Use of twitter data for waste minimisation in beef supply chain

Mishra, Nishikant; Singh, Akshit

Abstract

© 2016, The Author(s). Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer’s complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don’t leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well.

Citation

Mishra, N., & Singh, A. (2018). Use of twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 270(1-2), 337-359. https://doi.org/10.1007/s10479-016-2303-4

• Journal Article Type: Article
• Online Publication Date: Sep 28, 2016
• Publication Date: 2018-11
• Deposit Date: May 15, 2019
• Publicly Available Date: May 16, 2019
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 270
• Issue: 1-2
• Pages: 337-359
• DOI: https://doi.org/10.1007/s10479-016-2303-4
• Keywords: Big data; Beef supply chain; Waste minimisation; Twitter analytics
• Public URL: https://hull-repository.worktribe.com/output/1788719
• Publisher URL: https://link.springer.com/article/10.1007%2Fs10479-016-2303-4
• Contract Date: May 16, 2019