1. Materi 2 (Chapter 2) ntroduction to Quantitative Analysis

2. Learning Objectives Students will be able to:
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.

3. Chapter Outline 2.1 Introduction
2.2 What Is Quantitative Analysis (QA)?
2.3 The QA Approach
2.4 How to Develop a QA Model
2.5 The Role of Computers and Spreadsheet Models in the QA Approach
2.6 Possible Problems in the QA Approach
2.7 Implementation - Not Just the Final Step

4. 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.

5. 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.

6. Overview of Quantitative Analysis
A scientific approach to managerial decision
making whereby raw data are processed and
manipulated resulting in meaningful
information.
Quantitative
Analysis
Meaningful
Information
Raw Data
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. 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

8. 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.

9. Develop the Model Quantitative Analysis Model:
A realistic, solvable, and understandable
mathematical statement showing the relationship
between variables.
revenues
y = mx + b
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).

10. 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
Garbage Out =

11. 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).

12. 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!

13. 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 “what-
ifs” to be answered.

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

15. 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!

16. 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

17. 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

18. 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

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

20. 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.

21. 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.

22. 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.

23. Models: 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.

24. QM for Windows

25. QM for Windows

26. Excel QM

27. Excel QM’s Main Menu of Models

28. Excel QM’s Main Menu of Models continued
The highlighted area shows forecasting models
The End