AI in the Chain

Context

Edge computing moves data processing and analytics closer to where data is generated. In traditional logistics systems, sensor readings from vehicles, containers or production lines are sent to a central cloud, analysed there and then returned to the edge. This model works when connectivity is reliable and timing is not critical. However, in remote areas, on moving vehicles, or during network outages, the round trip introduces delay and creates single points of failure. Edge artificial intelligence (AI) combines on‑device processing with Internet‑of‑Things (IoT) sensors to bring intelligence to the “edge” of the network. Smart sensors, gateways and on‑board computers can run machine‑learning models locally, detecting anomalies, optimising routes and triggering control actions in milliseconds without waiting for instructions from a distant server. The trend toward edge AI is accelerating because modern supply chains depend on real‑time data and resiliency. Logistics operations now generate massive streams of temperature, vibration and location data. Sending all of this data to the cloud increases bandwidth costs, introduces latency and can overwhelm central systems. By 2025, analysts estimate that more than three‑quarters of enterprise data will be created and processed outside of traditional data centres. For logistics providers, edge computing is not just about speed – it is a resilience strategy that allows operations to continue even when connectivity drops.

Why it matters

Edge AI enables decentralised intelligence so that monitoring and decision making continue even when network connectivity is intermittent. A 2025 trend report by logistics firm DocShipper notes that edge computing combined with AI allows continuous monitoring and decision‑making regardless of network availability. This is especially valuable for fleets travelling through remote regions or ships on the open ocean. With local processing, sensors can detect temperature excursions in a refrigerated container or deviations in vibration signatures and take corrective action immediately. Edge AI also enables responsive automation. The same report notes that processing data at the edge enables sub‑millisecond responses to changing conditions, which is critical for coordinating autonomous vehicles and robotics. When a self‑driving truck detects an obstacle, an on‑board model must react instantly; round trips to the cloud would introduce unacceptable delays. At the same time, local intelligence reduces bandwidth costs and data transmission burdens. Instead of streaming every data point, edge devices can send only aggregated insights or anomalies to the cloud. Finally, edge AI enhances resilient operations. By continuing to function during network disruptions, edge‑enabled logistics maintain service continuity. Operations can still record and act on sensor data, maintain temperature control or perform autonomous manoeuvres even if satellite or cellular links drop out. This resilience will be increasingly important as supply chains move through regions with variable infrastructure and as global cyber risks grow.

Immediate impacts and challenges

The impact of edge AI in logistics is already tangible. For example, Maersk’s Remote Container Management (RCM) system places IoT sensors on refrigerated containers and uses machine‑learning algorithms to monitor temperature, humidity and CO₂ levels in real time. When anomalies are detected, the system triggers alerts and recommends corrective actions, reducing cargo spoilage by 60 % and cutting vessel fuel consumption by 12 %. The RCM platform also provides customers with proactive notifications via a virtual assistant, reducing customer service inquiries by 25 %. Another use case is predictive maintenance on trucking fleets. Vibration signatures from wheel bearings or engines can be analysed on board to spot subtle changes that precede failure. The system can trigger maintenance before a breakdown, minimising downtime. Despite these benefits, there are significant challenges. Edge AI requires robust data governance and integration. Training models locally means ensuring consistent data quality across thousands of devices. Cyber‑security is another concern: edge devices are physically accessible and may be harder to patch. Talent shortages also pose a barrier; supply‑chain teams need data engineers and machine‑learning specialists to design and maintain distributed architectures. Lastly, because edge AI still relies on some central coordination, companies must invest in orchestration platforms that synchronise local decisions with enterprise systems.

Limitations of traditional approaches

Traditional centralised systems collect data from sensors and transmit it to a cloud or data centre for processing. This model suffers from several limitations. First, high latency and bandwidth costs: shipping continuous streams of data over cellular or satellite networks is expensive, and cloud processing adds delays that are unacceptable for real‑time control of autonomous vehicles. Second, single points of failure: if connectivity to the cloud is lost, decision making halts. In remote regions or during disasters, operations can become blind and reactive. Third, limited scalability: as billions of IoT devices come online, centralised systems struggle to ingest, store and analyse all data; bottlenecks occur during peak activity, impacting service levels. Finally, regulatory and privacy risks: transmitting sensitive operational data across borders raises compliance and data‑sovereignty challenges; keeping data local can simplify compliance and reduce risk.

How edge AI reimagines logistics

By moving intelligence to the edge, supply‑chain organisations can rethink how they operate. Local anomaly detection and predictive maintenance: edge‑hosted models continuously analyse sensor inputs to identify patterns indicating potential failures. For example, vibration signatures from trailer axles can signal bearing wear; the system can trigger maintenance before a breakdown, minimising downtime. Dynamic routing and adaptive scheduling: edge gateways in trucks aggregate traffic, weather and road‑condition data and run optimisation algorithms in real time. They can reroute shipments around congestion or severe weather without waiting for instructions from a central control tower. At cross‑docks, yard‑management systems use edge AI to sequence trailers for unloading based on live demand and capacity. Autonomous control: self‑driving trucks and warehouse robots rely on on‑board perception, localisation and control models. Edge AI processes LIDAR, radar and camera feeds locally to make immediate steering and braking decisions; only summarised telemetry is sent to the cloud for oversight. Federated learning and privacy: instead of sending raw data to the cloud, edge devices can train models locally and transmit only model updates. This federated learning approach preserves privacy and reduces bandwidth while still improving global models. Integrated edge‑to‑cloud orchestration: central platforms remain crucial for strategic planning, compliance and analytics. They collect edge‑generated insights, coordinate inventory and demand forecasts, and push updated models back to devices. This two‑way loop creates a continuously learning supply chain.

Hands‑on adoption roadmap

The path to implementing edge AI starts with identifying high‑impact use cases. Begin with scenarios where latency, bandwidth or resilience issues create clear pain points—such as temperature‑sensitive shipments, autonomous yard trucks or operations in low‑connectivity regions. Assess infrastructure readiness by auditing existing sensors, gateways and networks; determine which assets can support on‑device processing and where upgrades (e.g., adding GPUs or AI accelerators) are needed. Build a unified data foundation; ensure that edge devices adhere to common data formats and security protocols; implement a central registry to track device metadata, configurations and firmware versions. Select and deploy edge AI platforms; evaluate open‑source frameworks such as TensorFlow Lite or OpenVINO and commercial offerings; choose solutions that support remote model management, over‑the‑air updates and integration with existing transport management systems. Develop and train models; start with simple anomaly detection models and gradually expand to more complex optimisation and control algorithms; use simulation environments to test models under various conditions before deploying on hardware. Pilot and validate; run controlled pilots on a small portion of the fleet or network; monitor metrics such as response times, fuel consumption, service reliability and cost savings; iterate based on feedback from drivers, warehouse operators and customers. Scale and integrate; after successful pilots, roll out edge AI across more assets; integrate edge insights into supply‑chain planning platforms, digital twins and AI co‑pilots; establish continuous improvement processes for model retraining and patching. Invest in talent and governance; form cross‑functional teams of IT, operations and data science personnel; provide training on edge development and cyber security; set up governance frameworks for device access control, patch management and monitoring.

Conclusion

Edge AI and IoT integration represent the next frontier in supply‑chain digitisation. By pushing intelligence closer to physical operations, companies gain speed, resilience and efficiency that cloud‑centric architectures cannot match. Success stories like Maersk’s RCM platform show dramatic reductions in spoilage and fuel use while improving customer experience. More broadly, decentralised intelligence supports autonomous vehicles, responsive robotics and proactive maintenance without overloading networks. However, achieving these benefits requires careful planning—assessing infrastructure, ensuring data governance, developing local models, and integrating edge and cloud systems. With a pragmatic roadmap and investment in skills, supply‑chain leaders can harness edge AI to build more adaptive, resilient and sustainable logistics networks.

References

• DocShipper, “How AI is Changing Logistics & Supply Chain in 2025?” – trend report highlighting edge AI’s benefits: decentralised intelligence, responsive automation and resilient operations.
• DocShipper, “Case Study – Maersk’s AI‑Driven Shipping Network” – discusses Maersk’s Remote Container Management system and its impact on spoilage, fuel consumption and customer service.