Show simple item record

dc.contributor.advisorJonas Jonasson and Stanley Gershwin.en_US
dc.contributor.authorAwuondo, Benjamin Martin Onyangoen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2018-09-17T15:52:20Z
dc.date.available2018-09-17T15:52:20Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117977
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 57-58).en_US
dc.description.abstractFirms developing an Operations Strategy need to make decisions across a wide spectrum. Within the field of operations strategy, common practice defines the stratification of these decisions into structural and infrastructural elements. Structural decisions relating to the amount of capacity and facilities a firm deploys can impact a firm's cost competitiveness if implemented incorrectly because of the large capital expenditures and time horizons involved. Boston Scientific, a medical device manufacturer, recognizes the importance of operations strategy in achieving competitive success and continually seeks tools that assist in the creation of strategy as it pursues growth. This thesis discusses the development of a scenario planning tool that is focused on estimation of manufacturing footprint requirements for the company's internal manufacturing network. The tool we develop takes a demand forecast as an input and converts it to a physical space requirement in square feet. Additionally, the tool exhibits significant flexibility in being able to develop multiple scenarios, especially given the ability to modify parameters ranging from growth rates to improvement factors within facilities. The tool also offers a deeper level of detail than previously available, with the critical decision unit being the value stream, rather than an aggregation of data to only present factory or network level results. Whilst this work is applied to the context of a medical device manufacturer, the methodology is easily transferable to a range of industries. The work can be applied to any manufacturing setting where investment decisions for new facilities take significant time and capital. Our research of the literature on this topic identified a gap, and the development of the tool is a positive addition to the field of estimation of manufacturing footprint.en_US
dc.description.statementofresponsibilityby Benjamin Martin Onyango Awuondo.en_US
dc.format.extent58 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.titleLong range planning of manufacturing footprinten_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.oclc1051238277en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record