Demand forecasting in supply chains: a review of aggregation and hierarchical approaches
Author:
Affiliation:
1. Centre of Excellence in Supply Chain (CESIT), Kedge Business School, Talence, France
2. Centre for Marketing Analytics and Forecasting, Lancaster University, Lancaster, UK
3. Cardiff Business School, Cardiff University, Cardiff, UK
Publisher
Informa UK Limited
Subject
Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management
Link
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.2005268
Reference131 articles.
1. Abolghasemi, M., R. Hyndman, E. Spiliotis, and C. Bergmeir. 2020. “Model Selection in Reconciling Hierarchical Time Series.” arXiv preprint: 2010.10742.
2. Abolghasemi, M., R. J. Hyndman, G. Tarr, and C. Bergmeir 2019. “Machine Learning Applications in Time Series Hierarchical Forecasting.” arXiv preprint arXiv:1912.00370.
3. Effects of correlation on intermittent demand forecasting and stock control
4. The Effect of Aggregation on Prediction in the Autoregressive Model
5. Forecasting with temporal hierarchies
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