dc.contributor.author | Meidutė-Kavaliauskienė, Ieva | |
dc.contributor.author | Taşkın, Kamil | |
dc.contributor.author | Ghorbani, Shahryar | |
dc.contributor.author | Činčikaitė, Renata | |
dc.contributor.author | Kačenauskaitė, Roberta | |
dc.date.accessioned | 2023-09-18T16:17:13Z | |
dc.date.available | 2023-09-18T16:17:13Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/112775 | |
dc.description.abstract | Supply chains have received significant attention in recent years. Neural networks (NN) are a technique available in artificial intelligence (AI) which has many supporters due to their diverse applications because they can be used to move towards complete harmony. NN, an emerging AI technique, have a strong appeal for a wide range of applications to overcome many issues associated with supply chains. This study aims to provide a comprehensive view of NN applications in supply chain management (SCM), working as a reference for future research directions for SCM researchers and application insight for SCM practitioners. This study generally introduces NNs and has explained the use of this method in five features identified by supply chain area, including optimization, forecasting, modelling and simulation, clustering, decision support, and the possibility of using NNs in supply chain management. The results showed that NN applications in SCM were still in a developmental stage since there were not enough high-yielding au-thors to form a strong group force in the research of NN applications in SCM. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-17 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | J-Gate | |
dc.relation.isreferencedby | Emerging Sources Citation Index (Web of Science) | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://www.mdpi.com/2078-2489/13/5/261/htm | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:130507594/datastreams/MAIN/content | |
dc.title | Reviewing the applications of neural networks in supply chain: exploring research propositions for future directions | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/
4.0/). | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 62 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | University of Sakarya, Turkija | |
dc.contributor.institution | Karo prievolės ir komplektavimo tarnyba | |
dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | |
dc.subject.researchfield | S 003 - Vadyba / Management | |
dc.subject.studydirection | L02 - Vadyba / Management studies | |
dc.subject.vgtuprioritizedfields | EV03 - Dinamiškoji vadyba / Dynamic Management | |
dc.subject.ltspecializations | L103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society | |
dc.subject.en | neural network | |
dc.subject.en | decision support | |
dc.subject.en | supply chain management | |
dc.subject.en | systematic review | |
dcterms.sourcetitle | Information: Special issue: Information for business and management–software development for data processing and management | |
dc.description.issue | iss. 5 | |
dc.description.volume | vol. 13 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 000802624500001 | |
dc.identifier.doi | 10.3390/info13050261 | |
dc.identifier.elaba | 130507594 | |