A model for reliable forecasting of supply chain demand with a neural network approach
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Abstract
Demand forecasting has always been a challenging issue in the supply chain. For this reason, it is known as the main tool for
success in balancing supply and demand. There are many methods, such as regression, time series for prediction. If causal
relationships between the influential factors of the model are not clear, all of these methods will lose their accuracy.On the other
hand, considering all causal relationships, despite increasing the accuracy of the model, makes it an NP-Hard model.If the
demand for several customers is considered, solving this model will be more difficult and sometimes impossible.In this paper,
using a combination of several artificial neural networks such as Principal Component Analysis, Self-Organization Map, and
Multi-Layer Perceptron network, a sustainable hybrid model is presented.The purpose of this model is to provide a solution to
overcome this challenge by giving a reliable forecast for demand, with acceptable accuracy. The results of this study all testify to
the validity of this claim.
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