Design of self-regulating planning model

Maria Paula Espitia Rincon, David Alejandro Sanabria Martínez, Kevin Alberto Abril Juzga and Andrés Felipe Santos Hernández

A chapter in Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains, 2019, pp 507-539 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: 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.

Keywords: Linear programming; Linear regression; Aggregate planning; Cost minimization (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:209383

DOI: 10.15480/882.2482

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