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dc.contributor.advisorRoy Welsch and Charles L. Cooney.en_US
dc.contributor.authorWilson, Christopher J. (Christopher James)en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2014-10-08T15:26:50Z
dc.date.available2014-10-08T15:26:50Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/90750
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 55-56).en_US
dc.description.abstractIn pursuit of Novartis Pharmaceutical's vision to "Make Quality Medicine-On Time, Every Time" Novartis Ringaskiddy Limited (NRL), is pursuing Class A Manufacturing (MRP II) certification. Achieving Class A certification requires production plans be established and met with great accuracy. The Multi Synthesis Production Unit (MSPU) at NRL sets rolling eighteen month production commitments on a semi-annual basis. During planning a rudimentary tool is used to allocate production campaigns for over 40 different Active Pharmaceutical Ingredients (API) and intermediate products across roughly 200 pieces of equipment. This manual tool is time intensive to operate, prone to errors, and requires extensive knowledge of the facility. To address this problem, a simulation based model was developed using the software package, SchedulePro". The aim of this project was to create a system to support more effective and reliable production planning. The approach requires accuracy in modeling, a process to deploy the tool and a connection to people who use and benefit from the process. In order to evaluate the proposed model and understand potential benefits to Novartis, five case studies are used to compare the proposed planning process with traditional methods. First, when planning a future production year the proposed planning process demonstrates a reduction in required time as well as an increase in accuracy. Second, when evaluating the response to a change in demand a user with little knowledge of the plant can attain comparable response time as an experienced user of traditional methods. Third, when faced with an unplanned equipment failure the user is able to explore alternative production plans which minimize disturbance to the established production plan. Fourth, through the evaluation of alternative resource allocation plans the user can determine the lowest cost approach. Finally, when applied to a product launch evaluation the model is shown to reduce the number of planning cycles, focusing specifically on the ability of the site to support a product launch in a previously allocated year.en_US
dc.description.statementofresponsibilityby Christopher J. Wilson.en_US
dc.format.extent61 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about 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.titleA practical application of simulation for production planning in a flexible pharmaceutical manufacturing environmenten_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.oclc891366566en_US


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