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TRUVACE RECORD VERSION
record: TRV-2026-1898
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-19T10:42:46.233460Z
status: published
lens: trace
sector: business
headline: Artificial Intelligence and Machine Learning in Supply Chain Management: Applications, Evidence, Implementation Constraints, and Future Directions
dek: 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…
gain_title: The literature supports the practical relevance of AI and machine learning for data-driven prediction, pattern detection and decision augmentation across supply chain functions.
problem_title: 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.
trace_subject: AI and machine learning decision support in supply chain management
gain_reading: The literature supports the practical relevance of AI and machine learning for data-driven prediction, pattern detection and decision augmentation across supply chain functions.
gain_evidence: The literature supports the practical relevance of data-driven prediction, pattern detection and decision augmentation
problem_reading: 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.
problem_evidence: it does not establish uniform improvements in cost, forecast accuracy, resilience or environmental performance across settings | integration costs, fragmented data, process misalignment, model drift, limited transparency and governance concerns as barriers
quick_read: This critical narrative review surveys how AI and machine learning are applied across supply chain functions including demand forecasting, inventory planning, sourcing, manufacturing, logistics, risk management and sustainability. It separates evidence from operational deployments from results reported in literature syntheses or simulations.

The review matters because companies considering these tools cannot assume predictable returns; benefits depend on the decision type, data availability and organisational capability. Field validation of newer generative AI uses remains limited, so many claimed advantages are still unproven in practice.
limitation: The review states that the literature does not establish uniform improvements across settings and that field-based validation of generative AI applications remains limited.
tag: Dual reading
key_points: The review covers demand forecasting, inventory planning, sourcing, manufacturing, logistics, risk management and sustainability. | Reported barriers include integration costs, fragmented data, process misalignment, model drift, limited transparency and governance concerns. | Generative AI may extend to document-intensive processes, synthetic scenarios and interactive decision support, but field-based validation is limited. | The authors propose a process-oriented framework where analytics inform decisions under continuous human oversight.
rundown: The paper distinguishes results from real operational deployments from those reported in literature syntheses or simulations, a distinction that tempers how much weight claimed improvements deserve.

It argues that evaluations should compare AI-supported decisions against relevant operational baselines, account for service levels and cost trade-offs, and document transferability across sectors and resource settings.

Future research priorities named are longitudinal field studies, cross-organisational coordination, responsible deployment and context-sensitive applications in emerging economies.
sources:
- peer_reviewed | Journal of Scientific Research and Reports | https://doi.org/10.9734/jsrr/2026/v32i114635 | 2026-10-18
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