Artificial Intelligence has moved beyond exploratory pilots in transport and logistics. While autonomous trucks and drone delivery continue to grab headlines, the real transformation is happening deeper inside the supply chain stack: in planning, forecasting, routing, exception management, terminal operations, data orchestration, and real-time decision support.
The exploratory study on AI in transport & logistics by the Amsterdam University of Applied Sciences shows that the sector is entering a period in which digital automation, machine learning, and predictive analytics are converging into operational capabilities that directly affect costs, reliability, sustainability, and resilience.
This shift matters. Transport and logistics face simultaneous pressure from energy prices, sustainability mandates, labor scarcity, congestion, and the need for more granular service levels. AI acts as a force multiplier on the core performance levers of logistics: assets, capacity, information, time, and risk.
Three layers of AI adoption relevant to transport & logistics
The report suggests three practical layers of AI adoption relevant for supply-chain operators, forwarders, carriers, terminals, and shippers:
1. Intelligent Prediction and Forecasting
Machine-learning models are increasingly embedded in tactical planning functions—demand forecasting, network planning, allocation, inventory positioning, pricing, and lead-time forecasting. Companies use historical and real-time datasets to improve the accuracy of demand and replenishment decisions, with spillover benefits for transport capacity planning and vendor alignment. Notably, AI-based predictive analytics supports:
- freight rate prediction
- ETA/ETD forecasting
- volume forecasting for terminals
- stockout avoidance in retail and healthcare
These prediction functions are foundational: once forecasting improves, downstream planning stabilizes, helping operators reduce buffers, excess capacity, and emergency transport.
2. Intelligent Operations and Routing
The second layer focuses on execution. AI engines optimize routing, dispatching, and flow control in congested networks. In road logistics, models use traffic patterns, geospatial data, and constraints to optimize and replan routes dynamically. In multimodal environments, similar techniques improve slotting, yard management, container allocation, and berth planning. Predictive-maintenance models reduce equipment downtime in ports, airports, and fleets—lowering operational volatility.
The report highlights use cases in port logistics, airport collaborative decision-making (A-CDM), and autonomous vehicle/robotics integration where AI reduces turnaround times, increases throughput, and supports crew and gate scheduling.
3. Cognitive and Automated Back-Office
The third layer concerns business processes rather than physical flows: document processing, claims handling, customer service, compliance, and document matching. Robotic Process Automation (RPA) combined with natural language processing (NLP) digitizes CMRs, manifests, invoices, and customs filings. This streamlines latency in order-to-cash, improves billing accuracy, and reduces labor intensity. For global forwarders and integrators, automation in these domains is becoming a competitive differentiator.
From point solutions to data ecosystems
A major insight from the study is that AI adoption in logistics is constrained far less by algorithmic maturity than by data interoperability and data availability. Two implications stand out:
- Vertical data exchange (shipper ↔ carrier ↔ terminal) supports operational optimization.
- Horizontal data sharing (competitors or sector peers) enables ecosystem optimization such as asset pooling, risk mitigation, or predictive maintenance.
Initiatives such as GAIA-X and IDS are emblematic of a shift toward secure, sovereign data spaces supporting multimodal transport, Mobility-as-a-Service (MaaS), and logistics corridors. In practice, this means the value of AI in supply chains increases non-linearly as more participants share data.
Strategic applications for supply-chain leaders
From a supply-chain management perspective, the most promising AI domains include:
Adaptive Network Planning
AI enables continuous network replanning based on real-time constraints rather than static master schedules. This supports resilience in the face of disruptions (weather, strikes, geopolitical shocks).
Transport Capacity Management
Carriers use AI to:
- predict lane imbalances
- adjust pricing dynamically
- allocate fleet capacity efficiently
This is particularly relevant in fragmented markets with volatile spot demand.
Sustainability Optimization
Given rising CO₂ regulation, AI enables:
- energy-optimal routing
- modal substitution
- empty-km reduction
- equipment right-sizing
These tools directly support zero-emission freight and EU Fit-for-55 trajectories.
Customer-Facing Reliability
Retailers and 3PLs deploy AI for proactive exception management and ETA transparency—now a customer-experience differentiator in B2B as much as B2C.
Barriers: AI is not plug-and-play for logistics
Key constraints highlighted in the report include:
- fragmented data ownership
- lack of interoperability standards
- limited explainability of models for regulated environments
- cybersecurity and data-sovereignty concerns
- shortage of AI talent
- uneven digital maturity across actors (SME vs tier-one integrator gap)
These barriers suggest that ecosystem-level governance will determine competitive outcomes in logistics—not just technology maturity.
Conclusion: AI will reprice time, risk, and capacity in logistics
AI in logistics is entering a scaling phase. Early adopters are gaining compound advantages in responsiveness, asset productivity, and resilience. For supply-chain professionals, the real strategic shift is that AI transforms how networks operate in the face of uncertainty. In a sector constrained by labor, emissions, and congestion, AI will increasingly serve as the mechanism for allocating scarce capacity, repricing time, and arbitrage risk across global supply networks.
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