General
Managers worldwide, beyond their personal experience, rely more and more on the use of
quantitative decision models which allow to take advantage of today’s data availability. Morover,
new computational tools, including algorithms, cloud computing and distributed processing, make
it possible to both develop and compute analytical models in a very short time, meeting the
requirement of practical applications and often using real time data. Data Driven Decision Making
is the new paradigm for managers to make better, evidence based, more rational, transparent and
reliable decisions.
In this context, the primary educational objective of the course is students' learning of the main
decision problems that arise in real world and the quantitative methods to model them and to
feed them with adequate data. Students must also be able to correctly use, for decision-making
and management purposes, computer tools to analyze data generated by real problems in
different contexts (e.g. service management, marketing, transportation, operations management
and production, and finance) through the analysis of several case studies.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students know and classify the main decision problems arising in
real world organization and the main analytical methods (decision and optimization models and
algorithms) to be used to support a Manager during his/her decision process.

b) Ability to apply knowledge and understanding
At the end of the course the students are able to formalize real problems in terms of decision
problems and to apply the specific methods taught in the course to solve them. They are also able
to classify the type of problem to it the most appropriate quantitative method, experimenting the
effectiveness for decisional purposes also on real problems.

c) Autonomy of judgment
Students develop critical skills through the application of modeling, decision analysis and multi
objective optimization methodologies to a broad set of practical problems. They also develop the
critical sense through the comparison between alternative solutions to the same problem
obtained using methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.

d) Communication skills
Students, through the study and the carrying out of practical exercises, acquire the technical-
scientific language of the course, which must be properly used both in the intermediate and final
written tests and in the oral tests. Communication skills are also developed through group
activities.

e) Learning ability
Students who pass the exam have learned methods of decision analysis and multiobjective
optimization that allow them to face, decision-making problems and optimization on complex
organizations.