An extreme-value model of concept testing

We model concept testing in new product development as a search for the most profitable solution to a design problem. When allocating resources, developers must balance the cost of testing multiple designs against the potential profits that may result. We propose extreme-value theory as a mathematical abstraction of the concept-testing process. We investigate the trade-off between the benefits and costs of parallel concept testing and derive closed-form solutions for the case of profits that follow extreme-value distributions. We analyze the roles of the scale and tail-shape parameters of the profit distribution as well as the cost of testing in determining the optimal number of tests and total budget for the concept phase of NPD. Using an example, we illustrate how to estimate and interpret the scale and tail-shape parameters. We find that the impact of declining concept-testing costs on expected profits, the number of concepts tested, and total spending depend on the scale/cost ratio and tail-shape parameter of the profit distribution.