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dc.contributor.advisorDavid Simchi-Levi and Sean P. Willems.en_US
dc.contributor.authorNeff, Margaret E.(Margaret Ellen)en_US
dc.contributor.otherSloan School of Management.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2020-09-03T15:52:50Z
dc.date.available2020-09-03T15:52:50Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/126910
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 74-75).en_US
dc.description.abstractJohnson & Johnson Vision (JJV) is a leading manufacturer of contact lenses offering a variety of vision correction products. They have a strong commitment to innovation--defining new product categories and improving existing ones--to better patient outcomes, but this poses a challenge for machine production capacity and long-term planning. As medical devices, contact lenses must first be qualified and validated to run on lines. Additionally, capital equipment has a multi-year lead time from design and order to onsite implementation. Taken together, these constraints add great complexity to JJV's supply chain. The JJV team has a strong capability in aggregate demand planning but determining the right product mix can be difficult as consumer tastes change, new products are uncertain, and the future cannot be predicted.en_US
dc.description.abstractThis complexity faced in manufacturing contact lenses along with forecasting product mix highlights the importance of having the right capacity at the right time in maintaining high customer service standards. Strategic capacity planning, looking out 3-5 years, is currently viewed deterministically, meaning that a single number is decided upon for each product line for both demand and supply. An aggregate production plan using the various machines is then built around this deterministic forecast. This thesis attempts to address strategic capacity planning through quantification of risk relative to a plan of record using various techniques, specifically looking at risk factors as inputs to demand and production planning. The focus of this research is to probabilistically model the risk in manufacturing line variability as an input to production capability and planning at JJV.en_US
dc.description.abstractA proof-of-concept was developed for each technique with a focus of Monte Carlo simulation as a model for uncertainty in production, which was then expanded to all other lines where appropriate conditions were met. Under the analysis assumptions, 75% of the fleet across both manufacturing sites was able to be analyzed and able to be identified as high risk in their current plan for both over- and under-production, which helps to inform capital needs. Ultimately, the results of this project intend to smooth out Long Range Financial Planning and challenge existing forecasting methods and metrics.en_US
dc.description.statementofresponsibilityby Margaret E. Neff.en_US
dc.format.extent77 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleEvaluating modeling techniques for quantifying production risk in contact lens manufacturingen_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.contributor.departmentLeaders for Global Operations Programen_US
dc.identifier.oclc1191623853en_US
dc.description.collectionM.B.A. Massachusetts Institute of Technology, Sloan School of Managementen_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineeringen_US
dspace.imported2020-09-03T15:52:46Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentCivEngen_US


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