To study the effect of congestion on the fundamental trade-off between diagnostic accuracy and speed, we empirically test the predictions of a formal sequential testing model in a setting where the gathering of additional information can improve diagnostic accuracy, but may also take time and...

Starting with historical demand trajectories of similar products and their covariate information, we develop a data-driven approach to dynamic inventory planning for new products. Specifically, we employ non-parametric machine learning methods to model the uncertain future demand and incorporate...