Implementing Digitization Potential

An approach using apps for the industrial shop floor

Christian Knecht, Andreas Schuller
JournalIndustrie 4.0 Management
Issue Volume 35, 2019, Edition 3, Pages 51-54
Open Accesshttps://doi.org/10.30844/I40M_19-3_S51-54
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Abstract

Small and medium-sized enterprises can hardly exploit the potential of digital transformation. In the BMBF research project »ScaleIT« an Industry 4.0 platform was developed with which individual process steps can be improved with the help of apps. There are both ready to use apps and open source tools that make it easy to develop new apps. Companies do not run the risk of a profound change in their IT processes, but can optimize their value chain step-by-step by implementing and installing new Industry 4.0 apps. A methodology helps to uncover the greatest digitization potential in companies.

Keywords

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Bibliography

[1] Schwarz, T.: Leitfaden Online-Marketing: das Wissen der Branche. 2. Auflage. Waghäusel 2013.
[2] Behrendt, B.: Was ist eine App? Gründerszene Lexikon. URL: https://www.gruenderszene. de/lexikon/begriffe/app Abrufdatum 01.03.2010.
[3] Wilhelm, T.: Was zeichnet eine gute App aus? Usabilityblog. URL: https://www. usabilityblog.de/was-ze – ichnet-eine-gute-app-aus/ Abrufdatum 14.01.2019.
[4] Sendler, U.: Industrie 4.0. Berlin Heidelberg 2013.
[5] Ganschar, O.; Gerlach, S.; Hämmerle, M.; Krause, T.; Schlund, S.: Produktionsarbeit der Zukunft – Industrie 4.0. Stuttgart 2013.
[6] Lieser, R.: Deshalb bremsen ERP-Monolithen Ihre Digitale Transformation aus. Blog 98 – Aktuelle Themen zu E-Commerce & Magento. 28.02.1018. URL: https://www.netz98.de/ blog/digitalisierung/so-bremsen-erp-monolithen-ihre-digitale-transformation-aus/ Abrufdatum 14.01.2019.
[7] Bauernhansl, T.; Hompel, M.; Vogel-Heuser, B.: Industrie 4.0 in Produktion, Automatisierung und Logistik. Wiesbaden 2014.
[8] Richter, M.; Flückiger, D.: Usability Engineering kompakt benutzbare Produkte gezielt entwickeln. 3. Auflage. Berlin 2013.

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