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Is the Edge Too Far for TMS?

TMSCloud computing is becoming an important component in cutting-edge transportation management systems (TMS). The internet of things (IoT) has created a push to increase speed and efficiency by shifting processing even further from center, called edge computing. Is this the future of TMS, or is the edge simply too far?

TMS Makes a Home in the Cloud

Current TMS solutions provide high levels of connectivity, supply chain visibility, and fast data transfers by leveraging the power of cloud computing. The cloud essentially removes latency issues caused by hard drive processing and raises the central system to a place where each user can interact with it directly. In an industry with millions of moving parts, this presents a major advantage. Shippers and carriers can communicate, track transportation vehicles, and anticipate regulations at every point along the way. A constant push toward even greater speed and efficiency has led to the rise of edge computing – also called fog computing – it exists beyond the cloud.

What Lies Beyond the Cloud?

Cloud computing employs a centralized system but relocates it for faster data processing. Edge computing, however, moves the active processing components further from the center. Fueled by the rise of the IoT, it leverages nodes on the outskirts of a network to manage data, like an ATM. Rather than working from a central system, it can perform on its own, processing and sending data as appropriate. Without the need to connect to center, latency in transactions is reduced.

Is Edge Computing Too Far?

Edge computing in TMS would leverage smart shipping containers and vessels. With millions of shipping containers moving around the planet, there is a huge amount of information to capture. Some of these data points are valuable in streamlining shipping and logistics. However, the sheer density of information is too much to transfer quickly. By moving this processing to the edge, it would function at top speed, sending only necessary data back to centralized systems.

Employing new data makes predictive analytics more relevant. Edge processing allows machines to respond to situations that arise and provides analysts with high-quality data for future planning. Many think this would also represent a decreased security risk. By completing data processing in closed devices, the chances of being hacked are less extreme than in open-air transfers.

Proposed edge solutions offer many positive features, but concerns still exist. In any business, encouraging employees to be autonomous speeds workflow, cutting time and energy required for check-ins. However, if one leaves an employee without directions for months at a time, their initiatives may no longer align with the company’s trajectory. This holds true in computing as well.

By moving processing to the edge, users relinquish some degree of control over their nodes. The speed of data analysis and transfer is, in large part, due to a lack of management. If applications at the edge misbehave, they are much harder to correct, placing an emphasis on computer learning to ensure smooth processing and data application.

The Fog Beyond the Cloud

The edge is still a work in progress. Many analysts believe a middle point will arise – a cloud-edge hybrid system that makes the most of each. Some edge computing solutions will likely find their way into TMS as one part of a complex system.



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