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Human–Machine Integration in Processes within Industry 4.0 Management

The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management s...

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Detalles Bibliográficos
Autores principales: Villalba-Diez, Javier, Ordieres-Meré, Joaquín
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434634/
https://www.ncbi.nlm.nih.gov/pubmed/34502819
http://dx.doi.org/10.3390/s21175928
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author Villalba-Diez, Javier
Ordieres-Meré, Joaquín
author_facet Villalba-Diez, Javier
Ordieres-Meré, Joaquín
author_sort Villalba-Diez, Javier
collection PubMed
description The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them.
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spelling pubmed-84346342021-09-12 Human–Machine Integration in Processes within Industry 4.0 Management Villalba-Diez, Javier Ordieres-Meré, Joaquín Sensors (Basel) Article The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them. MDPI 2021-09-03 /pmc/articles/PMC8434634/ /pubmed/34502819 http://dx.doi.org/10.3390/s21175928 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Villalba-Diez, Javier
Ordieres-Meré, Joaquín
Human–Machine Integration in Processes within Industry 4.0 Management
title Human–Machine Integration in Processes within Industry 4.0 Management
title_full Human–Machine Integration in Processes within Industry 4.0 Management
title_fullStr Human–Machine Integration in Processes within Industry 4.0 Management
title_full_unstemmed Human–Machine Integration in Processes within Industry 4.0 Management
title_short Human–Machine Integration in Processes within Industry 4.0 Management
title_sort human–machine integration in processes within industry 4.0 management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434634/
https://www.ncbi.nlm.nih.gov/pubmed/34502819
http://dx.doi.org/10.3390/s21175928
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