Lists
Lists
Kumar, Rajesh, Samadhiya, Ashutosh, Kumar, Anil, Luthra, Sunil and El Jaouhari, Asmae (2025) Implementing AI in agri-food supply chain security from the multi-stakeholder perspective: an exploratory review and future directions. International Journal of Industrial Engineering and Operations Management. ISSN 2690-6104 (In Press)
Purpose: In Agri-food supply chains (AFSC), food waste can be minimized, and food security can be improved with the assistance of Artificial Intelligence (AI). But, the implementation of AI in AFSC is difficult due to various barriers. Therefore, this paper aims to examine the barriers in the AFSC and explores how these challenges can be addressed using AI.
Design/methodology/approach: This article draws on academic research, business best practices and legislative frameworks to provide suggestions from a conceptual and qualitative perspective. This critical assessment takes into account the viewpoints of many stakeholders and examines the difficulties of using AI technology in AFSC.
Findings: Our findings reveal the various barriers, such as for producers (lack of expertise, initial cost, data privacy concerns), for food processors (regulatory compliance, legacy systems, quality control, regulations and standards), for distributors (logistical challenges, seasonal variability, sustainability concerns, regulatory compliance), and for consumers (limited access to information, quality and freshness, complexity of the supply chain, cost fluctuations).
Originality: This study does an in-depth analysis focusing on the application of AI or the challenges faced by it from the perspective of all major stakeholders involved in AFSC. Our study not only identifies these challenges, it also recommends what efforts are necessary to mitigate these challenges.
![[thumbnail of Manuscript for Submission.pdf]](https://repository.londonmet.ac.uk/style/images/fileicons/text.png "Manuscript for Submission.pdf")
Restricted to Repository staff only until 7 December 2025.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
Download (586kB) | Request a copy
 |
View Item |