Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique

Mishra, Nishikant, Singh, Akshit ORCID: 0000-0001-6498-4190, Rana, Nripendra P and Dwivedi, Yogesh K (2017) Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique. Production Planning & Control, 28 (11-12). pp. 945-963.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/09537...

Abstract

The food retailers have to make their supply chains more customer-driven to sustain in modern competitive environment. It is essential for them to assimilate consumer’s perception to improve their market share. The firms usually utilise customer’s opinion in the form of structured data collected from various means such as conducting market survey, customer interviews and market research to explore the interrelationships among factors influencing consumer purchasing behaviour and associated supply chain. However, there is abundance of unstructured consumer’s opinion available on social media (Twitter). Usually, retailers struggle to employ unstructured data in above decision-making process. In this paper, firstly, by the help of literature and social media Big Data, factors influencing consumer’s beef purchasing decisions are identified. Thereafter, interrelationships between these factors are established using big data supplemented with ISM and Fuzzy MICMAC analysis. Factors are divided as per their dependence and driving power. The proposed frameworks enable to enforce decree on the intricacy of the factors. Finally, recommendations are prescribed. The proposed approach will assist retailers to design consumer centric supply chain.

Item Type:	Article
Uncontrolled Keywords:	big data, interpretive structural modelling (ISM), fuzzy MICMAC, beef supply chain, twitter
Depositing User:	Symplectic Admin
Date Deposited:	13 Aug 2018 07:14
Last Modified:	19 Jan 2023 01:29
DOI:	10.1080/09537287.2017.1336789
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3024824