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.

Blockchain has the potential to improve sustainable food security due to its unique features like traceability, decentralized and immutable database, and smart contract mechanisms. However, blockchain technology is still in the early stages of adoption in particular in agricultural applications. In this context, this article aims to identify blockchain drivers to achieve sustainable food security in the Indian context and model them using an integrated MCDM (Multiple Criteria Decision Making) approach. The blockchain adoption drivers are identified through an exhaustive literature review and opinions from domain experts from industry, academia, and Agriculture Supply Chain (ASC) stakeholders. Subsequently, the integrated MCDM approach is developed by combining Total Interpretive Structural Modelling (TISM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which does not only investigate the interrelation between the identified constructs and builds hierarchy but also determines the intensity of the causal interrelationships. At a later stage, Fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) is used to cluster the identified drivers to evaluate the importance of each driver. The results reveal that Traceability, Real-time information availability to agro-stakeholder, and Decentralized and immutable database are the most significant drivers. Policymakers, governmental organizations and other relevant stakeholders may utilize the information about the interrelationship between these drivers and their influential power, to frame suitable strategies for enhancing the adoption rate of blockchain in the Indian ASC.

Purpose: Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.Design/methodology/approach: A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.Findings: Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.Research limitations/implications: This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.Originality/value: For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.