Quantitative Management

1. To accompany
Quantitative
1-1 © 2006 by
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Chapter 1Chapter 1
Introduction toIntroduction to
QuantitativeQuantitative
AnalysisAnalysis
Prepared by Lee Revere and John LargePrepared by Lee Revere and John Large

2. To accompany
Quantitative
1-2 © 2006 by
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Learning ObjectivesLearning Objectives
Students will be able to:
1. Describe the quantitative
analysis (QA) approach.
2. Understand the application of
QA in a real situation.
3. Describe the use of modeling in
QA.
4. Use computers and spreadsheet
models to perform QA.
5. Discuss possible problems in
using quantitative analysis.
6. Perform a break-even analysis.

3. To accompany
Quantitative
1-3 © 2006 by
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Chapter OutlineChapter Outline
1.1 Introduction
1.2 What Is Quantitative Analysis
(QA)?
1.3 The QA Approach
1.4 How to Develop a QA Model
1.5 The Role of Computers and
Spreadsheet Models in the QA
Approach
1.6 Possible Problems in the QA
Approach
1.7 Implementation - Not Just the
Final Step

4. To accompany
Quantitative
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IntroductionIntroduction
 Mathematical tools have been
used for thousands of years.
 QA can be applied to a wide
variety of problems.
 One must understand the
specific applicability of the
technique, its limitations, and its
assumptions.

5. To accompany
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Examples ofExamples of
Quantitative AnalysesQuantitative Analyses
 Taco Bell saved over $150 million
using forecasting and scheduling QA
models.
 NBC increased revenues by over $200
million by using QA to develop better
sales plans.
 Continental Airlines saved over $40
million using QA models to quickly
recover from weather and other
disruptions.

6. To accompany
Quantitative
1-6 © 2006 by
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Quantitative Analysis:
A scientific approach to managerial decision
making whereby raw data are processed and
manipulated resulting in meaningful information.
Raw Data
Quantitative
Analysis
Meaningful
Information
Overview ofOverview of
Quantitative AnalysisQuantitative Analysis
Qualitative Factors:
Information that may be difficult to quantify but
can affect the decision-making process such as
the weather, state, and federal legislation.

7. To accompany
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The QA Approach:The QA Approach:
Fig 1.1Fig 1.1
Define
the problem
Develop
a model
Acquire
input data
Develop
a solution
Test
the solution
Analyze
the results
Implement
the results

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Define the ProblemDefine the Problem
Problem Definition:
A clear and concise statement that gives
direction and meaning to the subsequent QA steps
and requires specific, measurable objectives.
THIS MAY BE THE MOST DIFFICULT STEP!
…because true problem causes must be identified
and the relationship of the problem to other
organizational processes must be considered.

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Develop the ModelDevelop the Model
Quantitative Analysis Model:
A realistic, solvable, and understandable
mathematical statement showing the relationship
between variables.
sales
revenues
y = mx + b
Models contain both controllable (decision variables)
and uncontrollable variables and parameters. Typically,
parameters are known quantities (salary of sales force)
while variables are unknown (sales quantity).

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Acquire DataAcquire Data
Model Data:
Accurate input data that may come from a variety
of sources such as company reports, company
documents, interviews, on-site direct measurement,
or statistical sampling.
Garbage InGarbage In Garbage OutGarbage Out=

11. To accompany
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Develop a SolutionDevelop a Solution
Model Solution:
 The best model solution is found by
manipulating the model variables until a
practical and implemental solution is
obtained.
 Manipulation can be done by solving
the equation(s), trying various
approaches (trial and error), trying all
possible variables (complete
enumeration), and/or implementing an
algorithm (repeating a series of steps).

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Test the SolutionTest the Solution
Model Testing:
The collection of data from a different
source to validate the accuracy and
completeness and sensibility of both
the model and model input data ~
consistency of results is key!

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Analyze the ResultsAnalyze the Results
Results Analysis:
Understanding actions implied by the
solution and their implications, as well
as conducting a sensitivity analysis (a
change to input values or the model) to
evaluate the impact of a change in model
parameters.
Sensitivity analyses allow the “what-
ifs” to be answered.

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Implement the ResultsImplement the Results
Results Implementation:
The incorporation of the solution
into the company and the monitoring of
the results.

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Modeling in the RealModeling in the Real
WorldWorld
Real World Models can be:
 Complex,
 expensive, and
 difficult to sell.
BUT…
Real world models are used in the real
world by real organizations to solve
real problems!

16. To accompany
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Possible Pitfalls inPossible Pitfalls in
Using ModelsUsing Models
Prior to developing and implementing models,Prior to developing and implementing models,
managers should be aware of the potentialmanagers should be aware of the potential
pitfalls.pitfalls.
Define the Problem
 Conflicting viewpoints
 Departmental impacts
 Assumptions
Develop a Model
 Fitting the model
 Understanding the model
Acquire Input Data
 Availability of data
 Validity of data

17. To accompany
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Possible PitfallsPossible Pitfalls
(Continued)(Continued)
Develop a Solution
 Complex mathematics
 Solutions become quickly outdated
Test the Solution
 Identifying appropriate test procedures
Analyze the Results
 Holding all other conditions constant
 Identifying cause and effect
Implement the Solution
 Selling the solution to others

18. To accompany
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Bagels R Us QA ModelBagels R Us QA Model
ExampleExample
Profits = Revenue - Expenses
Profits = $1Q - $100 - $.5Q
Assume you are the new owner of Bagels R Us and
you want to develop a mathematical model for your
daily profits and breakeven point. Your fixed
overhead is $100 per day and your variable costs
are 0.50 per bagel (these are GREAT bagels). You
charge $1 per bagel.
(Price per Unit) × (Number Sold)
- Fixed Cost
- (Variable Cost/Unit) × (Number Sold)

19. To accompany
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Bagels R Us QA ModelBagels R Us QA Model
Breakeven ExampleBreakeven Example
Breakeven point occurs whenBreakeven point occurs when
Revenue = ExpensesRevenue = Expenses
Where, Q = quantity of bagels sold
F = fixed cost per day of operation
V = variable cost/bagel
So,
$1Q = $100 + $.5Q
Solve for Q
$1Q - .5Q = 100 => Q = 200
Breakeven Quantity = F/(P-V)Breakeven Quantity = F/(P-V)

20. To accompany
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ConclusionsConclusions
Models can help
managers:
 Gain deeper insight into the
nature of business relationships.
 Find better ways to assess
values in such relationships; and
 See a way of reducing, or at
least understanding, uncertainty
that surrounds business plans
and actions.

21. To accompany
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ConclusionsConclusions
(continued)(continued)
Models:
 Are less expensive and disruptive than
experimenting with real world systems,
but may be expensive to develop and test.
 Allow “What ifWhat if” questions to be asked.
 Are built for management problems and
encourage input, but may be
misunderstood due to the mathematical
complexity.
 Enforce consistency in approach.
 Require specific constraints and goals, but
tend to downplay qualitative information.
 Help communicate problem solutions to
others, but may oversimplify assumptions
and variables.

22. To accompany
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Models: The Up SideModels: The Up Side
Models:
 accurately represent reality.
 help a decision maker
understand the problem.
 save time and money in problem
solving and decision making.
 help communicate problems and
solutions to others.
 provide the only way to solve
large or complex problems in a
timely fashion.

23. To accompany
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Models: The Down SideModels: The Down Side
Models:
 may be expensive and time-
consuming to develop and test.
 are often misused and
misunderstood (and feared)
because of their mathematical
complexity.
 tend to downplay the role and
value of nonquantifiable
information.
 often have assumptions that
oversimplify the variables of the
real world.

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QM for WindowsQM for Windows

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QM for WindowsQM for Windows

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Excel QMExcel QM

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Excel QM’s MainExcel QM’s Main
Menu of ModelsMenu of Models

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Excel QM’s Main Menu ofExcel QM’s Main Menu of
Models continuedModels continued
The highlighted area shows forecasting modelsThe highlighted area shows forecasting models