<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T02:40:58Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/117797" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/117797</identifier><datestamp>2026-06-06T01:04:14Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Jarrod Goentzel.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">García Castillo, Jorge, M.Eng. Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Supply Chain Management Program.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Supply Chain Management Program</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2018-09-17T14:50:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2018-09-17T14:50:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/117797</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1051223158</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">This electronic version was submitted by the student author.  The certified thesis is available in the Institute Archives and Special Collections.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from student-submitted PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 87-89).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">The 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.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Jorge García Castillo.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.Eng. in Supply Chain Management</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">97 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT 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.</dim:field>
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   <dim:field mdschema="dc" element="subject" lang="en_US">Supply Chain Management Program.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Effects and mitigation of natural hazards in retail networks</dim:field>
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   	&lt;Title>Effects and mitigation of natural hazards in retail networks&lt;/Title>
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   	&lt;PublicationDate>2018&lt;/PublicationDate>
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    &lt;Keyword>Supply Chain Management Program.&lt;/Keyword>
   	&lt;Abstract>The 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.&lt;/Abstract>
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