Consumer return rates have been steadily rising in recent years, resulting in growing costs for retailers who must manage the returns process and the disposition of returned products. This cost pressure is driven in part by extremely generous return policies, such as giving consumers a full refund upon return. Interestingly, this common retail practice of full refunds is inconsistent with the recommendations of many analytical models of returns, which nearly always show that a partial refund is optimal. Such inconsistencies between theory and practice might arise when the decision drivers included in the analytical models do not match the decision drivers in practice. It might also be the case that retailers are overly optimistic about the value that consumers assign to a full refund, and thus assume that the value of such a policy outweighs its costs. In this paper, we use data collected from eBay, where identical products are sold with different return policies, to investigate these open questions in the literature. We analyze both the return policy drivers from the retailer's perspective and the return policy value from the consumer's perspective. Our results suggest that the value of a full refund policy to consumers may not be as large as one might expect, and it also exhibits a large heterogeneity across buyers with different levels of online purchase experience. In addition, we provide empirical evidence for what has long been suspected by online retailers e that a non-refundable forward shipping charge quickly erodes any value that consumers assign to return policies. The generality of our results is limited by the fact that eBay differs from traditional retail contexts in many respects, including the fact that eBay buyers may not be representative of the general buyer population. However, our study of how eBay consumers value free returns provides new insights into an understudied area, and it can serve as a starting point for future studies of the value of return policies in other retail contexts.

We study a firm which serves customers that are sensitive to quoted price and leadtime, with pricing and leadtime decisions being made by the marketing and production departments, respectively. We analyze the inefficiencies created by the decentralization of the price and leadtime decisions. In the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm's profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments. We also provide insights on the sensitivity of the optimal decisions with respect to market characteristics, sequence of decisions and the firm's capacity level.

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Under changing market conditions for the hospitality industry, the Carlson Rezidor Hotel Group (CRHG) collaborated with JDA Software Group to use operations research to drive higher revenue for its hoteliers and to stay ahead of the competition. This highly innovative revenue optimization project, Stay Night Automated Pricing (SNAP), started with enterprise demand forecasting across 600 US hotels in 2007. It was followed by a large-scale network optimization solution to dynamically optimize hotel room rates based on price elasticity of demand, competitor rates, availability of remaining inventory, demand forecasts, and business rules. All North American hotels were operational in SNAP by March 2011. Starting from the optimization prototyping results in 2008, CRHG consistently measured a 2–4 percent revenue improvement in compliant hotels over noncompliant ones. To date, compliant hotels have increased revenue by more than $16 million annually. After a successful deployment in the Americas, CRHG extended the partnership with JDA to globally roll out SNAP, with an initial focus on Europe, the Middle East, Africa, and the Asia Pacific region. CRHG anticipates that the worldwide incremental revenue from this solution will exceed $30 million annually.