This paper addresses the simultaneous determination of pricing and inventory replenishment strategies in the face of demand uncertainty. More specifically, we analyze the following single item, periodic review model. Demands in consecutive periods are independent, but their distributions depend on the item's price in accordance with general stochastic demand functions. The price charged in any given period can be specified dynamically as a function of the state of the system. A replenishment order may be placed at the beginning of some or all of the periods. Stockouts are fully backlogged. We address both finite and infinite horizon models, with the objective of maximizing total expected discounted profit or its time average value, assuming that prices can either be adjusted arbitrarily (upward or downward) or that they can only be decreased. We characterize the structure of an optimal combined pricing and inventory strategy for all of the above types of models. We also develop an efficient value iteration method to compute these optimal strategies. Finally, we report on an extensive numerical study that characterizes various qualitative properties of the optimal strategies and corresponding optimal profit values.

Despite the existence of a large number of clustering algorithms, clustering remains a challenging problem. As large datasets become increasingly common in a number of different domains, it is often the case that clustering algorithms must be applied to heterogeneous sets of variables, creating an acute need for robust and scalable clustering methods for mixed continuous and categorical scale data. We show that current clustering methods for mixed-type data are generally unable to equitably balance the contribution of continuous and categorical variables without strong parametric assumptions. We develop KAMILA (KAymeans for MIxed LArge data), a clustering method that addresses this fundamental problem directly. We study theoretical aspects of our method and demonstrate its effectiveness in a series of Monte Carlo simulation studies and a set of real-world applications.

The appearance of this special issue of Organization Science reflects—and will surely increase—the attention organizational researchers are paying to studies of “complex systems.” There has been a remarkable wave of interest in this synthesis of concepts arising from the intersection of biology, physics, and computer science. However, if this line of work is to develop into a permanent source of valuable ideas for organizational researchers, rather than to prove a passing fancy, we need to begin sharpening our appraisal of the promise and limitations of complex systems theories in the study of organization. To have real value, such new ideas cannot for very long be characterized as the potential answer to almost every question. A period of testing their applicability across a spectrum of issues is needed. This will help us to determine on which problems the ideas work best, and which are best attacked with other tools.

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