MANAGERIAL DECISION MAKING AND MODELLING

This course allows students to gain solid methodological and modelling skills in mathematics useful to understand economic and business systems and to support managerial decision making.

The Managerial Decision Making and Modeling course applies management science tools and methodologies to analyze and solve problems arising in economic and business areas. It is designed to develop analytical problem-solving skills and to teach you decision-making techniques. The course covers methodologies for optimization that is a field of applied mathematics, the principles and methods of which are used to solve quantitative problems.

By the end of this course, students should be able to:
- Think critically and analytically about economic and business problems encountered in real life.
- Be able to model real-life situations using the mathematical tools presented during the class.

Specifically, students should achieve the following:
1. Knowledge and understanding
- know the terminology and basic concepts of a decision process.
- know how to formulate a model.
- know how to use and interpret the information a model produces.
2. Ability to apply knowledge and understanding
- understand and be able to apply basic quantitative reasoning and methodologies to real business problems.
3. Making judgements
- be able to understand the appropriate use of models in business and the potential pitfalls from using models incorrectly or inappropriately.
- be able to evaluate the information available in real-life situations effectively and appropriately.
4. Communication
- know how to gather the necessary information from stakeholders to develop valid models.
- know how to argue business decisions from the quantitative perspective by making use of mathematical models.

Students should have a bachelor degree level knowledge of mathematics and statistics.
Specifically, students should know basic concepts and methods concerning: systems of linear equalities and inequalities; matrix algebra; interest rates; minima and maxima of functions; general rules of probability; random variables and distributions; elements of inferential statistics; basic statistical models.

1. The decision-making process:
1.1 steps in a decision-making process and data-driven decision making
1.2 business analytics
1.3 prescriptive analytics and automation
2. The modeling process:
2.1 Problem analysis.
2.2 Formulating a Model
a. Data gathering
b. Making simplifying assumptions and documenting them
c. Determining variables and units
d. Establishing relationships among variables and submodels
e. Determining equations and functions
2.3. Solving the model
2.4. Verifying and interpreting the model’s solution
2.5. Reporting on the model
2.6. Maintaining the model
3. Case studies.

Notes, slides, exercises, videos, and other material necessary to follow lectures and to attain the intended learning outcomes are downloadable from the e-learning platform moodle.unive.it.

Additional readings
M. Watson, S. Lewis, P. Cacioppi, Jay Jayaraman, Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain, Pearson FT Press, Boston, 2012
D. Bertsimas, A. O'Hair and B. Pulleyblank, The Analytics Edge, Dynamic Ideas, 2016

The assessment of the students' learning occurs in two phases: during the course and at the end of the course.
During the course, students are encouraged to self-assess their learning by answering the exercises and tests proposed on the e-learning platform.
At the end of the course students will have to take the final exam.
The exam consists of an oral/practical test that lasts about 45 minutes to verify that the students have acquired analytical problem-solving skills and are familiar with decision-making techniques.
In the final exam students are required to implement, solve, and analyze a mathematical model to support managerial decision making concerning a typical operational business problem.
For example, students may be asked:
- to justify the hypotheses that they assume to hold true
- to discuss the role, purpose, and limitations of a model that they have developed as it is a simplified representation of a real situation
- to prove the validity of the results obtained and to discuss their economic effectiveness

Problems similar to ones proposed during the final exam can be found on the university e-learning platform.

Face-to-face classes and study material on the university e-learning platform moodle.unive.it. This material includes case studies that students are invited to solve. This material also includes the solutions (possibly presented through videos) of the case studies to allow students to compare their solutions with the ones proposed by the instructor or used in the real word.

During the course, the instructor also introduces and solves some case studies with the help of software packages.
In doing so he encourages thought and discussion about the considered cases. In particular, he:
- stresses the systemic approach that he uses to tackle considered problems and the mathematical concepts that lie behind the possible solution methods
- discusses the inherent difficulties of modeling a real-world problem, such as data collection,
- shows how to use the results provided by the mathematical model

English

1) Students should register for the course on the related page (Managerial Decision Making and Modelling) of the university e-learning platform moodle.unive.it

2) Accessibility, Disability and Inclusion
Accommodation and support services for students with disabilities and students with specific learning impairments

Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

written and oral