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Business Analytics with Management Science Models and Methods

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Business Analytics with Management Science Models and Methods


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The first practical, managerial guide to decision modeling and analytics 

  • Improve decision-making by applying practical management science techniques to business analytics
  • Teaches through realistic examples and intuitive explanations proven through 20+ years of MBA-level teaching experience
  • Helps practitioners focus more on key decision-making inputs and outputs, not internal model complexities they can rely on software to handle
  • Presents success stories showing how each chapter's key concepts can be applied to improve decision-making
  • Provides Excel templates, heuristics, algorithms, tips, and user-friendly interfaces that make "big data" decision models easier to create and use


  • Copyright 2015
  • Dimensions: 6" x 9"
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-376035-9
  • ISBN-13: 978-0-13-376035-4

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.



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Business Analytics with Management Science

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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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