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A review on the application of neural networks for decreasing bullwhip effect in supply chain

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dc.citedby1
dc.contributor.authorYousefi M.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorFerreira R.P.M.en_US
dc.contributor.authorid55247052200en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid15623226000en_US
dc.date.accessioned2023-05-29T05:59:57Z
dc.date.available2023-05-29T05:59:57Z
dc.date.issued2015
dc.description.abstractThe bullwhip effect can be described as increased demand variability in supply chains. Bullwhip effect is one of the most popular research problems in the field of supply chain management because in the case of bullwhip effect the small changes in demands of customers may lead to a large variations in the orders consequently, the organization faces the overestimated in their orders. There are different methods of forecasting which are used in supply chain in order to decrease the bullwhip effect. In recent years many researches are using neural networks as one of the useful tools for forecasting. The aim of this literature review is to describe the concept of bullwhip effect and its disadvantages and causes of it. Moreover, to provide a list of applications of neural networks in decreasing bullwhip effect in supply chain and suggest future studies based on the literature. � 2015 Praise Worthy Prize S.r.l. -All rights reserveden_US
dc.description.natureFinalen_US
dc.identifier.doi10.15866/ireme.v9i5.6271
dc.identifier.epage442
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84947606556
dc.identifier.spage438
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84947606556&doi=10.15866%2fireme.v9i5.6271&partnerID=40&md5=8cc27e8a0ca5f7d12891cf4c29380f4d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22270
dc.identifier.volume9
dc.publisherPraise Worthy Prizeen_US
dc.sourceScopus
dc.sourcetitleInternational Review of Mechanical Engineering
dc.titleA review on the application of neural networks for decreasing bullwhip effect in supply chainen_US
dc.typeReviewen_US
dspace.entity.typePublication
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