CHAPTER 1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT 1
1-1 The Meaning of Quality and Quality Improvement 2
1-1.1 Dimensions of Quality 2
1-1.2 Quality Engineering Terminology 6
1-2 A Brief History of Quality Methodology 8
1-3 Statistical Methods for Quality Improvement 12
1-4 Total Quality Management 17
1-4.1 Quality Philosophy 17
1-4.2 The Link Between Quality and Productivity 21
1-4.3 Quality Costs 22
1-4.4 Legal Aspects of Quality 28
1-4.5 Implementing Quality Improvement 30
PART Ⅰ STATISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT 33
CHAPTER 2 MODELING PROCESS QUALITY 34
2-1 Describing Variation 35
2-1.1 The Stem and Leaf Plot 35
2-1.2 The Frequency Distribution and Histogram 38
2-1.3 Numerical Summary of Data 40
2-1.4 The Box Plot 43
2-1.5 Sample Computer Output 44
2-1.6 Probability Distributions 46
2-2 Important Discrete Distributions 51
2-2.1 The Hypergeometric Distribution 51
2-2.2 The Binomial Distribution 52
2-2.3 The Poisson Distribution 55
2-2.4 The Pascal and Related Distributions 56
2-3 Important Continuous Distributions 57
2-3.1 The Normal Distribution 57
2-3.2 The Exponential Distribution 62
2-3.3 The Gamma Distribution 65
2-3.4 The Weibull Distribution 67
2-4 Some Useful Approximations 69
2-4.1 The Binomial Approximation to the Hypergeometric 69
2-4.2 The Poisson Approximation to the Binomial 69
2-4.3 The Normal Approximation to the Binomial 70
2-4.4 Comments on Approximations 70
2-5 Exercises 71
CHAPTER 3 INFERENCES ABOUT PROCESS QUALITY 77
3-1 Statistics and Sampling Distributions 78
3-1.1 Sampling from a Normal Distribution 79
3-1.2 Sampling from a Bernoulli Distribution 83
3-1.3 Sampling from a Poisson Distribution 84
3-2 Estimation of Process Parameters 85
3-2.1 Point Estimation 85
3-2.2 Interval Estimation 86
3-3 Hypothesis Testing on Process Parameters 96
3-3.1 Tests on Means, Variance Known 97
3-3.2 The Use of P-Values in Hypothesis Testing 100
3-3.3 Tests on Means of Normal Distributions, Variance Unknown 101
3-3.4 Tests on Variances of Normal Distributions 107
3-3.5 Tests on Binomial Parameters 109
3-3.6 Tests on Poisson Parameters 110
3-3.7 Probability Plotting 113
3-3.8 The Probability of Type Ⅱ Error 116
3-4 Exercises 119
PART Ⅱ STATISTICAL PROCESS CONTROL 127
CHAPTER 4 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL 129
4-1 Introduction 130
4-2 Chance and Assignable Causes of Quality Variation 130
4-3 Statistical Basis of the Control Chart 132
4-3.1 Basic Principles 132
4-3.2 Choice of Control Limits 138
4-3.3 Sample Size and Sampling Frequency 140
4-3.4 Rational Subgroups 143
4-3.5 Analysis of Patterns on Control Charts 146
4-3.6 Discussion of Sensitizing Rules for Control Charts 149
4-4 The Rest of the “Magnificent Seven” 150
4-5 Implementing SPC 158
4-6 An Application of SPC 159
4-7 Nonmanufacturing Applications of Statistical Process Control 167
4-8 Exercises 174
CHAPTER 5 CONTROL CHARTS FOR VARIABLES 179
5-1 Introduction 180
5-2 Control Charts for x and R 181
5-2.1 Statistical Basis of the Charts 181
5-2.2 Development and Use of x and R Charts 186
5-2.3 Charts Based onStandard Values 201
5-2.4 Interpretation of x and R Charts 202
5-2.5 The Effect of Nonnormality on x and R Charts 205
5-2.6 The Operating-Characteristic Function 206
5-2.7 The Average Run Length for the x Chart 209
5-3 Control Charts for x and S 211
5-3.1 Construction and Operation of x and S Charts 212
5-3.2 The x and S Control Charts with Variable Sample Size 217
5-3.3 The S2 Control Chart 221
5-4 Control Charts for Individual Measurements 221
5-5 Summary of Procedures for x, R, and S Charts 229
5-6 Applications of Variables Control Charts 230
5-7 Exercises 235
CHAPTER 6 CONTROL CHARTS FOR ATTRIBUTES 250
6-1 Introduction 251
6-2 The Control Chart for Fraction Nonconforming 251
6-2.1 Development and Operation of the Control Chart 253
6-2.2 Variable Sample Size 265
6-2.3 Nonmanufacturing Applications 270
6-2.4 The Operating-Characteristic Function and Average Run Length Calculations 271
6-3 Control Charts for Nonconformities （Defects） 275
6-3.1 Procedures with Constant Sample Size 275
6-3.2 Procedures with Variable Sample Size 285
6-3.3 Demerit Systems 287
6-3.4 The Operating-Characteristic Function 289
6-3.5 Dealing with Low Defect Levels 290
6-3.6 Nonmanufacturing Applications 294
6-4 Choice Between Attributes and Variables Control Charts 294
6-5 Guidelines for Implementing Control Charts 299
6-6 Exercises 304
CHAPTER 7 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS 313
7-1 The Cumulative-Sum Control Chart 314
7-1.1 Basic Principles: The Cusum Control Chart for Monitoring the Process Mean 314
7-1.2 The Tabular or Algorithmic Cusum for Monitoring the Process Mean 317
7-1.3 Recommendations for Cusum Design 322
7-1.4 The Standardized Cusum 324
7-1.5 Rational Subgroups 325
7-1.6 Improving Cusum Responsiveness for Large Shifts 325
7-1.7 The Fast Initial Response or Headstart Feature 325
7-1.8 One-Sided Cusums 327
7-1.9 A Cusum for Monitoring Process Variability 328
7-1.10 Cusums for Other Sample Statistics 329
7-1.11 The V-Mask Procedure 329
7-2 The Exponentially Weighted Moving-Average Control Chart 332
7-2.1 The Exponentially Weighted Moving-Average Control Chart for Monitoring the Process Mean 333
7-2.2 Design of an EWMA Control Chart 337
7-2.3 Rational Subgroups 339
7-2.4 Extensions of the EWMA 339
7-3 The Moving Average Control Chart 341
7-4 Exercises 344
CHAPTER 8 OTHER STATISTICAL PROCESS CONTROL TECHNIQUES 348
8-1 Statistical Process Control for Short Production Runs 349
8-1.1 x and R Charts for Short Production Runs 349
8-1.2 Attribute Control Charts for Short Production Runs 352
8-1.3 Other Methods 353
8-2 Modified and Acceptance Control Charts 354
8-2.1 Modified Control Limits for the x Chart 354
8-2.2 Acceptance Control Charts 357
8-3 Group Control Charts for Multiple-Stream Processes 358
8-4 Multivariate Quality Control 360
8-4.1 Monitoring of Means 362
8-4.2 Monitoring Process Variability 372
8-5 SPC with Correlated Data 374
8-6 Interfacing Statistical Process Control and Engineering Process Control 386
8-6.1 Process Monitoring and Process Regulation 386
8-6.2 Combining SPC and EPC 395
8-7 Economic Design of Control Charts 399
8-7.1 Designing a Control Chart 399
8-7.2 Process Characteristics 399
8-7.3 Cost Parameters 400
8-7.4 Early Work and Semi-Economic Design 402
8-7.5 An Economic Model of the x Control Chart 403
8-7.6 Other Work 412
8-8 Overview of Other Procedures 413
8-8.1 Tool Wear 413
8-8.2 Control Charts Based on Other Sample Statistics 414
8-8.3 Adaptive Schemes 415
8-8.4 Selecting the Optimum Target Value for a Process 417
8-8.5 Fill Control 419
8-8.6 Precontrol 419
8-9 Exercises 421
CHAPTER 9 PROCESS CAPABILITY ANALYSIS 430
9-1 Introduction 431
9-2 Process-Capability Analysis Using a Histogram or a Probability Plot 433
9-2.1 Using the Histogram 433
9-2.2 Probability Plotting 434
9-3 Process Capability Ratios 438
9-3.1 Use and Interpretation of PCR 438
9-3.2 Process-Capability Ratio for an Off-Center Process 442
9-3.3 Normality and the Process Capability Ratio 444
9-3.4 More About Process Centering 444
9-3.5 Confidence Intervals and Tests on Process Capability Ratios 447
9-4 Process-Capability Analysis Using a Control Chart 451
9-5 Process-Capability Analysis Using Designed Experiments 453
9-6 Gage and Measurement System Capability Studies 455
9-7 Setting Specification Limits on Discrete Components 461
9-7.1 Linear Combinations 461
9-7.2 Nonlinear Combinations 465
9-8 Estimating the Natural Tolerance Limits of a Process 467
9-8.1 Tolerance Limits Based on the Normal Distribution 468
9-8.2 Nonparametric Tolerance Limits 469
9-9 Exercises 470
PART Ⅲ PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS 475
CHAPTER 10 THE FUNDAMENTALS OF EXPERIMENTAL DESIGN 477
10-1 What is Experimental Design? 478
10-2 Examples of Designed Experiments in Quality and Process Improvement 479
10-3 Experiments with One Factor 483
10-3.1 An Example 483
10-3.2 The Analysis of Variance 485
10-3.3 Residual Analysis 490
10-3.4 Comparison of Individual Means 491
10-3.5 Using the Computer 494
10-3.6 A Components-of-Variance Model 496
10-4 Blocking and Nuisance Factors 499
10-4.1 The Randomized Block Design 499
10-4.2 Residual Analysis 504
10-5 Guidelines for Designing Experiments 506
10-6 Exercises 508
CHAPTER 11 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT 512
11-1 Factorial Experiments 513
11-1.1 An Example 517
11-1.2 Statistical Analysis 517
11-1.3 Residual Analysis 523
11-2 The 2k Factorial Design 525
11-2.1 The 2 2 Design 525
11-2.2 The 2k Design for k ≥ 3 Factors 532
11-2.3 A Single Replicate of the 2k Design 545
11-2.4 Addition of Center Points to the 2k Design 549
11-2.5 Blocking and Confounding in the 2k Design 553
11-3 Fractional Replication of the 2k Design 555
11-3.1 The One-Half Fraction of the 2k 555
11-3.2 Smaller Fractions: The 2k-p Fractional Factorial Design 562
11-4 Exercises 569
CHAPTER 12 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS 572
12-1 Response Surface Methods and Designs 573
12-1.1 The Method of Steepest Ascent 575
12-1.2 Analysis of a Second-Order Response Surface 578
12-2 Evolutionary Operation 583
12-3 Taguchi's Contributions to Quality Engineering 589
12-3.1 The Taguchi Philosophy 590
12-3.2 The Taguchi Approach to Parameter Design 591
12-3.3 Improved Robust Parameter Design 600
12-4 Exercises 601
PART Ⅳ ACCEPTANCE SAMPLING 605
CHAPTER 13 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES 606
13-1 The Acceptance Sampling Problem 607
13-1.1 Advantages and Disadvantages of Sampling 608
13-1.2 Types of Sampling Plans 609
13-1.3 Lot Formation 610
13-1.4 Random Sampling 610
13-1.5 Guidelines for Using Acceptance Sampling 611
13-2 Single-Sampling Plans for Attributes 613
13-2.1 Definition of a Single-Sampling Plan 613
13-2.2 The OC Curve 613
13-2.3 Designing a Single-Sampling Plan with a Specified OC Curve 619
13-2.4 Rectifying Inspection 621
13-3 Double, Multiple, and Sequential Sampling 625
13-3.1 Double-Sampling Plans 625
13-3.2 Multiple-Sampling Plans 632
13-3.3 Sequential-Sampling Plans 632
13-4 Military Standard 105E （ANSI/ASQC Z1.4, ISO 2859） 636
13-4.1 Description of the Standard 636
13-4.2 Procedure 638
13-4.3 Discussion 643
13-5 The Dodge-Romig Sampling Plans 645
13-5.1 AOQL Plans 646
13-5.2 LTPD Plans 648
13-5.3 Estimation of Process Average 649
13-6 Exercises 649
CHAPTER 14 OTHER ACCEPTANCE SAMPLING TECHNIQUES 652
14-1 Acceptance Sampling by Variables 653
14-1.1 Advantages and Disadvantages of Variables Sampling 653
14-1.2 Types of Sampling Plans Available 654
14-1.3 Caution in the Use of Variables Sampling 655
14-2 Designing a Variables Sampling Plan with a Specified OCCurve 656
14-3 MIL STD 414 （ANSI/ASQC Z1.9） 659
14-3.1 General Description of the Standard 659
14-3.2 Use of the Tables 660
14-3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9 663
14-4 Other Variables Sampling Procedures 664
14-4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean 664
14-4.2 Sequential Sampling by Variables 665
14-5 Chain Sampling 665
14-6 Continuous Sampling 667
14-6.1 CSP-1 668
14-6.2 Other Continuous Sampling Plans 670
14-7 Skip-Lot Sampling Plans 671
14-8 Exercises 675
APPENDIX A-1 679
Ⅰ Cumulative Poisson Distribution A-3 681
Ⅱ Cumulative Standard Normal Distribution A-6 684
Ⅲ Percentage Points of the x2 Distribution A-8 686
Ⅳ Percentage Points of the t Distribution A-9 687
Ⅴ Percentage Points of the F Distribution A-10 688
Ⅵ Factors for Constructing Variables Control Charts A-15 693
Ⅶ Factors for Two-Sided Normal Tolerance Limits A-16 694
Ⅷ Factors for One-Sided Normal Tolerance Limits A-17 695
Ⅸ Random Numbers A-18 696
BIBLIOGRAPHY B-1 697
ANSWERS TO SELECTED EXERCISES ANS-1 707
INDEX I-1 719