Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Bonakdar, L., Duarte de Almeida, I. & Cardoso-Grilo T (2025). Knowns and Unknowns: Probing Supply Chain Sustainability in the AI Era. GFIC / CIKI / ICKM,.
Exportar Referência (IEEE) 
L. Bonakdar et al.,  "Knowns and Unknowns: Probing Supply Chain Sustainability in the AI Era", in GFIC / CIKI / ICKM, 2025
Exportar BibTeX 
@misc{bonakdar2025_1793850742584,
	author = "Bonakdar, L. and Duarte de Almeida, I. and Cardoso-Grilo T",
	title = "Knowns and Unknowns: Probing Supply Chain Sustainability in the AI Era",
	year = "2025"
}
Exportar RIS 
TY  - CPAPER
TI  - Knowns and Unknowns: Probing Supply Chain Sustainability in the AI Era
T2  - GFIC / CIKI / ICKM
AU  - Bonakdar, L.
AU  - Duarte de Almeida, I.
AU  - Cardoso-Grilo T
PY  - 2025
AB  - This study explores the role of Artificial Intelligence (AI) in advancing sustainabil-ity across global supply chains (SC), supply chain management (SCM), and sus-tainable supply chains (SSC), with a focus on the Triple Bottom Line (TBL) dimen-sions: economic, environmental, and social. Using a systematic literature review guided by the PRISMA framework, the analysis includes 111 peer-reviewed articles from Scopus, Web of Science, and ProQuest. Findings show that most research em-phasizes AI’s benefits for environmental and economic outcomes, such as emis-sions reduction, operational efficiency, and predictive planning, while largely ne-glecting its implications for social sustainability. The review identifies key sec-toral applications in agriculture, logistics, urban infrastructure, and apparel, and highlights the potential of AI, particularly when combined with IoT, blockchain, and big data analytics, to improve traceability and circularity. However, critical re-search gaps persist, including algorithmic opacity, underexplored trade-offs across TBL pillars, limited attention to managerial authority shifts, and insufficient focus on developing economies. These challenges raise concerns about fairness, ethical governance, and organizational blind spots. Grounded in the principles of Industry 5.0 and aligned with the UN Sustainable Development Goals (SDGs), the study calls for more reflexive, ethically informed, and context-sensitive approaches to AI adoption in SSCM.
ER  -