Espitia Rincon, Maria PaulaMaria PaulaEspitia RinconSanabria Martínez, David AlejandroDavid AlejandroSanabria MartínezAbril Juzga, Kevin AlbertoKevin AlbertoAbril JuzgaSantos Hernández, Andrés FelipeAndrés FelipeSantos Hernández2019-11-132019-11-132019-09-26Hamburg International Conference of Logistics (HICL) 27: 507-539 (2019)http://hdl.handle.net/11420/3753Purpose: 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.en2019507539epubli GmbHhttps://creativecommons.org/licenses/by-sa/4.0/Linear programmingLinear regressionAggregate planningCost minimizationInformatikWirtschaftHandel, Kommunikation, VerkehrConference Paperurn:nbn:de:gbv:830-882.05447610.15480/882.2482https://www.epubli.de/shop/buch/Artificial-Intelligence-and-Digital-Transformation-in-Supply-Chain-Management-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783750249479/9209510.15480/882.2482Other