• Design of Self-regulating Plan...

Treffer: Design of Self-regulating Planning Model

Title:
Design of Self-regulating Planning Model
Authors:
Espitia Rincon, Maria Paula, Sanabria Martínez, David Alejandro, Abril Juzga, Kevin Alberto, Santos Hernández, Andrés Felipe
Contributors:
Manufactura y Servicios
Source:
https://www.econstor.eu/handle/10419/209383?locale=en.
Publisher Information:
Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL)
Berlín
Publication Year:
2019
Subject Terms:
Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos, Regresión lineal, Análisis de regresión, Programación lineal, Machine learning - Mathematical models, Linear Programming, Linear Regression, Aggregate Planning, Cost Minimization
Document Type:
Buch book part<br />article in journal/newspaper
File Description:
33 páginas.; application/pdf
Language:
Spanish; Castilian
ISBN:
978-958-52-3330-0
958-52-3330-4
Relation:
N/A; Artificial Intelligence and Digital Transformation in Supply Chain Management; Alonso, Martínez, Dorado,Páez, Lota, 2018. National logistics survey 2018, Bogotá: www.puntoaparte.com.co.; Aldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.; Anon., 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].; Borissova, D., 2008. Bibliography. Cybernetics and information technologies, 8(2), pp. 102-103.; Chopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.; Columbus, 2018. 10 Ways Machine Learning Is Revolutionizing Supply Chain Management, New York: Forbes.; Dinero, 2015. Competencia ragulacion farmacias. [Online] Available at: https://www.dinero.com/edicion-impresa/negocios/articulo/competencia-regulacion-farmacias/215331 [Accessed 01 May 2019].; Dinero, 2019.Accelerated expansion plan in Farmatodo, Bogotá: s.n.; Espectador, 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].; Fernández, I. A., 2011. Production and consumption: 49(1), pp. 179-191.; Gandhi, R., 2018. towards data science. [Online] Available at: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression14c4e325882a [Accessed 25 April 2019].; Gholamian, M.-M., 2015. Comprehensive fuzzy multi-objective multi-product multisite. 134(42), pp. 585-607.; Granja, A.-L., 2014. An optimization-based on a simulation approach to patient admission. Journal of Biomedical Informatics, Issue 52, pp. 427-437.; Julian, D., 2016. Designing Machine Learning Systems with Python. 1 ed. Birmingham B3 2PB: Packt Publishing Ltd.; Pereira, 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].; Rüssmann, 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.; Souza, C., 2018. Direct stockpile scheduling: Mathematical formulation •. 85(204), pp. 296-301.; https://repositorio.escuelaing.edu.co/handle/001/1855
Availability:
https://repositorio.escuelaing.edu.co/handle/001/1855
Rights:
info:eu-repo/semantics/openAccess
Accession Number:
edsbas.61419A65
Database:
BASE

Weitere Informationen

Purpose: 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. ; Propósito: 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.