Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3961
DC FieldValueLanguage
dc.contributor.authorBrylowski, Martin-
dc.contributor.authorSchröder, Meike-
dc.contributor.authorLodemann, Sebastian-
dc.contributor.authorKersten, Wolfgang-
dc.date.accessioned2021-12-08T15:01:20Z-
dc.date.available2021-12-08T15:01:20Z-
dc.date.issued2021-12-01-
dc.identifier.citationHamburg International Conference of Logistics (HICL 2021)de_DE
dc.identifier.isbn978-3-754927-70-0de_DE
dc.identifier.issn2365-5070de_DE
dc.identifier.urihttp://hdl.handle.net/11420/11176-
dc.description.abstractPurpose: Especially in supply chain management (SCM), data has become essential to the success of companies. Traditional analytical methods are being augmented by machine learning (ML), which is considered the foremost relevant branch of artificial intelligence. This article maps various ML use-cases and assigns them to the appropriate SCM tasks. Methodology: We applied a scoping review and checked scientific databases for relevant literature. Subsequently, the articles were assigned to different categories to map the research area. In the categorization, we considered, amongst others, the ML tasks and algorithms, data source and type, and the field of application. Findings: The results show that there are numerous ML use cases in SCM. These range from predictive demand forecasting and intelligent partner selection to the use of assistance systems for resource management. Various data sources, such as internal company data and publicly available data, are used for these applications. Originality: By mapping ML use cases in SCM, this complex and multifaceted field of research is presented in a transparent and structured way. Science and practice can deploy the results to improve existing ML use cases in SCM on the one hand and to identify promising areas of application on the other.en
dc.language.isoende_DE
dc.publisherepublide_DE
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/de_DE
dc.subjectArtificial Intelligencede_DE
dc.subjectBlockchainde_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.titleMachine learning in supply chain management: A scoping reviewde_DE
dc.typeinProceedingsde_DE
dc.identifier.doi10.15480/882.3961-
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0161734-
tuhh.oai.showtruede_DE
tuhh.abstract.englishPurpose: Especially in supply chain management (SCM), data has become essential to the success of companies. Traditional analytical methods are being augmented by machine learning (ML), which is considered the foremost relevant branch of artificial intelligence. This article maps various ML use-cases and assigns them to the appropriate SCM tasks. Methodology: We applied a scoping review and checked scientific databases for relevant literature. Subsequently, the articles were assigned to different categories to map the research area. In the categorization, we considered, amongst others, the ML tasks and algorithms, data source and type, and the field of application. Findings: The results show that there are numerous ML use cases in SCM. These range from predictive demand forecasting and intelligent partner selection to the use of assistance systems for resource management. Various data sources, such as internal company data and publicly available data, are used for these applications. Originality: By mapping ML use cases in SCM, this complex and multifaceted field of research is presented in a transparent and structured way. Science and practice can deploy the results to improve existing ML use cases in SCM on the one hand and to identify promising areas of application on the other.de_DE
tuhh.publisher.urlhttps://www.epubli.de/shop/buch/Adapting-to-the-Future-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783754927700/121489-
tuhh.publication.instituteLogistik und Unternehmensführung W-2de_DE
tuhh.identifier.doi10.15480/882.3961-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.gvk.hasppnfalse-
tuhh.hasurnfalse-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage377de_DE
tuhh.container.endpage406de_DE
dc.relation.conferenceHamburg International Conference of Logistics (HICL) 2021de_DE
dc.rights.nationallicensefalsede_DE
tuhh.relation.ispartofseriesProceedings of the Hamburg International Conference of Logistics (HICL)de_DE
tuhh.relation.ispartofseriesnumber31de_DE
dc.identifier.scopus2-s2.0-85127657599de_DE
local.contributorPerson.editorKersten, Wolfgang-
local.contributorPerson.editorRingle, Christian M.-
local.contributorPerson.editorBlecker, Thorsten-
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
local.publisher.peerreviewedtruede_DE
datacite.resourceTypeConference Paper-
datacite.resourceTypeGeneralText-
item.openairetypeinProceedings-
item.creatorOrcidBrylowski, Martin-
item.creatorOrcidSchröder, Meike-
item.creatorOrcidLodemann, Sebastian-
item.creatorOrcidKersten, Wolfgang-
item.tuhhseriesidProceedings of the Hamburg International Conference of Logistics (HICL)-
item.contributorGNDKersten, Wolfgang-
item.contributorGNDRingle, Christian M.-
item.contributorGNDBlecker, Thorsten-
item.contributorOrcidKersten, Wolfgang-
item.contributorOrcidRingle, Christian M.-
item.contributorOrcidBlecker, Thorsten-
item.grantfulltextopen-
item.creatorGNDBrylowski, Martin-
item.creatorGNDSchröder, Meike-
item.creatorGNDLodemann, Sebastian-
item.creatorGNDKersten, Wolfgang-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.seriesrefProceedings of the Hamburg International Conference of Logistics (HICL);31-
item.mappedtypeinProceedings-
crisitem.author.deptLogistik und Unternehmensführung W-2-
crisitem.author.deptLogistik und Unternehmensführung W-2-
crisitem.author.deptLogistik und Unternehmensführung W-2-
crisitem.author.deptLogistik und Unternehmensführung W-2-
crisitem.author.orcid0000-0002-2880-3564-
crisitem.author.orcid0000-0003-0894-4121-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
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