Lead Time Demand Modeling in Continuous Review Supply Chain Models

dc.contributor.authorCobb, Barry R.
dc.contributor.authorJohnson, Alan W.
dc.contributor.corporateLogistics Management (NPS-LM)
dc.contributor.corporateAcquisition Research Program (ARP)
dc.contributor.departmentGraduate School of Business & Public Policy (GSBPP)
dc.contributor.otherAcquisition Research Program
dc.date.accessioned2013-07-24T16:43:50Z
dc.date.available2013-07-24T16:43:50Z
dc.date.issued2013-04-01
dc.descriptionDisclaimer: The views represented in this report are those of the authors and do not reflect the official policy position of the Navy, the Department of Defense, or the federal government.en_US
dc.descriptionExcerpt from the Proceedings of the Tenth Annual Acquisition Research Symposium Logistics Managementen_US
dc.description.abstractThis paper introduces a mixture distribution approach to modeling the probability density function for lead time demand (LTD) in problems where a continuous review inventory system is implemented. The method differs from the typical “moment-matching” approach by focusing on building up an accurate, closed-form approximation to the LTD distribution from its components by using mixtures of truncated exponential (MTE) functions. First, construction of the LTD is illustrated and the approach is compared to two other possible LTDs. This distribution is then utilized to determine optimal order policies in cases where a buyer makes its decisions alone, and later in a situation where members of a two-level supply chain coordinate their actions. en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.sponsorshipPrepared for the Naval Postgraduate School, Monterey, CA 93943.en_US
dc.identifier.npsreportNPS-LM-13-C10P05R07-065
dc.identifier.urihttps://hdl.handle.net/10945/34589
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.relation.ispartofseriesAcquisition Research Symposium
dc.titleLead Time Demand Modeling in Continuous Review Supply Chain Modelsen_US
dc.typeTechnical Reporten_US
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