Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and circular economy perspective

The majority of the food grain supply chain (FGSC) is run in a linear fashion, requiring substantial inputs that produce mostly inedible by-products, environmental damage and wastage. Moreover, population increase, declining food resources, shifting weather patterns, and dwindling supplies pose seri...

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Bibliographic Details
Published in:Operations management research 2023-12, Vol.16 (4), p.2104-2124
Main Authors: Das, Sumanta, Barve, Akhilesh, Sahu, Naresh Chandra, Muduli, Kamalakanta
Format: Article
Language:English
Subjects:
Artificial intelligence
Business and Management
Circular economy
Decision making
Ecological effects
Economic aspects
Engineering Economics
Environmental degradation
Food resources
Food supply
Grain
Industrial and Production Engineering
Innovation/Technology Management
Literature reviews
Logistics
Management
Marketing
Operations Management
Operations research
Operations Research/Decision Theory
Organization
Paradigms
Population decline
Resource consumption
Supply chains
Sustainable agriculture
Technology adoption
Weather patterns
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Summary:The majority of the food grain supply chain (FGSC) is run in a linear fashion, requiring substantial inputs that produce mostly inedible by-products, environmental damage and wastage. Moreover, population increase, declining food resources, shifting weather patterns, and dwindling supplies pose serious problems to the FGSC. Effective usage and consumption of resources to harmonize ecological, economic, and social elements is the need of the hour from the Agri 5.0 and circular economy (CE) perspective. Fortunately, modern technological developments like artificial intelligence (AI) might represent a paradigm change in this context. However, enablers for AI adoption haven't been studied sufficiently despite AI's popularity. Hence, the fundamental objective of this research is to identify and examine key enablers that facilitate rapid AI adoption in FGSC, empowering Agri 5.0 and CE in India. The primary facilitators for AI adoption have been explored via a literature review and expert interviews followed by a questionnaire survey. The fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) approach was then used to create a causal model of the identified enablers. The F-DEMATEL method helps resolve the uncertainty of researching enabler interactions. Research findings suggest that “Legal and regulatory interventions from the government (E7)” and “Green IoT-driven total automation (E5)” have a significant influence in integrating AI in FGSC. The results have major ramifications for policymakers. The results may be used to justify future investments and will also aid decision-makers in India in advancing AI initiatives.
ISSN:1936-9735
1936-9743
DOI:10.1007/s12063-023-00390-z