We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration. We then use this guarantee of global optimality to benchmark the performance of more practical heuristic methods. We use two sources of data: (1) a conjoint study previously conducted for a real product line design problem, and (2) simulated problems of various sizes. For both data sources, several of the heuristic methods consistently find optimal or near-optimal solutions, including simulated annealing, divide-and-conquer, product-swapping, and genetic algorithms.product line design, conjoint, optimization
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We model how quality concerns affect the relationship between a firm and its supplier. A firm concerned about uncontractible quality for a customizable good has to pay higher prices to sustain a relationship with the supplier. If the customizable good has sufficiently volatile demand, then a contract that includes a constant unit price premium only for this good cannot be sustained. Instead, the downstream firm pays a premium both for the customizable good and also for a good with more stable demand that is correlated with the demand for the customizable good. Our results imply that a supplier of customized goods should also supply other products, which can include goods that do not require customization, and both the supplier and buyer benefit from the greater pricing flexibility they achieve by trading multiple goods. History: Ganesh Iyer served as the senior editor and Dmitri Kuksov served as associate editor for this article.
This paper presents a dynamic investment game in which firms that are initially identical develop assets which are specialized to different market segments. The model assumes there are increasing returns to investment in a segment, for example, due to word-ofmouth or learning curve effects. I derive three key results: (1) Under certain conditions there is a unique equilibrium in which firms that are only slightly different focus all of their investment in different segments, causing small random differences to expand into large permanent differences. (2) On the other hand, if firms are sufficiently patient or if sufficiently large random shocks are possible, there is always an equilibrium in which the firm focused on the smaller segment changes its strategy, attacking its rival until it drives the rival out of the larger segment.
I develop a dynamic investment game with a "memoryless" R&D process in which an incumbent and an entrant can invest in a new technology, and the entrant can also invest in the old technology. I show that an increase in the probability of successfully implementing a technology can cause the incumbent to reduce its investment. Under certain conditions, if the success probability is high, the incumbent allows the entrant to win the new technology so that firms reach an equilibrium in which they use different technologies, and threats of retaliation prevent attacks; but if the success probability is low, such an equilibrium cannot be sustained, and both firms eventually implement both technologies.
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