DSc 8240 and 4240 / Final Exam

Due by midnight, Tuesday August 6^th.

Answer any five of the following questions. Email your responses in a Word document back to me. Limit you response to each question to one page each.

This is individual work.

1. Outline the process of developing and using a Decision Support System. Briefly explain the purpose of sensitivity, scenario, and goal-seeking analyses.

2. Outline the purpose of performing Monte-Carlo Simulation analysis. How is MCS performed?

3. Explain the general structure of an optimization situation. Provide three examples of situations (objective and constraints) where optimization techniques may be appropriate.

4. Outline an integrated framework for enterprise-wide decisions and enterprise-wide DSS.

5. Outline an integrated framework for utilizing Business Intelligence applications in the extended enterprise (SCM, ERP, and CRM).

6. Describe a Business Analysis framework for decision-making (Intelligence, Design, Choice) with reference to appropriate techniques, methods and technologies.

OR, you may choose to address the following objectives (very concisely).

Model Building and Decision Support Systems:

1. Explain the need for decision support models.
2. Draw (and explain) influence diagrams. Distinguish between outcome, external and decision variables.
3. Design, Develop and Implement decision support models in a spreadsheet (Excel).
4. Discuss ways in which a DSS can be validated.
5. Use various analysis techniques like (sensitivity and scenario analyses) to evaluate outcomes. What is the purpose of various analyses that can be performed in a DSS?
6. Apply best and worst case scenarios and explain why this should be done.
7. Implement various design considerations into the development of DSS.
8. Describe an extended (integrated) framework for DSS.
9. Describe a framework for enterprise-wide decision support for a business organization.

Risk Analysis (Monte-Carlo Simulation):

1. Explain when Monte Carlo simulation methodology should be used in conjunction with decision support models.
2. Distinguish among the common theoretical distributions used in risk analysis and determine when each distribution is appropriate.
3. Determine the input variables that should be modeled as uncertain variables.
4. Perform Monte-Carlo Simulation and interpret its output.
5. Evaluate the pros and cons of using risk analysis.

Optimization:

1. Explain the nature of optimization modeling.
2. Be able to formulate objective functions and relevant constraints.
3. Obtain and interpret optimized results in a DSS.
4. Understand broad categories of optimization applications.
5. Relate optimization to goal seeking and sensitivity analyses.