Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/102071
DC FieldValueLanguage
dc.contributor.authorSilva, Nathalie Santos da-
dc.contributor.authorFerreira, Luis Miguel D. F.-
dc.contributor.authorSilva, Cristovão-
dc.contributor.authorMagalhães, Vanessa Sofia Melo-
dc.contributor.authorNeto, Pedro-
dc.date.accessioned2022-09-23T09:43:35Z-
dc.date.available2022-09-23T09:43:35Z-
dc.date.issued2017-
dc.identifier.issn23519789pt
dc.identifier.urihttps://hdl.handle.net/10316/102071-
dc.description.abstractThe vulnerability of supply chains has been increasing and to properly respond to disruptions, visibility across the supply chain is required. This paper addresses these challenges by relying on the use of artificial neural networks to predict the capacity of a simulated supply chain to fulfil incoming orders and to anticipate which supply chain nodes will receive an order for the next period. To assess the effectiveness of the approach two experiments were conducted. The findings contribute to the understanding of on how artificial neural networks can be applied to reduce the vulnerability of supply chains.pt
dc.language.isoengpt
dc.relationPortugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE2020pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt
dc.subjectArtificial neural networkspt
dc.subjectexperimentalpt
dc.subjectsimulationpt
dc.subjectSupply chainpt
dc.subjectvisibilitypt
dc.titleImproving Supply Chain Visibility With Artificial Neural Networkspt
dc.typearticle-
degois.publication.firstPage2083pt
degois.publication.lastPage2090pt
degois.publication.titleProcedia Manufacturingpt
dc.peerreviewedyespt
dc.identifier.doi10.1016/j.promfg.2017.07.329pt
degois.publication.volume11pt
dc.date.embargo2017-01-01*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.orcid0000-0003-0459-0020-
crisitem.author.orcid0000-0002-7693-9570-
crisitem.author.orcid0000-0003-2177-5078-
Appears in Collections:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais
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