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The future of industrial automation

Pragya Vaishwanar
13th June 2024

How interoperability, connectivity, and AI are revolutionizing manufacturing

“Manufacturing is more than just putting parts together. It’s coming up with ideas, testing principles and perfecting the engineering, as well as final assembly.”

James Dyson, British inventor, entrepreneur and industrial designer

This blog post delves into the transformative potential of interoperability, connectivity, and artificial intelligence (AI) to revolutionize industrial automation. It explores how seamless integration between diverse systems, intelligent networks enabled by the Industrial Internet of Things (IIoT), and AI-driven optimization are shaping manufacturing’s future.

From predictive maintenance to smart factories, discover how these technologies are converging to create a more efficient, agile, and data-driven industrial landscape.

The evolution of industrial automation

For centuries, industrial automation has been a cornerstone of efficiency and productivity in the manufacturing world. It began with simple mechanization – for instance, moving from manual operation in which workers had to manipulate objects using their own strength, to the use of machines (eg. hydraulic lifts or pneumatic systems) which are far more capable. Today it has evolved to complex systems driven by interconnected technologies, where interoperability, connectivity, and artificial intelligence (AI) play pivotal roles.

As the term implies, ‘industrial automation’ involves the use of control systems – like machines, actuators, sensors, processors, networks, and robots – to automate production, increasing efficiencies and enabling continuous improvement of manufacturing processes. But the technologies that underpin industrial automation are continuously evolving – and therefore the roadmap for such automation systems must also evolve.

That said, industrial automation has already come a long way, driven by advancements in interoperability, connectivity, cloud-native applications, and AI. Today, interconnected systems communicate seamlessly, allowing for real-time monitoring, predictive maintenance, and intelligent decision-making. This requires new ways of collecting data, and developing automation applications that are intelligent and can scale. Technologies like AI, cloud-native orchestration and edge computing are becoming increasingly relevant for industrial automation. And, as technology continues to evolve, the future of industrial automation holds even greater promise, with smart factories and autonomous systems reshaping the manufacturing landscape.

Interoperability: the key to seamless operations

In the industrial context, interoperability ensures seamless operation across various components of the production process, regardless of their origins or manufacturers. In the past, industrial systems often operated in silos, with each machine or process having its own proprietary software and communication protocols. This made integration and data exchange between different systems challenging and sometimes impossible.

Each device in the industrial automation context is essentially an edge computer – it hosts applications, shares data, and manages states. However, each of these edge devices are manufactured by different OEMs and each of them potentially can have different run-time stacks and orchestration systems. This incompatibility makes application development difficult, hard to scale and even harder to interoperate.

However, with the advent of industrial IoT (Internet of Things) platforms and standards like OPC UA (Open Platform Communications Unified Architecture) and Margo, interoperability has finally become a reality. These standards provide a common language for devices and systems to communicate, enabling seamless data exchange and integration across the industrial ecosystem.

In the pursuit of seamless industrial interoperability, the Linux Foundation launched the aforementioned Margo Initiative, a collaborative effort aimed at standardizing communication protocols and interfaces within the industrial automation domain. The initiative seeks to build upon existing standards, like OPC UA, and further enhance interoperability across diverse industrial systems. Workgroups within the initiative address specific challenges, such as data modeling, security, and real-time communication, ensuring that the resulting standards meet the diverse needs of industrial applications.

Connectivity: the backbone of Industry 4.0

Connectivity forms the backbone of modern industrial automation. With the proliferation of sensors, actuators, and smart devices, industrial machinery is now more connected than ever before. This connectivity enables real-time monitoring, remote operation, and data-driven decision-making, leading to improved efficiency and reduced downtime.

Through technologies like Ethernet, Wi-Fi, and 5G, separate pieces of industrial equipment can communicate with each other and with centralized control systems, forming a networked environment known as Industry 4.0. This connectivity allows for predictive maintenance, where machines can detect potential faults and schedule repairs before breakdowns occur, saving both time and money.

Industrial AI: enhancing automation with intelligence

Artificial intelligence builds upon this connectivity; revolutionizing industrial automation by adding a layer of intelligence to these connected machines and processes. Machine learning algorithms analyze vast amounts of data collected from sensors and actuators to identify patterns, optimize processes, and make autonomous decisions.

Edge AI brings computational power directly to the device, enabling real-time analytics and reducing latency, by processing data locally (rather than relying on cloud servers). This is crucial for applications requiring immediate responses, like autonomous vehicles and some networked industrial devices. Distributed AI, on the other hand, involves multiple interconnected nodes working collaboratively to solve complex problems. This approach enhances scalability, fault tolerance, and resource optimization. Together, edge AI and distributed AI empower efficient, decentralized processing, fostering advancements in smart cities, healthcare, and the IoT.

Edge AI and distributed AI are transformative technologies reshaping data processing and decision-making. For example, in predictive maintenance, AI algorithms can analyze equipment performance data to predict when maintenance is required, optimizing maintenance schedules and minimizing downtime. Similarly, in quality control, AI-powered vision systems can inspect products in real-time, identifying defects with greater accuracy and speed than human operators.

Furthermore, AI enables adaptive manufacturing, where production processes can dynamically adjust to changing conditions or customer demands. This flexibility improves responsiveness and agility, allowing manufacturers to meet market demands more efficiently. With AI infusion, we can have real time data collection, data analysis using machine learning (ML), dynamic process adjustment (ie. monitoring and adjusting parameters based on machine conditions, using real time data), demand forecasting and improved quality control.

We’ve come a long way, but we still have a long way to go

Industrial automation has come a long way from its humble beginnings, driven by advancements in interoperability, connectivity, and AI.

We are now living through the Fourth Industrial Revolution. Today, interconnected systems communicate seamlessly, allowing for real-time monitoring, predictive maintenance, and intelligent decision-making. As technology continues to evolve, the future of industrial automation holds even greater promise, with smart factories and autonomous systems reshaping the manufacturing landscape – offering competitive advantages to the companies that can best apply these tools.

Learn more on how Capgemini Digital Engineering can support you in your digital transformation journey. Talk to our expert about interoperability at the edge.


Pragya Vaishwanar

Director GTM, Market and Sales Enablement for Digital Engineering, Capgemini Engineering
Pragya is focused on helping our customers transform and adopt to the new digital age, and integrate digital engineering innovations into their business. She is focused on driving the expansion and delivery of digital transformation and helping companies to get a grasp on future technologies. She focuses on market and sales enablement and supports the go-to-market strategy for digital engineering.