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dc.contributor.authorAssuad, Carla Susana Aqudelo
dc.contributor.authorTvenge, Nina
dc.contributor.authorMartinsen, Kristian
dc.date.accessioned2021-10-22T08:50:08Z
dc.date.available2021-10-22T08:50:08Z
dc.date.created2020-05-07T07:47:18Z
dc.date.issued2020
dc.identifier.citationProcedia CIRP. 2020, 88 15-18.en_US
dc.identifier.issn2212-8271
dc.identifier.urihttps://hdl.handle.net/11250/2824931
dc.description.abstractManufacturing systems are complex socio-technical systems with non-linearities, accumulation, flows and delays that challenge decision-making processes. System Dynamics (SD) is a valuable approach to analyse and understand complex interlinkages. This paper discusses how SD can be applied to learning factories (LF) at manufacturing education for enhanced learning outcome. LF are physical, full scale high-fidelity simulators for manufacturing education, where students act as operators and train on interaction with humans, machines, software and technology. Using simulations both virtual and full-scale add value in learning outcomes. By using SD, learners can play “what-if” analysis to understand the effects of their decisions.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleSystem dynamics modelling and learning factories for manufacturing systems educationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber15-18en_US
dc.source.volume88en_US
dc.source.journalProcedia CIRPen_US
dc.identifier.doi10.1016/j.procir.2020.05.003
dc.identifier.cristin1809728
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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