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dc.contributor.advisorJarrod Goentzel.en_US
dc.contributor.authorGarcía Castillo, Jorge, M.Eng. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Supply Chain Management Program.en_US
dc.date.accessioned2018-09-17T14:50:03Z
dc.date.available2018-09-17T14:50:03Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117797
dc.descriptionThesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 87-89).en_US
dc.description.abstractThe number of natural hazards has been increasing over the last 10 years. Understanding the impact of natural hazards on retail networks is crucial to make effective planning against disruptions. We used daily sales and inventory data from a country-wide retail network and natural emergencies historic data to quantify the consequences triggered by these events in product and financial flows. We analyze sales and inventory flow through points of sale and distribution centers. We propose the Resilience Investment Model (RIM) to invest in resilience against the effects of natural hazards. This model takes into account the operational details of the organization. RIM is a two-stage multi-period inventory flow stochastic program. The resilience investments consist in acquiring additional inventory to buffer against disruptions and the use of real options contracts with suppliers to execute when a declared emergency happens. We use a set of risk profiles over the future costs to align the investment with the financials and preferences of the organization. This research shows how the risk profiles of the decision maker shape the location and distribution of backup stock in a retail network. We show that risk averse profiles reduce worst-case cost by 15% while increasing average cost by 2%. We recommend the use of risk profiles with cost targets to quantify the Value at Risk of the network due to natural hazards.en_US
dc.description.statementofresponsibilityby Jorge García Castillo.en_US
dc.format.extent97 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSupply Chain Management Program.en_US
dc.titleEffects and mitigation of natural hazards in retail networksen_US
dc.typeThesisen_US
dc.description.degreeM. Eng. in Supply Chain Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Supply Chain Management Program
dc.identifier.oclc1051223158en_US


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