Business analytics with management science models and methods / Arben Asllani.
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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Full Text (via O'Reilly/Safari) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Upper Saddle River, NJ :
Pearson Education,
[2015].
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| Subjects: |
MARC
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| 100 | 1 | |a Asllani, Arben, |e author. | |
| 245 | 1 | 0 | |a Business analytics with management science models and methods / |c Arben Asllani. |
| 264 | 1 | |a Upper Saddle River, NJ : |b Pearson Education, |c [2015]. | |
| 264 | 4 | |c ©2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 588 | |a Description based on online resource; title from title page (Safari, viewed December 5, 2014). | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |
| 505 | 0 | |a 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 | |
| 505 | 0 | |a 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 | |
| 505 | 0 | |a 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 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems. | ||
| 650 | 0 | |a Business intelligence. | |
| 650 | 0 | |a Business planning. | |
| 650 | 0 | |a Business |x Decision making. | |
| 650 | 0 | |a Commercial statistics. | |
| 650 | 7 | |a Business |x Decision making |2 fast | |
| 650 | 7 | |a Business intelligence |2 fast | |
| 650 | 7 | |a Business planning |2 fast | |
| 650 | 7 | |a Commercial statistics |2 fast | |
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