MGS 3100 - Business Analysis

Spring Semester 2016


Course:    MGS 3100 - Business Analysis
Classroom:  Aderhold Learning Center 213
Instructor:  Steve S. Wong

(678) 467-8199

Office Hours: By appointment

Please add MGS 3100 to the beginning of the subject line.





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, Math1111 or the equivalent; Algebra and Excel competency



A custom book of selected chapters:



Class Participation Participation, Attendance and Homework


Group Project 1  Profitability Analysis 


Group Project 2 Forecasting 


Group Project 3 Decision Analysis


Exam 1  General Modeling 15%
Exam 2  Forecasting  15%
Exam 3 Decision Analysis 15%
Final Exam  Common Departmental Exam  15%
Total 100%


Plus/Minus Grading Policy



Attendance/Class Participation:

Class attendance is expected and necessary component of class participation. In the event that you must miss class, you are still responsible for material covered, and should make arrangements with fellow classmates to remain current with the class. Assignments remain due on the designated date regardless of class attendance. This is a project-oriented class using various computer application programs which require some time commitment. More than 3 missed class periods will result in your final grade marked down one grade. Additional absences may result in a WF.


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:

·         Demonstrate competence in analysis/development of some common models mathematically

·         Demonstrate competence in analysis/development of some common models graphically

·         Demonstrate competence in using a spreadsheet for analysis

·         Interpret model results in the context of the business situation and explain in plain language


General Modeling:

·         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, etc.

·         Explain the modeling process, including model types, data collection, analysis, interpretation

·         Analyze a business situation to identify revenues, costs, and other relevant parameters

·         Draw an influence diagram to map the relationships between different variables of interest

·         Build a basic profit model both with a spreadsheet and without

·         Perform Breakeven and Crossover analysis algebraically and graphically, both with a spreadsheet and without, and interpret the results of each



·         Compare and contrast simulation with other types of modeling

·         Determine when simulation is an appropriate technique to use

·         Use random numbers from a random number table or a spreadsheet function

·         Apply simulation techniques to machine break-down, queuing, and inventory problems

·         Graph and interpret the results of the simulations



·         Define the types of forecasting - Quantitative (causal and time series) and Qualitative.

·         Forecast using the following methods for time-series data (on a spreadsheet):

·         Naïve

·         Moving Averages

·         Simple Exponential Smoothing

·         Trend (linear only)

·         Seasonal Analysis (simplified approach)

·         Regression

·         Compute Bias, MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), Standard Error, and R-Squared

·         Compare, contrast, and interpret the different forecasting methods


Decision Analysis

·         Differentiate between decision making under ignorance, risk, and certainty

·         Define the terms Decision Alternative, States of Nature, Payoff

·         Compute payoff matrix for a given business scenario

·         Define the criteria for choosing the best decision

·         Determine the best decision using the MAXIMAX, MAXIMIN

·         Compute Expected Value (EV or ER), EV under/with Perfect Information (EVUPI or EVwPI), and EV of Perfect Information (EVPI)

·         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

·         Combine sample data with prior probabilities using Bayes’ Theorem, and incorporate these “posterior” probabilities into a decision tree analysis




Brightspace / Desire2Learn Skills:

You are expected to be proficient in the use of Brightspace and all the assignments need to be submitted via the DropBox in Brightspace. Specifically, you should be able to read, upload, and download files; read and send e-mail messages, read and post messages on discussion boards. You are also expected to check the section site daily for any changes, updates, and announcements. A knowledge of these applications is a prerequisite for any course offered by RCB.  The University offers remedial courses in any of these applications.


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.



Exams will be administered in class according to the attached schedule. Exams may be a mixture of short questions, multiple choice and true/false.  Class exams 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 exams and the final exam will be covered in class.  A sample final exam and answer key can be found on the departmental web site (see page one of this syllabus).  All students are required to take the final exam. 


PowerPoint Slides:

Copies of the PowerPoint slides for this course can be found on this website (see the "Schedule of Classes" Wiki Page or the Quick Launch menu "Class PPT Slides").  To minimize note taking, you should print the slides for each class in advance and bring them to class.