dc.contributor.author | García-Barrios, David | |
dc.contributor.other | Palomino, Kevin | |
dc.contributor.other | García-Solan, Ethel | |
dc.contributor.other | Cuello-Quiroz, Ana | |
dc.date.accessioned | 2022-11-15T20:46:51Z | |
dc.date.available | 2022-11-15T20:46:51Z | |
dc.date.issued | 2021-04-02 | |
dc.date.submitted | 2020-11-03 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12834/878 | |
dc.description.abstract | Recently, 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.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.source | Journal of Engineering Science and Technology Review | spa |
dc.title | A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach | spa |
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datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.resourcetype | http://purl.org/coar/resource_type/c_6501 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.audience | Público general | spa |
dc.identifier.doi | 10.25103/jestr.141.02 | |
dc.identifier.instname | Universidad del Atlántico | spa |
dc.identifier.reponame | Repositorio Universidad del Atlántico | spa |
dc.rights.cc | Attribution-NonCommercial 4.0 International | * |
dc.subject.keywords | : Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineering | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | spa |
dc.type.spa | Artículo | spa |
dc.publisher.place | Barranquilla | spa |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | spa |
dc.publisher.discipline | Ingeniería Industrial | spa |
dc.publisher.sede | Sede Norte | spa |