Business Analytics Principles, Concepts, and Applications

Marc J. Schniederjans is the C. Wheaton Battey Distinguished Professor of Business in the College of Business Administration at the University of Nebraska-Lincoln and has served on the faculty of three other universities. Professor Schniederjans is a Fellow of the Decision Sciences Institute (DSI) and in 2014–2015 will serve as DSI’s President. His prior experience includes owning and operating his own truck leasing business. He is currently a member of the Institute of Supply Management (ISM), the Production and Operations Management Society (POMS), and Decision Sciences Institute (DSI). Professor Schniederjans has taught extensively in operations management and management science. He has won numerous teaching awards and is an honorary member of the Golden Key honor society and the Alpha Kappa Psi business honor society. He has published more than one hundred journal articles and has authored or coauthored twenty books in the field of management. The title of his most recent book is Reinventing the Supply Chain Life Cycle, and his research has encompassed a wide range of operations management and decision science topics. He has also presented more than one hundred research papers at academic meetings. Professor Schniederjans is serving on five journal editorial review boards, including Computers & Operations Research, International Journal of Information & Decision Sciences, International Journal of Information Systems in the Service Sector, and Journal of Operations Management, Production, and Operations Management. He is also serving as an area editor for the journal Operations Management Research and as an associate editor for the International Journal of Strategic Decision Sciences and International Journal of the Society Systems Science and Management Review: An International Journal (Korea). Professor Schniederjans has served as a consultant and trainer to various business and government agencies. Dara G. Schniederjans is an assistant professor of Supply Chain Management at the University of Rhode Island, College of Business Administration. She has published articles in journals such as Decision Support Systems, Journal of the Operational Research Society, and Business Process Management Journal. She has also co-authored two text books and co-edited a readings book. She has contributed chapters to readings utilizing quantitative and statistical methods. Dara has served as a guest co-editor for a special issue on Business Ethics in Social Sciences in the International Journal of Society Systems Science. She has also served as a website coordinator for Decisions Sciences Institute. She currently teaches courses in Supplier Relationship Management and Operations Management. Christopher M. Starkey is an Economics student at the University of Connecticut-Storrs. He has presented papers at the Academy of Management and Production and Operations Management Society meetings. He currently teaches courses in Principles of Microeconomics and has taught Principles of Macroeconomics. His current research interests include macroeconomic and monetary policy, as well as other decision-making methodologies.

Preface   xvi
PART I:  WHAT ARE BUSINESS ANALYTICS   1
Chapter 1:  What Are Business Analytics?   3
1.1 Terminology   3
1.2 Business Analytics Process   7
1.3 Relationship of BA Process and Organization Decision-Making   10
1.4 Organization of This Book   12
Summary   13
Discussion Questions   13
References    14

PART II:  WHY ARE BUSINESS ANALYTICS IMPORTANT    15
Chapter 2:  Why Are Business Analytics Important?    17
2.1 Introduction    17
2.2 Why BA Is Important: Providing Answers to Questions    18
2.3 Why BA Is Important: Strategy for Competitive Advantage    20
2.4 Other Reasons Why BA Is Important    23
   2.4.1 Applied Reasons Why BA Is Important    23
   2.4.2 The Importance of BA with New Sources of Data   24
Summary   26
Discussion Questions    26
References   26
Chapter 3:  What Resource Considerations Are Important to
Support Business Analytics?    29
3.1 Introduction   29
3.2 Business Analytics Personnel   30
3.3 Business Analytics Data   33
   3.3.1 Categorizing Data   33
   3.3.2 Data Issues   35
3.4 Business Analytics Technology   36
Summary   41
Discussion Questions   41
References   42

PART III:  HOW CAN BUSINESS ANALYTICS BE APPLIED   43
Chapter 4:  How Do We Align Resources to Support Business Analytics within an Organization?   45
4.1 Organization Structures Aligning Business Analytics   45
   4.1.1 Organization Structures   46
   4.1.2 Teams   51
4.2 Management Issues   54
   4.2.1 Establishing an Information Policy   54
   4.2.2 Outsourcing Business Analytics   55
   4.2.3 Ensuring Data Quality   56
   4.2.4 Measuring Business Analytics Contribution   58
   4.2.5 Managing Change   58
Summary   60
Discussion Questions   61
References . 61
Chapter 5:  What Are Descriptive Analytics?   63
5.1 Introduction   63
5.2 Visualizing and Exploring Data   64
5.3 Descriptive Statistics   67
5.4 Sampling and Estimation   72
   5.4.1 Sampling Methods   73
   5.4.2 Sampling Estimation   76
5.5 Introduction to Probability Distributions   78
5.6 Marketing/Planning Case Study Example: Descriptive Analytics Step in the BA Process   80
   5.6.1 Case Study Background   81
   5.6.2 Descriptive Analytics Analysis   82
Summary   91
Discussion Questions   91
Problems   92
Chapter 6:  What Are Predictive Analytics   93
6.1 Introduction   93
6.2 Predictive Modeling   94
   6.2.1 Logic-Driven Models   94
   6.2.2 Data-Driven Models   96
6.3 Data Mining   97
   6.3.1 A Simple Illustration of Data Mining   98
   6.3.2 Data Mining Methodologies   99
6.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Analytics Step in the BA Process   102
   6.4.1 Case Study Background Review   103
   6.4.2 Predictive Analytics Analysis   104
Summary   114
Discussion Questions   115
Problems   115
References   117
Chapter 7:  What Are Prescriptive Analytics?   119
7.1 Introduction   119
7.2 Prescriptive Modeling   120
7.3 Nonlinear Optimization   122
7.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Step in the BA Analysis   129
   7.4.1 Case Background Review   129
   7.4.2 Prescriptive Analysis   129
Summary   134
Addendum   134
Discussion Questions   135
Problems   135
References   .137
Chapter 8:  A Final Business Analytics Case Problem   139
8.1 Introduction   139
8.2 Case Study: Problem Background and Data   140
8.3 Descriptive Analytics Analysis   141
8.4 Predictive Analytics Analysis   147
   8.4.1 Developing the Forecasting Models   147
   8.4.2 Validating the Forecasting Models   155
   8.4.3 Resulting Warehouse Customer Demand Forecasts   157
8.5 Prescriptive Analytics Analysis   158
   8.5.1 Selecting and Developing an Optimization Shipping Model   158
   8.5.2 Determining the Optimal Shipping Schedule   159
   8.5.3 Summary of BA Procedure for the Manufacturer   161
   8.5.4 Demonstrating Business Performance Improvement   162
Summary   163
Discussion Questions   164
Problems   164

PART IV:  APPENDIXES   165
Appendix A:  Statistical Tools   167
A.1 Introduction   167
A.2 Counting   167
A.3 Probability Concepts   171
A.4 Probability Distributions   177
A.5 Statistical Testing   193
Appendix B:  Linear Programming   201
B.1 Introduction   201
B.2 Types of Linear Programming Problems/Models   201
B.3 Linear Programming Problem/Model Elements   202
B.4 Linear Programming Problem/Model Formulation Procedure   207
B.5 Computer-Based Solutions for Linear Programming
Using the Simplex Method   217
B.6 Linear Programming Complications   227
B.7 Necessary Assumptions for Linear Programming Models   232
B.8 Linear Programming Practice Problems   233
Appendix C:  Duality and Sensitivity Analysis in Linear Programming   241
C.1 Introduction   241
C.2 What Is Duality?   241
C.3 Duality and Sensitivity Analysis Problems   243
C.4 Determining the Economic Value of a Resource with Duality   258
C.5 Duality Practice Problems   259
Appendix D:  Integer Programming 263
D.1 Introduction   263
D.2 Solving IP Problems/Models   264
D.3 Solving Zero-One Programming Problems/Models   268
D.4 Integer Programming Practice Problems   270
Appendix E:  Forecasting   271
E.1 Introduction   271
E.2 Types of Variation in Time Series Data   272
E.3 Simple Regression Model   276
E.4 Multiple Regression Models   281
E.5 Simple Exponential Smoothing   284
E.6 Smoothing Averages   286
E.7 Fitting Models to Data   288
E.8 How to Select Models and Parameters for Models   291
E.9 Forecasting Practice Problems   292
Appendix F:  Simulation   295
F.1 Introduction   295
F.2 Types of Simulation   295
F.3 Simulation Practice Problems   302
Appendix G:  Decision Theory   303
G.1 Introduction   303
G.2 Decision Theory Model Elements   304
G.3 Types of Decision Environments   304
G.4 Decision Theory Formulation   305
G.5 Decision-Making Under Certainty   306
G.6 Decision-Making Under Risk   307
G.7 Decision-Making under Uncertainty   311
G.8 Expected Value of Perfect Information   315
G.9 Sequential Decisions and Decision Trees   317
G.10 The Value of Imperfect Information: Bayes’ Theorem   321
G.11 Decision Theory Practice Problems   328
Index   335