1. Introduction to Modeling
DECISION MODELING WITH
MICROSOFT EXCEL
Chapter 1
Introduction to Modeling
Copyright 2001
Prentice Hall

2. MGS 3100 Business Analysis Why is this class worth taking?
Knowledge of business analysis and MS Excel are core skills that can be applied to almost any job.
What is this class about?
Applying models in support of decision making within a business.

3. What is a model
A model is a carefully selected abstraction of reality.

4. TYPES OF MODELS Physical Model Characteristics Examples Tangible
Easy to Comprehend
Difficult to Duplicate and Share
Difficult to Modify and Manipulate
Lowest Scope of Use
Characteristics
Model Airplane
Model House
Model City
Examples

5. TYPES OF MODELS Analog Model Characteristics Examples Intangible
(A set of relationships through a different, but analogous, medium.)
Intangible
Harder to Comprehend
Easier to Duplicate and Share
Easier to Modify and Manipulate
Wider Scope of Use
Characteristics
Road Map
Speedometer
Pie Chart
Examples

6. TYPES OF MODELS Symbolic Model Characteristics Examples
(Relationships are represented mathematically.)
Intangible
Hardest to Comprehend
Easiest to Duplicate and Share
Easiest to Modify and Manipulate
Widest Scope of Use
Characteristics
Simulation Model
Algebraic Model
Spreadsheet Model
Examples

7. THE MODELING PROCESS Decision Support Models force you to
be explicit about your objectives. 1.
identify and record the types of decisions that influence those objectives. 2.
identify and record interactions and trade-offs among those decisions. 3.
think carefully about which variables to include. 4.
consider what data are pertinent and their interactions. 5.
recognize constraints or limitations on the values. 6.
Models allow communication of your ideas and understanding to facilitate teamwork. 7.
Models allow us to use the analytical power of spreadsheets hand in hand with the data storage and computational speed of computers.

8. MORE ON DECISION MODELS
Decision models typically include an explicit performance measure that gauges the attainment of that objective.
For example, the objective may be to maximize profit or minimize cost in relation to a performance measure (such as sales revenue, interest income, etc).
In summary, decision models
1. selectively describe the managerial situation.
2. designate decision variables.
3. designate performance measure(s) that reflect objective(s).

9. BUILDING MODELS The “Black Box” View of a Model Model Endogenous
Performance
Measure(s)
Decisions
(Controllable)
Parameters
(Uncontrollable)
Exogenous
Variables
Model
Consequence
Endogenous

10. DETERMINISTIC AND PROBABILISTIC 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.

11. DETERMINISTIC AND PROBABILISTIC 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.

12. Summary What is a model? How can models help business?
A model is a carefully selected abstraction of reality.
How can models help business?
allows a run-through of the situation

13. Summary Why are there different types of models?
Different situations demand models with different types of characteristics
Tangible
Comprehensible
Ease of modification and manipulation
Scope of use
What are the types of models?
Physical
Analog
Symbolic

14. Summary Exogenous Variables
Quantitative variables whose values are determined external to a symbolic model (i.e. inputs to a symbolic model)
Endogenous Variables
Quantitative variables whose values are determined by the relationships of a symbolic model (i.e. outputs of a symbolic model)