Red Hat

Edge computing and AI are driving an inevitable transformation in the industrial sector. Industrial needs are evolving rapidly and therefore necessitating advanced solutions that provide a consistent platform to simplify the complexity of deployment. With edge-enabled platforms, the industrial sector is bringing data processing to the data source, simplifying the complexity of industrial systems. This is just one-way manufacturers are breaking down the barriers between their information technology (IT) and operational technology (OT) infrastructure.

The gradual blending of IT and OT in industrial systems goes hand in hand with the need for more efficient manufacturing. Over time, bringing together IT and OT delivers lower operating costs and accelerated time-to-market with quality, reliable and secure solutions for manufacturers.

However, as manufacturers race to realise the potential of these innovations, data management is now more critical than ever in providing business insights and supporting real-time control over processes and operations.

Unlocking the full potential of advancements such as AI and edge computing requires the transformation of the underlying system to allow the full manageability of data and a way around the limitations of vertically integrated offerings from OT vendors.

For instance, while organisations can introduce “bolted-on” computer vision systems, they will lack the ability to fully realise these technologies until their underlying system is transformed, allowing them full manageability of their data.

So how can manufacturers bring together their disparate data sources to fully realise edge computing and AI? This is where an open-source approach to technology is critical to accelerating the level of innovation.

Because open source solutions are, by their very nature, flexible, open, and interoperable, they revolve around a collaborative ecosystem approach to building, testing, and innovating. This makes for unparalleled flexibility to accelerate the innovation feedback loop in continuous manufacturing processes.

Implementing autonomous operations on the shop floor through a consistent platform approach

Smart factories, or software-defined factories, are playing a crucial role in amplifying the speed at which manufacturers can innovate. According to a report by McKinsey, smart manufacturing has the potential to create up to US$3.7 trillion in value by 2025, driving growth, innovation, and competitiveness across sectors. But this doesn’t happen overnight.

To make smart factories a reality, manufacturers need to change the traditional bespoke, single-function software/hardware integrated systems to platforms that can host multiple functions, leverage automation, and be managed at scale. They also need to build and operate these functions in the same way from the cloud to the edge and from large scale-out systems to small form factors for easier development and testing, and eventually deployment and management.

Getting the most out of data with intelligent data services

Growing supply chains, smart devices, sensors, and more have created an influx of data in modern manufacturing. With this comes a lot of complexity, but also the potential for efficient, and sustainable data-driven operations.

By integrating operations with digitisation initiatives, and manufacturing execution system (MES) adoption, the visibility of activities from the shop floor to the top floor is enhanced.

The integration of operational data across both IT and OT environments can provide a comprehensive, unified view of the organisation and optimisations. It’s here where open source technologies can play a vital role in improving system security postures and better protecting potentially sensitive data.

A shift to private networks for advanced connectivity

Private networks and edge computing are two inextricably linked technologies that are poised to improve the performance of applications and enable huge amounts of data to be processed in real time.

Service providers will play a big role in this shift as many manufacturers will need to turn to private 5G networks to handle the massive increase in the volume of machine-generated data for faster decision-making, more secure and reliable connectivity, analytics, AI, and more.

For example, video cameras, robotics, and conveyor belts can all be connected through a private 5G wireless network, with each device serving an edge application with an embedded AI/ML (machine learning) model that helps in inferencing and making quick decisions.

Through private 5G networks, manufacturers will be able to realise greater cost reduction, preventative maintenance in real-time, energy self-sufficiency, operational efficiency, more flexibility, and faster time-to-market.

Greater security and standardisation

When you have rapid changes to adapt to business needs – platform security and standardisation must stay top of mind for true scalability. In a 2023 S & P Global Report on the state of edge security, nearly half (47%) of decision-makers surveyed said that data, network, and physical/digital device security are among their biggest challenges in edge deployment.

The more devices you have, the greater the attack surface and this can be a complex problem to have. Organisations need more standardisation with a common platform that has consistent and reliable security capabilities that can help manufacturers manage security at scale. This reduces operational complexity and helps organisations achieve greater interoperability.

As we implement the platforms needed to manage data and infuse AI on the factory floor with modernised, secure, scalable, and manageable edge infrastructure, it’s enticing to think about how much more we can do in the manufacturing sector.

There’s so much potential in areas like worker safety, sustainable product design, predictive maintenance, and supply chain optimisation. And the open-source technology ecosystem will continue to collaborate and innovate together on the possibilities.

 

 

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