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Über dieses Buch

This book provides an accessible one-volume introduction to Lean Six Sigma and statistics in engineering for students and industry practitioners. Lean production has long been regarded as critical to business success in many industries. Over the last ten years, instruction in Six Sigma has been linked more and more with learning about the elements of lean production. Building on the success of the first and second editions, this book expands substantially on major topics of increasing relevance to organizations interested in Lean Six Sigma.

Each chapter includes summaries and review examples plus problems with their solutions. As well as providing detailed definitions and case studies of all Six Sigma methods, the book uniquely describes the relationship between operations research techniques and Lean Six Sigma. Further, this new edition features more introductory material on probability and inference and information about Deming's philosophy, human factors engineering, and the motivating potential score – the material is tied more directly to the Certified Quality Engineer (CQE) exam.

New sections that explore motivation and change management, which are critical subjects for achieving valuable results have also been added. The book examines in detail Design For Six Sigma (DFSS), which is critical for many organizations seeking to deliver desirable products. It covers reliability, maintenance, and product safety, to fully span the CQE body of knowledge. It also incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on experiment design, and includes practical experiments that will help improve students’ intuition and retention.

The emphasis on lean production, combined with recent methods relating to DFSS, makes this book a practical, up-to-date resource for advanced students, educators and practitioners.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

In this chapter, six sigma is defined as a method for problem solving. It is perhaps true that the main benefits of six sigma are: (1) the method slows people down when they solve problems, preventing them from prematurely jumping to poor recommendations that lose money; and (2) six sigma forces people to evaluate quantitatively and carefully their proposed recommendations. These evaluations can aid by encouraging adoption of project results and in the assignment of credit to participants.
Theodore T. Allen

Statistical Quality Control

Frontmatter

Chapter 2. Quality Control and Six Sigma

The phrase “statistical quality control” (SQC) refers to the application of statistical methods to monitor and evaluate systems and to determine whether changing key input variable (KIV) settings is appropriate. Specifically, SQC is associated with Shewhart’s statistical process charting (SPC) methods. These SPC methods include several charting procedures for visually evaluating the consistency of key process outputs (KOVs) and identifying unusual circumstances that might merit attention.
Theodore T. Allen

Chapter 3. Define Phase and Strategy

This chapter focuses on the definition of a project, including the designation of who is responsible for what progress by when. By definition, those applying six sigma methods must answer some or all of these questions in the first phase of their system improvement or new system design projects. Also, according to what may be regarded as a defining principle of six sigma, projects must be cost-justified or they should not be completed. Often in practice, the needed cost justification must be established by the end of the “define” phase.
Theodore T. Allen

Chapter 4. Measure Phase and Statistical Charting

In Chap 2, it was suggested that projects are useful for developing recommendations to change system key input variable (KIV) settings. The measure phase in six sigma for improvement projects quantitatively evaluates the current or default system KIVs, using thorough measurements of key output variables (KOVs) before changes are made. This information aids in evaluating effects of project-related changes and assuring that the project team is not harming the system. In general, quantitative evaluation of performance and improvement is critical for the acceptance of project recommendations. The more data, the less disagreement.
Theodore T. Allen

Chapter 5. Analyze Phase

In Chap. 3, the development and documentation of project goals was discussed. Chapter 4 described the process of evaluating relevant systems, including measurement systems, before any system changes are recommended by the project team. The analyze phase involves establishing cause-and-effect relationships between system inputs and outputs.
Theodore T. Allen

Chapter 6. Improve or Design Phase

In Chap. 5, methods were described with goals that included clarifying the input-output relationships of systems. The purpose of this chapter is to describe methods for using the information from previous phases to tune the inputs and develop tentative recommendations. The phrase “improvement phase” refers to the situation in which an existing system is being improved. The phrase “design phase” refers to the case in which a new product is being designed.
Theodore T. Allen

Chapter 7. Control or Verify Phase

If the project involves an improvement to existing systems, the term “control” is used to refer to the final six sigma project phase in which tentative recommendations are confirmed and institutionalized. This follows because inspection controls are being put in place to confirm that the changes do initially increase quality and that they continue to do so. If the associated project involves new product or service design, this phase also involves confirmation. Since there is less emphasis on evaluating a process on an on-going basis, the term “verify” refers evaluation on a one-time, off-line basis.
Theodore T. Allen

Chapter 8. Advanced SQC Methods

In the previous chapters several methods are described for achieving various objectives. Each of these methods can be viewed as representative of many other similar methods developed by researchers. Many of these methods are published in such respected journals as the Journal of Quality Technology, Technometrics, and The Bell System Technical Journal. In general, the other methods offer additional features and advantages.
Theodore T. Allen

Chapter 9. SQC Case Studies

This chapter contains two descriptions of real projects in which a student played a major role in saving millions of dollars: the printed circuit board study and the wire harness voids study. The objectives of this chapter include: (1) providing direct evidence that the methods are widely used and associated with monetary savings and (2) challenging the reader to identify situations in which specific methods could help.
Theodore T. Allen

Chapter 10. SQC Theory

Some people view statistical material as a way to push students to sharpen their minds, but as having little vocational or practical value. Furthermore, practitioners of six sigma have demonstrated that it is possible to derive value from statistical methods while having little or no knowledge of statistical theory. However, understanding the implications of probability theory (assumptions to predictions) and inference theory (data to informed assumptions) can be intellectually satisfying and enhance the chances of successful implementations in at least some cases.
Theodore T. Allen

Design of Experiments (DOE) and Regression

Frontmatter

Chapter 11. DOE: The Jewel of Quality Engineering

Design of experiments (DOE ) methods are among the most complicated and useful of statistical quality control techniques. DOE methods can be an important part of a thorough system optimization, yielding definitive system design or redesign recommendations. These methods all involve the activities of experimental planning, conducting experiments, and fitting models to the outputs. An essential ingredient in applying DOE methods is the use of procedure called “randomization ” which is defined at the end of this chapter. To preview, randomization involves making many experimental planning decisions using a random or unpatterned approach.
Theodore T. Allen

Chapter 12. DOE: Screening Using Fractional Factorials

The methods presented in this chapter are primarily relevant when it is desired to determine simultaneously which of many possible changes in system inputs cause average outputs to change. “Factor screening ” is the process of starting with a long list of possibly influential factors and ending with a usually smaller list of factors believed to affect the average response. More specifically, the methods described in this section permit the simultaneous screening of several (m) factors using a number of runs, n, comparable to but greater than the number of factors (n ~ m and n > m).
Theodore T. Allen

Chapter 13. DOE: Response Surface Methods

Response surface methods (RSM ) are primarily relevant when the decision-maker desires (1) to create a relatively accurate prediction of engineered system input-output relationships and (2) to “tune” or optimize thoroughly of the system being designed. Since these methods require more runs for a given number of factors than screening using fractional factorials , they are generally reserved for cases in which the importance of all factors is assumed, perhaps because of previous experimentation.
Theodore T. Allen

Chapter 14. DOE: Robust Design

In Chapt. 4, it is claimed that perhaps the majority of quality problems are caused by variation in quality characteristics. The evidence is that typically only a small fraction of units fail to conform to specifications. If characteristic values were consistent, then either 100% of units would conform or 0%. Robust design methods seek to reduce the effects of input variation on a system’s outputs to improve quality.
Theodore T. Allen

Chapter 15. Regression

Regression is a family of curve-fitting methods for (1) predicting average response performance for new combinations of factors and (2) understanding which factor changes cause changes in average outputs. In this chapter, the uses of regression for prediction and performing hypothesis tests are described. Regression methods are perhaps the most widely used statistics or operations research techniques. Also, even though some people think of regression as merely the “curve fitting method” in Excel, the methods are surprisingly subtle with much potential for misuse (and benefit).
Theodore T. Allen

Chapter 16. Advanced Regression and Alternatives

Linear regression models are not the only curve-fitting methods in wide use. Also, these methods are not useful for analyzing data for categorical responses. In this chapter, so-called “kriging” models, “artificial neural nets” (ANNs), and logistic regression methods are briefly described. ANNs and logistic regression methods are relevant for categorical responses. Each of the modeling methods described here offers advantages in specific contexts. However, all of these alternatives have a practical disadvantage in that formal optimization must be used in their fitting process.
Theodore T. Allen

Chapter 17. DOE and Regression Case Studies

In this chapter, two additional case studies illustrate design of experiments (DOE) and regression being applied in real-world manufacturing. The first study involved the application of screening methods for identifying the cause of a major quality problem and resolving that problem. The second derives from Allen et al. (2000) and relates to the application of a type of response surface method. In this second study, the design of an automotive part was tuned to greatly improve its mechanical performance characteristics.
Theodore T. Allen

Chapter 18. DOE and Regression Theory

As is the case for other six sigma-related methods, practitioners of six sigma have demonstrated that it is possible to derive value from design of experiments (DOE) and regression with little or no knowledge of statistical theory. However, understanding the implications of probability theory can be intellectually satisfying and enhance the chances of successful implementations.
Theodore T. Allen

Optimization and Management

Frontmatter

Chapter 19. Optimization and Strategy

The selection of confirmed key system input (KIV) settings is the main outcome of a six sigma project. The term “optimization problem” refers to the selection of settings to derive to formally maximize or minimize a quantitative objective. Chapter 6 described how formal optimization methods are sometimes applied in the assumption phase of projects to develop recommended settings to be evaluated in the control or verify phases.
Theodore T. Allen

Chapter 20. Tolerance Design

Tolerance design” refers to the selection of specifications for individual components using formal optimization. Specifications might relate to the acceptable length of a shaft, for example, or the acceptable resistance of a specific resistor in a printed circuit board. Choices about the specifications are important in part because conforming component parts can cause the entire engineered system to fail to conform to specifications. Also, sometimes the specification limits may be needlessly “tight” requiring expensive manufacturing equipment that does not benefit the customer.
Theodore T. Allen

Chapter 21. Design for Six Sigma

Design for six sigma (DFSS) methods can be viewed as part of six sigma or an alternative method as described in Chap. 1. These methods generally involve teams that have control over the design nominals and specifications. Having this “design control” often means that the teams have relatively great power to improve the system quality. It has been said that 80+% of product quality is determined by the product design specifications meaning that manufacturing and delivery can play only a relatively small role. Design teams, therefore, have great responsibility to ensure that new products or processes are put in place smoothly and foster high quality levels.
Theodore T. Allen

Chapter 22. Lean Sigma Project Design

The purposes of this chapter are: (1) to describe six sigma strategy and (2) to propose opportunities for additional research and evolution of six sigma. Part I of this book describes several methods that can structure activities within a project. Part II focuses on design of experiment (DOE) methods that can be used inside six sigma projects. DOE methods are complicated to the extent that decision-making about them might seem roughly comparable to decision-making about an entire project.
Theodore T. Allen

Chapter 23. Motivation and Change Management

The purposes of this chapter are (1) to describe some of the main contributions of Edwards Deming, (2) to clarify the motivating potential score and the implications for job design, (3) to explain the relevance of the field of human factors engineering, and (4) to offer brief highlights from the change management literature. Each of these subjects relates to topics on the ASQ Certified Quality Engineer (CQE) exam. Also, these topics are relevant to lean six sigma projects and general discussions of business management.
Theodore T. Allen

Chapter 24. Software Overview and Methods Review: Minitab

The purposes of this chapter are to review many of the most powerful statistical techniques from the previous chapters and to illustrate their application with Minitab® software. To focus the review, ten questions about simple experimental systems are asked and answered using Minitab and previously mentioned techniques.
Theodore T. Allen

Backmatter

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