IoT & Edge AI in Supply Chain

The Internet of Things and edge AI address a fundamental limitation of traditional supply chain management: the gap between the physical world and digital planning systems. Supply chains move physical goods through physical infrastructure, and the quality of supply chain decisions depends on real-time, accurate information about what is happening on the ground. IoT sensors continuously generate data on location, temperature, humidity, vibration, weight, and other physical parameters, while edge AI processes this data locally — at the sensor, on the vehicle, or in the warehouse — rather than sending everything to the cloud for analysis.

IoT tracking has evolved from simple GPS location to rich, multi-sensor monitoring. Modern tracking devices (from companies like Tive, Sensitech, and Samsara) report location, temperature, humidity, shock/vibration, light exposure (detecting unauthorized opening), and tilt in near real time. This data powers AI applications throughout the supply chain: cold chain monitoring for pharma and food (predicting temperature excursions before they cause spoilage), condition monitoring for fragile goods (detecting handling damage in transit), and fleet telematics (monitoring driver behavior, fuel consumption, and vehicle health). The cost of IoT sensors has dropped below $50 per unit in many cases, making broad deployment economically viable.

Edge AI processes data at the point of generation rather than in the cloud, enabling real-time decisions without network latency or connectivity dependence. In a warehouse, edge AI runs computer vision models on local processors to inspect items at conveyor speed. On a delivery vehicle, edge AI optimizes the remaining route based on real-time conditions without waiting for cloud connectivity. At a manufacturing plant, edge AI monitors equipment sensors and triggers preventive maintenance actions milliseconds after detecting anomalies. The architecture is shifting from cloud-centric to hybrid: edge devices handle time-critical inference and data filtering, while the cloud manages model training, aggregated analytics, and long-term storage.