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dc.contributor.advisorDaniel Frey and Roy Welsch.en_US
dc.contributor.authorNechlani, Rajkumar aka Rahul Shankarlalen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2018-09-17T15:52:05Z
dc.date.available2018-09-17T15:52:05Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117971
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 92-95).en_US
dc.description.abstractThis 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.en_US
dc.description.statementofresponsibilityby Rajkumar aka Rahul Shankarlal Nechlani.en_US
dc.format.extent95 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.subjectMechanical Engineering.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleImprovement of cleaning effectiveness through Statistical Process Control in active pharmaceutical ingredient (API) manufacturingen_US
dc.title.alternativeImprovement of cleaning effectiveness through SPC API manufacturingen_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M.en_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1051237987en_US


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