Opis: Business Analytics with Management Science Models and Methods - Arben Asllani

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. "As Business Analytics has become a popular topic in recent years, a number of texts on the subject have appeared in the market. However, most of these books simply present a collection of topics in data mining, statistics, and management science tools. Dr. Asllani's book has a refreshing new approach to business analytics-a logical flow of design thinking for decision support with management science methods. This book emphasizes the creative thinking approach to decision making through practical, intuitive, and real success application examples. This is an excellent text for students and practitioners of business analytics." -Sang M. Lee, PhD, University Eminent Scholar Emeritus, University of Nebraska-Lincoln "Dr. Asllani illustrates the relevance of management science in the era of Big Data and Business Analytics. He demonstrates how predictive analytics can inform and enhance prescriptive analysis, and how the rapid growth in computing power has impacted tackling larger optimizations. It is a great primer for someone new to the topic, and a great reference to anyone in the field. After 10 years practicing management science and prior graduate level coursework, I have found that the content in Dr. Asllani's book has affected my professional modeling with a rigor and understanding that I didn't realize had been lacking. The book is well-written and paced, and each chapter builds on concepts from the prior. End-of-chapter questions challenge the reader to recall information from the chapter and consider its practical applications." -Brett Senentz, Business Optimization and Analytics Project Manager, McKee Foods Corporation "Dr. Asllani has delivered a practical guide for practitioners in the field and a priceless textbook for students with one brilliant stroke. This book is certain to serve as an invaluable reference in analytics and management science. The book covers a wide array of applications, from production, to logistics, to marketing. Dr. Asllani explains the intuition behind the concepts, avoiding heavy formulas and definitions, thus allowing for a guaranteed, solid grasp of each concept. He provides spreadsheet templates, which allow for easy application and reuse for a variety of optimization models. His step-by-step methodologies are sure to make the LP formulation process easier to apply by practitioners." -Alireza Lari, PhD, Professor of Practice of Management, Wake Forest University School of BusinessPreface 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

Szczegóły: Business Analytics with Management Science Models and Methods - Arben Asllani

Tytuł: Business Analytics with Management Science Models and Methods
Autor: Arben Asllani
Producent: Financial Times
ISBN: 9780133760354
Rok produkcji: 2014
Ilość stron: 400
Oprawa: Twarda
Waga: 0.67 kg

Recenzje: Business Analytics with Management Science Models and Methods - Arben Asllani