MBA 7025 Statistical Business Analysis Introduction - Why Business Analysis Jan 13, 2015

1. MBA 7025Statistical Business Analysis Introduction - Why Business AnalysisJan 13, 2015

2. Introduction to Decision Sciences Agenda Business Analysis - Models The Modeling Process

3. Analytical Methods Information Technology Decision Making Decision Sciences: Conceptualized!

4. What is Decision Sciences • Grocery Industry • Kroger • Travel Industry • Delta SkyMiles • Marriott Rewards • Gambling Industry • MGM Mirage Players Club • The Mirage • Treasure Island • Bellagio • New York New York • MGM Grand • Retail Business • Best Buy • Circuit City • Macy

5. Agenda Introduction to Decision Sciences Business Analysis - Models The Modeling Process

6. MBA 7025 Statistical Business AnalysisCourse Overview

7. Deterministic Models vs.Probabilistic (Stochastic) Models • Deterministic Models • are models in which all relevant data are assumed to be known with certainty. • can handle complex situations with many decisions and constraints • are very useful when there are few uncontrolled model inputs that are uncertain. • are useful for a variety of management problems. • are easy to incorporate constraints on variables. • software is available to optimize constrained models. • allows for managerial interpretation of results. • constrained optimization provides useful way to frame situations. • will help develop your ability to formulate models in general.

8. Deterministic Models vs.Probabilistic (Stochastic) Models • Probabilistic (Stochastic) Models • are models in which some inputs to the model are not known with certainty. • uncertainty is incorporated via probabilities on these “random” variables. • very useful when there are only a few uncertain model inputs and few or no constraints. • often used for strategic decision making involving an organization’s relationship to its environment.

9. Classification of Models • By problem type • Forecasting • Decision Analysis • Constrained Optimization • Monte Carlo Simulation • By data type • Time series • Exponential smoothing • Moving average • Cross sectional • Multiple linear regression • By causality • Causal: causal variable • Non-causal: surrogate variable • Methodologies • 1. Qualitative • Delphi Methods • 2. Quantitative - Non-statistical • Using “comparables” • 3. Quantitative - Statistical • Time-series • Regression

10. Analytical Methods • Qualitative Methods • Nominal Group Techniques • Heuristic Based Methods • Expert Systems / AI • Quantitative Methods • Mathematical / Algebraic / Calculus Methods • Statistical Modeling and Analysis • Management Science / Operations Research Techniques • Accounting / Financial Modeling

11. Decision Environment DATABASED MODELBASED KNOWLEDGEBASED UNCERTAINTY COMPLEXITY EQUIVOCALITY • Facts not known • Gather Information • Fact Finding /.Analysis • Too many facts • Generate Information • Simulation/Synthesis • Facts not Clear • Interpret Information • Application of Expertise

12. STRATEGIC TACTICAL OPERATIONAL Types and Levels of Decisions DECISION SUPPORT UNSTRUCTURED MANAGEMENT INFORMATION STRUCTURED TRANSACTION PROCESSING

13. Applications of Information Technology • Transaction Processing Systems • Management Information Systems • Decision Support Systems

14. Data Base Decision Support Systems Model Base Knowledge Base User Interface

15. Introduction to Decision Sciences Agenda Business Analysis - Models The Modeling Process

16. Organizational Context Managing OrganizationsInformed decision making as a prerequisite for success Vision Mission Values, Purpose, Structure, Politics, Environment, etc. Givens Strategic Direction Policies, Goals, and Objectives What should be done ? Decision Making Analytics, Decision Making When and how ?? Implementation Project Management Action

17. Complexity What does it add up to? Uncertainty What can happen? MODELS INTELLIGENCE DATA DESIGN CHOICE Managerial Decision MakingInformation Technology Solutions for Improving Effectiveness Variables (Measures and Estimates) Probabilities and Estimates Structuring Relationships Problem Representation Generation of Alternatives Decision Analysis and Influence Diagrams for Visualizing Models and Choices Spreadsheet Models for managing complex relationships and detail

18. The Modeling Process Quantitative - Statistical • Describe Problem / opportunity • Identify Overall Objective • Organize Sub-Objectives into a hierarchy Objective Hierarchies Variables and Attributes • Identify Model’s Objective • Determine all variables and their attributes • Decide on Measurement / Data Collection Influence Diagrams • Graphically depict relationships among variables • Distinguish between Decision and outcome variables Mathematical Representation • Determine mathematical relationships among variables • Develop mathematical model(s) Testing and Validation • Evaluate reliability and validity • Understand limitations Implementation and use • Implement models in DSSs • Clarify assumptions, inputs, and outputs

19. Manager analyzes situation (alternatives) These steps Use Spreadsheet Modeling Makes decision to resolve conflict Decisions are implemented Consequences of decision The Modeling Process Quantitative – Non-Statistical Managerial Approach to Decision Making