Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2476
DC FieldValueLanguage
dc.contributor.authorMoroff, Nikolas Ulrich-
dc.contributor.authorSardesai, Saskia-
dc.date.accessioned2019-11-08T10:56:27Z-
dc.date.available2019-11-08T10:56:27Z-
dc.date.issued2019-09-26-
dc.identifier.isbn978-3-750249-47-9de_DE
dc.identifier.issn2365-5070de_DE
dc.identifier.urihttp://hdl.handle.net/11420/3738-
dc.description.abstractPurpose: This paper aims to give an overview about the current state of research in the field of machine learning methods in demand planning. A cross-industry analysis for current machine learning approaches within the field of demand planning provides a decision-making support for the manufacturing industry. Methodology: Based on a literature research, the applied machine learning methods in the field of demand planning are identified. The literature research focuses on machine learning applications across industries wherein demand planning plays a major role. Findings: This comparative analysis of machine learning approaches provides/creates a decision support for the selection of algorithms and linked databases. Furthermore, the paper shows the industrial applicability of the presented methods in different use cases from various industries and formulates research needs to enable an integration of machine learning algorithms into the manufacturing industry. Originality: The article provides a systematic and cross-industry overview of the use of machine learning methods in demand planning. It shows the link between established planning processes and new technologies to identify future areas of researchen
dc.language.isoende_DE
dc.publisherepubli GmbHde_DE
dc.rightsCC BY-SA 4.0de_DE
dc.subjectMachine learningde_DE
dc.subjectDemand planningde_DE
dc.subjectArtificial intelligencede_DE
dc.subjectDigitalizationde_DE
dc.subject.ddc004: Informatikde_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.subject.ddc380: Handel, Kommunikation, Verkehrde_DE
dc.titleMachine learning in demand planning : cross-industry overviewde_DE
dc.typeinProceedingsde_DE
dc.identifier.urnurn:nbn:de:gbv:830-882.054309-
dc.identifier.doi10.15480/882.2476-
dc.type.dinicontributionToPeriodical-
dc.subject.ddccode380-
dc.subject.ddccode004-
dc.subject.ddccode330-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.054309-
tuhh.oai.showtruede_DE
tuhh.abstract.englishPurpose: This paper aims to give an overview about the current state of research in the field of machine learning methods in demand planning. A cross-industry analysis for current machine learning approaches within the field of demand planning provides a decision-making support for the manufacturing industry. Methodology: Based on a literature research, the applied machine learning methods in the field of demand planning are identified. The literature research focuses on machine learning applications across industries wherein demand planning plays a major role. Findings: This comparative analysis of machine learning approaches provides/creates a decision support for the selection of algorithms and linked databases. Furthermore, the paper shows the industrial applicability of the presented methods in different use cases from various industries and formulates research needs to enable an integration of machine learning algorithms into the manufacturing industry. Originality: The article provides a systematic and cross-industry overview of the use of machine learning methods in demand planning. It shows the link between established planning processes and new technologies to identify future areas of researchde_DE
tuhh.publisher.urlhttps://www.epubli.de/shop/buch/Artificial-Intelligence-and-Digital-Transformation-in-Supply-Chain-Management-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783750249479/92095-
tuhh.publication.instituteLogistik und Unternehmensführung W-2de_DE
tuhh.publication.institutePersonalwirtschaft und Arbeitsorganisation W-9de_DE
tuhh.identifier.doi10.15480/882.2476-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.institute.germanPersonalwirtschaft und Arbeitsorganisation W-9de
tuhh.institute.englishPersonalwirtschaft und Arbeitsorganisation W-9de_DE
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
dc.type.drivercontributionToPeriodical-
dc.rights.cchttps://creativecommons.org/licenses/by-sa/4.0/de_DE
dc.type.casraiConference Paper-
tuhh.container.startpage355de_DE
tuhh.container.endpage383de_DE
dc.relation.conferenceHamburg International Conference of Logistics (HICL) 2019de_DE
dc.rights.nationallicensefalsede_DE
tuhh.relation.ispartofseriesProceedings of the Hamburg International Conference of Logistics (HICL)de_DE
tuhh.relation.ispartofseriesnumber27de_DE
local.contributorPerson.editorKersten, Wolfgang-
local.contributorPerson.editorBlecker, Thorsten-
local.contributorPerson.editorRingle, Christian M.-
item.openairetypeinProceedings-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.fulltextWith Fulltext-
item.creatorOrcidMoroff, Nikolas Ulrich-
item.creatorOrcidSardesai, Saskia-
item.languageiso639-1en-
item.creatorGNDMoroff, Nikolas Ulrich-
item.creatorGNDSardesai, Saskia-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.tuhhseriesidProceedings of the Hamburg International Conference of Logistics (HICL)-
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