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Roy Welsch and Philip Gschwend. Lopez Marino, Maria Emilia. 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 2019-10-04T21:32:26Z 2019-10-04T21:32:26Z 2019 2019 2019 https://hdl.handle.net/1721.1/122400 1119722244 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2019 Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 96-102). Within the biopharmaceutical industry, material sciences is a rapidly growing field to continue to ensure reliable production and delivery of medicines. Consequently, there is an on-going need to evaluate and assess new materials, driven by novel process technologies and new modalities. Finding a solution to technically assess the impact of raw material attributes on the manufacturing process represents a significant opportunity to ensure supply. This study seeks to develop a novel predictive framework to assess the impact of raw material variability on the performance of commercial biologic manufacturing processes. Through machine learning techniques, the impact of two strategic raw materials is evaluated by modeling and predicting the outcomes of critical process performance variables and product quality attributes. As part of this research, we aimed to equip Amgen Inc. with a novel learning tool delivering the potential to uncover a deeper level of material variability understanding which: (1) ensures reliable supply through consistent performance, (2) provides insights to material attributes, and (3) delivers the capability to solve material-related investigations more efficiently. Models trained via machine learning showed 89 % average accuracy on predictions for new data. In addition to the demonstrated predictive power, the models developed were highly interpretable and illustrated correlations with several material attributes. Henceforth, the framework developed is the starting point of a novel methodology towards input material variability understanding. The predictive framework was implemented as a web-tool and is currently being piloted at Amgen Inc. The modular design of the predictive models and the web-tool enable the application to other production processes and associated raw materials, and could be generalized across the industry. by Maria Emilia Lopez Marino. 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 119 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Civil and Environmental Engineering. Leaders for Global Operations Program. Big data analysis interrogating raw material variability and the impact on process performance Thesis application/pdf 2019-10-04T21:32:25Z Publication Master Sloan unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3d9b7d22-3b49-4091-8e45-68d1f177a2b2"> <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>Big data analysis interrogating raw material variability and the impact on process performance</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Lopez Marino, Maria Emilia.</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>Within the biopharmaceutical industry, material sciences is a rapidly growing field to continue to ensure reliable production and delivery of medicines. Consequently, there is an on-going need to evaluate and assess new materials, driven by novel process technologies and new modalities. Finding a solution to technically assess the impact of raw material attributes on the manufacturing process represents a significant opportunity to ensure supply. This study seeks to develop a novel predictive framework to assess the impact of raw material variability on the performance of commercial biologic manufacturing processes. Through machine learning techniques, the impact of two strategic raw materials is evaluated by modeling and predicting the outcomes of critical process performance variables and product quality attributes. As part of this research, we aimed to equip Amgen Inc. with a novel learning tool delivering the potential to uncover a deeper level of material variability understanding which: (1) ensures reliable supply through consistent performance, (2) provides insights to material attributes, and (3) delivers the capability to solve material-related investigations more efficiently. Models trained via machine learning showed 89 % average accuracy on predictions for new data. In addition to the demonstrated predictive power, the models developed were highly interpretable and illustrated correlations with several material attributes. Henceforth, the framework developed is the starting point of a novel methodology towards input material variability understanding. The predictive framework was implemented as a web-tool and is currently being piloted at Amgen Inc. The modular design of the predictive models and the web-tool enable the application to other production processes and associated raw materials, and could be generalized across the industry.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>