General Modeling Fundamentals/Simulation

MGS3100 - Business Analysis
Fall 2008 Course Syllabus - Levine
CRN# 83945, Section #055, Tuesday: 7:15-9:45pm, ALC213
CRN# 82055, Section #060, Wednesday: 7:15-9:45pm, ALC325
Dr. Kenneth C. Levine
E-Mail: (Note: e-mail is the best form of contact!)
RCB1047 by appointment
770-633-9322 (cell)
Instructor Website: (then select MGS3100)
Departmental Course website:
Course Overview
This course provides a framework for using models in support of decision-making in an enterprise.
Some of the commonly used modeling approaches and principles are introduced. Topics covered
include general modeling concepts, spreadsheet modeling, simulation, forecasting, quality
management, statistical process control, and decision analysis. The course emphasizes hands-on
application of the techniques using commonly available software, and demonstrates the value of these
approaches in a variety of functional settings.
Math1070 or the equivalent
CSP 1-Basic microcomputing skills
CSP 2-Basic microcomputing spreadsheet skills
CSP 6-Basic word processing skills
Excel is required in this course.
Note: Although GSU’s registration technologies do not “block” registration for the lack of these
prerequisites, student transcript information is manually examined to assess whether the prerequisites
are met. By the time that this is done and a student is dropped from the course for not having the
prerequisites, it is too late to withdraw or receive a tuition refund!
Required Text:
Selected Chapters on Business Analysis, 2nd Ed., 2004 (ISBN: 0-536-83481-4)
Attendance/Class Participation/Homework:
Your class participation grade will be based on attendance only. All homework assignments will be
reviewed in class, but homework will not be collected. You are expected to attend classes. Class
attendance will be taken in the beginning of class. If you do miss a class, you are responsible for
obtaining notes and remaining current. It is not possible to repeat lectures for students missing class.
Late students are responsible for signing the class roll before leaving. Otherwise, you will be
considered absent. Excessively late students will be penalized.
All pagers and cell phones should be turned off or muted during class.
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General Course Objectives:
To demonstrate the application of models in support of decision making in an enterprise, using
some of the most commonly used modeling approaches and principles. Upon completion of the
course, the student should:
A. Demonstrate competence in analysis/development of some common models mathematically
B. Demonstrate competence in analysis/development of some common models graphically
C. Demonstrate competence in using a spreadsheet for analysis
D. Interpret model results in the context of the business situation and explain in plain language
Specific Course Objectives:
In order to earn a grade of ‘A’ in the course, the student should, upon completion, be able to:
General Modeling:
1. Define basic modeling terms, including (but not limited to) Physical model, Analog model,
Symbolic model, Deterministic model, Probabilistic model, Decision Variable, Random
Variable, Parameter, Performance measure, Objective function, Revenue, Fixed Cost,
Variable Cost, Overhead Cost, Sunk Cost, Demand, Price
2. Explain the modeling process, including model types, data collection, analysis, interpretation
3. Analyze a business situation to identify revenues, costs, and other relevant parameters
4. Draw an influence diagram to map the relationships between different variables of interest
5. Build a basic profit model both with a spreadsheet and without
6. Perform Breakeven analysis algebraically and graphically, both with a spreadsheet and without
7. Perform Crossover analysis algebraically and graphically, both with a spreadsheet and without
8. Interpret the results of Breakeven and Crossover analyses
9. Define the types of forecasting - Quantitative (causal and time series) and Qualitative.
10. Forecast using the following methods for time-series data (on a spreadsheet):
a) Naïve
b) Moving Averages
c) Simple Exponential Smoothing
d) Trend (linear only)
e) Seasonal Analysis (simplified approach)
f) Regression
11. Compute Bias, MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error),
Standard Error, and R-Squared
12. Compare and contrast the different forecasting methods
13. Interpret the results of the different forecasting methods
Decision Analysis
14. Differentiate between decision making under ignorance, risk, and certainty
15. Define the terms Decision Alternative, States of Nature, Payoff
16. Compute payoff matrix for a given business scenario
17. Define the criteria for choosing the best decision
18. Determine the best decision using the MAXIMAX, MAXIMIN
19. Compute Expected Value (EV or ER), EV under/with Perfect Information (EVUPI or EVwPI),
and EV of Perfect Information (EVPI)
20. Construct and solve a decision tree by assigning payoffs to branches, pruning of branches at
decision nodes, and assigning probabilities and calculating expected values at chance nodes
21. Combine sample data with prior probabilities using Bayes’ Theorem, and incorporate these
“posterior” probabilities into a decision tree analysis
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22. Compare and contrast simulation with other types of modeling
23. Determine when simulation is an appropriate technique to use
24. Use random numbers from a random number table or a spreadsheet function
25. Apply simulation techniques to machine break-down, queuing, and inventory problems
26. Graph and interpret the results of the simulations
Quality Management
27. Understand the basic concepts of Quality Management, including Six Sigma
28. Understand the difference between common cause and special cause variation
29. Understand how control charts can be used to help manage by exception
30. Create control charts for attribute and variable measures
31. Calculate process capability; understand how to determine the “sigma” level of a process
Competency Exercise
Final Exam
Excel Spreadsheet Analysis
3 Tests
2 Projects
Lose one point per absence
Common Departmental Exam
0 pts. (but required!)
60 pts. (20 pts. each)
20 pts. (10 pts. each)
0 pts. (not collected)
10 pts. (one free absence)
10 pts.
The grading scale for this class is as follows: A: 94-100; A-: 90-93.9; B+: 86-89.9; B: 82-85.9;
B-: 78-81.9; C+: 74-77.9; C: 70-73.9; C-: 66- 69.9; D: 60.0 - 65.9; F: < 60.0
Professional and personal circumstances that preclude you from performing at satisfactory levels will
not be considered in the determination of the course grade. The effect of your grade on overall GPA,
eligibility for graduation, loss of scholarship, loss of a United States resident card, placement on
academic probation, etc., are not considered in the determination of your grade. There are no extra
credit assignments. Individual requests for alternative ways to improve your course grade will not be
Honor Code:
Plagiarism in any form is not acceptable. While discussion with classmates regarding homework and
projects is encouraged, all work submitted must be your own. Evidence of plagiarism on an
assignment/exam will result in a failing grade for that assignment/exam.
Tests will be administered in class according to the attached schedule. Tests may be a mixture of
multiple choice and true/false. Class tests and the common final will test both your understanding of
concepts and problem solving ability, and will also include questions about the use of Excel to solve
problems in this course.
For in-class tests and the common final exam, you will need to bring a calculator (with a square root
button!) and one 8.5”x11” page of notes (two-sided). Students are required to provide their own pencils
and scratch paper. All material needed for tests and the final exam will be covered in class. A sample
final exam and answer key can be found on the departmental web site. All students are required to
take the final exam.
Individual Student Projects:
Individual class projects will be discussed in class. These are not group projects! Projects are to be
submitted on paper by each student by the designated date, including data output and formulas. No
diskettes will be accepted, as they are easily misplaced and damaged. Late projects will be penalized
at a rate of 5% per calendar day. In addition, once the deadline has passed, no further feedback will
be given. Use the “fit to one page” option to print your output on 8.5x11” sheets. No report covers,
please! You may discuss projects with your classmates, but the work you turn in must be your own!
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Schedule: The following is a tentative schedule; deviations may be necessary. Supplementary
homework assignments will be added as the course progresses.
Detailed Outline
General Modeling Fundamentals/Simulation
1. 8/19; 8/20
Overview of
Course introduction and overview;
Modeling for The nature of modeling for decision-making;
Implementation issues.
Discuss Excel Competency Exercise
2. 8/26; 8/27
Effective use of spreadsheets for modeling;
Sensitivity/what-if analysis;
Spreadsheet Review of key Excel functions;
Financial Models; Influence diagrams
Course Syllabus
HW1 (Discussion
questions): 1-1, 1-2,
1-4, 1-6, 1-12, 1-18
(not collected)
HW2: Excel
Exercise (on
3. 9/2; 9/3
2 (skim only);
BE/CO lecture;
9 (excl. pp.164-67,
174-82, 192, 210)
9 (excl. p.164-67,
174-82, 192-210)
4. 9/9; 9/10
Monte Carlo
Monte Carlo
5. 9/16; 9/17
Breakeven and crossover analysis;
Introduction to Simulation; Random
numbers; Probability distributions; Machine
breakdown problem; Queuing applications
Building a spreadsheet simulation:
Inventory applications;
Discuss Project #1 (Simulation);
Review for Quiz#1 (See Sample Final and
Key on departmental website.)
Quiz #1
Forecasting/Quality Management
5. 9/16; 9/17
Intro to Forecasting; Qualitative Models
6. 9/23; 9/24
7. 9/30; 10/1
8. 10/7; 10/8
9. 10/14;
10. 10/21;
11. 10/28;
Decision Analysis
11. 10/28;
12. 11/4; 11/5
13. 11/11;
14. 11/18;
15. 12/2; 12/3
Final Exam
Tues., 12/9;
Wed., 12/10
2 (skim only)
HW3: Simulation
HW4: Simulation
13 (excluding pp.
280-4, 300-2, 307-9)
Go over Quiz #1; Time-series forecasting
models: Naïve forecasts; Moving averages;
Error measures: Bias, MAD, MAPE.
Time-series forecasting models: Simple
exponential smoothing; Trend analysis.
Time series decomposition/seasonality;
Causal modeling/Regression;
Discuss Project #2 (Forecasting).
TQM Overview; Six Sigma philosophy;
Process improvement tools.
SPC Overview: Process measurement;
Control charts; Process capability.
Quiz #2
13 (excluding pp.
280-4, 300-2, 307-9)
Basic concepts; Ignorance, risk, and
certainty; Payoff tables; Alternatives; States
of nature; Payoffs; Decision making under
ignorance: Maximax, Maximin
Go over Quiz #2;
Decision making under risk; Expected
value; Expected value of perfect
information; Creating payoff matrices.
Sequential decisions and decision trees;
Conditional probability and Bayes’
Theorem; Expected value of sample
information; Efficiency
Sequential decisions and decision trees;
Conditional probability and Bayes’ Theorem
Quiz #3;
Review for Final
Comprehensive Departmental Final Exam:
8 (excluding pp.92,
94-101, 109-111)
13 (excluding pp.
280-4, 300-2, 307-9)
13 (excluding pp.
280-4, 300-2, 307-9)
Project#1 due
15 (excluding
pp.339-41, 357)
15 (excluding
pp.339-41, 357)
8 (excluding pp.92,
94-101, 109-111)
Project#2 due
8 (excluding pp.92,
94-101, 109-111)
8 (excluding pp.92,
94-101, 109-111)
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