|Συστήματα Υποστήριξης Αποφάσεων - Επιχειρηματική Αναλυτική|
|Title: ||Συστήματα Υποστήριξης Αποφάσεων - Επιχειρηματική Αναλυτική|
|Lesson Code: ||321-8504|
|Theory Hours: ||3|
|Lab Hours: ||2|
|Faculty: ||Alexopoulos Charalambos|
|Introduction. Categories of decisions in modern firms. Architecture of a Decision Support System. Analysis of discrete options’ decision problems. Influence Diagrams - Decision Trees. Creation of models, solution, risk profiles and sensitivity analysis. Utility functions and their use for decision support. Role and value of perfect and imperfect information - Bayes theorem use. Multi-criteria decision making. Structure and capabilities of software tools for the analysis of discrete options’ decision problems. Analysis of decision problems with continuous range of options - Linear Programming - Creation of models, solution and sensitivity analysis. Structure and capabilities of software tools for the analysis of decision problems with continuous range of options. Basic concepts, structure and design of data warehouses – star, constellation and snowflake schemes. Techniques of data mining for extraction of knowledge from data in order to provide decision support. Structure and capabilities of datawarehousing and datamining software tools. The laboratory of this course includes familiarization with software tools for the analysis of both discrete options and continuous ranges of options decision problems, and also data warehousing and data mining tools. |
|The main learning outcomes of this course are: |
1. Understanding basic methods for the analysis of decision problems of firms and public organizations based on the creation of models and the solution of them.
2. Understanding basic methods for supporting decision making in firms and public organizations based on the provision of appropriate forms of processed information to the decision-makers, and the extraction from the available data of knowledge useful for decision making.
3. Familiarization with software tools supporting the above tasks 1 and 2.
4. Development of ability to model decision problems, and then to solve the models, understand the results, and use them for drawing conclusions and formulate proposals-recommendations for the decision makers.
5. Development of ability to exploit the data of ‘traditional’ internal on-line transaction processing systems of firms and public organizations, and also other external sources, through appropriate processing, for providing support to various levels and types of decision makers.
|Basic knowledge on the main business functions and their electronic with through information systems. |
|1. Loukis, E., ‘Decision Support Systems Strategy – University Lectures’(in Greek). |
|1. Turban, E., Aronson, J., Liang, T. P., ‘Decision Support Systems and Intelligent Systems, Prentice-Hall International, 2007. |
2. Bertsimas, D., Freund, R. M., ‘Data, Models and Decisions’, Dynamic Ideas Publishing, 2004.
3. Clemen R. T., ‘Making Hard Decisions - An Introduction to Decision Analysis, Duxbury Press, 1996.
4. Laudon, K. C., Laudon, J. P., ‘Management Information Systems: Managing the Digital Firm’, Prentice Hall 2012.
5. Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P., ‘Fundamentals of Data Warehouses’, Springer-Verlag, 2003.
6. Siskos, J., ‘Linear Programming’, NewTech Publications, 1998.
7. Ypsilantis, P., ‘Operational Research – Applications in the Modern Enterprise’, Propompos Publications, 2008.
8. Mitchell, T. M., ‘Machine Learning, McGraw-Hill International Editions, 1997.
9. Nanopoulos, A., Manolopoulos, J., ‘Introduction to Data Mining and Data Warehousing’, NewTech Publications, 2010.
10. Doukidis, G., ‘Innovation, Strategy, Growth and Information Systems’, Andreas Sideris – Ioannis Sideris Publications, 2010 (in Greek).
|Learning Activities and Teaching Methods |
|Lectures, solution of exercises, Laboratory based on software tools for the analysis of both discrete options and continuous ranges of options decision problems, and also data warehousing and data mining tools. |
|Assessment/Grading Methods |
|Grade of final exam, grade of laboratory assignments. |
|Language of Instruction|
|Greek, English (for Erasmus students)|
|Μode of delivery |