Iftikhar, Rehan and Khan, Mohammad Saud (2020) Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework. Journal of Global Information Management, 28 (1). pp. 103-120. ISSN 1533-7995
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RI-Demand-Forecasting-2020
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Abstract
Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.
Item Type: | Article |
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Additional Information: | This article, originally published under IGI Global’s copyright on October 4, 2019 will proceed with publication as an Open Access article starting on January 11, 2021 in the gold Open Access journal, Journal of Global Information Management (converted to gold Open Access January 1, 2021), and will be distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited. Cite as: Iftikhar, Rehan and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework." JGIM vol.28, no.1 2020: pp.103-120. http://doi.org/10.4018/JGIM.2020010106 |
Keywords: | Apparel Supply Chain; Bass Emotion Model; Big Data; Demand Forecasting; Emotion Enhanced Model; Sentiment Analysis; Social Media; Supply Chain Management; |
Academic Unit: | Faculty of Social Sciences > School of Business |
Item ID: | 14957 |
Identification Number: | 10.4018/JGIM.2020010106 |
Depositing User: | Rehan Iftikhar |
Date Deposited: | 26 Oct 2021 15:41 |
Journal or Publication Title: | Journal of Global Information Management |
Publisher: | IGI Global |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/14957 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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