This paper proposes the Q-Lasso method for wait time prediction, which combines statistical learning with fluid model estimators. In historical data from four remarkably different hospitals, Q-Lasso predicts the emergency department (ED) wait time for low-acuity patients with greater accuracy than rolling average methods (currently used by hospitals), fluid model estimators (from the service operations management literature), and quantile regression methods (from the emergency medicine literature). Q-Lasso achieves greater accuracy largely by correcting errors of underestimation in which a patient waits for longer than predicted. Implemented on the external website and in the triage room of the San Mateo Medical Center (SMMC), Q-Lasso achieves over 30% lower mean squared prediction error than would occur with the best rolling average method. The paper describes challenges and insights from the implementation at SMMC.

W e study sourcing in a supply chain with three levels: a manufacturer, tier 1 suppliers, and tier 2 suppliers prone to disruption from, e.g., natural disasters such as earthquakes or floods. The manufacturer may not directly dictate which tier 2 suppliers are used but may influence the sourcing decisions of tier 1 suppliers via contract parameters. The manufacturer's optimal strategy depends critically on the degree of overlap in the supply chain: if tier 1 suppliers share tier 2 suppliers, resulting in a "diamond-shaped" supply chain, the manufacturer relies less on direct mitigation (procuring excess inventory and multisourcing in tier 1) and more on indirect mitigation (inducing tier 1 suppliers to mitigate disruption risk). We also show that while the manufacturer always prefers less overlap, tier 1 suppliers may prefer a more overlapped supply chain and hence may strategically choose to form a diamond-shaped supply chain. This preference conflict worsens as the manufacturer's profit margin increases, as disruptions become more severe, and as unreliable tier 2 suppliers become more heterogeneous in their probability of disruption; however, penalty contracts-in which the manufacturer penalizes tier 1 suppliers for a failure to deliver ordered units-alleviate this coordination problem.

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