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Cited by 2,974

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Design Principles for Industrie 4.0 Scenarios

Hermann, Mario, Pentek, Tobias, Otto, Boris·2016·2016 49th Hawaii International Conference on System Sciences (HICSS)

Abstract

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios.

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Metadata

Authors
Hermann, M., Pentek, T., & Otto, B.
Venue
2016 49th Hawaii International Conference on System Sciences (HICSS)
Publisher
IEEE
Published
2016-01
Pages
3928-3937
ISSN
1530-1605 (ISSN-L)
OpenAlex
W2295939521
Cited by
2,974
References
5
FWCI
520.63
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Design Principles for Industrie 4.0 Scenarios. https://app.cochat.ai/papers/10.1109/hicss.2016.488
The paper
Hermann, M., Pentek, T., & Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS). https://doi.org/10.1109/hicss.2016.488

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.