2026-09-20T08:56:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1075102026-06-06T01:03:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Matthias Winkenbach.
Rajendran, Krishna
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Supply Chain Management Program
s-bl---
2017-03-20T19:37:42Z
2017-03-20T19:37:42Z
2016
2016
http://hdl.handle.net/1721.1/107510
962730366
Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Supply Chain Management Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-58).
What are the key store related parameters that drive sales for large retail chains? This question has become increasingly important to Lojas Americanas, the sponsor company. In the last few years, the company has expanded rapidly to cater to a larger group of consumers in a wide range of locations across Brazil. With this expansion, it wishes to determine the key parameters that drive sales for each department and modify its assortment policy accordingly for each store, so as to optimize total sales. This thesis investigates the sales impact of a wide range of store related parameters such as location, size, and socio-economic profile of the surrounding population. Stepwise regression analysis is used here. For this regression, AIC and the p-value threshold are used as the criteria to identify statistically significant store related parameters that influence sales. Furthermore, cross validation is performed to check the explanatory power of the model. The analysis performed yields useful results. A total of 36 different retail departments are analyzed and an adjusted R-squared value (for the validation set) of over 0.6 is obtained for a vast majority of them, indicating that the model performs well in determining the key parameters that drive sales. Furthermore, for each department, the statistically significant set of parameters is obtained and for the company's overall revenue a set of 11 key parameters is identified as highlighted in the Discussion section of the thesis. LA can use the results of this analysis to guide its product assortment policy.
by Krishna Rajendran.
M.Eng. in Logistics
100 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Supply Chain Management Program.
Engineering Systems Division.
Parameters driving consumer demand in Brazil
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="79e219e2-6a5f-4d32-ad3d-8cd3b4d0833e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Parameters driving consumer demand in Brazil</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2016</PublicationDate>
<Authors>
<Author>
<DisplayName>Rajendran, Krishna</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Supply Chain Management Program.</Keyword>
<Keyword>Engineering Systems Division.</Keyword>
<Abstract>What are the key store related parameters that drive sales for large retail chains? This question has become increasingly important to Lojas Americanas, the sponsor company. In the last few years, the company has expanded rapidly to cater to a larger group of consumers in a wide range of locations across Brazil. With this expansion, it wishes to determine the key parameters that drive sales for each department and modify its assortment policy accordingly for each store, so as to optimize total sales. This thesis investigates the sales impact of a wide range of store related parameters such as location, size, and socio-economic profile of the surrounding population. Stepwise regression analysis is used here. For this regression, AIC and the p-value threshold are used as the criteria to identify statistically significant store related parameters that influence sales. Furthermore, cross validation is performed to check the explanatory power of the model. The analysis performed yields useful results. A total of 36 different retail departments are analyzed and an adjusted R-squared value (for the validation set) of over 0.6 is obtained for a vast majority of them, indicating that the model performs well in determining the key parameters that drive sales. Furthermore, for each department, the statistically significant set of parameters is obtained and for the company's overall revenue a set of 11 key parameters is identified as highlighted in the Discussion section of the thesis. LA can use the results of this analysis to guide its product assortment policy.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>