Remanufacturing is a production strategy whose goal is to recover the residual value of used products. Used products can be remanufactured at a lower cost than the initial production cost, but consumers value remanufactured products less than new products. The choice of production technology influences the value that can be recovered from a used product. In this paper, we solve the joint pricing and production technology selection problem faced by a manufacturer that considers introducing a remanufacturable product in a market that consists of heterogeneous consumers. Our analysis discusses the market and technology drivers of product remanufacturability and identifies some phenomena of managerial importance that are typical of a remanufacturing environment.product remanufacturing, market segmentation, technology management

M any products considered for remanufacturing are durables that exhibit a well-pronounced product life cycle-they diffuse gradually through the market. The remanufactured product, which is a cheaper substitute for the new product, is often put on the market during the life cycle of the new product and affects its sales dynamics. In this paper, we study the integrated dynamic management of a portfolio of new and remanufactured products that progressively penetrate a potential market over the product life cycle. To this end, we extend the Bass diffusion model in a way that maintains the two essential features of remanufacturing settings: (a) substitution between new and remanufactured products, and (b) a constraint on the diffusion of remanufactured products due to the limited supply of used products that can be remanufactured. We identify characteristics of the diffusion paths of new and remanufactured products. Finally, we analyze the impact of levers such as remanufacturability level, capacity profile and reverse channel speed on profitability. Figure 7 Improvement in discounted profits obtained by having a more responsive reverse channel (⌬ ‫؍‬ 0), without disposal costs, c d ‫؍‬ 0 (left panel), and with disposal costs, c d ‫؍‬ 0.2 (right panel). Debo, Toktay, and Van Wassenhove: Joint Life-Cycle Dynamics of New and Remanufactured Products Production and Operations Management 15(4), pp. 498 -513,

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In this paper, we study the impact of consumer-generated quality information (e.g., consumer reviews) on a firm’s dynamic pricing strategy in the presence of strategic consumers. Such information is useful, not only to the consumers that have not yet purchased the product but also to the firm. The informativeness of the consumer-generated quality information depends, however, on the volume of consumers who share their opinions and, thus, depends on the initial sales volume. Hence, via its initial price, the firm not only influences its revenue but also controls the quality information flow over time. The firm may either enhance or dampen the quality information flow via increasing or decreasing initial sales. The corresponding pricing strategy to steer the quality information flow is not always intuitive. Compared to the case without consumer-generated quality information, the firm may reduce the initial sales and lower the initial price. Interestingly, the firm may get strictly worse off due to the consumer-generated quality information. Even when the firm benefits from consumer-generated quality information, it may prefer less accurate information. Consumer surplus can also decrease due to the consumer-generated quality information, contrary to the conventional wisdom that word of mouth should help consumers. We examine extensions of our model that incorporate capacity investment, firm’s private information about quality, alternative updating mechanisms, as well as multiple sales periods, and show that our insights are robust. This paper was accepted by Yossi Aviv, operations management.

In the health care domain, diagnostic service centers provide advice to patients over the phone about what the most appropriate course of action is based on their symptoms. Managers of such centers must strike a balance between accuracy of advice, callers' waiting time and staffing costs by setting the appropriate capacity (staffing) and service depth. We model this problem as a multiple-server queueing system with the servers performing a sequential testing process, and the customers deciding whether to use the service or not, based on their expectation of accuracy and congestion. We find the dual concerns of accuracy and congestion lead to a counterintuitive impact of capacity: Increasing capacity might increase congestion. In addition, (i) Patient population size is an important driver in management decisions, not only in staffing, but also in accuracy of advice; (ii) Increasing asymmetry in error costs may not increase asymmetry in the corresponding error rates; and (iii) The error costs for the two major stake-holders -the service manager and the patient -may impact the optimal staffing level in different ways. Finally, we highlight the relevance of our model and results to challenges in practice elicited during interviews with current clinical researchers and practitioners.