Syllabus
Decision Support Systems
MS 5303 (Spring 2003)
Instructor:
Time/Room:
Office:
Phone:
E-mail:
Homepage:
Office Hours:
Dr. Kefeng Xu
Section 001 (M/W 05:30PM-06:45PM) BB 3.01.06 4.04.28
BB 4.04.18
(210) 458-5388
kefeng@lonestar.utsa.edu
http://faculty.business.utsa.edu/kxu
M/W 2:00PM-3:15PM and by appointment.
REQUIRED TEXT
1. Winston, Wayne L. and S. Christian Albright, “Practical Management Science:
Spreadsheet Modeling and Applications”, Second Edition, 2001, Duxbury Press.
Now bundled with new DecisionTools CD-ROM (software) and Study Guide. ISBN
0-534-42435-X. Available in the UTSA Bookstore.
COURSE OVERVIEW
In general, Decision Support Systems (DSS) are used by people who are skilled in their
jobs and who need to be supported rather than replaced by a computer system. The
broadest definition states that Decision Support Systems are interactive computer based
systems and subsystems that help decision makers utilize data, models and/or
communications to solve problems and make decisions. This Management Science
course focuses on Data-Driven DSS and Model-Driven DSS. In particular, Data-Driven
DSS emphasize using data; Model-Driven DSS emphasize using models to support
decision making.
Today’s managers face complex decision making environments, but typically need to
solve the problems quickly and frequently with solid justifications. Unfortunately, there
are few (if any) existing DSS packages geared for each specific task of a manager that
are installed in a firm or even available in the market. Thus the ability to capture
business decision problems using some broadly available tools such as spreadsheet or
database system is very useful and timely in modern business environment.
This course will help students structure business decision problems in some easy-to-use
software environment (mainly Excel & its Add-ins), set up the core fo the DSS modeling
system, solve the problems, and interpret the results. It will rely much less on a
student’s programming skills, other than the general sense of logic and other business
experiences.
COURSE OBJECTIVES
This Management Science course is aimed at extending business students’ knowledge
and expertise in various decision making scenarios. This course assumes prior
knowledge and work with computers and basic knowledge of management science.
Students are expected to:
 skillfully build customized or ad hoc computer models for use in decision support,
based on spreadsheet modeling;
 effectively present information derived from computer models, and
 interpret model results, gain insights from the models, and draw conclusions
supported by the results.
This will give students a first-hand experience with modeling a real world situation for
better decision making, and expose students to models from various business fields.
Students can use the project presentations to relay and discuss their experiences in
building DSSs.
STUDENT REQUIREMENTS
Some fundamental knowledge of management science and statistics (such as those in
MS 5023 or MS 3033) is very helpful. No strong computer experience is assumed,
although some operational exposure to microcomputers (through spreadsheets, word
processors, data bases, e-mail, etc...) and to computer programming would have some
value.
You do not have to own a microcomputer as there are many microcomputers available
for student use around campus (e.g. computer labs). The main computer tools used in
this class, Microsoft Excel and its Add-ins (@Risk, TopRank, Precision Tree, Premium
Solver), are installed in our Computer Classroom. Since all these Excel Add-ins come
with the textbooks you purchase, you can install them in your own computer at home
or at work for your own convenience. Unfortunately, although @Risk is readily
available in the Advanced Project Lab on the 3rd Floor of Business Building, other less
used Add-in components such as TopRank and Precision Tree are not.
You are expected to demonstrate mastery of the course materials through various
exercises including examinations, homework, and project.
COURSE FORMAT
Lectures, discussions, and labs will be used throughout the course. Students are
expected to prepare for assigned readings before class and are responsible for all
materials presented in class and outside assignments. You are strongly encouraged to
learn and work at the same pace as the instructor demonstrates in class.
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HOMEWORK ASSIGNMENTS
Assignment due date will be clearly marked on the assignment handout sheets to be
distributed. Late assignments will be accepted with the explicit understanding that the
homework score will be discounted 20% for each day late, unless arrangements are
made with me prior to the due date.
Each homework could be completed by a group of 2 persons. However, in case of doing
homework in group, you are required to change your group member every two
assignments, or penalty on grades will result.
Computations for all assignments are based on the spreadsheet program and thus there
is no need for handwritten works. To answer a typical homework question/case, the
following information should be included, in addition to your spreadsheet calculations:
a. Key assumptions relevant to the problem
b. Key formula or relationships clearly spelled out (and executed correctly!)
c. Major managerial conclusion/summary.
Naturally a case in a homework will include additional components such as
justifications of assumptions, discussions and sensitivity analysis as required by actual
situation, and answers to specific questions posed in the case. When space or format
allowed, you should try to fit these elements inside textboxes of related spreadsheets.
When deemed necessary, you could also include them in a separate word-processed
page.
A complete homework portfolio should include the printouts of the above information
in spreadsheet in a clear, understandable layout and order, and the diskette files of
Excel and Word (Please save all your file types as MS-Office format). These diskette files
will allow me to check your actual calculations if necessary. You should own a few
empty diskettes to turn in different assignments. An example homework disk file will
be available for you to follow.
SEMESTER PROJECT
A simple but practical project applying or even extending some general knowledge
learned in the course will bring satisfactions to the learning process. The project is a
testing ground for the exciting tool just acquired in this class. Students could choose
problems encountered in their works, previous experiences, or even friends’ works not
necessarily in the business environment. The emphasis is on having the real problems
and using the real data.
To facilitate cooperative learning while minimizing the chance of “free ride” (not
sharing the proper workload in the group), students are expected to form their own
groups of (at most) 2 persons around week 5. Each group will then meet among
themselves to plan the project, determine the problem scope, collect necessary data,
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formulate and test the DSS model, apply the model to solve the problem. Only when
you are determined to undertake a large, complex project you could form a group of
more than 2 with the permission of the instructor. You will demonstrate your
knowledge of the course materials by correctly executing the project in a scientific
fashion and briefly present the results to the class at the end of the semester.
Immediately after the Spring Break, a one-page project outline is due with the
instructor, to ensure the proper progress of the project. A managerial project report
with no more than 10 pages text for each group is due in the last class of the semester.
Each group is expected to present the project in the class near the end of the semester.
A copy of the project presentation slides should be made available to the instructor at
the time of the presentation. Tentatively each group is allowed 30 minutes to present
the project (e.g., 20-25 minutes for presenting your works, 5-10 minutes for answering
questions).
The grading of the project is 80% based on the report, while 20% based on the
presentation. Each member of the group usually gets the same grade, unless some “free
ride” is reported on particular member by the end of the last class.
EXAMINATIONS:
There will be one midterm and one final exams during the course. The exams will
include the similar types of questions/cases seen in your homework and will be
conducted on the computers in class. A doctor's excuse is necessary for a make-up
exam. Make-up exams are not scheduled for non-approved absences. If you will be
unable to make the exam for the medical or other emergency reasons, please call me at
the office and let me know PRIOR to the start of the exam. It is your responsibility to
notify me, not for me to guess.
ACADEMIC DISHONESTY:
Academic dishonesty will not be tolerated. Such matters will be subject to disciplinary
actions. All assignments are expected to be completed independently of other groups.
TENTATIVE COURSE GRADING*:
Homework Assignments
Semester Project
Midterm Exam
Final Exam
Class Participation & Attendance,
Instructor’s Discretion
25%
15%
25%
30%
5%
------Total:
100%
====
* The instructor reserves the right to modify the grading procedure.
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Tentative Course Schedule*:
Week
Date
Topic
1
1/13
1/15
2
1/20
1/22
Introduction & course overview
Introductory Spreadsheet Modeling
Monte Carlo Simulation
Classes do not meet (Univ. Holiday)
Simulation Using Built-in Excel
Tools– Walton Bookstore Example
Introduction to @Risk – Walton
Bookstore Revisited
Generating Other Distributions –
Additional Uncertainty at Walton
Correlation in @Risk – McDonald’s
Correlated Revenues Example
@Risk Simulation – Bidding Example
& Drug Production Example
Financial Planning Simulation –
Developing New Car at GF Auto
Maintaining a Cash Budget
Simulating Stock Prices & Options –
European Call, Portfolio, Asian Opt.
Marketing Models – Market Shares
for New Brands or Existing Brands
Using TopRankTM (Input Importance
Rating) – New Product Development
Decision Making Under Uncertainty
(Decision Tree with Precision TreeTM)
(continued) – More Single Stage Ex.
(continued) – Multistage Decision
Midterm exam
Linear Optimization Model (LP)
Spring Break. Classes do not meet.
Spring Break. Classes do not meet.
Managerial Information and
Sensitivity Analysis of LP
Integer Programming Models (IP)
IP Continued
Nonlinear Programming (NLP)
NLP continued
Multiple-Objective Decision Making
(continued)
(continued)
Regression Analysis and Applications
Forecasting
Student Project Presentations
Project Presentations +Course review
05:00pm-07:45pm, Monday, May 5.
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1/27
1/29
4
2/3
2/5
5
2/10
6
2/12
2/17
2/19
7
2/24
2/26
8
9
10
11
12
13
14
15
16
Final
3/3
3/5
3/10
3/12
3/17
3/19
3/24
3/26
3/31
4/2
4/7
4/9
4/14
4/16
4/21
4/23
4/28
4/30
Exam
Pre-class Readings/Special Note
(Textbook by Winston & Albright, 2001)
both Ch.1 of T1 & T2.
Excel Tutorial File from Instructor.
Ch. 2 (and its Appendix). Ch. 11 (11.1-11.3).
Ch. 11 (mainly 11.4).
Ch. 11 (mainly 11.6).
11.5-11.6.
11.7.
12.1-12.2.
12.3.
12.3.
12.3.
12.4.
12.6.
Ch. 10.
Computerized exam!
Ch. 3 & 4.
Ch. 3 (mainly 3.4).
*Project outline due.
Ch. 6.
Ch. 7.
Ch. 8.
Ch. 15
Ch. 16
tentative schedule
*The instructor reserves the right to modify this schedule when necessary.
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