A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management
Hierarchical time series demands are often associated with products, time frames, or geographic aggregations. Traditionally, these hierarchies have been forecasted using “top-down,” “bottom-up,” or “middle-out” approaches. This study advocates using child-level forecasts in a hierarchical supply cha...
Main Authors: | Sajjad Taghiyeh, David C. Lengacher, Amir Hossein Sadeghi, Amirreza Sahebi-Fakhrabad, Robert B. Handfield |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2023-09-01
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Series: | Supply Chain Analytics |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2949863523000316 |
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