This thesis presents a capacity planning model for pharmaceutical firms with long and risky development cycles. These firms must make capacity investment decisions under clinical trial and learning rate uncertainty in order to invest in the most cost-effective production strategy that enables a fast time to market. In addition, the companies also seek to minimise the investment losses in case the drug is not approved for commercialisation. Using a Markov Decision Process, this study demonstrates how a pharmaceutical company can lower its overall production and investment costs by re-evaluating the capacity investment decisions as information on the clinical trials becomes available. The model chooses the most cost-effective production strategy for the different scenarios that a company can encounter, and is able to stop the capacity investments if the drug is expected to fail.     «

This thesis presents a capacity planning model for pharmaceutical firms with long and risky development cycles. These firms must make capacity investment decisions under clinical trial and learning rate uncertainty in order to invest in the most cost-effective production strategy that enables a fast time to market. In addition, the companies also seek to minimise the investment losses in case the drug is not approved for commercialisation. Using a Markov Decision Process, this study demonstrates...     »