OVERVIEW

The teaching concerns the Student's training in the management of complex systems, starting from the basic concept of inferential Statistics, up to the most modern statistical techniques of Design of Experiment. The focus of teaching is on mathematical-statistical methods, paying attention to the types of reality in which they operate (deterministic / stochastic). Multiple exercises based on real business cases are provided to facilitate learning and to provide effective problem solving methods and tools.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching provides the basic notions relating to the application of statistical and simulation methods to the management of industrial processes.

AIMS AND LEARNING OUTCOMES

Provide Students with a theoretical and practical basis of the fundamental concepts and techniques of managing complex systems, such as the importance of knowing how to continuously evaluate and improve the performance of the plant in question, through the application of consolidated methodologies such as DOE, ANOVA; DES; OR.

Teach Students to identify, correct and prevent System / Facility problems and to use application tools to solve them or to mitigate their effects.

Develop Students' skills in the management of Production / Service projects, submitting them real business cases, to be solved with the methodologies learned.

Promote awareness of the transition of current production plants to the most modern dictates of Industry 4.0, through architectural, hardware and software transformation.

Help Students understand how to best use their know-how, in order to find, in the future, an ideal placement in the business context in order to be able to give value.

PREREQUISITES

It is appreciable, but not strictly necessary, to have previously attended the teaching of Statistics.

TEACHING METHODS

The teaching methods are divided into 4 macro-phases:

- Orientation

- Training

- Case studies (carried out)

- Exercises on business cases and on dedicated software

SYLLABUS/CONTENT

PROGRAM / CONTENT

1. Elements of inferential Statistics

2. ANOVA and DOE

3. Monovalent Classification, Duncan test, Dunnett test, Bartlett test

4. Bivalent Classification

5. Latin Square Design, Greco-Latin Square Design and Hyper-Square Designs

6. 2-Factor factorial designs

7. Factorial designs 2^K

8. RSM and pure quadratic curvature test

9. Daniel's test

10. Tutorials on real business cases

11. Introduction and tutorials on dedicated software

RECOMMENDED READING/BIBLIOGRAPHY

All the slides used during the lessons and other teaching material will be available on aul@web. In general, the notes taken during the lessons and the material on aul@web are sufficient for the preparation of the exam. The book indicated below is suggested as a supporting text.

Design and analysis of experiments – 10th Edition - D.C. Montgomery - Wiley 2020 - ISBN: 978-1-119-72210

TEACHERS AND EXAM BOARD

Exam Board

MARCO MOSCA (President)

MAURIZIO SCHENONE (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/10800/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written and oral, the dates of the sessions will be published in the calendar and communicated on aul@web.

The writing consists in carrying out a business case of equal difficulty to those performed in the classroom. The use of a computer software, available in the classroom, may be required, in the use of which the Students will be trained during the teaching. The oral exam includes the explanation by the Student of the exercise carried out in the written exam and, subsequently, theory questions.

ASSESSMENT METHODS

The verification of the actual achievement of the results will be obtained by evaluating the answers obtained by the Student during the exam. Specifically, the evaluation parameters will consist of:

Exercise on company cases: resolution autonomy, correctness of the result, ability to identify and request data that may have been omitted or to identify unnecessary data, ability to solve the exercise assigned to the oral exam within the standard timescales (2h), ability to compose an exhaustive and organic presentation, possibility of verifying the results by inserting screenshots from the Softwares used.

Theoretical questions: evaluation of the contents, exhaustiveness of the topic, level of detail, quality of the exposition, professional terminology, ability to connect from one topic to another, critical reasoning skills on the discussion addressed.

FURTHER INFORMATION

Pre-requisites :

Basic knowledge on fundamentals of Mathematics and Statistics.