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IEE Transactions 45, 7 (2013) 751-762
Open innovation, the process of soliciting innovation opportunities from the outside world, has been modeled and analyzed as innovation contests, where many individuals or teams submit plans or prototypes to an innovating firm. Innovation tournaments increase the capacity of idea generation by enabling access to a broad pool of solvers while avoiding exorbitant costs. To deliver on their promise, such tournaments must be designed carefully to enable effective generation and screening of new opportunities. In particular, given the large number of opportunities to be screened, such a tournament must be efficient, favoring quick judgments based on imperfect information over extensive data collection. It must also be accurate despite the uncertainty that still clouds the prospects for a particular opportunity. Through a simulation study, we show that tournaments may not necessarily be the best process for ranking and selecting innovation opportunities in an efficient way. Instead, we propose a ranking and selection approach based on ordinal optimization, which provides both efficiency and accuracy by dynamically allocating sampling effort away from inferior opportunities onto promising ones. A numerical example helps us quantify the benefits. Our approach should therefore further boost innovation tournaments’ capacity of idea generation and testing.