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dc.contributor.advisorThomas Roemer and Chris Caplice.en_US
dc.contributor.authorThoma, Andrew Josephen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2016-09-27T15:14:42Z
dc.date.available2016-09-27T15:14:42Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/104389
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.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.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 77-78).en_US
dc.description.abstractThe rapid growth of the Print on Demand (POD) business has necessitated a capacity expansion plan that spans the next five years. After analyzing sales data it was determined that more titles are selling in larger quantities. For these titles, the current make-to-order model does not represent the optimal manufacturing and fulfillment strategy. This preliminary insight led to the realization that an inventory model that uses demand forecasts and a cost analysis for each title should be used to determine the optimal ordering quantity for qualifying titles, in an initiative called high volume pre-builds. Additionally, an initiative called predictive manufacturing should be used concurrently to provide customer experience improvements to titles that sell in large quantities but do not qualify for high volume pre-builds. The development of a hybrid make-to-stock, make-to-order (MTS-MTO) production optimization model will lead to pre-building between 1.1M and 2.1M retail units per year, but could be scaled upward. Pre-building allows for cost savings through economies of scale in manufacturing and through transportation savings based on inventory placement and network topology. An additional 300K+ annual retail titles will be eligible for predictive manufacturing, which will also benefit from transportation savings. The customer experience improvements alone would make these initiatives worth pursuing even if they were NPV neutral or slightly negative. However, they are a clear win when also considering overall integration and cost savings. These initiatives will drive a lower cost structure for book manufacturing that benefits all stakeholders (Amazon, authors, and customers), which will lead to the continued, rapid growth of POD.en_US
dc.description.statementofresponsibilityby Andrew Joseph Thoma.en_US
dc.format.extent78 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.subjectSloan School of Management.en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleHybrid Make-to-Stock, Make-to-Order (MTS-MTO) production optimization and predictive manufacturing planen_US
dc.title.alternativeHybrid Make-to-Stock, Make-to-Order production optimization and predictive manufacturing planen_US
dc.title.alternativeHybrid MTS-MTO production optimization and predictive manufacturing planen_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M. in Engineering Systemsen_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.oclc958267558en_US


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