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

2. In the context of Values, Mission and Goals
Objective Hierarchy
Maximize Value to Customers and Stakeholders
Fundamental Objectives
Profitability
Quality
Service
Reputation
Max Revenues
Min Costs
Means Objectives
In the context of Values, Mission and Goals

3. Variables and Attributes
How Measured (Metrics)
Variable
Data Source

4. Influence Diagrams
A graphical representation of relationships among variables in a given problem situation
Informal rules for constructing influence diagrams
Identify all variables - decision, intermediate and outcome variables.
Connect variables to delineate relationships
Identify variables influencing the decision variables - both controllable and uncontrollable.
Use consistent representation scheme

5. Influence Diagrams - an example
Market Conditions
Room
Rate
Occupancy
Meals
Served
Room
Personnel
Room
Supplies
Room
Revenue
Operating
Cost
Meal
Cost
Meal
Revenue
Restaurant
Margin
Room
Margin
Fixed
Cost
Profit

6. Establishing Causality
To establish causality (X causes Y), one must show:
X precedes Y
Most cases easy to demonstrate.
A change in either is associated with a change in the other
Statistics can establish this.
There is no other plausible explanation for the occurrence of Y but X.
Impossible in the literal sense

7. Classification of Models
By what the model does
Descriptive
Prescriptive
Optimization
By type of problem
Forecasting
Decision Analysis
Constrained Optimization
Monte Carlo Simulation

8. Forecasting Methodologies
Qualitative
Delphi Methods
Quantitative - Non-statistical
Using “comparables”
Quantitative - Statistical
Time-series
Regression

9. Forecasting Models By data type Time series (surrogate variable)
Exponential smoothing
Moving averages
Cross sectional (causal variables)
Multiple linear regression