Operations & Supply Chain Management

We document a strong co-movement between the VIX, the stock market option-based implied volatility, and monetary policy. We decompose the VIX into two components, a proxy for risk aversion and expected stock market volatility ("uncertainty"), and analyze their dynamic interactions with monetary policy in a structural vector autoregressive framework. A lax monetary policy decreases risk aversion after about six months. Monetary authorities react to periods of high uncertainty by easing monetary policy. These results are robust to controlling for business cycle movements.

It is often claimed that we live in an information age. This has many implications for decision makers in general and business leaders in particular. For most businesses nowadays, increasingly, it is superior information that is at the origin of competitive advantage. The prime challenge in this new environment does not start with data analysis. Rather, the most important issue for the firm is to understand its information needs: what information, if it were available, would make a real difference for value creation and competitive advantage?

The U.S. government has mandated that, in a catastrophic event, metropolitan areas need to be capable of caring for 50 burn-injured patients per million population. In New York City, this corresponds to 400 patients. There are currently 140 burn beds in the region, which can be surged up to 210. To care for additional patients, hospitals without burn centers will be used to stabilize patients until burn beds become available.

We consider a multi-supplier, single-manufacturer supply chain where each supplier sells a different component at varying quality levels. The manufacturer has to decide on which quality level to choose for each component, trading-off the total cost and total quality. Each supplier decides on a price per unit quality level for its component. We characterize the strategic interaction among the suppliers and analyze the inefficiencies. We find that the inefficiencies due to such quality competition can be quite significant.

Modern electronic markets have been characterized by a relentless drive toward faster decision making. Significant technological investments have led to dramatic improvements in latency, the delay between a trading decision and the resulting trade execution. We describe a theoretical model for the quantitative valuation of latency. Our model measures the trading frictions created by the presence of latency, by considering the optimal execution problem of a representative investor.

In this article we argue that advances made in automation, communication, and manufacturing portend a dramatic reversal of the “bigger is better” approach to cost reductions prevalent in many basic infrastructure industries; for example, transportation, electric power generation, and raw material processing. We show that the traditional reductions in capital costs achieved by scaling up in size are generally matched by learning effects in the mass production process when scaling up in numbers instead. In addition, using the U.S.

It is apparent that severe weather should hamper the productivity of work that occurs outside. But what is the effect of extreme rain, snow, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, a week with six or more days of heat exceeding 90F reduces production in that week by 8% on average.

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers.

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers.