Business analytics with management science models and methods /

This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A l...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Asllani, Arben (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Upper Saddle River, NJ : Pearson Education, [2015].
Temas:
Business intelligence.
Business planning.
Business > Decision making.
Commercial statistics.
Affaires > Prise de décision.
Business > Decision making
Business intelligence
Business planning
Commercial statistics
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Business Analytics with Management Science
  • Chapter Objectives
  • Prescriptive Analytics in Action: Success Stories
  • Introduction
  • Implementing Business Analytics
  • Business Analytics Domain
  • Challenges with Business Analytics
  • Exploring Big Data with Prescriptive Analytics
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 2 Introduction to Linear Programming
  • Chapter Objectives
  • Prescriptive Analytics in Action: Chevron Optimizes Processing of Crude Oil
  • Introduction
  • LP Formulation
  • Solving LP Models: A Graphical Approach
  • Possible Outcome Solutions to LP Model
  • Exploring Big Data with LP Models
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 3 Business Analytics with Linear Programming
  • Chapter Objectives
  • Prescriptive Analytics in Action: Nu-kote Minimizes Shipment Cost
  • Introduction
  • General Formulation of LP Models
  • Formulating a Large LP Model
  • Note continued: Solving Linear Programming Models with Excel
  • Big Optimizations with Big Data
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 4 Business Analytics with Nonlinear Programming
  • Chapter Objectives
  • Prescriptive Analytics in Action: Netherlands Increases Protection from Flooding
  • Introduction
  • Challenges to NLP Models
  • Example 1: World Class Furniture
  • Example 2: Optimizing an Investment Portfolio
  • Exploring Big Data with Nonlinear Programming
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 5 Business Analytics with Goal Programming
  • Chapter Objectives
  • Prescriptive Analytics in Action: Airbus Uses Multi-Objective Optimization Models
  • Introduction
  • GP Formulation
  • Example 1: Rolls Bakery Revisited
  • Solving GP Models with Solver
  • Example 2: World Class Furniture
  • Exploring Big Data with Goal Programming
  • Wrap Up
  • Review Questions
  • Practice Problems
  • Note continued: ch. 6 Business Analytics with Integer Programming
  • Chapter Objectives
  • Prescriptive Analytics in Action: Zara Uses Mixed IP Modeling
  • Introduction
  • Formulation and Graphical Solution of IP Models
  • Types of Integer Programming Models
  • Solving Integer LP Models with Solver
  • Solving Nonlinear IP Models with Solver
  • Solving Integer GP Models with Solver
  • The Assignment Method
  • The Knapsack Problem
  • Exploring Big Data with Integer Programming
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 7 Business Analytics with Shipment Models
  • Chapter Objectives
  • Prescriptive Analytics in Action: Danaos Saves Time and Money with Shipment Models
  • Introduction
  • The Transportation Model
  • The Transshipment Method
  • Exploring Big Data with Shipment Models
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 8 Marketing Analytics with Linear Programming
  • Chapter Objectives
  • Note continued: Prescriptive Analytics in Action: Hewlett Packard Increases Profit with Marketing Optimization Models
  • Introduction
  • RFM Overview
  • RFM Analysis with Excel
  • Optimizing RFM-Based Marketing Campaigns
  • LP Models with Single RFM Dimension
  • Marketing Analytics and Big Data
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 9 Marketing Analytics with Multiple Goals
  • Chapter Objectives
  • Prescriptive Analytics in Action: First Tennessee Bank Improves Marketing Campaigns
  • Introduction
  • LP Models with Two RFM Dimensions
  • LP Model with Three Dimensions
  • A Goal Programming Model for RFM
  • Exploring Big Data with RFM Analytics
  • Wrap Up
  • Review Questions
  • Practice Problems
  • ch. 10 Business Analytics with Simulation
  • Chapter Objectives
  • Prescriptive Analytics in Action: Blood Assurance Uses Simulation to Manage Platelet Inventory
  • Introduction
  • Basic Simulation Terminology
  • Simulation Methodology
  • Note continued: Simulation Methodology in Action
  • Exploring Big Data with Simulation
  • Wrap Up
  • Review Questions
  • Practice Problems
  • Appendix A Excel Tools for the Management Scientist
  • 1.Shortcut Keys
  • 2.Sumif
  • 3.Averageif
  • 4.Countif
  • 5.Iferror
  • 6.Vlookup Or Hlookup
  • 7.transpose
  • 8.sumproduct
  • 9.if
  • 10.Pivot Table
  • Appendix B A Brief Tour of Solver
  • Setting Up Constraints and the Objective Function in Solver
  • Selecting Solver Options.

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