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Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 097340 - ADVANCED SUPPLY CHAIN PLANNING LAB
Docente Miragliotta Giovanni
Cfu 10.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - BV (479) MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE*AZZZZ097340 - ADVANCED SUPPLY CHAIN PLANNING LAB

Programma dettagliato e risultati di apprendimento attesi

Lab Objectives

The global business environment and the increased complexity in supply chain planning require an extended set of competences, ranging from new collaborative planning methods to advanced IT tools. In this scenario, the Lab objective is to provide state-of-the-art knowledge on techniques and tools for advanced Supply Chain planning and execution (SCP&E) tasks. By mixing traditional theoretical lessons with in-class numerical exercises, business games and application cases, a thorough yet practical knowledge will be transferred to the students.

 

Lab Contents

The Lab program will encompass the following contents:

  • Introduction / reprise about supply chain planning tasks, examples from different industries; the relevance of information in nowadays planning.
  • Models and techniques for advanced planning: from Vendor Managed Inventory to Supply Chain simulation.
  • Technologies for Advanced Planning: APS systems, Internet of Things, Industrial Analytics, Cloud platforms.
  • Methodologies for Value Assessment: a framework for tangible and intangible benefits, approaches and models for a quantitative assessment, and for business case constructing
  • Industries and vertical markets: special focus on Advanced planning in Automotive, Retailing, Food, Construction and Fashion Luxury industries.
  • Outlook on the job market: the role of human planner in the future technology playground.

 

Approach and Methodologies

Each topic will be first introduced through a short traditional lesson, and then followed by extensive applicative contents. Overall, the Lab will resort to the following teaching methodologies:

  • Traditional lessons, providing theoretical background, scenario analysis, outlook on market needs and technology solutions;
  • Numerical exercises, so as to practice with specific planning methods, techniques and tools in limited-size problems;
  • Computer aided learning (e.g. business games)
  • Visit to the Internet of Things Lab of Politecnico di Milano (if allowed by logistic constraints)
  • Company guest speakers
  • Autonomous work on Advanced Planning assignment(s)
  • Short literature reviews / Market searches as elective, bonus assignments

 


Note Sulla Modalità di valutazione

During traditional class exercises and business cases, students will be divided in groups (4/5 people, autonomously arranged), which will work on the given tasks using their own laptops. Class exercises and business cases will not be evaluated.

For the two assignments (project works), students will be divided in groups (4/5 people, arranged by the teachers so as to mix and balance people’s experience and background). The assignments will be given during the semester (1st after 30% of the course, 2nd after 60% of the course) and will require autonomous work of the team, with in-class assistance by Lab teachers.

The two assignments will rely on real-company data, disguised for the sake of confidentiality. Company involvement, in case, will be guided by teachers.

 Exam and evaluation

The evaluation will be based on one or two assignment(s), followed by an oral examination.

  • Assignment(s): The assignment(s) evaluation will represent 50% of the overall score, and will be valid for one academic year (till February 2017 sessions)
  • Oral examination: The oral examination will represent 50% of the overall score, and will cover both the assignment(s), so as to highlight the individual contribution to the project work, as well as the course’s topics.
  • Elective additional short assignments, awarded with bonus extra scores (+1 or +2 points, depending on assignments’ difficulty and quality of the work).

 

Class participation and motivation are strongly recommended.

 


Bibliografia

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
30.0
esercitazione
30.0
laboratorio informatico
40.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
60.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
19/01/2021