Implementing Six Sigma: smarter solutions using statistical methods ; smarter solutions using statistical methods
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2003
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| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Publisher description Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | XLII, 1187 S. graph. Darst. |
| ISBN: | 0471265721 |
Internformat
MARC
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| 245 | 1 | 0 | |a Implementing Six Sigma |b smarter solutions using statistical methods ; smarter solutions using statistical methods |c Forrest W. Breyfogle |
| 250 | |a 2. ed. | ||
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| 336 | |b txt |2 rdacontent | ||
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| 650 | 4 | |a Quality control -- Statistical methods | |
| 650 | 4 | |a Production management -- Statistical methods | |
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Datensatz im Suchindex
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|---|---|
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| DE-BY-FWS_katkey | 653106 |
| DE-BY-FWS_media_number | 083000517972 |
| _version_ | 1806176845939343360 |
| adam_text | CONTENTS
PREFACE xxxi
PART I S4/IEE DEPLOYMENT AND DEFINE PHASE
FROM DMAIC 1
1 Six Sigma Overview and S4/IEE Implementaton 3
1.1 Background of Six Sigma, 4
1.2 General Electric s Experiences with Six Sigma, 6
1.3 Additional Experiences with Six Sigma, 7
1.4 What Is Six Sigma and S4/IEE?. 10
1.5 The Six Sigma Metric, 12
1.6 Traditional Approach to the Deployment of Statistical
Methods, 15
1.7 Six Sigma Benchmarking Study, 16
1.8 S4/IEE Business Strategy Implementation. 17
1.9 Six Sigma as an S4/IEE Business Strategy, 19
1.10 Creating an S4/IEE Business Strategy with Roles and
Responsibilities, 22
1.11 Integration of Six Sigma with Lean, 31
1.12 Day to Day Business Management Using S4/IEE, 32
1.13 S4/IEE Project Initiation and Execution Roadmap, 33
1.14 Project Benefit Analysis, 36
1.15 Examples in This Book That Describe the Benefits and
Strategies of S4/IEE, 38
1.16 Effective Six Sigma Training and Implementation, 41
1.17 Computer Software, 43
viii CONTENTS
1.18 Selling the Benefits of Six Sigma, 44
1.19 S4/IEE Difference, 45
1.20 S4/IEE Assessment, 48
1.21 Exercises, 51
2 Voice of the Customer and the S4/IEE Define Phase 52
2.1 Voice of the Customer, 53
2.2 A Survey Methodology to Identify Customer Needs, 55
2.3 Goal Setting and Measurements, 57
2.4 Scorecard, 59
2.5 Problem Solving and Decision Making, 60
2.6 Answering the Right Question, 61
2.7 S4/IEE DMAIC Define Phase Execution, 61
2.8 S4/IEE Assessment, 63
2.9 Exercises, 64
PART II S4/IEE MEASURE PHASE FROM DMAIC 65
3 Measurements and the S4/IEE Measure Phase 71
3.1 Voice of the Customer, 71
3.2 Variability and Process Improvements, 72
3.3 Common Causes versus Special Causes and Chronic
versus Sporadic Problems, 74
3.4 Example 3.1: Reacting to Data, 75
3.5 Sampling, 79
3.6 Simple Graphic Presentations, 80
3.7 Example 3.2: Histogram and Dot Plot, 81
3.8 Sample Statistics (Mean, Range, Standard Deviation,
and Median), 81
3.9 Attribute versus Continuous Data Response, 85
3.10 Visual Inspections, 86
3.11 Hypothesis Testing and the Interpretation of Analysis of
Variance Computer Outputs, 87
3.12 Experimentation Traps, 89
3.13 Example 3.3: Experimentation Trap—Measurement
Error and Other Sources of Variability, 90
3.14 Example 3.4: Experimentation Trap—Lack of
Randomization, 92
3.15 Example 3.5: Experimentation Trap—Confused
Effects, 93
3.16 Example 3.6: Experimentation Trap—Independently
Designing and Conducting an Experiment, 94
3.17 Some Sampling Considerations, 96
3.18 DMAIC Measure Phase, 96
CONTENTS ix
3.19 S4/IEE Assessment, 97
3.20 Exercises, 99
4 Process Flowcharting/Process Mapping 102
4.1 S4/IEE Application Examples: Flowchart, 103
4.2 Description, 103
4.3 Defining a Process and Determining Key Process Input/
Output Variables, 104
4.4 Example 4.1: Defining a Development Process, 105
4.5 Focusing Efforts after Process Documentation, 107
4.6 S4/IEE Assessment, 107
4.7 Exercises, 109
5 Basic Tools 111
5.1 Descriptive Statistics, 112
5.2 Run Chart (Time Series Plot), 113
5.3 Control Chart, 114
5.4 Probability Plot, 115
5.5 Check Sheets, 115
5.6 Pareto Chart, 116
5.7 Benchmarking, 117
5.8 Brainstorming, 117
5.9 Nominal Group Technique (NGT), 119
5.10 Force Field Analysis, 119
5.11 Cause and Effect Diagram, 120
5.12 Affinity Diagram, 122
5.13 Interrelationship Digraph (ID), 123
5.14 Tree Diagram, 124
5.15 Why Why Diagram, 125
5.16 Matrix Diagram and Prioritization Matrices, 125
5.17 Process Decision Program Chart (PDPC), 127
5.18 Activity Network Diagram or Arrow Diagram, 129
5.19 Scatter Diagram (Plot of Two Variables), 130
5.20 Example 5.1: Improving a Process That Has
Defects, 130
5.21 Example 5.2: Reducing the Total Cycle Time of a
Process, 133
5.22 Example 5.3: Improving a Service Process, 137
5.23 Exercises, 139
6 Probability 141
6.1 Description, 141
6.2 Multiple Events, 142
6.3 Multiple Event Relationships, 143
x CONTENTS
6.4 Bayes Theorem, 144
6.5 S4/IEE Assessment, 145
6.6 Exercises, 146
7 Overview of Distributions and Statistical Processes 148
7.1 An Overview of the Application of Distributions, 148
7.2 Normal Distribution, 150
7.3 Example 7.1: Normal Distribution, 152
7.4 Binomial Distribution, 153
7.5 Example 7.2: Binomial Distribution—Number of
Combinations and Rolls of Die, 155
7.6 Example 7.3: Binomial—Probability of Failure, 156
7.7 Hypergeometric Distribution, 157
7.8 Poisson Distribution, 157
7.9 Example 7.4: Poisson Distribution, 159
7.10 Exponential Distribution, 159
7.11 Example 7.5: Exponential Distribution, 160
7.12 Weibull Distribution, 161
7.13 Example 7.6: Weibull Distribution, 163
7.14 Lognormal Distribution, 163
7.15 Tabulated Probability Distribution: Chi Square
Distribution, 163
7.16 Tabulated Probability Distribution: t Distribution, 165
7.17 Tabulated Probability Distribution: F Distribution, 166
7.18 Hazard Rate, 166
7.19 Nonhomogeneous Poisson Process (NHPP), 168
7.20 Homogeneous Poisson Process (HPP), 168
7.21 Applications for Various Types of Distributions and
Processes, 169
7.22 S4/IEE Assessment, 171
7.23 Exercises, 171
8 Probability and Hazard Plotting 175
8.1 S4/IEE Application Examples: Probability Plotting, 175
8.2 Description, 176
8.3 Probability Plotting, 176
8.4 Example 8.1: PDF, CDF, and Then a Probability
Plot, 177
8.5 Probability Plot Positions and Interpretation of
Plots, 180
8.6 Hazard Plots, 181
8.7 Example 8.2: Hazard Plotting, 182
8.8 Summarizing the Creation of Probability and Hazard
Plots, 183
CONTENTS xi
8.9 Percentage of Population Statement Considerations, 185
8.10 S4/IEE Assessment, 185
8.11 Exercises, 186
9 Six Sigma Measurements 188
9.1 Converting Defect Rates (DPMO or PPM) to Sigma
Quality Level Units, 188
9.2 Six Sigma Relationships, 189
9.3 Process Cycle Time, 190
9.4 Yield, 191
9.5 Example 9.1: Yield, 191
9.6 Z Variable Equivalent, 192
9.7 Example 9.2: Z Variable Equivalent, 192
9.8 Defects per Million Opportunities (DPMO), 192
9.9 Example 9.3: Defects per Million Opportunities
(DPMO), 193
9.10 Rolled Throughput Yield, 194
9.11 Example 9.4: Rolled Throughput Yield, 195
9.12 Example 9.5: Rolled Throughput Yield, 196
9.13 Yield Calculation, 196
9.14 Example 9.6: Yield Calculation, 196
9.15 Example 9.7: Normal Transformation (Z Value), 197
9.16 Normalized Yield and Z Value for Benchmarking, 198
9.17 Example 9.8: Normalized Yield and Z Value for
Benchmarking, 199
9.18 Six Sigma Assumptions, 199
9.19 S4/IEE Assessment, 199
9.20 Exercises, 200
10 Basic Control Charts 204
10.1 S4/IEE Application Examples: Control Charts, 205
10.2 Satellite Level View of the Organization, 206
10.3 A 30,000 Foot Level View of Operational and Project
Metrics, 207
10.4 AQL (Acceptable Quality Level) Sampling Can Be
Deceptive, 210
10.5 Example 10.1: Acceptable Quality Level, 213
10.6 Monitoring Processes, 213
10.7 Rational Sampling and Rational Subgrouping, 217
10.8 Statistical Process Control Charts, 219
10.9 Interpretation of Control Chart Patterns, 220
10.10 x and R and x and s Charts: Mean and Variability
Measurements, 222
10.11 Example 10.2: x and R Chart, 223
xii CONTENTS
10.12 XmR Charts: Individual Measurements, 226
10.13 Example 10.3: XmR Charts, 227
10.14 x and r versus XmR Charts, 229
10.15 Attribute Control Charts, 230
10.16 p Chart: Fraction Nonconforming Measurements, 231
10.17 Example 10.4: p Chart, 232
10.18 np Chart: Number of Nonconforming Items, 235
10.19 c Chart: Number of Nonconformities, 235
10.20 u Chart: Nonconformities per Unit, 236
10.21 Median Charts, 236
10.22 Example 10.5: Alternatives top Chart, «/? Chart,
c Chart, and M Chart Analyses, 237
10.23 Charts for Rare Events, 239
10.24 Example 10.6: Charts for Rare Events, 240
10.25 Discussion of Process Control Charting at the Satellite
Level and 30,000 Foot Level, 242
10.26 Control Charts at the 30,000 Foot Level: Attribute
Response, 245
10.27 XmR Chart of Subgroup Means and Standard Deviation:
An Alternative to Traditional x and R Charting, 245
10.28 Notes on the Shewhart Control Chart, 247
10.29 S4/IEE Assessment, 248
10.30 Exercises, 250
11 Process Capability and Process Performance Metrics 254
11.1 S4/IEE Application Examples: Process Capability/
Performance Metrics, 255
11.2 Definitions, 257
11.3 Misunderstandings, 258
11.4 Confusion: Short Term versus Long Term
Variability, 259
11.5 Calculating Standard Deviation, 260
11.6 Process Capability Indices: Cp and Cpk, 265
11.7 Process Capability /Performance Indices: Pp and
Ppk, 267
11.8 Process Capability and the Z Distribution, 268
11.9 Capability Ratios, 269
11.10 Cpm Index, 269
11.11 Example 11.1: Process Capability/Performance
Indices, 270
11.12 Example 11.2: Process Capability/Performance Indices
Study, 275
11.13 Example 11.3: Process Capability/Performance Index
Needs, 279
11.14 Process Capability Confidence Interval, 282
CONTENTS xiii
11.15 Example 11.4: Confidence Interval for Process
Capability, 282
11.16 Process Capability/Performance for Attribute Data, 283
11.17 Describing a Predictable Process Output When No
Specification Exists, 284
11.18 Example 11.5: Describing a Predictable Process Output
When No Specification Exists, 285
11.19 Process Capability/Performance Metrics from XmR
Chart of Subgroup Means and Standard Deviation, 290
11.20 Process Capability/Performance Metric for Nonnormal
Distribution, 290
11.21 Example 11.6: Process Capability/Performance Metric
for Nonnormal Distributions: Box Cox
Transformation, 292
11.22 Implementation Comments, 297
11.23 The S4/IEE Difference, 297
11.24 SVIEE Assessment, 299
11.25 Exercises, 300
12 Measurement Systems Analysis 306
12.1 MSA Philosophy, 308
12.2 Variability Sources in a 30,000 Foot Level Metric, 308
12.3 S4/IEE Application Examples: MSA, 309
12.4 Terminology, 310
12.5 Gage R R Considerations, 312
12.6 Gage R R Relationships, 314
12.7 Additional Ways to Express Gage R R
Relationships, 316
12.8 Preparation for a Measurement System Study, 317
12.9 Example 12.1: Gage R R, 318
12.10 Linearity, 322
12.11 Example 12.2: Linearity, 323
12.12 Attribute Gage Study, 323
12.13 Example 12.3: Attribute Gage Study, 324
12.14 Gage Study of Destructive Testing, 326
12.15 Example 12.4: Gage Study of Destructive Testing, 327
12.16 A 5 Step Measurement Improvement Process, 330
12.17 Example 12.5: A 5 Step Measurement Improvement
Process, 335
12.18 S4/IEE Assessment, 341
12.19 Exercises, 341
13 Cause and Effect Matrix and Quality Function Deployment 347
13.1 S4/IEE Application Examples: Cause and Effect
Matrix, 348
xiv CONTENTS
13.2 Quality Function Deployment (QFD), 349
13.3 Example 13.1: Creating a QFD Chart, 354
13.4 Cause and Effect Matrix, 356
13.5 Data Relationship Matrix, 358
13.6 SVIEE Assessment, 359
13.7 Exercises, 359
14 FMEA 360
14.1 S4/IEE Application Examples: FMEA, 361
14.2 Implementation, 362
14.3 Development of a Design FMEA, 363
14.4 Design FMEA Tabular Entries, 366
14.5 Development of a Process FMEA, 369
14.6 Process FMEA Tabular Entries, 371
14.7 Exercises, 381
PART III S4/IEE ANALYZE PHASE FROM DMAIC
(OR PASSIVE ANALYSIS PHASE) 383
15 Visualization of Data 385
15.1 S4/IEE Application Examples: Visualization of
Data, 386
15.2 Multi vari Charts, 386
15.3 Example 15.1: Multi vari Chart of Injection Molding
Data, 387
15.4 Box Plot, 389
15.5 Example 15.2: Plots of Injection Molding Data, 390
15.6 S4/IEE Assessment, 391
15.7 Exercises, 392
16 Confidence Intervals and Hypothesis Tests 399
16.1 Confidence Interval Statements, 400
16.2 Central Limit Theorem, 400
16.3 Hypothesis Testing, 401
16.4 Example 16.1: Hypothesis Testing, 404
16.5 S4/IEE Assessment, 405
16.6 Exercises, 405
17 Inferences: Continuous Response 407
17.1 Summarizing Sampled Data, 407
17.2 Sample Size: Hypothesis Test of a Mean Criterion for
Continuous Response Data, 408
CONTENTS xv
17.3 Example 17.1: Sample Size Determination for a Mean
Criterion Test, 408
17.4 Confidence Intervals on the Mean and Hypothesis Test
Criteria Alternatives, 409
17.5 Example 17.2: Confidence Intervals on the Mean, 411
17.6 Example 17.3: Sample Size—An Alternative
Approach, 413
17.7 Standard Deviation Confidence Interval, 413
17.8 Example 17.4: Standard Deviation Confidence
Statement, 413
17.9 Percentage of the Population Assessments, 414
17.10 Example 17.5: Percentage of the Population
Statements, 415
17.11 Statistical Tolerancing, 417
17.12 Example 17.6: Combining Analytical Data with
Statistical Tolerancing, 418
17.13 Nonparametric Estimates: Runs Test for
Randomization, 420
17.14 Example 17.7: Nonparametric Runs Test for
Randomization, 420
17.15 S4/IEE Assessment, 421
17.16 Exercises, 421
18 Inferences: Attribute (Pass/Fail) Response 426
18.1 Attribute Response Situations, 427
18.2 Sample Size: Hypothesis Test of an Attribute
Criterion, 427
18.3 Example 18.1: Sample Size—A Hypothesis Test of an
Attribute Criterion, 428
18.4 Confidence Intervals for Attribute Evaluations and
Alternative Sample Size Considerations, 428
18.5 Reduced Sample Size Testing for Attribute
Situations, 430
18.6 Example 18.2: Reduced Sample Size Testing—Attribute
Response Situations, 430
18.7 Attribute Sample Plan Alternatives, 432
18.8 S4/IEE Assessment, 432
18.9 Exercises, 433
19 Comparison Tests: Continuous Response 436
19.1 S4/IEE Application Examples: Comparison Tests, 436
19.2 Comparing Continuous Data Responses, 437
19.3 Sample Size: Comparing Means, 437
19.4 Comparing Two Means, 438
xvi CONTENTS
19.5 Example 19.1: Comparing the Means of Two
Samples, 439
19.6 Comparing Variances of Two Samples, 440
19.7 Example 19.2: Comparing the Variance of Two
Samples, 441
19.8 Comparing Populations Using a Probability Plot, 442
19.9 Example 19.3: Comparing Responses Using a
Probability Plot, 442
19.10 Paired Comparison Testing, 443
19.11 Example 19.4: Paired Comparison Testing. 443
19.12 Comparing More Than Two Samples, 445
19.13 Example 19.5: Comparing Means to Determine If
Process Improved, 445
19.14 S4/IEE Assessment, 450
19.15 Exercises, 451
20 Comparison Tests: Attribute (Pass/Fail) Response 455
20.1 SVIEE Application Examples: Attribute Comparison
Tests, 455
20.2 Comparing Attribute Data, 456
20.3 Sample Size: Comparing Proportions, 456
20.4 Comparing Proportions, 456
20.5 Example 20.1: Comparing Proportions, 457
20.6 Comparing Nonconformance Proportions and Count
Frequencies, 458
20.7 Example 20.2: Comparing Nonconformance
Proportions, 459
20.8 Example 20.3: Comparing Counts, 460
20.9 Example 20.4: Difference in Two Proportions, 461
20.10 S4/IEE Assessment, 462
20.11 Exercises, 462
21 Bootstrapping 465
21.1 Description, 465
21.2 Example 21.1: Bootstrapping to Determine Confidence
Interval for Mean, Standard Deviation, Pp and P/)A, 466
21.3 Example 21.2: Bootstrapping with Bias Correction, 471
21.4 Bootstrapping Applications, 471
21.5 Exercises, 472
22 Variance Components 474
22.1 S4/IEE Application Examples: Variance
Components, 474
CONTENTS xvii
22.2 Description, 475
22.3 Example 22.1: Variance Components of Pigment
Paste, 476
22.4 Example 22.2: Variance Components of a Manufactured
Door Including Measurement System Components, 478
22.5 Example 22.3: Determining Process Capability/
Performance Using Variance Components, 479
22.6 Example 22.4: Variance Components Analysis of
Injection Molding Data, 480
22.7 S4/IEE Assessment, 481
22.8 Exercises, 482
23 Correlation and Simple Linear Regression 484
23.1 S4/IEE Application Examples: Regression, 485
23.2 Scatter Plot (Dispersion Graph), 485
23.3 Correlation, 485
23.4 Example 23.1: Correlation, 487
23.5 Simple Linear Regression, 487
23.6 Analysis of Residuals, 492
23.7 Analysis of Residuals: Normality Assessment, 492
23.8 Analysis of Residuals: Time Sequence, 493
23.9 Analysis of Residuals: Fitted Values, 493
23.10 Example 23.2: Simple Linear Regression, 493
23.11 S4/IEE Assessment, 496
23.12 Exercises, 496
24 Single Factor (One Way) Analysis of Variance (ANOVA) and
Analysis of Means (ANOM) 500
24.1 S4/IEE Application Examples: ANOVA and
ANOM, 501
24.2 Application Steps, 501
24.3 Single Factor Analysis of Variance Hypothesis
Test, 502
24.4 Single Factor Analysis of Variance Table
Calculations, 503
24.5 Estimation of Model Parameters, 504
24.6 Unbalanced Data, 505
24.7 Model Adequacy, 505
24.8 Analysis of Residuals: Fitted Value Plots and Data
Transformations, 506
24.9 Comparing Pairs of Treatment Means, 507
24.10 Example 24.1: Single Factor Analysis of Variance, 507
24.11 Analysis of Means, 511
xviii CONTENTS
24.12 Example 24.2: Analysis of Means, 512
24.13 Example 24.3: Analysis of Means of Injection Molding
Data, 513
24.14 Six Sigma Considerations, 514
24.15 Example 24.4: Determining Process Capability Using
One Factor Analysis of Variance, 516
24.16 Nonparametric Estimate: Kruskal Wallis Test, 518
24.17 Example 24.5: Nonparametric Kruskal Wallis Test, 518
24.18 Nonparametric Estimate: Mood s Median Test, 519
24.19 Example 24.6: Nonparametric Mood s Median Test, 520
24.20 Other Considerations, 520
24.21 S4/IEE Assessment, 521
24.22 Exercises, 521
25 Two Factor (Two Way) Analysis of Variance 524
25.1 Two Factor Factorial Design, 524
25.2 Example 25.1: Two Factor Factorial Design, 526
25.3 Nonparametric Estimate: Friedman Test, 530
25.4 Example 25.2: Nonparametric Friedman Test, 531
25.5 SVIEE Assessment, 531
25.6 Exercises, 532
26 Multiple Regression, Logistic Regression, and Indicator
Variables 533
26.1 S4/IEE Application Examples: Multiple
Regression, 533
26.2 Description, 534
26.3 Example 26.1: Multiple Regression, 534
26.4 Other Considerations, 536
26.5 Example 26.2: Multiple Regression Best Subset
Analysis, 537
26.6 Indicator Variables (Dummy Variables) to Analyze
Categorical Data, 539
26.7 Example 26.3: Indicator Variables, 539
26.8 Example 26.4: Indicator Variables with Covariate, 541
26.9 Binary Logistic Regression, 542
26.10 Example 26.5: Binary Logistic Regression, 543
26.11 Exercises, 544
PART IV S4/IEE IMPROVE PHASE FROM DMAIC (OR
PROACTIVE TESTING PHASE) 547
27 Benefiting from Design of Experiments (DOE) 549
27.1 Terminology and Benefits, 550
CONTENTS xix
27.2 Example 27.1: Traditional Experimentation, 551
27.3 The Need for DOE, 552
27.4 Common Excuses for Not Using DOE, 553
27.5 Exercises, 554
28 Understanding the Creation of Full and Fractional Factorial
2* DOEs 555
28.1 S4/IEE Application Examples: DOE, 555
28.2 Conceptual Explanation: Two Level Full Factorial
Experiments and Two Factor Interactions, 557
28.3 Conceptual Explanation: Saturated Two Level
DOE, 559
28.4 Example 28.1: Applying DOE Techniques to a
Nonmanufacturing Process, 561
28.5 Exercises, 570
29 Planning 2* DOEs 571
29.1 Initial Thoughts When Setting Up a DOE, 571
29.2 Experiment Design Considerations, 572
29.3 Sample Size Considerations for a Continuous Response
Output DOE, 574
29.4 Experiment Design Considerations: Choosing Factors
and Levels, 575
29.5 Experiment Design Considerations: Factor Statistical
Significance, 577
29.6 Experiment Design Considerations: Experiment
Resolution, 578
29.7 Blocking and Randomization, 578
29.8 Curvature Check, 579
29.9 S4/IEE Assessment, 580
29.10 Exercises, 580
30 Design and Analysis of 2* DOEs 582
30.1 Two Level DOE Design Alternatives. 582
30.2 Designing a Two Level Fractional Experiment Using
Tables M and N, 584
30.3 Determining Statistically Significant Effects and
Probability Plotting Procedure, 584
30.4 Modeling Equation Format for a Two Level DOE, 585
30.5 Example 30.1: A Resolution V DOE, 586
30.6 DOE Alternatives, 599
30.7 Example 30.2: A DOE Development Test, 603
30.8 S4/IEE Assessment, 607
30.9 Exercises, 609
XX CONTENTS
31 Other DOE Considerations 613
31.1 Latin Square Designs and Youden Square Designs, 613
31.2 Evolutionary Operation (EVOP), 614
31.3 Example 31.1: EVOP, 615
31.4 Fold Over Designs, 615
31.5 DOE Experiment: Attribute Response, 617
31.6 DOE Experiment: Reliability Evaluations, 617
31.7 Factorial Designs That Have More Than Two
Levels, 617
31.8 Example 31.2: Creating a Two Level DOE Strategy
from a Many Level Full Factorial Initial Proposal, 618
31.9 Example 31.3: Resolution III DOE with Interaction
Consideration, 619
31.10 Example 31.4: Analysis of a Resolution III Experiment
with Two Factor Interaction Assessment, 620
31.11 Example 31.5: DOE with Attribute Response, 622
31.12 Example 31.6: A System DOE Stress to Fail Test, 622
31.13 S4/IEE Assessment, 627
31.14 Exercises, 629
32 Robust DOE 630
32.1 S4/IEE Application Examples: Robust DOE, 631
32.2 Test Strategies, 631
32.3 Loss Function, 632
32.4 Example 32.1: Loss Function, 634
32.5 Robust DOE Strategy, 635
32.6 Analyzing 2k Residuals for Sources of Variability
Reduction, 636
32.7 Example 32.2: Analyzing 2k Residuals for Sources of
Variability Reduction, 637
32.8 S4/IEE Assessment, 640
32.9 Exercises, 640
33 Response Surface Methodology 643
33.1 Modeling Equations, 643
33.2 Central Composite Design, 645
33.3 Example 33.1: Response Surface Design, 647
33.4 Box Behnken Designs, 649
33.5 Mixture Designs, 650
33.6 Simplex Lattice Designs for Exploring the Whole
Simplex Region, 652
33.7 Example 33.2: Simplex Lattice Designed Mixture
Experiment, 654
CONTENTS xxi
33.8 Mixture Designs with Process Variables, 656
33.9 Example 33.3: Mixture Experiment with Process
Variables, 658
33.10 Extreme Vertices Mixture Designs, 661
33.11 Example 33.4: Extreme Vertices Mixture
Experiment, 661
33.12 Computer Generated Mixture Designs/Analyses, 661
33.13 Example 33.5: Computer Generated Mixture Design/
Analysis, 663
33.14 Additional Response Surface Design
Considerations, 663
33.15 S4/IEE Assessment, 665
33.16 Exercises, 666
PART V S4/IEE CONTROL PHASE FROM DMAIC AND
APPLICATION EXAMPLES 667
34 Short Run and Target Control Charts 669
34.1 S4/IEE Application Examples: Target Control
Charts, 670
34.2 Difference Chart (Target Chart and Nominal
Chart), 671
34.3 Example 34.1: Target Chart, 671
34.4 Z Chart (Standardized Variables Control Chart), 673
34.5 Example 34.2: ZmR Chart, 674
34.6 Exercises, 675
35 Control Charting Alternatives 677
35.1 S4/IEE Application Examples: Three Way Control
Chart, 677
35.2 Three Way Control Chart (Monitoring within and
between Part Variability), 678
35.3 Example 35.1: Three Way Control Chart, 678
35.4 CUSUM Chart (Cumulative Sum Chart), 680
35.5 Example 35.2: CUSUM Chart, 683
35.6 Example 35.3: CUSUM Chart of Bearing
Diameter, 685
35.7 Zone Chart, 686
35.8 Example 35.4: Zone Chart, 686
35.9 S4/IEE Assessment, 687
35.10 Exercises, 687
xxii CONTENTS
36 Exponentially Weighted Moving Average (EWMA) and
Engineering Process Control (EPC) 690
36.1 S4/IEE Application Examples: EWMA and EPC, 690
36.2 Description, 692
36.3 Example 36.1: EWMA with Engineering Process
Control, 692
36.4 Exercises, 701
37 Pre control Charts 703
37.1 S4/IEE Application Examples: Pre control Charts, 703
37.2 Description, 704
37.3 Pre control Setup (Qualification Procedure), 704
37.4 Classical Pre control, 705
37.5 Two Stage Pre control, 705
37.6 Modified Pre control, 705
37.7 Application Considerations, 706
37.8 S4/IEE Assessment, 706
37.9 Exercises, 706
38 Control Plan, Poka yoke, Realistic Tolerancing, and Project
Completion 708
38.1 Control Plan: Overview, 709
38.2 Control Plan: Entries, 710
38.3 Poka yoke, 716
38.4 Realistic Tolerances, 716
38.5 Project Completion, 717
38.6 S4/IEE Assessment, 718
38.7 Exercises, 718
39 Reliability Testing/Assessment: Overview 719
39.1 Product Life Cycle, 719
39.2 Units, 721
39.3 Repairable versus Nonrepairable Testing, 721
39.4 Nonrepairable Device Testing, 722
39.5 Repairable System Testing, 723
39.6 Accelerated Testing: Discussion, 724
39.7 High Temperature Acceleration, 725
39.8 Example 39.1: High Temperature Acceleration
Testing, 727
39.9 Eyring Model, 727
39.10 Thermal Cycling: Coffin Manson Relationship, 728
39.11 Model Selection: Accelerated Testing, 729
39.12 S4/IEE Assessment, 730
CONTHNTS xxiii
39.13 Exercises. 732
40 Reliability Testing/Assessment: Repairable System 733
40.1 Considerations When Designing a Test of a Repairable
System Failure Criterion, 733
40.2 Sequential Testing: Poisson Distribution, 735
40.3 Example 40.1: Sequential Reliability Test, 737
40.4 Total Test Time: Hypothesis Test of a Failure Rate
Criterion, 738
40.5 Confidence Interval for Failure Rate Evaluations, 739
40.6 Example 40.2: Time Terminated Reliability Testing
Confidence Statement, 740
40.7 Reduced Sample Size Testing: Poisson Distribution, 741
40.8 Example 40.3: Reduced Sample Size Testing—Poisson
Distribution, 741
40.9 Reliability Test Design with Test Performance
Considerations, 742
40.10 Example 40.4: Time Terminated Reliability Test
Design—with Test Performance Considerations, 743
40.11 Posttest Assessments, 745
40.12 Example 40.5: Postreliability Test Confidence
Statements, 746
40.13 Repairable Systems with Changing Failure Rate, 747
40.14 Example 40.6: Repairable Systems with Changing
Failure Rate, 748
40.15 Example 40.7: An Ongoing Reliability Test (ORT)
Plan, 752
40.16 S4/IEE Assessment. 753
40.17 Exercises, 754
41 Reliability Testing/Assessment: Nonrepairable Devices 756
41.1 Reliability Test Considerations for a Nonrepairable
Device, 756
41.2 Weibull Probability Plotting and Hazard Plotting. 757
41.3 Example 41.1: Weibull Probability Plot for Failure
Data, 758
41.4 Example 41.2: Weibull Hazard Plot with Censored
Data, 759
41.5 Nonlinear Data Plots, 761
41.6 Reduced Sample Size Testing: Weibull
Distribution, 764
41.7 Example 41.3: A Zero Failure Weibull Test
Strategy, 765
41.8 Lognormal Distribution, 766
xxiv CONTENTS
41.9 Example 41.4: Lognormal Probability Plot
Analysis, 766
41.10 S4/IEE Assessment, 768
41.11 Exercises, 769
42 Pass/Fail Functional Testing 771
42.1 The Concept of Pass/Fail Functional Testing, 771
42.2 Example 42.1: Automotive Test—Pass/Fail Functional
Testing Considerations, 772
42.3 A Test Approach for Pass/Fail Functional Testing, 773
42.4 Example 42.2: A Pass/Fail System Functional Test, 775
42.5 Example 42.3: A Pass/Fail Hardware/Software System
Functional Test, 777
42.6 General Considerations When Assigning Factors, 778
42.7 Factor Levels Greater Than 2, 778
42.8 Example 42.4: A Software Interface Pass/Fail
Functional Test, 779
42.9 A Search Pattern Strategy to Determine the Source of
Failure, 781
42.10 Example 42.5: A Search Pattern Strategy to Determine
the Source of Failure, 781
42.11 Additional Applications, 785
42.12 A Process for Using DOEs with Product
Development, 786
42.13 Example 42.6: Managing Product Development Using
DOEs, 787
42.14 S4/IEE Assessment, 790
42.15 Exercises, 790
43 S4/IEE Application Examples 792
43.1 Example 43.1: Improving Product Development, 792
43.2 Example 43.2: A QFD Evaluation with DOE, 794
43.3 Example 43.3: A Reliability and Functional Test of an
Assembly, 800
43.4 Example 43.4: A Development Strategy for a Chemical
Product, 809
43.5 Example 43.5: Tracking Ongoing Product Compliance
from a Process Point of View, 811
43.6 Example 43.6: Tracking and Improving Times for
Change Orders, 813
43.7 Example 43.7: Improving the Effectiveness of Employee
Opinion Surveys, 815
43.8 Example 43.8: Tracking and Reducing the Time of
Customer Payment, 816
CONTKNTS xxv
43.9 Example 43.9: Automobile Test—Answering the Right
Question, 817
43.10 Example 43.10: Process Improvement and Exposing the
Hidden Factory, 823
43.11 Example 43.11: Applying DOE to Increase Website
Traffic—A Transactional Application, 826
43.12 Example 43.12: AQL Deception and Alternative, 829
43.13 Example 43.13: S4/IEE Project: Reduction of Incoming
Wait Time in a Call Center, 830
43.14 Example 43.14: S4/IEE Project: Reduction of Response
Time to Calls in a Call Center, 837
43.15 Example 43.15: S4/IEE Project: Reducing the Number
of Problem Reports in a Call Center, 842
43.16 Example 43.16: S4/IEE Project: AQL Test
Assessment, 847
43.17 Example 43.17: S4/IEE Project: Qualification of Capital
Equipment, 848
43.18 Example 43.18: S4/IEE Project: Qualification of
Supplier s Production Process and Ongoing
Certification, 851
43.19 Exercises, 852
PART VI S4/IEE LEAN AND THEORY OF CONSTRAINTS 855
44 Lean and Its Integration with S4/IEE 857
44.1 Waste Prevention, 858
44.2 Principles of Lean, 858
44.3 Kaizen, 860
44.4 S4/IEE Lean Implementation Steps, 861
44.5 Time Value Diagram, 862
44.6 Example 44.1: Development of a Bowling Ball, 864
44.7 Example 44.2: Sales Quoting Process, 867
44.8 5S Method, 872
44.9 Demand Management, 873
44.10 Total Productive Maintenance (TPM), 873
44.11 Changeover Reduction, 876
44.12 Kanban, 876
44.13 Value Stream Mapping. 877
44.14 Exercises, 885
45 Integration of Theory of Constraints (TOC) in S4/IEE 886
45.1 Discussion, 887
45.2 Measures of TOC, 887
xxvi CONTENTS
45.3 Five Focusing Steps of TOC, 888
45.4 S4/IEE TOC Application and the Development of
Strategic Plans, 889
45.5 TOC Questions, 890
45.6 Exercises, 891
PART VII DFSS AND 21 STEP INTEGRATION
OF THE TOOLS 893
46 Manufacturing Applications and a 21 Step Integration
of the Tools 895
46.1 A 21 Step Integration of the Tools: Manufacturing
Processes, 896
47 Service/Transactional Applications and a 21 Step
Integration of the Tools 901
47.1 Measuring and Improving Service/Transactional
Processes, 902
47.2 21 Step Integration of the Tools: Service/Transactional
Processes, 903
48 DFSS Overview and Tools 908
48.1 DMADV, 909
48.2 Using Previously Described Methodologies within
DFSS, 909
48.3 Design for X (DFX), 910
48.4 Axiomatic Design, 911
48.5 TRIZ, 912
48.6 Exercise, 914
49 Product DFSS 915
49.1 Measuring and Improving Development Processes, 916
49.2 A 21 Step Integration of the Tools: Product DFSS, 918
49.3 Example 49.1: Notebook Computer Development, 923
49.4 Product DFSS Examples, 924
50 Process DFSS 926
50.1 A 21 Step Integration of the Tools: Process DFSS, 927
PART VIII MANAGEMENT OF INFRASTRUCTURE AND
TEAM EXECUTION 933
CONTENTS xxvii
51 Change Management 935
51.1 Seeking Pleasure and Fear of Pain, 936
51.2 Cavespeak, 938
51.3 The Eight Stages of Change and S4/IEE, 939
51.4 Managing Change and Transition, 943
51.5 How Does an Organization Learn?, 944
52 Project Management and Financial Analysis 946
52.1 Project Management: Planning, 946
52.2 Project Management: Measures, 948
52.3 Example 52.1: CPM/PERT, 951
52.4 Financial Analysis, 953
52.5 S4/IEE Assessment, 955
52.6 Exercises, 955
53 Team Effectiveness 957
53.1 Orming Model, 957
53.2 Interaction Styles, 958
53.3 Making a Successful Team, 959
53.4 Team Member Feedback, 963
53.5 Reacting to Common Team Problems, 963
53.6 Exercise, 966
54 Creativity 967
54.1 Alignment of Creativity with S4/IEE, 968
54.2 Creative Problem Solving, 968
54.3 Inventive Thinking as a Process, 969
54.4 Exercise, 970
55 Alignment of Management Initiatives and Strategies
with S4/IEE 971
55.1 Quality Philosophies and Approaches, 971
55.2 Deming s 7 Deadly Diseases and 14 Points for
Management, 973
55.3 Organization Management and Quality Leadership, 978
55.4 Quality Management and Planning, 981
55.5 ISO 9000:2000, 982
55.6 Malcolm Baldrige Assessment, 984
55.7 Shingo Prize, 985
55.8 GE Work Out, 986
55.9 S4/IEE Assessment, 987
55.10 Exercises, 987
xxviii CONTENTS
Appendix A: Supplemental Information 989
A.I S4/IEE Project Execution Roadmap, 989
A.2 Six Sigma Benchmarking Study: Best Practices
and Lessons Learned, 989
A.3 Choosing a Six Sigma Provider, 1001
A.4 Agenda for Management and Employee
S4/IEE Training, 1005
A.5 8D (8 Disciplines), 1006
A.6 ASQ Black Belt Certification Test, 1011
Appendix B: Equations for the Distributions 1014
B.I Normal Distribution, 1014
B.2 Binomial Distribution, 1015
B.3 Hypergeometric Distribution, 1015
B.4 Poisson Distribution, 1016
B.5 Exponential Distribution, 1016
B.6 Weibull Distributions, 1017
Appendix C: Mathematical Relationships 1019
C.I Creating Histograms Manually, 1019
C.2 Example C.I: Histogram Plot, 1020
C.3 Theoretical Concept of Probability
Plotting, 1021
C.4 Plotting Positions, 1022
C.5 Manual Estimation of a Best Fit Probability
Plot Line, 1023
C.6 Computer Generated Plots and Lack of
Fit, 1026
C.7 Mathematically Determining the c4
Constant, 1026
Appendix D: DOE Supplement 1028
D. 1 DOE: Sample Size for Mean Factor
Effects, 1028
D.2 DOE: Estimating Experimental Error, 1030
D.3 DOE: Derivation of Equation to Determine
Contrast Column Sum of Squares, 1030
D.4 DOE: A Significance Test Procedure for
Two Level Experiments, 1032
D.5 DOE: Application Example, 1033
D.6 Illustration That a Standard Order DOE Design
from Statistical Software Is Equivalent to a
Table M Design, 1039
CONTKNTS xxix
Appendix E: Reference Tables 1040
List of Symbols 1092
Glossary 1098
References 1125
Index 1139
|
| any_adam_object | 1 |
| author | Breyfogle, Forrest W. |
| author_facet | Breyfogle, Forrest W. |
| author_role | aut |
| author_sort | Breyfogle, Forrest W. |
| author_variant | f w b fw fwb |
| building | Verbundindex |
| bvnumber | BV014740644 |
| callnumber-first | T - Technology |
| callnumber-label | TS156 |
| callnumber-raw | TS156.B75 2003 |
| callnumber-search | TS156.B75 2003 |
| callnumber-sort | TS 3156 B75 42003 |
| callnumber-subject | TS - Manufactures |
| classification_rvk | QP 321 ZG 9260 |
| classification_tum | WIR 896f MAT 620f FER 060f |
| ctrlnum | (OCoLC)632981520 (DE-599)BVBBV014740644 |
| dewey-full | 658.5/6221 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.5/62 21 |
| dewey-search | 658.5/62 21 |
| dewey-sort | 3658.5 262 221 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Technik Mathematik Fertigungstechnik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV014740644 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T11:27:47Z |
| institution | BVB |
| isbn | 0471265721 |
| language | English |
| lccn | 2002033192 |
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| publisher | Wiley |
| record_format | marc |
| spellingShingle | Breyfogle, Forrest W. Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods Quality control -- Statistical methods Production management -- Statistical methods Fertigungsfehler (DE-588)4286623-6 gnd Statistische Qualitätskontrolle (DE-588)4182962-1 gnd Statistik (DE-588)4056995-0 gnd Qualitätskontrolle (DE-588)4047968-7 gnd Prozessmanagement (DE-588)4353072-2 gnd Fehlerverhütung (DE-588)4276213-3 gnd |
| subject_GND | (DE-588)4286623-6 (DE-588)4182962-1 (DE-588)4056995-0 (DE-588)4047968-7 (DE-588)4353072-2 (DE-588)4276213-3 |
| title | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods |
| title_auth | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods |
| title_exact_search | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods |
| title_full | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods Forrest W. Breyfogle |
| title_fullStr | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods Forrest W. Breyfogle |
| title_full_unstemmed | Implementing Six Sigma smarter solutions using statistical methods ; smarter solutions using statistical methods Forrest W. Breyfogle |
| title_short | Implementing Six Sigma |
| title_sort | implementing six sigma smarter solutions using statistical methods smarter solutions using statistical methods |
| title_sub | smarter solutions using statistical methods ; smarter solutions using statistical methods |
| topic | Quality control -- Statistical methods Production management -- Statistical methods Fertigungsfehler (DE-588)4286623-6 gnd Statistische Qualitätskontrolle (DE-588)4182962-1 gnd Statistik (DE-588)4056995-0 gnd Qualitätskontrolle (DE-588)4047968-7 gnd Prozessmanagement (DE-588)4353072-2 gnd Fehlerverhütung (DE-588)4276213-3 gnd |
| topic_facet | Quality control -- Statistical methods Production management -- Statistical methods Fertigungsfehler Statistische Qualitätskontrolle Statistik Qualitätskontrolle Prozessmanagement Fehlerverhütung |
| url | http://www.loc.gov/catdir/description/wiley037/2002033192.html http://www.loc.gov/catdir/toc/wiley031/2002033192.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009985627&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT breyfogleforrestw implementingsixsigmasmartersolutionsusingstatisticalmethodssmartersolutionsusingstatisticalmethods |
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Sonderstandort Fakultät
| Signatur: |
2000 ZG 9260 B848(2) |
|---|---|
| Exemplar 1 | nicht ausleihbar Checked out – Rückgabe bis: 31.12.2099 Vormerken |