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dc.contributor.advisorMaria Yang and Charles Fine.en_US
dc.contributor.authorLandis, Jordan Riley.en_US
dc.contributor.otherSloan School of Management.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2020-09-03T15:52:08Z
dc.date.available2020-09-03T15:52:08Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/126906
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 Mechanical 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 84-86).en_US
dc.description.abstractLi & Fung works with over 10,000 factories distributed across 50 countries to design, produce, and deliver hard- and soft- goods to over 2,000 apparel and consumer goods customers. An increasingly prevalent focus of the industry, driven both by regulation and consumer preferences, is to measure, benchmark, and reduce the overall environmental impact of the supply chain. Currently the measurement mechanisms in place rely on a traditional two-phase approach involving factory self-reporting and verification via independent audits. The scope of this project is to assess the efficacy of currently available measurement data in order to inform the requirements for real-time collected data. This project will be broken into four phases. First, existing industry data sources will be described and evaluated in order to assess data quality, understand requirements, and provide recommendations for future data collection. Second, the features of the data will be analyzed in order to develop an understanding of the underlining relationships. Third, using a set of selected features from the second phase, a predictive clustering algorithm for factory-level resource efficiency will be developed and used to benchmark factories. Finally, an analysis will be performed to evaluate the requirements of real time data and how real-time data could improve the benchmarking tool and future tools and services.en_US
dc.description.statementofresponsibilityby Jordan Riley Landis.en_US
dc.format.extent86 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.subjectMechanical Engineering.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleBenchmarking environmental efficiency of garment factories to understand the value of real-time environmental dataen_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 Mechanical Engineeringen_US
dc.contributor.departmentLeaders for Global Operations Programen_US
dc.identifier.oclc1191623548en_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 Mechanical Engineeringen_US
dspace.imported2020-09-03T15:52:08Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentMechEen_US


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