Materi 2Materi 2(Chapter 2)(Chapter 2)

ntroduction to ntroduction to Quantitative Quantitative

AnalysisAnalysis

II

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.

Chapter OutlineChapter Outline

2.1 Introduction2.2 What Is Quantitative Analysis

(QA)?2.3 The QA Approach2.4 How to Develop a QA Model2.5 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

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.

Examples of Examples 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.

Quantitative Analysis:

A scientific approach to managerial decisionmaking whereby raw data are processed

and manipulated resulting in meaningfulinformation.

Raw DataQuantitative

AnalysisMeaningfulInformation

Overview of Overview 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.

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

Define the ProblemDefine the Problem

Problem Definition:A clear and concise statement

thatgives direction and meaning to

thesubsequent QA steps and requiresspecific, 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.

Develop the ModelDevelop the Model

Quantitative Analysis Model:A realistic, solvable, and understandable

mathematical statement showing the relationship

between variables.

sales

reve

nu

es

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).

Acquire DataAcquire Data

Model Data:Accurate input data that may come

from avariety of sources such as company

reports,company documents, interviews, on-

sitedirect measurement, or statistical

sampling.Garbage InGarbage In Garbage OutGarbage Out=

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).

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!

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.

Implement the ResultsImplement the Results

Results Implementation:

The incorporation of the solution

into the company and the monitoring of

the results.

Modeling in the Real Modeling 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!

Possible Pitfalls in Possible Pitfalls in Using ModelsUsing Models

Prior to developing and implementingmodels, managers should be aware of

thepotential 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

Possible Pitfalls Possible Pitfalls (Continued)(Continued)

Develop a Solution Complex mathematics Solutions become quickly

outdated

Test the Solution Identifying appropriate test

proceduresAnalyze the Results Holding all other conditions

constant Identifying cause and effectImplement the Solution Selling the solution to others

Bagels R Us QA Model Bagels 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 yourdaily 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)

Bagels R Us QA Model Bagels R Us QA Model Breakeven ExampleBreakeven Example

Breakeven point occurs when Breakeven 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)

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.

Conclusions Conclusions (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.

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.

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.

QM for WindowsQM for Windows

QM for WindowsQM for Windows

Excel QMExcel QM

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

Excel QM’s Main Menu of Excel QM’s Main Menu of Models continuedModels continued

The highlighted area shows forecasting models

The EndThe End