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Prof. Dr.-Ing. Sebastian Grundstein, MBA

Lehrcampus Cham

Professor:innen

Professor

Professur für Cyberphysische Produktionssysteme

DMS C114

09971/99673-62

Sprechzeiten

Nach Vereinbarung

Sortierung:

Labore

IT-Labor / Lean Digital / Production Simulation

Forschungs- und Lehrgebiete

  • Smart Factory & Cyberphysical Production Systems
  • Production Data Management / Applied AI in Production
  • ERP / MES / IIoT
  • Simulation & Optimization in Production
  • Business Planning

Vita

  • 03/24 - dato: Professor an der Technischen Hochschule Deggendorf
  • 05/17 - 02/24: Partner bei ROI-EFESO Management Consulting AG
  • 11/12 - 05/17: Doktorand im Fachbereich „Produktionstechnik“ der Universität Bremen; z.T. tätig am BIBA - Bremer Institut für Produktion und Logistik GmbH; Abschluss: Dr.-Ing.
  • 07/13 – 06/16: Berufsbegleitendes Studium „General Management“ an der HFH; Abschluss: Master of Business Administration (MBA)
  • 10/07 – 09/12: Studium „Maschinenbau & Management“ an der Fakultät Maschinenwesen der Technischen Universität München; Abschluss: Dipl.-Ing. Univ.,

Sonstiges

Current thesis fields in the labs:

  • A generic simulation model for digital factory twins (Anylogic)
  • Digital Shopfloor Management Framework and Pilot Implementation
  • Impact of generative AI on traditional business models – A case study across selected industries
  • A use case based reference model for cloud architectures with a pilot implementation of ML-based visual inspection
  • Potentials of composable MES in theory and practice
  • AI-based material replenishment - beyond Kanban 4.0
  • Pilot implementation of an I4.0 learning factory for the process industry
  • A learning factory for AI experience
  • Development of a VR-based training concept
  • Implementation and evaluation of an AR-supported assembly process
  • Potentials of 3D-laserscanning in Quality Control with pilot implementation in assembly
  • A case study using NVIDIA Omniverse in factory simulation
  • Evaluation framework & pilot for Agent-based RPA implementation
  • Coupling of cobot automation and visual quality control in a pilot process
  • Applied Process Mining in Manufacturing
  • AI-based variant configuration - concept & pilot implementation