Blockchain adoption in food supply chain for new business opportunities: an integrated approach

Monica Sharma, Akshay Patidar (), Neha Anchliya, Neeraj Prabhu, Amal Asok and Anjesh Jhajhriya
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Monica Sharma: Malaviya National Institute of Technology Jaipur
Akshay Patidar: Malaviya National Institute of Technology Jaipur
Neha Anchliya: Malaviya National Institute of Technology Jaipur
Neeraj Prabhu: Malaviya National Institute of Technology Jaipur
Amal Asok: Malaviya National Institute of Technology Jaipur
Anjesh Jhajhriya: Malaviya National Institute of Technology Jaipur

Operations Management Research, 2023, vol. 16, issue 4, No 16, 1949-1967

Abstract: Abstract Blockchain technology identifies and categorises product waste in supply chains, helps identify food contamination risks, and improves transit security by decreasing food degradation. This study aims to understand blockchain's adoption in the food supply chain. It is an attempt to determine causal relationship between the factors. The research includes a case study of a food processing unit, and the authors, drawing on a review of the relevant literature and the insights of subject-matter experts, have identified ten distinct factors. Fuzzy Interpretive Structure Modelling (F-ISM) is used to gain an understanding of the linkages among the factors and develop a hierarchical digraph. Furthermore, to understand the causal relationship, Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL) was applied. The outcomes of Fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (F-MICMAC) and F-DEMATEL analysis appeared to be almost identical, confirming the cause-and-effect relationship among factors. The factor's association was validated using sensitivity analysis. Altering the experts' input weights affected the factor's causal analysis, and the results were robust. The findings of the study depict a) Decentralisation (FII), Data Sovereignty (FIX), Interoperability (FVIII)) in the independent region and two factor (Infrastructure (FX), Smart Systems (FIII)) in the linkage region; representing causes and b) Data Management (FI), Operation Responsiveness (FIV), Data Documentation (FV), Third Party Involvement (FVI), and Cost (FVII in independent region representing effects. Further sensitivity in the inputs revealed very less change in outputs thereby representing robustness of the results. The nodding of the experts from case organisation further validated the findings. The research assists policy makers in assessment of existing systems, creation of laws and frameworks pertaining to system and can create social awareness about health hazards particularly in the food supply chain. The manuscript assists managers and decision-makers in evaluating their existing supply-chain practices and develop efficient and effective blockchain system that is not only transparent but is also robust.

Keywords: Industry 4.0; Blockchain technology; Supply chain; DEMATEL; ISM; Sensitivity Analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12063-023-00416-6

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