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Thomas Roemer and Luca Daniel. Regele, Oliver Brian. Sloan School of Management. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Sloan School of Management Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-09-03T15:26:00Z 2020-09-03T15:26:00Z 2020 2020 https://hdl.handle.net/1721.1/126896 1191624227 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, May, 2020 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 137-141). Digital Transformation of the Biopharmaceutical industry is enabling improved operations through smart manufacturing. One area of interest is the application of advanced data analytics techniques to supplement traditional workflows. The focus of this research was developing a process simulation model to address a defect observed at a manufacturing line at the Sanofi Pasteur Lyon site. This defect entailed a series of Out-of-Trend batches with abnormally low content of a certain attribute, at the end of a two-year process with complex product batch genealogy, which complicated the use of a traditional approaches to Root Cause Analysis. This study performed a statistical analysis of the defect batch attribute content through production stages to determine which contained a Root Cause. Once this analysis identified the Valence Assembly process as a stage of origin, a Discrete Event Simulator for this process was developed based on historical process data and specifications. This simulator was able to model the current process and replicate the defect in-silico. The simulator identified a specific Root Cause in the batch testing protocol as well as the expected incidence rate of the defect over future campaigns. Finally, the simulator evaluated the efficacy of two potential Corrective Process Changes. This work functions as a practical exploration of integrating novel data analysis and simulation techniques into traditional vaccine manufacturing activities. by Oliver Brian Regele. M.B.A. S.M. M.B.A. Massachusetts Institute of Technology, Sloan School of Management S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 141 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 Sloan School of Management. Electrical Engineering and Computer Science. Applied discrete event simulation for root cause analysis and evaluation of corrective process change Efficacy within vaccine manufacturing Thesis application/pdf 2020-09-03T15:26:00Z Publication Master Sloan EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a70cb5a7-a2a9-4ccb-8309-581c39daf394"> <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>Applied discrete event simulation for root cause analysis and evaluation of corrective process change Efficacy within vaccine manufacturing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Regele, Oliver Brian.</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>Sloan School of Management.</Keyword> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Digital Transformation of the Biopharmaceutical industry is enabling improved operations through smart manufacturing. One area of interest is the application of advanced data analytics techniques to supplement traditional workflows. The focus of this research was developing a process simulation model to address a defect observed at a manufacturing line at the Sanofi Pasteur Lyon site. This defect entailed a series of Out-of-Trend batches with abnormally low content of a certain attribute, at the end of a two-year process with complex product batch genealogy, which complicated the use of a traditional approaches to Root Cause Analysis. This study performed a statistical analysis of the defect batch attribute content through production stages to determine which contained a Root Cause. Once this analysis identified the Valence Assembly process as a stage of origin, a Discrete Event Simulator for this process was developed based on historical process data and specifications. This simulator was able to model the current process and replicate the defect in-silico. The simulator identified a specific Root Cause in the batch testing protocol as well as the expected incidence rate of the defect over future campaigns. Finally, the simulator evaluated the efficacy of two potential Corrective Process Changes. This work functions as a practical exploration of integrating novel data analysis and simulation techniques into traditional vaccine manufacturing activities.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>