Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

dc.contributor.authorNayal K.
dc.contributor.authorRaut R.D.
dc.contributor.authorQueiroz M.M.
dc.contributor.authorYadav V.S.
dc.contributor.authorNarkhede B.E.
dc.date.accessioned2024-03-12T19:10:00Z
dc.date.available2024-03-12T19:10:00Z
dc.date.issued2023
dc.description.abstract© 2021, Emerald Publishing Limited.Purpose: This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context. Design/methodology/approach: 20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used. Findings: The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties. Research limitations/implications: This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care. Originality/value: This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.
dc.description.firstpage304
dc.description.issuenumber2
dc.description.lastpage335
dc.description.volume34
dc.identifier.doi10.1108/IJLM-01-2021-0002
dc.identifier.issn1758-6550
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34098
dc.relation.ispartofInternational Journal of Logistics Management
dc.rightsAcesso Restrito
dc.subject.otherlanguageAgricultural supply chain (ASC)
dc.subject.otherlanguageArtificial intelligence-machine learning (AI-ML)
dc.subject.otherlanguageChallenges
dc.subject.otherlanguageCOVID-19
dc.subject.otherlanguageDelphi
dc.subject.otherlanguageFuzzy-MICMAC-ANP
dc.titleAre artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective
dc.typeArtigo
local.scopus.citations29
local.scopus.eid2-s2.0-85108146250
local.scopus.updated2024-06-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108146250&origin=inward
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