MODULBESCHREIBUNG

SF-Smart Factory

Kurzzeichen:
M_SFFACTORY
Durchführungszeitraum:
HS/19
ECTS-Punkte:
4
Lernziele:

Expertise - the participants can:

  • understand the improvement potential of a Smart Factory production system compared to a traditional production set up
  • understand the importance and connection of data throughout the company to improve production process applying digital technologies
  • use certain digital technologies such as simulation, data analysis and machine leaning techniques to analyze, evaluate and improve production processes

Methodological Skills - the participants can:

  • identify relevant use case for a Smart Factory
  • apply Smart Factory technologies to address the relevant use case
  • implement a Smart Factory and know how to drive the change process

Social Skills - the participants can:

  • Realistically plan work-sharing tasks in complex case studies in a team and solve conflicts in individual deviations in a team
Verantwortliche Person:
Hänggi Roman
Zusätzlich vorausgesetzte Kenntnisse:

Fundamental knowledge of production management
Fundamental knowledge of probability theory
Solid command of the English language, as the lectures are held in English

Skriptablage:
Modultyp:
Standard-Modul für Wirtschaftsingenieurwesen STD_18(Keine Semester Empfehlung)
Standard-Modul für Wirtschaftsingenieurwesen U_18(Keine Semester Empfehlung)

Modulbewertung

Bewertungsart:
Note von 1 - 6

Leistungsbewertung

Während der Prüfungssession:
Schriftliche Prüfung, 90 Minuten

Kurse in diesem Modul

SF-Smart Factory

Kurzzeichen:
SFFACTORY
Lernziele:
  1. Concept & Implementation Approach for a Smart Factory
  2. Data integrity & availability through CAD - PLM - ERP – MES – Machine Controls – sensors – digital twin ("understand through touch & feel")
  3. Digital Technologies for a Smart Factory
  4. Simulation of production processes
  5. Learning from Data (Machine Learning) for a Smart Factory
Plan und Lerninhalt:
  1. Understand what is a Smart Factory
  2. Data integrity & availability through CAD - PLM - ERP – MES – Machine Controls – sensors – digital twin
  3. Digital Technologies for a Smart Factory
  4. Simulation of production processes
    1.        Production process deep understanding
    2.        Data collection and analysis – implementation approach for simulation
    3.        Simulation result analysis, process optimization
  5. Learning from Data (Machine Learning)
    1.        Supervised learning
    2.        Unsupervised learning
    3.        Reinforcement learning
  6. Outlook of Smart Factory for CH to generate long term cost advantage
Kursart:

(Durchführung gemäss Stundenplan)

Vorlesung mit 2 Lektionen pro Woche
   - Max. Teilnehmer: 108
   - Harte Grenze: ja
Uebung mit 2 Lektionen pro Woche
   - Max. Teilnehmer: 18
   - Harte Grenze: ja