This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes. We use hospital operational factors as instrumental variables to handle the endogeneity of the admission decisions and identify important specification issues that are required for this approach to be valid. Using the quantified cost estimates, we then provide a simulation framework for evaluating various admission strategies' performance. By simulating a hospital with 21 ICU beds, we find that we could save about $1.9 million per year by using an optimal policy based on observables designed to reduce readmissions and hospital length of stay. We also discuss the role of unobserved patient factors, which physicians may discretionarily account for when making admission decisions, and show that including these unobservables could result in a more than threefold increase in benefits compared to just optimizing the policy over the observable patient factors.This paper was accepted by Serguei Netessine, operations management.

We conduct an empirical study to analyze how waiting in queue in the context of a retail store affects customers' purchasing behavior. Our methodology combines a novel data set with periodic information about the queuing system (collected via video recognition technology) with point-of-sales data. We find that waiting in queue has a nonlinear impact on purchase incidence and that customers appear to focus mostly on the length of the queue, without adjusting enough for the speed at which the line moves. An implication of this finding is that pooling multiple queues into a single queue may increase the length of the queue observed by customers and thereby lead to lower revenues. We also find that customers' sensitivity to waiting is heterogeneous and negatively correlated with price sensitivity, which has important implications for pricing in a multiproduct category subject to congestion effects. This paper was accepted by Martin Lariviere, operations management.

We develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.

The newsvendor model captures the trade-off faced by a decision maker that needs to place a firm bet prior to the occurrence of a random event. Previous research in operations management has mostly focused on deriving the decision that minimizes the expected mismatch costs. In contrast, we present two methods that estimate the unobservable cost parameters characterizing the mismatch cost function. We present a structural estimation framework that accounts for heterogeneity in the uncertainty faced by the newsvendor as well as in the cost parameters. We develop statistical methods that give consistent estimates of the model primitives, and derive their asymptotic distribution, which is useful to do hypothesis testing. We apply our econometric model to a hospital that balances the costs of reserving too much versus too little operating room capacity to cardiac surgery cases. Our results reveal that the hospital places more emphasis on the tangible costs of having idle capacity than on the costs of schedule overrun and long working hours for the staff. We also extend our structural models to incorporate external information on forecasting biases and mismatch costs reported by the medical literature. Our analysis suggests that overconfidence and incentive conflicts are important drivers of the frequency of schedule overruns observed in our sample.

We study the following question: How does competition influence the inventory holdings of General Motors' dealerships operating in isolated U.S. markets? We wish to disentangle two mechanisms by which local competition influences a dealer's inventory: (1) the entry or exit of a competitor can change a retailer's demand (a sales effect); and (2) the entry or exit of a competitor can change the amount of buffer stock a retailer holds, which influences the probability that a consumer finds a desired product in stock (a service-level effect). Theory is clear on the sales effect—an increase in sales leads to an increase in inventory (albeit a less than proportional increase). However, theoretical models of inventory competition are ambiguous on the expected sign of the service-level effect. Via a Web crawler, we obtained data on inventory and sales for more than 200 dealerships over a six-month period. Using cross-sectional variation, we estimated the effect of the number and type of local competitors on inventory holdings. We used several instrumental variables to control for the endogeneity of market entry decisions. Our results suggest that the service-level effect is strong, nonlinear, and positive. Hence, we observe that dealers carry more inventory (controlling for sales) when they face additional competition.

Automobile manufacturers in the U.S. supply chain exhibit significant differences in their days of supply of finished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days of supply than General Motors. This suggests that Toyota's well-documented advantage in manufacturing efficiency, product design, and upstream supply chain management extends to their finished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the effect of several factors on inventory holdings. We find that two factors, the number of dealerships in a manufacturer's distribution network and a manufacturer's production flexibility, explain essentially all of the difference in finished-goods inventory between Toyota and three other manufacturers: Chrysler, Ford, and General Motors.