We examine whether firm internal governance with respect to headquarter-plant monitoring affects workplace safety. We use travel time reductions due to new airline routes as exogenous shocks to the cost of monitoring, and find that workplace safety improves as a result of greater proximity between

Modern decision analytics frequently involves the optimization of an objective over a finite horizon where the functional form of the objective is unknown. The decision analyst observes covariates and tries to learn and optimize the objective by experimenting with the decision variables. We present

We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning

We study a service system which does not have the capability of monitoring and disclosing its real-time congestion level. However, the customers can observe and post their observations online, and future arrivals can take into account such user-generated information when deciding whether to go to

In this paper, we offer a new network perspective on one of the central topics in Operations Management - demand variability and the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with a puzzling observation: while the traditional intra-firm measure of the

Expanding on the zero-inflated Poisson (ZIP) model, the multiple-inflated Poisson (MIP) model is applied to analyze count data with multiple inflated values. The existing studies on the MIP model determined the inflated values by inspecting the histogram of count response and fitting the model with

This paper aims to reduce the length of stay (LOS) in Emergency Departments (EDs) by designing a systematic response policy for various specialists depending on the demands of their consultation. We model the specialist consultation (SC) demands via non-homogeneous Poisson process of a daily cycle;

In many services, a customer's valuation for the service depends on the amount of time that customer requires for service. We thus consider a queueing model to explicitly capture such dependence in congestion-prone services. Our goal is to study the impact of the dependence on the service

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,