Publication Date
Authored By
Topic(s) Covered:
  • Healthcare
  • Manufacturing
  • Product Development
  • Simulation

Supply chains in the pharmaceutical industry are growing increasingly more complex and expanding their geographic reach both in manufacturing production and to the end consumer, the patient. Physical development, manufacturing and distribution of these drugs, both of biologics and small molecules, is extremely technical in science and processes. Additionally, the industry is highly regulated with nuanced requirements that vary by country of origin and consumption, adding complexity to the drug development process. For these reasons, companies are pushing for longer range planning and forecasting of their drug pipelines, beginning the process earlier for drugs that are in pre-clinical phases of production in order to adequately plan for capacity in manufacturing and distribution. Working with data on a number of small molecules across different lines of treatment in the drug development pipeline, a discrete event simulation model was developed to simulate production quantity outputs given varying levels of stochastic parameters such as drug dosage, treatment duration, patient population, patient compliance, and competitive market share. Results from the simulations were used to assess manufacturing capacity risk given capacity and resource capabilities. The outputs of the model built in this thesis can be used to better inform capacity planning decisions for these early stage molecules.