The Low-Code Effect on IIoT Edge Modernization

The Low-Code Effect on IIoT Edge Modernization

Manufacturers are using increasingly sophisticated Industrial IoT (IIoT) capabilities, and they should, given competitive demands to keep pace with innovation and efficiency in this industry. But these more modern factory environments require more and more advanced computing architectures and applications. Efficient use of an array of connected robots, sensors, cameras, calibrators and more requires instantaneous data processing to be effective.

As reliance on the edge intensifies, CIOs and other technical leaders in manufacturing need to think carefully about how to develop their longer-term technology strategies. They must ensure that edge applications always remain active and they must avoid bringing down their networks. This is where low-code strategies provide an opportunity to modernize edge infrastructure with lighter code. With a low-code approach, manufacturers can scale and continue to leverage the ever-increasing amounts of data generated by IIoT applications, while maintaining the low-latency network connectivity needed to make split-second decisions in the factory.

Powerful cutting-edge AI-driven applications support intensive data demands

AI is at the heart of the most impactful edge deployments, and the relative capabilities of these AI edge applications depend on Data. Accurate AI behavior requires large volumes of data, and the best results require, well, even more. But the latency of data connectivity – from the IIoT decision-making mechanisms in the factory, to the local network, to the cloud and back again – becomes an issue when data is not streamlined and scaled efficiently. By their nature, IIoT devices deliver value by making real-time business decisions.

Manufacturing CIOs are facing availability demands unparalleled in other industries. While CIOs in other domains can often pursue and deploy technology where brief gaps in availability – even a few minutes per year – are acceptable, manufacturing CIOs don’t have that luxury. If factory lines stop moving because overloaded peripheral applications miss a key warning sign, every minute of downtime can cost industrial companies hundreds of thousands of dollars in lost production. Because they have zero margin for error, manufacturers welcome advanced cutting-edge servers with GPU-centric architectures, as well as improvements to the AI ​​itself that make cutting-edge AI applications more advanced. more resilient and reliable.

Low-code enables efficiency in IIoT data and applications

The IIoT requires robust data-intensive applications that won’t crash networks. Low-code application development offers very useful lightweight code that fits these parameters. With low-code development, developers take advantage of drag-and-drop interfaces where advanced technology capabilities including sensors, AI/ML, and analytics are contained in abstract modules. Developers can then assemble these modules to create complete applications. While traditional IIoT application development requires intensive time and resource investments and expert talent, low-code offers CIOs a strategy in which their existing developers can leverage technologies that are more advanced than their skillset would have. other.

In practice, most manufacturers rely on single-purpose, high-end computing systems with very limited computing power, which must communicate with factory machines running legacy Windows or Linux operating systems. Forcing these systems to run bloated and tedious code is problematic. At the same time, applications based on traditional open source tools tend to be cumbersome and particularly taxing on older systems. While CIOs would like to replace existing hardware now, these modernization initiatives are often further along on the roadmap. Lightweight low-code is the ideal solution for these use cases because it works well with legacy operating systems and apply little stress to limited edge computing systems.

Implement edge infrastructure best practices

Edge modernization efforts can go awry without the right principles in place. Projects that exceed their budgets and fail to deliver compelling benefits often rely on packaged solutions from a single vendor rather than best-in-class options. To avoid vendor lock-in and unify technology silos in a connected ecosystem, use low-code to introduce connective middleware. Carefully balance on-premises and cloud infrastructure, making sure to take advantage of components that are easy to package and redeploy as you scale.

Manufacturing Presents a “Golden Data” Opportunity

The data produced by manufacturing systems is particularly relevant and actionable “golden data”. While data collected in industries such as retail is more often fraught with noise and irrelevant bias, manufacturing data comes from machines that directly and accurately communicate its status, and is therefore far more valuable.

The use of lightweight, low-code AI/ML application processes in edge computing systems provides the ability to capture and perform analysis on this data in a more passive way that does not disrupt manufacturing systems. When needed, these same lean processes can also take decisive action faster and more efficiently to protect manufacturing systems and prevent downtime.

Additionally, manufacturing data requires strict security measures, with sole ownership of the data often being split between different entities, such as the OEM and the subcontractor. Edge devices using distributed processing can help with security protections by segmenting data by their owners, even when processing critical data at the edge and transferring the remaining data to the cloud.

Manufacturing CIOs: Take the Lead in Cutting-Edge Modernization

Modernizing the edge is a tremendous opportunity for manufacturing CIOs, but with real risks. By designing a strategy that addresses the data challenges of the IIoT while capitalizing on its “master data” strengths, leveraging small, lightweight code, and adhering to best practices, manufacturers can ensure their availability and even increase their productivity.

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