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dc.contributor.advisorMaria Yang and Charles H. Fine.en_US
dc.contributor.authorTalampas, Joseph P.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.accessioned2019-10-11T22:25:25Z
dc.date.available2019-10-11T22:25:25Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122600
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MITen_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MITen_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 53-54).en_US
dc.description.abstractCost pressures on factories and the growing movement towards sustainability are key motivations behind Li & Fung's (LF) drive towards innovation. Supply chain digitalization through initiatives such as the Internet of Things (IoT) and big data is one of LF's pillars in its Three Year Plan. LF is committed to making data visible, digitally connecting, and understanding and improving resource efficiency in its supply chain. However, there is an immense amount of data across LF's -16,000 partner factories that remains largely invisible and untapped to fulfill these objectives. This thesis serves as a case study that explored the use of sensors to monitor electricity consumption in factories. Prior to this thesis, small-scale pilots in China and India yielded energy saving opportunities of up to 15% of a factory's consumption, in a payback period of 1-2 years. Given these, a factory-wide sensor installation was developed and tested in one partner factory.en_US
dc.description.abstractA six-step framework, and tools to support the initiative, were also developed and tested. Elements of this framework include: i) a method for prioritizing factories for rolling-out the sensor installation, 2) capacity-building materials enabling factories to install sensors and derive insights from the data, 3) environmental metrics for Li & Fung to benchmark factories, and 4) financing or incentive scheme to encourage factory participation. The case study in a factory in Dongguan, China yielded 45% energy savings in the air compressor, a payback period of three months, and additional savings opportunities from improving the use of CNC and injection molding machines. Although the sensors identified energy savings, feedback from the case study and from vendor road shows reveals that using sensors may be attractive to some, but not all factories, due to upfront cost, sensitivity to data, or competing investments or initiatives to reduce costs and/or improve sustainability.en_US
dc.description.abstractLF may consider relying the Higg Index to improve visibility into the resource efficiency and sustainability of its network, and to segment the market for the electricity sensors project. Using the Higg Index can also provide insight to appropriate measures that a factory can take, ranging not only from installing electricity sensors but also with energy audits or direct investments in energy-efficient equipment. Sensors can also be part of a portfolio of digital, operations, and sustainability initiatives to develop a holistic way of collaborating with factories and driving change in the supply chain. Moving forward, enhancements to the electricity sensors offering whether to reduce the upfront cost, or by bundling the sensors with other supplier capabilities are recommended to improve project viability.en_US
dc.description.statementofresponsibilityby Joseph P. Talampas.en_US
dc.format.extent62 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.titleElectricity sensors for resource efficiency and supply chain visibility in factoriesen_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.oclc1119537784en_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.imported2019-10-11T22:25:24Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentMechEen_US


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