Although several methods for inventory management are proposed in the literature, the required assumptions can hinder their application in practice. This work proposes a methodology for stock replenishment in two-echelon logistic networks through stochastic programming, considering a single item, periodic review and uncertain demands. The proposed approach is flexible enough to consider backlogs and lost sales cases without limitations on the number of outstanding orders. Also, in order to achieve better customer service, we introduce a variable rationing rule for quantities of the item in short at the distribution center to meet simultaneously all the demands of the retailers, dealing with imbalances or negative allocations of quantities of the item in short. The optimal review periodicity and the target level for inventory position are determined through two-stage stochastic programming models and a Monte Carlo simulation based-technique, known as Sample Average Approximation, which takes into account the uncertain nature of the item demand levels through the generation of finite sets of scenarios. The deterministic equivalent models are presented as mixed-integer non-linear programming models, which are then linearized. Numerical experiments with the proposed approach for instances of the problem randomly generated shows its potential, as the errors of the obtained results are around 1 percent.