Demand Forecasting in Supply Chain: Comparing Multiple Linear Regression and Artificial Neural Networks Approaches

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Forecasting plays an important role, in supply chain management. It allows anticipating and meeting future demands and expectations of customers. This article presents a contribution to improve the quality of forecasts by application of artificial neuron networks (ANNs). The neural network model is compared with the multiple linear regression (MLR). The results show that ANNs are more efficient and are very promising for solving forecast problems.
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Demand Forecasting; Supply Chain; Time Series; Causal Method; Multiple Regression; Artificial Neural Networks

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