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Article

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

by
Daniel Schmidt
1,2,*,†,
Javier Villalba Diez
1,3,4,*,†,
Joaquín Ordieres-Meré
1,
Roman Gevers
2,
Joerg Schwiep
2 and
Martin Molina
4
1
Department of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain
2
Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany
3
Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
4
Department of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to the work.
Sensors 2020, 20(10), 2860; https://doi.org/10.3390/s20102860
Submission received: 30 April 2020 / Revised: 12 May 2020 / Accepted: 12 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)
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Abstract

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the “HOSHIN KANRI TREE” (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain’s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.
Keywords: EEG sensors; manufacturing systems; shopfloor management; machine learning; deep learning EEG sensors; manufacturing systems; shopfloor management; machine learning; deep learning

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MDPI and ACS Style

Schmidt, D.; Villalba Diez, J.; Ordieres-Meré, J.; Gevers, R.; Schwiep, J.; Molina, M. Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning. Sensors 2020, 20, 2860. https://doi.org/10.3390/s20102860

AMA Style

Schmidt D, Villalba Diez J, Ordieres-Meré J, Gevers R, Schwiep J, Molina M. Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning. Sensors. 2020; 20(10):2860. https://doi.org/10.3390/s20102860

Chicago/Turabian Style

Schmidt, Daniel, Javier Villalba Diez, Joaquín Ordieres-Meré, Roman Gevers, Joerg Schwiep, and Martin Molina. 2020. "Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning" Sensors 20, no. 10: 2860. https://doi.org/10.3390/s20102860

APA Style

Schmidt, D., Villalba Diez, J., Ordieres-Meré, J., Gevers, R., Schwiep, J., & Molina, M. (2020). Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning. Sensors, 20(10), 2860. https://doi.org/10.3390/s20102860

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