Business Analytics with Management Science Models and Methods
Pearson FT Press (Verlag)
978-0-13-376035-4 (ISBN)
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Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.
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
Erscheint lt. Verlag | 12.3.2015 |
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Verlagsort | NJ |
Sprache | englisch |
Maße | 162 x 236 mm |
Gewicht | 670 g |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
Technik ► Maschinenbau | |
Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion | |
Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
ISBN-10 | 0-13-376035-9 / 0133760359 |
ISBN-13 | 978-0-13-376035-4 / 9780133760354 |
Zustand | Neuware |
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