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dc.contributor.advisorDavid Hardt and Roy Welsch.en_US
dc.contributor.authorDavis, Daniel Jacoben_US
dc.contributor.otherLeaders for Manufacturing Program.en_US
dc.date.accessioned2008-12-11T18:35:00Z
dc.date.available2008-12-11T18:35:00Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/43831
dc.descriptionThesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.en_US
dc.descriptionIncludes bibliographical references (p. 139-143).en_US
dc.description.abstractPrinted circuit board assemblies (PCBAs) are the backbone of the electronics industry. PCBA technologies are keeping pace with Moore's Law and will soon enable the convergence of video, voice, data, and mobility onto a single device. With the rapid advancements in product and component technologies, manufacturing tests are being pushed to the limits as consumers are demanding higher quality and more reliable electronics than ever before. Cisco Systems, Inc. (Cisco) currently manufactures over one thousand different types of printed circuit board assemblies (PCBAs) per quarter all over the world. Each PCBA in Cisco's portfolio has an associated complexity to its design determined by the number of interconnects, components, and other variables. PCBA manufacturing yields have historically been quite variable. In order to remain competitive, there is an imminent need to attain Six Sigma PCBA yields while controlling capital expenditures and innovating manufacturing test development and execution. Recently, Cisco kicked off the Test Excellence initiative to improve overall PCBA manufacturing yields and provided the backdrop to this work study. This thesis provides a first step on the journey to attaining Six Sigma PCBA manufacturing yields. Using Six Sigma techniques, two hypotheses are developed that will enable yield improvements: (1) PCBA yields can be improved by optimizing component selection across the product portfolio by analyzing component cost and quality levels, and (2) Using the Six Sigma DMAIC (define-measure-analyze-improve-control) method and the TOPSIS (Technique for Order Preferences by Similarity to Ideal Solutions) algorithm, PCBA yields will improve by optimally prioritizing manufacturing resources on the most important PCBAs first.en_US
dc.description.abstract(cont.) The two analytical tools derived in this thesis will provide insights into how PCBA manufacturing yields can be improved today while enabling future yield improvements to occur.en_US
dc.description.statementofresponsibilityby Daniel Jacob Davis.en_US
dc.format.extent143 p.en_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 Manufacturing Program.en_US
dc.titleAchieving Six Sigma printed circuit board yields by improving incoming component quality and using a PCBA prioritization algorithmen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Manufacturing Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentSloan School of Management
dc.identifier.oclc262694782en_US


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