We estimate the effect of supply chain proximity on product quality. Merging four automotive data sets, we create a supply chain sample that reports the failure rate of 27,807 auto components, the location of 529 upstream component factories, and the location of 275 downstream assembly plants. We find that defect rates are higher when upstream and downstream factories are farther apart. Specifically, we estimate that increasing the distance between an upstream component factory and a downstream assembly plant by an order of magnitude increases the component’s expected defect rate by 3.9%. We find that quality improves more slowly across geographically dispersed supply chains. We also find that supply chain distance is more detrimental to quality when automakers produce early-generation models or high-end products, when they buy components with more complex configurations, or when they source from suppliers who invest relatively little in research and development. This paper was accepted by Vishal Gaur, operations management.

No abstract

No abstract

We model how a judge schedules cases as a multi-armed bandit problem. The model indicates that a first-in-first-out (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 Appeals-a court that exhibits increasing hazard rates-to switch from hearing-level FIFO to case-level FIFO. Tracking these judges for eight years, we estimate that our intervention decreased the average case duration by 12% and the probability of a decision being appealed to the Italian supreme court by 3.8%, relative to a 44-judge control sample.

T he bullwhip effect and production smoothing appear antithetical because their empirical tests oppose one another: production variability exceeding sales variability for bullwhip, and vice versa for smoothing. But this is a false dichotomy. We distinguish between the phenomena with a new production smoothing measure, which estimates how much more variable production would be absent production volatility costs. We apply our metric to an automotive manufacturing sample comprising 162 car models and find 75% smooth production by at least 5%, despite the fact that 99% exhibit the bullwhip effect. Indeed, we estimate both a strong bullwhip (on average, production is 220% as variable as sales) and robust 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 order-up-to policy.Keywords: production smoothing; bullwhip effect; demand signal processing; generalized order-up-to policy; martingale model of forecast evolution History: