2026-09-18T20:49:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/438292022-01-28T20:01:57Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Simchi-Levi Roy Welsch and David E. Hardt.
Bankston, Susan D
Leaders for Manufacturing Program.
Leaders for Manufacturing Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2008-12-11T18:34:40Z
2008-12-11T18:34:40Z
2008
2008
http://hdl.handle.net/1721.1/43829
262693644
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 Manufacturing Program at MIT, 2008.
Includes bibliographical references (p. 57).
One of the biggest opportunities for this consumer goods company today is reducing retail stockouts at its Direct Store Delivery (DSD) customers via pre-selling, which represents approximately 70% of the company's total sales volume. But reducing retail stock-outs is becoming constantly more challenging with an ever-burgeoning number of SKUs due to new product introductions and packaging innovations. The main tool this consumer goods company uses to combat retail stock-outs is the pre-sell handheld, which the company provides to all field sales reps. The handheld runs proprietary software developed by this consumer goods company that creates suggested orders based on a number of factors including: * Baseline forecast (specific to store-item combination) * Seasonality effects (i.e., higher demand for products during particular seasons) * Promotional effects (i.e., lift created from sale prices) * Presence of in-store displays (i.e., more space for product than just shelf space) * Weekday effects (i.e., selling more on weekends when most people shop) * Holiday effects (i.e., higher demand for products at holidays) * Inventory levels on the shelves and in the back room * In-transit orders (i.e., orders that may already be on their way to the customer) The more accurate that the suggested orders are, the fewer retail stock-outs will occur. This project seeks to increase the accuracy of the consumer demand forecast, and ultimately the suggested orders, by improving the baseline forecast and accounting for the effect of cannibalization on demand.
by Susan D. Bankston.
S.M.
M.B.A.
57 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 Manufacturing Program.
Improving the consumer demand forecast to generate more accurate suggested orders at the store-item level
Thesis
application/pdf
Publication
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<Abstract>One of the biggest opportunities for this consumer goods company today is reducing retail stockouts at its Direct Store Delivery (DSD) customers via pre-selling, which represents approximately 70% of the company's total sales volume. But reducing retail stock-outs is becoming constantly more challenging with an ever-burgeoning number of SKUs due to new product introductions and packaging innovations. The main tool this consumer goods company uses to combat retail stock-outs is the pre-sell handheld, which the company provides to all field sales reps. The handheld runs proprietary software developed by this consumer goods company that creates suggested orders based on a number of factors including: * Baseline forecast (specific to store-item combination) * Seasonality effects (i.e., higher demand for products during particular seasons) * Promotional effects (i.e., lift created from sale prices) * Presence of in-store displays (i.e., more space for product than just shelf space) * Weekday effects (i.e., selling more on weekends when most people shop) * Holiday effects (i.e., higher demand for products at holidays) * Inventory levels on the shelves and in the back room * In-transit orders (i.e., orders that may already be on their way to the customer) The more accurate that the suggested orders are, the fewer retail stock-outs will occur. This project seeks to increase the accuracy of the consumer demand forecast, and ultimately the suggested orders, by improving the baseline forecast and accounting for the effect of cannibalization on demand.</Abstract>
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