Introduction to Statistical Quality Control, EMEA Edition - Douglas C. Montgomery

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Résumé :

About the Author iii

Preface v

Part 1 Introduction 1

1 Quality Improvement in the Modern Business Environment 3

Chapter Overview and Learning Objectives 3

1.1. The Meaning of Quality and Quality Improvement 3

1.2. A Brief History of Quality Control and Improvement 9

1.3. Statistical Methods for Quality Control and Improvement 13

1.4. Management Aspects of Quality Improvement 16

2 The DMAIC Process 47

Chapter Overview and Learning Objectives 47

2.1. Overview of DMAIC 47

2.2. The Define Step 50

2.3. The Measure Step 52

2.4. The Analyze Step 53

2.5. The Improve Step 54

2.6. The Control Step 55

2.7. Examples of DMAIC 56

Part 2 Statistical Methods Useful in Quality Control and Improvement 63

3 Modeling Process Quality 65

Chapter Overview and Learning Objectives 65

3.1. Describing Variation 65

3.2. Important Discrete Distributions 79

3.3. Important Continuous Distributions 85

3.4. Probability Plots 96

3.5. Some Useful Approximations 100

4 Inferences About Process Quality 103

Chapter Overview and Learning Objectives 103

4.1. Statistics and Sampling Distributions 104

4.2. Point Estimation of Process Parameters 109

4.3. Statistical Inference for a Single Sample 111

4.4. Statistical Inference for Two Samples 128

4.5. What if There are More than Two Populations? The Analysis of Variance 143

4.6. Linear Regression Models 152

Part 3 Basic Methods of Statistical Process Control and Capability Analysis 173

5 Methods and Philosophy of Statistical Process Control 175

Chapter Overview and Learning Objectives 175

5.1. Introduction 175

5.2. Chance and Assignable Causes of Quality Variation 176

5.3. Statistical Basis of the Control Chart 177

5.4. The Rest of the Magnificent Seven 195

5.5. Implementing SPC in a Quality Improvement Program 201

5.6. An Application of SPC 202

5.7. Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses 208

6 Control Charts for Variables 218

Chapter Overview and Learning Objectives 218

6.1. Introduction 218

6.2. Control Charts for x? and R 219

6.3. Control Charts for x? and s 242

6.4. The Shewhart Control Chart for Individual Measurements 250

6.5. Summary of Procedures for x?, R, and s Charts 260

6.6. Applications of Variables Control Charts 261

7 Control Charts for Attributes 265

Chapter Overview and Learning Objectives 265

7.1. Introduction 265

7.2. The Control Chart for Fraction Nonconforming 266

7.3. Control Charts for Nonconformities (Defects) 289

7.4. Choice Between Attributes and Variables Control Charts 307

7.5. Guidelines for Implementing Control Charts 311

8 Process and Measurement System Capability Analysis 317

Chapter Overview and Learning Objectives 317

8.1. Introduction 317

8.2. Process Capability Analysis Using a Histogram or a Probability Plot 319

8.3. Process Capability Ratios 323

8.4. Process Capability Analysis Using a Control Chart 336

8.5. Process Capability Analysis Using Designed Experiments 338

8.6. Process Capability Analysis with Attribute Data 338

8.7. Describing Capability for Many Processes 340

8.8. Gauge and Measurement System Capability Studies 341

8.9. Setting Specification Limits on Discrete Components 360

8.10. Estimating the...

Sommaire:

Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.

Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.

A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge.