Photo via FreightWaves
Class 8, a logistics analytics firm, has developed a detection system that harnesses vehicle telemetry and satellite imagery to identify suspicious cargo movements. The company analyzed more than 3.2 million unload events across a network of 68,340 commercial vehicles using a three-stage analytical pipeline. By combining OEM axle weight data with geospatial imagery and behavioral analysis, the platform flagged nearly 42,600 unload events as posing high or critical theft risk, according to company CEO Chris Atkinson.
The detection methodology integrates multiple data layers to spot anomalies indicative of cargo diversion or theft. Vehicle weight telemetry captures sudden changes in load during unload sequences, while satellite imagery confirms location authenticity and verifies whether unloads occur at authorized facilities. The behavioral analysis component examines patterns such as unusual timing, unexpected routes, or deviations from standard operational protocols.
Class 8 has validated its findings against a statistically significant sample of confirmed cargo theft investigations, lending credibility to its predictive models. The approach represents a shift toward data-driven loss prevention in freight logistics, potentially offering shippers and carriers a scalable tool to reduce theft risk across national supply chains.
