Probabilistic Demand Signal Decomposition in Multi-Echelon Networks: Machine Learning Architectures for Supply Chain Forecasting Accuracy
Keywords:
probabilistic demand signal decomposition, multi-echelon networks, machine learning architectures, supply chain forecasting accuracyAbstract
Forecasting is challenging in the face of high demand uncertainty; precisely integrating these market demands into decisions on sourcing, manufacturing, and logistics directly impacts the operational efficiencies and effectiveness of many industrial concerns. With these interrelated uncertainties, many studies on supply chain planning and operations rely on the use of forecasting techniques.Downloads
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