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dc.contributor.advisorRichard de Neufville.en_US
dc.contributor.authorQuispez-Asin, Nestoren_US
dc.contributor.otherMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.date.accessioned2008-12-11T18:42:03Z
dc.date.available2008-12-11T18:42:03Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/43866
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.en_US
dc.descriptionMIT Barker Engineering Library copy: issued printed in pages.en_US
dc.descriptionAlso issued printed in pages.en_US
dc.descriptionIncludes bibliographical references (leaves 115-116).en_US
dc.description.abstractIn many real world systems, two types of uncertainties exist: those that evolve in small, continuous increments and those that may create large, discrete changes in the system. The field of engineering real options posits that flexible system designs can improve system performance in the face of such uncertainties. However, up to now, most analyses of engineering real options deal with one type of uncertainty at a time. One common analysis method for the incremental uncertainty is done by using binomial lattices, while the discrete changes are typically analyzed using traditional decision analysis. This thesis develops a new hybrid method which combines the lattice and decision analyses for the evaluation of real options. This method makes it possible to account for and display both types of uncertainties at the same time while drawing on the strengths of the two traditional methods. The main advantage is that decision makers are able to compare distributions resulting from strategies rather than only comparing single value evaluations such as expected net present value. The description of the distributions is made via Value at Risk and Gain (VARG) graphs. Also, risk preferences of decision makers are considered directly, rather than by the use of artificial utility functions or by evading the issue entirely. The main disadvantage of the method is that its complexity grows exponentially if many time periods, decision, and chance events are introduced. Therefore, the procedure is outlined for two stages of analysis step by step, and it has been programmed in Excel. To illustrate the method, an application to a supply chain strategy is developed for a computer wholesale company. The situation facing the company is whether to set up a local distribution mode (LDM) in a region experiencing increasing demand.en_US
dc.description.abstract(cont.) The competition may also decide to establish local distribution in the region. In this light, the incremental uncertainty is the growth of demand while the discrete uncertainty is the competition's decision to enter the market locally.en_US
dc.description.statementofresponsibilityby Nestor Quispez-Asin.en_US
dc.format.extent116 leavesen_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.subjectEngineering Systems Division.en_US
dc.titleHybrid lattice and decision analysis of real options : application to a supply chain strategyen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.identifier.oclc263168946en_US


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