2026-09-18T22:15:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/660722022-01-28T17:42:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jérémie Gallien and David Hardt.
Garro, Andres
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2011-09-27T18:39:37Z
2011-09-27T18:39:37Z
2011
2011
http://hdl.handle.net/1721.1/66072
753709870
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.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 191-194).
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.
by Andres Garro.
S.M.
M.B.A.
194 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
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http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
New product demand forecasting and distribution optimization : a case study at Zara
Thesis
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<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.</Abstract>
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