Industry 5.0: People and Machines Interact in Real-time to Transform Manufacturing Processes

Summary

Keep manufacturing human-centric

From this Industry 4.0 foundation we are now moving towards Industry 5.0. A European Commission Policy Brief defines the target of Industry 5.0 to: “aim beyond efficiency and productivity as the sole goals, and reinforce the role and contribution of industry to society.” It should complement the existing Industry 4.0 approach by specifically putting research and innovation at the center of the transition to a sustainable, human-centric and resilient manufacturing industry.
 
North American organizations too, see human workers at the core of Industry 5.0 with company growth spearheaded through a combination of innovation and human capital. Alongside this, there are also pressing opportunities to capitalize on AI all while keeping tabs on human centricity, sustainability and resilience. To enable this requires a technical balance to ensure humans and machines work together–each learning from the other.
 
Achieving this harmony begins at the data level. Industry 5.0 triggers further convergence of IT and OT–aggregating shop floor data in real-time to help bridge the gap; continuously collecting and analyzing data; and offering a comprehensive view of manufacturing operations. This data can then be delivered to enable key stakeholders, especially workers on the factory frontline, to make critical decisions based on accurate, real-time data.

A switch in mindset to connect multiple systems seamlessly

Imagine a world where business operations are no longer constrained by delays or bottlenecks. This applies not only on the factory floor but also across the rest of the enterprise and in each aspect of the business. What if you could connect everything, even if you were using multiple systems to manage manufacturing in the front and back office?
 
This could be systems in the OT layer with systems in the IT layer in a secure, guaranteed, throttled, multi-site and cloud, and most importantly, real-time streaming manner. Specifically, operational systems such as MES, Programmable Logic Controllers (PLCs), SCADAs, OPC Unified Architecture (OPC UA) layers bisectionally connect with Master Data, Product Lifecycle Management (PLM) systems, Customer Relationship Management (CRMs), Incident Management systems from SAP, Salesforce, ServiceNow, Microsoft, Oracle and many others.
 
Any of these systems can be connected seamlessly with each other across a wide variety of factory, inventory and logistics use cases. To enable this, integration needs to re-invent itself, especially when it must bring in other technologies that are becoming prevalent in factory operations, such as Artificial Intelligence. For this Industry 5.0 and AI-driven world to happen, we need to think differently.

Now for the event mesh!

The creation of the industrial metaverse–transforming factory floors

The industrial metaverse, as highlighted by EY, has the potential to revolutionize production floor processes and management optimization. It’s become possible through data-driven digital twins, model simulations and data analytics–paving a path that will transform how work is performed. Nvidia Omniverse is an example of this.
 
But the metaverse needs to connect to the physical universe in real time. It is cost and energy prohibitive to keep training AI models in real time. So how do we integrate AI with real time data? Agentic AI and Retrieval Augmented Generation (RAG) are being used to integrate GenAI capabilities with real-time data. This real-time data is essentially event streams on the event mesh, flowing into Agents or RAG vector databases, keeping them up to date with real-time context.
 
Event-driven integration can set up manufacturers to adapt to all these new changes on the factory floor, better capitalize on new technologies such as AI models, or connect to growing industrial metaverse applications in manufacturing. The enabler for AI is an event-driven integration mechanism, providing real time context to AI in autonomous systems, through something called a “context mesh.”
 
A context mesh brings together the entire context of an enterprise, which can serve as the bedrock of sophisticated AI-based applications–from intelligent assistants using GenAI models and complex analytical tools, to machine language-based recommendation models and purpose-built AI applications using deep or reinforcement learning. This becomes the fabric that pushes that real-time context onto these digital twins.

Put Industry 5.0 into perspective: Learn from real-world examples  

Take the example of one leading multinational engineering and technology company that uses event-driven integration to connect its global manufacturing facilities. Specifically, the manufacturer has deployed event brokers in each of its 160 plants, and also in its IT layers on-premise and in the cloud, forming a large interconnected event mesh.

Master data management at scale

With an event mesh, the company has been able to streamline the distribution of huge volumes of product master data across its enterprise.
 
For instance, its global operations produce 7,000 parts per minute. To achieve this, the company needed to send the production master data, such as a master Bill of Material, to multiple production facilities. This involved sending 6,000,000 messages, equivalent to three terabytes of information per day, to different plants worldwide.
 
As these products are rolled out to be manufactured at more plants or master data changes, the data requirements become more vast and more complex – creating a huge end-to-end scenario.
 
But with an event mesh, the manufacturer has been able to confidently share production master data to all of its required environments in real-time. This ability to connect factories around the world is underpinned by a stable asynchronous connection between core data centers around the world and its different plants.

AI deployment to minimize errors

This company has also been able to deploy AI across production lines to concentrate error levels to lower than one part per 10 million produced parts. The company is now looking to speed up production lines with these different models and reduce the error rate.
 
By relying on an event-driven integration backbone to underpin the data requirements of new AI models, the company aims to further increase its success rate within production lines. In fact, the manufacturer estimates it can scale to minimize errors to one part per 100 million produced parts in the future.

Don’t forget about the humans

Traditional manufacturing integration methods are no longer enough in an industry quickly evolving towards Industry 5.0. This new manufacturing era requires seamless collaboration between humans and machines, powered by real-time data exchange.
 
The key enabler lies in event-driven integration backed by humans.