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Review

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), 157-80 Athens, Greece
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Academic Editor: Carlos A. Iglesias
Electronics 2021, 10(7), 828; https://doi.org/10.3390/electronics10070828
Received: 9 March 2021 / Revised: 21 March 2021 / Accepted: 25 March 2021 / Published: 31 March 2021
(This article belongs to the Section Computer Science & Engineering)
Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. 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. View Full-Text
Keywords: Internet of Things; intelligent decision-making; data analytics; big data; predictive maintenance Internet of Things; intelligent decision-making; data analytics; big data; predictive maintenance
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MDPI and ACS Style

Bousdekis, A.; Lepenioti, K.; Apostolou, D.; Mentzas, G. A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications. Electronics 2021, 10, 828. https://doi.org/10.3390/electronics10070828

AMA Style

Bousdekis A, Lepenioti K, Apostolou D, Mentzas G. A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications. Electronics. 2021; 10(7):828. https://doi.org/10.3390/electronics10070828

Chicago/Turabian Style

Bousdekis, Alexandros, Katerina Lepenioti, Dimitris Apostolou, and Gregoris Mentzas. 2021. "A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications" Electronics 10, no. 7: 828. https://doi.org/10.3390/electronics10070828

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