Modeling the Usage of Knowledge for Industry 4.0

JournalIndustrie 4.0 Management
Issue Volume 37, 2021, Edition 3, Pages 6-10
Open Accesshttps://doi.org/10.30844/I40M_21-3_S6-10
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Abstract

This paper describes an analysis and design method for knowledge management integrating man and machine in the age of the 4th Industrial revolution (Industry 4.0). Digitized work p rocesses require employees in an Industry 4.0 environment to have the competence to adequately deal with fluid situations on the basis of their own knowledge and the ability to place this knowledge in situation-specific contexts. To this end, the development of a comprehensive understanding of processes is elementary.

Keywords


Bibliography

[1] Teichmann, M. u. a.: Mobile IIoT-Technologien in hybriden Lernfabriken. Industrie 4.0 Management 34 (2018) 3, S. 21-24.
[2] Spötl, G.; Gorldt, C.; Windelband, L.; Grantz, T.; Richter, T.: Industrie 4.0 – Auswirkungen auf Aus- und Weiterbildung in der M+E Industrie. München 2016.
[3] Baum, G.; Borcherding, H.; Broy, M.; Eigner, M.; Huber, A.; Kohler, H.; Russwurm, S.; Stümpfle, M.: Industrie 4.0: Beherrschung der industriellen Komplexität mit SysLM. Wiesbaden 2013.
[4] Gronau, N.: Enzyklopaedie der Wirtschaftsinformatik – Industrie 4.0. 2014. URL: www.enzyklopaedie-der-wirt- schaftsinformatik.de/wi-enzyklopaedie/lexikon/ informationssysteme/Sektorspezifische-Anwendungssysteme/cyber-physische-systeme/industrie-4.0, Abrufdatum 04.06.2020.
[5] Bettenhausen, K.: Erfolgsfaktoren Industrie 4.0: Qualifikation, Geschwindigkeit und Infrastruktur. Baden-Baden 2014.
[6] Hergesell, M.: Mit Apps auf dem Milkrun In: WITTENSTEIN bastian (be)lebt Industrie 4.0, Wittenstein AG – Kundenma- gazin move, Nr. 13 (2014), S. 12-17.
[7] Davenport, T. H.; Prusak, L.: Working knowledge: How organizations manage what they know. Boston 1998.
[8] Gronau, N.: Wissen prozessorientiert managen. München 2009.
[9] Polanyi,M.:Thetacitdimension. Glocester, USA 1966.
[10] Nonaka, I.; Takeuchi, H.: The knowledge creating compa- ny: how Japanese companies create the dynamics of innovation. New York 1995.
[11] Gronau, N.: Der Einfluss von Cyber-Physical Systems auf die Gestaltung von Produktionssystemen. In: Industrie Management 31 (2015) 3, S. 16-20.
[12] Turing, A. M.: Computing machinery and intelligence. Mind 1950.
[13] Gronau, N.: Handbuch Prozessorientiertes Wissensmanagement: Methoden und Praxis. Berlin 2014.
[14] Renzl, B.: Zentrale Aspekte des Wissensbegriffs – Kernelemente der Organisation von Wissen. In: Wyssusek, B. (Hrsg): Wissensmanagement komplex. Perspektiven und soziale Praxis. Berlin 2004.
[15] Faber, S.: Entwicklung eines integrativen Referenzmodells für das Wissensmanagement in Unternehmen. Frankfurt am Main 2007.
[16] Rehäuser, J.; Krcmar, H.: Wissensmanagement im Unternehmen. In: Schreyögg, G.; Conrad, P. (Hrsg): Managementforschung 6: Wissensmanagement. Berlin 1996.
[17] Gronau, N.; Fröming, J.: KMDL – Eine semiformale Beschrei- bungssprache zur Modellierung von Wissenskonversionen. Wirtschaftsinformatik 48 (2006) 5, S. 349-360.
[18] Windt, K.; Knollmann, M.; Meyer, M.: Anwendung von Data Mining Methoden zur Wissensgenerierung in der Logistik – Kritische Reflexion der Analysefähigkeit zur Termintreueverbesserung. In: Spath, D. (Hrsg): Wissensarbeit – Zwischen strengen Prozessen und kreativem Spielraum. Berlin 2011.
[19] Freitag, M.; Kück, M.; Ait Alla, A.; Lütjen, M.: Potenziale von Data Science in Produktion und Logistik. Teil 1 – Eine Ein- führung in aktuelle Ansätze der Data Science. In: Industrie Management 5 (2015), S. 22-26.
[20] Gronau, N.: Knowledge Modeling and Description Language 3.0. Eine Einführung. Berlin 2020.

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