Edge Computing

IoT, cloud, AI, ML and Edge have been quite familiar terms for technology lovers. There has been a wrong idea or approach that Edge and Cloud are mutually independent. Though they may operate in different ways; leveraging one does not prevent the utilization of the other. In fact, they powerfully complement each other.

Edge Computing in Manufacturing

With the growth and penetration of the Internet of Things in different sectors, the edge computing framework is also findings its way in several sectors. Today, the most promising edge computing use cases are present in the manufacturing industry as it welcomes new technologies, and these advanced technologies effectively improve performance as well as productivity.

IoT is already providing its best for the optimal result in the manufacturing industry; manufacturers are looking for some platform to boost the responsiveness of their production systems. To accomplish this, companies are adopting smart manufacturing with edge computing as its leading enabler.

Smart manufacturing indicates a futuristic factory where equipment can make autonomous decisions based on operations going on the factory floor.

The new technology allows businesses to integrate all steps of the manufacturing process like design, manufacturing, supply chain, and operations. This provides better flexibility and reactivity at competitive markets. But no doubt, this whole vision requires a combination of related technologies like IoT, AI, ML and Edge computing.

One of the critical reason for gathering analytics at the edge of the network is that it enables us to analyze and execute on real-time data without bandwidth costs that come with sending data offsite for analysis.
We all are well-aware that manufacturing is time-sensitive in terms of avoiding the production of out-of-spec components, equipment downtime, worker injury, or death.

In fact, for more complex, longer-term tasks, data can be transferred to the cloud and coupled with other structured and unstructured forms of data. Thus, this supports that the application of these two different computing frameworks is not mutually exclusive but its a symbiotic relationship leveraging the benefits provided by each.

Why businesses need Edge for Manufacturing?

In the manufacturing sector, the purpose of edge computing is to process and analyze data near a machine that require prompt action in a time-sensitive manner. It demands a quick decision right away without any delay. In traditional IoT platform set up, data produced by a device is collected through an IoT device is sent back to the central network server (cloud).

In the cloud, all the collected data is processed in a centralized location, usually in a data centre. This implies that all the devices which need access to this data or use applications associated with it should be connected to the cloud. Thus everything is centralized, and the cloud is easy to secure and control even if it allows for reliable remote access to data. Well, data processing is completed in the cloud; it can be accessed through IoT platforms in several ways, i.e. via real-time visualization, diagnostic analytics, reporting to support better decision making based on real data.

Now, the question which triggers is that, if everything is quite favourable, then why do we need edge computing. The main problem is that the whole process takes time, and the situation turns complicated when there is a need to take prompt decision based on data.

In the traditional process, the data travels the distance from the edge device back to the cloud, and a slight delay can be critical for taking a specific decision like stopping a machine tool from avoiding breaking. In fact, these IoT connected machines produce a massive amount of data and all the data travelling back and forth between edge and cloud disrupts the communication bandwidth.

The only way to achieve real-time decision making is to adopt edge computing. Edge enabled machines to collect and process data in real-time at the edge of the machine that allows them to respond promptly and effectively.

Edge Use Cases in Manufacturing:

Let’s now check the practical reasons to add edge computing as a necessary thing in manufacturing. There are many business benefits to ensure that all networks are correctly connected to the cloud while providing on-time delivery of powerful computing resources at the edge.

1) Updated equipment uptime:

The adoption of edge computing in manufacturing predicts failure in a subsystem, component or impact of running in a degraded state in real-time. It regularly refines as more data is analyzed and is used to boost operational purposes and maintenance schedule.

2) Decreased sustenance costs:

Better analysis of data for required maintenance means that maintenance can be completed on first visits by providing mechanics detailed guidance about the cause of the problem, required action, what part requires extra attention which ultimately deduces repair cost.

3) Lower spare parts inventory:

Edge analytics models are business-friendly; they can be tailored as per the need of an individual device or system. This implies reading sensors directly associated with specific components/subsystems.

Thus, the edge model describes how the system should be optimally configured to accomplish the business goal, making spare parts inventory more efficient at a minimum cost.

4) Critical failure prevention:

By collecting, analyzing and monitoring data related to components, edge analytics detect a cause for future failure before it affects actualize. This enables early problem detection and prevention.

5) Condition-based monitoring:

The convergence of I.T. and O.T. has allowed manufacturers to access machine data to know the condition of their equipment on the factory floor; either it is new or legacy equipment.

6) New business models:

This is an essential point because edge analytics helps in shaping new business models to catch opportunities. Let’s check an example; edge analytics can enhance just-in-time parts management systems using self-monitoring analysis to predict machine component failure and provides parts replacement notification throughout the value chain. This affirms for a needed maintenance schedule to reduce downtime and parts inventory and ensures an efficient model.

In the CNC machine tool, in-cycle stoppages to the tool are edge decision, whereas end-of-cycles can be a cloud decision. The reason behind this is that in-cycle stoppages require a very low, near-zero, lag time whereas end-of-cycle stoppages have a more lenient lag time. Thus in the former scenario, the machine would have to leverage edge analytics when in-cycle to adapt and shut down the machine automatically to avoid potential costly downtime and maintenance.

Edge and cloud computing

As we already know that IIoT aims to apply the latest analytics to large quantities of machine data to reduce unplanned downtime, reduction in the overall cost of machine maintenance and potentially utilizing the machine learning capabilities. The cloud has been responsible for making this kind of massive data acquisition, transfer, and analysis.

So, if data speed is high and connectivity should be stable and then adopting edge solution is the best option. Therefore it is clear that edge computing will not replace cloud computing but it will complement each other for the optimal result. Thus, integration of edge computing with cloud computing capabilities can enhance efficiency and maximize the productivity of the business.