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Chap 1 and Chap 13 Mansci

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What is Quantitative Analysis?
Mathematical tools have been used for
thousands of years.
Quantitative analysis can be applied to a
wide variety of problems.
• It’s not enough to just know the
mathematics of a technique.
• One must understand the specific
applicability of the technique, its
limitations, and its assumptions.
Body of Knowledge
The body of knowledge involving
quantitative approaches to decision
making is referred to as
• Management Science
• Operations Research
• Decision Science
It had its early roots in World War II
and is flourishing in business and
industry due, in part, to:
• numerous methodological
developments (e.g. simplex
method for solving linear
programming problems)
a virtual explosion in computing power
Examples of Quantitative Analyses
In the mid 1990s, Taco Bell saved
over $150 million using forecasting
and scheduling quantitative analysis
NBC television increased revenues
by over $200 million between 1996
and 2000 by using quantitative
analysis to develop better sales
Continental Airlines saved over $40
million in 2001 using quantitative
analysis models to quickly recover
Quantitative analysis is a scientific
approach to managerial decision making in
which raw data are processed and
manipulated to produce meaningful
Raw data
◼ Quantitative factors are data that
can be accurately calculated.
Examples include:
◼ Different investment
◼ Interest rates
◼ Inventory levels
◼ Demand
◼ Labor cost
◼ Qualitative factors are more
difficult to quantify but affect the
decision process.
Examples include:
◼ The weather
◼ State legislations, laws, bills
◼ Technological
The Quantitative Analysis Approach
Defining the Problem
Developing a Model
Acquiring Input Data
Developing a Solution
Testing the Solution
Analyzing the Results
Implementing the Results
Step 1: Defining the Problem
Develop a clear and concise statement that
gives direction and meaning to subsequent
◼ This may be the most important
and difficult step.
◼ It is essential to go beyond
symptoms and identify true causes.
◼ It may be necessary to concentrate
on only a few of the problems –
selecting the right problems is very
◼ Specific and measurable objectives
may have to be developed.
Data may come from a variety of
sources such as company reports,
company documents, interviews, on-site
direct measurement, or statistical
Step 4: Developing a Solution
The best (optimal) solution to a problem
is found by manipulating the model
variables until a solution is found that is
practical and can be implemented.
Common techniques are
◼ Solving equations.
Step 2: Developing a Model
Quantitative analysis models are realistic,
solvable, and understandable mathematical
representations of a situation.
There are different types of models:
1. Scale Models
2. Schematic Models
Models generally contain variables
(controllable and uncontrollable) and
o Controllable variables are the
decision variables and are
generally unknown.
▪ How many items
should be ordered for
o Parameters are known
quantities that are a part of
the model.
▪ What is the holding
cost of the inventory?
Step 3: Acquiring Input Data
Input data must be accurate – GIGO rule:
1. Garbage In
2. Process
3. Garbage Out
◼ Trial and error – trying
various approaches and
picking the best result.
◼ Complete enumeration –
trying all possible values.
◼ Using an algorithm – a series
of repeating steps to reach a
Step 5: Testing the Solution
Both input data and the model should be
tested for accuracy before analysis and
◼ New data can be collected to
test the model.
◼ Results should be logical,
consistent, and represent the
real situation.
Step 6: Analyzing the Results
Determine the implications of the solution:
◼ Implementing results often
requires change in an
◼ The impact of actions or
changes needs to be studied
and understood before
Sensitivity analysis determines how much
the results will change if the model or input
data changes.
◼ Sensitive models should be
very thoroughly tested.
How To Develop a Quantitative Analysis
A mathematical model of profit:
Profit = Revenue – Expenses
Expenses can be represented as the sum of
fixed and variable costs. Variable costs are
the product of unit costs times the number
of units.
Step 7: Implementing the Results
Implementation incorporates the solution
into the company.
◼ Implementation can be very
=Revenue – (Fixed cost + Variable cost)
=(Selling price per unit)(number of units
sold) – [Fixed cost + (Variable costs per
unit)(Number of units sold)]
◼ People may be resistant to
◼ Many quantitative analysis
efforts have failed because a
good, workable solution was
not properly implemented.
Changes occur over time, so even
successful implementations must be
monitored to determine if modifications are
=sX – [f + vX]
=sX – f – vX
s = selling price per unit
v = variable cost per unit
f = fixed cost
X = number of units sold
Modeling in the Real World
The parameters of this model are f, v, and s
as these are the inputs inherent in the
Quantitative analysis models are used
extensively by real organizations to solve
real problems.
The decision variable of interest is X
◼ In the real world, quantitative
analysis models can be
complex, expensive, and
difficult to sell.
◼ Following the steps in the
process is an important
component of success.
Advantages of Mathematical Modeling
1. Models can accurately represent
2. Models can help a decision maker
formulate problems.
3. Models can give us insight and
4. Models can save time and money in
decision making and problem
5. A model may be the only way to
solve large or complex problems in a
timely fashion.
6. A model can be used to
communicate problems and
solutions to others.
Models Categorized by Risk
◼ Mathematical models that do not
involve risk are called deterministic
◼ All of the values used in the
model are known with
complete certainty.
◼ Mathematical models that involve
risk, chance, or uncertainty are
called probabilistic models.
◼ Values used in the model are
estimates based on
Computers and Spreadsheet Models
QM for Windows
◼ An easy to use decision support
system for use in POM and QM
◼ This is the main menu of quantitative
◼ There may be an impact on
other departments.
◼ Beginning assumptions may
lead to a particular
◼ The solution may be
Developing a model
◼ Manager’s perception may
not fit a textbook model.
◼ There is a trade-off between
complexity and ease of
Acquiring accurate input data
◼ Accounting data may not be
collected for quantitative
◼ The validity of the data may
be suspect.
Developing an appropriate solution
◼ The mathematics may be
hard to understand.
◼ Having only one answer may
be limiting.
Testing the solution for validity
Analyzing the results in terms of the whole
Implementation – Not Just the Final Step
Excel QM’s Main Menu (2010)
◼ Works automatically within Excel
Possible Problems in the Quantitative
Analysis Approach
Defining the problem
◼ Problems may not be easily
◼ There may be conflicting
There may be an institutional lack of
commitment and resistance to change.
◼ Management may fear the
use of formal analysis
processes will reduce their
decision-making power.
◼ Action-oriented managers
may want “quick and dirty”
◼ Management support and
user involvement are
There may be a lack of commitment by
quantitative analysts.
◼ Analysts should be involved
with the problem and care
about the solution.
◼ Analysts should work with
users and take their feelings
into account.
◼ What is involved in making a good
◼ Decision theory is an analytic and
systematic approach to the study of
decision making.
◼ A good decision is one that is based
on logic, considers all available data
and possible alternatives, and the
quantitative approach described
The Six Steps in Decision Making
1. Clearly define the problem at hand.
2. List the possible alternatives.
3. Identify the possible outcomes or
states of nature.
4. List the payoff (typically profit) of
each combination of alternatives and
5. Select one of the mathematical
decision theory models.
6. Apply the model and make your
Types of Decision-Making Environments
Type 1: Decision making under certainty
◼ The decision maker knows
with certainty the
consequences of every
alternative or decision
Type 2: Decision making under uncertainty
◼ The decision maker does not
know the probabilities of the
various outcomes.
Type 3: Decision making under risk
◼ The decision maker knows
the probabilities of the
various outcomes.
Decision Making Under Uncertainty
Maximax (optimistic)
Maximin (pessimistic)
Criterion of realism (Hurwicz)
Equally likely (Laplace)
Minimax regret
Maximax (best of the best)
Used to find the alternative that maximizes
the maximum payoff.
• Locate the maximum payoff for each
• Select the alternative with the
maximum number
Maximin (best of the worst)
Used to find the alternative that maximizes
the minimum payoff.
• Locate the minimum payoff for each
• Select the alternative with the
maximum number.
Criterion of Realism (Hurwicz)
This is a weighted average compromise
between optimism and pessimism.
◼ Select a coefficient of realism
, with 0≤α≤1.
◼ A value of 1 is perfectly
optimistic, while a value of 0
is perfectly pessimistic.
◼ Compute the weighted
averages for each
◼ Select the alternative with the
highest value.
◼ Weighted average = (maximum in
row) + (1 – )(minimum in row)
Equally Likely (Laplace)
Considers all the payoffs for each
Find the average payoff for each
Select the alternative with the
highest average.
Minimax Regret
Based on opportunity loss or regret, this is
the difference between the optimal profit
and actual payoff for a decision.
◼ Create an opportunity loss
table by determining the
opportunity loss from not
choosing the best alternative.
◼ Opportunity loss is calculated
by subtracting each payoff in
the column from the best
payoff in the column.
◼ Find the maximum
opportunity loss for each
alternative and pick the
alternative with the minimum
Decision Making Under Risk
◼ This is decision making when there
are several possible states of nature,
and the probabilities associated with
each possible state are known.
◼ The most popular method is to
choose the alternative with the
highest expected monetary value
◼ This is very similar to the
expected value calculated in
the last chapter.
◼ EMV (alternative i) = (payoff of
first state of nature) x (probability of
first state of nature) + (payoff of
second state of nature) x (probability
of second state of nature) + … +
(payoff of last state of nature) x
(probability of last state of nature)
Expected Value of Perfect Information
◼ EVPI places an upper bound on
what you should pay for additional
EVPI = EVwPI – Maximum EMV
◼ EVwPI is the long run average return
if we have perfect information before
a decision is made.
◼ EVwPI = (best payoff for first state of
nature) x (probability of first state of
nature) + (best payoff for second
state of nature) x (probability of
second state of nature) + … + (best
payoff for last state of nature) x
(probability of last state of nature)
Expected Opportunity Loss
◼ Expected opportunity loss (EOL) is
the cost of not picking the best
◼ First construct an opportunity loss
◼ For each alternative, multiply the
opportunity loss by the probability of
that loss for each possible outcome
and add these together.
◼ Minimum EOL will always result in
the same decision as maximum
◼ Minimum EOL will always equal
Sensitivity Analysis
Sensitivity analysis examines how the
decision might change with different
input data.
Structure of Decision Trees
◼ Trees start from left to right.
◼ Trees represent decisions and
outcomes in sequential order.
For the Thompson Lumber example:
◼ Squares represent decision
P = probability of a favorable
(1 – P) = probability of an
unfavorable market
◼ Circles represent states of
nature nodes.
◼ Lines or branches connect
the decisions nodes and the
states of nature.
Decision Trees
◼ Any problem that can be presented
in a decision table can also be
graphically represented in a decision
◼ Decision trees are most beneficial
when a sequence of decisions must
be made.
◼ All decision trees contain decision
points or nodes, from which one of
several alternatives may be chosen.
◼ All decision trees contain state-ofnature points or nodes, out of which
one state of nature will occur.
Five Steps of Decision Tree Analysis
1. Define the problem.
2. Structure or draw the decision tree.
3. Assign probabilities to the states of
4. Estimate payoffs for each possible
combination of alternatives and
states of nature.
5. Solve the problem by computing
expected monetary values (EMVs)
for each state of nature node.
Expected Value of Sample Information
◼ Suppose Thompson wants to know
the actual value of doing the survey.
Expected value
with sample
no cost to
gather it
Expected value
of best decision
without sample
= (EV with sample information + cost) – (EV
without sample information)