ISBN-10:
0133760359
ISBN-13:
2900133760353
Pub. Date:
11/28/2014
Publisher:
Pearson FT Press
Business Analytics with Management Science Models and Methods / Edition 1

Business Analytics with Management Science Models and Methods / Edition 1

by Arben Asllani
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  • Product Details

    ISBN-13: 2900133760353
    Publisher: Pearson FT Press
    Publication date: 11/28/2014
    Series: FT Press Analytics Series
    Edition description: New Edition
    Pages: 400
    Product dimensions: 6.50(w) x 1.50(h) x 9.50(d)

    About the Author

    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.

    Table of Contents

    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

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