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dc.contributor.authorGarcía-Barrios, David
dc.contributor.otherPalomino, Kevin
dc.contributor.otherGarcía-Solan, Ethel
dc.contributor.otherCuello-Quiroz, Ana
dc.date.accessioned2022-11-15T20:46:51Z
dc.date.available2022-11-15T20:46:51Z
dc.date.issued2021-04-02
dc.date.submitted2020-11-03
dc.identifier.urihttps://hdl.handle.net/20.500.12834/878
dc.description.abstractRecently, some studies have begun to explore the potential that inventory management combined with machine learning algorithms could provide as a means of producing efficient and flexible inventory management methods. In this way, although there are some methods to carry out this practice, none are set up for impulse purchase products. This article illustrates this perspective within the context of an impulse purchase product provisioning problem and shows how group policies based on a clustering process can result in better (lower cost) groupings. To solve this problem, a method is proposed for finding a near-optimal inventory grouping solution. The key innovation in this solution is the idea to form groups for the items that have similar demand or ordering and cost characteristics. Subsequently, once the clusters have been formed, it was necessary to look at aggregating impulse purchase SKUs, and then two grouping techniques or heuristics that both consider common characteristics and develop some ordering decision rules are presented. The results show that the proposed method can be used to cluster impulse purchase products more effectively and the grouping techniques applied were efficient in terms of solution quality. The aim of the proposed unsupervised clustering-based method was not only to provide a classification of SKUs free of subjectivity processes but also to provide an approach to apply more efficient inventory policies for impulse purchase products.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceJournal of Engineering Science and Technology Reviewspa
dc.titleA Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approachspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.audiencePúblico generalspa
dc.identifier.doi10.25103/jestr.141.02
dc.identifier.instnameUniversidad del Atlánticospa
dc.identifier.reponameRepositorio Universidad del Atlánticospa
dc.rights.ccAttribution-NonCommercial 4.0 International*
dc.subject.keywords: Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineeringspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.type.spaArtículospa
dc.publisher.placeBarranquillaspa
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessspa
dc.publisher.disciplineIngeniería Industrialspa
dc.publisher.sedeSede Nortespa


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