A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management

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...

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Bibliographic Details
Main Authors: Sajjad Taghiyeh, David C. Lengacher, Amir Hossein Sadeghi, Amirreza Sahebi-Fakhrabad, Robert B. Handfield
Format: Article
Language:English
Published: Elsevier 2023-09-01
Series:Supply Chain Analytics
Subjects:
Hierarchical forecasting
Machine learning
Time series forecasting
Supply chain management
Demand forecasting
Online Access:http://www.sciencedirect.com/science/article/pii/S2949863523000316

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http://www.sciencedirect.com/science/article/pii/S2949863523000316

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