Decision-making and Quantitative Modeling
Chapter1:
What do all of these have in common?
They are models. They can be physical models such as a miniature model of an airplane, or of a
building, a globe, etc. Physical models are built to look like the real thing and to resemble the
larger object. Two dimensional models such as maps also show the shape of the object.
The second type of models are Analog Models. Analog Models try to represent something but
they do not look like the object they are modeling. For example, a thermometer is a model that
measures the heat in the room but it does not look like the heat. Other examples are
hourglasses, speedometers etc.
Finally, there are Mathematical Models. These are models that represent a certain object using
a mathematical relationship or equation. For example, Profit = Revenues Expenses.
Mathematical
Relationships
Independent or
decision variables
(controllable
factors)
Parameters
(uncontrollable
factors)
Dependent
Variables
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Area
Dependent Variables
Inde
Para
Finance
Profit
Rate of Return
Earnings per Share
Investment amount
Period of investment
Timing of investment
Inflation rate
Interest rate
Marketing
Market share
Customer satisfaction
Advertising budget
Zonal sales reps
price
Disposable income
Competitors actions
Manufacturing
Total cost
Quality measures
spoilage
Production amounts
Inventory levels
Machine capacity
Technology
Materials prices
Services
Customer satisfaction
Number of servers
Demand for service
What is a model?
A simplified representation or an abstraction of reality.
Physical models are simplified because they are smaller and have less detail.
Mathematical models look at the more detailed aspects and have an abstraction
Important qualities:
o Validity (it should look like what it tries to represent)
o Usability (should be usable)
o Value (there is a cost associated in building it that we should get back)
Benefits of Modeling
Models enable the compression of time
Allow for easier manipulation than the real system
Lower cost that experimenting with the real system
Allows us to consider risk
Cost of mistakes is smaller
Models help us learn and understand the real system
The modeling process forces the use of rigorous thinking
Mathematical models let us analyze a large number of possible solutions
Drawbacks of Modeling
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Can be time-consuming and expensive
Data collection can be expensive or impossible
It may be difficult to assess uncertainties
Oversimplification may produce poor results
Managers must agree to accept results
Common perception that if it is done on a computer, it must be correct
Uses of Models
Prescriptive
o Used to find the best or optimal solution to a problem by
Enumeration or
Algorithm
Descriptive
o Characterize things as they are
o Investigate outcomes and consequences
o Predict the behaviour of systems in certain situations
o Solutions are not necessarily optimal
Effective Modelers
Internal skills
o Creativity
o Sensitivity to the client
o Persistence
Interpersonal skills
o Communication
o Teamwork
Expertise in quantitative business modeling
The Modeling Process
1. Opportunity / problem recognition
2. Model formulation
3. Data collection
4. Analysis of
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