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Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework

Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework

Rehan Iftikhar, Mohammad Saud Khan
Copyright: © 2020 |Volume: 28 |Issue: 1 |Pages: 18
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799804109|DOI: 10.4018/JGIM.2020010106
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MLA

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

APA

Iftikhar, R. & Khan, M. S. (2020). Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework. Journal of Global Information Management (JGIM), 28(1), 103-120. http://doi.org/10.4018/JGIM.2020010106

Chicago

Iftikhar, Rehan, and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework," Journal of Global Information Management (JGIM) 28, no.1: 103-120. http://doi.org/10.4018/JGIM.2020010106

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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.