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**IBM401 Quantitative Analysis in Business**

To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created by Jeff Heyl

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**Question Doing Business effectively What do you need for this journey?**

…………………… Why do you study this subject? Ans. It is in this BBA.course. More from you….

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**IBM401 / Quantitative Analysis in Business**

1. Course Code and Course Name IBM401 / Quantitative Analysis in Business 2. Credit (Lecture hour – Lab. Hour – Self Study) 3(3-0-6) 4. Coordinated Lecturer and Lecturer 4.1 Coordinated Lecturer Asst.Prof. Dr W.Chalermkiat (WCK) 4.2 Lecturer Asst. Prof. Dr. R. Rathavoot (RR) 4.3 Lecturer Aj. P. Pongpat(PP) 4.4 Lecturer (IE)

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1. Subject Purpose Understand and know how to apply statistical and basic econometrics in evaluating business decisions Describe the main assumptions associated with regression and time series models Understand the effects of violation of importance assumptions underlying regression models Able to detect and correct violations concerning the assumptions of regression and time series model

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1. Course Description The unit presents the basic statistical and econometric methodologies in model building and model evaluation in general, and the treatment of autocorrelation, lagged relationship, qualitative variables, multicollinearity and heteroskedasticity, in particular it covers estimation and evaluation of multiple regression models, and testing for the validity of various theories in the areas of business. It also introduces the recent literature on unit root and cointegration in business data analysis.

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**and Teaching aids media**

1. Lesson plan Week Content Description Study Period Learning Activities and Teaching aids media Lecturer 1 19/6 Overview Introduction to Quantitative Analysis Lecture Exercise Q&A, PowerPoint/Excel WCK 2 26/6 Basic Probability & Statistics /Linear Programming IE 3 3/7 Linear Programming 4 10/7 5 17/7 6 24/7 Decision Theory /Project Management1 7 31/7 Project Management2 Test1 8 Midterm -

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**and Teaching aids media**

1. Lesson plan Week Content Description Study Period Learning Activities and Teaching aids media Lecturer 9 14/8 Regression Models Lecture Exercise Q&A, PowerPoint/Excel WCK 10 21/8 Forecasting PP 11 28/8 Inventory Control Models 12 4/9 Transportation and Assignment Model RR 13 11/9 Network Models 14 18/9 Waiting Lines and Queuing Theory Models 15 25/9 Simulation Modeling and Game Theory 16 2/10 Presentation and Wrap Up/Test2 Presentation 17 Final Examination -

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**2. Learning Evaluation Plan**

Skill Evaluation Method Week of Evaluation Score (%) 1, 2, 3, 4, 5 Work assignment 16 20 1, 2, 3, 4 Class Participations 1 - 16 10 1, 2, 3, 5 Midterm Examination 8 30 Final Examination 17 40

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1. Course Materials Textbook, Class notes (PowerPoint Slides), Internet Resources 2. Important Textbooks and References Render/Stair/Hana Quantitative Analysis for Management, 10th edn, Pearson Prentice Hall 2009

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**Introduction to Quantitative Analysis Modified by WCK**

Chapter 1 Introduction to Quantitative Analysis Modified by WCK To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created by Jeff Heyl © 2009 Prentice-Hall, Inc.

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Learning Objectives After completing this chapter, students will be able to: Describe the quantitative analysis approach Understand the application of quantitative analysis in a real situation Describe the use of modeling in quantitative analysis Use computers and spreadsheet models to perform quantitative analysis(Road Show) Discuss possible problems in using quantitative analysis Perform a break-even analysis

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**Chapter Outline 1.1 Introduction 1.2 What Is Quantitative Analysis?**

1.3 The Quantitative Analysis Approach 1.4 How to Develop a Quantitative Analysis Model 1.5 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach 1.6 Possible Problems in the Quantitative Analysis Approach 1.7 Implementation — Not Just the Final Step

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**Introduction Mathematical tools have been used for thousands of years**

Quantitative analysis can be applied to a wide variety of problems It’s not enough to just know the mathematics of a technique One must understand the specific applicability of the technique, its limitations, and its assumptions

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**Examples of Quantitative Analyses**

Taco Bell saved over $150 million using forecasting and scheduling quantitative analysis models NBC television increased revenues by over $200 million by using quantitative analysis to develop better sales plans Continental Airlines saved over $40 million using quantitative analysis models to quickly recover from weather delays and other disruptions

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**What is Quantitative Analysis?**

Quantitative analysis is a scientific approach to managerial decision making whereby raw data are processed and manipulated resulting in meaningful information Raw Data Quantitative Analysis Meaningful Information

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**What is Quantitative Analysis?**

Quantitative factors might be different investment alternatives, interest rates, inventory levels, demand, or labor cost Qualitative factors such as the weather, state and federal legislation, and technology breakthroughs should also be considered Information may be difficult to quantify but can affect the decision-making process

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**The Quantitative Analysis Approach**

Defining the Problem Developing a Model Acquiring Input Data Developing a Solution Testing the Solution Analyzing the Results Implementing the Results Figure 1.1

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Defining the Problem Need to develop a clear and concise statement that gives direction and meaning to the following steps This may be the most important and difficult step It is essential to go beyond symptoms and identify true causes May be necessary to concentrate on only a few of the problems – selecting the right problems is very important Specific and measurable objectives may have to be developed

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Developing a Model Quantitative analysis models are realistic, solvable, and understandable mathematical representations of a situation $ Advertising $ Sales Y = b0 + b1X There are different types of models Schematic models Scale models

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Developing a Model Models generally contain variables (controllable and uncontrollable) and parameters Controllable variables are generally the decision variables and are generally unknown Parameters are known quantities that are a part of the problem

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**Acquiring Input Data Input data must be accurate – GIGO rule**

Garbage In Process Garbage Out Data may come from a variety of sources such as company reports, company documents, interviews, on-site direct measurement, or statistical sampling

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Developing a Solution The best (optimal) solution to a problem is found by manipulating the model variables until a solution is found that is practical and can be implemented Common techniques are Solving equations Trial and error – trying various approaches and picking the best result Complete enumeration – trying all possible values Using an algorithm – a series of repeating steps to reach a solution

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**How many should apples be in Balance C?**

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**How many apples are there?**

A = Apple B = Banana C = Tomato a : A+B = 6C ………….[1] b : B = A+2C……….[2] c : B+2C = XA………….[3] [1]-[2]; A =6C-A-2C 2A = 4C A = 2C [2],[4]; B = 2C+2C =4C = 2A 2A+A =XA 3A =XA Find X = 3

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Math. Model 1 The objective of Business Profit = ? More detail…..

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**Math. Model 2 In AAA Shop A Boy has 10 Baht.**

He buys a toy that cost is 4 Baht. How much is the change? Pls. answer.

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Testing the Solution Both input data and the model should be tested for accuracy before analysis and implementation New data can be collected to test the model Results should be logical, consistent, and represent the real situation

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**Analyzing the Results Determine the implications of the solution**

Implementing results often requires change in an organization The impact of actions or changes needs to be studied and understood before implementation Sensitivity analysis determines how much the results of the analysis will change if the model or input data changes Sensitive models should be very thoroughly tested

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**Implementing the Results**

Implementation incorporates the solution into the company Implementation can be very difficult People can resist changes Many quantitative analysis efforts have failed because a good, workable solution was not properly implemented Changes occur over time, so even successful implementations must be monitored to determine if modifications are necessary

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**Modeling in the Real World**

Quantitative analysis models are used extensively by real organizations to solve real problems In the real world, quantitative analysis models can be complex, expensive, and difficult to sell Following the steps in the process is an important component of success

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**How To Develop a Quantitative Analysis Model**

An important part of the quantitative analysis approach Let’s look at a simple mathematical model of profit Profit = Revenue – Expenses

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**How To Develop a Quantitative Analysis Model**

Expenses can be represented as the sum of fixed and variable costs and variable costs are the product of unit costs times the number of units Profit = Revenue – (Fixed cost + Variable cost) Profit = (Selling price per unit)(number of units sold) – [Fixed cost + (Variable costs per unit)(Number of units sold)] Profit = sX – [f + vX] Profit = sX – f – vX where s = selling price per unit v = variable cost per unit f = fixed cost X = number of units sold

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**How To Develop a Quantitative Analysis Model**

Expenses can be represented as the sum of fixed and variable costs and variable costs are the product of unit costs times the number of units Profit = Revenue – (Fixed cost + Variable cost) Profit = (Selling price per unit)(number of units sold) – [Fixed cost + (Variable costs per unit)(Number of units sold)] Profit = sX – [f + vX] Profit = sX – f – vX The parameters of this model are f, v, and s as these are the inputs inherent in the model The decision variable of interest is X where s = selling price per unit v = variable cost per unit f = fixed cost X = number of units sold

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**EXAMPLE: PRITCHETT’S PRECIOUS TIME PIECES**

We will use the Bill Pritchett clock repair shop example to demonstrate the use of mathematical models. Bill’s company, Pritchett’s Precious Time Pieces, buys, sells, and repairs old clocks and clock parts. Bill sells rebuilt springs for a price per unit of $10. The fixed cost of the equipment to build the springs is $1,000. The variable cost per unit is $5 for spring material. In this example,

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**Pritchett’s Precious Time Pieces**

The company buys, sells, and repairs old clocks. Rebuilt springs sell for $10 per unit. Fixed cost of equipment to build springs is $1,000. Variable cost for spring material is $5 per unit. s = 10 f = 1,000 v = 5 Number of spring sets sold = X Profits = sX – f – vX If sales = 0, profits = –$1,000 If sales = 1,000, profits = [(10)(1,000) – 1,000 – (5)(1,000)] = $4,000

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**Pritchett’s Precious Time Pieces**

Companies are often interested in their break-even point (BEP). The BEP is the number of units sold that will result in $0 profit. 0 = sX – f – vX, or = (s – v)X – f Solving for X, we have f = (s – v)X X = f s – v BEP = Fixed cost (Selling price per unit) – (Variable cost per unit)

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**Pritchett’s Precious Time Pieces**

Companies are often interested in their break-even point (BEP). The BEP is the number of units sold that will result in $0 profit. 0 = sX – f – vX, or = (s – v)X – f Solving for X, we have f = (s – v)X X = f s – v BEP = Fixed cost (Selling price per unit) – (Variable cost per unit)

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**BEP for Pritchett’s Precious Time Pieces**

BEP = $1,000/($10 – $5) = 200 units Sales of less than 200 units of rebuilt springs will result in a loss Sales of over 200 units of rebuilt springs will result in a profit

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**Others Example Siam Eng Co.LTD. Max Profit items Fountain**

Obtaining Resource Ladder F. Smooth F. Motor(item/unit) 1 200 Production time (hrs/unit) 9 6 1566 Pipe (ft/unit) 12 16 2880 Profit($) 12000 9500 -

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**Advantages of Mathematical Modeling**

Models can accurately represent reality Models can help a decision maker formulate problems Models can give us insight and information Models can save time and money in decision making and problem solving A model may be the only way to solve large or complex problems in a timely fashion A model can be used to communicate problems and solutions to others

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**Models Categorized by Risk**

Mathematical models that do not involve risk are called deterministic models We know all the values used in the model with complete certainty Mathematical models that involve risk, chance, or uncertainty are called probabilistic models Values used in the model are estimates based on probabilities

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**Computers and Spreadsheet Models**

QM for Windows An easy to use decision support system for use in POM and QM courses This is the main menu of quantitative models Program 1.1

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**Computers and Spreadsheet Models**

Excel QM’s Main Menu (2003) Works automatically within Excel spreadsheets Program 1.2A

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**Computers and Spreadsheet Models**

Excel QM Solution to the Break-Even Problem Program 1.3B

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**Possible Problems in the Quantitative Analysis Approach**

Defining the problem Problems are not easily identified Conflicting viewpoints Impact on other departments Beginning assumptions Solution outdated Developing a model Fitting the textbook models Understanding the model

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**Possible Problems in the Quantitative Analysis Approach**

Acquiring input data Using accounting data Validity of data Developing a solution Hard-to-understand mathematics Only one answer is limiting Testing the solution Analyzing the results

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**Implementation – Not Just the Final Step**

Lack of commitment and resistance to change Management may fear the use of formal analysis processes will reduce their decision-making power Action-oriented managers may want “quick and dirty” techniques Management support and user involvement are important

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**Implementation – Not Just the Final Step**

Lack of commitment by quantitative analysts An analysts should be involved with the problem and care about the solution Analysts should work with users and take their feelings into account

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Problem

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Model Confuse detail in class

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Summary Quantitative analysis is a scientific approach to decision making The approach includes Defining the problem Acquiring input data Developing a solution Testing the solution Analyzing the results Implementing the results

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**Summary Potential problems include Conflicting viewpoints**

The impact on other departments Beginning assumptions Outdated solutions Fitting textbook models Understanding the model Acquiring good input data Hard-to-understand mathematics Obtaining only one answer Testing the solution Analyzing the results

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**Summary Implementation is not the final step**

Problems can occur because of Lack of commitment to the approach Resistance to change

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