The trend of Edge Computing refers to the decentralization of IT architecture, bringing computer processing closer to sensors and other data sources – at the edge of a network – and away from remote cloud servers and data centers.
Deploying computing and storage resources at the location where data is produced, edge computing minimizes the need for continuous, long-distance communication between clients and servers, improving processing time and the speed of response to surrounding changes.
The volume of data generated by internet-connected devices is growing far too quickly for traditional data center infrastructures to cope. Gartner predicts that by 2025 75% of enterprise-generated data will be created outside centralized data centers. Moving such vast amounts of data via the internet is often time- and disruption-sensitive.
With edge computing and the ability to decentralize IT architecture with the growing capabilities of mobile computing and the Internet of Things (IoT), organizations gain near real-time insights with a lower demand for cloud server bandwidth. This also adds an extra layer of security for sensitive data, as decentralization means data can be more safely stored near to its source.
With global market value predicted to hit 116.5 billion USD by 2030, and expanding at a CAGR of 12.4% between 2022 and 2030 , this trend and its wide application across many different sectors also triggers other innovative technologies, accelerating development and new capabilities.
It is said that the rise of 5G networks across the globe actually positions edge computing as the next evolution of cloud computing. More than ever seen in the past, organizations can now harness comprehensive data analysis without the IT infrastructure that was necessary in previous years. Considering that edge computing is in relative infancy, its maximum potential is still far from full realization, although it is already accelerating digital transformation across organizations, including logistics and supply chain facilities. As edge computing streamlines how much detailed data the organization can process at any given time, it enables companies to learn more and it delivers insights at a faster rate than before. This helps businesses to predict, manage, prepare, and adapt, meaning they remain resilient in the face of future demands.
In the logistics industry, the ongoing development of self-driving vehicles is a prime example of an edge computing application. Driverless cars must react and adapt to surroundings in real time rather than wait for commands from data centers in far-away locations.
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.
Relevance to the Future of Logistics
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.
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.
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.
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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- TechTarget (2021): What is edge computing? Everything you need to know
- GlobeNewswire (2022): Edge computing market size to hit at USD 116.5 billion by 2030
- Hewlett Packard Enterprise (2022): What is edge computing?
- Atos (2022): Why edge computing is about to solve major IoT issues
- STL Partners (2022): Edge use cases for retail, warehousing and logistics
- Cloudflare (2022): What is edge computing?
- DC Velocity (2022): Edge computing holds rising value for logistics and manufacturing firms, Gartner says
- STL Partners (2022): Edge computing market trends