A model for reliable forecasting of supply chain demand with a neural network approach (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 asthe main tool for success in balancing supply and demand. There are many methods, such as regression, timeseries for prediction. If causal relationships between the influential factors of the model are not clear, all of thesemethods will lose their accuracy.On the other hand, considering all causal relationships, despite increasing theaccuracy of the model, makes it an NP-Hard model.If the demand for several customers is considered, solvingthis model will be more difficult and sometimes impossible.In this paper, using a combination of severalartificial neural networks such as Principal Component Analysis, Self-Organization Map, and Multi-LayerPerceptron network, a sustainable hybrid model is presented.The purpose of this model is to provide a solutionto overcome this challenge by giving a reliable forecast for demand, with acceptable accuracy. The results ofthis study all testify to the validity of this claim.