MGS 8150 – Business Modeling

Course Syllabus

Dr. Alok Srivastava

829 RCB (by appointment)

404-413-7548

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 forecasting, optimization, simulation, decision analysis, and classification. 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.

DETAILED COURSE DESCRIPTION: 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 unknown parameters, evaluating trends for forecasting and 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. Overall, the course will provide the student with an overview of analytical techniques for decision support.

This course will provide the student with several opportunities to apply concepts and techniques to "real-world like" cases. The approach is applications oriented and students are encouraged to apply concepts to real world cases. Students are also encouraged to apply the concepts to their work-related datasets for a more meaningful learning experience. The content of this course is “interdisciplinary” covering applications from marketing, finance, operations and strategy. A key focus of this course is the Six Sigma methodology for process improvement.

Text:

Data Analysis and Decision Making with Microsoft® Excel (with CD-ROM, InfoTrac®, and Decision Tools and Statistic Tools Suite), 3rd Edition, 2006

Albright/Winston/Zappe, ISBN-10: 0-324-40082-9

Course Policies:

All projects and assignments are subject to grade penalty if not turned in at the beginning of class on the due date.
The internet will be used extensively for communications throughout the quarter. You are expected to become proficient using e-mail and web-publishing skills. It will be your responsibility to read your mail on a regular basis. The three-fold purpose is to familiarize you with online capabilities, to facilitate communication with other class members and me, and to reduce paper usage.
Class attendance is expected and a necessary component of class participation.
Assignments remain due on the designated date regardless of class attendance. This is a project-oriented class using various computer application programs, which require significant time commitment.

Grading

Details will be made available in class and the course site. You are expected to make your reports available on your website on the due date.

Group or Individual Projects (5) 40%

Midterm (individual) 25%

Final Project (individual) 25%

Class Participation 10%

Cognitive Objectives:

To receive a grade of "A', you should develop theoretical knowledge, modeling know-how, and computer skills and be able to:

Understand and explain in your own words ways in which model-based support systems are needed and can be utilized in managerial decision processes.
Explain how and why modeling is used in the support system environment.
Identify and differentiate different model components.
Understand and explain the modeling process AND be able to apply it in a variety of different situations.
Compare and contrast different decision structuring techniques and to use these techniques to analyze various situations.
Evaluate models applying good modeling and validation techniques.
Implement model-based DSS using Excel, Crystal Ball, @Risk and other software packages.
Develop and demonstrate presentation skills and be able to post reports on the web.
Develop and demonstrate group skills: leadership and group management.
Use Internet (e.g., Web) and its tools to access necessary information for model development.

Course Schedule:

The detailed course schedule is posted on the course website. The schedule is tentative and changes may be necessary. Visit this page regularly. I will be posting assignments, review materials, and other resources every week.

Week1: Overview of Decision Sciences

· Overview of Decision Support Systems

· Publishing to the Web

Week2: Introduction to Modeling

· Why Model?

· The Modeling Process

· MB-DSS : Modeling Uncertainty

· Putting it all together: Decision Sciences in Context

Week3: Decision Support Systems

· Model-Based DSS

· Influence Diagrams

· Business Intelligence

· Finance, Marketing and Operations Models

Week4: Time Series Forecasting Models

· Forecasting Models

· Time Series Analysis

· Moving Averages

· Trend Modeling

· Time Series Decomposition

Week5: Forecasting Models using Regression Analysis

· Regression Analysis

· Interpreting Regression Results

· Hypothesis Testing

· Backward, Forward and Stepwise Regression

· Implementing Regression Models for Decision Support

Week6: Demand Forecasting Models

· DSS for Marketing

· Modeling Firm Demand

· Data for demand forecasting

· DSS for marketing decisions

· DSS for financial decisions

· DSS development project

Week7: The Modeling Process

· Model Development, Implementation and Use

· Sensitivity Analyses and Goal Seeking

· Auditing and Reverse Engineering

· DSS Design and Development Issues

Week8: MIDTERM EXAM

Week9: Simulation Models (Monte-Carlo Simulation)

· General Overview of Simulation

· Modeling Process for MCS

· MCS Methodology

· @Risk Video Tutorial

Week10: Monte-Carlo Simulation - continued

· Time dependent simulation

· Risk Analysis

· MCS example

· Project 4:

· Monte-Carlo Simulation Project

Week11: Optimization Techniques

· Modeling process for optimization

· Linear Programming

· Graphical Method

· Solving LP Models

Week 12: Optimization Techniques - continued

· Network Models

· Integer Programming

Week 13: Overview of Data Mining Techniques

Weeks 14 and 15 : Final Project Presentations and in-class Final

Teaching Philosophy:

This is more about "facilitation of learning," even though I am calling this my teaching philosophy. I have outlined the general nature of my approach to create opportunities for you to acquire and develop skills that will prove to be valuable in your life. In an era of continuous improvement, interdisciplinary integration, and short lifecycle of skills, my motivation is to help you build confidence and prepare you for the era of "Just In Time" training. The world is rapidly adopting the "open-systems" model for knowledge creation, dissemination, and use - which is making it necessary for me to try the approaches described below. Please provide me your input as I try to make a transition into developing a more meaningful environment for your learning.

Collaborative Learning:

I strongly advocate team-oriented learning in my class. Research is indicating that for this kind of a course the best strategy is to create a structure of topics, provide several opportunities to bring related topics into perspective, and create an environment that facilitates implementation of concepts into meaningful applications. The motivation is to accomplish synergy through sharing of information and skills.

"Learning by Doing" Model for Pedagogy:

This model (constuctivism) calls for facilitation of learning versus the traditional approach of instructor-imparted teaching (objectivism). I will provide you with several opportunities to apply concepts and techniques to "real-world like" cases. This kind of integration/synthesis of knowledge from diverse sources is necessary to be able to create meaningful IT solutions/applications. An important part of this approach is "reverse engineering" to learn systems development. I will provide examples of solutions to cases and we will reverse engineer these solutions to gain a better understanding of the modeling process.

Student-Centered Learning:

This approach encourages students to develop their own context for learning. Meaningfulness and relevancy of concepts can be highly enhanced if you think of an application scenario in your profession and be able to use ideas covered in the course to enhance existing methods. Those seeking to acquire marketable skills use opportunities in class to develop/refine skills that are needed in today's environment. Your projects should reflect applications that demonstrate improvement over conventional methods and cover technology skills that are considered current.

Covering all aspects of Knowledge Acquisition:

I will try to create opportunities for you to acquire all aspects of knowledge associated with the content of this course. These aspects are : Know-what, Know-how, Know-why, and Care-why. Know-what is associated with the understanding of concepts and techniques. Know-how is the understanding related to the application of concepts/techniques. Know-why is an understanding of the relevance and appropriateness of the application to real-world situations. Care-why is something you need to think about (this aspect of knowledge acquisition is more about you than the content of any course).