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Point-of-sale demand forecasting

Author(s)
Holbrook, Blair Sato
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Other Contributors
Leaders for Global Operations Program.
Advisor
Tauhid Zaman and David Simchi-Levi.
Terms of use
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
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Abstract
Nike Always Available (AA) is a significant global business unit within Nike that allows retail customers to purchase athletic essentials at weekly replenishment intervals and 95% availability. However, demand fluctuations and current forecasting processes have resulted in frequent stock-outs and inventory surpluses, which in turn affect revenue, profitability, and brand trust. Potential root causes for demand fluctuations have included: -- Erratic customer behavior, including unplanned promotional events, allocation of open-to- buy dollars for futures (i.e., contract) versus replenishment (i.e., AA), and product inventory loading to protect from anticipated stock-outs; -- Lack of incentives and accountability to encourage accurate forecasting by customers. Current forecasting processes, which utilize historical sell-in data (i.e., product sold to retail customers) were found to be significantly inaccurate - 100% MAPE. The goal of this project was to develop a more accurate forecast based on historical sell-through data (i.e., product sold to consumers), which were recently made available. Forecast error was drastically reduced using the new forecasting method - 35% MAPE. A pilot was initiated with a major retail customer in order to test the new forecast model and determine the effects of a more transparent ordering partnership. The pilot is ongoing at the time of thesis completion.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
 
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (page 38).
 
Date issued
2016
URI
http://hdl.handle.net/1721.1/104397
Department
Leaders for Global Operations Program at MIT; Massachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Institute for Data, Systems, and Society; Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management., Institute for Data, Systems, and Society., Engineering Systems Division., Leaders for Global Operations Program.

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