<?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>2026-09-19T02:23:44Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/90756" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/90756</identifier><datestamp>2022-01-28T17:19:08Z</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">Donald Rosenfield and Josef Oehmen.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Ma, Jan, S.M. Massachusetts Institute of Technology</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. Department of Mechanical Engineering</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Sloan School of Management</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2014-10-08T15:27:12Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2014-10-08T15:27:12Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2014</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2014</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/90756</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">891370291</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014. In conjunction with the Leaders for Global Operations Program at MIT.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">20</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 (pages 59-60).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">This project explores portfolio management and planning through effectively reducing complexity within operations. We apply this to a major healthcare company (referred to as Company X). The anticipated launch of new molecules and formulations into the existing high mix product portfolio presents significant challenges to contain cost and maintain the standard service level of Company X. Complexity costs associated with manufacturing and supply chain activities are not entirely accounted for in direct production costs. Having transparency to these costs at a brand or SKU level would allow significant improvements in strategic decision making throughout the life cycle of a product. The work outlined in this thesis describes the development of a quantification model to capture operational complexity costs as well as an analysis of potential impact for Company X associated with implementation of the model. This is accomplished through first, identifying and prioritizing complexity cost generators; second, quantifying the costs through application of activity based accounting; third, building and piloting a decision support tool and NPV model. Lastly, process for implementation and application of the model was defined. The findings from this project provide financial rationale for a 27% reduction in the total product portfolio size, which results in a potential savings of $75M over the next five years, and 50% human resource savings across the Technical Operations and key support functions at Company X. The model can be a powerful tool for optimizing product portfolios with attention to financial, operational, and strategic considerations. Reducing complexity creates the ability to become more discerning about the portfolio composition and enable Company X to focus even more on high growth and life-saving brands.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Jan Ma.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.B.A.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">60 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">M.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.</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">Mechanical Engineering.</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">Complexity cost quantification and modeling for strategic portfolio management</dim:field>
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   	&lt;Title>Complexity cost quantification and modeling for strategic portfolio management&lt;/Title>
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   	&lt;PublicationDate>2014&lt;/PublicationDate>
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        	&lt;DisplayName>Ma, Jan, S.M. Massachusetts Institute of Technology&lt;/DisplayName>
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    &lt;Keyword>Sloan School of Management.&lt;/Keyword>
    &lt;Keyword>Mechanical Engineering.&lt;/Keyword>
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   	&lt;Abstract>This project explores portfolio management and planning through effectively reducing complexity within operations. We apply this to a major healthcare company (referred to as Company X). The anticipated launch of new molecules and formulations into the existing high mix product portfolio presents significant challenges to contain cost and maintain the standard service level of Company X. Complexity costs associated with manufacturing and supply chain activities are not entirely accounted for in direct production costs. Having transparency to these costs at a brand or SKU level would allow significant improvements in strategic decision making throughout the life cycle of a product. The work outlined in this thesis describes the development of a quantification model to capture operational complexity costs as well as an analysis of potential impact for Company X associated with implementation of the model. This is accomplished through first, identifying and prioritizing complexity cost generators; second, quantifying the costs through application of activity based accounting; third, building and piloting a decision support tool and NPV model. Lastly, process for implementation and application of the model was defined. The findings from this project provide financial rationale for a 27% reduction in the total product portfolio size, which results in a potential savings of $75M over the next five years, and 50% human resource savings across the Technical Operations and key support functions at Company X. The model can be a powerful tool for optimizing product portfolios with attention to financial, operational, and strategic considerations. Reducing complexity creates the ability to become more discerning about the portfolio composition and enable Company X to focus even more on high growth and life-saving brands.&lt;/Abstract>
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