Laboratory of Data Driven Decision Making (3 CFU)
Paolo Dell’Olmo

Objectives
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. A variety of different computational model and software platform is available to transform data in valuable evaluation and decisions
Our aim is to give to the student the experience of processing data to feed evaluation and optimization software tools applied to a number of 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 laboratory, the students know the process of elaborating data to feed decision-optimization analytical models to support managers in problems arising in real world organization.

b) Ability to apply knowledge and understanding
At the end of the laboratory the students are able to select an adequate model or platform for a specific decision problem, to analyze the data available and to execute the to solve them.

c) Autonomy of judgment
Students develop the skills through the use of decision analysis and multi objective optimization tools applied to a broad set of practical problems. They also develop the critical sense through the experiments and of sensitivity analysis and dependencies of decision from the. 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 laboratory, which must be properly used both in the project. Communication skills are also developed through group activities.

e) Learning ability
Students who pass the exam have learned tools of decision analysis and multi-objective optimization which allow them to face decision-making problems and optimization on complex organizations.

References
1. D. Bertsimas, and R. Freund. Data, Models, and Decisions: The Fundamentals of Management Science. Dynamic Ideas, Wiley, 2004. ISBN: 9780975914601.
2. M. Ehrgott, Multicriteria Optimization, Springer, 2005.
3. A. Ishizaka, P. Nemery, Multi-criteria Decision Analysis: Methods and Software, ISBN: 978-1-119-97407-9, WIley, 2013.
4. Software manuals available on line and on the e-learning platform