Introduction to Statistical Quality Control, 8/e (AE-Paperback)

CHAPTER ORGANIZATION AND TOPICAL COVERAGE

The book contains five parts. Part 1 is introductory. The first chapter is an introduction to the philosophy and basic concepts of quality improvement. It notes that quality has become a major business strategy and that organizations that successfully improve quality can increase their productivity, enhance their market penetration. and achieve greater profitability and a strong competitive advantage. Some of the managerial and implementation aspects of quality improvement are included. Chapter 2 describes DMAIC, an acronym for Define, Measure, Analyze, Improve, and Control. The DMAIC process is an excellent framework to use in conducting quality-improvement projects. DMAIC often is associated with Six Sigma, but regardless of the approach taken by an organization strategically9 DMAIC is an excellent tactical tool for quality professionals to employ.

Part 2 is a description of statistical methods useful in quality improvement. Topics include sampling and descriptive statistics, the basic notions of probability and probability distributions. point and interval estimation of parameters. and statistical hypothesis testing. These topics are usually covered in a basic course in statistical methods: however, their presentation in this text is from the quality-engineering viewpoint. My experience has been that even readers with a strong statistical background will find the approach to this material useful and somewhat different from a standard statistics textbook.

Part 3 contains four chapters covering the basic methods of statistical process control (SPC) and methods for process capability analysis. Even though several SPC problem-solving tools are discussed (including Pareto charts and cause-and-effect diagrams. for example), the primary focus in this section is on the Shewhart control chart. The Shewhart control Chart certainly is  new, but its use in modern-day business and industry is of tremendous value.

There are four chapters in Part 4 that present more advanced SPC methods Included are the cumulative sum and exponentially weighted moving average control charts (Chapter 9),  several  important univariate control charts such as procedures for short production runs, autocorrelated data, and multiple stream processes (Chapter 10), multivariate process monitoring and control (Chapter 11), and feedback adjustment techniques (Chapter 12). Some of this material is at a higher level than Part 3, but much of it is accessible by advanced undergraduates or first-year graduate students. This material forms the basis of a second course in statistical quality control and improvement for this audience.

Part 5 contains two chapters that show how statistically designed experiments can be used for process design. development, and improvement. Chapter 13 presents the fundamental Concepts of designed experiments and introduces factorial and fractional factorial designs, with particular emphasis on the two-level system of designs. These designs are used extensively in the industry for factor screening and process characterization. Although the treatment of the subject is not extensive and is no substitute for a formal course in experimental design, it will enable the reader to appreciate more sophisticated examples of experimental design. Chapter 14 introduces response surface methods and designs. illustrates evolutionary operation (EVOP) for process monitoring, and shows how statistically designed experiments can be used for process robustness studies Chapters 13 and 14 emphasize the important interrelationship between statistical process control and experimental design for process improvement.

Two chapters deal with acceptance sampling in Part 6. The focus is on lot-by-lot acceptance sampling, although there is some discussion of continuous sampling and MIL STD 1235C in Chapter 14. Other sampling topics presented include various aspects of the design of acceptance-sampling plans, a discussion of MIL STD 105E, and MIL STD 414 (and their civilian counterparts: ANSI/ASQC ZI.4 and ANSI/ASQC ZI.9), and other techniques such as chain sampling and skip-lot sampling.

Throughout the book, guidelines are given for selecting the proper type of statistical technique to use in a wide variety of situations. In addition, extensive references to journal articles and other technical literature should assist the reader in applying the methods described. I also have shown how the different techniques presented are used in the DMAIC process.

NEW TO THIS EDITION
The 8th edition of the book has new material on several topics, including implementing quality improvement, applying quality tools in nonmanufacturing settings, monitoring Bernoulli processes, monitoring processes with low defect levels, and designing experiments for process and product improvement In addition, I have rewritten and updated many sections of the book. Many new references have been added to the bibliography. I think that has led to a clearer and more current exposition of many topics. I have also added over 120 new exercises to the end-of-chapter problem sets.

The 8th edition is published for the first time as an enhanced eText (also available bundled with an abridged print companion) The new format allows integrated media, highlighting, notes, and more interactivity, such as select problems that have complete solutions that can be accessed with a click or tap. The new format also allows easy access to supplemental text material and data sels directly from the eText.

章節組織和主題涵蓋

本書分為五個部分。第一部分是介紹性的。第一章是關於品質改進的哲學和基本概念的介紹。它指出品質已成為一個重要的商業策略，成功改善品質的組織可以提高生產力，增強市場滲透力，實現更大的盈利能力和強大的競爭優勢。其中包括一些管理和實施品質改進的方面。第二章介紹了DMAIC，即定義、測量、分析、改進和控制的縮寫。DMAIC過程是進行品質改進項目的優秀框架。DMAIC通常與六西格瑪相關聯，但無論組織採取何種策略，DMAIC都是品質專業人員優秀的戰術工具。

第二部分是關於品質改進中有用的統計方法的描述。主題包括抽樣和描述性統計、概率和概率分佈的基本概念、參數的點估計和區間估計，以及統計假設檢驗。這些主題通常在基礎統計方法的課程中涵蓋，但在本教材中的呈現是從品質工程的觀點出發的。我的經驗是，即使具有強大統計背景的讀者也會發現這種方法對他們有用，並且與標準統計教材有所不同。

第三部分包含四章，介紹了統計過程控制（SPC）的基本方法和過程能力分析的方法。儘管討論了幾種SPC問題解決工具（例如帕累托圖和因果圖），但本節的主要重點是舍華特控制圖。舍華特控制圖在現代商業和工業中的應用具有巨大的價值。

第四部分有四章，介紹了更高級的SPC方法。包括累積和指數加權移動平均控制圖（第9章），幾個重要的單變量控制圖，如短期生產運行、自相關數據和多流程（第10章），多變量過程監控和控制（第11章），以及反饋調整技術（第12章）。這些材料中的一些比第三部分更高級，但其中很多對高年級本科生或一年級研究生來說是可以理解的。這些材料為這個讀者群體的統計品質控制和改進的第二門課程奠定了基礎。

第五部分包含兩章，展示了如何使用統計設計的實驗來進行過程設計、開發和改進。第13章介紹了設計實驗的基本概念，並介紹了因子和分數因子設計，特別強調兩級系統設計。這些設計在工業中廣泛用於因子篩選和過程表徵。雖然對這個主題的處理不是很廣泛，也不能替代正式的實驗設計課程，但它將使讀者能夠更好地理解更複雜的實驗設計示例。第14章介紹了響應曲面方法和設計，演示了用於過程監控的演化運算（EVOP），並展示了如何使用統計設計的實驗進行過程的穩健性研究。第13章和第14章強調了統計過程控制和實驗設計在過程改進中的重要相互關係。

第六部分有兩章，介紹了接受抽樣。重點是逐批接受抽樣，雖然第14章中也有一些關於連續抽樣和MIL STD 1235C的討論。其他抽樣主題的介紹

Table of Contents
Part 1 Introduction
1 Quality Improvement in the Modern Business Environment
2 The DMAIC Process

Part 2 Statistical Methods Useful in Quality Control and Improvement
3 Modeling Process Quality
4 Inferences About Process Quality

Part 3 Basic Methods of Statistical Process Control and Capability Analysis
5 Methods and Philosophy of Statistical Process Control
6 Control Charts for Variables
7 Control Charts for Attributes
8 Process and Measurement System Capability Analysis

Part 4 Other Statistical Process-Monitoring and Control Techniques
9 Cumulative Sum and Exponentially Weighted Moving Average Control Charts
10 Other Univariate Statistical Process-Monitoring and Control Techniques
11 Multivariate Process Monitoring and Control
12 Engineering Process Control and SPC

Part 5 Process Design and Improvement with Designed Experiments
13 Factorial and Fractional Factorial Experiments for Process Design and Improvement
14 Process Optimization with Designed Experiments

Part 6 Acceptance Sampling
15 Lot-by-Lot Acceptance Sampling for Attributes
16 Other Acceptance-Sampling Techniques

目錄

第一部分 簡介

1 現代商業環境中的品質改進

2 DMAIC流程



第二部分 品質控制和改進中有用的統計方法

3 模擬流程品質

4 關於流程品質的推論



第三部分 統計過程控制和能力分析的基本方法

5 統計過程控制的方法和理念

6 變量的控制圖

7 屬性的控制圖

8 流程和測量系統能力分析



第四部分 其他統計過程監控和控制技術

9 累計和指數加權移動平均控制圖

10 其他單變量統計過程監控和控制技術

11 多變量過程監控和控制

12 工程過程控制和SPC



第五部分 設計實驗的過程設計和改進

13 流程設計和改進的階乘和分數階乘實驗

14 設計實驗的流程優化



第六部分 接受抽樣

15 屬性的逐批接受抽樣

16 其他接受抽樣技術