<?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>2026-09-22T19:26:00Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/81029" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/81029</identifier><datestamp>2022-01-28T15:40:12Z</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">Georgia Perakis and Mort Webster.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Yoder, Brent E. (Brent Edward)</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Leaders for Global Operations Program.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Leaders for Global Operations Program at MIT</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Engineering Systems Division</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Sloan School of Management</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2013-09-24T19:38:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2013-09-24T19:38:03Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2013</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2013</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/81029</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">857790829</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 136-138).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">This thesis covers work done at Tracks Energy, a regulated utility, to develop a strategic roadmap for supply chain process improvement. The focus of Tracks Energy has always been on keeping the lights on and the gas flowing for its customers, and the organizational structure of the company has been aligned by functional expertise to accomplish this goal. Existing supply chain operations span across the areas of responsibility for four senior executives and ten different operational groups. The cost and responsiveness of the supply chain has been negatively impacted by groups working to improve performance directly associated with their tasks, at the expense of the supply chain as a complete system. We propose a methodology for developing a strategic supply chain process improvement roadmap based on process map development, benchmarking, and data analysis to outline projected performance. We also present two different inventory models for developing inventory policies based on minimizing total material cost. The first inventory policy model applies a common framework based on stochastic optimization using normal distribution assumptions for demand and lead time. The objective of this model is to minimize costs over an infinite horizon given desired service levels. The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. The opportunities presented as a strategic roadmap represent the potential for significant capital and operational savings by focusing on the end to end supply chain over individual department functions.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Brent E. Yoder.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.B.A.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">138 p.</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">M.I.T. theses are protected by &#xd;
copyright. They may be viewed from this source for any purpose, but &#xd;
reproduction or distribution in any format is prohibited without written &#xd;
permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Sloan School of Management.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Leaders for Global Operations Program.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Developing a strategic roadmap for supply chain process improvement in a regulated utility</dim:field>
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   	&lt;Title>Developing a strategic roadmap for supply chain process improvement in a regulated utility&lt;/Title>
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   	&lt;PublicationDate>2013&lt;/PublicationDate>
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        	&lt;DisplayName>Yoder, Brent E. (Brent Edward)&lt;/DisplayName>
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    &lt;Keyword>Sloan School of Management.&lt;/Keyword>
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   	&lt;Abstract>This thesis covers work done at Tracks Energy, a regulated utility, to develop a strategic roadmap for supply chain process improvement. The focus of Tracks Energy has always been on keeping the lights on and the gas flowing for its customers, and the organizational structure of the company has been aligned by functional expertise to accomplish this goal. Existing supply chain operations span across the areas of responsibility for four senior executives and ten different operational groups. The cost and responsiveness of the supply chain has been negatively impacted by groups working to improve performance directly associated with their tasks, at the expense of the supply chain as a complete system. We propose a methodology for developing a strategic supply chain process improvement roadmap based on process map development, benchmarking, and data analysis to outline projected performance. We also present two different inventory models for developing inventory policies based on minimizing total material cost. The first inventory policy model applies a common framework based on stochastic optimization using normal distribution assumptions for demand and lead time. The objective of this model is to minimize costs over an infinite horizon given desired service levels. The second model is a multi-period model adapted from a robust framework. The objective of the second model is to minimize costs given unfavorable demand bounded by potential values unrestricted by a specific probability distribution function. The strategic roadmap for supply chain process improvements presented in this thesis is currently being pursued through the development of a newly developed supply chain management team. The opportunities presented as a strategic roadmap represent the potential for significant capital and operational savings by focusing on the end to end supply chain over individual department functions.&lt;/Abstract>
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