MGS3100_01.ppt/Jan 13, 2010/Page 1

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MGS3100_01.ppt/Jan 13, 2010/Page 1

  1. 1. MGS 3100 Business Analysis Introduction - Why Business Analysis Jan 13, 2010
  2. 2. Agenda Business Analysis - Models The Modeling Process Introduction to Decision Sciences
  3. 3. What is Decision Sciences <ul><li>Grocery Industry </li></ul><ul><li>Kroger </li></ul><ul><li>Travel Industry </li></ul><ul><li>Delta SkyMiles </li></ul><ul><li>Marriott Rewards </li></ul><ul><li>Gambling Industry </li></ul><ul><li>MGM Mirage Players Club </li></ul><ul><ul><li>The Mirage </li></ul></ul><ul><ul><li>Treasure Island </li></ul></ul><ul><ul><li>Bellagio </li></ul></ul><ul><ul><li>New York New York </li></ul></ul><ul><ul><li>MGM Grand </li></ul></ul><ul><li>Retail Business </li></ul><ul><li>Best Buy </li></ul><ul><li>Circuit City </li></ul><ul><li>Macy </li></ul>
  4. 4. Agenda Business Analysis - Models The Modeling Process Introduction to Decision Sciences
  5. 5. MGS 3100 Business Analysis Course Overview
  6. 6. Deterministic Models vs. Probabilistic (Stochastic) Models <ul><li>Deterministic Models </li></ul><ul><li>are models in which all relevant data are assumed to be known with certainty. </li></ul><ul><li>can handle complex situations with many decisions and constraints </li></ul><ul><li>are very useful when there are few uncontrolled model inputs that are uncertain. </li></ul><ul><li>are useful for a variety of management problems. </li></ul><ul><li>are easy to incorporate constraints on variables. </li></ul><ul><li>software is available to optimize constrained models. </li></ul><ul><li>allows for managerial interpretation of results. </li></ul><ul><li>constrained optimization provides useful way to frame situations. </li></ul><ul><li>will help develop your ability to formulate models in general. </li></ul>
  7. 7. Deterministic Models vs. Probabilistic (Stochastic) Models <ul><li>Probabilistic (Stochastic) Models </li></ul><ul><li>are models in which some inputs to the model are not known with certainty. </li></ul><ul><li>uncertainty is incorporated via probabilities on these “random” variables. </li></ul><ul><li>very useful when there are only a few uncertain model inputs and few or no constraints. </li></ul><ul><li>often used for strategic decision making involving an organization’s relationship to its environment. </li></ul>
  8. 8. Classification of Models <ul><li>By problem type </li></ul><ul><ul><li>Forecasting </li></ul></ul><ul><ul><li>Decision Analysis </li></ul></ul><ul><ul><li>Constrained Optimization </li></ul></ul><ul><ul><li>Monte Carlo Simulation </li></ul></ul><ul><li>By data type </li></ul><ul><ul><li>Time series </li></ul></ul><ul><ul><ul><li>Exponential smoothing </li></ul></ul></ul><ul><ul><ul><li>Moving average </li></ul></ul></ul><ul><ul><li>Cross sectional </li></ul></ul><ul><ul><ul><li>Multiple linear regression </li></ul></ul></ul><ul><li>By causality </li></ul><ul><ul><li>Causal: causal variable </li></ul></ul><ul><ul><li>Non-causal: surrogate variable </li></ul></ul><ul><li>Methodologies </li></ul><ul><li>1. Qualitative </li></ul><ul><ul><ul><li>Delphi Methods </li></ul></ul></ul><ul><ul><ul><li>2. Quantitative - Non-statistical </li></ul></ul></ul><ul><ul><ul><li>Using “comparables ” </li></ul></ul></ul><ul><ul><li>3. Quantitative - Statistical </li></ul></ul><ul><ul><ul><li>Time-series </li></ul></ul></ul><ul><ul><ul><li>Regression </li></ul></ul></ul>
  9. 9. Reasons for Using Models <ul><li>Models force you to: </li></ul><ul><li>Be explicit about your objectives </li></ul><ul><li>Identify and record the decisions that influence those objectives </li></ul><ul><li>Identify and record interactions and trade-offs among those decisions </li></ul><ul><li>Think carefully about variables to include and their definitions in terms that are quantifiable </li></ul><ul><li>Consider what data are pertinent for quantification of those variables and determining their interactions </li></ul><ul><li>Recognize constraints (limitations) on the values that those quantified variables may assume </li></ul><ul><li>Allow communication of your ideas and understanding to facilitate teamwork </li></ul>
  10. 10. Agenda Business Analysis - Models The Modeling Process Introduction to Decision Sciences
  11. 11. The Modeling Process Quantitative - Statistical Variables and Attributes Objective Hierarchies Influence Diagrams Mathematical Representation Testing and Validation Implementation and use <ul><li>Describe Problem / opportunity </li></ul><ul><li>Identify Overall Objective </li></ul><ul><li>Organize Sub-Objectives into a hierarchy </li></ul><ul><li>Identify Model’s Objective </li></ul><ul><li>Determine all variables and their attributes </li></ul><ul><li>Decide on Measurement / Data Collection </li></ul><ul><li>Graphically depict relationships among variables </li></ul><ul><li>Distinguish between Decision and outcome variables </li></ul><ul><li>Determine mathematical relationships among variables </li></ul><ul><li>Develop mathematical model(s) </li></ul><ul><li>Evaluate reliability and validity </li></ul><ul><li>Understand limitations </li></ul><ul><li>Implement models in DSSs </li></ul><ul><li>Clarify assumptions, inputs, and outputs </li></ul>
  12. 12. The Modeling Process Quantitative – Non-Statistical Managerial Approach to Decision Making Manager analyzes situation (alternatives) Makes decision to resolve conflict Decisions are implemented Consequences of decision These steps Use Spreadsheet Modeling
  13. 13. The Modeling Process Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World As applied to the first two stages of decision making
  14. 14. The Modeling Process Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World Managerial Judgment The Role of Managerial Judgment in the Modeling Process:
  15. 15. Building Models <ul><li>To model a situation, you first have to frame it (i.e. develop an organized way of thinking about the situation). </li></ul><ul><li>A problem statement involves possible decisions and a method for measuring their effectiveness. </li></ul><ul><li>Steps in modeling: </li></ul><ul><ul><li>Study the Environment to Frame the Managerial Situation </li></ul></ul><ul><ul><li>Formulate a selective representation </li></ul></ul><ul><ul><li>Construct a symbolic (quantitative) model </li></ul></ul>
  16. 16. Building Models <ul><li>Studying the Environment </li></ul><ul><ul><li>Select those aspects of reality relevant to the situation at hand. </li></ul></ul><ul><li>Formulation </li></ul><ul><ul><li>Specific assumptions and simplifications are made. </li></ul></ul><ul><ul><li>Decisions and objectives must be explicitly identified and defined. </li></ul></ul><ul><ul><li>Identify the model’s major conceptual ingredients using “Black Box” approach. </li></ul></ul>The “Black Box” View of a Model Performance Measure(s) Decisions (Controllable) Parameters (Uncontrollable) Exogenous Variables Model Consequence Variables Endogenous Variables
  17. 17. Building Models <ul><li>Study the Environment to Frame the Managerial Situation </li></ul><ul><ul><li>The next step is to construct a symbolic model. </li></ul></ul><ul><ul><li>Mathematical relationships are developed. Graphing the variables may help define the relationship. </li></ul></ul><ul><ul><li>To do this, use “Modeling with Data” technique. </li></ul></ul>Var. X Var. Y Cost A Cost B A + B
  18. 18. Iterative Model Building DEDUCTIVE MODELING INFERENTIAL MODELING PROBABILISTIC MODELS DETERMINISTIC MODELS Model Building Process Models Models Models Models Decision Modeling (‘What If?’ Projections, Decision Analysis, Decision Trees, Queuing) Decision Modeling (‘What If?’ Projections, Optimization) Data Analysis (Forecasting, Simulation Analysis, Statistical Analysis, Parameter Estimation) Data Analysis (Data Base Query, Parameter Evaluation
  19. 19. Modeling and Real World Decision Making <ul><li>Four Stages of applying modeling to real world decision making: </li></ul><ul><li>Stage 1: Study the environment, formulate the model and construct the model. </li></ul><ul><li>Stage 2: Analyze the model to generate results. </li></ul><ul><li>Stage 3: Interpret and validate model results. </li></ul><ul><li>Stage 4: Implement validated knowledge. </li></ul>
  20. 20. Modeling and Real World Decision Making Modeling Term Management Lingo Formal Definition Example Decision Variable Lever Controllable Exogenous Investment Input Quantity Amount Parameter Gauge Uncontrollable Exogenous Interest Rate Input Quantity Consequence Outcome Endogenous Output Commissions Variable Variable Paid Performance Yardstick Endogenous Variable Return on Measure Used for Evaluation Investment (Objective Function Value)

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