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A Professional's Guide to Decision Science and Problem Solving provides an integrated, start-to-finish framework for more effective problem solving and decision making in corporations. Drawing on vast experience in the field, the authors show how to apply state-of-the-art decision science, statistical modeling, benchmarking, and processing modeling techniques together to create a robust analytical framework for better decision making in any field, especially those that rely on advanced operations management. They integrate both newly-developed and time-tested techniques into a logical, structured approach for assessing corporate issues, developing solutions, and making decisions that drive the successful achievement of corporate objectives. Coverage includes: defining objectives, exploring the environment; scoping problems and evaluating their importance; bringing data mining and statistical analysis to bear; solving problems and measuring the results; evaluating the results and performing sensitivity analysis, and more. The book concludes with three case study chapters that walk through the effective use of its methods, step-by-step. Representing a wide variety of corporate environments, these case studies underscore and demonstrate the method's exceptional adaptability. This book will be valuable in a wide range of industries, notably finance, pharmaceutical, healthcare, economics, and manufacturing.
Part I: The Method 1
Chapter 1: Define the Objectives and Identify Metrics 3
Chapter 2: Explore the Environment 31
Chapter 3: Explore the Scope of the Problem and Its Importance 47
Chapter 4: Data Mining and Statistical Analysis 59
Chapter 5: Solve the Problem and Measure the Results 71
Chapter 6: Evaluate the Results and Do Sensitivity Analysis 81
Chapter 7: Summary of Part I 91
Part II: Case Studies 95
Chapter 8: Logistics Service Provider 97
Chapter 9: New Product Development 131
Chapter 10: Airline Merger 159
Appendix A: Overview of Methodologies 205
Appendix B: Detailed Methodologies 221
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