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Export Reference (APA) 
Bonakdar L, Duarte de Almeida, I. & Cardoso-Grilo T (2025). Knowns and unknowns: Probing supply chain sustainability in the AI era. Green Future Innovation Conference (GFIC 2025).
Export Reference (IEEE) 
L. Bonakdar et al.,  "Knowns and unknowns: Probing supply chain sustainability in the AI era", in Green Future Innovation Conf. (GFIC 2025), Lisbon, 2025
Export BibTeX 
@misc{bonakdar2025_1771853735959,
	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",
	howpublished = "Digital",
	url = "https://gfic.icaa.pt/gfic-2025-agenda/"
}
Export RIS 
TY  - CPAPER
TI  - Knowns and unknowns: Probing supply chain sustainability in the AI era
T2  - Green Future Innovation Conference (GFIC 2025)
AU  - Bonakdar L
AU  - Duarte de Almeida, I.
AU  - Cardoso-Grilo T
PY  - 2025
CY  - Lisbon
UR  - https://gfic.icaa.pt/gfic-2025-agenda/
AB  - This study explores the role of Artificial Intelligence (AI) in advancing sustainability
across global supply chains (SC), supply chain management (SCM), and sustainable supply
chains (SSC), with a focus on the Triple Bottom Line (TBL) dimensions: 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 emphasizes AI’s benefits for environmental and
economic outcomes, such as emissions reduction, operational efficiency, and predictive planning,
while largely neglecting its implications for social sustainability. The review identifies key
sectoral 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 research 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  -