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New product demand forecasting and distribution optimization : a case study at Zara

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dc.contributor.advisor Jérémie Gallien and David Hardt. en_US
dc.contributor.author Garro, Andres en_US
dc.contributor.other Leaders for Global Operations Program. en_US
dc.date.accessioned 2011-09-27T18:39:37Z
dc.date.available 2011-09-27T18:39:37Z
dc.date.copyright 2011 en_US
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/66072
dc.description Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 191-194). en_US
dc.description.abstract The problem of optimally distributing new products is common to many companies and industries. This thesis describes how this challenge was addressed at Zara, a leading retailer in the "fast fashion" industry. The thesis discusses the development and evaluation of a modular system including distributional demand forecasting and dynamic programming distribution optimization. The demand forecasting module combined the practice of using similar products to predict the demand of a new product with a new store or customer cluster data aggregation scheme. Moreover, distributional forecasts were generated using a generic distribution of the expected relative forecast error constructed based on historical forecast performance. Finally, an empirical study of expert or qualitative forecasting within Zara was performed to evaluate the potential for forecast improvement. The distribution optimization module leveraged the distributional forecasts and dynamic programming to determine the optimal initial shipment quantities. The dynamic program directly accounted for the inventory constraints as well as the information dynamics that result from the improvement in forecast accuracy after initial sales are observed. The complete system was validated using extensive simulation. Overall, the new demand forecast reduced forecasting error by over 30% and the final simulation results showed that the overall system would be expected to improve initial sales by over 12%. Given Zara's scale, these results would translate to hundreds of millions in additional profit. Thus, a live pilot was approved and initiated by Zara with the goal of confirming the simulated impact of the system under real conditions. Assuming a successful pilot, full system implementation is expected in 2011. en_US
dc.description.statementofresponsibility by Andres Garro. en_US
dc.format.extent 194 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Sloan School of Management. en_US
dc.subject Mechanical Engineering. en_US
dc.subject Leaders for Global Operations Program. en_US
dc.title New product demand forecasting and distribution optimization : a case study at Zara en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.description.degree M.B.A. en_US
dc.contributor.department Sloan School of Management. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.contributor.department Leaders for Global Operations Program. en_US
dc.identifier.oclc 753709870 en_US


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