Abstract
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.
Highlights
The current trend of automation and data exchange in manufacturing is enabled by emerging technological advancements including the Internet of Things (IoT), cloud computing, and cyber-physical systems
On the basis of these data, one can apply to the stochastic degradation process welltoashandle the uncertainty in prognostic output and advanced data analytics techniques inas order the uncertainty due to the stochastic to support decision-making under time constraints
The human cyber physical system (H-cyber physical systems (CPS)) concept has arisen [81], which paves the way for the use of emerging technologies implementing human–machine symbiosis
Summary
The current trend of automation and data exchange in manufacturing is enabled by emerging technological advancements including the Internet of Things (IoT), cloud computing, and cyber-physical systems. This trend is often cited as “Industry 4.0”, “smart manufacturing”, and “digital factory” [1]. This paper starts by surveying prominent decision-making approaches for manufacturing maintenance operations, a well-studied application area, and analyses decision-making algorithms that are triggered by real-time, data-driven analytics. The analysis of the state of the art leads to a synthesis of research challenges and the definition of a research agenda for data-driven decision-making for Industry 4.0 maintenance applications. Research challenges and the definition of a research agenda for data-driven decision-making for Industry 4.0 maintenance applications
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