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Design of self-regulating planning model
Citation Link: https://doi.org/10.15480/882.2482
Publikationstyp
Conference Paper
Date Issued
2019-09-26
Sprache
English
TORE-DOI
TORE-URI
First published in
Number in series
27
Start Page
507
End Page
539
Citation
Hamburg International Conference of Logistics (HICL) 27: 507-539 (2019)
Contribution to Conference
Publisher
epubli GmbH
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.
Subjects
Linear programming
Linear regression
Aggregate planning
Cost minimization
DDC Class
004: Informatik
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
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Rincon_Martinez_Juzga_Hernandez-Design_of_Self-regulating_Planning_Model_hicl_2019.pdf
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