Massachusetts Institute of Technology
Improving the consumer demand forecast to generate more accurate suggested orders at the store-item level
Abstract
dc:description.abstractOne 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.
Degree
thesis:*- Department dc:contributor.department
- Leaders for Manufacturing Program at MIT
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2008
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Bankston, Susan D
- Advisor dc:contributor.advisor
-
- David Simchi-Levi Roy Welsch and David E. Hardt.
Subjects
dc:subject × 3Rights
dc:rights- Statement 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.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/43829
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/43829