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Title: Machine learning in demand planning : cross-industry overview
Language: English
Authors: Moroff, Nikolas Ulrich 
Sardesai, Saskia 
Keywords: Machine learning;Demand planning;Artificial intelligence;Digitalization
Issue Date: 26-Sep-2019
Publisher: epubli GmbH
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 27
Abstract (english): 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
Conference: Hamburg International Conference of Logistics (HICL) 2019 
DOI: 10.15480/882.2476
ISBN: 978-3-750249-47-9
ISSN: 2365-5070
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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