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dc.contributor.authorEspitia Rincon, Maria Paula
dc.contributor.authorSanabria Martínez, David Alejandro
dc.contributor.authorAbril Juzga, Kevin Alberto
dc.contributor.authorSantos Hernández, Andrés Felipe
dc.date.accessioned2021-11-24T17:51:20Z
dc.date.available2021-11-24T17:51:20Z
dc.date.issued2019
dc.identifier.isbn9789585233300
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/1855
dc.description.abstractPurpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the solver excel tool to make optimal decisions at the distribution center to reduce costs and guarantee the level of service. Findings: The findings after this study pertain to planning supply tactics in real-time, self-regulation of information in real-time and optimization of the frequency of the supply. Originality: An application capable of being updated in real-time by updating data by the planning director, which will show the optimal aggregate planning and the indicators of the costs associated with the picking operation of a company with 12000 SKU's (Stock Keeping Unit), in which a retail trade of 65 stores is carried out.eng
dc.description.abstractPropósito: Esta investigación tiene como objetivo desarrollar una aplicación dinámica y autorregulada que considere los pronósticos de demanda, basados ​​en la regresión lineal como algoritmo básico para el aprendizaje automático. Metodología: Esta investigación utiliza la planificación agregada y el aprendizaje automático junto con las políticas de inventario a través de la herramienta solver excel para tomar decisiones óptimas en el centro de distribución para reducir costos y garantizar el nivel de servicio. Hallazgos: Los hallazgos de este estudio se refieren a la planificación de tácticas de suministro en tiempo real, la autorregulación de la información en tiempo real y la optimización de la frecuencia del suministro. Originalidad: Una aplicación susceptible de ser actualizada en tiempo real mediante la actualización de datos por parte del director de planificación, que mostrará la planificación agregada óptima y los indicadores de los costos asociados a la operación de picking de una empresa con 12000 SKU's (Stock Keeping Unit), en el que se realiza un comercio minorista de 65 tiendas.spa
dc.format.extent33 páginas.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherArtificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL)spa
dc.sourcehttps://www.econstor.eu/handle/10419/209383?locale=enspa
dc.titleDesign of Self-regulating Planning Modelspa
dc.typeCapítulo - Parte de Librospa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.contributor.researchgroupManufactura y Serviciosspa
dc.publisher.placeBerlínspa
dc.relation.indexedN/Aspa
dc.relation.ispartofbookArtificial Intelligence and Digital Transformation in Supply Chain Managementeng
dc.relation.referencesAlonso, Martínez, Dorado,Páez, Lota, 2018. National logistics survey 2018, Bogotá: www.puntoaparte.com.co.spa
dc.relation.referencesAldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.spa
dc.relation.referencesAldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.spa
dc.relation.referencesAnon., 2017. ingenieriaindustrialonline. [Online] Available at: https://www.ingenieriaindustrialonline.com/herramientas-para-el-ingeniero-industrial/producci%C3%B3n/planeacion-agregada-mediante-programacion-lineal/ [Accessed 01 May 2019].spa
dc.relation.referencesBorissova, D., 2008. Bibliography. Cybernetics and information technologies, 8(2), pp. 102-103.spa
dc.relation.referencesChopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.spa
dc.relation.referencesChopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.spa
dc.relation.referencesColumbus, 2018. 10 Ways Machine Learning Is Revolutionizing Supply Chain Management, New York: Forbes.spa
dc.relation.referencesDinero, 2015. Competencia ragulacion farmacias. [Online] Available at: https://www.dinero.com/edicion-impresa/negocios/articulo/competencia-regulacion-farmacias/215331 [Accessed 01 May 2019].spa
dc.relation.referencesDinero, 2019.Accelerated expansion plan in Farmatodo, Bogotá: s.n.spa
dc.relation.referencesEspectador, E., 2016. El Espectador. [Online] Available at: https://www.elespectador.com/noticias/economia/colombia-hay-menos-3000-droguerias-de-barrioarticulo-654947 [Accessed 01 May 2019].spa
dc.relation.referencesFernández, I. A., 2011. Production and consumption: 49(1), pp. 179-191.spa
dc.relation.referencesGandhi, R., 2018. towards data science. [Online] Available at: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression14c4e325882a [Accessed 25 April 2019].spa
dc.relation.referencesGholamian, M.-M., 2015. Comprehensive fuzzy multi-objective multi-product multisite. 134(42), pp. 585-607.spa
dc.relation.referencesGranja, A.-L., 2014. An optimization-based on a simulation approach to patient admission. Journal of Biomedical Informatics, Issue 52, pp. 427-437.spa
dc.relation.referencesJulian, D., 2016. Designing Machine Learning Systems with Python. 1 ed. Birmingham B3 2PB: Packt Publishing Ltd.spa
dc.relation.referencesPereira, J., 2018. BigData mazine. [Online] Available at: https://bigdatamagazine.es/utilizacion-de-big-data-y-machine-learning-en-la-industria-4-0 [Accessed 01 May 2019].spa
dc.relation.referencesRüssmann, L., 2015. Bibliography. En: I. 2. A. r. r. The Boston Consulting Group, ed. The Future of Productivity and Growth in Manufacturing Industries. Boston: The Boston Consulting Group, p. 5.spa
dc.relation.referencesSouza, C., 2018. Direct stockpile scheduling: Mathematical formulation •. 85(204), pp. 296-301.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.subject.armarcAprendizaje automático (Inteligencia artificial) - Modelos matemáticosspa
dc.subject.armarcMachine learning - Mathematical modelseng
dc.subject.armarcRegresión linealspa
dc.subject.armarcAnálisis de regresiónspa
dc.subject.armarcProgramación linealspa
dc.subject.proposalLinear Programmingeng
dc.subject.proposalLinear Regression,eng
dc.subject.proposalAggregate Planningeng
dc.subject.proposalCost Minimizationeng
dc.type.coarhttp://purl.org/coar/resource_type/c_3248spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa


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