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PART 1 INTRODUCTION
1 1 MODERN QUALITY MANAGEMENT AND IMPROVEMENT 3 Chapter Overview and Learning Objectives 3
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1 The Meaning of Quality and Quality Improvement 4
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1 Dimensions of Quality 4
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2 Quality Engineering Terminology 8
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2 A Brief History of Quality Control and Improvement 9
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3 Statistical Methods for Quality Control and Improvement 13
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4 Management Aspects of Quality Improvement 16
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1 Quality Philosophy and Management Strategies 17
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2 The Link Between Quality and Productivity 35
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3 Supply Chain Quality Management 36
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4 Quality Costs 38
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5 Legal Aspects of Quality 44
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6 Implementing Quality Improvement 45 2 THE DMAIC PROBLEM SOLVING PROCESS 48 Chapter Overview and Learning Objectives 48
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1 Overview of DMAIC 49
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2 The Define
Step 52
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3 The Measure
Step 54
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4 The Analyze
Step 55
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5 The Improve
Step 56
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6 The Control
Step 57
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7 Examples of DMAIC 57
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1 Litigation Documents 57
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2 Improving On-Time Delivery 59
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3 Improving Service Quality in a Bank 62

PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT 65 3 STATISTICAL MODELS OR QUALITY CONTROL AND IMPROVEMENT 67 Chapter Overview and Learning Objectives 68
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1 Describing Variation 68
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1 The Stem-and-Leaf Plot 68
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2 The Histogram 70
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3 Numerical Summary of Data 73
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4 The Box Plot 75
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5 Probability Distributions 76
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2 Important Discrete Distributions 80
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1 The Hypergeometric Distribution 80
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2 The Binomial Distribution 81
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3 The Poisson Distribution 83
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4 The Negative Binomial and Geometric Distributions 86
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3 Important Continuous Distributions 88
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1 The Normal Distribution 88
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2 The Lognormal Distribution 90
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3 The Exponential Distribution 92
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4 The Gamma Distribution 93
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5 The Weibull Distribution 95
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4 Probability Plots 97
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1 Normal Probability Plots 97
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2 Other Probability Plots 99
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5 Some Useful Approximations 100
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1 The Binomial Approximation to the Hypergeometric 100
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2 The Poisson Approximation to the Binomial 100
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3 The Normal Approximation to the Binomial 101
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4 Comments on Approximations 102 4 STATISTICAL INFERENCE IN QUALITY CONTROL AND IMPROVEMENT 108 Chapter Overview and Learning Objectives 109
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1 Statistics and Sampling Distributions 110
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1 Sampling from a Normal Distribution 111
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2 Sampling from a Bernoulli Distribution 113
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3 Sampling from a Poisson Distribution 114
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2 Point Estimation of Process Parameters 115
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3 Statistical Inference for a Single Sample 117
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1 Inference on the Mean of a Population, Variance Known 118
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2 The Use of P-Values for Hypothesis Testing 121
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3 Inference on the Mean of a Normal Distribution, Variance Unknown 122
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4 Inference on the Variance of a Normal Distribution 126
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5 Inference on a Population Proportion 128
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6 The Probability of Type II Error and Sample Size Decisions 130
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4 Statistical Inference for Two Samples 133
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1 Inference for a Difference in Means, Variances Known 134
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2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown 136
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3 Inference on the Variances of Two Normal Distributions 143
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4 Inference on Two Population Proportions 145
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5 What If There Are More Than Two Populations? The Analysis of Variance 146
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1 An Example 146
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2 The Analysis of Variance 148
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3 Checking Assumptions: Residual Analysis 154
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6 Linear Regression Models 156
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1 Estimation of the Parameters in Linear Regression Models 157
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2 Hypothesis Testing in Multiple Regression 163
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3 Confidance Intervals in Multiple Regression 169
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4 Prediction of New Observations 170
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5 Regression Model Diagnostics 171

PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS 185 5 HOW SPC WORKS 187 Chapter Overview and Learning Objectives 187
5.
1 Introduction
188
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2 Chance and Assignable Causes of Quality Variation 189
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3 Statistical Basis of the Control Chart 190
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3.
1 Basic Principles 190
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2 Choice of Control Limits 197
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3 Sample Size and Sampling Frequency 199
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4 Rational Subgroups 201
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5 Analysis of Patterns on Control Charts 203
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6 Discussion of Sensitizing Rules for Control Charts 205
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7 Phase I and Phase II of Control Chart Application 206
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4 The Rest of the Magnificent Seven 207
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5 Implementing SPC in a Quality Improvement Program 213
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6 An Application of SPC 214
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7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses 221 6 VARIABLES CONTROL CHARTS 234 Chapter Overview and Learning Objectives 235
6.
1 Introduction
235
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2 Control Charts for x and R 236
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1 Statistical Basis of the Charts 236
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2 Development and Use of x and R Charts 239
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3 Charts Based on Standard Values 250
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4 Interpretation of x and R Charts 251
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5 The Effect of Nonnormality on x and R Charts 254
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6 The Operating-Characteristic Function 254
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7 The Average Run Length for the x Chart 257
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3 Control Charts for x and s 259
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1 Construction and Operation of x and s Charts 259
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2 The x and s Control Charts with Variable Sample Size 263
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3 The s2 Control Chart 267
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4 The Shewhart Control Chart for Individual Measurements 267
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5 Summary of Procedures for x , R, and s Charts 276
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6 Applications of Variables Control Charts 276 7 ATTRIBUTES CONTROL CHARTS 297 Chapter Overview and Learning Objectives 297
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1 Introduction
298
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2 The Control Chart for Fraction Nonconforming 299
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1 Development and Operation of the Control Chart 299
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2 Variable Sample Size 310
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3 Applications in Transactional and Service Businesses 315
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4 The Operating-Characteristic Function and Average Run Length Calculations 315
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3 Control Charts for Nonconformities (Defects) 317
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1 Procedures with Constant Sample Size 318
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2 Procedures with Variable Sample Size 328
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3 Demerit Systems 330
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4 The Operating-Characteristic Function 331
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5 Dealing with Low Defect Levels 332
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6 Nonmanufacturing Applications 335
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4 Choice Between Attributes and Variables Control Charts 335
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5 Guidelines for Implementing Control Charts 339 8 DETERMINING PROCESS AND MEASUREMENT SYSTEMS CAPABILITY 355 Chapter Overview and Learning Objectives 356
8.
1 Introduction
356
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2 Process Capability Analysis Using a Histogram or a Probability Plot 358
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2.
1 Using the Histogram 358
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2 Probability Plotting 360
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3 Process Capability Ratios 362
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3.
1 Use and Interpretation of Cp 362
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2 Process Capability Ratio for an Off-Center Process 365
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3 Normality and the Process Capability Ratio 367
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4 More about Process Centering 368
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5 Confidence Intervals and Tests on Process Capability Ratios 370
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4 Process Capability Analysis Using a Control Chart 375
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5 Process Capability Analysis Using Designed Experiments 377
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6 Process Capability Analysis with Attribute Data 378
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7 Gauge and Measurement System Capability Studies 379
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1 Basic Concepts of Gauge Capability 379
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2 The Analysis of Variance Method 384
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3 Confidence Intervals in Gauge R & R Studies 387
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4 False Defectives and Passed Defectives 388
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5 Attribute Gauge Capability 392
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6 Comparing Customer and Supplier Measurement Systems 394
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8 Setting Specification Limits on Discrete Components 396
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1 Linear Combinations 397
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2 Nonlinear Combinations 400
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9 Estimating the Natural Tolerance Limits of a Process 401
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1 Tolerance Limits Based on the Normal Distribution 402
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2 Nonparametric Tolerance Limits 403

PART 4 OTHER STATISTICAL PROCESSMONITORING AND CONTROL TECHNIQUES 411 9 TIME-WEIGHTED CONTROL CHARTS 413 Chapter Overview and Learning Objectives 414
9.
1 The Cumulative Sum Control Chart 414
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1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean 414
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2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean 417
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3 Recommendations for CUSUM Design 422
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4 The Standardized CUSUM 424
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5 Improving CUSUM Responsiveness for Large Shifts 424
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6 The Fast Initial Response or Headstart Feature 424
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7 One-Sided CUSUMs 427
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8 A CUSUM for Monitoring Process Variability 427
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9 Rational Subgroups 428
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10 CUSUMs for Other Sample Statistics 428
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11 The V-Mask Procedure 429
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12 The Self-Starting CUSUM 431
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2 The Exponentially Weighted Moving Average Control Chart 433
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1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean 433
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2 Design of an EWMA Control Chart 436
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3 Robustness of the EWMA to Nonnormality 438
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4 Rational Subgroups 439
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5 Extensions of the EWMA 439
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3 The Moving Average Control