VILLANOVA UNIVERSITY
SCHOOL OF BUSINESS
MANAGEMENT AND OPERATIONS DEPARTMENT
DIT 2160 Business Decision Making
Spring 2008
Dr. Robert Nydick
Office No. Bartley 3077
Office Hours: By Appointment
Voice Phone No.: 610-519-6444
Fax Phone No.: 610-519-6566
Cell Phone No.: 610-329-0207
E-mail Address: robert.nydick@villanova.edu
Web Page: www.homepage.villanova.edu/robert.nydick
SYLLABUS
TEXT:
There is no required text for this course. Chapters and articles are available for you to download through
our WebCT Vista courseware. The lectures and PowerPoint files for AHP are based on the material in the
Liberatore files for Chapters 9, 10, and 11. The financial simulation material is based on the Winston
chapters. Other files are available for additional resources if needed.
SOFTWARE:
Decision Lens for the analytic hierarchy process, @Risk for financial simulation, and Excel for simulation
concepts and input/output interfacing with Decision Lens and @Risk. Details on how to gain access to
Decision Lens and @Risk will be discussed in class.
PREREQUISITES:
DIT 1141 AND JUNIOR OR SENIOR STANDING.
It is the student’s responsibility to be certain that the prerequisites have been successfully completed. If at
any time during the semester it is determined that a student has not completed the prerequisites, the
student can be administratively dropped from the course without credit or tuition refund.
PERIODICALS:
Management Science, Operations Research, Interfaces, Computers & Operations Research, European
Journal of Operations Research, and Decision Sciences.
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CATALOG DESCRIPTION
Integrate behavioral science and quantitative approaches to decision-making; descriptive and prescriptive
models in individual, group, and organizational settings, expected value, utility theory, the analytic
hierarchy process, and financial risk analysis.
OBJECTIVE:
To examine how decision support modeling and software can be applied successfully to the solution of
practical business problems.
METHOD OF INSTRUCTION:
PowerPoint presentations facilitate discussion of the assigned topics. The use of modeling software
supports the presentation of the material. This class emphasizes the practical application of the theory and
methods presented. Students complete assigned homework and other assignments, a group project, and
participate in class discussions and exercises.
COURSE MODULES:
This course consists of an introduction, a decision making module, and a financial simulation module. The
introduction discusses the role of business decision making in organizations and provides an overview of
the modeling approaches used in this course. Although this material can be applied to all areas of business,
we will emphasize its application to the MIS field.
The decision making module includes a review of classical decision analysis including utility theory and a
detailed discussion of the analytic hierarchy process. Applications include: problems of prioritizing
alternatives and resource planning, such as product, project, and job selection; employee evaluation
systems; facility location; vendor selection; and transport mode/carrier selection.
The financial simulation module uses @Risk to study the impact of adding randomness and uncertainty to
any spreadsheet. Specific concepts covered include the impact of a single source of randomness versus
multiple sources of randomness and the measurement of correlated demand values on the output variable.
In addition, it will be demonstrated how @Risk can evaluate different input parameter values to identify
which ones have the most positive impact on an output parameter.
PROJECT:
All students will participate in a group project. Groups and subgroups within each group will be formed as
the semester progresses. The goal of the project is to successfully apply the AHP to an actual problem in a
business organization. Additional details about all project requirements will be discussed as the semester
progresses.
ATTENDANCE:
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Students are expected to attend all classes and participate in the class discussions.
SUBMISSION POLICY FOR LATE ASSIGNMENTS:
One letter grade will be deducted from the assignment grade for each day late.
MAKEUP EXAMINATION POLICY:
There will be no makeup examinations given. Points associated with a missed exam will be added to a
comprehensive final examination.
GRADING:
FINAL GRADES:
Homework and Class Assignments 10%
Project Report
60%
Examination 1
30%
100%
A
AB+
B
BC+
C
CD+
D
DF
90 – 100
88 – Below 90
86 – Below 88
80 – Below 86
78 – Below 80
76 – Below 78
70 – Below 76
68 – Below 70
66 – Below 68
60 – Below 66
58 – Below 60
Below 58
ACADEMIC INTEGRITY POLICY:
The Code of Academic Integrity of Villanova University addresses cheating, fabrication of submitted
work, plagiarism, handing in work completed for another course without the instructor’s approval, and
other forms of dishonesty. For the first offense, a student who violates the Code of Villanova University
will receive 0 points for the assignment. The violation will be reported by the instructor to the Dean’s
office and recorded in the student’s file. In addition, the student will be expected to complete an
education program. For the second offense, the student will be dismissed from the University and the
reason noted on the student’s official transcript.
DISABILITY STATEMENT:
It is the policy of Villanova to make reasonable academic accommodations for qualified individuals with
disabilities. If you are a person with a disability please contact me after class or during office hours and
make arrangements to register with the Learning Support Office by contacting 610-519-5636 or
nancy.mott@villanova.edu as soon as possible. Registration is needed in order to receive
accommodations.
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TENTATIVE CLASS SCHEDULE
Week
Description
_____________________________________________________________________________________
1
Introduction to the Course; and Classical Decision Analysis – Chapters 1 and 9
The role of decision sciences in organizations is presented. This will provide a framework and overview
for the course. We also begin a discussion of classical decision analysis including expected value and
utility theory.
_____________________________________________________________________________________
2
Classical Decision Analysis; and AHP Using Decision Lens: Part I – Chapters 9 and 10
We finish the material on decision analysis. An introduction to the application of the Analytic Hierarchy
Process (AHP) is also presented. The AHP is a decision-making methodology that is used for prioritizing
alternatives when multi-criteria must be considered.
_____________________________________________________________________________________
3
AHP Using Decision Lens: Part II – Chapter 10
We continue the introduction of the AHP and discuss an AHP software package (Decision Lens).
_____________________________________________________________________________________
4
AHP Using Decision Lens: Part III – Chapter 10
The theory and selected applications of the AHP are presented. Computations of the rankings and
consistency are also described. The application of Decision Lens to a variety of problems will be shown.
_____________________________________________________________________________________
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AHP Using Decision Lens: Part IV – Chapter 11
A continuing discussion of the AHP is presented. Distributive versus ideal synthesis, sensitivity analysis,
a validation exercise, and structuring multi-level hierarchies are discussed and illustrated through the
application of Decision Lens.
_____________________________________________________________________________________
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AHP Using Decision Lens: Part V – Chapter 11
A continuing discussion of the AHP is presented. AHP-based ratings systems and group decision making
are discussed and illustrated through the application of Decision Lens.
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7
Project Organization
Right before our semester break we will spend time to discuss the various project options and have
students self-select a project and get organized.
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8
Financial Simulation with @Risk
An overview of financial simulation using @Risk is discussed. Specific topics include: fitting data to
probability distributions using goodness of fit tests; an inventory problem is used to illustrate the power of
@Risk including sampling methods, interpretation of output, and computation of confidence intervals;
using the Risksimtable command to evaluate different inventory ordering policies; using the Risk
Optimizer feature to identify the ideal order size; extensions of the basic inventory problem to include
additional sources of uncertainty, using tornado graphs for sensitivity analysis, and the impact of
correlated variables in financial simulation modeling.
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9
Semester Break - No Classes
During the semester, an exam will be scheduled. After the Semester Break we will begin work on the class
projects. Additional details will be provided as the semester progresses. Our meetings will take many
forms. We will meet as a class to update everyone on our progress. The subgroups will meet with me for
guidance. Subgroups will also meet with their client as well as outside vendors and other constituencies as
needed.
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