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For autonomous vehicles, platooning or truck moving in convoy is likely to be one of the first use cases. Multiple trucks will travel closely following one another, enabling savings in fuel costs and decreasing congestion.

Thanks to edge computing, this will remove the need for a driver per truck; just one will be required in the first truck, and the following ones will be able to communicate with each other with ultra-low latency.

Source: STL Partners

Relevance to the Future of Logistics

Enabled Connectivity in Dark Zones

In remote locations and in regions lacking legacy infrastructure (dark zones), edge computing can overcome IoT device connectivity limitations. By processing data at the edge of the network, rather than running data through remote data centers or network infrastructures, edge computing allows captured data to be analyzed at source. Once data is processed at the edge, it can then still be transmitted to cloud applications. Highly relevant to tracking and visibility use cases, this gives logistics providers and customers alike uninterrupted access to real-time shipment locations.

For high-value shipments, this is essential if another party attempts to hack shipment location data. Edge computing increases the level of IT ownership and security, and therefore reduces the risks both of data theft and product theft. Similarly, this is important for shipment of sensitive goods; for example, when temperatures must be maintained to ensure product quality and any deviation may be harmful to human consumption. Here, edge computing ensures manufacturers and consumers know about the product’s environment and condition at all times, regardless of whether at any stage of the supply chain it entered a dark zone with no connectivity.

Supply Chain Resilience

At the granularity of individual product level, logistics companies need to know their stock at all stages of the supply chain and at all times so they can meet the growing demand for visibility and enable increasing e-commerce. To address this, logistics providers as well as retailers use IoT devices to monitor temperature, track real-time location, and watch stock levels in order to make data-driven business decisions.

There are numerous opportunities to increase resilience by leveraging edge computing in various supply chain processes. During transportation (trucking and last-mile delivery), an autonomous vehicle is vulnerable to increasing 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 all 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 the cloud, so that employees are actioned to rectify the situation.

Another good example is how edge computing supports the efficient management of volume fluctuation during high seasons, helping warehouses to cope with a surge in demand for consumer goods.

Operational Efficiency & Security

Manufacturing and warehouse operations benefit from the way edge computing enables close monitoring. For example, watching the efficiency of equipment and production lines helps to detect failures before they occur, avoiding costly delays caused by downtime. In the energy sector, companies can keep a close eye on assets in order to avoid disruption in meeting consumer demand and overcoming global shortages.

Within a traditional operational setup, closed-circuit television (CCTV) cameras in a warehouse continuously output raw video signals which stream constantly to a cloud server. From there, a motion-detection application identifies any footage containing activity, ensuring this is saved to the cloud server – this represents a constant strain on the warehouse’s internet infrastructure as large bandwidth is needed to transfer all this footage; it also imposes a heavy load on the cloud server which must process all camera footage simultaneously. Edge computing offers an excellent alternative. If each camera’s motion sensor computation is moved to the network edge, using its own internal computer to run the motion-detection application, the camera need only send this footage to the cloud server, resulting in a significant reduction in bandwidth use. Now the cloud server would only be responsible for storing footage of significance and it would be able to communicate with a larger number of cameras without being overloaded.


Managing disparate networks and storage systems to edge compute is complex and requires specialized IT expertise and talent at multiple geographical locations simultaneously.
As the trend is still at a relatively early stage, expect developments in data security when computing moves to the edge; the IT infrastructure will go beyond the multiple layers of physical and virtual network security offered by centralized computing and will, therefore, introduce more potential targets for cybercrime.
The physical isolation of devices powered by edge computing, and therefore the data being exchanged by multiple devices, makes it difficult to monitor, authenticate, and authorize data access.
Edge computing requires time and investment which can be challenging while running hundreds of container clusters simultaneously with different microservices provided at different edge locations at different times.

This trend should be ACTIVELY monitored, with imminent developments and applications.


Experts predict that one quarter of supply chain decisions will be made using edge ecosystems by 2025, as organizations shift away from centralized systems towards more distributed networks enabled by developments in Wi-Fi, Bluetooth, and 5G data communication. With edge processing of real-time data, we here at DHL anticipate supply chains becoming more dynamic while covering larger networks, with data and decisions originating from the edge. This impacts operators, machines, and sensors, as well as devices.

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