Chapter 1
Introduction to
Quantitative
Analysis
Prepared by Lee Revere and John Large
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-1
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Learning Objectives
Students will be able to:
1.
2.
3.
4.
5.
6.
Describe the quantitative
analysis (QA) approach.
Understand the application of
QA in a real situation.
Describe the use of modeling in
QA.
Use computers and spreadsheet
models to perform QA.
Discuss possible problems in
using quantitative analysis.
Perform a break-even analysis.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-2
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Chapter Outline
1.1
1.2
1.3
1.4
1.5
1.6
1.7
Introduction
What Is Quantitative Analysis
(QA)?
The QA Approach
How to Develop a QA Model
The Role of Computers and
Spreadsheet Models in the QA
Approach
Possible Problems in the QA
Approach
Implementation - Not Just the
Final Step
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-3
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Introduction
 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-4
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Examples of
Quantitative 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-5
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Overview of
Quantitative Analysis
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
Qualitative Factors:
Information that may be difficult to quantify but
can affect the decision-making process such as
the weather, state, and federal legislation.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-6
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
The QA Approach:
Fig 1.1
Define
the problem
Develop
a model
Acquire
input data
Develop
a solution
Test
the solution
Analyze
the results
Implement
the results
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-7
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Define 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-8
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Develop the Model
Quantitative Analysis Model:
A realistic, solvable, and understandable
mathematical statement showing the relationship
revenues
between variables.
sales
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).
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-9
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Acquire 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 In
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
=
1-10
Garbage Out
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Develop 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).
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-11
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Test 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!
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-12
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Analyze 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 “whatifs” to be answered.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-13
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Implement the Results
Results Implementation:
The incorporation of the solution
into the company and the monitoring of
the results.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-14
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Modeling in the Real
World
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!
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-15
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Possible Pitfalls in
Using Models
Prior to developing and implementing models,
managers should be aware of the potential
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
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-16
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Possible Pitfalls
(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
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-17
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Bagels R Us QA Model
Example
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.
Profits = Revenue - Expenses
(Price per Unit)  (Number Sold)
- Fixed Cost
- (Variable Cost/Unit)  (Number Sold)
Profits = $1Q - $100 - $.5Q
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-18
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Bagels R Us QA Model
Breakeven Example
Breakeven point occurs when
Revenue = Expenses
So,
$1Q = $100 + $.5Q
Solve for Q
$1Q - .5Q = 100 => Q = 200
Where, Q = quantity of bagels sold
F = fixed cost per day of operation
V = variable cost/bagel
Breakeven Quantity = F/(P-V)
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-19
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Conclusions
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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-20
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Conclusions
(continued)
Models:
 Are less expensive and disruptive than
experimenting with real world systems, but
may be expensive to develop and test.
 Allow “What 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-21
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Models: 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-22
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Models: The Down Side
Models:
 may be expensive and timeconsuming 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.
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-23
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
QM for Windows
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-24
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
QM for Windows
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-25
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Excel QM
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-26
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Excel QM’s Main
Menu of Models
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-27
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458
Excel QM’s Main Menu of
Models continued
The highlighted area shows forecasting models
To accompany Quantitative Analysis
for Management, 9e
by Render/Stair/Hanna
1-28
© 2006 by Prentice Hall, Inc.
Upper Saddle River, NJ 07458