A model for reliable forecasting of supply chain demand with a neural network approach - Mehdi Safaei, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
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.