The condition of the used items acquired by remanufacturers is often highly variable, and sorting is an important aspect of remanufacturing operations. Sorting policies—the rules specifying which used products should be remanufactured and which should be scrapped—have received limited attention in the literature. In this paper, we examine the case of a remanufacturer who acquires unsorted used products as needed from third party brokers. As more used items are acquired for a given demand, the remanufacturer can be more selective when sorting. Thus, two related decisions are made: how many used items to acquire, and how selective to be during the sorting process. We derive optimal acquisition and sorting policies in the presence of used product condition variability for a remanufacturer facing both deterministic and uncertain demand. We show the existence of a single optimal acquisition and sorting policy with a simple structure and show that this policy is independent of production amount when acquisition costs are linear.

T he condition of the used products acquired by remanufacturing firms often varies widely. A firm can manage this variation by acquiring a quantity of used items that exceeds demand, enabling it to remanufacture a subset of the acquired items in the best condition. As more excess items are acquired, the firm can increase its selectivity and lower its remanufacturing costs. In this paper, we examine the tradeoff of acquisition and scrapping costs vs. remanufacturing costs when used product condition is widely varying and uncertain. We derive acquisition quantities that minimize total expected costs for several representations of condition variability and remanufacturing cost structures. We find that, when costs are linear, the optimal acquisition quantity has a closed form and increases with the square root of the degree of condition variability. Our models are based on experience with remanufacturers of cell phones and imaging supplies, and application of our results is illustrated using example data from industry.

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.

M ost models of product reuse do not consider the fact that firms might be required to innovate their products over time in order to continue to appeal to the tastes of customers. We consider how the rate of this required innovation, which might be fast or slow depending on the product, affects reuse decisions. We consider two types of reuse-remanufacturing to original specifications, and upgrading used items by replacing components that have experienced innovation since the item was originally produced. We find that optimal reuse decreases with the rate of innovation, implying that models that ignore innovation overestimate the optimal amount of reuse that a company should pursue. Furthermore, we show that reuse can be encouraged in two ways-the intuitive approach of increasing end-of-life costs, and the less intuitive approach of raising the cost to make items reusable. We also examine the environmental impact of reuse, measured in terms of virgin material usage, finding that reuse can actually increase total virgin material usage in some cases. In an extension, we show how the results and insights change when the rate of innovation is uncertain.

S ocial sharing of information goods-wherein a single good is purchased and shared through a network of acquaintances such as friends or coworkers-is a significant concern for the providers of these goods. The effect of social sharing on firm pricing and profits depends critically on two elements: the structure of the underlying consumer network and the mechanism used by groups to decide whether to purchase at a given price. We examine the effect of social sharing under different network structures (decentralized, centralized, and complete), which reflect a range of market conditions. Moreover, we draw from the mechanism design literature to examine several approaches to group decision making. Our results suggest that a firm can benefit from increased social sharing if the level of sharing is already high, enabling a pricing strategy targeted primarily at sharing groups rather than individuals. However, the point at which sharing becomes marginally beneficial for a firm depends on both the distribution of group sizes (which derives from the network structure) and the group decision mechanism. Additional insights are obtained when we extend the model to capture homophily in group formation and the potential that a subset of consumers will never share for ethical reasons.