Robert L. Bray 
Kellogg School of Management Research Interests Supply Chaining and Empirical Operations Management Curriculum Vitae 
Articles 
Abstract: This work distinguishes between two related conceptsthe bullwhip effect and production smoothing. These phenomena appear antithetical because they share opposing empirical tests: production variability exceeding sales variability for bullwhip, and vice versa for smoothing. But this is a false dichotomy. We differentiate between the two with a new production smoothing measure, which estimates how much more volatile production would be absent production volatility costs. We apply this metric to an automotive manufacturing sample comprising 162 car models. We find 75% of our sample smooths production by at least 5%, despite the fact that 99% exhibits the bullwhip effect; indeed, we estimate both a strong bullwhip (on average, production is 220% as variable as sales) and a strong degree of smoothing (on average, production would be 22% more variable without deliberate stabilization). We find firms smooth both production variability and production uncertainty. We measure production smoothing with a structural econometric production scheduling model, based on the Generalized OrderUpTo Policy.
Abstract: We model how a judge schedules cases as a multiarmed bandit problem. The model indicates that a firstinfirstout (FIFO) scheduling policy is optimal when the case completion hazard rate function is monotonic. But there are two ways to implement FIFO in this context: at the hearing level or at the case level. Our model indicates that the former policy, prioritizing the oldest hearing, is optimal when the case completion hazard rate function decreases, and the latter policy, prioritizing the oldest case, is optimal when the case completion hazard rate function increases. This result convinced six judges of the Roman Labor Court of Appealsa court that exhibits increasing hazard ratesto switch from hearinglevel FIFO to caselevel FIFO. Tracking these judges for eight years, we estimate that our intervention decreased their case flow times by 12\% and the likelihood that their decisions were appealed to the Italian supreme court by 3.8\%, relative to a 44 judge control sample.
Abstract: Most supply chain works suppose retailers can credibly communicate costs to suppliers. But honest cost disclosure can be untenable because stores can shift the inventory burden upstream by inflating marginal costs (e.g., they can reduce stockout rates by exaggerating the goodwill lost to unsatisfied demand). So suppliers may not receive true cost estimates, preventing them from instituting optimal inventory policies. We develop an empirical means to resolve this supply chain ``cheap talk'' problemto compel the supplier and retailer to coordinate optimally, even when the latter is dishonest. Rather than ask a retailer for its private costs, we estimate them directly with a dynamic discrete choice inventory model. We illustrate this approach with a 5,320SKU, 1,371day sample from a Chinese supermarket. We conclude the distribution center stocking out of the median product costs the store the equivalent of .03 shipments, .51 lost sales, or 37 days of storage.
Abstract: We model a manufacturer's and regulator's joint recall decisions as an asymmetric dynamic discrete choice game. We estimate our model with a data set comprising 14,124 recalls and 976,062 defect reports. The agents use these reports to learn component quality, trading off between a fixed recall cost and a variable liability cost. Both agents perceive a recall to be less costly when initiated by the other. Hence, initiating auto recalls is a game of chicken: The agents want faulty products off the road but hold out for the other to act, increasing the average product's recall time by 1.81 years.

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