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Discovering actionable knowledge for industry 4.0: from data mining to predictive and prescriptive analytics

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dc.contributor.authorSchuetz, C.G.
dc.contributor.authorSchrefl, M.
dc.contributor.authorSelway, M.
dc.contributor.authorThalmann, S.
dc.contributor.editorVogel-Heuser, B.
dc.contributor.editorWimmer, M.
dc.date.issued2023
dc.description.abstractMaking sense of the vast amounts of data generated by modern production operations—and thus realizing the full potential of digitization—requires adequate means of data analysis. In this regard, data mining represents the employment of statistical methods to look for patterns in data. Predictive analytics then puts the thus gathered knowledge to good use by making predictions about future events, e.g., equipment failure in process industries and manufacturing or animal illness in farming operations. Finally, prescriptive analytics derives from the predicted events suggestions for action, e.g., optimized production plans or ideal animal feed composition. In this chapter, we provide an over view of common techniques for data mining as well as predictive and prescriptive analytics, with a specific focus on applications in production. In particular, we focus on association and correlation, classification, cluster analysis and outlier detection. We illustrate selected methods of data analysis using examples inspired from real-world settings in process industries, manufacturing, and precision farming.
dc.identifier.citationSource details - Title: Digital Transformation Core Technologies and Emerging Topics from a Computer Science Perspective, 2023 / Vogel-Heuser, B., Wimmer, M. (ed./s), Ch.14, pp.337-362
dc.identifier.doi10.1007/978-3-662-65004-2_14
dc.identifier.isbn9783662650035
dc.identifier.orcidSelway, M. [0000-0001-6220-6352]
dc.identifier.urihttps://hdl.handle.net/11541.2/32725
dc.language.isoen
dc.publisherSpringer Vieweg, Berlin, Heidelberg
dc.publisher.placeGermany
dc.rightsCopyright 2022 Access Condition Notes: The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature
dc.source.urihttps://doi.org/10.1007/978-3-662-65004-2_14
dc.subjectdata mining
dc.subjectprescriptive analytics
dc.subjectindustry
dc.subjectdata analysis
dc.titleDiscovering actionable knowledge for industry 4.0: from data mining to predictive and prescriptive analytics
dc.typeBook chapter
pubs.publication-statusPublished
ror.mmsid9916716826601831

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