The impact of project introduction heuristics on research and development performance

Assuming a fixed total R&D budget, the product pipeline management (PPM) problem has two parts: (1) Which and how many projects should be initiated? (2) Which projects should continue to be invested in or terminated? We use a dynamic model calibrated to a pharmaceutical company to study PPM, focusing on three types of heuristics — gradual increase or decrease, random-normal choice, and target-based search — to evaluate the impact of the introduction of innovation projects in the pipeline on the performance in R&D. We find that a gradual decrease of project introduction rates results in convergence, but the size of the adjustments and delays in the pipeline can limit the precision of the results. A random choice is detrimental to performance even when the average value is the optimal. A target-based search results in oscillation. The results of our analysis show that the specific problem of choosing the project introduction rate can be significantly improved by using an adequate rule of thumb or heuristic.