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Business Analytics with Management Science Models and Methods

Business Analytics with Management Science Models and Methods

          
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About the Book

Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics.   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.

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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Product Details
  • ISBN-13: 9780133760354
  • Publisher: Pearson Education (US)
  • Publisher Imprint: Pearson FT Press
  • Depth: 25
  • Language: English
  • Returnable: N
  • Spine Width: 27 mm
  • Width: 162 mm
  • ISBN-10: 0133760359
  • Publisher Date: 12 Mar 2015
  • Binding: Hardback
  • Height: 236 mm
  • No of Pages: 400
  • Series Title: Ft Press Analytics
  • Weight: 670 gr


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