Application of machine learning techniques for supply chain demand forecasting
By Réal André Carbonneau, Kevin Laframboise, Rustam M. Vahidov. European Journal of Operational Research, 2008
625 citations recorded by the source corpus.
Subfields: Management Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/1edfaf279f01d6ea3469b20c57cff6fa69dec219
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Information Systems
- Mathias Kraus
- Asil Oztekin
- Nikolaos Kourentzes
- Robert Fildes
- Fereshteh Mafakheri
- Juan Ramón Trapero
- Stefan Feuerriegel
- Hokey Min
In Management Science and Operations Research
- Nikolaos Kourentzes
- Mathias Kraus
- Shreshth Tuli
- Gerald Woo
- Steven C. H. Hoi
- Asil Oztekin
- Chenghao Liu
- Robert Fildes
Citations in the corpus
- Impact of Information Exchange on Supplier Forecasting Performance (2012, background): “In [36] NN were shown to be more accurate than MA and regression, however with no statistically significant differences, when applied to supply chain forecasting problems.”
- Artificial intelligence in supply chain management: theory and applications (2010, methodology): “For instance, Carbonneau et al. (2008) recently used machine learning to forecast the distorted demand information at the end of a SC, namely the bullwhip effect, if that demand information was not shared among the SC partners due to lack of collaboration.”
- Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020, methodology): “A key application of BDA in SCM is to provide accurate forecasting, especially demand forecasting, with the aim of reducing the bullwhip effect [14, 40–42].”
- Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “These analyses help in predicting trends, patterns and outliers that can improve decisions [37] and save millions of dollars in computing world.”
- Deep learning in business analytics and operations research: Models, applications and managerial implications (2018, positive): “…the areas of, for instance, healthcare ( Misiunas, Oztekin, Chen, & Chandra, 2016; Oztekin, Al-Ebbini, Sevkli, & Delen, 2018 ), demand forecasts ( Carbonneau et al., 2008; Venkatesh, Ravi, Prinzie, & van den Poel, 2014 ) and maintenance ( Mahamad, Saon, & Hiyama, 2010; Mazhar, Kara, &…”
- CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (2022, methodology): “1 INTRODUCTION Time series forecasting has been widely applied to various domains, such as electricity pricing (Cuaresma et al., 2004), demand forecasting (Carbonneau et al., 2008), capacity planning and management (Kim, 2003), and anomaly detection (Laptev et al., 2017).”