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Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production
Open accessAbstract
Industry is currently undergoing a transformation towards full digitalization and intelligentization of manufacturing processes. Visionary but quite realistic concepts such as the Internet of Things, Industrial Internet, Cloud-based Manufacturing and Smart Manufacturing are drivers of the so called Fourth Industrial Revolution which is commonly referred to as Industry 4.0. Although a common agreement exists on the necessity for technological advancement of production technologies and business models in the sense of Industry 4.0, a major obstacle lies in the perceived complexity and abstractness which partly hinders its quick transformation into industrial practice. To overcome these burdens, we suggest a Scenario-based Industry 4.0 Learning Factory concept that we are currently planning to implement in Austria's first Industry 4.0 Pilot Factory. The concept is built upon a tentative competency model for Industry 4.0 and the use of scenarios for problem-oriented learning of future production engineering.
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“The global Industry 4.0 initiative is centered around the integration of AI at nearly every level to drive predictive maintenance, future business strategies, and floor production, with humans working alongside AI to make vital decisions for the company (Erol et al., 2016; Kang et al., 2016; Lee et al., 2015; Mabkhot et al., 2018; Omron, 2020).”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Erol et al., 2016
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Cited by
Literature review of Industry 4.0 and related technologies
Oztemel, E., & Gursev, S. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1433-8 · Cited by 2,143
Ivanov, D., Dolgui, A., & Sokolov, B. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1488086 · Cited by 2,055
Vaidya, S., Ambad, P., & Bhosle, S. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.02.034 · Cited by 1,352
Horváth, D., & Szabó, R. Z. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.05.021 · Cited by 1,281
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Metadata
- Authors
- Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W.
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 54
- Pages
- 13-18
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W2538102286
- Cited by
- 636
- References
- 0
- FWCI
- 62.57
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production. Procedia CIRP. https://doi.org/10.1016/j.procir.2016.03.162
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
