A hybrid learning framework for forecasting uncertainty and adaptive inventory planning in retail supply chains

Demand forecasting and quantification of uncertainty is an essential asset of the retail supply chain optimization and risk-based inventory decisions. This study will introduce a new hybrid conditional variance model (combining gradient boosting machines (XGBoost, LightGBM), recurrent neural network...

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Autores principales:	Zizi Mohammed, Chafi Anas, Mohammed El Hammoume
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2026-03-01
Colección:	Supply Chain Analytics
Materias:	Demand forecasting Predictive analytics Uncertainty modeling Ensemble learning Inventory optimization Retail supply chains
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2949863525000809
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