(Source: Suriyo / stock.adobe.com; generated with AI)
From a business continuity perspective, improved resilience is one of the most significant impacts of the Industry 5.0 movement. In this context, resilience refers to an organization's capacity to endure and adjust to shocks, disturbances, and variability that impact its normal operations.
Generally, resilience has two key aspects. The first is the ability to withstand disruptive situations like pandemics, wars, cyberattacks, and natural disasters for as long as they last without experiencing significant negative impacts. The second is the ability to adapt to the disruption and emerge better after the situation is resolved.
In this blog, we examine some fundamental Industry 5.0 technologies that support new paradigms, such as predictive maintenance, adaptive manufacturing, and digital twins, and how they contribute to a more resilient manufacturing sector.
Predictive maintenance is an approach to maintaining industrial machinery that monitors and analyzes its condition and performance data to predict when maintenance will be required.
This process relies on Internet of Things (IoT) sensors installed on industrial equipment to collect real-time data on parameters like vibration, temperature, and pressure. These data are fed into artificial intelligence (AI) and machine learning (ML) models trained to recognize normal operating patterns. By continuously analyzing these data, the models can identify anomalies that may indicate an impending failure and accurately determine the best time for maintenance.
For example, increased vibration levels in a motor or bearing could signal the need for lubrication or replacement weeks or months before it breaks down. Here, the AI system raises an alert, allowing maintenance to be scheduled at the optimal time—after the current production cycle but before the component fails. This paradigm leads to significantly lower operating costs than the traditional corrective or preventive maintenance strategies from the past.
In fact, this predictive approach has several advantages over traditional preventive maintenance schedules, which are based on usage hours or calendar intervals. Predictive maintenance minimizes unplanned downtime by avoiding unexpected breakdowns. Moreover, equipment lifetime is extended by repairing or replacing components only when truly required. Finally, overall maintenance costs are reduced through optimized planning and inventory management.
Naturally, predictive maintenance is a major contributor to resilience in manufacturing operations. By avoiding disruptive breakdowns, it allows production to continue uninterrupted and optimizes overall equipment effectiveness (OEE).
Furthermore, this approach’s AI and analytics capabilities also have potential to improve processes, maximize product quality, and reduce waste and energy consumption. It can also provide resilience during supply chain issues or demand fluctuations. Such a holistic approach enhances the organization’s ability to withstand shocks, recover quickly from any disruption, and emerge in an improved state.
Adaptive manufacturing refers to an organization’s ability to adapt its manufacturing processes and operations rapidly in response to disruptions or changing demands. This technique is enabled through a confluence of AI, automation, and flexible production systems.
At its core, adaptive manufacturing is about having flexible and reconfigurable production lines that can be reprogrammed to change the product mix or production volumes with minimal changeover time and costs. AI is key in optimizing these reconfigurations based on live data regarding forecasts, inventory, and supply chain constraints.
For example, if a particular product model experiences a surge in demand, the AI system can adjust the manufacturing schedule, reallocate resources, and even modify the product design for easier production—all to prioritize the high-demand item. Conversely, adaptive manufacturing can switch quickly to an alternative design using available components if certain parts face a supply crunch.
This responsiveness makes manufacturers more resilient to fluctuations in customer demand and supply chain disruptions resulting from natural disasters, geopolitical events, or pandemics. They can continue fulfilling orders and generating revenue instead of being forced to stop production. Adaptive manufacturing, enabled by AI and automation, enhances resilience by improving quality, reducing inventory costs, and extending product life cycles.
Overall, the ability to adapt manufacturing processes gives organizations the agility to withstand and recover rapidly from internal and external disruptions. They can respond nimbly to changing market conditions, thereby mitigating risks and maintaining business continuity.
Digital twins are computerized representations of physical assets or systems that use live data to enable simulation, monitoring, and optimization. In the context of manufacturing, digital twins enhance resilience in several ways.
First, digital twins allow organizations to validate changes in their system’s behavior in a risk-free digital environment before introducing changes in the physical world. This “trial without error” approach minimizes disruptions and downtime when introducing new products, processes, or technologies. Companies can experiment, identify potential issues, and find optimal solutions before real-world deployment.
Second, digital twins leverage machine learning and data analytics to monitor the performance and health of physical assets and processes continuously. Any deviations from the ideal operating parameters are quickly detected, enabling predictive maintenance and timely interventions before failures occur. The result is minimized downtime and extended operational life of equipment while operating at optimal performance.
Moreover, the insights from digital twin simulations allow organizations to optimize their overall manufacturing operations for improved efficiency, quality, and sustainability. AI-driven “what if” analyses can identify bottlenecks, wastages, and opportunities for process improvements. Such insights allow operators to reduce costs and environmental impact while boosting productivity.
Finally, digital twins play a role in supply chain resilience. By integrating data from suppliers, logistics providers, and other partners, companies can model and stress test their entire value chain. This end-to-end visibility enables risk mitigation strategies like diversifying suppliers, rerouting shipments, or building redundancy.
The goal of any business is to be resilient, adapt to disruptions, and emerge better as a result. With Industry 5.0, technologies like IoT sensors and AI enable new paradigms, including predictive maintenance, adaptive manufacturing, and digital twins.
With these advances, companies can become more resilient to inevitable market volatility and unexpected disruptions, leading to a more secure and reliable global industrial sector.
Hector Barresi is an award-winning Industrial Technology Advisor, Consultant, and Public Speaker specialized in Industrial Automation, Smart Manufacturing, and Digitalization. He has held executive positions at Honeywell, Danaher, IDEX and General Electric, and he is renowned for shaping top-tier Product Innovation organizations globally. Notably, he pioneered the Honeywell XYR5000, the first industrial wireless sensor family on the market, and the groundbreaking Tintelligence smart tinting platform, revolutionizing the paint industry.