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Self-Learning Production Control using Algorithms of Artificial Intelligence
[conference paper]

dc.contributor.authorLuetkehoff, Bende
dc.contributor.authorBlum, Matthiasde
dc.contributor.authorSchroeter, Moritzde
dc.date.accessioned2020-07-13T06:24:00Z
dc.date.available2020-07-13T06:24:00Z
dc.date.issued2017de
dc.identifier.issn1868-422Xde
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/68375
dc.description.abstractManufacturing companies are facing an increasingly turbulent market - a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.de
dc.languageende
dc.subject.ddcWirtschaftde
dc.subject.ddcEconomicsen
dc.subject.otherproduction control; self-learning algorithms; data analyticsde
dc.titleSelf-Learning Production Control using Algorithms of Artificial Intelligencede
dc.title.alternativeSelf-Learning Production Control using Algorithms of Artificial Intelligencede
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalIFIP Advances in Information and Communication Technology
dc.publisher.countryDEU
dc.subject.classozProduktion, Fertigungde
dc.subject.classozManufacturingen
dc.identifier.urnurn:nbn:de:0168-ssoar-68375-6
dc.rights.licenceCreative Commons - Namensnennung 4.0de
dc.rights.licenceCreative Commons - Attribution 4.0en
ssoar.contributor.institutionFIR at RWTH Aachen Universityde
internal.statusformal und inhaltlich fertig erschlossende
dc.type.stockarticlede
dc.type.documentKonferenzbeitragde
dc.type.documentconference paperen
dc.source.pageinfo293-300de
internal.identifier.classoz1090404
internal.identifier.journal1781
internal.identifier.document16
internal.identifier.ddc330
dc.description.pubstatusVeröffentlichungsversionde
dc.description.pubstatusPublished Versionen
internal.identifier.licence16
internal.identifier.pubstatus1
internal.identifier.review1
dc.subject.classhort20800de
internal.pdf.wellformedtrue
internal.pdf.encryptedfalse


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