@conference {Slimani2016266,
	title = {Artificial neural networks for demand forecasting: Application using Moroccan supermarket data},
	booktitle = {International Conference on Intelligent Systems Design and Applications, ISDA},
	volume = {2016-June},
	year = {2016},
	note = {cited By 0},
	pages = {266-271},
	abstract = {The accuracy of sales forecasts in a supply chain is certainly an important key to competitiveness. Because, for any member of the supply chain system, having a clear vision of the future demand affects his planning, his performance so his profit. In the first study of this work, various Artificial Neural Network models were presented and utilized to predict demand of a costumer{\textquoteright}s product. The training and validating data are provided from a known supermarket in Morocco. In a previous study, the results indicated that the best neural network structure for demand forecasting is the Multi Layer Perceptron, which is by the way, the most commonly used model in the literature. This work focuses on finding the optimal Multi Layer Perceptron structure for demand forecasting. We also present a review of selected works done in the application of game theory and neural networks in the context of management science. The main contribution of our work is the use of neural networks in order to predict the consumer{\textquoteright}s demand and implement this demand forecasting in a two-echelon supply chain with a game theoretic approach. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/ISDA.2015.7489236},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978383954\&doi=10.1109\%2fISDA.2015.7489236\&partnerID=40\&md5=e101e792765c4a46c2bf395db5f4b1b1},
	author = {Slimani, I.a and El Farissi, I.b and Achchab, S.a}
}
@article {Slimani2015411,
	title = {Application of game theory and neural network to study the behavioral probabilities in supply chain},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {82},
	number = {3},
	year = {2015},
	note = {cited By 3},
	pages = {411-416},
	abstract = {As a review of game theoretic approach to optimize the logistic costs with the objective of modeling interactions among players in a basic supply chain, this paper focuses on a single channel, two-echelon supply chain with a retailer and his supplier of a one-product where careful attention is given to information sharing in general and demand forecasting in particular. Therefore, in the industrial world, firms cannot risk waiting for the actual demand to occur so they can react and determine the quantities to purchase, produce or deliver. Demand forecasts are important and necessary to any member of the supply chain as they gave them the advantage of planning and anticipating for future needs. However, demand forecasting is one of those crucial decisions where an error can cost too much. This is why we choose to implement the artificial neural network as a forecasting technique. Obviously the closest actor to the market i.e the retailer has the best view of demand levels than the supplier, so sharing demand information with the other actors has an impact on the performance of the whole supply chain but it is not necessarily the case since the retailer can choose to withhold this information. This is why we focus in this investigation on the demand{\textquoteright}s prediction when information is not shared using the artificial intelligence of neural networks. {\textcopyright} 2005 - 2015 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952779428\&partnerID=40\&md5=12ffaf47b965757c2f2c6cf341401aac},
	author = {Slimani, I.a and El Farissi, I.b and Achchab, S.c}
}
