Kharazmi University International Journal of Supply and Operations Management 2383-1359 4 2 2017 05 01 The Comparison of Neural Networks’ Structures for Forecasting 105 114 2729 10.22034/2017.2.01 EN Ilham Slimani Al-Qualsadi Research and Development Team, National Higher School for Computer Science and System analysis (ENSIAS), Mohammed V University, Rabat, Morocco Ilhame El Farissi Laboratory LSE2I, National School of Applied Sciences (ENSAO), Mohammed first University, Oujda, Morocco Said Achchab Al-Qualsadi Research and Development Team, National Higher School for Computer Science and System analysis (ENSIAS), Mohammed V University, Rabat, Morocco Journal Article 2016 02 24 This paper considers the application of neural networks to demand forecasting in a simple supply chain composed of a single retailer and his supplier with a game theoretic approach. This work analyses the problem from the supplier’s point of view and the employed dataset in our experimentation is provided from a recognized supermarket in Morocco. Various attempts were made in order to optimize the total network error and the findings indicate that different neural net structures can be used to forecast demand such as Adaline, Multi-Layer Perceptron (MLP), or Radial Basis Function (RBF) Network. However, the most adequate one with optimal error is the MLP architecture. http://www.ijsom.com/article_2729_b7f1f29db7c23648f2bb8d6a8ee0469b.pdf