Introduction. Categories of decisions in modern firms. General 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. 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. Structure and capabilities of datawarehousing and datamining software tools. Predictive Analytics using Machine Learning and Regression. 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:

• Understanding basic methods for the analysis of various kinds of decision problems of firms and public organizations based on the creation of models and the solution of them.
• 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.
• Familiarization with software tools supporting the above tasks 1 and 2.
• 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.
• 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.

Not required.

Clemen R. T., ‘Making Hard Decisions - An Introduction to Decision Analysis', Duxbury Press, 1997.

Λουκής, Ε., ‘Συστήματα Υποστήριξης Αποφάσεων – Πανεπιστημιακές Παραδόσεις’.

Decision Support Systems

Information Systems Mnagement

European Journal of Information Systems

International Journal of Information Management

The final grade is determined as follows:

0.4 * (Laboratory Grade) + 0.6 * (Written Exam Grade)

Both the written exam and the laboratory grade must be ≥ 5.

For the assessment of the written exam and the laboratory assignments have been clearly defined specific assessment criteria, which are stated. Students have the opportunity to view their written exam and identify their mistakes. The overall grade distribution for the class is announced on eClass, allowing students to evaluate their performance

Face-to-face