Visual Six Sigma Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions Titles in the Wiley & SAS Business Series include: Agile by Design: An Implementation Guide to Analytic Lifecycle Management by Rachel AltSimmons Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs Business Forecasting: Practical Problems and Solutions edited by Michael Gilliland, Len Tashman, and Udo Sglavo Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S Gendron Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry by Laura Madsen Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A Davis Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments by Gene Pease, Barbara Beresford, and Lew Walker Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard Financial Institution Advantage and the Optimization of Information Processing by Sean C Keenan Financial Risk Management: Applications in Market, Credit, Asset, and Liability Management and Firmwide Risk by Jimmy Skoglund and Wei Chen Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection by Bart Baesens, Veronique Van Vlasselaer, and Wouter Verbeke Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models by Keith Holdaway Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World by Carlos Andre, Reis Pinheiro, and Fiona McNeill Hotel Pricing in a Social World: Driving Value in the Digital Economy by Kelly McGuire Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp Killer Analytics: Top 20 Metrics Missing from your Balance Sheet by Mark Brown Mobile Learning: A Handbook for Developers, Educators, and Learners by Scott McQuiggan, Lucy Kosturko, Jamie McQuiggan, and Jennifer Sabourin The Patient Revolution: How Big Data and Analytics Are Transforming the Healthcare Experience by Krisa Tailor Predictive Analytics for Human Resources by Jac Fitz-enz and John Mattox II Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance by Lawrence Maisel and Gary Cokins Statistical Thinking: Improving Business Performance, Second Edition by Roger W Hoerl and Ronald D Snee Too Big to Ignore: The Business Case for Big Data by Phil Simon Trade-Based Money Laundering: The Next Frontier in International Money Laundering Enforcement by John Cassara The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions by Phil Simon Understanding the Predictive Analytics Lifecycle by Al Cordoba Unleashing Your Inner Leader: An Executive Coach Tells All by Vickie Bevenour Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean Visual Six Sigma, Second Edition by Ian Cox, Marie Gaudard and Mia Stephens For more information on any of the above titles, please visit www.wiley.com Visual Six Sigma Making Data Analysis Lean Ian Cox Marie A Gaudard Mia L Stephens Second Edition Copyright © 2016 by SAS Institute, Inc All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley publishes in a variety of print and electronic formats and by print-on-demand Some material included with standard print versions of this book may not be included in e-books or in print-on-demand If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com For more information about Wiley products, visit www.wiley.com Library of Congress Cataloging-in-Publication Data: Names: Cox, Ian, 1956– Title: Visual six sigma : making data analysis lean / Ian Cox, Marie A Gaudard, Mia L Stephens Description: Second edition | Hoboken : Wiley, 2016 | Series: Wiley and SAS business series | Revised edition of Visual six sigma, 2010 | Includes index Identifiers: LCCN 2016001878 (print) | LCCN 2016003459 (ebook) | ISBN 9781118905685 (hardback) | ISBN 9781119222262 (epdf) | ISBN 9781119222255 (epub) Subjects: LCSH: Six sigma (Quality control standard) | Decision support systems | Decision making—Statistical methods | Organizational effectiveness | BISAC: BUSINESS & ECONOMICS / Strategic Planning Classification: LCC HD30.213 C69 2016 (print) | LCC HD30.213 (ebook) | DDC 658.4/013—dc23 LC record available at http://lccn.loc.gov/2016001878 Cover Design: Wiley Cover Image: ©Studio-Pro/iStock.com Printed in the United States of America 10 Contents Preface to the Second Edition ix Preface to the First Edition xiii Acknowledgments xv About the Authors xvii PART ONE BACKGROUND Chapter Introduction Chapter Six Sigma and Visual Six Sigma Chapter A First Look at JMP 27 Chapter Managing Data and Data Quality PART TWO CASE STUDIES 67 101 Chapter Reducing Hospital Late Charge Incidents 103 Chapter Transforming Pricing Management in a Chemical Supplier 157 Chapter Improving the Quality of Anodized Parts 223 Chapter Informing Pharmaceutical Sales and Marketing 297 Chapter Improving a Polymer Manufacturing Process 345 Chapter 10 Classification of Cells 437 PART THREE SUPPLEMENTARY MATERIAL 509 Chapter 11 Beyond “Point and Click” with JMP 511 Index 539 vii Preface to the Second Edition The first edition of this book appeared in 2010, so we decided to produce an updated and expanded second edition The purpose of the book remains unchanged—to show how, using the three principles of Visual Six Sigma, you can exploit data to make better decisions more quickly and easily than you would otherwise And, as you might expect given their power and utility, these principles are also unchanged However, production of this second edition allows us to take advantage of some interim developments that make the implementation of Visual Six Sigma even easier, further increasing the scope and efficacy of its application It also allows us to improve and enhance the content and form of the first edition The staying power of Six Sigma as a methodology can be attributed to the fact that it can provide a common language for, and approach to, project-based improvement initiatives Nonetheless, as we pointed out in the first edition, there is a clear need to evolve the mechanics of Six Sigma both to accommodate the greater availability of data and to address the fact that, historically, approaches to analyzing data were overly concerned with hypothesis testing, to the detriment of the hypothesis generation and discovery needed for improvement We believe that Visual Six Sigma can foster this evolution, and this is part of our motivation for keeping this text current At the same time, the past five years have seen the explosion of “big data,” at least as an identifiable area that software providers and implementation consultants make strenuous efforts to market to In this language, the increased data availability mentioned above is measured using three dimensions: volume, variety, and velocity Even though the precise definition of big data is not always clear, we think there is much for would-be data scientists to learn from the principles of Visual Six Sigma and their application In addition, if a project-based approach is warranted, the language of Six Sigma may also be useful Although the principles of Visual Six Sigma are general, their effective and efficient adoption in practice is reliant on good enabling software The first edition was tied to version 8.01 of JMP, Statistical Discovery software from SAS Institute® This second edition has been revised to be consistent with the version current at the time of writing, JMP 12.2.0 Generally, JMP aims to exploit the synergy between visualization and analysis, and its continuing development has opened up new possibilities for Visual Six Sigma In some cases, these are simply matters of detail and efficiency, but in others there are important new capabilities we can use ix x PREFACE TO THE SECOND EDITION A key feature of the book remains the six self-contained case studies Given feedback from the first edition, we are even more convinced of the advantage of this format in showing how seemingly disparate techniques can be used in concert to accomplish something useful We interweave the new capabilities of JMP where they usefully support or extend the case studies Consistent with the requirements of Visual Six Sigma in the new era of big data, we have introduced two new chapters: ◾ Chapter 4, “Managing Data and Data Quality,” precedes the case studies and addresses the management of data and data quality Data quality, at an organizational level, is a ubiquitous topic that is often seen as mainstream to the point of being boring However, the importance of data quality for project teams and anyone making decisions with data cannot be overstated As we shall see, the Visual Six Sigma context leads to some important and interesting nuances ◾ Chapter 11, “Beyond ‘Point and Click’ with JMP,” follows the case studies and shows how to go beyond the interactive usage of JMP for discovery and improvement No matter how simple or complex, the performance of empirical models always degrades over time Once improvements are made, there is always the need to monitor and adapt with an appropriate frequency In turn, this means that analyses need to be repeated as new data arrive, and this is often best done with an element of automation The case studies appear in Part Two of the book Chapter is appended to Part One, making this section four chapters long Given the nature of the content, Chapter 11 appears as a singleton chapter in Part Three Finally, we have tried to make the case studies easier to use by having clearer typographic separation between the narrative (consisting of the why, the what, and the findings of each technique as it is used in a specific context) and the “how to” steps required in JMP As well as helping to keep things concise, this arrangement better accommodates users with different levels of prior familiarity with JMP, and may make it easier to use other software should this be required or mandated As in the first edition, we have used different fonts to help identify the names of data tables, of columns in data tables, and commands Data table names are shown in MeridienLTStd-Bold, the names of columns (which are variable names) are shown in italic Helvetica, and the names of commands and other elements of the user interface are shown in bold Helvetica We are now living through a time of rapid change in the world of data analysis We have tried to reflect this in our changes and additions We hope that this second edition on Visual Six Sigma contains even more of interest for current or would-be Six Sigma practitioners, or more generally for anyone with PREFACE TO THE SECOND EDITION xi a stake in exploiting data for the purpose of gaining new understanding or of driving improvement Supplemental Materials We anticipate that you will follow along, using JMP, as you work through the case studies and Chapters and 11 You can download a trial copy of JMP at www.jmp.com/try Chapter 10 requires JMP Pro You can request a trial version of JMP Pro at www.jmp.com/en_us/software/jmp-pro-eval.html JMP instructions in this book are based on JMP 12.2.0 Although the menu structure may differ if you use a different version of JMP, all the functionality described in this book is available in JMP 12.2.0 or newer versions The data sets used in the book are available at http://support.sas.com/ visualsixsigma This folder contains a journal file, Visual Six Sigma.jrn, that contains links to the data tables, scripts, and add-ins discussed in this book The color versions of the exhibits shown in the book are also available here Exhibits showing JMP results were taken using JMP 12.2.0 running on Windows Preface to the First Edition The purpose of this book is to show how, using the principles of Visual Six Sigma, you can exploit data to make better decisions more quickly and easily than you would otherwise We emphasize that your company does not need to have a Six Sigma initiative for this book to be useful Clearly there are many data-driven decisions that, by necessity or by design, fall outside the scope of a Six Sigma effort, and in such cases we believe that Visual Six Sigma is ideal We seek to show that Visual Six Sigma can be used by a lone associate, as well as a team, to address data-driven questions, with or without the support of a formal initiative like Six Sigma To this end, we present six case studies that show Visual Six Sigma in action These case studies address complex problems and opportunities faced by individuals and teams in a variety of application areas Each case study was addressed using the Visual Six Sigma Roadmap, described in Chapters and As these case studies illustrate, Visual Six Sigma is about exploration and discovery, which means that it is not, and never could be, an entirely prescriptive framework As well as using the case studies to convey the Visual Six Sigma Roadmap, we also want to use them to illustrate Visual Six Sigma techniques that you can reuse in your own setting To meet this goal, sometimes we have deliberately compromised the lean nature of the Visual Six Sigma Roadmap in order to take the opportunity to show you extra techniques that may not be strictly necessary to reach the conclusion or business decision Striking the balance this way means that you will see a wider repertoire of techniques from which to synthesize an approach to Visual Six Sigma that works for you Because of its visual emphasis, Visual Six Sigma opens the doors for non-statisticians to take active roles in data-driven decision making, empowering them to leverage their contextual knowledge to pose relevant questions, get good answers, and make sound decisions You may find yourself working on a Six Sigma improvement project, a design project, a data mining inquiry, or a scientific study—all of which require decision making based on data After working through this book, we hope that you will be able to make data-driven decisions in your specific situation quickly, easily, and with greater assurance How This Book Is Organized This book is organized in two parts Part I contains an introductory chapter that presents the three Visual Six Sigma strategies, a chapter on Visual Six Sigma, xiii Index A B absolute values, ranking, 492 Actual By Predicted plot, 265, 406, 409 Add-In missing data, 525–527 structure of, 528–529 Add-Ins menu, 525–526 Advanced Controls outline, 483 Agreement Comparisons panel, 237, 238 Agreement within Raters panel, 238 All Pairs option, 183, 324 All Possible Models option, 410–411 Alt key, 37, 231 amounts, analyzing, 122–125 analysis of amounts, 122–125 baseline, 171–174 cluster, 520–525 conducting, 368 in JMP, 47–54 missing data, 314–318, 443 results using Process Capability platform, 287 analysis data sets checking, 460–461 constructing, 161–164 Analysis of Variance table, 203, 332 analysis sets constructing, 454 distribution of, 455 Analyze menu, 33–34, 48–49, 236, 363, 378, 518–519, 528 anatomy, of JMP, 28–40 Application Builder, 58 Augment Design platform, 54 automatic splitting, 467 Average Chart, 369–370 axis settings, copying and pasting, 233, 234 baseline analysis, 171–174 baseline data, 166–174, 239–241 Bayesian Information Criterion (BIC), 405 bias, 373 Bias Factors Std Dev, in Profiler, 383 BIC (Bayesian Information Criterion), 405 bivariate plots, adding horizontal reference lines to, 140, 141 black belt, 13 Boosted Tree model, 471–478 Boosted Tree report, 475 Boosting panel, 497 Box, George, boxplots, displaying in Graph Builder, 251 broadcasting commands to all reports, 180 Bubble Plot (Graph menu), 47, 335–341 Bubble Size slider, 319 ButtonBox(), 528, 531 buyer’s viewpoint, 164 C capability obtaining analysis of, 426 projected, 283–292 simulated, 286–289 Capability Box Plots, 288, 289 case studies Classification of Cells, 437–508 Improving a Polymer Manufacturing Process, 345–435 Improving the Quality of Anodized Parts, 223–296 Informing Pharmaceutical Sales and Marketing, 297–343 Reducing Hospital Late Charge Incidents, 103–156 Visual Six Sigma: Making Data Analysis Lean, Second Edition Ian Cox, Marie A Gaudard, Mia L Stephens © 2016 by SAS Institute, Inc Published by John Wiley & Sons, Inc 539 540 INDEX case studies (Continuted) Transforming Pricing Management in a Chemical Supplier, 157–221 Categorical Profiler, 499 cause-and-effect diagram, constructing in JMP, 206 causes combining, 142, 144 types of, CDA (Confirmatory Data Analysis), 16–18, 19 Cell Plot script, 518 champion, 13 Char() command, 514 chunking variables, 309 CIELAB (Commission Internationale de l’Eclairage), 227–228 classification models, comparing, 503–507 Classification of Cells case study about, 438–439, 507–508 background, 440–441 Collect Data step, 441–442 comparing classification models, 503–507 constructing training, validation, and test sets, 452–461 data exploration, 442–452 Frame Problem step, 441–442 Generalized Regression platform, 482–494 neural net models, 494–503 prediction models, 461–463 recursive partitioning, 463–478 Stepwise Logistic Model, 478–482 clbChangeScript, 531 Clean task, in data management, 70, 71 Clear button, 389 Clear Row States command, 50 closing reports, 317–318, 414, 429 cluster analysis, 520–525 Collect Data step, in VSS Data Analysis Process about, 20, 70–71, 295 in Classification of Cells case study, 441–442 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 256 examples of, 71–99 in Improving the Quality of Anodize Parts case study, 228–242 in Informing Pharmaceutical Sales and Marketing case study, 300–302 in Reducing Hospital Late Charge Incidents case study, 106–108 in Transforming Pricing Management in a Chemical Supplier case study, 166–174 coloring points, 447–448 colors, changing in reports, 188–190 Colors and Markers script, 464 Column Contribution report, 476, 477 Column Info command, 51 Column Info window, 52, 239 Column Property, 189 Column Switcher, 248–249, 389–392, 457–458, 460–461 Column Viewer, 305–308, 518 columns adjusting structure of, 430–431 excluding, 303–304 grouping, 304, 305 hiding, 303–304 inserting descriptions in, 302, 303 properties of, 51 saving specification limits as, 401–403 Columns menu about, 71 displays in, 51–53 illustrated, 52 Columns panel (JMP), 31, 33, 134, 168, 239, 262, 351, 402, 412, 448, 454, 471, 477, 518–519 Columns Viewer report, 109–113, 443 Combine task, in data management, 70, 71 Combine Windows feature, 532 INDEX commands, 180 See also specific commands Commission Internationale de l’Eclairage (CIELAB), 227–228 Compare Means option, 183 Comparison Circles, obtaining, 183–187 Concat() command, 514 Concatenate (Tables menu), 50 concatenating data tables, 431 concavity, 441 confirmation runs, 23, 282–283 Confirmatory Data Analysis (CDA), 15–19 confirmatory study, exploratory study versus, 15 Confusion Matrices, constructing, 489 Confusion Matrix report, 469–470, 475, 481–482, 505 Containers outline, 532 context sensitive commands, 37 Contingency Table, 172 contour plot, creating, 278, 279 Contour Profiler (Graph menu), 47, 277–280 Control Chart Builder, 220, 292, 353, 389–392, 424–426 Control Charts, 219, 361–363, 389–392, 426–428 Control key, 84–85, 177, 180 Control Limit property, 52 control limits, 219–220 Control Matrix, 206–207 control panel (Boosted Tree model), 473–475 correlations pairwise, 449–451 Scatterplot Matrix and, 445 crisis yields, filtering, 353–354 critical to quality (CTQ), 14, 226 Critical to Quality Characteristics (CTQs), 59 Critical to Quality Tree, 356–357 CTQ (critical to quality), 14, 226 CTQs (Critical to Quality Characteristics), 59 541 Cumulative Validation plot, 475–476 Current Estimates panel, 479 Custom Design platform, 54, 259, 273 customers, in data sets, 162 D data See also Collect Data step, in VSS Data Analysis Process; data quality baseline, 239–241 collection examples, 71–99 exploring, 442–452 free trade, 516–518 importing, 72–74 missing, 74–77, 113–122, 314–318, 443, 525–537 observational versus experimental, 11–12 scoping, 302–318 splitting, 190–194 validating, 302–318 verifying integrity of, 168–171 data analysis See also Visual Six Sigma (VSS) Data Analysis Process making lean, 5–6 obtaining, 169, 170 Data Filter dialog, 50–51, 389 data grid, 31 data management, 70, 71 See also data data mining, 18, 438, 440 data quality, 68–70 See also data data table panels, 31, 273 Data Table toolbar, 33 data tables adding descriptions for variables in, 108 concatenating, 431 creating views of, 146, 147 dynamic linking to, 40–46 in JMP, 31–33 joining, 94 preparing, 303–305 542 INDEX data tables (Continued) saving, 304–305 using two, 91 Data Type, 52 Data View table, 116, 122, 127, 144, 150 dates, in JMP, 110–112 Decision Tree model, 464–471 Defect table, 422 Define, Measure, Analyze, Design, and Validate (DMADV), Define, Measure, Analyze, Improve, and Control approach See DMAIC (Define, Measure, Analyze, Improve, and Control) approach Define Abs [Amount] script, 133–134 democracy, trade policy and, 515–528 dendogram, 524 Derive task, in data management, 70, 71 Describe task, in data management, 70, 71 descriptions, adding for variables in data tables, 108 descriptive variables, using Local Data Filter for, 309–310 Design for Six Sigma (DFSS), 59 design of experiments (DOE), 11–12 Design panel, 261 designs, developing, 257–264 Desirability Functions, 273, 415 desirability traces, 280 detective, statistics as, 15–19, 24, 224 DFSS (Design for Six Sigma), 59 Diagram plot, 499 disclosure icons, 39 distribution about, 35–36, 256, 295 of analysis sets, 455 dynamic linking and, 445 dynamic visualization of variables using, 175–177 exploring, 444–445 using, 305–308 of variables, 461 Distribution (Analyze menu), 48 Distribution Analysis, obtaining, 210–211, 360 Distribution platform (JMP), 35, 77, 175, 242–244, 389 Distribution plot, 152, 153, 154, 316, 318–319, 359–361, 385–389 Distribution report, 55, 78, 110, 154, 173, 211, 212, 213, 242, 243, 306, 307–308, 444 Distribution script, 286 DMADV (Define, Measure, Analyze, Design, and Validate), 4, 14 DMAIC (Define, Measure, Analyze, Improve, and Control) approach application of, 256–257 defined, using to deliver bottom-line results in short or medium term, 14 DOE (design of experiments), 11–12 DOE Dialog script, 262, 367 DOE menu, 53–54, 228 duplicate rows, keys and, 98–99 dynamic linking to data tables, 40–46 distribution and, 445 dynamic visualization See also visualizing of multiple variables at once, 187–200 of prescriptions with tabular displays, 320–321 of sales reps and practices geographically, 318–319 using, 16–18 of variables one and two at a time, 305–314 of variables two at a time, 177–187 of variables using distribution, 175–177 E Each Pair option, 324 Early Stopping, 475 EDA (Exploratory Data Analysis), 15–19, 24, 224 Edit toolbar, 33 INDEX Effect Summary report, 203, 265, 269, 406–408, 409 Effect Tests table, 268 effectiveness, of measurement system, 238 Effectiveness Report, 238 Elastic Net model, fitting, 490–494 Elastic Net Prediction Equation, saving, 493–494 EMP Gauge R&R Results panel, 372, 376–378 EMP Gauge R&R Results report, 373, 380–381, 382, 384 entry order, viewing by, 127–132 error, 9, 10 Estimation Details outline, 484 ETL (extract, transform and load) processes, 68 Evaluate Design platform, 54 examples, of Collect Data step in VSS Data Analysis Process, 71–99 excluding columns, 303–304 outliers, 459 points on histograms, 123 rows, 33, 401 executive committee, 13 experimental data, observational data versus, 11–12 experimental design, 53 experiments, conducting, 264 Exploratory Data Analysis (EDA), 15–19, 24, 224 exploratory study, confirmatory study versus, 15 extract, transform and load (ETL) processes, 68 F factors, specifying, 259, 260 Factors panel, 259 File toolbar, 33 filtering crisis yields, 353–354 data values with Local Data Filter, 353–354 by month, 310 543 findings, summarizing, 112–113 Fit All Prediction Formulas script, 507 Fit Model (Analyze menu), 48, 188, 200, 264, 326–329, 378, 478 Fit Model report, 48, 273, 328 Fit Special, 138, 140 Fit Y by X (Analyze menu), 48, 145, 151, 152, 177, 180, 187, 323–326, 329–342 fitting Boosted Tree model, 473 Decision Tree model, 464 Elastic Net model, 490–494 Lasso model, 484–490 lines, 138, 140 logistic model, 479–481 models, 267 Neural Net models, 498, 501–502 Fitting Options panel, 497 Fixed Effects Tests report, 328 FontColor(), 529–530 For() loop, 514 Formula column property, 52 Formula Editor, 119–121, 126, 168, 239 formulas creating, 125–126 viewing in Formula Editor, 239 fractal dimension, 441 Frame Problem step, in VSS Data Analysis Process about, 20, 22, 295 in Classification of Cells case study, 441–442 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 256 in Improving a Polymer Manufacturing Process case study, 350–357 in Improving the Quality of Anodize Parts case study, 226–228 in Reducing Hospital Late Charge Incidents case study, 104–106 544 INDEX in Transforming Pricing Management in a Chemical Supplier case study, 160–166 free trade data, 516–518 frequency, of measurements, 77–82 Full Factorial Design platform, 54, 228, 366 G Gauge Repeatability and Reproducibility (Gauge R&R) study, 22–23, 228, 234 Gauge R&R Std Dev, 381 gbUpdateScript, 531 Generalized Regression modeling, 462, 482–484 Generalized Regression report, 483 Go button, 467, 479 Goal Plot, 287, 288, 289, 290 Goos, Peter Optimal Design of Experiments: A Case Study Approach, 11 Graph Builder (Graph menu) about, 47, 256 checking analysis data sets, 460–461 constructing a plot for two variables with, 399–400 using, 244–251, 313, 314 viewing outliers with, 457–458 visualizing two variables at a time with, 392–394 Graph menu (JMP) about, 267, 273 displays in, 47 getting reports from, 33–34 illustrated, 34 GraphBox(), 531 graphs creating, 244, 248 displaying elements of in Graph Builder, 251 green belt, 13 grouping columns, 304, 305 predictors, 442–443 guidelines, Visual Six Sigma and, 23–24 H Hidden Layer Structure panel, 497 hiding columns, 303–304 outliers, 459 panes in JMP, 29 points on histograms, 123 rows, 33, 401 hierarchical processes, 22 histograms, selecting outliers on, 123 historical data, reviewing, 358–363 HListBox(), 530 horizontal reference lines, adding to bivariate plots, 140, 141 Hot Xs, 21–22, 23, 59, 141–155, 187, 264–270 I Identify, Design, Optimize, and Validate (IDOV), 4, 14 importing data, 72–74 improvements confirming, 422–423 planning, 207–209 tracking, 428–434 verifying, 209–218 Improving a Polymer Manufacturing Process case study about, 346–348, 434–435 background, 348 forming teams, 350–351 Frame Problem step, 351–357 manufacturing process, 348 Measurement System Analysis (MSA), 363–385 Model Relationships step, 400–412 reviewing historical data, 358–363 Revise Knowledge step, 412–423 typical crisis, 349 Uncover Relationships step, 385–400 Utilize Knowledge step, 423–434 Improving the Quality of Anodized Parts case study about, 224, 295–296 background, 224–226 INDEX Collect Data step, 228–242 Frame Problem step, 226–228 Model Relationships step, 257–270 Revise Knowledge step, 270–292 Roadmap, 256–257 Uncover Relationships step, 242–256 Utilize Knowledge step, 292–294 In-Control Part Std Dev, 382, 383 Individual Measurement chart, 130, 131, 240–241, 292–293, 294, 352, 361 Informing Pharmaceutical Sales and Marketing case study about, 298–300, 342–343 background, 300 Collect Data step, 300–302 promotional activity, 321–333 regional differences for, 333–342 scoping data, 302–318 Uncover Relationships step, 318–321 validating data, 302–318 infrastructure, of typical Six Sigma deployment, 13 Input/Output process map, 358 integrity, of data, 168–171 interactions, 373 interoperability, with R, 533–537 Intraclass Correlation, 373, 378, 381, 385 IR charts, obtaining, 432 Is Missing() function, 521 545 Graph menu, 33–34, 47, 267, 273 opening, 29–30 personalizing, 58 programming in, 512–515 reports, 33–40 Rows menu, 50–51, 71 scripts, 55–58 Tables menu, 49–50, 71 techniques, 66 visual displays, 33–40 Visual Six Sigma Roadmap and, 58–66 VSS Data Analysis Process and, 58–66 window management, 46–47 JMP Home Window, 29–30, 39, 46, 110–112, 495–496 JMP Pro version, 12 29 JMP Scripting Language (JSL), 55, 514 JMP Starter Window, 29–30, 33 Join (Tables menu), 50 Jones, Bradley Optimal Design of Experiments: A Case Study Approach, 11 JSL (JMP Scripting Language), 55, 514 K kappa value, 237 Key Performance Indicator (KPI), 226 keys, duplicate rows and, 98–99 KPI (Key Performance Indicator), 226 J JMPⓇ about, 28, 66, 69–70, 512 Analyze menu, 33–34, 48–49, 236, 363, 378, 518–519, 528 anatomy of, 28–40 application building in, 512–515 Columns menu, 51–53, 71 data tables, 31–33 DOE menu, 53–54, 228 dynamic linking to data tables, 40–46 featured analyses, 47–54 featured visual displays, 47–54 L labelled rows, 33 Lack Of Fit Test, 265 Lasso model, fitting, 484–490 Lasso Prediction Equation, saving, 489–490 Lasso tool, 130 Lasso with Validation Column Validation, 487 launch platforms, 33, 35 lawyer, statistics as, 15–19 Leaf Report, 196, 199 Learning Rate, 474 546 INDEX Least Squares Mean Table, 204 Legend window, 449 lines, fitting, 138, 140 LineUpBox(), 530 linking with Contour Profiler, 277–280 ListBox(), 531 Local Data Filter about, 50, 55, 215, 254–255 filtering data values with, 353–354 finding relationships using, 313–314 using for descriptive variables, 309–310 using for response variables, 310–313 Lock Columns, 191 Lock Scales option, 89 logistic model, fitting, 479–481 long term, 288 M Magnifier tool, 319 Make Validation Column script, 454 management, 13 manufacturing process, in Improving a Polymer Manufacturing Process case study, 348 marking points, 447–448 Mast, Jeroen de, 16, 19 master black belt, 13 Maximize Desirability, 416, 419–420 mean fractal dimension, 451–452 Means Comparison report, 187, 325 Measurement System Analysis (MSA) study about, 22–23, 224, 228, 236–239, 346 following-up with, 380–385 in Improving a Polymer Manufacturing Process case study, 363–385 for MFI, 363–374 for Xf, 374–378 measurement systems, fixing, 378–380 measurements, 10–11, 77–82 Measures of Fit for Diagnosis report, 504–505 Minimum Size Split, 190, 475 missing data add-in for, 525–527 analyzing, 314–318, 443 application building, 528–537 identifying, 74–77 JMP 12 functionality for, 527–528 understanding, 113–122 Missing Data Pattern (Tables menu), 50, 113–114, 314–318, 443, 516–525 Missing Data Pattern report, 114, 115 Missing Value Clustering, 527 Missing Value report, 527 Missing Value Snapshot, 527 Model Comparison report, 504–505 Model Launch outline, panels in, 497 Model Launch panel, 483, 501–502 Model NTanH(3) report, 498–501 Model NTanH(3)NBoost(10) report, 502 Model Relationships step, in VSS Data Analysis Process about, 20, 21, 22–23, 60, 257, 295 conducting the experiment, 264 design development, 257–264 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 256 Improving a Polymer Manufacturing Process case study, 400–412 Improving the Quality of Anodize Parts case study, 257–270 Transforming Pricing Management in a Chemical Supplier case study, 200–205 uncovering Hot Xs, 264–270 Model script, 261, 264, 367 Model Summary report, 484, 490 Model with Random Effect script, 327 modeling, planning for, 404 Modeling menu, 48 Modeling Type, 52 Modeling>Model Comparison (Analyze menu), 49 Modeling>Neural Net (Analyze menu), 48 INDEX Modeling>Partition (Analyze menu), 48 models about, 8–10 building, 201–202, 404–412 fitting, 267 prediction, 462–463 month, filtering by, 310 mosaic plots, creating, 151, 152 MSA (Measurement System Analysis) study about, 22–23, 224, 228, 236–239, 346 following-up with, 380–385 for MFI, 363–374 for Xf, 374–378 Multiple Fits over Splits and learning rate, 475 Multi-Vari chart, creating, 229–230 Multivariate k-Nearest Neighbor Outliers option, 456 Multivariate Methods>Cluster (Analyze menu), 49 Multivariate Methods>Multivariate (Analyze menu), 49 Multivariate Methods>Principal Components (Analyze menu), 49 Multivariate Normal Imputation, 527 multivariate outliers, identifying, 456 Multivariate Robust Outliers option, 456 Multivariate SVD Imputation, 528 N Neural modeling, 462 neural net model launch dialog, obtaining, 496–497 Neural Net models about, 494 background, 494–495 Neural Net, 496–501 Neural Net, 501–503 Neural platform in JMP, 495–496 noise, 10 noise function, Non Critical quadrant, in Product Categorization Matrix, 165 nonparametric fit, 426 notes, viewing for variables, 108 547 Notes Column property, 303 Number of Subgroups, in Profiler, 382 numbers, recoding, 94–97 O Objects outline, 532 Objects panel, 532 observational data, experimental data versus, 11–12 one way plot, creating, 145 opening JMP, 29–30 Optimal Design of Experiments: A Case Study Approach (Goos and Jones), 11 optimal factor level settings, determining, 271–277 optimal strategies, identifying, 205–207 optimization simultaneous, 271, 414–417 using Profiler for multiple, 412–413 options See specific options for Add-Ins menu, 525 in Classification of Cells case study, 439 for Decision Tree model, 470–471 in Improving a Polymer Manufacturing Process case study, 348 in Informing Pharmaceutical Sales and Marketing case study, 299 Outlier Boxplot Option, 457 outliers checking for, 456–460 excluding, 459 hiding, 459 selecting on histogram, 123 overall process capability, 288 Overall Sigma Summary Report, 290, 291 Overall Statistics report, 475 Overfit Penalty, 475 P pairwise correlations, 449–451 PanelBox(), 530, 532 panes, manipulating in JMP, 29 548 INDEX Parallelism Plots, 370 Parameter Estimates for Centered and Scaled Predictors report, 487, 492 Parameter Estimates for Original Predictors outline, 484, 487 Parameter Estimates report, 487, 492 Pareto Plot, 116, 118, 142, 143 Part Mean Shift, in Profiler, 383 Part Std Dev, in Profiler, 383 partition analysis, obtaining, 188 Partition platform, 463–464, 467 Partition report, 187–188, 464–465 partitioning, recursive, 463–478 Partitioning modeling, 461 PCA (principal components analysis), 518–520 Percent format, 239 percentages, calculating using Formula Editor, 120–121 performance gap, 12 platforms See also specific platforms in Classification of Cells case study, 439 in Improving a Polymer Manufacturing Process case study, 348 in Informing Pharmaceutical Sales and Marketing case study, 299 Platforms option, 39 Potential Outliers, 459 "The Power to KnowTM ", 68 Ppk, 288–289 prediction formulas entering as column properties, 284–285 saving, 411–412 saving to data table, 273, 411–412 prediction models, 462–463 Prediction Profiler, 273, 275, 276–277, 279–280, 281, 283–284, 285–286, 295, 412–413, 418 predictive analytics, 18, 438, 440 predictors assessing sensitivity to settings, 417–421 contribution of, 481, 487–488, 492–494 grouping, 442–443 Preferences menu, 58 pricing deficit, 168, 174 pricing management process, 160 primary key, finding, 91–93 principal components analysis (PCA), 518–520 Print() command, 514 Process Capability platform, 287, 291, 426–428 process maps, 160–161 process owner, 13 processes defining, 160–161 verifying stability, 424–426 Product Categorization Matrix, 164–166 Product Variation variance component, 377–378 products, in data sets, 162 Profiler (Graph menu), 47, 267, 275–276, 280, 412–414 Profit Matrix, 462–463, 506 project charters, developing, 351–354 projected capability, 283–292 projects, identifying, 155–156 promotional activity, 321–333 properties, of good measurement systems, 364 Properties outline, 532 Prune button, 191, 193 Q Quality and Process>Control Chart Builder (Analyze menu), 49 Quality and Process>Diagram (Analyze menu), 49 Quality and Process>Measurement Systems Analysis (Analyze menu), 49 Quality and Process>Pareto Plot (Analyze menu), 49 Quality and Process>Process Capability (Analyze menu), 49 INDEX Quality and Process>Variability/Attribute Gauge Chart (Analyze menu), 49 Quantile Range Outliers option, 456 Quantiles panel, 386 Quantiles report, 113, 125, 173 R R chart, constructing, 425, 433–434 Range Chart, 369–370 recoding numbers, 94–97 recursive partitioning about, 463–464 Boosted Tree model, 471–478 Decision Tree model, 464–471 red triangle icons, 37 Red X See Hot Xs Reducing Hospital Late Charge Incidents case study about, 104 Collect Data step, 106–108 Frame Problem step, 104–106 identifying projects, 155–156 Uncover Relationships step, 109–141 uncovering Hot Xs, 141–155 Reformat Script option, 515 regional differences, 333–342 Regression Plot, 409 relationships exploring for two variables at a time, 445–452 finding using Local Data Filter, 313–314 REML Variance Component Estimates report, 328 Remove button, 269, 270 Repeatability variance component, 377–378 reports See also specific reports broadcasting commands to all, 180 changing colors in, 188–190 closing, 317–318, 414, 429 defined, 35 549 in JMP, 33–40 sorting, 196, 198 Reproducibility variance component, 377–378 Reshape task, in data management, 70, 71 response distribution analysis, 283 Response Goal window, 415 response variables adding, 249 using Local Data Filter for, 310–313 responses, specifying, 259, 260 results analyzing using Process Capability platform, 287 interpreting, 369–374 Revise Knowledge step, in VSS Data Analysis Process about, 20, 21, 23, 60, 270–271, 295 addressing conclusions with, 282 confirmation runs, 282–283 determining optimal factor level settings, 271–277 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 257 Improving a Polymer Manufacturing Process case study, 412–423 Improving the Quality of Anodize Parts case study, 270–292 linking with Contour Profiler, 277–280 projected capability, 283–292 sensitivity, 280–282 Transforming Pricing Management in a Chemical Supplier case study, 205–218 RMSE (root mean squared error), 283 Roadmap Improving the Quality of Anodize Parts case study, 256–257 JMP and, 58–66 Visual Six Sigma, 21–23 Robust Fit Outliers option, 456 root mean squared error (RMSE), 283 550 INDEX row states, 33, 50 rows, excluding and hiding, 401 Rows menu about, 71 displays in, 50–51 illustrated, 51 Rows panel (JMP), 31, 33, 40, 397 Run Script, 55 running scripts in JMP, 31 S Sample Size and Power platform, 54 Save Formulas option, 501 saving data tables, 304–305 Elastic Net Prediction Equation, 493–494 Lasso Prediction Equation, 489–490 Logistic Prediction Equation, 482 Neural Net Prediction Equation, 502–503 prediction formulas, 273, 411–412 prediction formulas to data table, 273 Scale panel, 82 Scaled-LogLikelihood plot, 489 scatterplot, creating, 137, 138 Scatterplot 3D (Graph menu), 47, 252–255, 256, 295 Scatterplot Matrix (Graph menu), 47, 48, 251–252, 295, 394–400, 397, 445 Screening Design platform, 258 Scripting Index, 529–530, 535 scripts See also specific scripts JMP, 55–58 running in JMP, 31 Second function, 77 Select Columns window, 487–488 selected rows, 33 seller’s viewpoint, 164 senior executive, 13 sensitivity, 280–282, 417–421 Sensitivity Indicator, 280, 281, 418 serial processes, 22 SetWrap(), 529–530 Shift Detection Profiler, 382–384 Sigma column properties, 284 signal function, Simulate button, 285, 422 simulated capability, 286–289 simulating process outcomes, 421–422 Simulator, 283–286, 295, 421–422 simultaneous optimization, 271, 414–417 SIPOC (Suppliers, Inputs, Process, Outputs, and Customers) map, 351 Six Sigma See also Visual Six Sigma about, 8, 13, 24–25 background of, beyond traditional, 4–5 common perceptions and definitions of, 12 DMADV approach and, 14–15 DMAIC structure and, 14 dynamic visualization, 16–18 infrastructure, 13 measurements, 10–11 models, 8–10 observational versus experimental data, 11–12 questions related to deployment of, 12 statistics, 15–16 variation, 15–16 SliderBox(), 528, 531 Small Tree View option, 191–194, 471 Solution Path report, 486, 489, 490 Sort (Tables menu), 49 Sorted Parameter Estimates table, 268, 269 sorting reports, 196, 198 SpacerBox(), 530 Spec Limits column property, 52, 286, 401–403 specification limits saving as column properties, 401–403 setting, 264 specifications, proposing, 255–256 INDEX Specifications report, 475 Split button, 191, 319, 465 Split History report, 467–469 splitting about, 465–467 automatic, 467 data, 190–194 history for, 467–469 Stack command, 35, 37, 40, 122, 127 standard deviation plot, removing, 231 standard deviations, entering as column properties, 284–285 Statistical Discovery, 28 statistical modeling, 10, 16 statistical significance, 10, 187 statistics defined, 15 as detective, 15–19, 24, 224 as lawyer, 15–16, 19 Six Sigma and, 15–16 Std Dev chart, 230 Step button, 479 Step History panel, 479 Stepwise Logistic model, 478–479 stepwise models checking and revising, 406–409 variable selection with, 404–406 Stepwise Regression control panel, 405 modeling, 461 Stepwise report, 405 Stop button, 479 Strategic Critical quadrant, in Product Categorization Matrix, 165 Strategic Security quadrant, in Product Categorization Matrix, 165 strategies, Visual Six Sigma, 18–19 structure, of Add-Ins, 528–529 Subgroup Size, in Profiler, 383 Subset (Tables menu), 49 Summary (Tables menu), 49 Summary Reports, 289, 290 Summary Statistics report, 110, 173, 305–306 Summary tables, 118, 119, 135, 137, 322–323 551 Suppliers, Inputs, Process, Outputs, and Customers (SIPOC) map, 351 supply/demand balance, in data sets, 162–163 Surface Plot (Graph menu), 47, 280 Surface Profiler, 501 T Table panel, 261, 270 Table Variable, 459–460 tables, creating for summary statistics of variables, 110 Tables menu about, 71 displays in, 49–50 illustrated, 50 tabular displays, dynamic visualization of prescriptions with, 320–321 Tabulate (Analyze menu), 48, 489, 494 tabulation results, producing, 134 Tactical Profit quadrant, in Product Categorization Matrix, 165 TanH, 497 teams, forming, 350–351 techniques, JMP, 66 test sets comparing models on, 504 constructing, 452–461 testing and inference, 16 Test-Retest Std Dev, in Profiler, 383 TextBox(), 528, 530–531 timelines, setting new, 378–380 Tip of the Day window, 29–30 toolbars, customizing in JMP, 33 traces, visualizing, 414 trade policy, democracy and, 515–528 training sets, constructing, 452–461 transfer functions, 283 Transforming Pricing Management in a Chemical Supplier case study about, 158–159, 221 background, 162–163 Collect Data step, 166–174 Frame Problem step, 160–166 Model Relationships step, 200–205 552 INDEX Transforming Pricing Management in a Chemical Supplier case study (Continued) Revise Knowledge step, 205–218 Uncover Relationships step, 174–200 Utilize Knowledge step, 218–221 Transpose (Tables menu), 49 tree map, 146, 148, 149–150 tree structure, in JMP, 29 Treemap (Graph menu), 47, 518 Trip, Albert, 16, 19 Tukey HSD option, 183, 324 two-way interactions, adding, 261 U Uncover Relationships step, in VSS Data Analysis Process about, 20, 21, 22–23, 60, 295 in case studies, 323–326 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 256 Improving the Quality of Anodize Parts case study, 242–256 Informing Pharmaceutical Sales and Marketing case study, 318–321 in Reducing Hospital Late Charge Incidents case study, 109–141 Transforming Pricing Management in a Chemical Supplier case study, 174–200 uncovering Hot Xs, 141–155, 264–270 unhiding panes in JMP, 29 Utilize Knowledge step, in VSS Data Analysis Process about, 21, 292–294, 295 DMAIC (Define, Measure, Analyze, Improve, and Control) approach and, 257 Improving a Polymer Manufacturing Process case study, 423–424 Improving the Quality of Anodize Parts case study, 272–294 Transforming Pricing Management in a Chemical Supplier case study, 218–221 V Validation Column, 483, 484–489 Validation Method, 483 Validation Portion, 191 Validation report, 484–489 validation sets, constructing, 452–461 Value Colors column property, 189–190, 448–449 Value Labels property, 454 Var Comps Model, 378 Variability Chart, 232–233 Variable Importance feature, 280, 488 variables adding descriptions for in data tables, 108 chunking, 309 creating tables for summary statistics of, 110 distribution of, 461 dynamic visualization of multiples at once, 187–200 dynamic visualization of one and two at a time, 305–314 dynamic visualization of two at a time, 177–187 dynamic visualization of using distribution, 175–177 exploring relationships for two at a time, 445–452 response, 249, 310–313 viewing notes for, 108 visualizing one at a time, 109–113, 385–392 visualizing two at a time, 125–141, 392–400 variance components, 370–373 variation defined, Six Sigma and, 15–16 versions, JMP, 29 viewing by entry order, 127–132 formulas in Formula Editor, 239 notes for variables, 108 INDEX virtual column, 80–82 visual displays, in JMP, 33–40, 47–54 Visual Six Sigma See also Six Sigma; specific topics about, 4, 24–25 data analysis process, 19–21 data quality for, 68–70 guidelines for, 23–24 roadmap for, 21–23 strategies of, 4, 18–19 Visual Six Sigma (VSS) Data Analysis Process about, 19–21 illustrated, 200 JMP and, 58–66 visualizing See also dynamic visualization about, 16–18 one variable at a time, 109–113, 385–392 553 traces, 414 two variables at a time, 125–141, 392–400 Vital X See Hot X VListBox(), 530 voice of the customer (VOC), 355–356 W Weight column, 506 Window List pane (JMP Home Window), 29, 46 windows, managing in JMP, 46–47 Wisconsin Breast Cancer Diagnostic Data Set See Classification of Cells case study X XBar chart, constructing, 425, 433–434 ... Hampshire xvii Visual Six Sigma PART ONE Background Visual Six Sigma: Making Data Analysis Lean, Second Edition Ian Cox, Marie A Gaudard, Mia L Stephens © 2016 by SAS Institute, Inc Published by John... Introduction Visual Six Sigma: Making Data Analysis Lean, Second Edition Ian Cox, Marie A Gaudard, Mia L Stephens © 2016 by SAS Institute, Inc Published by John Wiley & Sons, Inc VISUAL SIX SIGMA WHAT... Sigma Visual Six Sigma: Making Data Analysis Lean, Second Edition Ian Cox, Marie A Gaudard, Mia L Stephens © 2016 by SAS Institute, Inc Published by John Wiley & Sons, Inc VISUAL SIX SIGMA T his