MGS 3100 Business Analysis Introduction - Why Business Analysis Jan 14, 2016

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

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

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

MGS 3100 Business Analysis Course Overview

Deterministic Models vs. Probabilistic (Stochastic) 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.

Deterministic Models vs. 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.

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

Reasons for Using Models Models force you to: Be explicit about your objectives Identify and record the decisions that influence those objectives Identify and record interactions and trade-offs among those decisions Think carefully about variables to include and their definitions in terms that are quantifiable Consider what data are pertinent for quantification of those variables and determining their interactions Recognize constraints (limitations) on the values that those quantified variables may assume Allow communication of your ideas and understanding to facilitate teamwork

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

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

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

The Modeling Process As applied to the first two stages of decision making Model Results Analysis Symbolic World Abstraction Interpretation Real World Management Situation Decisions Intuition

The Modeling Process The Role of Managerial Judgment in the Modeling Process: Analysis Model Results Symbolic World Managerial Judgment Abstraction Interpretation Real World Management Situation Decisions Intuition

Building Models To model a situation, you first have to frame it (i.e. develop an organized way of thinking about the situation). A problem statement involves possible decisions and a method for measuring their effectiveness. Steps in modeling: Study the Environment to Frame the Managerial Situation Formulate a selective representation Construct a symbolic (quantitative) model

Building Models Studying the Environment Select those aspects of reality relevant to the situation at hand. Formulation Specific assumptions and simplifications are made. Decisions and objectives must be explicitly identified and defined. Identify the model’s major conceptual ingredients using “Black Box” approach. The “Black Box” View of a Model Performance Measure(s) Decisions (Controllable) Parameters (Uncontrollable) Exogenous Variables Model Consequence Endogenous

Building Models Study the Environment to Frame the Managerial Situation The next step is to construct a symbolic model. Mathematical relationships are developed. Graphing the variables may help define the relationship. To do this, use “Modeling with Data” technique. Var. X Var. Y Cost A Cost B A + B

Iterative Model Building DEDUCTIVE MODELING (‘What If?’ Projections, Decision Modeling Optimization) (‘What If?’ Projections, Decision Analysis, Decision Trees, Queuing) Decision Modeling Models Models Model Building Process PROBABILISTIC MODELS DETERMINISTIC MODELS Models Models Analysis, Statistical Analysis, (Forecasting, Simulation Data Analysis Parameter Estimation) Data Analysis (Data Base Query, Parameter Evaluation INFERENTIAL MODELING

Modeling and Real World Decision Making Four Stages of applying modeling to real world decision making: Stage 1: Study the environment, formulate the model and construct the model. Stage 2: Analyze the model to generate results. Stage 3: Interpret and validate model results. Stage 4: Implement validated knowledge.

Modeling and Real World Decision Making Management Lingo Modeling Term 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)