Collaborative direct to store distribution : the consumer packaged goods network of the future

Promotional events are a common occurrence in the grocery and drug industries. These events require consumer packaged goods manufacturers to deliver a large volume of product, beyond the typical demand, to the retailer in a short period of time. Two of these manufacturers, Manufacturer A and General Mills, are interested in exploring the benefits of an innovative distribution strategy: collaboratively shipping their promotional products direct to the retailer stores. This thesis describes a modified minimum cost flow optimization model, which was developed to compare the costs of this multi-manufacturer collaborative distribution strategy with two more traditional distribution approaches in which each company would deliver product independently. The first traditional strategy entails independently delivering product to the retailer distribution center, from where the retailer would transport the product to the stores. The second traditional strategy involves each manufacturer independently delivering directly to the retailer stores. Using a retailer that participated in a trial implementation of this collaborative distribution strategy in 2010 as a case study, the model is solved to find the lowest cost distribution strategy for the region served by each retailer distribution center. Results show that collaborative distribution is the most cost effective strategy in two thirds of the regions that were studied, and that this finding is fairly robust with respect to the input parameters. However, cost savings to the supply chain from employing the optimal strategy are relatively small, with savings to the retailer coming at an additional expense to the manufacturers. Therefore, this thesis concludes that the manufacturers' incentive to employ collaborative distribution depends upon a method of sharing savings with the retailer, or upon the expectation of increased revenue due to higher sales from employing this distribution strategy.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Authors dc:creator

• Le, Nanette Thi
• Sheerr, Melanie Ann

Advisor dc:contributor.advisor

• Stephen C. Graves.

• Engineering Systems Division.

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/1721.1/68826

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/68826