ABSTRACT
PurposeThis 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/approach20 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.FindingsThe 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/implicationsThis 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/valueThis 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.
ABSTRACT
Purpose>The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.Design/methodology/approach>This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.Findings>This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.Practical implications>The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.Originality/value>This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.