Many industries that rely on physical assets, from construction to manufacturing, to energy & utilities, are still working to fully realize the benefits of Industry 4.0. But the data they are gathering now will help them build that next step, cementing the foundations of Industry 5.0.
Industry 4.0, or the Fourth Industrial Revolution (FIR), is a concept that has been around since the mid-2010s when Klaus Schwab at WEF coined the term. This revolution was rooted in digitalization and the implementation of digital transformation. This was marked by using connected devices, data analytics, and automation for process-driven shifts. Combined, these digital elements play a huge role in our everyday lives, whether we’re at work or at home. Industry 5.0 is the next rung on that digital ladder and the natural progression to what we’ve learned so far.
When Industry 5.0 emerges, we can expect to see the convergence of all that work and collected data. The next industrial revolution will be steeped in bridging the physical and the digital realms. Effectively this goes back to that human versus machine argument, but optimizing both human and machine to enhance their capabilities. AI and cloud computing will reach a harmony where workers can produce their best results, which can be replicated in processes throughout the supply chain. Industrial AI powers our lives in the back end. Industrial AI capabilities will enable power decision-making, and won’t be a force for contention despite speculation. While AI is set to join the disparate data and physical elements to create Industry 5.0, remember that AI is only as good as the data it’s trained on.
Data Begets Innovation
Using AI to integrate the data points from physical assets will unlock new avenues for innovation and variation. Real-time insights from production machinery and equipment will help drive operational excellence and provide an edge over competitors.
This is where Industrial AI truly makes a difference.
As well as operational excellence, Industry 5.0 sees the intersection of AI and Environmental, Social, and Governance (ESG) frameworks. AI presents a serious opportunity for businesses to drive sustainability throughout their workflows, physical operations and, to that extent, the larger supply chain. By harnessing AI-driven insights companies can optimize their processes from a manufacturing-based level, with AI proposing opportunities to reduce waste of all kinds to achieve greater profitability, use of time, and sustainability.
For businesses that manage physical assets, this integration is a reality not an overstatement. The decision-making processes for businesses with significant capital assets will be transformed. Through advanced decision analytics, asset-rich enterprises can optimize capital allocation, manage risk, and drive more precise, data-driven business decisions. In an increasingly data-centric environment, industrial AI can provide a competitive edge by helping companies prioritize high-impact investments, adapt to changing regulatory and market conditions, and align with sustainability goals.
We live in a world of servitization where companies increasingly rent or lease industrial equipment instead of buying it outright. Think of robotics, aircraft engines, heavy construction machinery, or even delivery vehicles. As a result, manufacturers will be designing and building higher-quality machinery with in-build smart technologies to meet the demands of the servitization era.
AI can detect anomalies and maintenance issues in this equipment before determining the proper court of action. Monitoring workflows for redundancy in the network and calling out a field service engineer to remedy the machine. At the same time, rerouting the planners of other field service engineers to recoup any time losses. Again, this is streamlining operations and minimizing industry downtime until the machinery is up and running again. Industrial AI will change the entire value proposition of production in the circular economy, identifying parts and components that need servicing before they show any physical signs of wear and tear.
Building Digital Twins Into Strategy
Taking something from the physical world and replicating it virtually is a technical concept. A concept that becomes crucial in an environment with integrated Manufacturing Execution Systems. Historically, these worlds were separate. Data would generate manufacturing orders and necessitate translation. Digital twins bridge this gap by processing information in real-time, breaking down the silos between the virtual and physical environments quicker than humans can. Again, achieving true optimization in milliseconds rather than seconds or even minutes.
There’s a cycle that occurs: simulation informs business practices which change the parameters for simulation, and so on. Simulations help identify the areas needed for improvement, allowing for iterative adjustments until the desired outcome is tested, proven and achieved. An easy way to visualize this process is to think of a farm. With the farmer’s current systems in place, it takes him three days to harvest. The farmer is sure, however, that there is a more efficient way of doing things but is hesitant to experiment with his live environment. With Digital Twin technology, the farmer can optimize tool usage, harvesting routes, and crop storage before muddy boots even hit the soil. Taking a three-day and optimizing it so it’s achievable in just a single day.
Considering the time-to-value aspect, whether implementing universal AI across the business or focusing on specific edge cases, is crucial. In the above example, it’s before the harvest even happens in August. Lifecycle production time can be reduced, production waste minimized, and service efficiency increased. These, in turn, lead to environmental gains with minimal energy consumption, optimized delivery routes and reduced charging times for electric vehicles.
Innovation in Industry 5.0 is using technological advancement to attain industry foresight and adaptation. Digital twins can empower customers to drive operational efficiencies and unlock productivity in a way never seen before. Integrating Industrial AI gives businesses a holistic view of their resources to find other areas of opportunity with informed decision-making at a granular and investment-based level.
Eliminating the Complexity
From the regulatory complexities of data collection and storage to varying levels of AI adoption within businesses, a successful transition into Industry 5.0 requires expert support. Costs of AI investments can snowball, so you must be strategic and targeted at improving specific areas of your business. Generic, off-the-shelf AI tools trained on irrelevant data won’t help here. To remain competitive at a global scale, companies need to invest in this technology and work with proven partners.
Businesses that can effectively harness the data they collect and employ AI to create actionable insights will be ready for Industry 5.0. Delivering more value to their customers, improving their employees’ working lives with better everyday processes, to become a true industry leader. Whether in manufacturing, construction, or any other physical asset-focused industry, the businesses that can’t see the wood through the trees with their collected data will miss out on Industry 5.0.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
This articles is written by : Nermeen Nabil Khear Abdelmalak
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By Nermeen Nabil Khear 