The main purpose of this paper is to discuss how a Bayesian framework is appropriate to incorporate the uncertainty on the parameters of the model that is used for demand forecasting. We first present a general Bayesian framework that allows us to consider a complex model for forecasting. Using this framework we specialize (for simplicity) in the continuous-review (Q,R) system to illustrate how the main performance measures that are required for inventory management can be estimated from the output of simulation experiments. We discuss the use of sampling from the posterior distribution (SPD) and show that, under suitable regularity conditions, the estimators obtained from SPD satisfy a corresponding Central Limit Theorem, so that they are consistent, and the accuracy of each estimator can be assessed by computing an asymptotically valid halfwidth from the output of the simulation experiments.