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Ozgu Turgut. Chen, Emily, M. Eng. Massachusetts Institute of Technology Massachusetts Institute of Technology. Supply Chain Management Program. Massachusetts Institute of Technology. Supply Chain Management Program 2017-12-20T18:15:35Z 2017-12-20T18:15:35Z 2017 2017 http://hdl.handle.net/1721.1/112872 1014339797 Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-41). 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. by Emily Chen. M.Eng. in Supply Chain Management 41 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Supply Chain Management Program. Manufacturing risk assessment and uncertainty analysis for early stage (Pre-phase III) pharmaceutical drug production Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="721df428-d607-4ac6-a847-af43e755f5c9"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Manufacturing risk assessment and uncertainty analysis for early stage (Pre-phase III) pharmaceutical drug production</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Chen, Emily, M. Eng. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Supply Chain Management Program.</Keyword> <Abstract>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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>