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dc.contributor.advisorChristopher Caplice.en_US
dc.contributor.authorXu, Zhiyu, 1973-en_US
dc.contributor.otherMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.date.accessioned2005-09-27T16:46:55Z
dc.date.available2005-09-27T16:46:55Z
dc.date.copyright2004en_US
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/28520
dc.descriptionThesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2004.en_US
dc.descriptionIncludes bibliographical references (leaves 66-67).en_US
dc.description.abstract(cont.) boundary and leave more demand uncertainty to the pull part of the system.en_US
dc.description.abstractBased on a particular case study, this paper presents two approaches to buffer management under demand uncertainty, which is characterized by high lumpiness, dispersion and volatility. The common theme of both of the two approaches is not to find an advanced statistical method to improve demand forecast on the basis of historical data. Rather, these approaches provide new business paradigms to deal with demand uncertainty. The first approach, make-to-anticipated-order (MTAO), takes advantage of the mechanism of make-to-order (MTO) and develops a process that the production is pulled by anticipated orders instead of being pushed by the forecast of unpredictable future demand. The implementation of this method, on one hand, breaks through the precondition of MTO that the total production cycle time should be less than customers' desired lead-time. On the other hand, MTAO enjoys the advantage of arranging production by responding to customer demand to reduce inventory costs and obsolescence risks of MPS level items. The second approach makes use of postponement and commonality strategy to lower demand uncertainty. The basic principle is that aggregate demand is more stable than disaggregate demand. Thus, if a common module instead of various individual modules in a module family acts as a MPS item, the demand of the common module will represent the aggregate demand of all individual modules in the module family and more accurate forecast can be made. Then by using the forecasted demand distribution of the common module, we can figure out optimized multistage inventory placement to buffer demand uncertainty with the minimum holding cost of total safety stock. In effect, by implementing postponement and commonality strategy, we change the push-pullen_US
dc.description.statementofresponsibilityby Zhiyu Xu.en_US
dc.format.extent67 leavesen_US
dc.format.extent3024259 bytes
dc.format.extent3030747 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_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/1721.1/7582
dc.subjectEngineering Systems Division.en_US
dc.titleTwo approaches to buffer management under demand uncertainty : an analytical processen_US
dc.title.alternativeHybrid approaches to buffer management under demand uncertainty : an analytical processen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.in Logisticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.identifier.oclc57350259en_US


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