Papanagnou, C 
ORCID: https://orcid.org/0000-0002-5889-4209 and Matthews-Amune, O
2018,
'Coping with demand volatility in retail pharmacies with the
aid of big data exploration'
, Computers & Operations Research, 98
, pp. 343-354.
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Abstract
Data management tools and analytics have provided managers with the opportunity to contemplate inventory performance as an ongoing activity by no longer examining only data agglomerated from ERP systems, but also, considering internet information derived from customers' online buying behaviour. The realisation of this complex relationship has increased interest in business intelligence through data and text mining of structured, semi-structured and unstructured data, commonly referred to as "big data" to uncover underlying patterns which might explain customer behaviour and improve the response to demand volatility. This paper explores how sales structured data can be used in conjunction with non-structured customer data to improve inventory management either in terms of forecasting or treating some inventory as "top-selling" based on specific customer tendency to acquire more information through the internet. A medical condition is considered - namely pain - by examining 129 weeks of sales data regarding analgesics and information seeking data by customers through Google, online newspapers and YouTube. In order to facilitate our study we consider a VARX model with non-structured data as exogenous to obtain the best estimation and we perform tests against several univariate models in terms of best fit performance and forecasting.
| Item Type: | Article |
|---|---|
| Schools: | Schools > Salford Business School > Salford Business School Research Centre |
| Journal or Publication Title: | Computers & Operations Research |
| Publisher: | Elsevier |
| ISSN: | 0305-0548 |
| Related URLs: | |
| Depositing User: | Dr Christos Papanagnou |
| Date Deposited: | 02 Oct 2017 15:02 |
| Last Modified: | 15 Feb 2022 22:28 |
| URI: | https://usir.salford.ac.uk/id/eprint/43887 |
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