Smart Objects - A Smart Alternative to Isolated Applications

von der Planung bis zum Betrieb

Timur Ripke, Sven Kägebein
JournalIndustrie 4.0 Management
Issue Volume 35, 2019, Edition 5, Pages 21-24
Open Accesshttps://doi.org/10.30844/I40M_19-5_S21-24
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Abstract

Media disruption interferes with consistent and universal digitalization. Data is easily lost, time and resources wasted. Heterogeneous and isolated applications produce partial relief; however they fail to integrate redundant information from separately operated systems into a homogeneously processible data mass. The employment of a centralized data hub proposes a strategy to effectively advance digitalization in process management, connecting scheduling of involved parties, defect tracking and progress processes. It also automatizes reportings on project progress.

Keywords

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Bibliography

[1] Bracht, U. u a. (2011). Digitale Fabrik. Methoden und Praxisbeispiele. Basis für Industrie 4.0. VDI Buch.
[2] Sonnenberg, V. (2018). „Predictive Maintenance: Effizienz in der Industrie 4.0.“ Maschinenmarkt. Vogel Communications GmbH & Co.KG. URL: www.maschinenmarkt.vogel.de/predictive-maintenance-effizienz-in-der-industrie-40-a-730126/, Abrufdatum 14.01.2019.
[3] Stahl R., Staab P. (2017). „Ausgangslage, Vision und Wegbeschreibung.“ In: Die Vermessung des Datenuniversums.
[4] „Datenspeicherung: Warum Datensilos massive Schwierigkeiten verursachen.“A-bas-erp. URL: www.abas-erp.com/de/news/datenspeicherung-warum-datensilos-massive-schwierigkeiten-verursachen, Abrufdatum 20.03.2019.
[5] Schober, K.-S. (2016). “Digitization in the Construction Industry. Building Europe’s Road to ‘Construction 4.0.’” Think Act. Beyond Mainstream, Ausg. 6-2016, Roland Berger GmbH. URL: www.rolandberger.com/publications/publication_pdf/tab_digitization_construction_industry_e_fi nal.pdf, Abrufdatum, 14.03.2019.
[6] VDI-Fachbereich Fabrikplanung und -betrieb (2008): „Digitale Fabrik – Grundlagen.“ VDI-Richtlinien, VDI 4499 Blatt 1.
[7] Böhmer, H. u. a. (2012). Erkennen und Vermeiden von Planungs- und Ausführungsfehlern. Institut für Bauforschung e.V. (Hrsg).
[8] Mossman, A. (2017): “Last Planner: 5 + 1 Crucial; Collaborative Conversations for Predictable Design & Construction Delivery.” ResearchGate. URL: www.researchgate.net/publication/321804312_Last_Planner_5_1_crucial_collaborative_conversations_for_predictable_design_construction_delivery_Dec_2017, Abrufdatum 14.01.2019.
[9] Kupsch, F.: Framework zur dezentralen Integration systemübergreifender Geschäftsprozesse. Köln 2006.
[10] Morkos, B. u. a. (2012). “Mobile Devices Within Manufacturing Environments: A BMW Applicability Study.” International Journal on Interactive Design and Manufacturing, 6(2), S. 1-11.

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