Regenerative Supply Chain Through Digitalization in Diary

Sarvari P. A., Martin S., Baskurt G., Nozari M., Khadraoui D.

Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), ELECTR NETWORK, 14 - 15 August 2020, pp.377-389 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-030-76724-2_28
  • Country: ELECTR NETWORK
  • Page Numbers: pp.377-389
  • Keywords: Big data, Natural language processing, Support vector machines, Supply chain management, Sentiment analysis, Agro-food supply chain, SOCIAL MEDIA
  • Istanbul Technical University Affiliated: No

Abstract

Globally, enterprises are leveraging social media to promote their brands, monitor consumer trends, research new product ideas, drive business growth, and improve business processes. Integrating social media into existing supply chain networks is essential to provide instant access to real user data. This study defines tailored metrics by examining the current supply chain considering the data gathered from social media in order to have a re-designed supply chain based on the requirements defined by end-users alongside utilizing organizations' strategy, technology, process, and evaluation metrics. The target is to define a framework to take full advantage of intelligent automation in retail and consumer feedback for creating efficiency and creativity. As a case study, this study introduces a social data-driven causal analytics-based methodology that reflects Tweeter data for diagnosing supply chain management issues and determining its capabilities in a milk products company in Luxembourg.