Forecasting Customer Demand with Deep Neural NetworksGrace Hopper Stage
Accurate forecasts of the customer demand are key for a successful supply chain management. We apply deep feedforward neural networks to explore demand patterns in the sales time series of more than 1.000 products. The approach incorporates an automated model building, training and evaluation scheme. The forecasts are integrated into enterprise resource planning system of a Siemens factory. We benchmark the forecast accuracy of our approach with state-of-the-art machine learning methods.
Cédric Villani, Mathematician - member of the Academy of Sciences - 1st vice-president of the Parliamentary Office For Scientific and Technological Assessment (OPECST) – Member of Parliament Fields Medal,