Summary
This systematic review synthesises evidence on the transformative potential of artificial intelligence in food manufacturing between 2019 and 2024. The authors document how AI-driven approaches—including predictive analytics, real-time monitoring, and computer vision—can streamline workflows, minimise environmental impact, and ensure product consistency, whilst identifying critical barriers to adoption and proposing strategies for cross-sector collaboration to realise a more sustainable food manufacturing ecosystem.
UK applicability
The findings are directly relevant to UK food manufacturing policy and practice, particularly given the sector's sustainability commitments and digital transformation agendas. However, applicability will depend on whether evidence reviewed included UK-based case studies and whether identified barriers (infrastructure, regulatory frameworks) align with the UK's technology adoption landscape.
Key measures
AI technologies deployed (predictive analytics, real-time monitoring, computer vision); environmental footprint reduction; waste reduction metrics; production optimisation; product consistency; barriers to adoption; policy and stakeholder recommendations
Outcomes reported
The review synthesised peer-reviewed evidence (2019–2024) on AI applications across food manufacturing, identifying how predictive analytics, real-time monitoring, and computer vision optimise production efficiency, reduce waste, and enhance product consistency. The study also documented barriers to AI adoption (infrastructure, ethics, economics) and proposed cross-sector collaboration strategies.
Topic tags
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