Operations & Supply Chain Management

We propose a tractable, data-driven demand estimation procedure based on the use of maximum entropy (ME) distributions, and apply it to a stochastic capacity control problem motivated from airline revenue management. Specifically, we study the two fare-class "Littlewood" problem in a setting where the firm has access to only potentially censored sales observations. We propose a heuristic that iteratively fits an ME distribution to all observed sales data, and in each iteration selects a protection level based on the estimated distribution.

In many markets, it is common for headquarters to create a price list but grant local salespeople discretion to negotiate prices for individual transactions. How much (if any) pricing discretion headquarters should grant is a topic of debate within many firms. We investigate this issue using a unique data set from an indirect lender with local pricing discretion. We estimate that the local sales force adjusted prices in a way that improved profits by approximately 10% on average.

Many factors introduce the prospect of changes for the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, such changes affect the delicate balance between demand and supply and thus should be anticipated to the extent possible. We study the dynamic pricing problem of a retailer facing the prospect of a change in the demand function during a finite selling season with no inventory replenishment opportunity.

We propose a simple approach to dynamic multi-period portfolio choice with quadratic transaction costs. The approach is tractable in settings with a large number of securities, realistic return dynamics with multiple risk factors, many predictor variables, and stochastic volatility. We obtain a closed-form solution for a trading rule that is optimal if the problem is restricted to a broad class of strategies we define as "linearity generating strategies" (LGS).

We study a scenario in which a firm designs the compensation contract for a salesperson who exerts unobservable effort to increase the level of uncertain demand and, jointly, the firm also decides the inventory level to be stocked. We use a newsvendor-type model in which actual sales depend on the realized demand but are limited by the inventory available, and unfulfilled demand cannot be observed. In this setup, under the optimal contract, the agent is paid a bonus for meeting a sales quota.

We consider a repeated newsvendor problem in which the decision-maker (DM) does not have access to the underlying distribution of discrete demand. We analyze three informational settings: i) the DM observes realized demand in each period; ii) the DM only observes realized sales; and iii) the DM observes realized sales but also a lost sales indicator that records whether demand was censored or not. We analyze the implications of censoring on performance and key characteristics that effective policies should possess.

Systemic risk is an issue of great concern in modern financial markets as well as, more broadly, in the management of complex systems. We propose an axiomatic framework for systemic risk. Our framework allows for an independent specification of (1) a functional of the cross-sectional profile of outcomes across agents in the system in a single scenario of nature, and (2) a functional of the profile of aggregated outcomes across scenarios of nature.

Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited battery resources. Conversely, processing user-queries at remote servers can have slow response times due to communication latency incurred during transmission of the potentially large query.  We evaluate a network-assisted mobile computing scenario where mid-network nodes with "leasing" capabilities are deployed by a service provider.