How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time

Understand

We study the classical newsvendor problem in which the decision-maker must trade-off underage and overage costs.

• In contrast to the typical setting, we assume that the decision-maker does not know the underlying distribution driving uncertainty but has only access to historical data.
• In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size.
• We analyze the classical setting with access to past samples drawn from the distribution (e.g., past demand), focusing not only on asymptotic performance but also on what we call the transient regime of learning, i.e., performance for arbitrary data sizes.