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Business Analytics

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Business Analytics Methods, Models, and Decisions

James R. Evans University of Cincinnati

SECOND EDITION

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Library of Congress Cataloging-in-Publication Data

Evans, James R. (James Robert), 1950– Business analytics: methods, models, and decisions / James R. Evans, University of Cincinnati.—2 Edition. pages cm Includes bibliographical references and index. ISBN 978-0-321-99782-1 (alk. paper) 1. Business planning. 2. Strategic planning. 3. Industrial management—Statistical methods. I. Title. HD30.28.E824 2016 658.4'01—dc23 2014017342

1 2 3 4 5 6 7 8 9 10—XXX—18 17 16 15 14

ISBN 10: 0-321-99782-4 ISBN 13: 978-0-321-99782-1

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Preface xviii About the Author xxiii Credits xxv

Part 1 Foundations of Business Analytics

Chapter 1 Introduction to Business Analytics 1

Chapter 2 Analytics on Spreadsheets 37

Part 2 Descriptive Analytics

Chapter 3 Visualizing and Exploring Data 53

Chapter 4 Descriptive Statistical Measures 95

Chapter 5 Probability Distributions and Data Modeling 131

Chapter 6 Sampling and Estimation 181

Chapter 7 Statistical Inference 205

Part 3 Predictive Analytics

Chapter 8 Trendlines and Regression Analysis 233

Chapter 9 Forecasting Techniques 273

Chapter 10 Introduction to Data Mining 301

Chapter 11 Spreadsheet Modeling and Analysis 341

Chapter 12 Monte Carlo Simulation and Risk Analysis 377

Part 4 Prescriptive Analytics

Chapter 13 Linear Optimization 415

Chapter 14 Applications of Linear Optimization 457

Chapter 15 Integer Optimization 513

Chapter 16 Decision Analysis 553

Supplementary Chapter A (online) Nonlinear and Non-Smooth Optimization

Supplementary Chapter B (online) Optimization Models with Uncertainty

Appendix A 585 Glossary 609 Index 617

Brief Contents

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Preface xviii About the Author xxiii Credits xxv

Part 1: Foundations of Business Analytics

Chapter 1: Introduction to Business Analytics 1 Learning Objectives 1 What Is Business Analytics? 4 Evolution of Business Analytics 5

Impacts and Challenges 8 Scope of Business Analytics 9

Software Support 12 Data for Business Analytics 13

Data Sets and Databases 14 • Big Data 15 • Metrics and Data Classification 16 • Data Reliability and Validity 18

Models in Business Analytics 18 Decision Models 21 • Model Assumptions 24 • Uncertainty and Risk 26 • Prescriptive Decision Models 26

Problem Solving with Analytics 27 Recognizing a Problem 28 • Defining the Problem 28 • Structuring the Problem 28 • Analyzing the Problem 29 • Interpreting Results and Making a Decision 29 • Implementing the Solution 29

Key Terms 30 • Fun with Analytics 31 • Problems and Exercises 31 • Case: Drout Advertising Research Project 33 • Case: Performance Lawn Equipment 34

Chapter 2: Analytics on Spreadsheets 37 Learning Objectives 37 Basic Excel Skills 39

Excel Formulas 40 • Copying Formulas 40 • Other Useful Excel Tips 41 Excel Functions 42

Basic Excel Functions 42 • Functions for Specific Applications 43 • Insert Function 44 • Logical Functions 45

Using Excel Lookup Functions for Database Queries 47 Spreadsheet Add-Ins for Business Analytics 50

Key Terms 50 • Problems and Exercises 50 • Case: Performance Lawn Equipment 52

Contents

viii Contents

Part 2: Descriptive Analytics

Chapter 3: Visualizing and Exploring Data 53 Learning Objectives 53 Data Visualization 54

Dashboards 55 • Tools and Software for Data Visualization 55 Creating Charts in Microsoft Excel 56

Column and Bar Charts 57 • Data Labels and Data Tables Chart Options 59 • Line Charts 59 • Pie Charts 59 • Area Charts 60 • Scatter Chart 60 • Bubble Charts 62 • Miscellaneous Excel Charts 63 • Geographic Data 63

Other Excel Data Visualization Tools 64 Data Bars, Color Scales, and Icon Sets 64 • Sparklines 65 • Excel Camera Tool 66

Data Queries: Tables, Sorting, and Filtering 67 Sorting Data in Excel 68 • Pareto Analysis 68 • Filtering Data 70

Statistical Methods for Summarizing Data 72 Frequency Distributions for Categorical Data 73 • Relative Frequency Distributions 74 • Frequency Distributions for Numerical Data 75 • Excel Histogram Tool 75 • Cumulative Relative Frequency Distributions 79 • Percentiles and Quartiles 80 • Cross-Tabulations 82

Exploring Data Using PivotTables 84 PivotCharts 86 • Slicers and PivotTable Dashboards 87

Key Terms 90 • Problems and Exercises 91 • Case: Drout Advertising Research Project 93 • Case: Performance Lawn Equipment 94

Chapter 4: Descriptive Statistical Measures 95 Learning Objectives 95 Populations and Samples 96

Understanding Statistical Notation 96 Measures of Location 97

Arithmetic Mean 97 • Median 98 • Mode 99 • Midrange 99 • Using Measures of Location in Business Decisions 100

Measures of Dispersion 101 Range 101 • Interquartile Range 101 • Variance 102 • Standard Deviation 103 • Chebyshev’s Theorem and the Empirical Rules 104 • Standardized Values 107 • Coefficient of Variation 108

Measures of Shape 109 Excel Descriptive Statistics Tool 110 Descriptive Statistics for Grouped Data 112 Descriptive Statistics for Categorical Data: The Proportion 114 Statistics in PivotTables 114

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Measures of Association 115 Covariance 116 • Correlation 117 • Excel Correlation Tool 119

Outliers 120 Statistical Thinking in Business Decisions 122

Variability in Samples 123

Key Terms 125 • Problems and Exercises 126 • Case: Drout Advertising Research Project 129 • Case: Performance Lawn Equipment 129

Chapter 5: Probability Distributions and Data Modeling 131 Learning Objectives 131 Basic Concepts of Probability 132

Probability Rules and Formulas 134 • Joint and Marginal Probability 135 • Conditional Probability 137

Random Variables and Probability Distributions 140 Discrete Probability Distributions 142

Expected Value of a Discrete Random Variable 143 • Using Expected Value in Making Decisions 144 • Variance of a Discrete Random Variable 146 • Bernoulli Distribution 147 • Binomial Distribution 147 • Poisson Distribution 149

Continuous Probability Distributions 150 Properties of Probability Density Functions 151 • Uniform Distribution 152 • Normal Distribution 154 • The NORM.INV Function 156 • Standard Normal Distribution 156 • Using Standard Normal Distribution Tables 158 • Exponential Distribution 158 • Other Useful Distributions 160 • Continuous Distributions 160

Random Sampling from Probability Distributions 161 Sampling from Discrete Probability Distributions 162 • Sampling from Common Probability Distributions 163 • Probability Distribution Functions in Analytic Solver Platform 166

Data Modeling and Distribution Fitting 168 Goodness of Fit 170 • Distribution Fitting with Analytic Solver Platform 170

Key Terms 172 • Problems and Exercises 173 • Case: Performance Lawn Equipment 179

Chapter 6: Sampling and Estimation 181 Learning Objectives 181 Statistical Sampling 182

Sampling Methods 182 Estimating Population Parameters 185

Unbiased Estimators 186 • Errors in Point Estimation 186 Sampling Error 187

Understanding Sampling Error 187

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Sampling Distributions 189 Sampling Distribution of the Mean 189 • Applying the Sampling Distribution of the Mean 190

Interval Estimates 190 Confidence Intervals 191

Confidence Interval for the Mean with Known Population Standard Deviation 192 • The t-Distribution 193 • Confidence Interval for the Mean with Unknown Population Standard Deviation 194 • Confidence Interval for a Proportion 194 • Additional Types of Confidence Intervals 196

Using Confidence Intervals for Decision Making 196 Prediction Intervals 197 Confidence Intervals and Sample Size 198

Key Terms 200 • Problems and Exercises 200 • Case: Drout Advertising Research Project 202 • Case: Performance Lawn Equipment 203

Chapter 7: Statistical Inference 205 Learning Objectives 205 Hypothesis Testing 206

Hypothesis-Testing Procedure 207 One-Sample Hypothesis Tests 207

Understanding Potential Errors in Hypothesis Testing 208 • Selecting the Test Statistic 209 • Drawing a Conclusion 210

Two-Tailed Test of Hypothesis for the Mean 212 p-Values 212 • One-Sample Tests for Proportions 213 • Confidence Intervals and Hypothesis Tests 214

Two-Sample Hypothesis Tests 215 Two-Sample Tests for Differences in Means 215 • Two-Sample Test for Means with Paired Samples 218 • Test for Equality of Variances 219

Analysis of Variance (ANOVA) 221 Assumptions of ANOVA 223

Chi-Square Test for Independence 224 Cautions in Using the Chi-Square Test 226

Key Terms 227 • Problems and Exercises 228 • Case: Drout Advertising Research Project 231 • Case: Performance Lawn Equipment 231

Part 3: Predictive Analytics

Chapter 8: Trendlines and Regression Analysis 233 Learning Objectives 233 Modeling Relationships and Trends in Data 234 Simple Linear Regression 238

Finding the Best-Fitting Regression Line 239 • Least-Squares Regression 241 Simple Linear Regression with Excel 243 • Regression as Analysis of Variance 245 • Testing Hypotheses for Regression Coefficients 245 • Confidence Intervals for Regression Coefficients 246

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Residual Analysis and Regression Assumptions 246 Checking Assumptions 248

Multiple Linear Regression 249 Building Good Regression Models 254

Correlation and Multicollinearity 256 • Practical Issues in Trendline and Regression Modeling 257

Regression with Categorical Independent Variables 258 Categorical Variables with More Than Two Levels 261

Regression Models with Nonlinear Terms 263 Advanced Techniques for Regression Modeling using XLMiner 265

Key Terms 268 • Problems and Exercises 268 • Case: Performance Lawn Equipment 272

Chapter 9: Forecasting Techniques 273 Learning Objectives 273 Qualitative and Judgmental Forecasting 274

Historical Analogy 274 • The Delphi Method 275 • Indicators and Indexes 275 Statistical Forecasting Models 276 Forecasting Models for Stationary Time Series 278

Moving Average Models 278 • Error Metrics and Forecast Accuracy 282 • Exponential Smoothing Models 284

Forecasting Models for Time Series with a Linear Trend 286 Double Exponential Smoothing 287 • Regression-Based Forecasting for Time Series with a Linear Trend 288

Forecasting Time Series with Seasonality 290 Regression-Based Seasonal Forecasting Models 290 • Holt-Winters Forecasting for Seasonal Time Series 292 • Holt-Winters Models for Forecasting Time Series with Seasonality and Trend 292

Selecting Appropriate Time-Series-Based Forecasting Models 294 Regression Forecasting with Causal Variables 295 The Practice of Forecasting 296

Key Terms 298 • Problems and Exercises 298 • Case: Performance Lawn Equipment 300

Chapter 10: Introduction to Data Mining 301 Learning Objectives 301 The Scope of Data Mining 303 Data Exploration and Reduction 304

Sampling 304 • Data Visualization 306 • Dirty Data 308 • Cluster Analysis 310

Classification 315 An Intuitive Explanation of Classification 316 • Measuring Classification Performance 316 • Using Training and Validation Data 318 • Classifying New Data 320

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Classification Techniques 320 k-Nearest Neighbors (k-NN) 321 • Discriminant Analysis 324 • Logistic Regression 327 • Association Rule Mining 331

Cause-and-Effect Modeling 334

Key Terms 338 • Problems and Exercises 338 • Case: Performance Lawn Equipment 340

Chapter 11: Spreadsheet Modeling and Analysis 341 Learning Objectives 341 Strategies for Predictive Decision Modeling 342

Building Models Using Simple Mathematics 342 • Building Models Using Influence Diagrams 343

Implementing Models on Spreadsheets 344 Spreadsheet Design 344 • Spreadsheet Quality 346

Spreadsheet Applications in Business Analytics 349 Models Involving Multiple Time Periods 351 • Single-Period Purchase Decisions 353 • Overbooking Decisions 354

Model Assumptions, Complexity, and Realism 356 Data and Models 356

Developing User-Friendly Excel Applications 359 Data Validation 359 • Range Names 359 • Form Controls 360

Analyzing Uncertainty and Model Assumptions 362 What-If Analysis 362 • Data Tables 364 • Scenario Manager 366 • Goal Seek 367

Model Analysis Using Analytic Solver Platform 368 Parametric Sensitivity Analysis 368 • Tornado Charts 370

Key Terms 371 • Problems and Exercises 371 • Case: Performance Lawn Equipment 376

Chapter 12: Monte Carlo Simulation and Risk Analysis 377 Learning Objectives 377 Spreadsheet Models with Random Variables 379

Monte Carlo Simulation 379 Monte Carlo Simulation Using Analytic Solver Platform 381

Defining Uncertain Model Inputs 381 • Defining Output Cells 384 • Running a Simulation 384 • Viewing and Analyzing Results 386

New-Product Development Model 388 Confidence Interval for the Mean 391 • Sensitivity Chart 392 • Overlay Charts 392 • Trend Charts 394 • Box-Whisker Charts 394 • Simulation Reports 395

Newsvendor Model 395 The Flaw of Averages 395 • Monte Carlo Simulation Using Historical Data 396 • Monte Carlo Simulation Using a Fitted Distribution 397

Overbooking Model 398 The Custom Distribution in Analytic Solver Platform 399

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Cash Budget Model 400 Correlating Uncertain Variables 403

Key Terms 407 • Problems and Exercises 407 • Case: Performance Lawn Equipment 414

Part 4: Prescriptive Analytics

Chapter 13: Linear Optimization 415 Learning Objectives 415 Building Linear Optimization Models 416

Identifying Elements for an Optimization Model 416 • Translating Model Information into Mathematical Expressions 417 • More about Constraints 419 • Characteristics of Linear Optimization Models 420

Implementing Linear Optimization Models on Spreadsheets 420 Excel Functions to Avoid in Linear Optimization 422

Solving Linear Optimization Models 422 Using the Standard Solver 423 • Using Premium Solver 425 • Solver Answer Report 426

Graphical Interpretation of Linear Optimization 428 How Solver Works 433

How Solver Creates Names in Reports 435 Solver Outcomes and Solution Messages 435

Unique Optimal Solution 436 • Alternative (Multiple) Optimal Solutions 436 • Unbounded Solution 437 • Infeasibility 438

Using Optimization Models for Prediction and Insight 439 Solver Sensitivity Report 441 • Using the Sensitivity Report 444 • Parameter Analysis in Analytic Solver Platform 446

Key Terms 450 • Problems and Exercises 450 • Case: Performance Lawn Equipment 455

Chapter 14: Applications of Linear Optimization 457 Learning Objectives 457 Types of Constraints in Optimization Models 459 Process Selection Models 460

Spreadsheet Design and Solver Reports 461 Solver Output and Data Visualization 463 Blending Models 467

Dealing with Infeasibility 468 Portfolio Investment Models 471

Evaluating Risk versus Reward 473 • Scaling Issues in Using Solver 474 Transportation Models 476

Formatting the Sensitivity Report 478 • Degeneracy 480 Multiperiod Production Planning Models 480

Building Alternative Models 482 Multiperiod Financial Planning Models 485

Models with Bounded Variables 489 Auxiliary Variables for Bound Constraints 493

A Production/Marketing Allocation Model 495 Using Sensitivity Information Correctly 497

Key Terms 499 • Problems and Exercises 499 • Case: Performance Lawn Equipment 511

Chapter 15: Integer Optimization 513 Learning Objectives 513 Solving Models with General Integer Variables 514

Workforce-Scheduling Models 518 • Alternative Optimal Solutions 519 Integer Optimization Models with Binary Variables 523

Project-Selection Models 524 • Using Binary Variables to Model Logical Constraints 526 • Location Models 527 • Parameter Analysis 529 • A Customer-Assignment Model for Supply Chain Optimization 530

Mixed-Integer Optimization Models 533 Plant Location and Distribution Models 533 • Binary Variables, IF Functions, and Nonlinearities in Model Formulation 534 • Fixed-Cost Models 536

Key Terms 538 • Problems and Exercises 538 • Case: Performance Lawn Equipment 547

Chapter 16: Decision Analysis 553 Learning Objectives 553 Formulating Decision Problems 555 Decision Strategies without Outcome Probabilities 556

Decision Strategies for a Minimize Objective 556 • Decision Strategies for a Maximize Objective 557 • Decisions with Conflicting Objectives 558

Decision Strategies with Outcome Probabilities 560 Average Payoff Strategy 560 • Expected Value Strategy 560 • Evaluating Risk 561

Decision Trees 562 Decision Trees and Monte Carlo Simulation 566 • Decision Trees and Risk 566 • Sensitivity Analysis in Decision Trees 568

The Value of Information 569 Decisions with Sample Information 570 • Bayes’s Rule 570

Utility and Decision Making 572 Constructing a Utility Function 573 • Exponential Utility Functions 576

Key Terms 578 • Problems and Exercises 578 • Case: Performance Lawn Equipment 582

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Supplementary Chapter A (online) Nonlinear and Non-Smooth Optimization

Supplementary Chapter B (online) Optimization Models with Uncertainty

Online chapters are available for download at www.pearsonhighered.com/evans.

Appendix A 585 Glossary 609 Index 617

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In 2007, Thomas H. Davenport and Jeanne G. Harris wrote a groundbreaking book, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press). They described how many organizations are using analytics strategically to make better decisions and improve customer and shareholder value. Over the past several years, we have seen remarkable growth in analytics among all types of organizations. The In- stitute for Operations Research and the Management Sciences (INFORMS) noted that analytics software as a service is predicted to grow three times the rate of other business segments in upcoming years.1 In addition, the MIT Sloan Management Review in collabo- ration with the IBM Institute for Business Value surveyed a global sample of nearly 3,000 executives, managers, and analysts.2 This study concluded that top-performing organiza- tions use analytics five times more than lower performers, that improvement of informa- tion and analytics was a top priority in these organizations, and that many organizations felt they were under significant pressure to adopt advanced information and analytics approaches. Since these reports were published, the interest in and the use of analytics has grown dramatically.

In reality, business analytics has been around for more than a half-century. Business schools have long taught many of the core topics in business analytics—statistics, data analysis, information and decision support systems, and management science. However, these topics have traditionally been presented in separate and independent courses and supported by textbooks with little topical integration. This book is uniquely designed to present the emerging discipline of business analytics in a unified fashion consistent with the contemporary definition of the field.

About the Book

This book provides undergraduate business students and introductory graduate students with the fundamental concepts and tools needed to understand the emerging role of business analytics in organizations, to apply basic business analytics tools in a spread- sheet environment, and to communicate with analytics professionals to effectively use and interpret analytic models and results for making better business decisions. We take a balanced, holistic approach in viewing business analytics from descriptive, predictive, and prescriptive perspectives that today define the discipline.

Preface

1Anne Robinson, Jack Levis, and Gary Bennett, INFORMS News: INFORMS to Officially Join Analyt- ics Movement. http://www.informs.org/ORMS-Today/Public-Articles/October-Volume-37-Number-5/ INFORMS-News-INFORMS-to-Officially-Join-Analytics-Movement. 2“Analytics: The New Path to Value,” MIT Sloan Management Review Research Report, Fall 2010.

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xviii Preface

This book is organized in five parts.

1. Foundations of Business Analytics

The first two chapters provide the basic foundations needed to understand busi- ness analytics, and to manipulate data using Microsoft Excel.

2. Descriptive Analytics

Chapters 3 through 7 focus on the fundamental tools and methods of data analysis and statistics, focusing on data visualization, descriptive statistical mea- sures, probability distributions and data modeling, sampling and estimation, and statistical inference. We subscribe to the American Statistical Association’s recommendations for teaching introductory statistics, which include emphasiz- ing statistical literacy and developing statistical thinking, stressing conceptual understanding rather than mere knowledge of procedures, and using technology for developing conceptual understanding and analyzing data. We believe these goals can be accomplished without introducing every conceivable technique into an 800–1,000 page book as many mainstream books currently do. In fact, we cover all essential content that the state of Ohio has mandated for undergraduate business statistics across all public colleges and universities.

3. Predictive Analytics

In this section, Chapters 8 through 12 develop approaches for applying regression, forecasting, and data mining techniques, building and analyzing predictive mod- els on spreadsheets, and simulation and risk analysis.

4. Prescriptive Analytics

Chapters 13 through 15, along with two online supplementary chapters, explore linear, integer, and nonlinear optimization models and applications, including optimization with uncertainty.

5. Making Decisions

Chapter 16 focuses on philosophies, tools, and techniques of decision analysis.

The second edition has been carefully revised to improve both the content and pedagogical organization of the material. Specifically, this edition has a much stronger emphasis on data visualization, incorporates the use of additional Excel tools, new features of Analytic Solver Platform for Education, and many new data sets and problems. Chapters 8 through 12 have been re-ordered from the first edi- tion to improve the logical flow of the topics and provide a better transition to spreadsheet modeling and applications.

Features of the Book

• Numbered Examples—numerous, short examples throughout all chapters illus- trate concepts and techniques and help students learn to apply the techniques and understand the results.

• “Analytics in Practice”—at least one per chapter, this feature describes real applications in business.

• Learning Objectives—lists the goals the students should be able to achieve after studying the chapter.

Preface xix

• Key Terms—bolded within the text and listed at the end of each chapter, these words will assist students as they review the chapter and study for exams. Key terms and their definitions are contained in the glossary at the end of the book.

• End-of-Chapter Problems and Exercises—help to reinforce the material cov- ered through the chapter.

• Integrated Cases—allows students to think independently and apply the relevant tools at a higher level of learning.

• Data Sets and Excel Models—used in examples and problems and are available to students at www.pearsonhighered.com/evans.

Software Support

While many different types of software packages are used in business analytics applica- tions in the industry, this book uses Microsoft Excel and Frontline Systems’ powerful Excel add-in, Analytic Solver Platform for Education, which together provide exten- sive capabilities for business analytics. Many statistical software packages are available and provide very powerful capabilities; however, they often require special (and costly) licenses and additional learning requirements. These packages are certainly appropriate for analytics professionals and students in master’s programs dedicated to preparing such professionals. However, for the general business student, we believe that Microsoft Ex- cel with proper add-ins is more appropriate. Although Microsoft Excel may have some deficiencies in its statistical capabilities, the fact remains that every business student will use Excel throughout their careers. Excel has good support for data visualization, basic statistical analysis, what-if analysis, and many other key aspects of business analytics. In fact, in using this book, students will gain a high level of proficiency with many features of Excel that will serve them well in their future careers. Furthermore Frontline Systems’ Analytic Solver Platform for Education Excel add-ins are integrated throughout the book. This add-in, which is used among the top business organizations in the world, provides a comprehensive coverage of many other business analytics topics in a common platform. This add-in provides support for data modeling, forecasting, Monte Carlo simulation and risk analysis, data mining, optimization, and decision analysis. Together with Excel, it provides a comprehensive basis to learn business analytics effectively.

To the Students

To get the most out of this book, you need to do much more than simply read it! Many ex- amples describe in detail how to use and apply various Excel tools or add-ins. We highly recommend that you work through these examples on your computer to replicate the out- puts and results shown in the text. You should also compare mathematical formulas with spreadsheet formulas and work through basic numerical calculations by hand. Only in this fashion will you learn how to use the tools and techniques effectively, gain a better under- standing of the underlying concepts of business analytics, and increase your proficiency in using Microsoft Excel, which will serve you well in your future career.

Visit the Companion Web site (www.pearsonhighered.com/evans) for access to the following:

• Online Files: Data Sets and Excel Models—files for use with the numbered examples and the end-of-chapter problems (For easy reference, the relevant file names are italicized and clearly stated when used in examples.)

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• Software Download Instructions: Access to Analytic Solver Platform for Education—a free, semester-long license of this special version of Frontline Systems’ Analytic Solver Platform software for Microsoft Excel.

Integrated throughout the book, Frontline Systems’ Analytic Solver Platform for Educa- tion Excel add-in software provides a comprehensive basis to learn business analytics effectively that includes:

• Risk Solver Pro—This program is a tool for risk analysis, simulation, and optimi- zation in Excel. There is a link where you will learn more about this software at www.solver.com.

• XLMiner—This program is a data mining add-in for Excel. There is a link where you will learn more about this software at www.solver.com/xlminer.

• Premium Solver Platform, a large superset of Premium Solver and by far the most powerful spreadsheet optimizer, with its PSI interpreter for model analysis and five built-in Solver Engines for linear, quadratic, SOCP, mixed-integer, nonlinear, non-smooth and global optimization.

• Ability to solve optimization models with uncertainty and recourse decisions, using simulation optimization, stochastic programming, robust optimization, and stochastic decomposition.

• New integrated sensitivity analysis and decision tree capabilities, developed in cooperation with Prof. Chris Albright (SolverTable), Profs. Stephen Powell and Ken Baker (Sensitivity Toolkit), and Prof. Mike Middleton (TreePlan).

• A special version of the Gurobi Solver—the ultra-high-performance linear mixed- integer optimizer created by the respected computational scientists at Gurobi Optimization.

To register and download the software successfully, you will need a Texbook Code and a Course Code. The Textbook Code is EBA2 and your instructor will provide the Course Code. This download includes a 140-day license to use the software. Visit www.pearson- highered.com/evans for complete download instructions.

To the Instructors

Instructor’s Resource Center—Reached through a link at www.pearsonhighered.com/ evans, the Instructor’s Resource Center contains the electronic files for the complete In- structor’s Solutions Manual, PowerPoint lecture presentations, and the Test Item File.

• Register, redeem, log in at www.pearsonhighered.com/irc, instructors can ac- cess a variety of print, media, and presentation resources that are available with this book in downloadable digital format. Resources are also available for course management platforms such as Blackboard, WebCT, and CourseCompass.

• Need help? Pearson Education’s dedicated technical support team is ready to as- sist instructors with questions about the media supplements that accompany this text. Visit http://247pearsoned.com for answers to frequently asked questions and toll-free user support phone numbers. The supplements are available to adopting instructors. Detailed descriptions are provided at the Instructor’s Resource Center.

• Instructor’s Solutions Manual—The Instructor’s Solutions Manual, updated and revised for the second edition by the author, includes Excel-based solu- tions for all end-of-chapter problems, exercises, and cases. The Instructor’s

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Solutions Manual is available for download by visiting www.pearsonhighered. com/evans and clicking on the Instructor Resources link.

• PowerPoint presentations—The PowerPoint slides, revised and updated by the author, are available for download by visiting www.pearsonhighered.com/ evans and clicking on the Instructor Resources link. The PowerPoint slides provide an instructor with individual lecture outlines to accompany the text. The slides include nearly all of the figures, tables, and examples from the text. Instructors can use these lecture notes as they are or can easily modify the notes to reflect specific presentation needs.

• Test Bank—The TestBank, prepared by Paolo Catasti from Virginia Common- wealth University, is available for download by visiting www.pearsonhighered. com/evans and clicking on the Instructor Resources link.

• Analytic Solver Platform for Education (ASPE)—This is a special version of Frontline Systems’ Analytic Solver Platform software for Microsoft Excel. For further information on Analytic Solver Platform for Education, contact Frontline Systems at (888) 831–0333 (U.S. and Canada), 775-831-0300, or ac- ademic@solver.com. They will be pleased to provide free evaluation licenses to faculty members considering adoption of the software, and create a unique Course Code for your course, which your students will need to download the software. They can help you with conversion of simulation models you might have created with other software to work with Analytic Solver Platform (it’s very straightforward).

Acknowledgements

I would like to thank the staff at Pearson Education for their professionalism and dedication to making this book a reality. In particular, I want to thank Kerri Consalvo, Tatiana Anacki, Erin Kelly, Nicholas Sweeney, and Patrick Barbera; Jen Carley at Lumina Datamatics Ltd.; accuracy checker Annie Puciloski; and solutions checker Regina Krahenbuhl for their out- standing contributions to producing this book. I also want to acknowledge Daniel Fylstra and his staff at Frontline Systems for working closely with me to allow this book to have been the first to include XLMiner with Analytic Solver Platform. If you have any sugges- tions or corrections, please contact the author via email at james.evans@uc.edu.

James R. Evans Department of Operations, Business Analytics, and Information Systems University of Cincinnati Cincinnati, Ohio

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James R. Evans Professor, University of Cincinnati College of Business

James R. Evans is professor in the Department of Operations, Business Analytics, and Information Systems in the College of Business at the University of Cincinnati. He holds BSIE and MSIE degrees from Purdue and a PhD in Industrial and Systems Engineering from Georgia Tech.

Dr. Evans has published numerous textbooks in a variety of business disciplines, in- cluding statistics, decision models, and analytics, simulation and risk analysis, network optimization, operations management, quality management, and creative thinking. He has published over 90 papers in journals such as Management Science, IIE Transactions, Decision Sciences, Interfaces, the Journal of Operations Management, the Quality Man- agement Journal, and many others, and wrote a series of columns in Interfaces on creativ- ity in management science and operations research during the 1990s. He has also served on numerous journal editorial boards and is a past-president and Fellow of the Decision Sciences Institute. In 1996, he was an INFORMS Edelman Award Finalist as part of a project in supply chain optimization with Procter & Gamble that was credited with help- ing P&G save over $250,000,000 annually in their North American supply chain, and consulted on risk analysis modeling for Cincinnati 2012’s Olympic Games bid proposal.

A recognized international expert on quality management, he served on the Board of Examiners and the Panel of Judges for the Malcolm Baldrige National Quality Award. Much of his current research focuses on organizational performance excellence and mea- surement practices.

About the Author

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Credits

Text Credits

Chapter 1 Pages 2–3 “The Cincinnati Zoo & Botanical Garden” from Cincinnati Zoo Transforms Customer Experience and Boosts Profits, Copyright © 2012. Used by permis- sion of IBM Corporation. Pages 4–5 “Common Types of Decisions that can be Enhanced by Using Analytics” by Thomas H. Davenport from How Organizations Make Better Decisions. Published by SAS Institute, Inc. Pages 10–11 Analytics in the Home Lending and Mortgage Industry by Craig Zielazny. Used by permission of Craig Zielazny. Page 26 Excerpt by Thomas Olavson, Chris Fry from Spreadsheet Decision-Support Tools: Lessons Learned at Hewlett-Packard. Published by Interfaces. Pages 29–30 Analytics in Practice: Developing Effective Analytical Tools at Hewlett-Packard: Thomas Olvason; Chris Fry; Interfaces Page 33 Drout Advertising Research Project by Jamie Drout. Used by permis- sion of Jamie Drout.

Chapter 5 Page 151 Excerpt by Chris K. Anderson from Setting Prices on Priceline. Published by Interfaces.

Chapter 7 Page 227 Help Desk Service Improvement Project by Francisco Endara M from Help Desk Improves Service and Saves Money With Six Sigma. Used by permission of The American Society for Quality.

Chapter 12 Pages 410–411 Implementing Large-Scale Monte Carlo Spreadsheet Models by Yusuf Jafry from Hypo International Strengthens Risk Management with a Large-Scale, Secure Spreadsheet-Management Framework. Published by Interfaces, © 2008.

Chapter 13 Pages 452–453 Excerpt by Srinivas Bollapragada from NBC’s Optimiza- tion Systems Increase Revenues and Productivity. Copyright © 2002. Used by permission of Interfaces.

Chapter 15 Pages 536–537 Supply Chain Optimization at Procter & Gamble by Jeffrey D. Camm from Blending OR/MS, Judgment, and GIS: Restructuring P&G’s Supply Chain. Published by Interfaces, © 1997.

Chapter 16 Pages 580–581 Excerpt from How Bayer Makes Decisions to Develop New Drugs by Jeffrey S Stonebraker. Published by Interfaces.

Photo Credits

Chapter 1 Page 1 Analytics Business Analysis: Mindscanner/Fotolia Page 30 Computer, calculator, and spreadsheet: Hans12/Fotolia

Chapter 2 Page 37 Computer with Spreadsheet: Gunnar Pippel/Shutterstock

xxvi Credits

Chapter 3 Page 53 Spreadsheet with magnifying glass: Poles/Fotolia Page 72 Data Analysis: 2jenn/Shutterstock

Chapter 4 Page 95 Pattern of colorful numbers: JonnyDrake/Shutterstock Page 125 Computer screen with financial data: NAN728/Shutterstock

Chapter 5 Page 131 Faded spreadsheet: Fantasista/Fotolia Page 151 Probability and cost graph with pencil: Fantasista/Fotolia Page 172 Business concepts: Victor Correia/ Shutterstock

Chapter 6 Page 181 Series of bar graphs: Kalabukhava Iryna/Shutterstock Page 185 Brewery truck: Stephen Finn/Shutterstock

Chapter 7 Page 205 Business man solving problems with illustrated graph display: Serg Nvns/Fotolia Page 227 People working at a helpdesk: StockLite/Shutterstock

Chapter 8 Page 233 Trendline 3D graph: Sheelamohanachandran/Fotolia Page 253 Computer and Risk: Gunnar Pippel/Shutterstock Page 254C 4 blank square shape naviga- tion web 2.0 button slider: Claudio Divizia/Shutterstock Page 254L Graph chart illustra- tions of growth and recession: Vector Illustration/Shutterstock Page 254R Audio gauge: Shutterstock

Chapter 9 Page 273 Past and future road sign: Karen Roach/Fotolia Page 298 NBC Studios: Sean Pavone/Dreamstine

Chapter 10 Page 301 Data Mining Technology Strategy Concept: Kentoh/Shutterstock Page 337 Business man drawing a marketing diagram: Helder Almeida/Shutterstock

Chapter 11 Page 341 3D spreadsheet: Dmitry/Fotolia Page 349 Buildings: ZUMA Press/Newscom Page 355 Health Clinic: Poprostskiy Alexey/Shutterstock

Chapter 12 Page 377 Analyzing Risk in Business: iQoncept/Shutterstock Page 406 Office Building: Verdeskerde/Shutterstock

Chapter 13 Page 415 3D spreadsheet, graph, pen: Archerix/Shutterstock Page 449 Television acting sign: Bizoo_n/Fotolia

Chapter 14 Page 457 People working on spreadsheets: Pressmaster/Shutterstock Page 489 Colored Stock Market Chart: 2jenn/Shutterstock

Chapter 15 Page 513 Brainstorming Concept: Dusit/Shutterstock Page 523 Qantas Air- bus A380: Gordon Tipene/Dreamstine Page 533 Supply chain concept: Kheng Guan Toh/ Shutterstock

Chapter 16 Page 553 Person at crossroads: Michael D Brown/Shutterstock Page 578 Collage of several images from a drug store: Sokolov/Shutterstock

Supplementary Chapter A (online) Page 1 Various discount tags and labels: little Whale/Shutterstock Page 9 Red Cross facility: Littleny/Dreamstine

Supplementary Chapter B (online) Page 1 Confused man thinking over right deci- sion: StockThings/Shutterstock Page 7 Lockheed Constellation Cockpit: Brad Whitsitt/ Shutterstock

1

Learning Objectives

After studying this chapter, you will be able to:

• Define business analytics. • Explain why analytics is important in today’s business

environment.

• State some typical examples of business applications in which analytics would be beneficial.

• Summarize the evolution of business analytics and explain the concepts of business intelligence, operations research and management science, and decision support systems.

• Explain and provide examples of descriptive, predictive, and prescriptive analytics.

• State examples of how data are used in business. • Explain the difference between a data set and a

database.

• Define a metric and explain the concepts of measurement and measures.

• Explain the difference between a discrete metric and continuous metric, and provide examples of each.

• Describe the four groups of data classification, categorical, ordinal, interval, and ratio, and provide examples of each.

• Explain the concept of a model and various ways a model can be characterized.

• Define and list the elements of a decision model. • Define and provide an example of an influence

diagram.

• Use influence diagrams to build simple mathematical models.

• Use predictive models to compute model outputs. • Explain the difference between uncertainty and risk. • Define the terms optimization, objective function, and

optimal solution.

• Explain the difference between a deterministic and stochastic decision model.

• List and explain the steps in the problem-solving process.

Introduction to Business Analytics1Chapte

r

2 Chapter 1 Introduction to Business Analytics

Most of you have likely been to a zoo, seen the animals, had something to eat, and bought some souvenirs. You probably wouldn’t think that managing

a zoo is very difficult; after all, it’s just feeding and taking care of the ani-

mals, right? A zoo might be the last place that you would expect to find busi-

ness analytics being used, but not anymore. The Cincinnati Zoo & Botanical

Garden has been an “early adopter” and one of the first organizations of its

kind to exploit business analytics.1

Despite generating more than two-thirds of its budget through its own

fund-raising efforts, the zoo wanted to reduce its reliance on local tax subsidies

even further by increasing visitor attendance and revenues from secondary

sources such as membership, food and retail outlets. The zoo’s senior man-

agement surmised that the best way to realize more value from each visit was

to offer visitors a truly transformed customer experience. By using business

analytics to gain greater insight into visitors’ behavior and tailoring operations

to their preferences, the zoo expected to increase attendance, boost member-

ship, and maximize sales.

The project team—which consisted of consultants from IBM and BrightStar

Partners, as well as senior executives from the zoo—began translating the

organization’s goals into technical solutions. The zoo worked to create a

business analytics platform that was capable of delivering the desired goals

by combining data from ticketing and point-of-sale systems throughout the

zoo with membership information and geographical data gathered from the

ZIP codes of all visitors. This enabled the creation of reports and dashboards

that give everyone from senior managers to zoo staff access to real-time

information that helps them optimize operational management and transform

the customer experience.

By integrating weather forecast data, the zoo is able to compare current

forecasts with historic attendance and sales data, supporting better decision-

making for labor scheduling and inventory planning. Another area where the

solution delivers new insight is food service. By opening food outlets at spe-

cific times of day when demand is highest (for example, keeping ice cream

kiosks open in the final hour before the zoo closes), the zoo has been able

to increase sales significantly. The zoo has been able to increase attendance

and revenues dramatically, resulting in annual ROI of 411%. The business

1Source: IBM Software Business Analtyics, “Cincinnati Zoo transforms customer experience and boosts profits,” © IBM Corporation 2012.

Chapter 1 Introduction to Business Analytics 3

analytics initiative paid for itself within three months, and delivers, on aver-

age, benefits of $738,212 per year. Specifically,

• The zoo has seen a 4.2% rise in ticket sales by targeting potential visitors who live in specific ZIP codes.

• Food revenues increased by 25% by optimizing the mix of products on sale and adapting selling practices to match peak purchase times.

• Eliminating slow-selling products and targeting visitors with specific promotions enabled an 18% increase in merchandise sales.

• Cut marketing expenditure, saving $40,000 in the first year, and reduced advertising expenditure by 43% by eliminating ineffective campaigns and

segmenting customers for more targeted marketing.

Because of the zoo’s success, other organizations such as Point Defiance

Zoo & Aquarium, in Washington state, and History Colorado, a museum in

Denver, have embarked on similar initiatives.

In recent years, analytics has become increasingly important in the world

of business, particularly as organizations have access to more and more data.

Managers today no longer make decisions based on pure judgment and experi-

ence; they rely on factual data and the ability to manipulate and analyze data to

support their decisions. As a result, many companies have recently established

analytics departments; for instance, IBM reorganized its consulting business

and established a new 4,000-person organization focusing on analytics.2 Com-

panies are increasingly seeking business graduates with the ability to under-

stand and use analytics. In fact, in 2011, the U.S. Bureau of Labor Statistics

predicted a 24% increase in demand for professionals with analytics expertise.

No matter what your academic business concentration is, you will most

likely be a future user of analytics to some extent and work with analytics pro-

fessionals. The purpose of this book is to provide you with a basic introduc-

tion to the concepts, methods, and models used in business analytics so that

you will develop not only an appreciation for its capabilities to support and

enhance business decisions, but also the ability to use business analytics at

an elementary level in your work. In this chapter, we introduce you to the field

of business analytics, and set the foundation for many of the concepts and

techniques that you will learn.

2Matthew J. Liberatore and Wenhong Luo, “The Analytics Movement: Implications for Operations Research,” Interfaces, 40, 4 (July–August 2010): 313–324.

4 Chapter 1 Introduction to Business Analytics

What Is Business Analytics?

Everyone makes decisions. Individuals face personal decisions such as choosing a college or graduate program, making product purchases, selecting a mortgage instrument, and investing for retirement. Managers in business organizations make numerous decisions every day. Some of these decisions include what products to make and how to price them, where to locate facilities, how many people to hire, where to allocate advertising budgets, whether or not to outsource a business function or make a capital investment, and how to schedule production. Many of these decisions have significant economic consequences; moreover, they are difficult to make because of uncertain data and imperfect information about the future. Thus, managers need good information and assistance to make such criti- cal decisions that will impact not only their companies but also their careers. What makes business decisions complicated today is the overwhelming amount of available data and information. Data to support business decisions—including those specifically collected by firms as well as through the Internet and social media such as Facebook—are growing exponentially and becoming increasingly difficult to understand and use. This is one of the reasons why analytics is important in today’s business environment.

Business analytics, or simply analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact- based decisions. Business analytics is “a process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solv- ing.”3 Business analytics is supported by various tools such as Microsoft Excel and various Excel add-ins, commercial statistical software packages such as SAS or Minitab, and more- complex business intelligence suites that integrate data with analytical software.

Tools and techniques of business analytics are used across many areas in a wide va- riety of organizations to improve the management of customer relationships, financial and marketing activities, human capital, supply chains, and many other areas. Leading banks use analytics to predict and prevent credit fraud. Manufacturers use analytics for produc- tion planning, purchasing, and inventory management. Retailers use analytics to recom- mend products to customers and optimize marketing promotions. Pharmaceutical firms use it to get life-saving drugs to market more quickly. The leisure and vacation indus- tries use analytics to analyze historical sales data, understand customer behavior, improve Web site design, and optimize schedules and bookings. Airlines and hotels use analytics to dynamically set prices over time to maximize revenue. Even sports teams are using busi- ness analytics to determine both game strategy and optimal ticket prices.4 Among the many organizations that use analytics to make strategic decisions and manage day-to-day opera- tions are Harrah’s Entertainment, the Oakland Athletics baseball and New England Patriots football teams, Amazon.com, Procter & Gamble, United Parcel Service (UPS), and Capital One bank. It was reported that nearly all firms with revenues of more than $100 million are using some form of business analytics.

Some common types of decisions that can be enhanced by using analytics include

• pricing (for example, setting prices for consumer and industrial goods, govern- ment contracts, and maintenance contracts),

• customer segmentation (for example, identifying and targeting key customer groups in retail, insurance, and credit card industries),

3Liberatore and Luo, “The Analytics Movement.” 4Jim Davis, “8 Essentials of Business Analytics,” in “Brain Trust—Enabling the Confident Enterprise with Business Analytics” (Cary, NC: SAS Institute, Inc., 2010): 27–29. www.sas.com/bareport

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Chapter 1 Introduction to Business Analytics 5

• merchandising (for example, determining brands to buy, quantities, and allocations),

• location (for example, finding the best location for bank branches and ATMs, or where to service industrial equipment),

and many others in operations and supply chains, finance, marketing, and human resources—in fact, in every discipline of business.5

Various research studies have discovered strong relationships between a company’s performance in terms of profitability, revenue, and shareholder return and its use of analyt- ics. Top-performing organizations (those that outperform their competitors) are three times more likely to be sophisticated in their use of analytics than lower performers and are more likely to state that their use of analytics differentiates them from competitors.6 However, re- search has also suggested that organizations are overwhelmed by data and struggle to under- stand how to use data to achieve business results and that most organizations simply don’t understand how to use analytics to improve their businesses. Thus, understanding the ca- pabilities and techniques of analytics is vital to managing in today’s business environment.

One of the emerging applications of analytics is helping businesses learn from social media and exploit social media data for strategic advantage.7 Using analytics, firms can integrate social media data with traditional data sources such as customer surveys, focus groups, and sales data; understand trends and customer perceptions of their products; and create informative reports to assist marketing managers and product designers.

Evolution of Business Analytics

Analytical methods, in one form or another, have been used in business for more than a century. However, the modern evolution of analytics began with the introduction of com- puters in the late 1940s and their development through the 1960s and beyond. Early com- puters provided the ability to store and analyze data in ways that were either very difficult or impossible to do so manually. This facilitated the collection, management, analysis, and reporting of data, which is often called business intelligence (BI), a term that was coined in 1958 by an IBM researcher, Hans Peter Luhn.8 Business intelligence software can an- swer basic questions such as “How many units did we sell last month?” “What products did customers buy and how much did they spend?” “How many credit card transactions were completed yesterday?” Using BI, we can create simple rules to flag exceptions au- tomatically, for example, a bank can easily identify transactions greater than $10,000 to report to the Internal Revenue Service.9 BI has evolved into the modern discipline we now call information systems (IS).

5Thomas H. Davenport, “How Organizations Make Better Decisions,” edited excerpt of an article dis- tributed by the International Institute for Analytics published in “Brain Trust—Enabling the Confident Enterprise with Business Analytics” (Cary, NC: SAS Institute, Inc., 2010): 8–11. www.sas.com/bareport 6Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics (Boston: Harvard Business School Press, 2007): 46; Michael S. Hopkins, Steve LaValle, Fred Balboni, Nina Kruschwitz, and Rebecca Shockley, “10 Data Points: Information and Analytics at Work,” MIT Sloan Management Review, 52, 1 (Fall 2010): 27–31. 7Jim Davis, “Convergence—Taking Social Media from Talk to Action,” SASCOM (First Quarter 2011): 17.

9Jim Davis, “Business Analytics: Helping You Put an Informed Foot Forward,” in “Brain Trust— Enabling the Confident Enterprise with Business Analytics,” (Cary, NC: SAS Institute, Inc., 2010): 4–7. www.sas .com/bareport

8H. P. Luhn, “A Business Intelligence System.” IBM Journal (October 1958).

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6 Chapter 1 Introduction to Business Analytics

Statistics has a long and rich history, yet only rather recently has it been recognized as an important element of business, driven to a large extent by the massive growth of data in today’s world. Google’s chief economist stated that statisticians surely have the “really sexy job” for the next decade.10 Statistical methods allow us to gain a richer understanding of data that goes beyond business intelligence reporting by not only sum- marizing data succinctly but also finding unknown and interesting relationships among the data. Statistical methods include the basic tools of description, exploration, estima- tion, and inference, as well as more advanced techniques like regression, forecasting, and data mining.

Much of modern business analytics stems from the analysis and solution of com- plex decision problems using mathematical or computer-based models—a discipline known as operations research, or management science. Operations research (OR) was born from efforts to improve military operations prior to and during World War II. After the war, scientists recognized that the mathematical tools and techniques developed for military applications could be applied successfully to problems in business and industry. A significant amount of research was carried on in public and private think tanks during the late 1940s and through the 1950s. As the focus on business applications expanded, the term management science (MS) became more prevalent. Many people use the terms operations research and management science interchangeably, and the field became known as Opera- tions Research/Management Science (OR/MS). Many OR/MS applications use modeling and optimization—techniques for translating real problems into mathematics, spreadsheets, or other computer languages, and using them to find the best (“optimal”) solutions and deci- sions. INFORMS, the Institute for Operations Research and the Management Sciences, is the leading professional society devoted to OR/MS and analytics, and publishes a bimonthly magazine called Analytics (http://analytics-magazine.com/). Digital subscriptions may be obtained free of charge at the Web site.