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.

Table of Contents:
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 Natural Tolerance Limits of a Process 366 Part 4 Other Statistical Process-Monitoring and Control Techniques 369 9 Cumulative Sum and Exponentially Weighted Moving Average Control Charts 371 Chapter Overview and Learning Objectives 371 9.1. The Cumulative Sum Control Chart 372 9.2. The Exponentially Weighted Moving Average Control Chart 390 9.3. The Moving Average Control Chart 400 10 Other Univariate Statistical Process-Monitoring and Control Techniques 403 Chapter Overview and Learning Objectives 403 10.1. Statistical Process Control for Short Production Runs 404 10.2. Modified and Acceptance Control Charts 407 10.3. Control Charts for Multiple-Stream Processes 412 10.4. SPC with Autocorrelated Process Data 415 10.5. Adaptive Sampling Procedures 431 10.6. Economic Design of Control Charts 433 10.7. Cuscore Charts 442 10.8. The Changepoint Model for Process Monitoring 444 10.9. Profile Monitoring 445 10.10. Control Charts in Health Care Monitoring and Public Health Surveillance 449 10.11. Overview of Other Procedures 450 11 Multivariate Process Monitoring and Control 458 Chapter Overview and Learning Objectives 458 11.1. The Multivariate Quality-Control Problem 459 11.2. Description of Multivariate Data 460 11.3. The Hotelling T2 Control Chart 462 11.4. The Multivariate EWMA Control Chart 473 11.5. Regression Adjustment 476 11.6. Control Charts for Monitoring Variability 479 11.7. Latent Structure Methods 482 12 Engineering Process Control and SPC 488 Chapter Overview and Learning Objectives 488 12.1. Process Monitoring and Process Regulation 488 12.2. Process Control by Feedback Adjustment 489 12.3. Combining SPC and EPC 500 Part 5 Process Design and Improvement with Designed Experiments 505 13 Factorial and Fractional Factorial Experiments for Process Design and Improvement 507 Chapter Overview and Learning Objectives 507 13.1. What is Experimental Design? 507 13.2. Examples of Designed Experiments in Process and Product Improvement 509 13.3. Guidelines for Designing Experiments 512 13.4. Factorial Experiments 514 13.5. The 2k Factorial Design 523 13.6. Fractional Replication of the 2k Design 551 14 Process Optimization with Designed Experiments 563 Chapter Overview and Learning Objectives 563 14.1. Response Surface Methods and Designs 563 14.2. Process Robustness Studies 572 14.3. Evolutionary Operation 583 Part 6 Acceptance Sampling 589 15 Lot-by-Lot Acceptance Sampling for Attributes 591 Chapter Overview and Learning Objectives 591 15.1. The Acceptance-Sampling Problem 591 15.2. Single-Sampling Plans for Attributes 596 15.3. Double, Multiple, and Sequential Sampling 606 15.4. Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859) 615 15.5. The Dodge-Romig Sampling Plans 623 16 Other Acceptance-Sampling Techniques 627 Chapter Overview and Learning Objectives 627 16.1. Acceptance Sampling by Variables 627 16.2. Designing a Variables-Sampling Plan with a Specified OC Curve 630 16.3. MIL STD 414 (ANSI/ASQC Z1.9) 631 16.4. Other Variables Sampling Procedures 635 16.5. Chain Sampling 636 16.6. Continuous Sampling 638 16.7. Skip-Lot Sampling Plans 641 Problems (Available in e-text for students) P-1 Appendix A-1 Bibliography (Available in e-text for students) B-1 Index I-1