While the internet of things (IoT) is already proving to be a critical enabler on the factory floor, organizations are now looking to enhance the responsiveness of their manufacturing systems further. To achieve this, these organizations are adopting smart manufacturing with edge computing as its main enabler.
The emergence of the Internet of Things (IoT) has brought about the much-needed confluence of data-centric information technology (IT) and process-centric operational technology (OT). This has made manufacturing processes more integrated, streamlined, and better adapted to keep up with a dynamic market. Most modern manufacturing facilities now employ what is known as the Industrial IoT or IIoT, which takes the concept of automation to the next level. According to a report, over 77% of manufacturers have installed IoT solutions.
Although far superior in capability to the previous generations of manufacturing technology, IIoT-enabled facilities have areas that can be further improved and have inefficiencies that can be eliminated by adopting smarter means of operating. This need for further improvement has given rise to the concept of smart manufacturing with edge computing. Edge computing can make existing manufacturing processes more intelligent and autonomous for greater responsiveness and agility. Read on for a detailed account of how edge computing can build upon the already enhanced capabilities of IIoT.
Taking Automation to the Next Level with IoT
Automation has brought about the biggest wave of transformation in the manufacturing industry since the industrial revolution a century ago. Robotic mechanisms are rapidly replacing human workers, making manufacturing and assembly processes faster, more cost-effective, and more standardized. While automation enabled manufacturing systems to “act” by themselves without human intervention, IIoT gave manufacturing systems the ability to “sense” and “think” about the changes in the processes, and “react” to changes in exigent circumstances. Thus, the introduction of IoT in manufacturing has raised the performance and functionality of automated manufacturing systems to a whole new level.
IoT-enabled manufacturing units, in addition to demonstrating high efficiency and high quality of output, are now also able to monitor and regulate themselves. They can detect anomalies in process parameters such as temperature, power output, speed and positioning during machining operations, etc., and adjust them based on the changing needs. For instance, a manufacturing unit can use a combination of IoT sensors to inspect manufactured goods and identify any defects automatically. Based on the nature of the defect, the systems can either make adjustments to the process parameters or notify the concerned personnel to take remedial action. This enables factory personnel to immediately fix any issues and get the system functioning back at top efficiency. Similarly, integrating IoT into manufacturing systems can also help in improving other functions such as energy management, inventory control, equipment maintenance, and worker safety among others.
Understanding the Limitations of IIoT
While IIoT may offer all these benefits and applications, it also comes with a few limitations or added costs that are avoidable. The biggest cost associated with IoT is that of building and maintaining high capacity data centers to store, process, and analyze the vast volumes of data generated by IoT sensors across the factory floor and throughout the supply chain. The cost of transmitting large volumes of data between these centralized data centers and the connected devices across large distances at high speeds also lead to heavy recurring costs.
Moreover, the centralization of all processing power also makes the entire system less reliable as there is only a single point of failure. Any issue with the data center or the network connecting the data center to the IoT devices (both sensors and actuators) results in the breakdown of operations, leading to suboptimal utilization of resources and inefficiencies. And depending on the size and geographical spread of the supply chain, the communication between the central data center and the endpoints may not be as prompt as desired, leading to latency and delayed response to changing conditions and contingencies.
A solution to such issues is, in a way, decentralizing the network and distributing the control across the entire network towards the edge, i.e., closer to the endpoints and further from the servers.
Implementing Smart Manufacturing with Edge Computing
As a solution to the limitations inherent to IoT, organizations are exploring the implementation of smart manufacturing with edge computing. Edge computing implies having most of the processing and storage elements of the IoT network closer to the points where the data is gathered from and where the actions are required. This means distributing the IIoT’s thinking and decision-making capabilities closer to the sensing and acting capabilities. Using such an architecture, manufacturers can maximize the benefits associated with IoT and minimize the risks and effects of its limitations. Following are a few benefits that manufacturers can gain by powering smart manufacturing with edge computing:
Increasing Responsiveness
The primary benefit of implementing edge computing for smart manufacturing is the minimization of network latency, i.e., the time taken for a request to travel to the data center, the processing of information by the data center, and the response to reach back to the endpoints. Since the most frequently required processing modules are closer to the endpoints in edge computing, latency is drastically reduced. This change in architecture makes manufacturing more responsive to changes and hence, more agile. Thus, manufacturing facilities can host the core modules necessary to handle the day-to-day operations on site, while those that are rarely used can be stored on the central cloud servers. These systems can periodically upload logs and other key information to the centralized cloud servers for analytics and other high-level business functions. Deciding which functions to leave on the central cloud and which ones to bring to the edge can differ from business to business.
Improving Reliability
In a facility implementing smart manufacturing using edge computing, all the processing components essential for operating the facility are available onsite. Hence, the distributed manufacturing units become less reliant on constant connectivity with the central data center. Although connectivity with the cloud is desirable, it is no longer as critical as it should be without edge computing. Thus, lapses in communication won’t impact manufacturing operations, ensuring uninterrupted operations. Since there are multiple points of storage and data processing in an edge computing network, such networks are safe from data loss due to hardware failure and other causes like cyberattacks.
Minimizing Costs
With the processing and storage capabilities distributed across the supply chain, manufacturers can avoid the costs of having high-capacity cloud servers and high-volume data transfer capabilities. The data on the enterprise network, due to the use of edge computing, will not need transmission over long distances and in large quantities. This minimizes the cost of setting up, maintaining, or subscribing to high-bandwidth connectivity.
Although the benefits of IoT in manufacturing are obvious, it is still a fairly new technology. It is understandable, then, that only a fraction of all the devices that can be connected to the internet are currently part of IoT. Businesses seeking to gain a competitive edge are just beginning to use IoT and it has been reported that nearly IoT users have already seen a significant return, with revenues rising by as much as 67% depending on the scale of adoption. As the use of IoT becomes the standard across the global manufacturing industry, the need for a new competitive differentiator will arise. Once that happens, upgrading to smart manufacturing with edge computing might just be that new differentiator.
