2026-09-19T20:25:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/285202026-06-06T01:03:51Zcom_1721.1_7582com_1721.1_7581com_1721.1_101402col_1721.1_131023col_1721.1_101610
Christopher Caplice.
Xu, Zhiyu, 1973-
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Engineering Systems Division
2005-09-27T16:46:55Z
2005-09-27T16:46:55Z
2004
2004
http://hdl.handle.net/1721.1/28520
57350259
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2004.
Includes bibliographical references (leaves 66-67).
(cont.) boundary and leave more demand uncertainty to the pull part of the system.
Based 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-pull
by Zhiyu Xu.
M.Eng. in Logistics
67 leaves
3024259 bytes
3030747 bytes
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application/pdf
en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Two approaches to buffer management under demand uncertainty : an analytical process
Hybrid approaches to buffer management under demand uncertainty : an analytical process
Thesis
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<Abstract>(cont.) boundary and leave more demand uncertainty to the pull part of the system.</Abstract>
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