Pages that link to "Item:Q2384865"
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The following pages link to Application of machine learning techniques for supply chain demand forecasting (Q2384865):
Displaying 20 items.
- Easy, reliable method for mid-term demand forecasting based on the Bass model: a hybrid approach of NLS and OLS (Q320752) (← links)
- A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment (Q350365) (← links)
- Support vector regression for warranty claim forecasting (Q421592) (← links)
- Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system (Q847226) (← links)
- How collaborative forecasting can reduce forecast accuracy (Q1785377) (← links)
- A data-driven newsvendor problem: from data to decision (Q1999637) (← links)
- Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (Q2028883) (← links)
- Iterative pre-conditioning for expediting the distributed gradient-descent method: the case of linear least-squares problem (Q2071934) (← links)
- A comparative study of demand forecasting models for a multi-channel retail company: a novel hybrid machine learning approach (Q2084304) (← links)
- Distributional regression for demand forecasting in e-grocery (Q2240022) (← links)
- A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches (Q2241169) (← links)
- A combination selection algorithm on forecasting (Q2256180) (← links)
- Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research (Q2973352) (← links)
- Nonlinear identification of judgmental forecasts effects at SKU level (Q3088166) (← links)
- Management of supply chain: an alternative modelling technique for forecasting (Q3518829) (← links)
- Advances in Neural Networks – ISNN 2005 (Q5707501) (← links)
- Reconstructing production networks using machine learning (Q6164880) (← links)
- Inventory model using machine learning for demand forecast with imperfect deteriorating products and partial backlogging under carbon emissions (Q6546997) (← links)
- A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data (Q6572858) (← links)
- Interpretable generalized additive neural networks (Q6572862) (← links)
