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
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DOI: 10.15480/882.2476
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References listed on IDEAS
- Javier Arroyo & Rosa Espínola & Carlos Maté, 2011. "Different Approaches to Forecast Interval Time Series: A Comparison in Finance," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 169-191, February.
- Demary, Vera & Engels, Barbara & Röhl, Klaus-Heiner & Rusche, Christian, 2016. "Digitalisierung und Mittelstand: Eine Metastudie," IW-Analysen, Institut der deutschen Wirtschaft (IW) / German Economic Institute, volume 109, number 109.
- 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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Keywords
Machine learning; Demand planning; Artificial intelligence; Digitalization;All these keywords.
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