Business Analytics Principles, Concepts, and Applications
What, Why, and How
Published on 15. May 2014
Book
Hardback
368 pages
978-0-13-355218-8 (ISBN)
Description
Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives.
They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making.
Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.
Unlike most competitive guides, this text demonstrates the use of IBM's menu-based SPSS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.
A valuable resource for all beginning-to-intermediate-level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.
They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making.
Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.
Unlike most competitive guides, this text demonstrates the use of IBM's menu-based SPSS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.
A valuable resource for all beginning-to-intermediate-level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.
More details
Persons
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.
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.
Content
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
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