A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

[Display omitted] •Proposes a MES based data-driven algorithm to predict throughput bottlenecks.•Devised an approach to determine the size historical machine data to predict bottlenecks.•Proposes an evaluation framework to evaluate the algorithm performance.•Compared the performance of the algorithm...

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Veröffentlicht in:	Computers & industrial engineering Jg. 125; S. 533 - 544
Hauptverfasser:	Subramaniyan, Mukund, Skoogh, Anders, Salomonsson, Hans, Bangalore, Pramod, Bokrantz, Jon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2018
Schlagworte:	active period ARIMA Big data bottleneck Bottleneck prediction bottlenecks constraints data-driven decision-making Digitalisation Industry 4.0 machine learning Maintenance managing bottlenecks prediction Predictive analytics production Real-world shifting smart maintenance smart manufacturing Theory of Constraints throughput throughput bottlenecks Digitalisation Big data Industry 4.0 Bottleneck prediction Maintenance Predictive analytics
ISSN:	0360-8352, 1879-0550
Online-Zugang:	Volltext
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