Citation link: http://dx.doi.org/10.25819/ubsi/10134
DC FieldValueLanguage
crisitem.author.orcid0000-0001-9018-9959-
crisitem.author.orcid0000-0002-2552-2855-
dc.contributor.authorSteinberg, Fabian-
dc.contributor.authorBurggräf, Peter-
dc.contributor.authorWagner, Johannes-
dc.contributor.authorHeinbach, Benjamin-
dc.date.accessioned2022-06-09T07:56:09Z-
dc.date.available2022-06-09T07:56:09Z-
dc.date.issued2021de
dc.descriptionFinanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractFor manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms - six prediction models on every level of detail - and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/10134-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/2223-
dc.identifier.urnurn:nbn:de:hbz:467-22235-
dc.language.isoende
dc.sourceIEEE Access ; Vol. 9 (2021), S. 105926-105938. - https://dx.doi.org/10.1109/ACCESS.2021.3075620de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherProduction controlen
dc.subject.otherAssemblyen
dc.subject.otherPrediction methodsen
dc.subject.otherLead time reductionen
dc.subject.otherMachine learningen
dc.subject.otherSupervised learningen
dc.subject.otherClassification algorithmsen
dc.subject.otherRegression analysisen
dc.subject.otherData analysisen
dc.subject.otherProduktionssteuerungde
dc.subject.otherMontagede
dc.subject.otherVorhersagemethodende
dc.subject.otherReduzierung der Durchlaufzeitde
dc.subject.otherMaschinelles Lernende
dc.subject.otherÜberwachtes Lernende
dc.subject.otherAlgorithmen zur Klassifizierungde
dc.subject.otherRegressionsanalysede
dc.subject.otherDatenanalysede
dc.subject.swbProduktionssteuerungde
dc.subject.swbPrognosede
dc.subject.swbMaschinelles Lernende
dc.subject.swbRegressionsanalysede
dc.titleMachine learning-based prediction of missing components for assembly - a case study at an engineer-to-order manufactureren
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationDepartment Maschinenbaude
ubsi.source.doi10.1109/ACCESS.2021.3075620-
ubsi.source.issn2169-3536-
ubsi.source.issued2021de
ubsi.source.issuenumber9de
ubsi.source.pagefrom105926de
ubsi.source.pageto105938de
ubsi.source.placeNew Yorkde
ubsi.source.publisherIEEEde
ubsi.source.titleIEEE Accessde
ubsi.subject.ghbsZHXde
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