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Roy Welsch and John Williams. Liu, Zihuai. Sloan School of Management. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Leaders for Global Operations Program. Sloan School of Management Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Leaders for Global Operations Program 2020-09-03T15:52:36Z 2020-09-03T15:52:36Z 2020 2020 https://hdl.handle.net/1721.1/126909 1191623633 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 57-60). The development of a biopharmaceutical manufacturing process involves an assessment of all possible sources of variation throughout each of the unit operations in the drive toward six sigma manufacturing. The primary goal of this project is to develop a novel way to assess the variation in raw materials attributes throughout the life-cycle of the material and gain insights about the correlation between material variation to process performance and product quality. This thesis focuses on understanding the impact raw materials have on unit operations within biopharmaceutical manufacturing processes through machine learning techniques. To evaluate the impact of raw material attributes on process performance and exclude the variations explained by process operating parameters, a modeling framework is developed and tested. The framework contains three steps: (1) fitting models with only process operating data, (2) fitting models with process operating data and batch number information, (3) fitting models with process operating and raw material attributes data. By comparing the performance measurements from 3 different models, insights of correlations between raw materials and process outcomes could be obtained. by Zihuai Liu. M.B.A. S.M. M.B.A. Massachusetts Institute of Technology, Sloan School of Management S.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineering 60 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. Civil and Environmental Engineering. Leaders for Global Operations Program. Artificial intelligence infrastructure into material attributes insights Thesis application/pdf 2020-09-03T15:52:35Z Publication Master Sloan CivEng unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4bd72574-1f8f-4d99-95c6-6409f18f858e"> <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>Artificial intelligence infrastructure into material attributes insights</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Liu, Zihuai.</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>Civil and Environmental Engineering.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>The development of a biopharmaceutical manufacturing process involves an assessment of all possible sources of variation throughout each of the unit operations in the drive toward six sigma manufacturing. The primary goal of this project is to develop a novel way to assess the variation in raw materials attributes throughout the life-cycle of the material and gain insights about the correlation between material variation to process performance and product quality. This thesis focuses on understanding the impact raw materials have on unit operations within biopharmaceutical manufacturing processes through machine learning techniques. To evaluate the impact of raw material attributes on process performance and exclude the variations explained by process operating parameters, a modeling framework is developed and tested. The framework contains three steps: (1) fitting models with only process operating data, (2) fitting models with process operating data and batch number information, (3) fitting models with process operating and raw material attributes data. By comparing the performance measurements from 3 different models, insights of correlations between raw materials and process outcomes could be obtained.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>