MANAGERIAL DECISION MAKING AND MODELLING

This course allows students to gain solid methodological and modeling skills in mathematics useful for understanding economic and business systems and supporting managerial decision making.

The Managerial Decision Making and Modeling course applies the tools and methods of management science to analyze and solve problems that arise in business and economics. It is designed to develop analytical problem-solving skills and to teach you decision-making techniques. The course covers methods of optimization, an area of applied mathematics whose principles and methods are used to solve quantitative problems.

At the end of this course, students should be able to:
- Think critically and analytically about real-world economic and business problems.
- Model real-world situations using the mathematical tools presented in the course.

Specifically, students should be able to:
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 that a model produces.
2. Ability to apply knowledge and understanding
- Understand and be able to apply basic quantitative reasoning and methods to real-world business problems.
3. Make judgments
- Understand the appropriate use of models in business and the potential pitfalls of incorrect or inappropriate use of models.
- Be able to effectively and appropriately evaluate the information available in real-world situations.
4. Communication Skills
- Know how to gather the necessary information from stakeholders to develop valid models.
- Be able to argue business decisions from a quantitative perspective using mathematical models.

Students should have an undergraduate background in mathematics and statistics.
Specifically, students should know basic concepts and methods in: 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 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 Collection
b. Make and document simplifying assumptions
c. Determine variables and units
d. Establish relationships between variables and submodels
e. Identify equations and functions.
2.3. Solving a model
2.4. Verifying and Interpreting a model solution
2.5. Reporting a model
2.6. Maintaining a model
3. Case studies.

Notes, slides, exercises, videos and other materials necessary to follow the lectures and achieve the intended learning outcomes are available for download from the e-learning platform moodle.unive.it.

Additional readings
G. Ghiani, G. Laporte, R. Musmanno, Introduction to Logistics Systems Management: With Microsoft Excel and Python Examples, Wiley Series in Operations Research and Management Science, John Wiley & Sons, Inc., Hoboken, NJ
D.-S. Chen, R. G. Batson, Y. Dang. Applied integer programming: modeling and simulation, John Wiley & Sons, Inc., Hoboken, NJ
M. Watson, S. Lewis, P. Cacioppi, J. Jayaraman, Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain, Pearson FT Press, Boston,
D. Bertsimas, A. O'Hair and B. Pulleyblank. The Analytics Edge, Dynamic Ideas, Charlestown, MA
M. L. Pinedo. Planning and Scheduling in Manufacturing and Services. Springer, Berlin, D

Assessment of student learning takes place 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 completing the exercises and tests proposed in the e-learning platform.
At the end of the course, students must take the final exam.
The exam consists of an oral/practical test of approximately 45 minutes to verify that students have acquired analytical problem-solving skills and are familiar with decision-making techniques.
The final exam requires students to implement, solve, and analyze a mathematical model to support managerial decision making on a typical operational business problem.
For example, students may be asked to:
- justify the hypotheses they assume to be true
- discuss the role, purpose, and limitations of a model they have developed as a simplified representation of a real-world situation.
- demonstrate the validity of the results obtained and discuss their economic effectiveness.

Problems similar to those proposed for the final exam can be found on the university's e-learning platform.

To achieve a passing grade on the exam, it is necessary for the student to demonstrate the ability to mathematically formulate the assigned problem, particularly but not limited to correctly identifying: objective, constraints and decision variables.
To achieve an intermediate grade (up to 26) it is necessary for the student to be able to develop a program in python that solves the proposed problem.
To achieve the top grade, it is necessary that the student knows how to comment from an economic/business perspective on the results provided by the python program.

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

During the course, the instructor also presents and solves some case studies using software packages.
He encourages reflection and discussion on the cases. In particular, he
- emphasizes the systemic approach to the problems considered and the mathematical concepts 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 dedicated 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