Technical Note—Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation

Abstract

Sample Average Approximation in Data-Driven Newsvendor In the data-driven newsvendor problem, the manager makes sequential inventory decisions while learning the unknown demand distribution based on past demand samples. How does the widely used sample average approximation approach perform in this problem? In “Technical Note—Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation,” Lin, Huh, Krishnan, and Uichanco analyze the performance of the sample average approximation as the time horizon grows, which turns out to be the best possible. The authors also examine how the local flatness of the demand distribution around the optimal order quantity affects the complexity of the problem. They show that the sample average approximation has the best achievable performance in terms of not only the time horizon, but also the local flatness of the demand distribution.