MGS 4140 – Business Modeling

Course Syllabus for Fall 2014

Instructor: Dr. Satish Nargundkar  E-Mail : snargundkar@gmail.com Office: 827 College of Business  Office Hours:  By appointment

Website: www.nargund.com/gsu Phone: (678) 644 6838   CRN: 82337 Sparks 304: 4:30 – 7:00 PM, Tuesdays

Text (required):

Spreadsheet Modeling and Applications: Essentials of Practical Management Science, 1^st Ed., South-Western College Pub, 2004 (ISBN-10: 0534380328, ISBN-13: 978-0534380328).

Course Description

This course covers the development, implementation, and utilization of business models for managerial decision making. Students learn to utilize techniques for analytical modeling which include decision analysis, optimization and simulation. These mathematical models are implemented in decision support systems. Examples are introduced that cover applications in strategic planning, financial management, operations, project management, and marketing research.

Upon completion of the course, the student will be able to build Decision Support Systems (DSS) – apply mathematical, graphical and spreadsheet modeling techniques to business situations to aid decision-making. Students will go through the process of describing and visualizing data, estimating relationships between process inputs and outcomes. Students will also get an overview of using models for business intelligence and decision support and will be able to evaluate various scenarios to optimize business decisions.

General Policies:

· Students are expected to attend each class (who knows, you may actually enjoy the class!), arrive on time and participate in class discussions. As the instructor, I also make the commitment to come to class prepared!

· Turn off cell phones, stereos, TVs, etc. when in class. Treat the instructor and each other with courtesy.

Teaching Methods:

You learn best by doing; in a context that is of interest to you (use your own data if and when possible). You will work on real-world cases or realistic exercises to understand techniques (knowing how) and concepts (knowing what). You are also encouraged to think about the relevance of the topics to real-life issues (knowing why).

Grading:

		Course Average	Grade	Course Average	Grade
Assignments	20%	94-96, 97+	A, A+	77-79	C+
Projects	30%	90-93	A-	73-76	C
Tests/Final	50%	87-89	B+	70-72	C-
		83-86	B	60-69	D
Total	100%	80-82	B-	Less than 60	F

College Policy regarding grade distribution recommends approximately 35% A grades (including A- and A+) and certainly no more than 50%.

Learning Outcomes/Course Objectives

Upon successful completion of the course, students will be able to:

• Develop spreadsheet based decision support systems
• Develop models to analyze decision making scenarios, with probabilities.
• Analyze the value of information using Bayesian analysis and incorporate it into a decision making scenario.
• Formulate optimization problems mathematically, and solve them using Excel’s solver function
• Interpret Solver results, conduct sensitivity analysis and discuss the impact of individual input variables
• Create simulations of discrete and continuous variables. Develop Monte Carlo Simulation models to study business situations and use them to aid decision making.
• Explain the limitations of analytics in your own words.

Honor Code:

It is more honorable to get any grade with your own work than to get a better grade by using someone else’s work as yours. While discussion with classmates is encouraged, any work submitted must be your own (or your group’s, for group projects). Evidence of plagiarism/cheating on an assignment/exam will result in a failing grade for that assignment/exam, or even for the course.

Course Assessment:

Your constructive assessment of this course plays an indispensable role in shaping education at Georgia State. Upon completing the course, please take the time to fill out the online course evaluation.