First Advisor
Moras, Rafael
Second Advisor
Uhlig, Paul
Degree
Master of Science in Industrial Engineering (MSIE)
Date of Award
10-2018
Abstract
We describe a fully empirical study on demand forecasting, that is applicable to any real-world data. This is a hands-on case study on the power of social media in demand forecasting. We implement a Box-Jenkins methodology with exogenous variables, namely ARIMAX, to forecast Walmart's future sales. The social media components that we utilize are the number of likes and comments on the official Facebook page of Walmart. The details of the empirical investigation for fitting the best ARIMAX model are presented, and the results are discussed. With this thesis, we demonstrate that social media information should be considered in forecasting, as it is very valuable for any company when performing demand planning, and inventory management.
LCSH subject
Box-Jenkins forecasting
Document Type
Thesis
Format
Medium
Manuscript
Proquest Document ID
2155338519
Recommended Citation
Akici, Aybike, "Using social media data in demand forecasting: the case of Walmart" (2018). Theses. 6.
https://commons.stmarytx.edu/theses/6
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.