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Article

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning

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Fakultät Management und Vertrieb, Hochschule Heilbronn Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
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Departament of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain
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Departament of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 2006 Madrid, Spain
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Saueressig GmbH + Co. KG, Gutenbergstr. 1-3, 48691 Vreden, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 2841; https://doi.org/10.3390/s19132841
Received: 20 May 2019 / Revised: 9 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals. View Full-Text
Keywords: EEG sensors; manufacturing systems; problem-solving; deep learning EEG sensors; manufacturing systems; problem-solving; deep learning
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MDPI and ACS Style

Villalba-Diez, J.; Zheng, X.; Schmidt, D.; Molina, M. Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning. Sensors 2019, 19, 2841. https://doi.org/10.3390/s19132841

AMA Style

Villalba-Diez J, Zheng X, Schmidt D, Molina M. Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning. Sensors. 2019; 19(13):2841. https://doi.org/10.3390/s19132841

Chicago/Turabian Style

Villalba-Diez, Javier, Xiaochen Zheng, Daniel Schmidt, and Martin Molina. 2019. "Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning" Sensors 19, no. 13: 2841. https://doi.org/10.3390/s19132841

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