Title: Challenges in adopting artificial intelligence for food manufacturing and supply chain post-pandemic in Palestine

Authors: Kawther Mousa; Zenglian Zhang; Waleed Hemdan

Addresses: School of Economics and Management, University of Science and Technology, Beijing, China ' School of Economics and Management, University of Science and Technology, Beijing, China ' School of Economics and Management, University of Science and Technology, Beijing, China; Faculty of Commerce, Kafrelsheikh University, Kafr El-Sheikh, Egypt

Abstract: The COVID-19 outbreak has seriously caused food manufacturing and supply chain (FMSC) disruptions, thereby food insecurity. The paper purposes to demonstrate the challenges of adopting machine learning and artificial intelligence (MLAI) and for mitigating the effects of COVID-19 in Palestine' FMSC. This study uses an integrated MICMAC-FISM to identify 19 main challenges derived from an inclusive review of publications and professionals' opinions. Subsequently, the discovered challenges are prioritised using analytical network process (ANP). Results show that the most critical challenges of MLAI adoption in the FMSC are 'inadequate privacy and security of data' and 'absence of government's policies'. Besides, MLAI in the FMSC is an influential tool for predicting the future accurately to minimalise fears and uncertainty caused by pandemic. The paper is an initial attempt to assess the likelihood of MLAI in the FMSC post-COVID-19 using an integrated method of MICMAC, FISM, and ANP in Palestine.

Keywords: risk management; artificial intelligence; food manufacturing; food supply chain; COVID-19; MICMAC; fuzzy-ISM.

DOI: 10.1504/IJIMS.2024.142545

International Journal of Internet Manufacturing and Services, 2024 Vol.10 No.4, pp.391 - 412

Received: 05 Feb 2024
Accepted: 24 Feb 2024
Published online: 08 Nov 2024 *

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