UN ENFOQUE BAYESIANO PARA INCORPORAR PRONóSTICOS DE LA DEMANDA EN EXPERIMENTOS POR SIMULACIóN PARA LA ADMINISTRACIóN DE INVENTARIOS

in order to postpone production planning based on information obtained closer to the time of sale, decision support systems for inventory management often include demand forecasts based on little historical data and/or subjective information. particularly, when simulation models for analyzing decisions related to safety inventories, lot sizing or lead times are used, it is convenient to model demand considering historical data, as well as information (often subjective) of the near future. this article presents an approach for modeling a random input component (e.g., demand) in simulation experiments for inventory management. under this approach, the family of distributions proposed for modeling the random component include two types of parameters: the ones that capture information of historical data and the ones that depend on forecasts (often subjective) from the particular scenario that is to be simulated. the application of the proposed approach is illustrated with an example which models daily demand through a negative binomial distribution, where the system user provides the expected demand for the period that is to be simulated