Supply Chain Planning For Clinical Trials PDF Download

This dissertation investigates production and inventory decisions made within clinical trial supply chains in order to reduce drug supply costs. By investigating the SEC filings of public companies, we find that drug supply costs frequently account for a significant portion of pharmaceutical companies' R & D spending. To unlock value tied up in clinical trial supply chains, three unique aspects of clinical trial supply chains are explored and associated supply chain decisions are optimized. The first unique factor that differentiates the supply chains for clinical trials is the risk of failure, meaning that the investigational drug is proven unsafe or ineffective during human testing. Upon failure, any unused inventory is essentially wasted and needs to be destroyed. We explore the effect of this failure on production planning decisions and find the planner's decision to be a balancing act between waste and destruction costs versus production inefficiency. To optimally achieve this balance, we generalize the Wagner-Whitin model to incorporate the risk of failure. A second unique aspect of clinical trials is that demand can go from being quite unpredictable to fully predictable during the course of a trial. To take advantage of this demand learning, intra-trial batches can be produced, but at the expense of scale economies. Using various learning curves, we study this balance between learning and economies of scale in a finite horizon inventory model with fixed production costs and two production options: the pre-trial batch and the intra-trial batch. We characterize the optimal policy for both production batches in regards to optimally scheduling and sizing production. Lastly, we analyze the distribution networks of global clinical trial supply chains. Unique to these networks is their temporary existence; trials are ceased after patient enrollment goals are met. To manage these networks, we present a new class of multi-echelon inventory models to make stock positioning decisions, develop algorithms to identify lower and upper bounds on the optimal objective function for this new class, and leverage those algorithms to provide insights into optimal supply chain configurations.