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Professional's Guide to Decision Science and Problem Solving An Integrated Approach for Assessing Issues, Finding Solutions, and Reaching Corporate Objectives

ISBN-10: 0132869780

ISBN-13: 9780132869782

Edition: 2012 (Revised)

Authors: Frank A. Tillman, Deandra T. Cassone

List price: $64.99
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Description:

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.
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Book details

List price: $64.99
Copyright year: 2012
Publisher: Pearson Education, Limited
Publication date: 1/24/2012
Binding: Hardcover
Pages: 280
Size: 6.34" wide x 9.21" long x 0.91" tall
Weight: 1.078
Language: English

Dr.Frank A. Tillman(Gravois Mills, MO) is President of the management consultancies HTX International and IBES, Inc. He served as Department Head at Kansas State University, where he published more than 50 professional articles and two books, and advised several Masters and Ph.D. theses. Dr.Deandra T. Cassone(Overland Park, KS) is Logistics Manager at Sprint and Adjunct Assistant Professor, Systems Engineering at Missouri University of Science and Technology. She has spent over 25 years in industry consulting and management roles. Her interests include building structured decision-making models; she has submitted numerous business process patents.

Acknowledgments
About the Authors
Preface
The Method
Define the Objectives and Identify Metrics
Chapter Topic
Key Corporate Participants
Management Steps Required to Execute the Approach
Solving the Right Problem
Developing an Understanding of the Problem
Defining Goals and Objectives of a Company or Organization
Defining the Framework for the Decisions Being Made
Metrics for Measuring Success
Definition of a Metric
Developing Decision Criteria and Metrics
Data Used to Support Metrics
Structure and Definition of the Problem
Key Concepts in Defining the Objectives
Explore the Environment
Chapter Topic
Key Corporate Participants
Integrated Corporate Planning
Assess the Scope of the Problem
Develop the Activity Relationship Matrix
Quantify Performance with Industry Benchmarks and Performance Evaluations
Develop the Activity Relationship Diagram
Determine the Variability of the Metrics and Financial Contribution of the Individual Functions
Identify Specific Problem Areas to Improve
Key Concepts in Exploring the Environment
Explore the Scope of the Problem and Its Importance
Chapter Topic
Key Corporate Participants
How Does This Fit into the Overall Processes?
Discussion of Business Process Modeling
What Is the Panoramic View?
Unique Application of Techniques and Methods
Key Concepts in Exploring the Scope of the Problem and Its Importance
Data Mining and Statistical Analysis
Chapter Topic
Key Corporate Participants
Assess the Information and Its Availability
Data Summarization
Analysis and Decision Methods
Key Concepts in Data Mining and Statistical Analysis
Solve the Problem and Measure the Results
Chapter Topic
Key Corporate Participants
Select the Best Method That the Data Can Support
Model to Represent the Decision Process
Model Automation
Key Concepts to Solve the Problem and Measure the Results
Evaluate the Results and Do Sensitivity Analysis
Chapter Topic
Key Corporate Participants
Measure the Degree of Success
Economic Analysis
What-If and Sensitivity Analysis
Key Concepts to Evaluate the Results and Do Sensitivity Analysis
Summary of Part I
Summary of Integrated Approach
Case Studies
Logistics Service Provider
Introduction
Define the Objectives
Developing Decision Criteria and Metrics
Explore the Environment
Explore the Scope of the Problem and Its Importance
Data Mining and Statistical Analysis
Solve the Problem and Measure the Results
Evaluate the Results and Do Sensitivity Analysis
Summary
New Product Development
Introduction
Define the Objectives
Developing Decision Criteria and Metrics
Explore the Environment
Explore the Scope of the Problem and Its Importance
Data Mining and Statistical Analysis
Solve the Problem and Measure the Results
Evaluate the Results and Do Sensitivity Analysis
Summary
Airline Merger
Introduction
Define the Objectives
Developing Decision Criteria and Metrics
Explore the Environment
Explore the Scope of the Problem and Its Importance
Data Mining and Statistical Analysis
Solve the Problem and Measure the Results
Evaluate the Results and Do Sensitivity Analysis
Summary
Overview of Methodologies
Decision Methodologies
Multiple Criteria Decision Making
Multiple Objective Decision Making
Artificial Intelligence
Group Decision Making
Statistical Analysis
Forecasting
Expert Opinion
Fuzzy Logic
Simulation
Detailed Methodologies
Nominal Group Technique (NGT)
Normalized Direct Weighting
Analytical Hierarchy Process (Eigenvector Method)
Simple Additive Weighting Method
Borda's Function
TOPSIS
SPAN
Brainstorming
Brainwriting
Moving Averages
Weighted Moving Averages
Exponential Smoothing
Regression Analysis
References
Index