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Risorsa bibliografica obbligatoria |
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Risorsa bibliografica facoltativa |
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Anno Accademico
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2017/2018
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Scuola
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Scuola di Ingegneria Industriale e dell'Informazione |
Insegnamento
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097340 - ADVANCED SUPPLY CHAIN PLANNING LAB
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Docente |
Miragliotta Giovanni
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Cfu |
10.00
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Tipo insegnamento
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Monodisciplinare
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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 | * | A | ZZZZ | 097340 - 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
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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.
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Nessun software richiesto |
Tipo Forma Didattica
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Ore didattiche |
lezione
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30.0
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esercitazione
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30.0
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laboratorio informatico
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40.0
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laboratorio sperimentale
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0.0
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progetto
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0.0
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laboratorio di progetto
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60.0
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Informazioni in lingua inglese a supporto dell'internazionalizzazione |
Insegnamento erogato in lingua

Inglese
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Disponibilità di materiale didattico/slides in lingua inglese
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Disponibilità di libri di testo/bibliografia in lingua inglese
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Possibilità di sostenere l'esame in lingua inglese
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Disponibilità di supporto didattico in lingua inglese
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