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Fundamentals of Quality Control and Improvement

Mitra, Amitava

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5. Auflage Juni 2021
800 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-69233-1
John Wiley & Sons

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The newest edition of an insightful and practical statistical approach to quality control and management

In the newly revised and thoroughly updated Fifth Edition of Fundamentals of Quality Control and Improvement, accomplished academic, consultant, and author Dr. Amitava Mitra delivers a comprehensive and quantitative approach to quality management techniques. The book demonstrates how to integrate statistical concepts with quality assurance methods, incorporating modern ideas, strategies, and philosophies of quality management.

You'll discover experimental design concepts and the use of the Taguchi method to incorporate customer needs, improve lead time, and reduce costs. The new edition also includes brand-new case studies at the end of several chapters, references to the statistical software Minitab 19, and chapter updates that add discussions of trending and exciting topics in quality control.

The book includes access to supplementary material for instructors consisting of a new instructor's solutions manual and PowerPoint slides, as well as access to data sets for all readers.

Readers will also benefit from the inclusion of:
* A thorough introduction to the evolution of quality and definitions of quality, quality control, quality assurance, quality circles, and quality improvement teams
* An exploration of customer needs and market share, as well as the benefits of quality control and the total quality system
* Practical discussions of quality and reliability, quality improvement, product and service costing, and quality costs
* A concise treatment of how to measure quality costs, the management of quality, and the interrelationship between quality and productivity

Perfect for upper-level undergraduate and graduate students in quality control and improvement, the Fifth Edition of Fundamentals of Quality Control and Improvement will also earn a place in the libraries of business students and those undertaking training programs in Six Sigma.

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

Quality Characteristics 8

Variables and Attributes 8

Defects 9

Standard or Specification 9

Quality of Design 10

Quality of Conformance 10

Quality of Performance 11

1-4 Quality Control 12

Off-Line Quality Control 12

Statistical Process Control 12

Acceptance Sampling Plans 13

1-5 Quality Assurance 13

1-6 Quality Circles and Quality Improvement Teams 14

1-7 Customer Needs and Market Share 15

Kano Model 15

1-8 Benefits of Quality Control and the Total Quality System 16

Total Quality System 17

1-9 Quality and Reliability 18

1-10 Quality Improvement 18

1-11 Product and Service Costing 19

Activity-Based Costing 20

1-12 Quality Costs 23

Prevention Costs 23

Appraisal Costs 23

Internal Failure Costs 24

External Failure Costs 24

Hidden Failure Costs 24

Quality Costs Data Requirements 24

Process Cost Approach 26

1-13 Measuring Quality Costs 27

Impact of Quality Improvement on Quality Costs 29

1-14 Management of Quality 31

1-15 Quality and Productivity 34

Effect on Cost 34

Effect on Market 34

1-16 Total Quality Environmental Management 37

Green Supply Chain 39

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

Differences in the Manufacturing and Service Sectors 49

Service Quality Characteristics 50

Measuring Service Quality 52

Techniques for Evaluating Service Quality 52

2-3 Model for Service Quality 53

2-4 W. Edwards Deming's Philosophy 56

Extended Process 57

Deming's 14 Points for Management 58

Deming's Deadly Diseases 72

2-5 Philip B. Crosby's Philosophy 75

Four Absolutes of Quality Management 76

14-Step Plan for Quality Improvement 76

2-6 Joseph M. Juran's Philosophy 78

Quality Trilogy Process 79

Quality Planning 79

Quality Control 80

Quality Improvement 81

2-7 The Three Philosophies Compared 82

Definition of Quality 82

Management Commitment 82

Strategic Approach to a Quality System 83

Measurement of Quality 83

Never-Ending Process of Improvement 83

Education and Training 83

Eliminating the Causes of Problems 84

Goal Setting 84

Structural Plan 84

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

Total Quality Management 90

Vision and Quality Policy 92

Balanced Scorecard 94

Performance Standards 96

3-3 Quality Function Deployment 99

QFD Process 100

3-4 Benchmarking and Performance Evaluation 106

Benchmarking 107

Quality Auditing 110

Vendor Selection and Certification Programs 112

Vendor Rating and Selection 112

3-5 Health Care Analytics 115

Health Care Analytics and Big Data 116

Uniqueness of Health Care 116

Challenges in Health Care Quality 121

3-6 Tools for Continuous Quality Improvement 124

Pareto Diagrams 124

Flowcharts 124

Cause-and-Effect Diagrams 126

Scatterplots 126

Multivariable Charts 127

Matrix and Three-Dimensional Plots 129

Failure Mode and Effects Criticality Analysis 131

3-7 International Standards ISO 9000 and Other Derivatives 137

Features of ISO 9000 137

Other Industry Standards 138

Case Study 139

Summary 143

Key Terms 144

Exercises 145

References 149

PART II STATISTICAL FOUNDATIONS AND METHODS OF QUALITY IMPROVEMENT 151

4 Fundamentals of Statistical Concepts and Techniques in Quality Control and Improvement 153

4-1 Introduction and Chapter Objectives 154

4-2 Population and Sample 154

4-3 Parameter and Statistic 154

4-4 Probability 155

Relative Frequency Definition of Probability 155

Simple and Compound Events 155

Complementary Events 156

Additive Law 157

Multiplicative Law 158

Independence and Mutually Exclusive Events 158

4-5 Descriptive Statistics: Describing Product or Process Characteristics 160

Data Collection 160

Measurement Scales 162

Measures of Central Tendency 163

Measures of Dispersion 165

Measures of Skewness and Kurtosis 170

Measures of Association 173

4-6 Probability Distributions 177

Cumulative Distribution Function 179

Expected Value 179

Discrete Distributions 180

Continuous Distributions 184

4-7 Inferential Statistics: Drawing Conclusions on Product and Process Quality 193

Sampling Distributions 193

Estimation of Product and Process Parameters 194

Hypothesis Testing 203

Summary 216

Appendix: Approximations to Some Probability Distributions 216

Binomial Approximation to the Hypergeometric 216

Poisson Approximation to the Binomial 216

Normal Approximation to the Binomial 217

Normal Approximation to the Poisson 218

Key Terms 219

Exercises 220

References 232

5 Data Analyses and Sampling 233

5-1 Introduction and Chapter Objectives 233

5-2 Empirical Distribution Plots 234

Histograms 234

Stem-and-Leaf Plots 235

Box Plots 236

Variations of the Basic Box Plot 238

5-3 Randomness of a Sequence 239

Run Chart 239

5-4 Validating Distributional Assumptions 241

Probability Plotting 241

5-5 Transformations to Achieve Normality 244

Some Common Transformations 244

Power Transformations 244

Johnson Transformation 245

5-6 Analysis of Count Data 248

Hypothesis Test on Cell Probabilities 248

Contingency Tables 249

Measures of Association 251

5-7 Analysis of Customer Satisfaction Data 252

Customer Needs and Their Level of Satisfaction 252

Displaying Survey Results 257

Analysis of Survey Results 259

5-8 Concepts in Sampling 261

Sampling Designs and Schemes 262

Sample Size Determination 264

Bound on the Error of Estimation and Associated Confidence Level 264

Estimating the Difference of Two Population Means 266

Estimating the Difference of Two Population Proportions 266

Controlling the Type I Error Type II Error and Associated Parameter Shift 267

5-9 Bayes Rule and Decision Making Based on Samples 268

5-10 Deming's kp rule 272

Summary 274

Key Terms 275

Exercises 276

References 283

PART III STATISTICAL PROCESS CONTROL 285

6 Statistical Process Control Using Control Charts 287

6-1 Introduction and Chapter Objectives 287

6-2 Causes of Variation 289

Special Causes 289

Common Causes 289

6-3 Statistical Basis for Control Charts 289

Basic Principles 289

Selection of Control Limits 291

Errors in Making Inferences from Control Charts 293

Effect of Control Limits on Errors in Inference Making 297

Warning Limits 298

Effect of Sample Size on Control Limits 298

Average Run Length 299

6-4 Selection of Rational Samples 301

Sample Size 301

Frequency of Sampling 301

6-5 Analysis of Patterns in Control Charts 302

Some Rules for Identifying an Out-of-Control Process 302

Interpretation of Plots 304

Determination of Causes of Out-of-Control Points 306

6-6 Maintenance of Control Charts 306

Summary 307

Key Terms 307

Exercises 307

References 310

7 Control Charts for Variables 311

7-1 Introduction and Chapter Objectives 312

7-2 Selection of Characteristics for Investigation 313

7-3 Preliminary Decisions 314

Selection of Rational Samples 314

Sample Size 315

Frequency of Sampling 315

Choice of Measuring Instruments 315

Design of Data Recording Forms 315

7-4 Control Charts for the Mean and Range 315

Development of the Charts 315

Variable Sample Size 321

Standardized Control Charts 321

Control Limits for a Given Target or Standard 322

Interpretation and Inferences from the Charts 325

Control Chart Patterns and Corrective Actions 327

7-5 Control Charts for the Mean and Standard Deviation 333

No Given Standards 334

Given Standard 335

7-6 Control Charts for Individual Units 338

No Given Standards 339

Given Standard 340

7-7 Control Charts for Short Production Runs 342

_X- and R-Charts for Short Production Runs 342

Z-MR Chart 342

7-8 Other Control Charts 344

Cumulative Sum Control Chart for the Process Mean 344

Tabular Method 345

V-Mask Method 348

Cumulative Sum for Monitoring Process Variability 351

Moving-Average Control Chart 351

Exponentially Weighted Moving-Average or Geometric Moving-Average Control Chart 354

Modified Control Chart 357

Acceptance Control Chart 361

7-9 Risk-Adjusted Control Charts 363

Risk-Adjusted Cumulative Sum (RACUSUM) Chart 364

Risk-Adjusted Sequential Probability Ratio Test (RASPRT) 365

Risk-Adjusted Exponentially Weighted Moving-Average (RAEWMA) Chart 366

Variable Life-Adjusted Display (VLAD) Chart 367

7-10 Multivariate Control Charts 370

Controlling Several Related Quality Characteristics 370

Hotelling's T2 Control Chart and Its Variations 373

Phase 1 and Phase 2 Charts 374

Usage and Interpretations 376

Individual Observations with Unknown Process Parameters 377

Generalized Variance Chart 378

Case Study 384

Summary 388

Key Terms 389

Exercises 390

References 403

8 Control Charts for Attributes 405

8-1 Introduction and Chapter Objectives 406

8-2 Advantages and Disadvantages of Attribute Charts 406

Advantages 406

Disadvantages 407

8-3 Preliminary Decisions 408

8-4 Chart for Proportion Nonconforming: p-Chart 408

Construction and Interpretation 409

Variable Sample Size 416

Risk-Adjusted p-Charts in Health Care 420

Special Considerations for p-Charts 424

8-5 Chart for Number of Nonconforming Items: np-Chart 425

No Standard Given 425

Standard Given 426

8-6 Chart for Number of Nonconformities: c-Chart 427

No Standard Given 428

Standard Given 428

Probability Limits 430

Applications in Health Care When Nonoccurence of Nonconformities Are Not Observable 431

8-7 Chart for Number of Nonconformities Per Unit: u-Chart 433

Variable Sample Size and No Specified Standard 433

Risk-Adjusted u-Charts in Health Care 436

8-8 Chart for Demerits Per Unit: u-Chart 439

Classification of Nonconformities 439

Construction of a U-Chart 439

8-9 Charts for Highly Conforming Processes 442

Transformation to Normality 442

Use of Exponential Distribution for Continuous Variables 442

Use of Geometric Distribution for Discrete Variables 443

Probability Limits 443

Applications in Health Care of Low-Occurrence Nonconformities 445

8-10 Operating Characteristic Curves for Attribute Control Charts 447

Case Study 450

Summary 455

Key Terms 455

Exercises 456

References 469

9 Process Capability Analysis 471

9-1 Introduction and Chapter Objectives 471

9-2 Specification Limits and Control Limits 472

9-3 Process Capability Analysis 473

Process Capability 474

9-4 Natural Tolerance Limits 475

Statistical Tolerance Limits 476

9-5 Specifications and Process Capability 476

9-6 Process Capability Indices 479

Cp Index 479

Upper and Lower Capability Indices 480

Cpk Index 481

Capability Ratio 483

Taguchi Capability Index Cpm 484

Cpmk Index 484

Confidence Intervals and Hypothesis Testing on Capability Indices 485

Comparison of Capability Indices 486

Effect of Measurement Error on Capability Indices 490

Gage Repeatability and Reproducibility 492

Evaluation of Measurement Systems 493

Metrics for Evaluation of Measurement Systems 493

Preparation for a Gage Repeatability and Reproducibility Study 494

Cp Index and the Nonconformance Rate 497

9-7 Process Capability Analysis Procedures 498

Estimating Process Mean and Standard Deviation 498

9-8 Capability Analysis for Nonnormal Distributions 500

Identification of Appropriate Distribution 500

Box-Cox Transformation 500

Using Attribute Charts 500

Using a Nonparametric Approach 501

9-9 Setting Tolerances on Assemblies and Components 502

Tolerances on Assemblies and Subassemblies 502

Tolerance Limits on Individual Components 504

Tolerance on Mating Parts 505

Nonlinear Combinations of Random Variables 508

9-10 Estimating Statistical Tolerance Limits of a Process 509

Statistical Tolerance Limits Based on Normal Distribution 509

Nonparametric Statistical Tolerance Limits 510

Case Study 511

Summary 515

Key Terms 516

Exercises 516

References 525

PART IV PRODUCT AND PROCESS DESIGN 527

10 Reliability 529

10-1 Introduction and Chapter Objectives 529

10-2 Reliability 530

10-3 Life-Cycle Curve and Probability Distributions in Modeling Reliability 530

Probability Distributions to Model Failure Rate 531

Availability 534

10-4 System Reliability 534

Systems with Components in Series 535

Systems with Components in Parallel 537

Systems with Components in Series and in Parallel 539

Systems with Standby Components 540

10-5 Operating Characteristic Curves 542

10-6 Reliability and Life Testing Plans 544

Types of Tests 544

Life Testing Plans Using the Exponential Distribution 546

Standard Life Testing Plans Using Handbook H-108 548

10-7 Survival Analysis 552

Estimation of the Survival Function 552

Confidence Intervals for the Survival Function 557

Comparion of Survival Functions of Two Groups 559

Summary 563

Key Terms 563

Exercises 564

References 567

11 Experimental Design and the Taguchi Method 569

11-1 Introduction and Chapter Objectives 570

11-2 Experimental Design Fundamentals 570

Features of Experimentation 574

11-3 Some Experimental Designs 575

Completely Randomized Design 576

Randomized Block Design 582

Latin Square Design 587

11-4 Factorial Experiments 595

Two-Factor Factorial Experiment Using a Completely Randomized Design 596

Two-Factor Factorial Experiment Using a Randomized Block Design 600

Role of Contrasts 606

The 2k Factorial Experiment 612

Confounding in 2k Factorial Experiments 616

Fractional Replication in 2k Experiments 617

11-5 The Taguchi Method 623

11-6 The Taguchi Philosophy 624

11-7 Loss Functions 627

Target Is Best 628

Smaller Is Better 631

Larger Is Better 632

11-8 Signal-to-Noise Ratio and Performance Measures 634

Target Is Best 634

Smaller Is Better 637

Larger Is Better 637

11-9 Critique of S/N Ratios 637

11-10 Experimental Design in the Taguchi Method 638

Orthogonal Arrays and Linear Graphs 639

Estimation of Effects 649

11-11 Parameter Design in the Taguchi Method 654

Application to Attribute Data 656

11-12 Critique of Experimental Design and the Taguchi Method 658

Summary 660

Key Terms 661

Exercises 662

References 672

12 Process Modeling Through Regression Analysis 675

12-1 Introduction and Chapter Objectives 675

12-2 Deterministic and Probabilistic Models 676

12-3 Model Assumptions 678

12-4 Least Squares Method for Parameter Estimation 680

Performance Measures of a Regression Model 683

12-5 Model Validation and Remedial Measures 686

Linearity of Regression Function 686

Constancy of Error Variance 687

Normality of Error Component 689

Independence of Error Components 689

12-6 Estimation and Inferences from a Regression Model 690

Inferences on Individual ßi Parameters 691

Inferences on All ßi i= 1 2 . . . p . 1 Parameters 691

Simultaneous Inferences on Some ßi i= 1 2 . . . p . 1 691

Hypothesis Tests on a Subset of ßi Parameters 692

Estimation of Mean Response 692

Simultaneous Confidence Intervals for Several Mean Responses 693

Prediction of Individual Observations 693

Simultaneous Prediction Intervals for Several New Observations 693

12-7 Qualitative Independent Variables 696

Additive Model 696

Interaction Model 697

12-8 Issues in Multiple Regression 702

Data from a Retrospective Versus Designed Experiment 702

Outliers in the Space of the Independent Variables 703

Outliers for the Dependent Variable 704

Influential Observations 705

Multicollinearity 706

Detection of Multicollinearity 706

Effects of Multicollinearity 707

12-9 Logistic Regression 707

Binary Response Variable 708

Assumptions in Regression 709

Nominal Polytomous Response Variable 712

Ordinal Polytomous Response Variable 715

12-10 Classification Problems 719

Performance Measures in Classification Problems 720

Tests of Association in 2 × 2 Contingency Tables 722

Receiver Operating Characteristic Curve 723

Summary 725

Key Terms 725

Exercises 726

References 732

Appendixes 733

A-1 Cumulative Binomial Distribution 733

A-2 Cumulative Poisson Distribution 738

A-3 Cumulative Standard Normal Distribution 740

A-4 Values of t for a Specified Right-Tail Area 743

A-5 Chi-Squared Values for a Specified Right-Tail Area 745

A-6 Values of F for a Specified Right-Tail Area 747

A-7 Factors for Computing Centerline and Three-Sigma Control Limits 753

A-8 Uniform Random Numbers 754

Index 000
Amitava Mitra, PhD, is Professor in the Department of Systems and Technology and the former Associate Dean in the College of Business at Auburn University, Alabama. He has published over 70 journal articles and teaches quality assurance and improvement.

A. Mitra, Auburn University