Artificial intelligence for agricultural supply chain risk management: Constraints and potentials
| cg.contributor.crp | Big Data | |
| dc.contributor.author | Tzachor, Asaf | en |
| dc.date.accessioned | 2020-07-07T10:20:29Z | en |
| dc.date.available | 2020-07-07T10:20:29Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/108709 | |
| dc.title | Artificial intelligence for agricultural supply chain risk management: Constraints and potentials | en |
| dcterms.abstract | Supply chains of staple crops, in developed and developing regions, are vulnerable to an array of disturbances and disruptions. These include biotic, abiotic and institutional risk factors. Artificial intelligence (AI) systems have the potential to mitigate some of these vulnerabilities across supply chains, and thereby improve the state of global food security. However, the particular properties of each supply chain phase, from "the farm to the fork," might suggest that some phases are more vulnerable to risks than others. Furthermore, the social circumstances and technological environment of each phase may indicate that several phases of the supply chains will be more receptive to AI adoption and deployment than others. This research paper seeks to test these assumptions to inform the integration of AI in agricultural supply chains. It employs a supply chain risk management approach (SCRM) and draws on a mix-methods research design. In the qualitative component of the research, interviews are conducted with agricultural supply chain and food security experts from the Food and Agricultural Organization of the UN (FAO), the World Bank, CGIAR, the World Food Program (WFP) and the University of Cambridge. In the quantitative component of the paper, seventy-two scientists and researchers in the domains of digital agriculture, big data in agriculture and agricultural supply chains are surveyed. The survey is used to generate assessments of the vulnerability of different phases of supply chains to biotic, abiotic and institutional risks, and the ease of AI adoption and deployment in these phases. The findings show that respondents expect the vulnerability to risks of all but one supply chain phases to increase over the next ten years. Importantly, where the integration of AI systems will be most desirable, in highly vulnerable supply chain phases in developing countries, the potential for AI integration is likely to be limited. To the best of our knowledge, the methodical examination of AI through the prism of agricultural SCRM, drawing on expert insights, has never been conducted. This paper carries out a first assessment of this kind and provides preliminary prioritizations to benefit agricultural SCRM as well as to guide further research on AI for global food security. | en |
| dcterms.accessRights | Open Access | |
| dcterms.bibliographicCitation | Tzachor, A. (2020) Artificial intelligence for agricultural supply chain risk management: Constraints and potentials. CGIAR Big Data Platform. 27 p. | en |
| dcterms.extent | 27 p. | en |
| dcterms.issued | 2020 | |
| dcterms.license | Other | |
| dcterms.subject | artificial intelligence | en |
| dcterms.subject | agriculture | en |
| dcterms.subject | supply chains | en |
| dcterms.subject | supply change management | en |
| dcterms.subject | risk factors | en |
| dcterms.subject | food security | en |
| dcterms.subject | methods | en |
| dcterms.type | Report |