Quantitative Analysis-数量分析简介

Chapter 1 Introduction to Quantitative

Analysis

Learning 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.

https://www.sodocs.net/doc/4b17181939.html,e computers and spreadsheet

models to perform QA.

5.Discuss possible problems in

using quantitative analysis. 6.Perform a break-even analysis.

Chapter Outline

1.1Introduction

1.2What Is Quantitative Analysis

(QA)?

1.3The QA Approach

1.4How to Develop a QA Model

1.5The Role of Computers and

Spreadsheet Models in the QA

Approach

1.6Possible Problems in the QA

Approach

1.7Implementation -Not Just the

Final Step

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.

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.

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 of

Quantitative 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:

Fig 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 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.

Develop the Model

Quantitative Analysis Model:

A realistic, solvable, and understandable

mathematical statement showing the relationship between variables.

sales

r e v e n u e s y =

m x + 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 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 In Garbage Out Garbage Out =

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).

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!

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.

Implement the Results

Results Implementation:

The incorporation of the solution

into the company and the monitoring of the results.

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!

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

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

Example 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)

Breakeven Example Breakeven point occurs when Revenue = 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)

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.