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AI and machine learning decision support in supply chain management

Source article: Artificial 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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1898· cite & verify
Artificial Intelligence and Machine Learning in Supply Chain Management: Applications, Evidence, Implementation Constraints, and Future Directions
"Containers" by Jim Bahn is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.
Trace impact readingContested
P 73The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 71The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

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.

Main points

  1. The review covers demand forecasting, inventory planning, sourcing, manufacturing, logistics, risk management and sustainability.
  2. Reported barriers include integration costs, fragmented data, process misalignment, model drift, limited transparency and governance concerns.
  3. Generative AI may extend to document-intensive processes, synthetic scenarios and interactive decision support, but field-based validation is limited.
  4. The authors propose a process-oriented framework where analytics inform decisions under continuous human oversight.

The gain

The literature supports the practical relevance of AI and machine learning for data-driven prediction, pattern detection and decision augmentation across supply chain functions.

The problem

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 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.

What this doesn’t fix

The review states that the literature does not establish uniform improvements across settings and that field-based validation of generative AI applications remains limited.

Sources

  1. Peer-reviewedJournal of Scientific Research and Reports2026-10-18

The debate