Operations & Supply Chain Management

In this paper we conduct an empirical investigation of a large-scale combinatorial auction (CA); the Chilean auction for school meals in which the government procures half a billion dollars worth of meal services every year. Our empirical study is motivated by two fundamental aspects in the designof CAs: (1) which packages should bidders be allowed to bid on; and (2) diversifying the supplier base to promote competition. We use bidding data to uncover important aspects of the firms’ cost structure and their strategic behavior, both of which are not directly observed by the auctioneer.

We consider the Kiefer-Wolfowitz (KW) stochastic approximation algorithm and derive general upper bounds on its mean-squared error. The bounds are established using an elementary induction argument and phrased directly in the terms of tuning sequences of the algorithm. From this we deduce the non- necessity of one of the main assumptions imposed on the tuning sequences in the Kiefer-Wolfowitz paper and essentially all subsequent literature.

We consider a pricing problem in an environment where the customers' willingness-to-pay (WtP) distribution may change at some point over the selling horizon. Customers arrive sequentially and make purchase decisions based on a quoted price and their private reservation price. The seller knows the WtP distribution pre- and post-change, but does not know the time at which this change occurs. The performance of a pricing policy is measured in terms of regret: the loss in revenues relative to an oracle that knows the time of change prior to the start of the selling season.

This study investigates the information content of variation analysis—that is, analysis of year-over-year changes in the components of operating profit. Using industry level data, we find that the effects on profitability of changes in the prices of output products and costs of intermediate inputs are more persistent than the effects of changes in output volume, labor cost, labor productivity, and intermediate input productivity. We further show that this information is priced by investors.

This special issue features articles from the 9th Annual INFORMS Revenue Management and Pricing Section Conference at the Kellogg School of Management, Northwestern University during 22–23 June 2009. The conference featured 42 half hour talks by practitioners and researchers, as well as keynote addresses by Professor Anton Kleywegt of Georgia Tech and by Dr Matthew Schrag, the Director of Operations Research and Industrial Engineering at Delta Airlines. The conference was organized by Martin Lariviere and Baris Ata.

It is widely accepted that entrepreneurial creation affects destruction, as new and better organizations, technologies and transactions replace old ones. This phenomenon is labeled creative destruction, but it might more accurately be called destructive creation, given the driving role of creation in the process. We reverse the typical causal ordering, and ask whether destruction may drive creation. We argue that economic systems may get stuck in suboptimal equilibria due to path dependence, and that destruction may sweep away this inertia, and open the way for entrepreneurship.

This article starts by discussing the concept of "inflation hedging" and provides estimates of "inflation betas" for standard bond and well-diversified equity indices for over 45 countries. We show that such standard securities are poor inflation hedges. Expanding the menu of assets to Treasury bills, foreign bonds, real estate and gold improves matters but inflation risk remains difficult to hedge. We then describe how state-of-the-art term structure research has tried to uncover estimates of the inflation risk premium, the compensation for bearing inflation risk.

We consider a bandit problem which involves sequential sampling from two populations (arms). Each arm produces a noisy reward realization which depends on an observable random covariate. The goal is to maximize cumulative expected reward. We derive general lower bounds on the performance of any admissible policy, and develop an algorithm whose performance achieves the order of said lower bound up to logarithmic terms. This is done by decomposing the global problem into suitably "localized" bandit problems.