DECISION MODELING WITH
MICROSOFT EXCEL
Chapter 1
Introduction to Modeling
(cont)
Copyright 2001
Prentice Hall
MORE ON MODELS
A model is a carefully selected abstraction of reality.
Symbolic models
1. always simplify reality.
2. incorporate enough detail so that
• the result meets your needs,
• it is consistent with the data you have available,
• it can be quickly analyzed.
Decision models are symbolic models in which some of the
variables represent decisions that must or could be made.
Decision variables are variables whose values you can
control, change or set.
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).
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:
1. Study the Environment to Frame the Managerial Situation
2. Formulate a selective representation
3. Construct a symbolic (quantitative) model
BUILDING MODELS
1. Studying the Environment
Select those aspects of reality relevant to the situation
at hand.
2. 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.
BUILDING MODELS
The “Black Box” View of a Model
Decisions
(Controllable)
Parameters
(Uncontrollable)
Model
Performance
Measure(s)
Consequence
Variables
MODELING AND REAL
WORLD DECISION MAKING
Management
Modeling Term
Lingo
Formal Definition
Example
Decision Variable
Lever
Controllable Exogenous
Input Quantity
Parameter
Gauge
Uncontrollable Exogenous Interest Rate
Input Quantity
Consequence
Variable
Outcome
Performance
Measure
Yardstick
Endogenous Output
Variable
Endogenous Variable
Used for Evaluation
(Objective Function Value)
Investment
Amount
Commissions
Paid
Return on
Investment
BUILDING MODELS
3. Model Construction
The next step is to construct a symbolic model.
Var. Y
Mathematical relationships are developed. Graphing the
variables may help define the relationship.
Var. X
To do this, use “Modeling with Data” technique.
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.
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 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.
Course Overview
Modeling Techniques
Deterministic Models
Basic Profit Models
Breakeven
Pricing for Max Profit
Crossover
Time Series Forecasting
Naive / Exp. Smoothing
Regression (trend)
Classical Decomposition
(trend + seasonality)
Probabilistic Models
Decision Analysis
Alternatives, States, Payoffs
Decision Criteria
Decision Trees
Bayes Theorem
Simulation
Random numbers
Distributions
Discrete Variables
Continuous Variables
ITERATIVE MODEL BUILDING
DEDUCTIVE MODELING
Models
Models
Model Building
PROBABILISTIC
MODELS
DETERMINISTIC
MODELS
Process
Models
Models
INFERENTIAL MODELING
ITERATIVE MODEL BUILDING
Deductive Modeling
focuses on the variables themselves before data are collected.
variables are interrelated based on assumptions about algebraic
relationships and values of the parameters.
places importance on modeler’s prior knowledge and judgments of
both mathematical relationships and data values.
tends to be “data poor” initially.
Inferential Modeling
focuses on the variables as reflected in existing data collections.
variables are interrelated based on an analysis of data to
determine relationships and to estimate values of parameters.
available data need to be accurate and readily available.
tends to be “data rich” initially.
MODELING WITH DATA
DATA – numbers that reflect quantitative facts about the
Environment of a managerial situation
Which comes first – data or models - Both
Managerial Decision First
- Marketer knows from experience what will drive sales and
Designs a system to capture those variables
Data First
- See next slide
MODELING WITH DATA
Consider the following data.
Graphs are created to view any relationship(s) between the variables.
This is the first step in formulating the equations in the model.
Creating the Symbolic Model
Predicting Sales Based on Marketing Expenditures
Sales Revenue (y)
3000
y = 3.5853x + 357.7
R2 = 0.9316
2500
2000
1500
1000
500
0
0
100
200
300
400
500
Marketing Expenses (x)
600
700
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.