguchi's methods. This paper seeks to make a rational reconstruction of these types of improvement strategies, which results in a methodological framework. The paper gives a demarcation of the subject of study and proposes a reconstruction research approach. Thereupon, the elements of the methodological framework are listed and briefly discussed. Finally, the effectiveness of the framework is illustrated by showing to what extent it reconstructs Six Sigma's Breakthrough Cookbook. Copyright © 2003 John Wiley & Sons, Ltd.

Improving the Six Sigma Paradigm

Abstract: Since its inception more than a decade ago, Six Sigma as a quality improvement framework has been gaining increasing attention and acceptance in industry. Thus performance in both manufacturing and service operations can now be calibrated in terms of "sigma level", and companies eager to impress customers have begun to label themselves as "Six Sigma Organizations". In this paper, a realistic view is taken of the Six Sigma framework, with an examination of the basis of Six Sigma and its long term potential. It is argued that in the dynamic business environment of the 21st Century, a forwardlooking organization should aim beyond the Six Sigma benchmark; thus additional requirements are recommended to fortify the common Six Sigma approach, leading to an "EightS" paradigm for sustained excellence in performance.

Use Customer Feedback To Choose Six Sigma Projects

Abstract: Six Sigma methodology offers an organization significant improvement potential, but project selection is often a problem. A formalized customer feedback system can provide reliable and valuable information to a project selection team, since dissatisfied..

Special Issue Quality Improvement from the Viewpoint of Statistical Method

Abstract: With the purpose of guiding professionals in conducting improvement projects in industry, several quality improvement strategies have been proposed which strongly rely on statistical methods. Examples are the Six Sigma programme, the Shainin System and Taguchi’s methods. This paper seeks to make a rational reconstruction of these types of improvement strategies, which results in a methodological framework. The paper gives a demarcation of the subject of study and proposes a reconstruction research approach. Thereupon, the elements of the methodological framework are listed and briefly discussed. Finally, the effectiveness of the framework is illustrated by showing to what extent it reconstructs Six Sigma’s Breakthrough Cookbook. Copyright c � 2003 John Wiley & Sons, Ltd.

Selecting Six Sigma Projects That Matter

Abstract: Early in the deployment of Six Sigma, a company may have more projects than there are green and black belts to handle them, and unless management is selective about choosing future projects, the quality and impact on business diminishes over time. Compa..

A comparative analysis of trilateral and concurrent business improvement methodologies in the high technology sector

Abstract: Many organisations in the high technology sector have suffered from a large fall in market confidence. This fall has resulted in postponed investment and large-scale unemployment. This slowdown in the hi-tech sector has led to a renewed examination of business improvement methodologies to improve organisational competitiveness. Typical questions are, "Which methodology is best" and "Can different methods be used effectively concurrently"? In this study, a longitudinal and explanatory case analysis is used to conduct a comparative analysis on the application of six sigma, self managed teams and lean manufacturing business improvement methodologies. These methodologies were running concurrently in the subject organisation. The main conclusions emanating from this study are: six sigma, lean manufacturing and self-managed teams are compatible; six sigma is highly measurable; the deployment of six sigma has made the largest contribution; it is difficult to assess the contribution made by self-managed teams; the rationale changes over time for the deployment of any initiative; there is no formula for success other than the level of leadership, which is critical to success.

DFSS and Your Current Design Process

Abstract: practitioners make is to assume DFSS is a disruptive technology. It is not. DFSS relies heavily on the voice of the customer to determine the appropriate design approach and required level of performance. Customers often don’t know what the next leap in development will or can be; therefore, an organization may be eternally destined to make only incremental improvements if it relies solely on the voice of the customer to dictate product development strategies.

The Honeywell Experience

Abstract: Honeywell International�s Six Sigma Plus is a marriage of traditional Six Sigma variation reduction techniques with the waste reduction efforts of lean enterprise deployment. Design for Six Sigma (DFSS) is being applied to both new product development a..

Crystal Ball for Six Sigma Tutorial

Abstract: In an increasingly competitive market, businesses are turning to new practices like Six Sigma, a structured methodology for accelerated process improvement, to help reduce costs and increase efficiency. Monte Carlo simulation can help Six Sigma practitioners understand the variation inherent in a process or product, and in turn, can be used to identify and test potential improvements. The benefits of understanding and controlling the sources of variability include increased productivity, reduced waste, and sales driven through improved customer satisfaction. This tutorial uses Crystal Ball/spl reg/ Professional Edition, a suite of easy-to-use Microsoft Excel add-in software, to demonstrate how stochastic simulation and optimization can be used in a Six Sigma analysis of a technical support call center.

Empowering Engineers to Generate Six-Sigma Quality Designs

Abstract: Andreas Vlahinos Advanced Engineering Solutions, LLC Kenneth Kelly, Ahmad Pesaran & Terry Penney National Renewable Energy Laboratory ABSTRACT Although great advances have been made over the last two decades in the product development process, tradition and experience still govern many design decisions. The need for innovative tools is apparent now more than ever as the design team tries to cope with multiple requirements such as cost, performance, six-sigma quality, styling, packaging, safety, durability, environmental impact, etc. A DFSS technique that integrates FEA and probabilistic and robust design tools within the CAD environment is presented. An example from the automotive industry that demonstrates the process and how engineering quality into designs positively impacts the bottom line is presented. INTRODUCTION The need for innovative tools is apparent now more than ever as more complex design requirements are surfacing such as cost, performance, safety, quality, time to market, short life cycle, environmental impacts, WOW aesthetics andmajor changes in industries' business models. Moreover, the automotive industry’s cycle development time from concept to production is being compressed significantly. Some of the changes in the automotive industry’s business model include: vehicle designs are tailored to focused markets; vehicles are being manufactured more on a global scale; and vehicles are designed increasingly through multiple engineering sites around the world. Quality issues can be addressed early in the design cycle with robust esign dmethodologies. The goal of robust design is to deliver customer expectations at affordable cost regardless of customer usage, degradation over product life and variation in manufacturing, suppliers, distribution, delivery and installation. Since randomness and scatter are a part of reality everywhere, probabilistic design techniques are necessary to engineer quality into designs. Traditional deterministic approaches account for uncertainties through the use of empirical safety factors. The safety factors are derived based on past experience [Ref 5]; they do not guarantee satisfactory performance and do not provide sufficient information to achieve optimal use of available resources. The probabilistic design process has not been widely used because it has been intimidating and tedious due to its complexity. In recent years, CAD and FEA codes have introduced integrated design space exploration (PTC's Behavioral Modeling [Ref 14]) and probabilistic systems (e.g. ANSYS' PDS [Ref 1, 3, 5 and 7]) that makes probabilistic