Design for Six Sigma statistics : 59 tools for diagnosing and solving problems in DFSS initiatives

Title:

Personal Author:

Sleeper, Andrew D.

Publication Information:

New York : McGraw-Hill, 2006

ISBN:

9780071451628

Subject Term:

Six sigma (Quality control standard)

Quality control -- Statistical methods

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... SPACE KL Main Library	30000010078263	TS156 S575 2006	Open Access Book	Book	Searching... Unknown

Summary

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.

This rigorous text provides the user with statistical tools for rooting out and solving problems in Design for Six Sigma initiatives

In today's competitive environment, companies can no longer produce goods and services that are merely good with low defect levels, they have to be near-perfect. Design for Six Sigma Statistics is a rigorous mathematical roadmap to help companies reach this goal. As the sixth book in the Six Sigma operations series, this comprehensive book goes beyond an introduction to the statistical tools and methods found in most books but contains expert case studies, equations and step by step MINTAB instruction for performing: DFSS Design of Experiments, Measuring Process Capability, Statistical Tolerancing in DFSS and DFSS Techniques within the Supply Chain for Improved Results. The aim is to help you better diagnosis and root out potential problems before your product or service is even launched.

Author Notes

Andrew Sleeper is a DFSS expert and General Manager of Successful Statistics, LLC. He has worked with product development teams for 22 years as an engineer, statistician, project manager, Six Sigma Black Belt, and consultant. Mr. Sleeper holds degrees in electrical engineering and statistics, and is a licensed Professional Engineer. A senior member of the American Society for Quality, he is certified by ASQ as a Quality Manager, Reliability Engineer, and Quality Engineer, and has provided over 1,000 hours of instruction in countries around the world. His client list includes Anheuser-Busch, Intier Automotive Seating, New Belgium Brewing Company, and Ingersoll-Rand.

Foreword	p. xiii
Preface	p. xix
Chapter 1 Engineering in a Six Sigma Company	p. 1
1.1 Understanding Six Sigma and DFSS Terminology	p. 2
1.2 Laying the Foundation for DFSS	p. 11
1.3 Choosing the Best Statistical Tool	p. 14
1.4 Example of Statistical Tools in New Product Development	p. 21
Chapter 2 Visualizing Data	p. 31
2.1 Case Study: Data Graphed Out of Context Leads to Incorrect Conclusions	p. 34
2.2 Visualizing Time Series Data	p. 38
2.2.1 Concealing the Story with Art	p. 38
2.2.2 Concealing Patterns by Aggregating Data	p. 40
2.2.3 Choosing the Aspect Ratio to Reveal Patterns	p. 43
2.2.4 Revealing Instability with the IX, MR Control Chart	p. 46
2.3 Visualizing the Distribution of Data	p. 50
2.3.1 Visualizing Distributions with Dot Graphs	p. 51
2.3.2 Visualizing Distributions with Boxplots	p. 55
2.3.3 Visualizing Distributions with Histograms	p. 61
2.3.4 Visualizing Distributions with Stem-and-Leaf Displays	p. 69
2.3.5 Revealing Patterns by Transforming Data	p. 71
2.4 Visualizing Bivariate Data	p. 74
2.4.1 Visualizing Bivariate Data with Scatter Plots	p. 74
2.4.2 Visualizing Both Marginal and Joint Distributions	p. 76
2.4.3 Visualizing Paired Data	p. 79
2.5 Visualizing Multivariate Data	p. 85
2.5.1 Visualizing Historical Data with Scatter Plot Matrices	p. 86
2.5.2 Visualizing Experimental Data with Multi-Vari Charts	p. 88
2.6 Summary: Guidelines for Visualizing Data with Integrity	p. 93
Chapter 3 Describing Random Behavior	p. 97
3.1 Measuring Probability of Events	p. 98
3.1.1 Describing Collections of Events	p. 98
3.1.2 Calculating the Probability of Events	p. 101
3.1.2.1 Calculating Probability of Combinations of Events	p. 102
3.1.2.2 Calculating Probability of Conditional Chains of Events	p. 103
3.1.2.3 Calculating the Joint Probability of Independent Events	p. 104
3.1.3 Counting Possible Outcomes	p. 106
3.1.3.1 Counting Samples with Replacement	p. 106
3.1.3.2 Counting Ordered Samples without Replacement	p. 107
3.1.3.3 Counting Unordered Samples without Replacement	p. 108
3.1.4 Calculating Probabilities for Sampling Problems	p. 109
3.1.4.1 Calculating Probability Based on a Sample Space of Equally Likely Outcomes	p. 109
3.1.4.2 Calculating Sampling Probabilities from a Finite Population	p. 110
3.1.4.3 Calculating Sampling Probabilities from Populations with a Constant Probability of Defects	p. 113
3.1.4.4 Calculating Sampling Probabilities from a Continuous Medium	p. 115
3.2 Representing Random Processes by Random Variables	p. 116
3.2.1 Describing Random Variables	p. 117
3.2.2 Selecting the Appropriate Type of Random Variable	p. 118
3.2.3 Specifying a Random Variable as a Member of a Parametric Family	p. 118
3.2.4 Specifying the Cumulative Probability of a Random Variable	p. 120
3.2.5 Specifying the Probability Values of a Discrete Random Variable	p. 124
3.2.6 Specifying the Probability Density of a Continuous Random Variable	p. 125
3.3 Calculating Properties of Random Variables	p. 129
3.3.1 Calculating the Expected Value of a Random Variable	p. 129
3.3.2 Calculating Measures of Variation of a Random Variable	p. 135
3.3.3 Calculating Measures of Shape of a Random Variable	p. 138
3.3.4 Calculating Quantiles of a Random Variable	p. 139
Chapter 4 Estimating Population Properties	p. 145
4.1 Communicating Estimation	p. 146
4.1.1 Sampling for Accuracy and Precision	p. 147
4.1.2 Selecting Good Estimators	p. 153
4.2 Selecting Appropriate Distribution Models	p. 156
4.3 Estimating Properties of a Normal Population	p. 158
4.3.1 Estimating the Population Mean	p. 160
4.3.2 Estimating the Population Standard Deviation	p. 173
4.3.3 Estimating Short-Term and Long-Term Properties of a Normal Population	p. 184
4.3.3.1 Planning Samples to Identify Short-Term and Long-Term Properties	p. 185
4.3.3.2 Estimating Short-Term and Long-Term Properties from Subgrouped Data	p. 189
4.3.3.3 Estimating Short-Term and Long-Term Properties from Individual Data	p. 203
4.3.4 Estimating Statistical Tolerance Bounds and Intervals	p. 211
4.4 Estimating Properties of Failure Time Distributions	p. 216
4.4.1 Describing Failure Time Distributions	p. 217
4.4.2 Estimating Reliability from Complete Life Data	p. 223
4.4.3 Estimating Reliability from Censored Life Data	p. 230
4.4.4 Estimating Reliability from Life Data with Zero Failures	p. 234
4.5 Estimating the Probability of Defective Units by the Binomial Probability [pi]	p. 238
4.5.1 Estimating the Probability of Defective Units [pi]	p. 239
4.5.2 Testing a Process for Stability in the Proportion of Defective Units	p. 244
4.6 Estimating the Rate of Defects by the Poisson Rate Parameter [lambda]	p. 248
4.6.1 Estimating the Poisson Rate Parameter [lambda]	p. 249
4.6.2 Testing a Process for Stability in the Rate of Defects	p. 255
Chapter 5 Assessing Measurement Systems	p. 261
5.1 Assessing Measurement System Repeatability Using a Control Chart	p. 265
5.2 Assessing Measurement System Precision Using Gage R&R Studies	p. 271
5.2.1 Conducting a Gage R&R Study	p. 272
5.2.1.1 Step 1: Define Measurement System and Objective for MSA	p. 272
5.2.1.2 Step 2: Select n Parts for Measurement	p. 274
5.2.1.3 Step 3: Select k Appraisers	p. 275
5.2.1.4 Step 4: Select r, the Number of Replications	p. 276
5.2.1.5 Step 5: Randomize Measurement Order	p. 279
5.2.1.6 Step 6: Perform nkr Measurements	p. 280
5.2.1.7 Step 7: Analyze Data	p. 281
5.2.1.8 Step 8: Compute MSA Metrics	p. 287
5.2.1.9 Step 9: Reach Conclusions	p. 293
5.2.2 Assessing Sensory Evaluation with Gage R&R	p. 296
5.2.3 Investigating a Broken Measurement System	p. 301
5.3 Assessing Attribute Measurement Systems	p. 307
5.3.1 Assessing Agreement of Attribute Measurement Systems	p. 308
5.3.2 Assessing Bias and Repeatability of Attribute Measurement Systems	p. 313
Chapter 6 Measuring Process Capability	p. 319
6.1 Verifying Process Stability	p. 321
6.1.1 Selecting the Most Appropriate Control Chart	p. 324
6.1.1.1 Continuous Measurement Data	p. 324
6.1.1.2 Count Data	p. 326
6.1.2 Interpreting Control Charts for Signs of Instability	p. 326
6.2 Calculating Measures of Process Capability	p. 333
6.2.1 Measuring Potential Capability	p. 336
6.2.1.1 Measuring Potential Capability with Bilateral Tolerances	p. 336
6.2.1.2 Measuring Potential Capability with Unilateral Tolerances	p. 342
6.2.2 Measuring Actual Capability	p. 346
6.2.2.1 Measuring Actual Capability with Bilateral Tolerances	p. 346
6.2.2.2 Measuring Actual Capability with Unilateral Tolerances	p. 359
6.3 Predicting Process Defect Rates	p. 361
6.4 Conducting a Process Capability Study	p. 369
6.5 Applying Process Capability Methods in a Six Sigma Company	p. 371
6.5.1 Dealing with Inconsistent Terminology	p. 371
6.5.2 Understanding the Mean Shift	p. 372
6.5.3 Converting between Long-Term and Short-Term	p. 374
6.6 Applying the DFSS Scorecard	p. 376
6.6.1 Building a Basic DFSS Scorecard	p. 379
Chapter 7 Detecting Changes	p. 385
7.1 Conducting a Hypothesis Test	p. 387
7.1.1 Define Objective and State Hypothesis	p. 388
7.1.2 Choose Risks [alpha] and [beta] and Select Sample Size n	p. 392
7.1.3 Collect Data and Test Assumptions	p. 400
7.1.4 Calculate Statistics and Make Decision	p. 405
7.2 Detecting Changes in Variation	p. 410
7.2.1 Comparing Variation to a Specific Value	p. 410
7.2.2 Comparing Variations of Two Processes	p. 420
7.2.3 Comparing Variations of Three or More Processes	p. 433
7.3 Detecting Changes in Process Average	p. 440
7.3.1 Comparing Process Average to a Specific Value	p. 441
7.3.2 Comparing Averages of Two Processes	p. 450
7.3.3 Comparing Repeated Measures of Process Average	p. 459
7.3.4 Comparing Averages of Three or More Processes	p. 467
Chapter 8 Detecting Changes in Discrete Data	p. 477
8.1 Detecting Changes in Proportions	p. 478
8.1.1 Comparing a Proportion to a Specific Value	p. 480
8.1.2 Comparing Two Proportions	p. 490
8.2 Detecting Changes in Defect Rates	p. 496
8.3 Detecting Associations in Categorical Data	p. 505
Chapter 9 Detecting Changes in Nonnormal Data	p. 517
9.1 Detecting Changes Without Assuming a Distribution	p. 518
9.1.1 Comparing a Median to a Specific Value	p. 521
9.1.2 Comparing Two Process Distributions	p. 535
9.1.3 Comparing Two or More Process Medians	p. 539
9.2 Testing for Goodness of Fit	p. 543
9.3 Normalizing Data with Transformations	p. 560
9.3.1 Normalizing Data with the Box-Cox Transformation	p. 561
9.3.2 Normalizing Data with the Johnson Transformation	p. 570
Chapter 10 Conducting Efficient Experiments	p. 575
10.1 Conducting Simple Experiments	p. 578
10.1.1 Changing Everything at Once	p. 578
10.1.2 Analyzing a Simple Experiment	p. 582
10.1.3 Insuring Against Experimental Risks	p. 590
10.1.4 Conducting a Computer-Aided Experiment	p. 599
10.1.5 Selecting a More Efficient Treatment Structure	p. 613
10.2 Understanding the Terminology and Procedure for Efficient Experiments	p. 619
10.2.1 Understanding Experimental Terminology	p. 619
10.2.2 Following a Procedure for Efficient Experiments	p. 622
10.2.2.1 Step 1: Define the Objective	p. 623
10.2.2.2 Step 2: Define the IPO Structure	p. 624
10.2.2.3 Step 3: Select Treatment Structure	p. 626
10.2.2.4 Step 4: Select Design Structure	p. 627
10.2.2.5 Step 5: Select Sample Size	p. 628
10.2.2.6 Step 6: Prepare to Collect Data	p. 629
10.2.2.7 Step 7: Collect Data	p. 630
10.2.2.8 Step 8: Determine Significant Effects	p. 631
10.2.2.9 Step 9: Reach Conclusions	p. 632
10.2.2.10 Step 10: Verify Conclusions	p. 632
10.3 Conducting Two-Level Experiments	p. 633
10.3.1 Selecting the Most Efficient Treatment Structure	p. 635
10.3.2 Calculating Sample Size	p. 643
10.3.3 Analyzing Screening Experiments	p. 648
10.3.4 Analyzing Modeling Experiments	p. 655
10.3.5 Testing a System for Nonlinearity with a Center Point Run	p. 663
10.4 Conducting Three-Level Experiments	p. 669
10.5 Improving Robustness with Experiments	p. 680
Chapter 11 Predicting the Variation Caused by Tolerances	p. 685
11.1 Selecting Critical to Quality (CTQ) Characteristics	p. 692
11.2 Implementing Consistent Tolerance Design	p. 698
11.3 Predicting the Effects of Tolerances in Linear Systems	p. 704
11.3.1 Developing Linear Transfer Functions	p. 704
11.3.2 Calculating Worst-Case Limits	p. 711
11.3.3 Predicting the Variation of Linear Systems	p. 716
11.3.4 Applying the Root-Sum-Square Method to Tolerances	p. 724
11.4 Predicting the Effects of Tolerances in Nonlinear Systems	p. 731
11.5 Predicting Variation with Dependent Components	p. 754
11.6 Predicting Variation with Geometric Dimensioning and Tolerancing	p. 765
11.7 Optimizing System Variation	p. 771
Appendix	p. 791
References	p. 833
Index	p. 837

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Table of Contents