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Learning Factory: The Path to Industry 4.0

Open access
Baena, Felipe, Guarin, Alvaro, Mora, Julian, Sauza, Joel, Retat, Sebastian·2017·Procedia Manufacturing

Abstract

Nowadays, there are plenty of studies that seek to determine which are the skills that should be met by an engineer. Communication and teamwork are some of the most recurrent ones associated with a knowledge of the engineering sciences. However, their application is not straight forward, due to the lack of educational approaches that contributes to develop experience-based knowledge. Learning Factories (LF) have shown to be effective for developing theoretical and practical knowledge in a real production environment. This article describes the transformation process of a training-addressed manufacturing workshop, in order to structure a Learning Factory for the production engineering program at EAFIT University. The proposed transformations were based on the definition of three pillars (didactic, integrative and engineering) for the development of an LF. We argue that a proper transformation process may contribute to ease the path towards new manufacturing trends such as industry 4.0 into an academic context that strengths the engineering training process.

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Metadata

Authors
Baena, F., Guarin, A., Mora, J., Sauza, J., & Retat, S.
Venue
Procedia Manufacturing
Publisher
Elsevier BV
Published
2017
Volume
9
Pages
73-80
ISSN
2351-9789 (ISSN-L)
OpenAlex
W2653635971
Cited by
323
References
0
FWCI
525.57
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Learning Factory: The Path to Industry 4.0. https://app.cochat.ai/papers/10.1016/j.promfg.2017.04.022
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Baena, F., Guarin, A., Mora, J., Sauza, J., & Retat, S. (2017). Learning Factory: The Path to Industry 4.0. Procedia Manufacturing. https://doi.org/10.1016/j.promfg.2017.04.022

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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.