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Stephen Graves and David Simchi-Levi. Patel, Jalpa (Jalpa N.) Leaders for Global Operations Program. Leaders for Global Operations Program at MIT Massachusetts Institute of Technology. Engineering Systems Division Sloan School of Management 2012-09-27T15:29:03Z 2012-09-27T15:29:03Z 2012 2012 http://hdl.handle.net/1721.1/73396 810131287 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, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 83-84). Some of the biggest challenges in the retail sourcing lie in predicting demand for a new article and making purchase decisions such as quantity, source, transportation mode and time of the order. Such decisions become more complex and time consuming as the number of SKUs and suppliers increase. The thesis addresses the issue of managing retail sourcing using forecasting and optimization based decision system developed for Zara, a leading fast-fashion clothing retailer. We started with an existing pre-season demand forecasting method that uses POS data from a comparable older article to forecast demand for a new article after adjusting for stock-outs and seasonality. We developed and compared various forecast updating methods for accuracy and found that an exponential smoothing-based model, modified to accommodate for changes in level few steps ahead, resulted in highest accuracy using Cumulative Absolute Percentage Error (CAPE). Next, we implemented a profit-maximizing optimization model to produce explicit sourcing decisions such as quantity, time and source of orders. The model takes in distributional forecasts, supply constraints, holding cost, pricing information and outputs explicit sourcing decisions mentioned above. A prototype for forecasting and optimization code is ready and currently being evaluated to secure approval for a live pilot for Summer 2013 campaign sourcing. by Jalpa Patel. S.M. M.B.A. 84, [2] p. eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Engineering Systems Division. Leaders for Global Operations Program. Optimization-based decision support system for retail souring Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ff0d3532-f826-460a-8b29-50581534c110"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Optimization-based decision support system for retail souring</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Patel, Jalpa (Jalpa N.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Sloan School of Management.</Keyword> <Keyword>Engineering Systems Division.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>Some of the biggest challenges in the retail sourcing lie in predicting demand for a new article and making purchase decisions such as quantity, source, transportation mode and time of the order. Such decisions become more complex and time consuming as the number of SKUs and suppliers increase. The thesis addresses the issue of managing retail sourcing using forecasting and optimization based decision system developed for Zara, a leading fast-fashion clothing retailer. We started with an existing pre-season demand forecasting method that uses POS data from a comparable older article to forecast demand for a new article after adjusting for stock-outs and seasonality. We developed and compared various forecast updating methods for accuracy and found that an exponential smoothing-based model, modified to accommodate for changes in level few steps ahead, resulted in highest accuracy using Cumulative Absolute Percentage Error (CAPE). Next, we implemented a profit-maximizing optimization model to produce explicit sourcing decisions such as quantity, time and source of orders. The model takes in distributional forecasts, supply constraints, holding cost, pricing information and outputs explicit sourcing decisions mentioned above. A prototype for forecasting and optimization code is ready and currently being evaluated to secure approval for a live pilot for Summer 2013 campaign sourcing.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>