Edge Computing

Data privacy and protecting individual worker identity are essential in logistics and the supply chain. With its decentralized approach, edge computing helps companies comply with stringent data regulations such as the European Union’s General Data Protection Regulation (GDPR).

Processing data where it is collected at the edge reduces the need to transmit sensitive information from logistics operations to centralized or cloud servers. After processing data locally, it may still be necessary to transmit some of this information to central servers or the cloud. By using encryption at the edge, the data is unreadable without the required decryption key, keeping information secure even if intercepted during transmission or storage.

As an additional layer of security, established techniques such as tokenization can be used, replacing personally identifiable information (PII) with unique tokens. This way, visual data can be used to enhance operational efficiency but worker anonymity is also assured in the warehouse or yard.

Telematics plays a significant role in fleet management by providing real-time data and insights about vehicles and drivers. With edge computing, relevant data can be captured, processed, and used for rapid analysis and decision making. Examples include tracking the location, speed, and movement of vehicles in real time, monitoring engine performance and fuel consumption, diagnosing vehicles and receiving real-time maintenance alerts, and analyzing driver behavior.

American IoT company Samsara uses edge computing in an integrated platform solution to improve fleet safety, efficiency, and sustainability. This covers dash cams, telematics, compliance, trip management, driver vehicle inspection reporting (DVIR), trailer tracking, and speed monitoring. For example, the highly effective dash cam uses sensors, computer vision, and AI to analyze both the road and the driver at the edge. Through video analysis, edge computing identifies and documents safety-related events such as instances of high-risk driver behavior or driver fatigue. Edge computing then selects recordings of only relevant events to automatically upload to cloud for fleet managers to access.

Edge computing offers several specific applications in strengthening supply chain resilience.

To meet growing demands for visibility and enable increasing levels of e-commerce, better stock transparency is required in logistics. This transparency is essential at all times and all stages of the supply chain, ideally with granularity to individual product level. Responding to this need, companies are using IoT devices to monitor temperature, track real-time location, and watch stock levels. This information enables data-driven business decisions.

There are numerous opportunities to increase resilience by leveraging edge computing in supply chain processes. During transportation (trucking and last-mile delivery), an autonomous vehicle is vulnerable to an increasing number of cyberattacks; if vehicle control is compromised, this endangers other road users and the shipment itself. When a self-driving vehicle is connected to the edge, however, it is able to react to situations in real time, rectifying any malfunction and correctly responding to cyberattack without requiring human intervention.

Within a warehouse, edge-enabled devices share and process data in real time. This improves the speed and accuracy of warehouse operations. For example, edge-enabled cameras can scan barcodes on individual pallets to monitor stock per micro-location in the facility. The cameras can read barcode metadata and indicate if a box has been placed in the correct location. Crunching metadata in real time, the analytics platform can trigger an alert of fraudulent barcodes and incorrect placements. This data can then be streamed, connecting the edge-enable devices to the warehouse management system and enterprise resource planning system via cloud, so that employees are actioned to rectify the situation.