Optimal Portfolio Strategies for New Product Development

We study the portfolio selection problem in a new product development setting with many projects in parallel, each lasting several stages, in the face of uncertainty. Each stage of the process performs an experiment on a selected number of projects in the stage, depending on the amount of (scarce) budget allocated to the stage. Projects become differentiated through their experimental results, and all available results for a project determine its category.

We model the problem as a Markov decision process. We seek an optimal policy that specifies, for every configuration of projects in categories, which projects to test and/or terminate. For two special cases we characterize the optimal project promotion policy as following a new type of strategy, state-dependent non-congestive promotion (SDNCP). SDNCP implies that a project with the highest expected reward in any stage is advanced to the next stage if and only if the number of projects in each successor category is below a congestion-dependent threshold. For the general problem, numerical experiments reveal the outstanding performance of SDNCP (optimal in 72 of 77 instances with maximum deviation from optimal of 0.67%), highlighting when and how a fixed non-congestive promotion policy, which is easier to implement, may fall short.