MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Applied discrete event simulation for root cause analysis and evaluation of corrective process change Efficacy within vaccine manufacturing

Author(s)
Regele, Oliver Brian.
Thumbnail
Download1191624227-MIT.pdf (18.01Mb)
Other Contributors
Sloan School of Management.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Thomas Roemer and Luca Daniel.
Terms of use
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
Metadata
Show full item record
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.
Description
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).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/126896
Department
Sloan School of Management; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management., Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.