A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach
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...
- Autores:
-
García-Barrios, David
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/878
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/878
- Palabra clave:
- : Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineering
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| title |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| spellingShingle |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach : Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineering |
| title_short |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| title_full |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| title_fullStr |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| title_full_unstemmed |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| title_sort |
A Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management Approach |
| dc.creator.fl_str_mv |
García-Barrios, David |
| dc.contributor.author.none.fl_str_mv |
García-Barrios, David |
| dc.contributor.other.none.fl_str_mv |
Palomino, Kevin García-Solan, Ethel Cuello-Quiroz, Ana |
| dc.subject.keywords.spa.fl_str_mv |
: Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineering |
| topic |
: Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineering |
| description |
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. |
| publishDate |
2020 |
| dc.date.submitted.none.fl_str_mv |
2020-11-03 |
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2021-04-02 |
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2022-11-15T20:46:51Z |
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2022-11-15T20:46:51Z |
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Artículo |
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https://hdl.handle.net/20.500.12834/878 |
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10.25103/jestr.141.02 |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://hdl.handle.net/20.500.12834/878 |
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10.25103/jestr.141.02 Universidad del Atlántico Repositorio Universidad del Atlántico |
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eng |
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Ingeniería Industrial |
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Sede Norte |
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García-Barrios, David1e8dfd3b-baca-4441-84b5-c585d865e0d4Palomino, KevinGarcía-Solan, EthelCuello-Quiroz, Ana2022-11-15T20:46:51Z2022-11-15T20:46:51Z2021-04-022020-11-03https://hdl.handle.net/20.500.12834/87810.25103/jestr.141.02Universidad del AtlánticoRepositorio Universidad del AtlánticoRecently, 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.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Engineering Science and Technology ReviewA Machine Learning based Method for Managing Multiple Impulse Purchase Products: An Inventory Management ApproachPúblico general: Cluster analysis, Impulse purchase products, Inventory management, Supply chain management, Industrial engineeringinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaIngeniería IndustrialSede Norte1. H. Kartal, A. Oztekin, A. Gunasekaran, and F. Cebi, “An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification.,” Computers & Industrial Engineering. vol. 101, pp. 599–613, 2016.2. M. Khan, M.Y. Jaber, and A.-R. Ahmad, “An integrated supply chain model with errors in quality inspection and learning in production.,” Omega. vol. 42, no. 1, pp. 16–24, 20143. A.J. Badgaiyan, A. Verma, and S. Dixit, “Impulsive buying tendency: Measuring important relationships with a new perspective and an indigenous scale.,” IIMB Management Review. vol. 28, no. 4, pp. 186–199, 2016.4. C. Horváth and M. van Birgelen, “The role of brands in the behavior and purchase decisions of compulsive versus noncompulsive buyers.,” European Journal of Marketing. vol. 49, no. 1/2, pp. 2–21, 2015.5. J.J. Kacen, J.D. Hess, and D. 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Niknafs, “NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set.,” Journal of Statistical Software. vol. 61, no. 6, pp. 1–36, 2014.http://purl.org/coar/resource_type/c_6501ORIGINALfulltext21412021.pdffulltext21412021.pdfapplication/pdf637991https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/878/1/fulltext21412021.pdfa643fea728bc9d4c4cc4c65be2b42bc9MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/878/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/878/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/878oai:repositorio.uniatlantico.edu.co:20.500.12834/8782022-11-15 15:46:52.756DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.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 |