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Machine learning in demand planning: Cross-industry overview

 
: Moroff, Nikolas Ulrich; Sardesai, Saskia

:
Volltext urn:nbn:de:gbv:830-882.054309 (1019 KByte PDF)
MD5 Fingerprint: 38c3bb6d84d432b816ca18e18c312e63
Erstellt am: 9.1.2020


Kersten, W. ; TU Hamburg-Harburg:
Artificial Intelligence and Digital Transformation in Supply Chain Management. Innovative Approaches for Supply Chains : Proceedings of the Hamburg International Conference of Logistics (HICL), 25-27 September 2019, Hamburg
Berlin: epubli, 2019 (Proceedings of the Hamburg International Conference of Logistics (HICL) 27)
ISBN: 978-3-7502-4947-9
ISBN: 3-7502-4947-4
S.354-383
Hamburg International Conference of Logistics (HICL) <2019, Hamburg>
Deutsch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IML ()
Digitalization; machine learning; demand planning

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-569807.html