The role of technology for the competitive advantage of the firm and industries; Basic concepts, models and tools for the management of technology; managerial decision making regarding the identification, selection and protection of technology (make or buy, keep or sell, current and future technologies). Theories, practical examples (cases), lectures, interactive sessions and group study.
This lecture is part of the Module Technology Management and can not separately choosen.
TeilnehmerInnen:
This module has a limited capacity of 40 seats.
Until the end of the registration period you can only reserve a spot on the waiting list. You have to attend the mandatory introductory session on 24th of October to be able to attend the course.
Voraussetzungen:
Bachelor knowledge in business management.
Leistungsnachweis:
To pass this module, every student has to a) give one presentation during the Übung or Seminar, b) give their fellow students feedback for their presentation once, and c) pass the written exam at the end of the semester.
Sonstiges:
REGISTRATION PROCESS: Until the end of the registration period you can only register on the waiting list (FOR BOTH LECTURE AND SEMINAR). We will upload the final enrolment list around the the 15th of October.
If you don't get a spot right away there is still the chance of getting a spot later if you are on the waiting list and students don't attend the mandatory introductory session on 24th of October. You will get a notification if you can join the course then.
As every year, we will try our best to make sure that every student can at least participate one of the two modules Technology Management or Product Planning.
ECTS-Kreditpunkte:
3
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.
Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.
Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.
Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.
2020
Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.
Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.