An estimation model for hypertension drug demand in retail pharmacies with the aid of big data analytics

Papanagnou, C; Matthews-Amune, O

Abstract

The unpredictability of consumer preference observed
in the last few years has coincided with the global digital
data explosion as consumers are increasingly relying on internet
information to guide their buying behaviour. The emergence of
this trend has resulted in demand volatility and uncertainty in
the retail industry, leading to negative consequences on inventory
control and on shareholder profits in the long-run. This paper
examines whether retail pharmacies in Abuja, Nigeria may
exploit the increasing availability of relevant big data (structured,
semi-structured and unstructured) from different sources to
anticipate the changes on demand profiles for antihypertensive
medication. In order to examine this, we consider a VARX
model with non-structured data as exogenous to obtain the best
estimation

Citation

Papanagnou, C., & Matthews-Amune, O. (2017, July). An estimation model for hypertension drug demand in retail pharmacies with the aid of big data analytics. Presented at 19th IEEE Conference on Business Informatics, Thessaloniki, Greece

• Presentation Conference Type: Other
• Conference Name: 19th IEEE Conference on Business Informatics
• Conference Location: Thessaloniki, Greece
• Start Date: Jul 24, 2017
• End Date: Jul 26, 2017
• Online Publication Date: Aug 21, 2017
• Publication Date: Aug 21, 2017
• Deposit Date: Jul 6, 2017
• Publicly Available Date: Oct 2, 2017
• DOI: https://doi.org/10.1109/CBI.2017.18
• Publisher URL: http://dx.doi.org/10.1109/CBI.2017.18
• Related Public URLs: https://conferences.cwa.gr/cbi2017/; http://ieeexplore.ieee.org/Xplore/home.jsp
• Additional Information: Event Type : Conference