An Asymptotic Analysis of Inventory Planning with Censored Demand

We study stochastic inventory planning with lost sales, where contrary to classical inventory theory, the knowledge of the demand distribution is not available a priori. While the manager observes the sales quantities in each period, lost sales are unobservable, i.e., demand data is censored. The decision in each period depends only on historical sales data. Excess inventory is either perishable or carried over to the next period. In this setting, we propose non-parametric adaptive policies that generate ordering decisions over time. We show that the T-period average expected cost of our policy diers from the bench