Machine learning-bases approach with a deep neural network(DNN) at its core, solves an inventory optimization task more effectively than other approacches do. We skip the demand forecasting stage and calculate inventory directly. This means that we won’t have to make wild guesses about demand probability distribution. Instead, a DNN will scrutinize most detailed historical sales data and will consider multiple diverse factors that range from product promotions to store locations, weather conditions, etc. Then it will apply a loss function that will weight holding costs against shortage costs for the projected inventory figure to return the optimal one.