Survival in the highly competitive business today requires an accurate view of demand to implement production plans, inventory, distribution and purchase within business; the pharmaceutical industry is no exception, as the effects of seasonality, promotions, price changes, advertising, products with low or high motion and outliers generally affect the determination of the same. In this context, above forecast demand among its consequences the drug excess inventory, obsolescence or expiration, and on the other hand, below the forecast demand has resulted in the loss of sales and a possible increase in costs . As mentioned, the theme focuses on the development of a computational model, which the techniques of artificial neural networks and genetic algorithms for forecasting demand for products used._x000D_ In the present work an analysis and comparison of computational models and then the ...