Many industries that rely on physical assets, from construction, to manufacturing, to energy and utilities, are still working to fully realize the benefits of Industry 4.0. But the data they collect now will help them take the next step and strengthen 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 WEF’s Klaus Schwab coined the term. This revolution had its origins in digitalization and the implementation of digital transformation. This was characterized by the use of connected devices, data analytics and automation for process-driven services. Combined, these digital elements play a major role in our daily lives, whether we are at work or at home. Industry 5.0 is the next rung on the digital ladder and the natural progression from what we have learned so far.
When Industry 5.0 arrives, we can expect the convergence of all that work and data collected. The next industrial revolution will bridge the physical and digital worlds. In fact, this goes back to the human versus machine argument, but about optimizing both humans and machines to increase their capabilities. AI and cloud computing will achieve a harmony where employees can achieve the best results, which can be replicated in processes throughout the supply chain. Industrial AI powers our lives at the back end. Industrial AI capabilities will enable power decisions and will not be a cause for debate despite speculation. While AI is ready to combine the disparate data and physical elements to create Industry 5.0, remember that AI is only as good as the data it is trained on.
Chief Product Officer at IFS.
Data drives innovation
Using AI to integrate the data points of physical assets will unlock new avenues for innovation and variation. Real-time insights from production machines and equipment help drive operational excellence and provide a competitive edge.
This is where industrial AI really makes a difference.
In addition to operational excellence, Industry 5.0 sees the intersection of AI and Environmental, Social and Governance (ESG) frameworks. AI presents a serious opportunity for companies to drive sustainability in their workflows, physical operations and, to that extent, in the larger supply chain. Using AI-driven insights, companies can optimize their processes from a production-based level, with AI providing opportunities to reduce all kinds of waste to achieve greater profitability, time efficiency and sustainability.
For companies that manage physical assets, this integration is a reality and not an exaggeration. The decision-making processes for companies with significant capital assets will be transformed. Through advanced decision analytics, asset-rich enterprises can optimize capital allocation, manage risk, and make more accurate, data-driven business decisions. In an increasingly data-centric environment, industrial AI can provide a competitive advantage by helping companies prioritize high-impact investments, adapt to changing regulations and market conditions, and adapt to sustainability goals.
We live in a world of servitization where companies increasingly rent or lease industrial equipment instead of purchasing it outright. Think of robotics, aircraft engines, heavy construction equipment or even vans. As a result, manufacturers will design and build higher quality machines with built-in smart technologies to meet the demands of the servitization era.
AI can detect anomalies and maintenance issues in this equipment before determining the appropriate course of action. Monitoring workflows for redundancy in the network and calling in a field service engineer to troubleshoot the machine. At the same time, the schedulers of other field service technicians are rerouted to make up for any time losses. Again, this streamlines operations and minimizes downtime in the industry until machines are operational again. Industrial AI will change the entire value proposition of manufacturing in the circular economy, identifying parts and components that require maintenance before they show physical signs of wear.
Building digital twins into strategy
Taking something from the physical world and replicating it virtually is an engineering concept. A concept that becomes crucial in an environment with integrated Manufacturing Execution Systems. Historically, these worlds were separate. Data would generate production 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 faster than humans can. Again, achieving true optimization in milliseconds rather than seconds or even minutes.
A cycle occurs: simulation informs business practices that change the parameters for simulation, and so on. Simulations help identify areas needed for improvement, allowing 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 it takes him three days to harvest. However, the farmer is sure there is a more efficient way to do things, but is hesitant to experiment with his living environment. Digital Twin technology allows the farmer to optimize tool use, harvest routes and crop storage before muddy boots even hit the ground. We take a three-day one and optimize it so that it is achievable in just one day.
It is critical to consider the time-to-value aspect, whether implementing universal AI across the business or focusing on specific edge cases. In the example above, this is before the harvest takes place in August. Life cycle production time can be shortened, production waste can be minimized and service efficiency can be increased. These in turn lead to environmental benefits with minimal energy consumption, optimized delivery routes and shorter charging times for electric vehicles.
Innovation in Industry 5.0 uses technological advancements to bring foresight and adaptation in the industry. Digital twins can enable customers to increase operational efficiency and unlock productivity in a way never seen before. The integration of industrial AI gives companies a holistic view of their resources to find other opportunities with informed decision-making at a granular and investment-based level.
Eliminating complexity
From the regulatory complexities of data collection and storage to the varying levels of AI adoption within companies, a successful transition to Industry 5.0 requires expert support. The costs of AI investments can be enormous, so you need to be strategic and focused on improving specific areas of your business. Generic, off-the-shelf AI tools trained on irrelevant data won’t help here. To remain competitive on a global scale, companies must invest in this technology and collaborate with proven partners.
Companies that can effectively leverage the data they collect and leverage AI to create actionable insights will be ready for Industry 5.0. Delivering more value to their customers, improving the work life of their employees with better daily processes, to become a true market leader. Whether it’s manufacturing, construction or any other physical asset-oriented industry, those companies that can’t see the forest for the trees with their collected data will miss Industry 5.0.
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