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Machine learning in demand planning : cross-industry overview
Citation Link: https://doi.org/10.15480/882.2476
Publikationstyp
Conference Paper
Date Issued
2019-09-26
Sprache
English
Author(s)
TORE-DOI
TORE-URI
First published in
Number in series
27
Start Page
355
End Page
383
Citation
Hamburg International Conference of Logistics (HICL) 27: 355-383 (2019)
Contribution to Conference
Publisher
epubli GmbH
Purpose: This paper aims to give an overview about the current state of research in the field of machine learning methods in demand planning. A cross-industry analysis for current machine learning approaches within the field of demand planning provides a decision-making support for the manufacturing industry. Methodology: Based on a literature research, the applied machine learning methods in the field of demand planning are identified. The literature research focuses on machine learning applications across industries wherein demand planning plays a major role. Findings: This comparative analysis of machine learning approaches provides/creates a decision support for the selection of algorithms and linked databases. Furthermore, the paper shows the industrial applicability of the presented methods in different use cases from various industries and formulates research needs to enable an integration of machine learning algorithms into the manufacturing industry. Originality: The article provides a systematic and cross-industry overview of the use of machine learning methods in demand planning. It shows the link between established planning processes and new technologies to identify future areas of research
Subjects
Machine learning
Demand planning
Artificial intelligence
Digitalization
DDC Class
004: Informatik
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
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Moroff_Sardesai-Machine_Learning_in_Demand_Planning_Crossindustry_Overview_hicl_2019.pdf
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