A Review of data-driven decision-making methods for Industry 4.0. Maintenance applications

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<title>Review of data-driven decision-making methods for Industry 4.0. Maintenance applications</title>
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<namePart>Bousdekis,  Alexandros</namePart>
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<abstract displayLabel="Summary"> The current paper reviews the literature on data-driven decision- making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives</abstract>
<note type="statement of responsibility">Alexandros Bousdekis... [et al.]</note>
<note>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/)</note>
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<topic>Análisis de datos</topic>
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<topic>Data driven</topic>
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<topic>Toma de decisiones</topic>
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<topic>Internet de las cosas</topic>
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<topic>Realidad aumentada</topic>
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<topic>Big data</topic>
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<title>Electronics.- Basel, Switzerland : MDPI, A.G</title>
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<text>Vol. 10, nº 828, 31 March 2021 ; 20 p.</text>
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