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dc.date.accessioned2023-01-02T12:20:58Z
dc.date.available2023-01-02T12:20:58Z
dc.date.issued2022-06-01
dc.identifierdoi:10.17170/kobra-202212157232
dc.identifier.urihttp://hdl.handle.net/123456789/14317
dc.description.sponsorshipGefördert im Rahmen des Projekts DEALger
dc.language.isoengeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectindustry 4.0eng
dc.subjectcircular economyeng
dc.subjectsupply chain managementeng
dc.subjectquantitative methodseng
dc.subject.ddc330
dc.titleIndustry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysiseng
dc.typeAufsatz
dcterms.abstractThe Industry 4.0 (I4.0) concept paves the way for the circular economy (CE) as advanced digital technologies enable sustainability initiatives. Hence, I4.0-driven CE-oriented supply chains (SCs) have improved sustainable performance, flexibility and interoperability. In order to smoothly embrace circular practices in digitally enabled SCs, quantitative techniques have been identified as crucial. Therefore, the intersection of I4.0, CE, supply chain management (SCM) and quantitative techniques is an emerging research arena worthy of investigation. This article presents a bibliometric analysis to identify the established and evolving research clusters in the topological analysis by identifying collaboration patterns, interrelations and the studies that significantly dominate the intersection of the analysed fields. Further, this study investigates the current research trends and presents potential directions for future research. The bibliometric analysis highlights that additive manufacturing (AM), big data analytics (BDA) and the Internet of Things (IoT) are the most researched technologies within the intersection of CE and sustainable SCM. Evaluation of intellectual, conceptual and social structures revealed that I4.0-driven sustainable operations and manufacturing are emerging research fields. This study provides research directions to guide scholars in the further investigation of these four identified fields while exploring the potential quantitative methods and techniques that can be applied in I4.0-enabled SCs in the CE context.eng
dcterms.accessRightsopen access
dcterms.creatorHettiarachchi, Biman Darshana
dcterms.creatorSeuring, Stefan
dcterms.creatorBrandenburg, Marcus
dc.relation.doidoi:10.1007/s12063-022-00275-7
dc.relation.projectidgrant agreement number 814247 (ReTraCE project)eng
dc.subject.swdIndustrie 4.0ger
dc.subject.swdKreislaufwirtschaftger
dc.subject.swdSupply Chain Managementger
dc.subject.swdQuantitative Methodeger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:1936-9743
dcterms.source.issueIssue 3-4
dcterms.source.journalOperations Management Researcheng
dcterms.source.pageinfo858-878
dcterms.source.volumeVolume 15
kup.iskupfalse


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