27 www.drivesncontrols.com October 2026 ARTIFICIAL INTELLIGENCE n equipment too early or too late. Predictive maintenance, powered by AI, offers a more intelligent approach. It uses machinelearning algorithms to analyse data from sensors on equipment, identifying subtle patterns that could signal an impending failure. By monitoring variables such as temperature, vibration and performance metrics, AI can predict when a component is likely to fail. This allows maintenance teams to intervene proactively, scheduling repairs during planned downtime and avoiding catastrophic failures. This data-driven approach reduces unplanned operational stoppages significantly, extends the lifespan of expensive equipment, and lowers maintenance costs. The shift is from a preventative “maintain it on a fixed schedule” or a reactive “fix it when it breaks” model, to a predictive, strategic one that optimises maintenance time and effort and reduces equipment failures. Some manufacturers take this further by using a digital twin – a virtual model kept in synch with real-time sensor data – to assess patterns in equipment behaviour and performance. These systems can alert operators about potential issues early, allowing them to prevent breakdowns before they escalate. Automobile manufacturers, for example, employ predictive maintenance widely on assembly-line robots, reducing unplanned downtime significantly and resulting in substantial cost savings. 2. Enhancing Quality and Throughput Achieving peak efficiency on a production line requires constant monitoring and adjustment. AI excels at this complex task by analysing vast datasets from the manufacturing process in real time. Machine-learning models can identify bottlenecks, inefficiencies and deviations from optimal parameters that might be invisible to human operators. For instance, in precision manufacturing, AI can analyse images captured by cameras on the production line to detect microscopic defects in products, thus ensuring higher quality control standards. In chemical production, algorithms can adjust ingredient mixtures or temperature settings to improve yields and reduce waste. Food and beverage manufacturers similarly use AI-powered systems to track the use of ingredients in real time. By learning continually from production data, AI systems refine processes to achieve greater speed, consistency and quality, leading to higher throughput and lower operational costs. Another example is generative AI in machining, where AI can “learn” from machining history and generate NC machining programs automatically based on the starting dimensions of the raw material and a 3D model of the part to be machined, helping to address the increasingly critical shortage of skilled workers in manufacturing. Similar AI capabilities can be applied in additive manufacturing and, in electronics manufacturing, intelligent detection systems powered by machine learning are already being used to reduce preparation time in tasks such as identifying PCB layers. 3. Intelligent Automation and Robotics AI is making automation smarter and more flexible. Traditional industrial robots are programmed to perform a single, repetitive task with high precision. AI-powered robots are different. They can perceive their environment, make decisions and, in the case of cobots (collaborative robots), can work safely alongside human employees. Equipped with advanced sensors and machine vision, these robots can handle more complex and varied tasks, such as assembling intricate products or sorting mixed items. They learn from experience, improving their performance over time. This new generation of automation equipment allows manufacturers to adapt quickly to changes in product design or demand, resulting in more agile production lines. It also frees human workers from dangerous or monotonous tasks, allowing them to focus on more creative and strategic responsibilities. This capability can be extended into the virtual world using generative AI. For manufacturers that need to relocate a production line, add a new line, or even build a new plant, a virtual model can be created based on a point cloud generated by scanning the existing facility. AI can then be used to identify specific robots in the point cloud and replace them with precise 3D engineering models of those robots. 4. AI-Driven Inventory Management Effective inventory management is a delicate balance. Too much stock ties up capital and increases storage costs, while too little risks production delays and lost sales. AI is revolutionising this area by providing highly accurate demand forecasting. Machine learning algorithms analyse historical sales data, market trends, supply chain disruptions and even weather patterns to predict future demand with remarkable precision. This allows companies to optimise their stock levels and supply plans, ensuring they have the right parts and products at the right time. AI-powered systems can also automate the re-ordering process, triggering purchase orders based on a combination of stock levels, production activity and demand plans. The result is a leaner, more responsive supply chain that reduces waste and carrying costs, and improves cash flows. 5. Optimising Energy Consumption Energy is one of the largest operational costs for many manufacturers. AI offers a powerful tool for reducing this expense and improving sustainability. By analysing data from plant-wide sensors, AI systems can build a comprehensive model of a facility’s energy usage. This can be used to identify equipment, processes and times of day that consume the most power. With these insights, machine-learning algorithms can recommend or even automate adjustments to reduce energy consumption. This could involve shutting down non-essential machinery during periods of low production, adjusting HVAC systems based on real-time occupancy and weather conditions, or optimising machine settings to run at their most energy-efficient levels. These incremental improvements can lead to substantial cost savings and a significant reduction in a company’s carbon footprint – combining material, process and equipment data to suggest ways to cut energy costs, optimise labour, and improve reliability across an operation. The Size of the Opportunity According to Markets and Markets, the value of AI in manufacturing is projected to reach $20bn within a few years, growing much faster than the broader industrial software market. This growth is being driven less by novelty, and more by measurable results: predictive maintenance programs consistently reduce unplanned downtime, and quality-inspection systems built on computer vision routinely catch defects that human inspectors miss on fast-moving lines. Despite this momentum, adoption still varies significantly with company size and sector. Larger manufacturers with more mature data infrastructures tend to move faster, while smaller manufacturers are more likely to start with a single, narrowly scoped use case – often predictive maintenance or inventory forecasting – before expanding further. Even with a lag in adoption in parts of industry, the broader trend is unmistakable: manufacturing as a whole is likely to continue embracing AI, not retreat from it. n
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