We develop a game-theoretic model to examine the entry of copycats and its implications by incorporating two salient features; these features are two product attributes, i.e., physical resemblance and product quality, and two consumer utilities, i.e., consumption utility and status utility. Our equilibrium analysis suggests that copycats with a high physical resemblance but low product quality are more likely to successfully enter the market by defying the deterrence of the incumbent. Furthermore, we show that higher quality can prevent the copycat from successfully entering the market. Finally, we show that the entry of copycats does not always improve consumer surplus and social welfare. In particular, when the quality of the copycat is sufficiently low, the loss in status utility from consumers of the incumbent product overshadows the small gain in consumption utility from buyers of the copycat, leading to an overall decrease in consumer surplus and social welfare.

Simchi-Levi et al. proposed a novel approach using the Time-To-Recover (TTR) notion to analyze the Risk Exposure Index (REI) of supply chains under disruption. However, this approach assumed that at most one node in the supply chain can be disrupted. In this paper, we proposed a new method to integrate probabilistic assessment of disruption risks into the REI approach, and measure supply chain resiliency by analyzing the Worst-case CVaR (WCVaR) of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. Moreover, the optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on suppliers and/or installations that will have greater impact on the performance of the supply chain when disrupted.

It is widely believed that a little flexibility added at the right place can reap significant benefits for operations. Unfortunately, despite the extensive literature on this topic, we are not aware of any general methodology that can be used to guide managers in designing sparse (i.e., slightly flexible) and yet efficient operations. We address this issue using a distributionally robust approach to model the performance of a stochastic system under different process structures. We use the dual prices obtained from a related conic program to guide managers in the design process. This leads to a general solution methodology for the construction of efficient sparse structures for several classes of operational problems. Our approach can be used to design simple yet efficient structures for workforce deployment and for any level of sparsity requirement, to respond to deviations and disruptions in the operational environment. Furthermore, in the case of the classical process flexibility problem, our methodology can recover the k-chain structures that are known to be extremely efficient for this type of problem when the system is balanced and symmetric. We can also obtain the analog of 2-chain for nonsymmetrical system using this methodology.

This article examines the implications of the potential entry of a copycat who produces and sells a copycat (i.e., imitation) product that competes with the incumbent product. By analyzing a two‐period dynamic noncooperative game between these two firms, we identify conditions under which the copycat can gain successful market entry. More importantly, we find that the potential entry of a copycat creates (implicit) pressure for the incumbent to lower its selling price; hence, it improves consumer welfare. Finally, we identify conditions under which the potential entry of a copycat can increase social welfare (i.e., consumer welfare and the profit of both firms).

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