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Machine learning in demand planning: Cross-industry overview

In: Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27

Author

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  • Moroff, Nikolas Ulrich
  • Sardesai, Saskia

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. It shows the link between established planning processes and new technologies to identify future areas of research

Suggested Citation

  • Moroff, Nikolas Ulrich & Sardesai, Saskia, 2019. "Machine learning in demand planning: Cross-industry overview," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 355-383, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:209378
    DOI: 10.15480/882.2476
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    3. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2017. "Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms," Energies, MDPI, vol. 10(10), pages 1-25, October.
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