Fundamentals of Quality Control and Improvement

A statistical approach to the principles of quality control and management Incorporating modern ideas, methods, and philosophies of quality management, Fundamentals of Quality Control and Improvement, Fourth Edition presents a quantitative approach to management-oriented techniques and enforces the integration of statistical concepts into quality assurance methods. Utilizing a sound theoretical foundation and illustrating procedural techniques through real-world examples, the timely new edition bridges the gap between statistical quality control and quality management.  Promoting a unique approach, the book focuses on the use of experimental design concepts as well as the Taguchi method for creating product/process designs that successfully incorporate customer needs, improve lead time, and reduce costs. The Fourth Edition of Fundamentals of Quality Control and Improvement also includes: New topical coverage on risk-adjustment, capability indices, model building using regression, and survival analysis  Updated examples and exercises that enhance the readers’ understanding of the concepts Discussions on the integration of statistical concepts to decision making in the realm of quality assurance  Additional concepts, tools, techniques, and issues in the field of health care and health care quality  A unique display and analysis of customer satisfaction data through surveys with strategic implications on decision making, based on the degree of satisfaction and the degree of importance of survey items Fundamentals of Quality Control and Improvement, Fourth Edition is an ideal book for undergraduate and graduate-level courses in management, technology, and engineering. The book also serves as a valuable reference for practitioners and professionals interested in expanding their knowledge of statistical quality control, quality assurance, product/process design, total quality management, and/or Six Sigma training in quality improvement.

Table of Contents:
Preface xix About the Companion Website xxiii Part I Philosophy and Fundamentals 1 1 Introduction to Quality Control and the Total Quality System 3 1-1 Introduction and Chapter Objectives 3 1-2 Evolution of Quality Control 4 1-3 Quality 7 1-4 Quality Control 12 1-5 Quality Assurance 13 1-6 Quality Circles and Quality Improvement Teams 14 1-7 Customer Needs and Market Share 15 1-8 Benefits of Quality Control and the Total Quality System 16 1-9 Quality and Reliability 18 1-10 Quality Improvement 18 1-11 Product and Service Costing 19 1-12 Quality Costs 23 1-13 Measuring Quality Costs 27 1-14 Management of Quality 31 1-15 Quality and Productivity 34 1-16 Total Quality Environmental Management 37 Summary 40 Key Terms 41 Exercises 41 References 46 2 Some Philosophies and Their Impact on Quality 47 2-1 Introduction and Chapter Objectives 47 2-2 Service Industries and Their Characteristics 47 2-3 Model for Service Quality 53 2-4 W. Edwards Deming’s Philosophy 56 2-5 Philip B. Crosby’s Philosophy 75 2-6 Joseph M. Juran’s Philosophy 78 2-7 The Three Philosophies Compared 82 Summary 85 Key Terms 85 Exercises 86 References 88 3 Quality Management: Practices, Tools, and Standards 89 3-1 Introduction and Chapter Objectives 89 3-2 Management Practices 90 3-3 Quality Function Deployment 99 3-4 Benchmarking and Performance Evaluation 106 3-5 Health Care Analytics 115 3-6 Tools for Continuous Quality Improvement 124 3-7 International Standards ISO 9000 and Other Derivatives 137 Part II Statistical Foundations and Methods of Quality Improvement 147 4 Fundamentals of Statistical Concepts and Techniques in Quality Control and Improvement 149 4-1 Introduction and Chapter Objectives 150 4-2 Population and Sample 150 4-3 Parameter and Statistic 150 4-4 Probability 151 4-5 Descriptive Statistics: Describing Product or Process Characteristics 156 4-6 Probability Distributions 173 4-7 Inferential Statistics: Drawing Conclusions on Product and Process Quality 189 Summary 212 Appendix: Approximations to Some Probability Distributions 212 Key Terms 215 Exercises 216 References 228 5 Data Analyses and Sampling 229 5-1 Introduction and Chapter Objectives 229 5-2 Empirical Distribution Plots 230 5-3 Randomness of a Sequence 235 5-4 Validating Distributional Assumptions 237 5-5 Transformations to Achieve Normality 240 5-6 Analysis of Count Data 244 5-7 Analysis of Customer Satisfaction Data 248 5-8 Concepts in Sampling 257 Summary 264 Key Terms 265 Exercises 266 References 272 Part III Statistical Process Control 273 6 Statistical Process Control Using Control Charts 275 6-1 Introduction and Chapter Objectives 275 6-2 Causes of Variation 277 6-3 Statistical Basis for Control Charts 277 6-4 Selection of Rational Samples 289 6-5 Analysis of Patterns in Control Charts 290 6-6 Maintenance of Control Charts 294 Summary 295 Key Terms 295 Exercises 295 References 298 7 Control Charts for Variables 299 7-1 Introduction and Chapter Objectives 300 7-2 Selection of Characteristics for Investigation 301 7-3 Preliminary Decisions 302 7-4 Control Charts for the Mean and Range 303 7-5 Control Charts for the Mean and Standard Deviation 321 7-6 Control Charts for Individual Units 326 7-7 Control Charts for Short Production Runs 330 7-8 Other Control Charts 332 7-9 Risk-Adjusted Control Charts 352 7-10 Multivariate Control Charts 359 Summary 372 Key Terms 373 Exercises 374 References 387 8 Control Charts for Attributes 389 8-1 Introduction and Chapter Objectives 390 8-2 Advantages and Disadvantages of Attribute Charts 390 8-3 Preliminary Decisions 392 8-4 Chart for Proportion Nonconforming: p-Chart 392 8-5 Chart for Number of Nonconforming Items: np-Chart 409 8-6 Chart for Number of Nonconformities: c-Chart 411 8-7 Chart for Number of Nonconformities Per Unit: u-Chart 417 8-8 Chart for Demerits Per Unit: u-Chart 423 8-9 Charts for Highly Conforming Processes 426 8-10 Operating Characteristic Curves for Attribute Control Charts 431 Summary 434 Key Terms 435 Exercises 435 References 448 9 Process Capability Analysis 449 9-1 Introduction and Chapter Objectives 449 9-2 Specification Limits and Control Limits 450 9-3 Process Capability Analysis 451 9-4 Natural Tolerance Limits 453 9-5 Specifications and Process Capability 454 9-6 Process Capability Indices 457 9-7 Process Capability Analysis Procedures 476 9-8 Capability Analysis for Nonnormal Distributions 478 9-9 Setting Tolerances on Assemblies and Components 480 9-10 Estimating Statistical Tolerance Limits of a Process 487 Summary 489 Key Terms 490 Exercises 490 References 499 Part IV Acceptance Sampling 501 10 Acceptance Sampling Plans for Attributes and Variables 503 10-1 Introduction and Chapter Objectives 504 10-2 Advantages and Disadvantages of Sampling 504 10-3 Producer and Consumer Risks 505 10-4 Operating Characteristic Curve 505 10-5 Types of Sampling Plans 509 10-6 Evaluating Sampling Plans 511 10-7 Bayes Rule and Decision Making Based on Samples 516 10-8 Lot-by-Lot Attribute Sampling Plans 519 10-9 Other Attribute Sampling Plans 537 10-10 Deming’s kp Rule 540 10-11 Sampling Plans for Variables 543 10-12 Variable Sampling Plans for a Process Parameter 544 10-13 Variable Sampling Plans for Estimating the Lot Proportion Nonconforming 550 Summary 555 Key Terms 556 Exercises 556 References 562 Part V Product and Process Design 563 11 Reliability 565 11-1 Introduction and Chapter Objectives 565 11-2 Reliability 566 11-3 Life-Cycle Curve and Probability Distributions in Modeling Reliability 566 11-4 System Reliability 570 11-5 Operating Characteristic Curves 578 11-6 Reliability and Life Testing Plans 580 11-7 Survival Analysis 588 Summary 599 Key Terms 599 Exercises 600 References 603 12 Experimental Design and the Taguchi Method 605 12-1 Introduction and Chapter Objectives 606 12-2 Experimental Design Fundamentals 606 12-3 Some Experimental Designs 611 12-4 Factorial Experiments 631 12-5 The Taguchi Method 659 12-6 The Taguchi Philosophy 660 12-7 Loss Functions 663 12-8 Signal-to-Noise Ratio and Performance Measures 670 12-9 Critique of S/N Ratios 673 12-10 Experimental Design in the Taguchi Method 674 12-11 Parameter Design in the Taguchi Method 690 12-12 Critique of Experimental Design and the Taguchi Method 694 Summary 696 Key Terms 697 Exercises 698 References 708 13 Process Modeling Through Regression Analysis 711 13-1 Introduction and Chapter Objectives 711 13-2 Deterministic and Probabilistic Models 712 13-3 Model Assumptions 714 13-4 Least Squares Method for Parameter Estimation 716 13-5 Model Validation and Remedial Measures 722 13-6 Estimation and Inferences from a Regression Model 726 13-7 Qualitative Independent Variables 732 13-9 Logistic Regression 742 Summary 746 Key Terms 747 Exercises 748 References 752 Appendixes 753 A-1 Cumulative Binomial Distribution 753 A-2 Cumulative Poisson Distribution 758 A-3 Cumulative Standard Normal Distribution 760 A-4 Values of t for a Specified Right-Tail Area 763 A-5 Chi-Squared Values for a Specified Right-Tail Area 765 A-6 Values of F for a Specified Right-Tail Area 767 A-7 Factors for Computing Centerline and Three-Sigma Control Limits 773 A-8 Uniform Random Numbers 774 Index 775