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dc.contributor.advisorDavid Simchi-Levi and Donald B. Rosenfield.en_US
dc.contributor.authorBakousseva, Renataen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2016-12-05T19:55:45Z
dc.date.available2016-12-05T19:55:45Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/105632
dc.descriptionThesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 59).en_US
dc.description.abstractAs Company X looks to improve customer service and deliver new growth opportunities, it is driving toward a more efficient, aligned and effective organization that eliminates waste through integration of its supply and production networks. The current manufacturing system is optimized for high volume products with low demand variation signals, and is used for all products regardless of demand characteristics. The effects of such a system on the supply network are higher holding cost and stale inventory, while the effects on the business are lost sales and higher total delivered cost. A more responsive production system is an opportunity to reduce strain on the supply network, reduce total delivered cost and improve product fulfillment. Analysis of a portfolio of products demonstrates two main findings: (1) considerable impact of inventory cost on the total delivered cost and (2) a definitive case for differentiated manufacturing strategy - for high and low volume products. Previously only manufacturing cost had been used to make the decision of which system might better fit the goals of providing products in a timely and cost efficient manner. However, the uncovering of the impact of inventory cost on the total delivered cost has challenged that perception. An analysis was also performed on various algorithms which optimize (1) the product lot size and (2) job scheduling on machines. EOQ and a Mixed Integer Program were both analyzed for lot size determination, with the latter demonstrating more cost efficient and production efficient results due to more flexibility with the time scale and the consideration of manufacturing capacity. Finally, a couple of bin packing algorithm heuristics were tested for job scheduling. The results demonstrated significant time savings in job scheduling and have highlighted the need to automate the scheduling process.en_US
dc.description.statementofresponsibilityby Renata Bakousseva.en_US
dc.format.extent59 pagesen_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.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectSloan School of Management.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleIntegrated supply and production network designen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Engineering Systemsen_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.contributor.departmentSloan School of Management
dc.identifier.oclc963212792en_US


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