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Daniel Frey and Roy Welsch. Nechlani, Rajkumar aka Rahul Shankarlal Leaders for Global Operations Program. Leaders for Global Operations Program at MIT Massachusetts Institute of Technology. Department of Mechanical Engineering Sloan School of Management 2018-09-17T15:52:05Z 2018-09-17T15:52:05Z 2018 2018 http://hdl.handle.net/1721.1/117971 1051237987 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018. Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 92-95). This thesis presents work that was done to improve the effectiveness of cleaning processes at an active pharmaceutical ingredient (API) manufacturing site that was in the phase of engineering trials and cleaning cycle development. Cleaning cycles executed on the site prior to the project were found to be inconsistent in cleaning the equipment to the desired specifications. Lack of repeatability of cleaning processes was hypothesized to be a resultant of inadequate process control and monitoring. Statistical Process Control (SPC) implemented using process automation was found to improve the success rate of cleaning processes significantly. SPC introduction required breaking down the cleaning operation into component steps, identifying critical process parameters (CPPs) and calculation of control limits using Shewhart Control Charts for these CPPs. Significant modifications were done to the automation controls for the recipe to ensure deviations from recipe are captured and appropriate actions are taken by the system or the operator to bring the process back in control. The success rate of cleaning processes improved from 38% to 72% post the implementation of Phase I of SPC with the newer non-conformances being associated to special external causes outside the control of the process. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) was also introduced and piloted as a future opportunity for enhanced control and continuous quality improvement. Multivariate statistical process control eliminates the need to monitor multiple control charts (one for each variable) at the same time accounting for the correlations among process variables. by Rajkumar aka Rahul Shankarlal Nechlani. M.B.A. S.M. 95 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. Mechanical Engineering. Leaders for Global Operations Program. Improvement of cleaning effectiveness through Statistical Process Control in active pharmaceutical ingredient (API) manufacturing Improvement of cleaning effectiveness through SPC API manufacturing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3daebc4b-2f31-4b7c-b7f1-4af3adf6a7b7"> <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>Improvement of cleaning effectiveness through Statistical Process Control in active pharmaceutical ingredient (API) manufacturing</Title> <Subtitle>Improvement of cleaning effectiveness through SPC API manufacturing</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Nechlani, Rajkumar aka Rahul Shankarlal</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>Mechanical Engineering.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>This thesis presents work that was done to improve the effectiveness of cleaning processes at an active pharmaceutical ingredient (API) manufacturing site that was in the phase of engineering trials and cleaning cycle development. Cleaning cycles executed on the site prior to the project were found to be inconsistent in cleaning the equipment to the desired specifications. Lack of repeatability of cleaning processes was hypothesized to be a resultant of inadequate process control and monitoring. Statistical Process Control (SPC) implemented using process automation was found to improve the success rate of cleaning processes significantly. SPC introduction required breaking down the cleaning operation into component steps, identifying critical process parameters (CPPs) and calculation of control limits using Shewhart Control Charts for these CPPs. Significant modifications were done to the automation controls for the recipe to ensure deviations from recipe are captured and appropriate actions are taken by the system or the operator to bring the process back in control. The success rate of cleaning processes improved from 38% to 72% post the implementation of Phase I of SPC with the newer non-conformances being associated to special external causes outside the control of the process. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) was also introduced and piloted as a future opportunity for enhanced control and continuous quality improvement. Multivariate statistical process control eliminates the need to monitor multiple control charts (one for each variable) at the same time accounting for the correlations among process variables.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>