Demand forecast for short life cycle products : Zara case study

The problem of optimally purchasing 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. This thesis discusses the development of a methodology to optimize the purchasing process for seasonal, short life-cycle articles. The methodology includes a process to develop a point forecast of demand of new articles, the top-down forecast at the color and size level and an optimization module to produce recommendations to define the optimal quantity to purchase and the optimal origin to source from. This thesis is the first phase of a two phases purchasing optimization process. The focus of this thesis is: a) the outline of an enhanced purchasing methodology b) the development of the most important input in the system: a point forecast of demand at the article, color, and size level, and c) the development of an IT prototype to automatically manage the purchasing methodology. The second phase of the purchasing optimization process focuses on the optimization module. The optimization module is beyond the reach of this thesis.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Bonnefoi, Tatiana (Bonnefoi Monroy)

Advisor dc:contributor.advisor

• Jeremie Gallien, Stephen C. Graves and David Simchi-Levi.

• Mechanical Engineering.
• Sloan School of Management.
• Leaders for Global Operations Program.

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

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/74454

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/74454