Fundamentals of Quality Control and Improvement
4th Edition
Published on 21. June 2016
Book
Hardback
816 pages
978-1-118-70514-8 (ISBN)
Description
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.
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Amitava Mitra
Fundamentals of Quality Control and Improvement
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4th Edition
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Amitava Mitra
Fundamentals of Quality Control and Improvement
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4th Edition
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Amitava Mitra
Fundamentals of Quality Control and Improvement
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Person
Amitava Mitra, PhD, is Professor in the Department of Systems and Technology and former associate dean in the College of Business at Auburn University, Alabama. He has published over seventy journal articles and currently teaches in the areas of quality assurance and improvement. Dr. Mitra has over thirty years of academic and professional experience, and has conducted courses for professionals in total quality management, quality assurance and statistical process control, design of experiments, and Six Sigma Black Belt training.
Content
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
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