Citation link: http://dx.doi.org/10.25819/ubsi/9967
DC FieldValueLanguage
crisitem.author.orcid0000-0001-9018-9959-
dc.contributor.authorBurggräf, Peter-
dc.contributor.authorWagner, Johannes-
dc.contributor.authorKoke, Benjamin-
dc.contributor.authorSteinberg, Fabian-
dc.date.accessioned2021-08-06T09:13:07Z-
dc.date.available2021-08-06T09:13:07Z-
dc.date.issued2020de
dc.descriptionFinanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractThe interest of manufacturing companies in a sufficient prediction of lead times is continuouslyincreasing - especially in engineer to order environments with typically a large number of individual parts andcomplex production processes. A multitude of approaches have been proposed in the literature for predictinglead times considering different data and methods or algorithms from operations research (OR) and machinelearning (ML). In order to provide guidance at setting up prediction models and developing new approaches,a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-twopublications were analyzed and synthetized: Based on a developed framework considering the used dataclass (e.g. product data or system status), the data origin (master data or real data) and the used methodand algorithm from OR and ML, the publications are classified. Based on the classification, a descriptiveanalysis is performed to identify common approaches in the existing literature as well as implications forfurther research. One result is, that mostly order data and the status of the production system are used forpredicting lead times whereas material data are used seldom. Additionally, ML approaches primarily useartificial neural networks and regression models for predicting lead times, while OR approaches use mainlycombinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of realdata decreased. Thus, we identified as an implication for further research to set up a complex data modelconsidering material data, which uses real data as data origin.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/9967-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/1953-
dc.identifier.urnurn:nbn:de:hbz:467-19536-
dc.language.isoende
dc.sourceIEEE Access ; vol. 8, S. 142434-142445. - DOI: https://doi.org/10.1109/ACCESS.2020.3010050de
dc.subject.ddc620de
dc.subject.otherDurchlaufzeitverkürzungde
dc.subject.otherMaschinelles Lernende
dc.subject.otherOperations Researchde
dc.subject.otherVorhersagemethodende
dc.subject.otherLead time reductionen
dc.subject.otherMachine learningen
dc.subject.otherOperations researchen
dc.subject.otherPrediction methodsen
dc.subject.swbMaschinelles Lernende
dc.subject.swbFabrikplanungde
dc.titleApproaches for the prediction of lead times in an engineer to order environment - a systematic reviewen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationFakultät IV - Naturwissenschaftlich-Technische Fakultätde
ubsi.source.authorIEEEde
ubsi.source.doi10.1109/ACCESS.2020.3010050-
ubsi.source.issn2169-3536-
ubsi.source.issued2020de
ubsi.source.linkhttps://www.ieee.org/de
ubsi.source.pagefrom142434de
ubsi.source.pageto142445de
ubsi.source.placeNew Yorkde
ubsi.source.publisherIEEEde
ubsi.source.titleIEEE Accesssde
ubsi.source.volume8de
ubsi.subject.ghbsZHXde
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