Business Analytics with Management Science Models and Methods

Arben Asllani is Marvin E. White Professor of Business Analytics at the University of Tennessee at Chattanooga. He has an M.A. and Ph. D. from the University of Nebraska at Lincoln and a B.S. degree from the University of Tirana, Albania. Dr. Asllani has been a member of the Decision Sciences Institute since 1997 and has joined several other traditional and online academic and practitioner-oriented conferences and organizations. He has won several faculty teaching and research awards and is a member of Alpha Honor Society at the University of Tennessee at Chattanooga. Dr. Asllani is Associate Editor of the American Journal of Business Research and serves on the editorial board of Service Business. Dr. Asllani has published more than 36 articles in journals including Omega, Transfusion, European Journal of Operational Research, Knowledge Management, Computers & Industrial Engineering, Total Quality Management and Business Excellence, and Service Business: An International Journal. He has also published and presented over 30 research papers at academic conferences. Dr. Asllani has a broad expertise in business analytics, especially in optimization techniques and computer-based simulations. He has served as a consultant and trainer to a variety of business and government agencies. Dr. Asllani has also taught extensively in management science, business analytics, and information systems courses, and has played an important role in developing business analytics programs in the United States and abroad.

Preface   xii
Chapter 1  Business Analytics with Management Science   1
   Chapter Objectives   1
   Prescriptive Analytics in Action: Success Stories   1
   Introduction   3
   Implementing Business Analytics   4
   Business Analytics Domain   5
   Challenges with Business Analytics   9
   Exploring Big Data with Prescriptive Analytics   14
   Wrap Up   16
   Review Questions   17
   Practice Problems   19
Chapter 2  Introduction to Linear Programming   23
   Chapter Objectives   23
   Prescriptive Analytics in Action: Chevron Optimizes Processing of Crude Oil   23
   Introduction   24
   LP Formulation   26
   Solving LP Models: A Graphical Approach   35
   Possible Outcome Solutions to LP Model   43
   Exploring Big Data with LP Models   53
   Wrap Up   55
   Review Questions   56
   Practice Problems   58
Chapter 3  Business Analytics with Linear Programming   65
   Chapter Objectives   65
   Prescriptive Analytics in Action: Nu-kote Minimizes Shipment Cost   66
   Introduction   66
   General Formulation of LP Models   68
   Formulating a Large LP Model   68
   Solving Linear Programming Models with Excel   77
   Big Optimizations with Big Data   86
   Wrap Up   87
   Review Questions   88
   Practice Problems   89
Chapter 4  Business Analytics with Nonlinear Programming   95
   Chapter Objectives   95
   Prescriptive Analytics in Action: Netherlands Increases Protection from Flooding   95
   Introduction   96
   Challenges to NLP Models   97
   Example 1: World Class Furniture   101
   Example 2: Optimizing an Investment Portfolio   110
   Exploring Big Data with Nonlinear Programming   117
   Wrap Up   118
   Review Questions   120
   Practice Problems   121
Chapter 5  Business Analytics with Goal Programming   127
   Chapter Objectives   127
   Prescriptive Analytics in Action: Airbus Uses Multi-Objective Optimization Models   128
   Introduction   129
   GP Formulation   130
   Example 1: Rolls Bakery Revisited   130
   Solving GP Models with Solver   139
   Example 2: World Class Furniture   142
   Exploring Big Data with Goal Programming   150
   Wrap Up   150
   Review Questions   152
   Practice Problems   153
Chapter 6  Business Analytics with Integer Programming   159
   Chapter Objectives   159
   Prescriptive Analytics in Action: Zara Uses Mixed IP Modeling   160
   Introduction   161
   Formulation and Graphical Solution of IP Models   161
   Types of Integer Programming Models   164
   Solving Integer LP Models with Solver   165
   Solving Nonlinear IP Models with Solver   167
   Solving Integer GP Models with Solver   169
   The Assignment Method   172
   The Knapsack Problem   179
   Exploring Big Data with Integer Programming   180
   Wrap Up   181
   Review Questions   182
   Practice Problems   183
Chapter 7  Business Analytics with Shipment Models   189
   Chapter Objectives   189
   Prescriptive Analytics in Action: Danaos Saves Time and Money with Shipment Models   190
   Introduction   190
   The Transportation Model   191
   The Transshipment Method   201
   Exploring Big Data with Shipment Models   208
   Wrap Up   209
   Review Questions   211
   Practice Problems   212
Chapter 8  Marketing Analytics with Linear Programming   223
   Chapter Objectives   223
   Prescriptive Analytics in Action: Hewlett Packard Increases Profit with Marketing Optimization Models   223
   Introduction   224
   RFM Overview   228
   RFM Analysis with Excel   231
   Optimizing RFM-Based Marketing Campaigns   237
   LP Models with Single RFM Dimension   238
   Marketing Analytics and Big Data   248
   Wrap Up   249
   Review Questions   250
   Practice Problems   251
Chapter 9  Marketing Analytics with Multiple Goals   259
   Chapter Objectives   259
   Prescriptive Analytics in Action: First Tennessee Bank Improves Marketing Campaigns   259
   Introduction   260
   LP Models with Two RFM Dimensions   261
   LP Model with Three Dimensions   279
   A Goal Programming Model for RFM   285
   Exploring Big Data with RFM Analytics   292
   Wrap Up   293
   Review Questions   293
   Practice Problems   294
Chapter 10  Business Analytics with Simulation   303
   Chapter Objectives   303
   Prescriptive Analytics in Action: Blood Assurance
   Uses Simulation to Manage Platelet Inventory   304
   Introduction   305
   Basic Simulation Terminology   305
   Simulation Methodology   308
   Simulation Methodology in Action   314
   Exploring Big Data with Simulation   319
   Wrap Up   319
   Review Questions   320
   Practice Problems   322
Appendix A  Excel Tools for the Management Scientist   329
   1: Shortcut Keys   329
   2: SUMIF   332
   3: AVERAGEIF   332
   4: COUNTIF   333
   5: IFERROR   333
   6: VLOOKUP or HLOOKUP   336
   7: TRANSPOSE   337
   8: SUMPRODUCT   338
   9: IF   340
   10: Pivot Table   343
Appendix B  A Brief Tour of Solver   349
   Setting Up Constraints and the Objective Function in Solver   349
   Selecting Solver Options   352
References   361
Index   369