Zukunftslabor Produktion-Reference-Cited by-同舟云学术

Zukunftslabor Produktion

Published:2024-04-30 Issue:5 Volume:119 Page:372-377
ISSN:2511-0896
Container-title:Zeitschrift für wirtschaftlichen Fabrikbetrieb
language:en
Short-container-title:

Author:

Kallisch Jonas¹,Denkena Berend²,Kramer Kathrin³,Stürenburg Lukas¹,Pachandrin Slava⁴,Rokicki Markus⁵,Walter Jörg⁶,Nein Marcus²,Voss Marvin⁷,Wunck Christoph¹,Niemann Karl-Heinz⁷,Schmidt Matthias⁸,Dilger Klaus⁴,Niederée Claudia⁵,Hoffmann Norbert⁴

Affiliation:

1. Hochschule Emden/Leer ; Constantinplatz 4 , Emden ; Germany

2. IFW, Leibniz Universität Hannover Germany

3. IPTS, Leuphana Universität Lüneburg Germany

4. IFS, Technische Universität Braunschweig Germany

5. Forschungszentrum L3S der Leibniz Universität Hannover Germany

6. OFFIS Institute for Information Technology Oldenburg , Germany

7. Hochschule Hannover Germany

8. IFL, Leibniz Universität Hannover Germany

Abstract

Abstract The Future Lab Production demonstrates the potentials of digitalisation by using the die casting process as an example process. The project shows how manufacturing companies can digitalise their existing machines, analyse their data and exchange information along the supply chain while maintaining data sovereignty. The aim is to support companies with digitalisation from the machine to data platforms. The article describes the methods used, the concepts developed and their benefits.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/zwf-2024-1061/pdf

Reference17 articles.

1. Kallisch, J.; Wunck, C.: Options for Connecting Decentralized Data Infrastructure to improve Supply-Chain Decision Making without Giving up Individual Data Property. Online unter https://dsi-annualconference2022.exordo.com/programme/presentation/219 [Abruf am 15.12.2023]

2. Kallisch, J.; Marx-Gómez, J.; Wunck, C.: Challenges and Opportunities of Intercompany Big Data Analytics in Supply Chains. In: Mora, M. et al. (Hrsg.): Development Methodologies for Big Data Analytics Systems. Springer-Verlag, Berlin, Heidelberg 2024, S. 249–262 10.1007/978-3-031-40956-1_10

3. Kallisch, J.; Wunck, C.: Development and Evaluation of a Process Management and Analytics Platform for Small and Medium-sized Enterprises. International Journal for Computers & Their Applications 30 (2023) 2, S. 130

4. Kallisch, J.; Wunck, C. (Hrsg.): Using Vertical Federated Learning in Industrial Supply Chains. Decision Science Institute, 11/2023

5. VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA) (Hrsg.): VDI/VDE 2182 Blatt 1: Informationssicherheit in der industriellen Automatisierung – Allgemeines Vorgehensmodell. Beuth Verlag, Berlin 2020