The censored newsvendor and the optimal acquisition of information

This paper investigates the effect of demand censoring on the optimal policy in newsvendor inventory models with general parametric demand distributions and unknown parameter values. We show that the newsvendor problem with observable lost sales reduces to a sequence of single-period problems, while the newsvendor problem with unobservable lost sales requires a dynamic analysis. Using a Bayesian Markov decision process approach we show that the optimal inventory level in the presence of censored demand is higher than would be determined using a Bayesian myopic policy. We explore the economic rationality for this observation and illustrate it with numerical examples.