aceArtificial Intelligence and Machine Learning in Supply Chain Management: Applications, Evidence, Implementation Constraints, and Future Directions
Artificial intelligence (AI) and machine learning (ML) are increasingly investigated as decision-support technologies for supply chain management (SCM), yet their value depends on the nature of the decision, data availability, organisational capability and the methods used to evaluate outcomes. This critical narrative review examines applications across demand forecasting, inventory planning, sourcing, manufacturing, logistics, risk management and sustainability. It brings together relevant peer-reviewed reviews…
The literature supports the practical relevance of AI and machine learning for data-driven prediction, pattern detection and decision augmentation across supply chain functions.
Evidence does not establish uniform gains in cost, forecast accuracy, resilience or environmental performance, and integration costs, fragmented data, model drift and limited transparency hinder realising benefits.
The review states that the literature does not establish uniform improvements across settings and that field-based validation of generative AI applications remains limited.
Evidence
- Peer-reviewedJournal of Scientific Research and Reports2026-10-18
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Truvace Impact Record TRV-2026-1898, v1: “Artificial Intelligence and Machine Learning in Supply Chain Management: Applications, Evidence, Implementation Constraints, and Future Directions.” Truvace, 2026-10-19. /record/TRV-2026-1898 (accessed at citation time). sha256 ff0017635a9577d8…
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