GenAI Could Rewire Global Supply Chains, if Trust, Security and Sustainability Catch Up

Generative AI is moving rapidly into supply-chain decision-making, from forecasting and logistics to manufacturing and quality control, but a major new review finds that research is advancing far faster in efficiency-driven applications than in areas such as resilience, cybersecurity, sustainability and reverse logistics.

Published in Logistics, the study "Generative Artificial Intelligence in Supply Chain: Review, Trends, and Future Directions" by Amlan Baruah and Mohammad Moshref-Javadi analyzes 692 peer-reviewed studies from 2020 to April 2025. It clearly states that GenAI is becoming more capable across supply chains, but the systems needed to make it secure, trustworthy and resilient are not developing at the same pace.

Transformers Are Becoming the Operating System of Supply-Chain AI

Transformer-based models have emerged as the dominant architecture in the research landscape. In the study's keyword analysis, "transformers" appeared 218 times, substantially ahead of other high-frequency terms such as data science, transportation, optimization and prediction. Their prominence reflects their flexibility across text, time-series, image and multimodal data rather than dependence on one narrowly defined task.

This versatility is pushing GenAI from isolated analytics toward broader decision support. Transformers are being studied for demand forecasting, network design, supplier evaluation, production scheduling, inventory optimization, route planning, quality inspection, warranty forecasting and customer service. Large language models add another layer by extracting information from contracts, technical documentation, supplier records and other unstructured sources that traditional systems often struggle to process efficiently.

The shift matters because supply-chain management is fundamentally an information-coordination problem. A model that can interpret market signals, supplier documents, transport conditions and operational data within the same architecture potentially creates a far more connected decision environment than a collection of separate forecasting or optimization tools.

The paper's temporal analysis suggests that this transition accelerated after 2022. Earlier work largely adapted machine-learning and generative models to individual problems, while newer studies increasingly focus on foundation models and context-aware systems capable of supporting multiple functions. The researchers describe this as a movement away from simple predictive accuracy toward integrated intelligent decision-making.

Efficiency Gains Are Advancing Faster Than Supply-Chain Resilience

Most current research concentrates on areas where commercial benefits are relatively easy to measure. Data analytics, optimization, forecasting, manufacturing, transportation, logistics and quality management dominate the literature, while sourcing, procurement, inventory management and warehousing still show notable research gaps.

The appeal is understandable. GenAI can generate synthetic demand scenarios when historical data are scarce, identify defects from limited training data, model traffic flows, optimize routing and help managers stress-test operations under alternative scenarios. These applications can translate into faster decisions, lower operating costs and better use of industrial assets.

However, the review exposes a strategic contradiction. Companies are adopting AI partly because supply chains have become more vulnerable to geopolitical disruption, natural disasters and volatile demand, yet resilience itself remains relatively under-researched compared with efficiency-oriented applications. Large-scale evidence on whether GenAI can consistently help businesses survive severe real-world disruptions is still limited.

The same caution applies to cybersecurity. GenAI may strengthen threat detection by identifying anomalies and interpreting unstructured cyber-intelligence data, but it simultaneously depends on access to sensitive operational, supplier and customer information. As AI becomes more deeply connected to logistics platforms, sensors and enterprise systems, a compromise of the underlying digital infrastructure could turn an efficiency tool into a systemic vulnerability.

The Most Important Opportunities May Be in the Areas Research Has Neglected

Reverse logistics, returns, refurbishment, repair, warranty management and customer service remain comparatively neglected, even though these activities are increasingly central to circular-economy strategies and resource efficiency. This is a major missed opportunity. GenAI could potentially help predict product returns, identify repair needs, automate warranty analysis, simulate component failure and support decisions about whether products should be reused, refurbished, remanufactured or recycled. These capabilities could reduce waste while helping companies recover more economic value from products after the initial sale.

Sustainability and ethics also remain underdeveloped relative to their importance. The paper highlights potential uses in supplier-compliance monitoring, labor-risk analysis, emissions reduction and sustainable packaging decisions. GenAI could generate scenarios showing how sourcing or logistics changes affect environmental performance, while language models could help detect risks related to forced labor, exploitation or regulatory non-compliance within complex supplier networks.

For developing economies, these applications could be particularly significant. Many firms operate with fragmented data, informal supplier networks and limited analytical capacity. Synthetic-data generation and automated information extraction could improve decision-making under those constraints, but unequal access to computing infrastructure, high-quality data, cybersecurity expertise and skilled personnel could also deepen the digital divide between advanced and lower-capacity supply chains.

The Next Competitive Battle Will Be About Trust, Not Just Better Models

The next phase of GenAI adoption will depend less on whether models can produce technically impressive outputs and more on whether companies can trust them enough to embed them in real operational decisions. Complex models remain difficult to interpret, and the authors identify explainability, privacy protection and trustworthy AI as major research priorities.

The challenge becomes more serious as AI shifts from advising managers to coordinating activities autonomously. The paper points to agentic AI as a future direction in which systems could connect procurement, inventory, transportation, manufacturing and other functions rather than optimizing each stage separately. Such integration could dramatically improve responsiveness, but it would also raise difficult questions about accountability when automated decisions produce financial losses, safety problems or labor and environmental harms.

The research itself carries limitations. Its evidence comes from Web of Science, English-language peer-reviewed journal articles and a defined search strategy, meaning relevant work outside those boundaries may be missing. The researchers also caution that their visualization and classifications are exploratory and that many promising GenAI applications have not yet been validated through large-scale industrial implementation.

The limitation may be the most important message for executives and policymakers. The supply-chain AI debate is moving quickly from "what can these models do?" toward "under what conditions should they be trusted to do it?" The organizations that gain the most from GenAI are unlikely to be those that automate fastest, but those that combine advanced models with secure infrastructure, human oversight, transparent governance and strong operational data.